2023/07/24 14:30:42 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.9.0 (default, Nov 15 2020, 14:28:56) [GCC 7.3.0] CUDA available: True numpy_random_seed: 990884726 GPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 PyTorch: 1.12.1+cu113 PyTorch compiling details: PyTorch built with: - GCC 9.3 - 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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_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 8.2 - Built with CuDNN 8.3.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.1+cu102 OpenCV: 4.7.0 MMEngine: 0.7.3 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: 16 ------------------------------------------------------------ 2023/07/24 14:30:42 - mmengine - INFO - Config: model = dict( type='Recognizer2D', backbone=dict( type='mmpretrain.MobileOne', arch='s4', out_indices=(3, ), init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.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='disk') train_pipeline = [ dict(type='DecordInit', io_backend='disk'), dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), 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='disk'), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, 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='disk'), 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=16, 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='disk'), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), 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=16, 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='disk'), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, 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='disk'), 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') auto_scale_lr = dict(enable=False, base_batch_size=256) checkpoint = 'https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth' launcher = 'pytorch' work_dir = 'work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2023/07/24 14:30:48 - 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 (NORMAL ) SyncBuffersHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train: (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 -------------------- 2023/07/24 14:30:50 - mmengine - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth Name of parameter - Initialization information backbone.stage0.branch_scale.conv.weight - torch.Size([64, 3, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage0.branch_scale.norm.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage0.branch_scale.norm.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage0.branch_conv_list.0.conv.weight - torch.Size([64, 3, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage0.branch_conv_list.0.norm.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage0.branch_conv_list.0.norm.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.0.branch_scale.conv.weight - torch.Size([64, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.0.branch_scale.norm.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.0.branch_scale.norm.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.0.branch_conv_list.0.conv.weight - torch.Size([64, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.0.branch_conv_list.0.norm.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.0.branch_conv_list.0.norm.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.1.branch_conv_list.0.conv.weight - torch.Size([192, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.1.branch_conv_list.0.norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.1.branch_conv_list.0.norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_scale.conv.weight - torch.Size([192, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_scale.norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_scale.norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_conv_list.0.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_conv_list.0.norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.2.branch_conv_list.0.norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.3.branch_norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.3.branch_norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.3.branch_conv_list.0.conv.weight - torch.Size([192, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.3.branch_conv_list.0.norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage1.3.branch_conv_list.0.norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.0.branch_scale.conv.weight - torch.Size([192, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.0.branch_scale.norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.0.branch_scale.norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.0.branch_conv_list.0.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.0.branch_conv_list.0.norm.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.0.branch_conv_list.0.norm.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.1.branch_conv_list.0.conv.weight - torch.Size([448, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.1.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.1.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_scale.conv.weight - torch.Size([448, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_scale.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_scale.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_conv_list.0.conv.weight - torch.Size([448, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.2.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.3.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.3.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.3.branch_conv_list.0.conv.weight - torch.Size([448, 448, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.3.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.3.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.4.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.4.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.4.branch_scale.conv.weight - torch.Size([448, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.4.branch_scale.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.4.branch_scale.norm.bias - torch.Size([448]): PretrainedInit: load from 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PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.6.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.7.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.7.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.7.branch_conv_list.0.conv.weight - torch.Size([448, 448, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.7.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.7.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.8.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.8.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.8.branch_scale.conv.weight - torch.Size([448, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.8.branch_scale.norm.weight - torch.Size([448]): PretrainedInit: 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PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.9.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.9.branch_conv_list.0.conv.weight - torch.Size([448, 448, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.9.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.9.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_scale.conv.weight - torch.Size([448, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_scale.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_scale.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_conv_list.0.conv.weight - torch.Size([448, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.10.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.11.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.11.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.11.branch_conv_list.0.conv.weight - torch.Size([448, 448, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.11.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.11.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_scale.conv.weight - torch.Size([448, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_scale.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_scale.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_conv_list.0.conv.weight - torch.Size([448, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.12.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.13.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.13.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.13.branch_conv_list.0.conv.weight - torch.Size([448, 448, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.13.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.13.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_scale.conv.weight - torch.Size([448, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_scale.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_scale.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_conv_list.0.conv.weight - torch.Size([448, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.14.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.15.branch_norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.15.branch_norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.15.branch_conv_list.0.conv.weight - torch.Size([448, 448, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.15.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage2.15.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.0.branch_scale.conv.weight - torch.Size([448, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.0.branch_scale.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.0.branch_scale.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.0.branch_conv_list.0.conv.weight - torch.Size([448, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.0.branch_conv_list.0.norm.weight - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.0.branch_conv_list.0.norm.bias - torch.Size([448]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.1.branch_conv_list.0.conv.weight - torch.Size([896, 448, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.1.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.1.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.2.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.3.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.3.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.3.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.3.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.3.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.4.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.4.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.4.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.4.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: 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PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.5.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.5.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.5.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.5.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.6.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.7.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.7.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.7.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.7.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.7.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.8.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.9.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.9.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.9.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.9.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.9.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.10.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.11.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.12.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.13.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.14.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.15.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.16.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.17.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.18.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.branch_norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.branch_norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.branch_conv_list.0.conv.weight - torch.Size([896, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage3.19.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.se.conv1.conv.weight - torch.Size([56, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.se.conv1.conv.bias - torch.Size([56]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.se.conv2.conv.weight - torch.Size([896, 56, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.se.conv2.conv.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.branch_scale.conv.weight - torch.Size([896, 1, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.branch_scale.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.branch_scale.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.branch_conv_list.0.conv.weight - torch.Size([896, 1, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.branch_conv_list.0.norm.weight - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.0.branch_conv_list.0.norm.bias - torch.Size([896]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.1.se.conv1.conv.weight - torch.Size([128, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.1.se.conv1.conv.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.1.se.conv2.conv.weight - torch.Size([2048, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.1.se.conv2.conv.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.1.branch_conv_list.0.conv.weight - torch.Size([2048, 896, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.1.branch_conv_list.0.norm.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.pth backbone.stage4.1.branch_conv_list.0.norm.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s4_8xb32_in1k_20221110-28d888cb.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 2023/07/24 14:30:51 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/07/24 14:30:51 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/07/24 14:30:51 - mmengine - INFO - Checkpoints will be saved to /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb. 2023/07/24 14:31:23 - mmengine - INFO - Epoch(train) [1][ 20/940] lr: 1.0000e-02 eta: 1 day, 17:41:39 time: 1.5971 data_time: 0.2380 memory: 15768 grad_norm: 1.6618 loss: 5.9398 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 5.9398 2023/07/24 14:31:44 - mmengine - INFO - Epoch(train) [1][ 40/940] lr: 1.0000e-02 eta: 1 day, 11:03:19 time: 1.0891 data_time: 0.0137 memory: 15768 grad_norm: 1.6875 loss: 5.6237 top1_acc: 0.1875 top5_acc: 0.1875 loss_cls: 5.6237 2023/07/24 14:32:06 - mmengine - INFO - Epoch(train) [1][ 60/940] lr: 1.0000e-02 eta: 1 day, 8:52:34 time: 1.0935 data_time: 0.0132 memory: 15768 grad_norm: 1.8281 loss: 5.4162 top1_acc: 0.0625 top5_acc: 0.1875 loss_cls: 5.4162 2023/07/24 14:32:28 - mmengine - INFO - Epoch(train) [1][ 80/940] lr: 1.0000e-02 eta: 1 day, 7:47:54 time: 1.0957 data_time: 0.0133 memory: 15768 grad_norm: 2.0137 loss: 4.9585 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 4.9585 2023/07/24 14:32:50 - mmengine - INFO - Epoch(train) [1][100/940] lr: 1.0000e-02 eta: 1 day, 7:09:36 time: 1.0978 data_time: 0.0129 memory: 15768 grad_norm: 2.4496 loss: 4.7682 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 4.7682 2023/07/24 14:33:12 - mmengine - INFO - Epoch(train) [1][120/940] lr: 1.0000e-02 eta: 1 day, 6:43:53 time: 1.0975 data_time: 0.0129 memory: 15768 grad_norm: 2.2635 loss: 4.4779 top1_acc: 0.1875 top5_acc: 0.3750 loss_cls: 4.4779 2023/07/24 14:33:34 - mmengine - INFO - Epoch(train) [1][140/940] lr: 1.0000e-02 eta: 1 day, 6:25:26 time: 1.0977 data_time: 0.0127 memory: 15768 grad_norm: 2.3595 loss: 4.2385 top1_acc: 0.1875 top5_acc: 0.1875 loss_cls: 4.2385 2023/07/24 14:33:56 - mmengine - INFO - Epoch(train) [1][160/940] lr: 1.0000e-02 eta: 1 day, 6:11:33 time: 1.0979 data_time: 0.0126 memory: 15768 grad_norm: 2.4116 loss: 4.0808 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 4.0808 2023/07/24 14:34:18 - mmengine - INFO - Epoch(train) [1][180/940] lr: 1.0000e-02 eta: 1 day, 6:00:35 time: 1.0974 data_time: 0.0127 memory: 15768 grad_norm: 2.4610 loss: 3.9078 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.9078 2023/07/24 14:34:40 - mmengine - INFO - Epoch(train) [1][200/940] lr: 1.0000e-02 eta: 1 day, 5:52:01 time: 1.0992 data_time: 0.0131 memory: 15768 grad_norm: 2.5073 loss: 3.8402 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 3.8402 2023/07/24 14:35:02 - mmengine - INFO - Epoch(train) [1][220/940] lr: 1.0000e-02 eta: 1 day, 5:44:52 time: 1.0986 data_time: 0.0125 memory: 15768 grad_norm: 2.5449 loss: 3.7547 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.7547 2023/07/24 14:35:24 - mmengine - INFO - Epoch(train) [1][240/940] lr: 1.0000e-02 eta: 1 day, 5:38:34 time: 1.0965 data_time: 0.0130 memory: 15768 grad_norm: 2.5643 loss: 3.6757 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.6757 2023/07/24 14:35:46 - mmengine - INFO - Epoch(train) [1][260/940] lr: 1.0000e-02 eta: 1 day, 5:33:28 time: 1.0989 data_time: 0.0124 memory: 15768 grad_norm: 2.5938 loss: 3.6786 top1_acc: 0.0625 top5_acc: 0.3750 loss_cls: 3.6786 2023/07/24 14:36:08 - mmengine - INFO - Epoch(train) [1][280/940] lr: 1.0000e-02 eta: 1 day, 5:28:50 time: 1.0970 data_time: 0.0129 memory: 15768 grad_norm: 2.6261 loss: 3.3776 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 3.3776 2023/07/24 14:36:30 - mmengine - INFO - Epoch(train) [1][300/940] lr: 1.0000e-02 eta: 1 day, 5:24:47 time: 1.0971 data_time: 0.0126 memory: 15768 grad_norm: 2.6333 loss: 3.3622 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 3.3622 2023/07/24 14:36:52 - mmengine - INFO - Epoch(train) [1][320/940] lr: 1.0000e-02 eta: 1 day, 5:21:10 time: 1.0968 data_time: 0.0127 memory: 15768 grad_norm: 2.6582 loss: 3.3977 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 3.3977 2023/07/24 14:37:14 - mmengine - INFO - Epoch(train) [1][340/940] lr: 1.0000e-02 eta: 1 day, 5:18:03 time: 1.0982 data_time: 0.0125 memory: 15768 grad_norm: 2.6847 loss: 3.1750 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.1750 2023/07/24 14:37:36 - mmengine - INFO - Epoch(train) [1][360/940] lr: 1.0000e-02 eta: 1 day, 5:15:13 time: 1.0978 data_time: 0.0131 memory: 15768 grad_norm: 2.6975 loss: 3.1452 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1452 2023/07/24 14:37:58 - mmengine - INFO - Epoch(train) [1][380/940] lr: 1.0000e-02 eta: 1 day, 5:12:38 time: 1.0979 data_time: 0.0125 memory: 15768 grad_norm: 2.7308 loss: 2.9875 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.9875 2023/07/24 14:38:20 - mmengine - INFO - Epoch(train) [1][400/940] lr: 1.0000e-02 eta: 1 day, 5:10:18 time: 1.0980 data_time: 0.0125 memory: 15768 grad_norm: 2.7344 loss: 3.1009 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 3.1009 2023/07/24 14:38:42 - mmengine - INFO - Epoch(train) [1][420/940] lr: 1.0000e-02 eta: 1 day, 5:08:04 time: 1.0969 data_time: 0.0125 memory: 15768 grad_norm: 2.7409 loss: 2.9990 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9990 2023/07/24 14:39:03 - mmengine - INFO - Epoch(train) [1][440/940] lr: 1.0000e-02 eta: 1 day, 5:05:59 time: 1.0968 data_time: 0.0125 memory: 15768 grad_norm: 2.7578 loss: 2.9358 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9358 2023/07/24 14:39:25 - mmengine - INFO - Epoch(train) [1][460/940] lr: 1.0000e-02 eta: 1 day, 5:04:07 time: 1.0976 data_time: 0.0125 memory: 15768 grad_norm: 2.7487 loss: 2.9811 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.9811 2023/07/24 14:39:47 - mmengine - INFO - Epoch(train) [1][480/940] lr: 1.0000e-02 eta: 1 day, 5:02:18 time: 1.0966 data_time: 0.0126 memory: 15768 grad_norm: 2.7552 loss: 2.6735 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 2.6735 2023/07/24 14:40:09 - mmengine - INFO - Epoch(train) [1][500/940] lr: 1.0000e-02 eta: 1 day, 5:00:38 time: 1.0971 data_time: 0.0123 memory: 15768 grad_norm: 2.7835 loss: 3.1573 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1573 2023/07/24 14:40:31 - mmengine - INFO - Epoch(train) [1][520/940] lr: 1.0000e-02 eta: 1 day, 4:59:09 time: 1.0985 data_time: 0.0124 memory: 15768 grad_norm: 2.8057 loss: 2.9077 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.9077 2023/07/24 14:40:53 - mmengine - INFO - Epoch(train) [1][540/940] lr: 1.0000e-02 eta: 1 day, 4:57:41 time: 1.0972 data_time: 0.0125 memory: 15768 grad_norm: 2.8004 loss: 2.8869 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.8869 2023/07/24 14:41:15 - mmengine - INFO - Epoch(train) [1][560/940] lr: 1.0000e-02 eta: 1 day, 4:56:17 time: 1.0972 data_time: 0.0129 memory: 15768 grad_norm: 2.8307 loss: 2.9385 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9385 2023/07/24 14:41:37 - mmengine - INFO - Epoch(train) [1][580/940] lr: 1.0000e-02 eta: 1 day, 4:55:03 time: 1.0987 data_time: 0.0127 memory: 15768 grad_norm: 2.8400 loss: 3.0132 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 3.0132 2023/07/24 14:41:59 - mmengine - INFO - Epoch(train) [1][600/940] lr: 1.0000e-02 eta: 1 day, 4:53:57 time: 1.1004 data_time: 0.0122 memory: 15768 grad_norm: 2.8534 loss: 2.7662 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.7662 2023/07/24 14:42:21 - mmengine - INFO - Epoch(train) [1][620/940] lr: 1.0000e-02 eta: 1 day, 4:53:02 time: 1.1029 data_time: 0.0128 memory: 15768 grad_norm: 2.8578 loss: 2.8414 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.8414 2023/07/24 14:42:43 - mmengine - INFO - Epoch(train) [1][640/940] lr: 1.0000e-02 eta: 1 day, 4:51:54 time: 1.0979 data_time: 0.0123 memory: 15768 grad_norm: 2.8585 loss: 2.8531 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.8531 2023/07/24 14:43:05 - mmengine - INFO - Epoch(train) [1][660/940] lr: 1.0000e-02 eta: 1 day, 4:51:02 time: 1.1025 data_time: 0.0125 memory: 15768 grad_norm: 2.8606 loss: 2.6185 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.6185 2023/07/24 14:43:27 - mmengine - INFO - Epoch(train) [1][680/940] lr: 1.0000e-02 eta: 1 day, 4:49:55 time: 1.0966 data_time: 0.0123 memory: 15768 grad_norm: 2.8824 loss: 2.6572 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.6572 2023/07/24 14:43:49 - mmengine - INFO - Epoch(train) [1][700/940] lr: 1.0000e-02 eta: 1 day, 4:48:54 time: 1.0978 data_time: 0.0124 memory: 15768 grad_norm: 2.9351 loss: 2.5826 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.5826 2023/07/24 14:44:11 - mmengine - INFO - Epoch(train) [1][720/940] lr: 1.0000e-02 eta: 1 day, 4:47:52 time: 1.0965 data_time: 0.0124 memory: 15768 grad_norm: 2.9067 loss: 2.7168 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.7168 2023/07/24 14:44:33 - mmengine - INFO - Epoch(train) [1][740/940] lr: 1.0000e-02 eta: 1 day, 4:46:52 time: 1.0964 data_time: 0.0123 memory: 15768 grad_norm: 2.8824 loss: 2.6397 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.6397 2023/07/24 14:44:55 - mmengine - INFO - Epoch(train) [1][760/940] lr: 1.0000e-02 eta: 1 day, 4:45:53 time: 1.0961 data_time: 0.0126 memory: 15768 grad_norm: 2.9525 loss: 2.4401 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.4401 2023/07/24 14:45:17 - mmengine - INFO - Epoch(train) [1][780/940] lr: 1.0000e-02 eta: 1 day, 4:44:53 time: 1.0948 data_time: 0.0122 memory: 15768 grad_norm: 2.9430 loss: 2.7845 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.7845 2023/07/24 14:45:39 - mmengine - INFO - Epoch(train) [1][800/940] lr: 1.0000e-02 eta: 1 day, 4:44:01 time: 1.0971 data_time: 0.0128 memory: 15768 grad_norm: 2.9429 loss: 2.7403 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.7403 2023/07/24 14:46:01 - mmengine - INFO - Epoch(train) [1][820/940] lr: 1.0000e-02 eta: 1 day, 4:43:09 time: 1.0971 data_time: 0.0124 memory: 15768 grad_norm: 2.9409 loss: 2.7591 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.7591 2023/07/24 14:46:23 - mmengine - INFO - Epoch(train) [1][840/940] lr: 1.0000e-02 eta: 1 day, 4:42:19 time: 1.0970 data_time: 0.0124 memory: 15768 grad_norm: 2.9235 loss: 2.6339 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.6339 2023/07/24 14:46:45 - mmengine - INFO - Epoch(train) [1][860/940] lr: 1.0000e-02 eta: 1 day, 4:41:34 time: 1.0987 data_time: 0.0123 memory: 15768 grad_norm: 2.9707 loss: 2.7685 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.7685 2023/07/24 14:47:07 - mmengine - INFO - Epoch(train) [1][880/940] lr: 1.0000e-02 eta: 1 day, 4:40:51 time: 1.0989 data_time: 0.0130 memory: 15768 grad_norm: 2.9685 loss: 2.4469 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 2.4469 2023/07/24 14:47:28 - mmengine - INFO - Epoch(train) [1][900/940] lr: 1.0000e-02 eta: 1 day, 4:40:06 time: 1.0981 data_time: 0.0125 memory: 15768 grad_norm: 2.9917 loss: 2.8758 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8758 2023/07/24 14:47:50 - mmengine - INFO - Epoch(train) [1][920/940] lr: 1.0000e-02 eta: 1 day, 4:39:23 time: 1.0983 data_time: 0.0124 memory: 15768 grad_norm: 2.9502 loss: 2.7030 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.7030 2023/07/24 14:48:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 14:48:11 - mmengine - INFO - Epoch(train) [1][940/940] lr: 1.0000e-02 eta: 1 day, 4:37:07 time: 1.0505 data_time: 0.0122 memory: 15768 grad_norm: 3.1384 loss: 2.5845 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5845 2023/07/24 14:48:22 - mmengine - INFO - Epoch(val) [1][20/78] eta: 0:00:31 time: 0.5439 data_time: 0.3864 memory: 2147 2023/07/24 14:48:29 - mmengine - INFO - Epoch(val) [1][40/78] eta: 0:00:17 time: 0.3518 data_time: 0.1955 memory: 2147 2023/07/24 14:48:38 - mmengine - INFO - Epoch(val) [1][60/78] eta: 0:00:08 time: 0.4430 data_time: 0.2868 memory: 2147 2023/07/24 14:48:50 - mmengine - INFO - Epoch(val) [1][78/78] acc/top1: 0.4441 acc/top5: 0.7170 acc/mean1: 0.4439 data_time: 0.2635 time: 0.4186 2023/07/24 14:48:51 - mmengine - INFO - The best checkpoint with 0.4441 acc/top1 at 1 epoch is saved to best_acc_top1_epoch_1.pth. 2023/07/24 14:49:16 - mmengine - INFO - Epoch(train) [2][ 20/940] lr: 1.0000e-02 eta: 1 day, 4:41:18 time: 1.2484 data_time: 0.1546 memory: 15768 grad_norm: 3.0331 loss: 2.6847 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.6847 2023/07/24 14:49:38 - mmengine - INFO - Epoch(train) [2][ 40/940] lr: 1.0000e-02 eta: 1 day, 4:40:30 time: 1.0965 data_time: 0.0126 memory: 15768 grad_norm: 2.9729 loss: 2.6148 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.6148 2023/07/24 14:50:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 14:50:00 - mmengine - INFO - Epoch(train) [2][ 60/940] lr: 1.0000e-02 eta: 1 day, 4:39:47 time: 1.0984 data_time: 0.0127 memory: 15768 grad_norm: 2.9650 loss: 2.5754 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5754 2023/07/24 14:50:21 - mmengine - INFO - Epoch(train) [2][ 80/940] lr: 1.0000e-02 eta: 1 day, 4:39:07 time: 1.0995 data_time: 0.0129 memory: 15768 grad_norm: 2.9985 loss: 2.5396 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.5396 2023/07/24 14:50:43 - mmengine - INFO - Epoch(train) [2][100/940] lr: 1.0000e-02 eta: 1 day, 4:38:23 time: 1.0974 data_time: 0.0125 memory: 15768 grad_norm: 3.0032 loss: 2.4959 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.4959 2023/07/24 14:51:05 - mmengine - INFO - Epoch(train) [2][120/940] lr: 1.0000e-02 eta: 1 day, 4:37:42 time: 1.0983 data_time: 0.0126 memory: 15768 grad_norm: 2.9998 loss: 2.6580 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.6580 2023/07/24 14:51:27 - mmengine - INFO - Epoch(train) [2][140/940] lr: 1.0000e-02 eta: 1 day, 4:37:05 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 3.0220 loss: 2.4846 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.4846 2023/07/24 14:51:49 - mmengine - INFO - Epoch(train) [2][160/940] lr: 1.0000e-02 eta: 1 day, 4:36:30 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.0003 loss: 2.3689 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.3689 2023/07/24 14:52:11 - mmengine - INFO - Epoch(train) [2][180/940] lr: 1.0000e-02 eta: 1 day, 4:35:50 time: 1.0978 data_time: 0.0124 memory: 15768 grad_norm: 3.0298 loss: 2.2682 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2682 2023/07/24 14:52:33 - mmengine - INFO - Epoch(train) [2][200/940] lr: 1.0000e-02 eta: 1 day, 4:35:12 time: 1.0984 data_time: 0.0131 memory: 15768 grad_norm: 3.0220 loss: 2.6498 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.6498 2023/07/24 14:52:55 - mmengine - INFO - Epoch(train) [2][220/940] lr: 1.0000e-02 eta: 1 day, 4:34:32 time: 1.0975 data_time: 0.0121 memory: 15768 grad_norm: 3.0817 loss: 2.4646 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4646 2023/07/24 14:53:17 - mmengine - INFO - Epoch(train) [2][240/940] lr: 1.0000e-02 eta: 1 day, 4:34:01 time: 1.1026 data_time: 0.0132 memory: 15768 grad_norm: 3.0594 loss: 2.3127 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3127 2023/07/24 14:53:39 - mmengine - INFO - Epoch(train) [2][260/940] lr: 1.0000e-02 eta: 1 day, 4:33:22 time: 1.0970 data_time: 0.0124 memory: 15768 grad_norm: 3.0423 loss: 2.2398 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.2398 2023/07/24 14:54:01 - mmengine - INFO - Epoch(train) [2][280/940] lr: 1.0000e-02 eta: 1 day, 4:32:44 time: 1.0971 data_time: 0.0123 memory: 15768 grad_norm: 3.0616 loss: 2.4379 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4379 2023/07/24 14:54:23 - mmengine - INFO - Epoch(train) [2][300/940] lr: 1.0000e-02 eta: 1 day, 4:32:06 time: 1.0973 data_time: 0.0124 memory: 15768 grad_norm: 3.0576 loss: 2.5540 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5540 2023/07/24 14:54:45 - mmengine - INFO - Epoch(train) [2][320/940] lr: 1.0000e-02 eta: 1 day, 4:31:28 time: 1.0968 data_time: 0.0127 memory: 15768 grad_norm: 3.0651 loss: 2.5481 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.5481 2023/07/24 14:55:07 - mmengine - INFO - Epoch(train) [2][340/940] lr: 1.0000e-02 eta: 1 day, 4:30:52 time: 1.0977 data_time: 0.0125 memory: 15768 grad_norm: 3.0401 loss: 2.5684 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5684 2023/07/24 14:55:29 - mmengine - INFO - Epoch(train) [2][360/940] lr: 1.0000e-02 eta: 1 day, 4:30:16 time: 1.0973 data_time: 0.0127 memory: 15768 grad_norm: 3.0545 loss: 2.4079 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4079 2023/07/24 14:55:51 - mmengine - INFO - Epoch(train) [2][380/940] lr: 1.0000e-02 eta: 1 day, 4:29:40 time: 1.0972 data_time: 0.0125 memory: 15768 grad_norm: 3.0373 loss: 2.4501 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.4501 2023/07/24 14:56:13 - mmengine - INFO - Epoch(train) [2][400/940] lr: 1.0000e-02 eta: 1 day, 4:29:04 time: 1.0963 data_time: 0.0125 memory: 15768 grad_norm: 3.0964 loss: 2.3485 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3485 2023/07/24 14:56:35 - mmengine - INFO - Epoch(train) [2][420/940] lr: 1.0000e-02 eta: 1 day, 4:28:29 time: 1.0977 data_time: 0.0121 memory: 15768 grad_norm: 3.0757 loss: 2.1902 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.1902 2023/07/24 14:56:57 - mmengine - INFO - Epoch(train) [2][440/940] lr: 1.0000e-02 eta: 1 day, 4:27:56 time: 1.0985 data_time: 0.0125 memory: 15768 grad_norm: 3.0517 loss: 2.3077 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.3077 2023/07/24 14:57:19 - mmengine - INFO - Epoch(train) [2][460/940] lr: 1.0000e-02 eta: 1 day, 4:27:24 time: 1.0990 data_time: 0.0124 memory: 15768 grad_norm: 3.0149 loss: 2.0322 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0322 2023/07/24 14:57:41 - mmengine - INFO - Epoch(train) [2][480/940] lr: 1.0000e-02 eta: 1 day, 4:26:50 time: 1.0966 data_time: 0.0125 memory: 15768 grad_norm: 3.0678 loss: 2.3669 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3669 2023/07/24 14:58:03 - mmengine - INFO - Epoch(train) [2][500/940] lr: 1.0000e-02 eta: 1 day, 4:26:17 time: 1.0982 data_time: 0.0124 memory: 15768 grad_norm: 3.0879 loss: 2.1551 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1551 2023/07/24 14:58:25 - mmengine - INFO - Epoch(train) [2][520/940] lr: 1.0000e-02 eta: 1 day, 4:25:46 time: 1.0989 data_time: 0.0129 memory: 15768 grad_norm: 3.0716 loss: 2.1727 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1727 2023/07/24 14:58:47 - mmengine - INFO - Epoch(train) [2][540/940] lr: 1.0000e-02 eta: 1 day, 4:25:17 time: 1.1002 data_time: 0.0123 memory: 15768 grad_norm: 3.1199 loss: 2.4516 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4516 2023/07/24 14:59:09 - mmengine - INFO - Epoch(train) [2][560/940] lr: 1.0000e-02 eta: 1 day, 4:24:44 time: 1.0971 data_time: 0.0123 memory: 15768 grad_norm: 3.1248 loss: 2.3399 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.3399 2023/07/24 14:59:31 - mmengine - INFO - Epoch(train) [2][580/940] lr: 1.0000e-02 eta: 1 day, 4:24:12 time: 1.0978 data_time: 0.0124 memory: 15768 grad_norm: 3.0752 loss: 2.3539 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.3539 2023/07/24 14:59:53 - mmengine - INFO - Epoch(train) [2][600/940] lr: 1.0000e-02 eta: 1 day, 4:23:40 time: 1.0977 data_time: 0.0125 memory: 15768 grad_norm: 3.0866 loss: 2.4563 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4563 2023/07/24 15:00:14 - mmengine - INFO - Epoch(train) [2][620/940] lr: 1.0000e-02 eta: 1 day, 4:23:08 time: 1.0975 data_time: 0.0123 memory: 15768 grad_norm: 3.0798 loss: 2.3588 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3588 2023/07/24 15:00:36 - mmengine - INFO - Epoch(train) [2][640/940] lr: 1.0000e-02 eta: 1 day, 4:22:37 time: 1.0977 data_time: 0.0124 memory: 15768 grad_norm: 3.0492 loss: 2.2479 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.2479 2023/07/24 15:00:58 - mmengine - INFO - Epoch(train) [2][660/940] lr: 1.0000e-02 eta: 1 day, 4:22:09 time: 1.1003 data_time: 0.0124 memory: 15768 grad_norm: 3.0693 loss: 2.3255 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3255 2023/07/24 15:01:20 - mmengine - INFO - Epoch(train) [2][680/940] lr: 1.0000e-02 eta: 1 day, 4:21:38 time: 1.0968 data_time: 0.0124 memory: 15768 grad_norm: 3.0674 loss: 2.3141 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3141 2023/07/24 15:01:42 - mmengine - INFO - Epoch(train) [2][700/940] lr: 1.0000e-02 eta: 1 day, 4:21:06 time: 1.0972 data_time: 0.0126 memory: 15768 grad_norm: 3.1058 loss: 2.3462 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3462 2023/07/24 15:02:04 - mmengine - INFO - Epoch(train) [2][720/940] lr: 1.0000e-02 eta: 1 day, 4:20:35 time: 1.0971 data_time: 0.0126 memory: 15768 grad_norm: 3.1277 loss: 2.1189 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1189 2023/07/24 15:02:26 - mmengine - INFO - Epoch(train) [2][740/940] lr: 1.0000e-02 eta: 1 day, 4:20:04 time: 1.0961 data_time: 0.0126 memory: 15768 grad_norm: 3.0985 loss: 2.3981 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3981 2023/07/24 15:02:48 - mmengine - INFO - Epoch(train) [2][760/940] lr: 1.0000e-02 eta: 1 day, 4:19:33 time: 1.0971 data_time: 0.0126 memory: 15768 grad_norm: 3.0930 loss: 2.3447 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3447 2023/07/24 15:03:10 - mmengine - INFO - Epoch(train) [2][780/940] lr: 1.0000e-02 eta: 1 day, 4:19:02 time: 1.0965 data_time: 0.0128 memory: 15768 grad_norm: 3.1404 loss: 2.0603 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0603 2023/07/24 15:03:32 - mmengine - INFO - Epoch(train) [2][800/940] lr: 1.0000e-02 eta: 1 day, 4:18:32 time: 1.0969 data_time: 0.0126 memory: 15768 grad_norm: 3.0751 loss: 2.2216 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2216 2023/07/24 15:03:54 - mmengine - INFO - Epoch(train) [2][820/940] lr: 1.0000e-02 eta: 1 day, 4:18:03 time: 1.0983 data_time: 0.0125 memory: 15768 grad_norm: 3.0886 loss: 2.1810 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1810 2023/07/24 15:04:16 - mmengine - INFO - Epoch(train) [2][840/940] lr: 1.0000e-02 eta: 1 day, 4:17:36 time: 1.1003 data_time: 0.0124 memory: 15768 grad_norm: 3.1111 loss: 2.3603 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3603 2023/07/24 15:04:38 - mmengine - INFO - Epoch(train) [2][860/940] lr: 1.0000e-02 eta: 1 day, 4:17:06 time: 1.0967 data_time: 0.0127 memory: 15768 grad_norm: 3.0783 loss: 2.2926 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2926 2023/07/24 15:05:00 - mmengine - INFO - Epoch(train) [2][880/940] lr: 1.0000e-02 eta: 1 day, 4:16:36 time: 1.0959 data_time: 0.0125 memory: 15768 grad_norm: 3.0949 loss: 2.1128 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1128 2023/07/24 15:05:22 - mmengine - INFO - Epoch(train) [2][900/940] lr: 1.0000e-02 eta: 1 day, 4:16:07 time: 1.0974 data_time: 0.0124 memory: 15768 grad_norm: 3.1380 loss: 2.3123 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3123 2023/07/24 15:05:44 - mmengine - INFO - Epoch(train) [2][920/940] lr: 1.0000e-02 eta: 1 day, 4:15:40 time: 1.1001 data_time: 0.0125 memory: 15768 grad_norm: 3.1572 loss: 2.3713 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3713 2023/07/24 15:06:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 15:06:05 - mmengine - INFO - Epoch(train) [2][940/940] lr: 1.0000e-02 eta: 1 day, 4:14:27 time: 1.0515 data_time: 0.0121 memory: 15768 grad_norm: 3.2556 loss: 2.3666 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3666 2023/07/24 15:06:14 - mmengine - INFO - Epoch(val) [2][20/78] eta: 0:00:28 time: 0.4830 data_time: 0.3263 memory: 2147 2023/07/24 15:06:21 - mmengine - INFO - Epoch(val) [2][40/78] eta: 0:00:15 time: 0.3201 data_time: 0.1641 memory: 2147 2023/07/24 15:06:29 - mmengine - INFO - Epoch(val) [2][60/78] eta: 0:00:07 time: 0.4220 data_time: 0.2661 memory: 2147 2023/07/24 15:06:41 - mmengine - INFO - Epoch(val) [2][78/78] acc/top1: 0.5345 acc/top5: 0.7920 acc/mean1: 0.5342 data_time: 0.2305 time: 0.3840 2023/07/24 15:06:41 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_1.pth is removed 2023/07/24 15:06:42 - mmengine - INFO - The best checkpoint with 0.5345 acc/top1 at 2 epoch is saved to best_acc_top1_epoch_2.pth. 2023/07/24 15:07:07 - mmengine - INFO - Epoch(train) [3][ 20/940] lr: 1.0000e-02 eta: 1 day, 4:16:36 time: 1.2595 data_time: 0.1339 memory: 15768 grad_norm: 3.1045 loss: 2.0299 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0299 2023/07/24 15:07:29 - mmengine - INFO - Epoch(train) [3][ 40/940] lr: 1.0000e-02 eta: 1 day, 4:16:07 time: 1.0980 data_time: 0.0124 memory: 15768 grad_norm: 3.1184 loss: 2.3267 top1_acc: 0.3750 top5_acc: 0.4375 loss_cls: 2.3267 2023/07/24 15:07:51 - mmengine - INFO - Epoch(train) [3][ 60/940] lr: 1.0000e-02 eta: 1 day, 4:15:38 time: 1.0987 data_time: 0.0124 memory: 15768 grad_norm: 3.1324 loss: 2.1441 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1441 2023/07/24 15:08:13 - mmengine - INFO - Epoch(train) [3][ 80/940] lr: 1.0000e-02 eta: 1 day, 4:15:09 time: 1.0976 data_time: 0.0126 memory: 15768 grad_norm: 3.1329 loss: 2.0521 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0521 2023/07/24 15:08:35 - mmengine - INFO - Epoch(train) [3][100/940] lr: 1.0000e-02 eta: 1 day, 4:14:41 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.1429 loss: 2.1556 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1556 2023/07/24 15:08:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 15:08:57 - mmengine - INFO - Epoch(train) [3][120/940] lr: 1.0000e-02 eta: 1 day, 4:14:12 time: 1.0973 data_time: 0.0129 memory: 15768 grad_norm: 3.1170 loss: 2.2906 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2906 2023/07/24 15:09:19 - mmengine - INFO - Epoch(train) [3][140/940] lr: 1.0000e-02 eta: 1 day, 4:13:44 time: 1.0986 data_time: 0.0123 memory: 15768 grad_norm: 3.1338 loss: 2.1674 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1674 2023/07/24 15:09:41 - mmengine - INFO - Epoch(train) [3][160/940] lr: 1.0000e-02 eta: 1 day, 4:13:15 time: 1.0971 data_time: 0.0126 memory: 15768 grad_norm: 3.1476 loss: 2.2297 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2297 2023/07/24 15:10:03 - mmengine - INFO - Epoch(train) [3][180/940] lr: 1.0000e-02 eta: 1 day, 4:12:47 time: 1.0977 data_time: 0.0128 memory: 15768 grad_norm: 3.1454 loss: 2.0771 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.0771 2023/07/24 15:10:25 - mmengine - INFO - Epoch(train) [3][200/940] lr: 1.0000e-02 eta: 1 day, 4:12:19 time: 1.0987 data_time: 0.0125 memory: 15768 grad_norm: 3.1851 loss: 2.1245 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 2.1245 2023/07/24 15:10:47 - mmengine - INFO - Epoch(train) [3][220/940] lr: 1.0000e-02 eta: 1 day, 4:11:51 time: 1.0972 data_time: 0.0122 memory: 15768 grad_norm: 3.1718 loss: 2.2391 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2391 2023/07/24 15:11:09 - mmengine - INFO - Epoch(train) [3][240/940] lr: 1.0000e-02 eta: 1 day, 4:11:23 time: 1.0986 data_time: 0.0126 memory: 15768 grad_norm: 3.1624 loss: 2.0468 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0468 2023/07/24 15:11:31 - mmengine - INFO - Epoch(train) [3][260/940] lr: 1.0000e-02 eta: 1 day, 4:10:56 time: 1.0988 data_time: 0.0125 memory: 15768 grad_norm: 3.1420 loss: 2.1306 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.1306 2023/07/24 15:11:53 - mmengine - INFO - Epoch(train) [3][280/940] lr: 1.0000e-02 eta: 1 day, 4:10:29 time: 1.0982 data_time: 0.0128 memory: 15768 grad_norm: 3.1688 loss: 2.2219 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2219 2023/07/24 15:12:15 - mmengine - INFO - Epoch(train) [3][300/940] lr: 1.0000e-02 eta: 1 day, 4:10:05 time: 1.1027 data_time: 0.0126 memory: 15768 grad_norm: 3.1754 loss: 2.1561 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1561 2023/07/24 15:12:37 - mmengine - INFO - Epoch(train) [3][320/940] lr: 1.0000e-02 eta: 1 day, 4:09:39 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 3.1580 loss: 2.3772 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3772 2023/07/24 15:12:59 - mmengine - INFO - Epoch(train) [3][340/940] lr: 1.0000e-02 eta: 1 day, 4:09:11 time: 1.0973 data_time: 0.0123 memory: 15768 grad_norm: 3.1387 loss: 2.1291 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1291 2023/07/24 15:13:21 - mmengine - INFO - Epoch(train) [3][360/940] lr: 1.0000e-02 eta: 1 day, 4:08:46 time: 1.1003 data_time: 0.0126 memory: 15768 grad_norm: 3.1353 loss: 2.2950 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 2.2950 2023/07/24 15:13:43 - mmengine - INFO - Epoch(train) [3][380/940] lr: 1.0000e-02 eta: 1 day, 4:08:19 time: 1.0986 data_time: 0.0126 memory: 15768 grad_norm: 3.1498 loss: 2.1587 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.1587 2023/07/24 15:14:05 - mmengine - INFO - Epoch(train) [3][400/940] lr: 1.0000e-02 eta: 1 day, 4:07:53 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 3.1754 loss: 2.0926 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0926 2023/07/24 15:14:27 - mmengine - INFO - Epoch(train) [3][420/940] lr: 1.0000e-02 eta: 1 day, 4:07:27 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 3.1717 loss: 2.1993 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1993 2023/07/24 15:14:49 - mmengine - INFO - Epoch(train) [3][440/940] lr: 1.0000e-02 eta: 1 day, 4:07:01 time: 1.0999 data_time: 0.0128 memory: 15768 grad_norm: 3.1550 loss: 2.2273 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2273 2023/07/24 15:15:11 - mmengine - INFO - Epoch(train) [3][460/940] lr: 1.0000e-02 eta: 1 day, 4:06:37 time: 1.1008 data_time: 0.0128 memory: 15768 grad_norm: 3.2017 loss: 2.1931 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1931 2023/07/24 15:15:33 - mmengine - INFO - Epoch(train) [3][480/940] lr: 1.0000e-02 eta: 1 day, 4:06:10 time: 1.0984 data_time: 0.0126 memory: 15768 grad_norm: 3.1664 loss: 2.0239 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.0239 2023/07/24 15:15:55 - mmengine - INFO - Epoch(train) [3][500/940] lr: 1.0000e-02 eta: 1 day, 4:05:44 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 3.1447 loss: 2.1776 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1776 2023/07/24 15:16:17 - mmengine - INFO - Epoch(train) [3][520/940] lr: 1.0000e-02 eta: 1 day, 4:05:21 time: 1.1033 data_time: 0.0125 memory: 15768 grad_norm: 3.1779 loss: 2.1287 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1287 2023/07/24 15:16:39 - mmengine - INFO - Epoch(train) [3][540/940] lr: 1.0000e-02 eta: 1 day, 4:04:56 time: 1.0992 data_time: 0.0125 memory: 15768 grad_norm: 3.1822 loss: 2.0195 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0195 2023/07/24 15:17:01 - mmengine - INFO - Epoch(train) [3][560/940] lr: 1.0000e-02 eta: 1 day, 4:04:30 time: 1.0992 data_time: 0.0126 memory: 15768 grad_norm: 3.1900 loss: 2.2323 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.2323 2023/07/24 15:17:23 - mmengine - INFO - Epoch(train) [3][580/940] lr: 1.0000e-02 eta: 1 day, 4:04:06 time: 1.1012 data_time: 0.0127 memory: 15768 grad_norm: 3.2108 loss: 2.0720 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0720 2023/07/24 15:17:45 - mmengine - INFO - Epoch(train) [3][600/940] lr: 1.0000e-02 eta: 1 day, 4:03:39 time: 1.0979 data_time: 0.0125 memory: 15768 grad_norm: 3.1933 loss: 2.1792 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1792 2023/07/24 15:18:07 - mmengine - INFO - Epoch(train) [3][620/940] lr: 1.0000e-02 eta: 1 day, 4:03:14 time: 1.0986 data_time: 0.0126 memory: 15768 grad_norm: 3.1557 loss: 2.0503 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.0503 2023/07/24 15:18:29 - mmengine - INFO - Epoch(train) [3][640/940] lr: 1.0000e-02 eta: 1 day, 4:02:48 time: 1.0987 data_time: 0.0130 memory: 15768 grad_norm: 3.1515 loss: 2.0917 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0917 2023/07/24 15:18:51 - mmengine - INFO - Epoch(train) [3][660/940] lr: 1.0000e-02 eta: 1 day, 4:02:21 time: 1.0969 data_time: 0.0124 memory: 15768 grad_norm: 3.2187 loss: 2.2284 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2284 2023/07/24 15:19:13 - mmengine - INFO - Epoch(train) [3][680/940] lr: 1.0000e-02 eta: 1 day, 4:01:56 time: 1.1001 data_time: 0.0126 memory: 15768 grad_norm: 3.1682 loss: 1.9579 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9579 2023/07/24 15:19:35 - mmengine - INFO - Epoch(train) [3][700/940] lr: 1.0000e-02 eta: 1 day, 4:01:32 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.1983 loss: 2.2374 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2374 2023/07/24 15:19:57 - mmengine - INFO - Epoch(train) [3][720/940] lr: 1.0000e-02 eta: 1 day, 4:01:06 time: 1.0986 data_time: 0.0128 memory: 15768 grad_norm: 3.1783 loss: 1.9425 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9425 2023/07/24 15:20:19 - mmengine - INFO - Epoch(train) [3][740/940] lr: 1.0000e-02 eta: 1 day, 4:00:39 time: 1.0963 data_time: 0.0126 memory: 15768 grad_norm: 3.1684 loss: 1.9672 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9672 2023/07/24 15:20:41 - mmengine - INFO - Epoch(train) [3][760/940] lr: 1.0000e-02 eta: 1 day, 4:00:14 time: 1.0993 data_time: 0.0122 memory: 15768 grad_norm: 3.1721 loss: 2.0495 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0495 2023/07/24 15:21:03 - mmengine - INFO - Epoch(train) [3][780/940] lr: 1.0000e-02 eta: 1 day, 3:59:49 time: 1.0984 data_time: 0.0123 memory: 15768 grad_norm: 3.1644 loss: 2.0964 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0964 2023/07/24 15:21:25 - mmengine - INFO - Epoch(train) [3][800/940] lr: 1.0000e-02 eta: 1 day, 3:59:22 time: 1.0970 data_time: 0.0125 memory: 15768 grad_norm: 3.1617 loss: 1.9292 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9292 2023/07/24 15:21:47 - mmengine - INFO - Epoch(train) [3][820/940] lr: 1.0000e-02 eta: 1 day, 3:58:56 time: 1.0976 data_time: 0.0131 memory: 15768 grad_norm: 3.2214 loss: 2.1200 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1200 2023/07/24 15:22:09 - mmengine - INFO - Epoch(train) [3][840/940] lr: 1.0000e-02 eta: 1 day, 3:58:32 time: 1.1009 data_time: 0.0124 memory: 15768 grad_norm: 3.1829 loss: 2.0417 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0417 2023/07/24 15:22:31 - mmengine - INFO - Epoch(train) [3][860/940] lr: 1.0000e-02 eta: 1 day, 3:58:08 time: 1.0992 data_time: 0.0126 memory: 15768 grad_norm: 3.1505 loss: 2.0617 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0617 2023/07/24 15:22:52 - mmengine - INFO - Epoch(train) [3][880/940] lr: 1.0000e-02 eta: 1 day, 3:57:42 time: 1.0975 data_time: 0.0128 memory: 15768 grad_norm: 3.1960 loss: 2.1451 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.1451 2023/07/24 15:23:14 - mmengine - INFO - Epoch(train) [3][900/940] lr: 1.0000e-02 eta: 1 day, 3:57:16 time: 1.0983 data_time: 0.0124 memory: 15768 grad_norm: 3.1927 loss: 2.1786 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1786 2023/07/24 15:23:36 - mmengine - INFO - Epoch(train) [3][920/940] lr: 1.0000e-02 eta: 1 day, 3:56:51 time: 1.0977 data_time: 0.0126 memory: 15768 grad_norm: 3.1767 loss: 1.9861 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9861 2023/07/24 15:23:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 15:23:58 - mmengine - INFO - Epoch(train) [3][940/940] lr: 1.0000e-02 eta: 1 day, 3:55:58 time: 1.0559 data_time: 0.0116 memory: 15768 grad_norm: 3.3135 loss: 2.1829 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.1829 2023/07/24 15:23:58 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/07/24 15:24:08 - mmengine - INFO - Epoch(val) [3][20/78] eta: 0:00:27 time: 0.4727 data_time: 0.3164 memory: 2147 2023/07/24 15:24:14 - mmengine - INFO - Epoch(val) [3][40/78] eta: 0:00:15 time: 0.3196 data_time: 0.1636 memory: 2147 2023/07/24 15:24:23 - mmengine - INFO - Epoch(val) [3][60/78] eta: 0:00:07 time: 0.4129 data_time: 0.2567 memory: 2147 2023/07/24 15:24:34 - mmengine - INFO - Epoch(val) [3][78/78] acc/top1: 0.5765 acc/top5: 0.8241 acc/mean1: 0.5763 data_time: 0.2230 time: 0.3765 2023/07/24 15:24:34 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_2.pth is removed 2023/07/24 15:24:35 - mmengine - INFO - The best checkpoint with 0.5765 acc/top1 at 3 epoch is saved to best_acc_top1_epoch_3.pth. 2023/07/24 15:25:00 - mmengine - INFO - Epoch(train) [4][ 20/940] lr: 1.0000e-02 eta: 1 day, 3:57:06 time: 1.2438 data_time: 0.1378 memory: 15768 grad_norm: 3.1345 loss: 2.1035 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.1035 2023/07/24 15:25:22 - mmengine - INFO - Epoch(train) [4][ 40/940] lr: 1.0000e-02 eta: 1 day, 3:56:41 time: 1.0985 data_time: 0.0127 memory: 15768 grad_norm: 3.1420 loss: 1.9651 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9651 2023/07/24 15:25:44 - mmengine - INFO - Epoch(train) [4][ 60/940] lr: 1.0000e-02 eta: 1 day, 3:56:17 time: 1.1000 data_time: 0.0126 memory: 15768 grad_norm: 3.1789 loss: 2.1263 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1263 2023/07/24 15:26:06 - mmengine - INFO - Epoch(train) [4][ 80/940] lr: 1.0000e-02 eta: 1 day, 3:55:52 time: 1.0996 data_time: 0.0128 memory: 15768 grad_norm: 3.1919 loss: 1.7756 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7756 2023/07/24 15:26:28 - mmengine - INFO - Epoch(train) [4][100/940] lr: 1.0000e-02 eta: 1 day, 3:55:27 time: 1.0986 data_time: 0.0129 memory: 15768 grad_norm: 3.1761 loss: 1.9804 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9804 2023/07/24 15:26:51 - mmengine - INFO - Epoch(train) [4][120/940] lr: 1.0000e-02 eta: 1 day, 3:55:36 time: 1.1543 data_time: 0.0131 memory: 15768 grad_norm: 3.1740 loss: 1.8917 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8917 2023/07/24 15:27:14 - mmengine - INFO - Epoch(train) [4][140/940] lr: 1.0000e-02 eta: 1 day, 3:55:49 time: 1.1618 data_time: 0.0130 memory: 15768 grad_norm: 3.2102 loss: 2.0800 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0800 2023/07/24 15:27:38 - mmengine - INFO - Epoch(train) [4][160/940] lr: 1.0000e-02 eta: 1 day, 3:56:03 time: 1.1635 data_time: 0.0126 memory: 15768 grad_norm: 3.1909 loss: 2.0253 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0253 2023/07/24 15:28:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 15:28:01 - mmengine - INFO - Epoch(train) [4][180/940] lr: 1.0000e-02 eta: 1 day, 3:56:19 time: 1.1668 data_time: 0.0126 memory: 15768 grad_norm: 3.2351 loss: 2.2394 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2394 2023/07/24 15:28:24 - mmengine - INFO - Epoch(train) [4][200/940] lr: 1.0000e-02 eta: 1 day, 3:56:27 time: 1.1549 data_time: 0.0131 memory: 15768 grad_norm: 3.2078 loss: 2.1161 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1161 2023/07/24 15:28:46 - mmengine - INFO - Epoch(train) [4][220/940] lr: 1.0000e-02 eta: 1 day, 3:56:00 time: 1.0986 data_time: 0.0125 memory: 15768 grad_norm: 3.2457 loss: 2.0066 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0066 2023/07/24 15:29:08 - mmengine - INFO - Epoch(train) [4][240/940] lr: 1.0000e-02 eta: 1 day, 3:55:34 time: 1.0995 data_time: 0.0127 memory: 15768 grad_norm: 3.1727 loss: 1.9269 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9269 2023/07/24 15:29:30 - mmengine - INFO - Epoch(train) [4][260/940] lr: 1.0000e-02 eta: 1 day, 3:55:10 time: 1.1008 data_time: 0.0128 memory: 15768 grad_norm: 3.2515 loss: 2.0412 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0412 2023/07/24 15:29:52 - mmengine - INFO - Epoch(train) [4][280/940] lr: 1.0000e-02 eta: 1 day, 3:54:45 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 3.1917 loss: 2.0399 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.0399 2023/07/24 15:30:14 - mmengine - INFO - Epoch(train) [4][300/940] lr: 1.0000e-02 eta: 1 day, 3:54:18 time: 1.0978 data_time: 0.0130 memory: 15768 grad_norm: 3.2335 loss: 1.9129 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9129 2023/07/24 15:30:36 - mmengine - INFO - Epoch(train) [4][320/940] lr: 1.0000e-02 eta: 1 day, 3:53:52 time: 1.0984 data_time: 0.0131 memory: 15768 grad_norm: 3.2203 loss: 2.0062 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0062 2023/07/24 15:30:58 - mmengine - INFO - Epoch(train) [4][340/940] lr: 1.0000e-02 eta: 1 day, 3:53:27 time: 1.1005 data_time: 0.0127 memory: 15768 grad_norm: 3.1839 loss: 2.0771 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 2.0771 2023/07/24 15:31:20 - mmengine - INFO - Epoch(train) [4][360/940] lr: 1.0000e-02 eta: 1 day, 3:53:01 time: 1.0983 data_time: 0.0128 memory: 15768 grad_norm: 3.1963 loss: 2.0367 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0367 2023/07/24 15:31:42 - mmengine - INFO - Epoch(train) [4][380/940] lr: 1.0000e-02 eta: 1 day, 3:52:36 time: 1.0996 data_time: 0.0127 memory: 15768 grad_norm: 3.2051 loss: 1.9445 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9445 2023/07/24 15:32:04 - mmengine - INFO - Epoch(train) [4][400/940] lr: 1.0000e-02 eta: 1 day, 3:52:10 time: 1.0985 data_time: 0.0127 memory: 15768 grad_norm: 3.2199 loss: 1.7597 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7597 2023/07/24 15:32:26 - mmengine - INFO - Epoch(train) [4][420/940] lr: 1.0000e-02 eta: 1 day, 3:51:46 time: 1.1028 data_time: 0.0123 memory: 15768 grad_norm: 3.2433 loss: 1.9264 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9264 2023/07/24 15:32:48 - mmengine - INFO - Epoch(train) [4][440/940] lr: 1.0000e-02 eta: 1 day, 3:51:20 time: 1.0982 data_time: 0.0128 memory: 15768 grad_norm: 3.2080 loss: 2.1137 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 2.1137 2023/07/24 15:33:10 - mmengine - INFO - Epoch(train) [4][460/940] lr: 1.0000e-02 eta: 1 day, 3:50:55 time: 1.0992 data_time: 0.0126 memory: 15768 grad_norm: 3.2466 loss: 2.0174 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0174 2023/07/24 15:33:32 - mmengine - INFO - Epoch(train) [4][480/940] lr: 1.0000e-02 eta: 1 day, 3:50:31 time: 1.1024 data_time: 0.0127 memory: 15768 grad_norm: 3.2245 loss: 2.0947 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0947 2023/07/24 15:33:54 - mmengine - INFO - Epoch(train) [4][500/940] lr: 1.0000e-02 eta: 1 day, 3:50:07 time: 1.1003 data_time: 0.0124 memory: 15768 grad_norm: 3.2033 loss: 1.9823 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9823 2023/07/24 15:34:16 - mmengine - INFO - Epoch(train) [4][520/940] lr: 1.0000e-02 eta: 1 day, 3:49:41 time: 1.0986 data_time: 0.0125 memory: 15768 grad_norm: 3.2435 loss: 2.0041 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0041 2023/07/24 15:34:38 - mmengine - INFO - Epoch(train) [4][540/940] lr: 1.0000e-02 eta: 1 day, 3:49:17 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 3.2138 loss: 1.9420 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9420 2023/07/24 15:35:00 - mmengine - INFO - Epoch(train) [4][560/940] lr: 1.0000e-02 eta: 1 day, 3:48:52 time: 1.1008 data_time: 0.0124 memory: 15768 grad_norm: 3.1930 loss: 1.8878 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8878 2023/07/24 15:35:22 - mmengine - INFO - Epoch(train) [4][580/940] lr: 1.0000e-02 eta: 1 day, 3:48:27 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.1725 loss: 2.0631 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 2.0631 2023/07/24 15:35:44 - mmengine - INFO - Epoch(train) [4][600/940] lr: 1.0000e-02 eta: 1 day, 3:48:02 time: 1.0986 data_time: 0.0127 memory: 15768 grad_norm: 3.2378 loss: 2.0174 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.0174 2023/07/24 15:36:06 - mmengine - INFO - Epoch(train) [4][620/940] lr: 1.0000e-02 eta: 1 day, 3:47:36 time: 1.0981 data_time: 0.0130 memory: 15768 grad_norm: 3.1803 loss: 1.9571 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9571 2023/07/24 15:36:28 - mmengine - INFO - Epoch(train) [4][640/940] lr: 1.0000e-02 eta: 1 day, 3:47:11 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 3.2179 loss: 1.9114 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9114 2023/07/24 15:36:50 - mmengine - INFO - Epoch(train) [4][660/940] lr: 1.0000e-02 eta: 1 day, 3:46:46 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.2105 loss: 1.9649 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.9649 2023/07/24 15:37:12 - mmengine - INFO - Epoch(train) [4][680/940] lr: 1.0000e-02 eta: 1 day, 3:46:20 time: 1.0972 data_time: 0.0127 memory: 15768 grad_norm: 3.2531 loss: 1.9427 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9427 2023/07/24 15:37:34 - mmengine - INFO - Epoch(train) [4][700/940] lr: 1.0000e-02 eta: 1 day, 3:45:55 time: 1.0983 data_time: 0.0126 memory: 15768 grad_norm: 3.2143 loss: 1.9061 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9061 2023/07/24 15:37:56 - mmengine - INFO - Epoch(train) [4][720/940] lr: 1.0000e-02 eta: 1 day, 3:45:30 time: 1.0996 data_time: 0.0126 memory: 15768 grad_norm: 3.2644 loss: 2.0871 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.0871 2023/07/24 15:38:18 - mmengine - INFO - Epoch(train) [4][740/940] lr: 1.0000e-02 eta: 1 day, 3:45:04 time: 1.0979 data_time: 0.0126 memory: 15768 grad_norm: 3.2429 loss: 2.1017 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1017 2023/07/24 15:38:40 - mmengine - INFO - Epoch(train) [4][760/940] lr: 1.0000e-02 eta: 1 day, 3:44:39 time: 1.0976 data_time: 0.0126 memory: 15768 grad_norm: 3.2592 loss: 1.9548 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9548 2023/07/24 15:39:02 - mmengine - INFO - Epoch(train) [4][780/940] lr: 1.0000e-02 eta: 1 day, 3:44:15 time: 1.1020 data_time: 0.0129 memory: 15768 grad_norm: 3.1993 loss: 1.9436 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9436 2023/07/24 15:39:24 - mmengine - INFO - Epoch(train) [4][800/940] lr: 1.0000e-02 eta: 1 day, 3:43:50 time: 1.0984 data_time: 0.0137 memory: 15768 grad_norm: 3.2126 loss: 2.1258 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1258 2023/07/24 15:39:46 - mmengine - INFO - Epoch(train) [4][820/940] lr: 1.0000e-02 eta: 1 day, 3:43:25 time: 1.0985 data_time: 0.0128 memory: 15768 grad_norm: 3.2542 loss: 2.1102 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1102 2023/07/24 15:40:08 - mmengine - INFO - Epoch(train) [4][840/940] lr: 1.0000e-02 eta: 1 day, 3:43:00 time: 1.0991 data_time: 0.0127 memory: 15768 grad_norm: 3.2486 loss: 1.8077 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8077 2023/07/24 15:40:30 - mmengine - INFO - Epoch(train) [4][860/940] lr: 1.0000e-02 eta: 1 day, 3:42:36 time: 1.0989 data_time: 0.0128 memory: 15768 grad_norm: 3.2113 loss: 2.0571 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0571 2023/07/24 15:40:52 - mmengine - INFO - Epoch(train) [4][880/940] lr: 1.0000e-02 eta: 1 day, 3:42:12 time: 1.1012 data_time: 0.0128 memory: 15768 grad_norm: 3.2209 loss: 1.8961 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8961 2023/07/24 15:41:14 - mmengine - INFO - Epoch(train) [4][900/940] lr: 1.0000e-02 eta: 1 day, 3:41:47 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.2019 loss: 1.7735 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7735 2023/07/24 15:41:36 - mmengine - INFO - Epoch(train) [4][920/940] lr: 1.0000e-02 eta: 1 day, 3:41:22 time: 1.0981 data_time: 0.0127 memory: 15768 grad_norm: 3.2665 loss: 1.8824 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8824 2023/07/24 15:41:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 15:41:57 - mmengine - INFO - Epoch(train) [4][940/940] lr: 1.0000e-02 eta: 1 day, 3:40:36 time: 1.0540 data_time: 0.0121 memory: 15768 grad_norm: 3.3562 loss: 1.9908 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.9908 2023/07/24 15:42:06 - mmengine - INFO - Epoch(val) [4][20/78] eta: 0:00:26 time: 0.4638 data_time: 0.3074 memory: 2147 2023/07/24 15:42:13 - mmengine - INFO - Epoch(val) [4][40/78] eta: 0:00:15 time: 0.3371 data_time: 0.1811 memory: 2147 2023/07/24 15:42:21 - mmengine - INFO - Epoch(val) [4][60/78] eta: 0:00:07 time: 0.4183 data_time: 0.2621 memory: 2147 2023/07/24 15:42:33 - mmengine - INFO - Epoch(val) [4][78/78] acc/top1: 0.5366 acc/top5: 0.7797 acc/mean1: 0.5361 data_time: 0.2285 time: 0.3821 2023/07/24 15:42:59 - mmengine - INFO - Epoch(train) [5][ 20/940] lr: 1.0000e-02 eta: 1 day, 3:41:46 time: 1.2969 data_time: 0.1507 memory: 15768 grad_norm: 3.2307 loss: 1.9004 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9004 2023/07/24 15:43:21 - mmengine - INFO - Epoch(train) [5][ 40/940] lr: 1.0000e-02 eta: 1 day, 3:41:22 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 3.1643 loss: 1.7877 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7877 2023/07/24 15:43:43 - mmengine - INFO - Epoch(train) [5][ 60/940] lr: 1.0000e-02 eta: 1 day, 3:40:57 time: 1.0988 data_time: 0.0125 memory: 15768 grad_norm: 3.2437 loss: 2.0189 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0189 2023/07/24 15:44:05 - mmengine - INFO - Epoch(train) [5][ 80/940] lr: 1.0000e-02 eta: 1 day, 3:40:31 time: 1.0972 data_time: 0.0126 memory: 15768 grad_norm: 3.2327 loss: 1.9210 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9210 2023/07/24 15:44:27 - mmengine - INFO - Epoch(train) [5][100/940] lr: 1.0000e-02 eta: 1 day, 3:40:07 time: 1.1020 data_time: 0.0127 memory: 15768 grad_norm: 3.1989 loss: 1.8642 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8642 2023/07/24 15:44:49 - mmengine - INFO - Epoch(train) [5][120/940] lr: 1.0000e-02 eta: 1 day, 3:39:43 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.2575 loss: 1.9499 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9499 2023/07/24 15:45:10 - mmengine - INFO - Epoch(train) [5][140/940] lr: 1.0000e-02 eta: 1 day, 3:39:17 time: 1.0971 data_time: 0.0128 memory: 15768 grad_norm: 3.2298 loss: 1.8358 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8358 2023/07/24 15:45:32 - mmengine - INFO - Epoch(train) [5][160/940] lr: 1.0000e-02 eta: 1 day, 3:38:52 time: 1.0980 data_time: 0.0126 memory: 15768 grad_norm: 3.2472 loss: 1.8538 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8538 2023/07/24 15:45:54 - mmengine - INFO - Epoch(train) [5][180/940] lr: 1.0000e-02 eta: 1 day, 3:38:27 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.2371 loss: 1.9593 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9593 2023/07/24 15:46:16 - mmengine - INFO - Epoch(train) [5][200/940] lr: 1.0000e-02 eta: 1 day, 3:38:04 time: 1.1031 data_time: 0.0130 memory: 15768 grad_norm: 3.2735 loss: 1.8603 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8603 2023/07/24 15:46:38 - mmengine - INFO - Epoch(train) [5][220/940] lr: 1.0000e-02 eta: 1 day, 3:37:39 time: 1.0996 data_time: 0.0124 memory: 15768 grad_norm: 3.2396 loss: 2.0372 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0372 2023/07/24 15:47:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 15:47:01 - mmengine - INFO - Epoch(train) [5][240/940] lr: 1.0000e-02 eta: 1 day, 3:37:16 time: 1.1021 data_time: 0.0129 memory: 15768 grad_norm: 3.2175 loss: 1.9279 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9279 2023/07/24 15:47:23 - mmengine - INFO - Epoch(train) [5][260/940] lr: 1.0000e-02 eta: 1 day, 3:36:52 time: 1.0995 data_time: 0.0127 memory: 15768 grad_norm: 3.2779 loss: 2.1165 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1165 2023/07/24 15:47:45 - mmengine - INFO - Epoch(train) [5][280/940] lr: 1.0000e-02 eta: 1 day, 3:36:27 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 3.2327 loss: 1.9992 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9992 2023/07/24 15:48:07 - mmengine - INFO - Epoch(train) [5][300/940] lr: 1.0000e-02 eta: 1 day, 3:36:04 time: 1.1032 data_time: 0.0125 memory: 15768 grad_norm: 3.2363 loss: 1.9979 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9979 2023/07/24 15:48:29 - mmengine - INFO - Epoch(train) [5][320/940] lr: 1.0000e-02 eta: 1 day, 3:35:41 time: 1.1014 data_time: 0.0129 memory: 15768 grad_norm: 3.2296 loss: 1.8671 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8671 2023/07/24 15:48:51 - mmengine - INFO - Epoch(train) [5][340/940] lr: 1.0000e-02 eta: 1 day, 3:35:16 time: 1.1001 data_time: 0.0129 memory: 15768 grad_norm: 3.2447 loss: 1.9637 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9637 2023/07/24 15:49:13 - mmengine - INFO - Epoch(train) [5][360/940] lr: 1.0000e-02 eta: 1 day, 3:34:54 time: 1.1032 data_time: 0.0127 memory: 15768 grad_norm: 3.2502 loss: 1.9709 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9709 2023/07/24 15:49:35 - mmengine - INFO - Epoch(train) [5][380/940] lr: 1.0000e-02 eta: 1 day, 3:34:29 time: 1.0998 data_time: 0.0128 memory: 15768 grad_norm: 3.2532 loss: 1.9737 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.9737 2023/07/24 15:49:57 - mmengine - INFO - Epoch(train) [5][400/940] lr: 1.0000e-02 eta: 1 day, 3:34:05 time: 1.0993 data_time: 0.0130 memory: 15768 grad_norm: 3.2283 loss: 1.9149 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9149 2023/07/24 15:50:19 - mmengine - INFO - Epoch(train) [5][420/940] lr: 1.0000e-02 eta: 1 day, 3:33:41 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 3.2535 loss: 1.8799 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8799 2023/07/24 15:50:41 - mmengine - INFO - Epoch(train) [5][440/940] lr: 1.0000e-02 eta: 1 day, 3:33:17 time: 1.1003 data_time: 0.0136 memory: 15768 grad_norm: 3.2597 loss: 1.9834 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9834 2023/07/24 15:51:03 - mmengine - INFO - Epoch(train) [5][460/940] lr: 1.0000e-02 eta: 1 day, 3:32:52 time: 1.0986 data_time: 0.0132 memory: 15768 grad_norm: 3.2848 loss: 1.9510 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9510 2023/07/24 15:51:25 - mmengine - INFO - Epoch(train) [5][480/940] lr: 1.0000e-02 eta: 1 day, 3:32:29 time: 1.1023 data_time: 0.0127 memory: 15768 grad_norm: 3.3025 loss: 1.8600 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8600 2023/07/24 15:51:47 - mmengine - INFO - Epoch(train) [5][500/940] lr: 1.0000e-02 eta: 1 day, 3:32:04 time: 1.0978 data_time: 0.0129 memory: 15768 grad_norm: 3.2208 loss: 1.8519 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8519 2023/07/24 15:52:09 - mmengine - INFO - Epoch(train) [5][520/940] lr: 1.0000e-02 eta: 1 day, 3:31:41 time: 1.1018 data_time: 0.0127 memory: 15768 grad_norm: 3.2624 loss: 1.7507 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7507 2023/07/24 15:52:31 - mmengine - INFO - Epoch(train) [5][540/940] lr: 1.0000e-02 eta: 1 day, 3:31:16 time: 1.0975 data_time: 0.0126 memory: 15768 grad_norm: 3.2239 loss: 1.8776 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8776 2023/07/24 15:52:53 - mmengine - INFO - Epoch(train) [5][560/940] lr: 1.0000e-02 eta: 1 day, 3:30:51 time: 1.0984 data_time: 0.0134 memory: 15768 grad_norm: 3.1914 loss: 1.8210 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8210 2023/07/24 15:53:15 - mmengine - INFO - Epoch(train) [5][580/940] lr: 1.0000e-02 eta: 1 day, 3:30:27 time: 1.0998 data_time: 0.0127 memory: 15768 grad_norm: 3.2794 loss: 2.1041 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1041 2023/07/24 15:53:37 - mmengine - INFO - Epoch(train) [5][600/940] lr: 1.0000e-02 eta: 1 day, 3:30:03 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.2086 loss: 1.8128 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8128 2023/07/24 15:53:59 - mmengine - INFO - Epoch(train) [5][620/940] lr: 1.0000e-02 eta: 1 day, 3:29:39 time: 1.0997 data_time: 0.0127 memory: 15768 grad_norm: 3.3478 loss: 1.8816 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8816 2023/07/24 15:54:21 - mmengine - INFO - Epoch(train) [5][640/940] lr: 1.0000e-02 eta: 1 day, 3:29:14 time: 1.0981 data_time: 0.0131 memory: 15768 grad_norm: 3.2567 loss: 1.8267 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8267 2023/07/24 15:54:43 - mmengine - INFO - Epoch(train) [5][660/940] lr: 1.0000e-02 eta: 1 day, 3:28:50 time: 1.0994 data_time: 0.0128 memory: 15768 grad_norm: 3.2909 loss: 1.9277 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9277 2023/07/24 15:55:04 - mmengine - INFO - Epoch(train) [5][680/940] lr: 1.0000e-02 eta: 1 day, 3:28:25 time: 1.0979 data_time: 0.0129 memory: 15768 grad_norm: 3.2056 loss: 1.7503 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7503 2023/07/24 15:55:26 - mmengine - INFO - Epoch(train) [5][700/940] lr: 1.0000e-02 eta: 1 day, 3:28:01 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 3.2397 loss: 1.9923 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9923 2023/07/24 15:55:48 - mmengine - INFO - Epoch(train) [5][720/940] lr: 1.0000e-02 eta: 1 day, 3:27:36 time: 1.0984 data_time: 0.0129 memory: 15768 grad_norm: 3.2413 loss: 1.8783 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8783 2023/07/24 15:56:10 - mmengine - INFO - Epoch(train) [5][740/940] lr: 1.0000e-02 eta: 1 day, 3:27:12 time: 1.0998 data_time: 0.0126 memory: 15768 grad_norm: 3.2840 loss: 1.9392 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9392 2023/07/24 15:56:32 - mmengine - INFO - Epoch(train) [5][760/940] lr: 1.0000e-02 eta: 1 day, 3:26:49 time: 1.1008 data_time: 0.0129 memory: 15768 grad_norm: 3.3365 loss: 1.8456 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8456 2023/07/24 15:56:55 - mmengine - INFO - Epoch(train) [5][780/940] lr: 1.0000e-02 eta: 1 day, 3:26:29 time: 1.1095 data_time: 0.0128 memory: 15768 grad_norm: 3.2771 loss: 2.0940 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0940 2023/07/24 15:57:17 - mmengine - INFO - Epoch(train) [5][800/940] lr: 1.0000e-02 eta: 1 day, 3:26:05 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 3.2668 loss: 1.9812 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9812 2023/07/24 15:57:39 - mmengine - INFO - Epoch(train) [5][820/940] lr: 1.0000e-02 eta: 1 day, 3:25:41 time: 1.1009 data_time: 0.0129 memory: 15768 grad_norm: 3.2940 loss: 1.7850 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7850 2023/07/24 15:58:01 - mmengine - INFO - Epoch(train) [5][840/940] lr: 1.0000e-02 eta: 1 day, 3:25:17 time: 1.0993 data_time: 0.0128 memory: 15768 grad_norm: 3.2717 loss: 1.7096 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7096 2023/07/24 15:58:23 - mmengine - INFO - Epoch(train) [5][860/940] lr: 1.0000e-02 eta: 1 day, 3:24:52 time: 1.0974 data_time: 0.0129 memory: 15768 grad_norm: 3.2625 loss: 2.0110 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0110 2023/07/24 15:58:44 - mmengine - INFO - Epoch(train) [5][880/940] lr: 1.0000e-02 eta: 1 day, 3:24:28 time: 1.0980 data_time: 0.0133 memory: 15768 grad_norm: 3.2576 loss: 1.7743 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7743 2023/07/24 15:59:07 - mmengine - INFO - Epoch(train) [5][900/940] lr: 1.0000e-02 eta: 1 day, 3:24:19 time: 1.1395 data_time: 0.0128 memory: 15768 grad_norm: 3.2214 loss: 1.8227 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8227 2023/07/24 15:59:31 - mmengine - INFO - Epoch(train) [5][920/940] lr: 1.0000e-02 eta: 1 day, 3:24:21 time: 1.1671 data_time: 0.0131 memory: 15768 grad_norm: 3.3171 loss: 1.9391 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9391 2023/07/24 15:59:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 15:59:53 - mmengine - INFO - Epoch(train) [5][940/940] lr: 1.0000e-02 eta: 1 day, 3:24:05 time: 1.1187 data_time: 0.0123 memory: 15768 grad_norm: 3.4388 loss: 1.7490 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.7490 2023/07/24 16:00:03 - mmengine - INFO - Epoch(val) [5][20/78] eta: 0:00:27 time: 0.4769 data_time: 0.3202 memory: 2147 2023/07/24 16:00:09 - mmengine - INFO - Epoch(val) [5][40/78] eta: 0:00:15 time: 0.3407 data_time: 0.1839 memory: 2147 2023/07/24 16:00:18 - mmengine - INFO - Epoch(val) [5][60/78] eta: 0:00:07 time: 0.4427 data_time: 0.2861 memory: 2147 2023/07/24 16:00:30 - mmengine - INFO - Epoch(val) [5][78/78] acc/top1: 0.5625 acc/top5: 0.8044 acc/mean1: 0.5622 data_time: 0.2402 time: 0.3941 2023/07/24 16:00:56 - mmengine - INFO - Epoch(train) [6][ 20/940] lr: 1.0000e-02 eta: 1 day, 3:24:55 time: 1.2974 data_time: 0.1361 memory: 15768 grad_norm: 3.2191 loss: 1.7630 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.7630 2023/07/24 16:01:18 - mmengine - INFO - Epoch(train) [6][ 40/940] lr: 1.0000e-02 eta: 1 day, 3:24:31 time: 1.0995 data_time: 0.0127 memory: 15768 grad_norm: 3.1927 loss: 1.7206 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7206 2023/07/24 16:01:40 - mmengine - INFO - Epoch(train) [6][ 60/940] lr: 1.0000e-02 eta: 1 day, 3:24:08 time: 1.1038 data_time: 0.0127 memory: 15768 grad_norm: 3.2296 loss: 1.9358 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9358 2023/07/24 16:02:02 - mmengine - INFO - Epoch(train) [6][ 80/940] lr: 1.0000e-02 eta: 1 day, 3:23:44 time: 1.0987 data_time: 0.0129 memory: 15768 grad_norm: 3.2339 loss: 1.7593 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7593 2023/07/24 16:02:24 - mmengine - INFO - Epoch(train) [6][100/940] lr: 1.0000e-02 eta: 1 day, 3:23:20 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 3.2004 loss: 1.8367 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8367 2023/07/24 16:02:46 - mmengine - INFO - Epoch(train) [6][120/940] lr: 1.0000e-02 eta: 1 day, 3:22:56 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 3.2753 loss: 2.0838 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0838 2023/07/24 16:03:08 - mmengine - INFO - Epoch(train) [6][140/940] lr: 1.0000e-02 eta: 1 day, 3:22:31 time: 1.0987 data_time: 0.0129 memory: 15768 grad_norm: 3.2478 loss: 1.8527 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.8527 2023/07/24 16:03:30 - mmengine - INFO - Epoch(train) [6][160/940] lr: 1.0000e-02 eta: 1 day, 3:22:07 time: 1.0982 data_time: 0.0129 memory: 15768 grad_norm: 3.2089 loss: 1.8659 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8659 2023/07/24 16:03:52 - mmengine - INFO - Epoch(train) [6][180/940] lr: 1.0000e-02 eta: 1 day, 3:21:42 time: 1.0986 data_time: 0.0130 memory: 15768 grad_norm: 3.2812 loss: 1.8623 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8623 2023/07/24 16:04:14 - mmengine - INFO - Epoch(train) [6][200/940] lr: 1.0000e-02 eta: 1 day, 3:21:17 time: 1.0967 data_time: 0.0131 memory: 15768 grad_norm: 3.3242 loss: 1.9165 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9165 2023/07/24 16:04:36 - mmengine - INFO - Epoch(train) [6][220/940] lr: 1.0000e-02 eta: 1 day, 3:20:54 time: 1.1017 data_time: 0.0127 memory: 15768 grad_norm: 3.2153 loss: 1.7765 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7765 2023/07/24 16:04:58 - mmengine - INFO - Epoch(train) [6][240/940] lr: 1.0000e-02 eta: 1 day, 3:20:29 time: 1.0982 data_time: 0.0130 memory: 15768 grad_norm: 3.2950 loss: 1.8072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8072 2023/07/24 16:05:20 - mmengine - INFO - Epoch(train) [6][260/940] lr: 1.0000e-02 eta: 1 day, 3:20:05 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 3.3293 loss: 1.9093 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9093 2023/07/24 16:05:42 - mmengine - INFO - Epoch(train) [6][280/940] lr: 1.0000e-02 eta: 1 day, 3:19:41 time: 1.0991 data_time: 0.0134 memory: 15768 grad_norm: 3.2410 loss: 1.9604 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9604 2023/07/24 16:06:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 16:06:04 - mmengine - INFO - Epoch(train) [6][300/940] lr: 1.0000e-02 eta: 1 day, 3:19:17 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 3.2620 loss: 1.6564 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6564 2023/07/24 16:06:26 - mmengine - INFO - Epoch(train) [6][320/940] lr: 1.0000e-02 eta: 1 day, 3:18:53 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 3.3083 loss: 1.6563 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6563 2023/07/24 16:06:48 - mmengine - INFO - Epoch(train) [6][340/940] lr: 1.0000e-02 eta: 1 day, 3:18:30 time: 1.1051 data_time: 0.0131 memory: 15768 grad_norm: 3.2251 loss: 1.8046 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8046 2023/07/24 16:07:10 - mmengine - INFO - Epoch(train) [6][360/940] lr: 1.0000e-02 eta: 1 day, 3:18:06 time: 1.0989 data_time: 0.0134 memory: 15768 grad_norm: 3.2687 loss: 1.8276 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8276 2023/07/24 16:07:32 - mmengine - INFO - Epoch(train) [6][380/940] lr: 1.0000e-02 eta: 1 day, 3:17:42 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.2515 loss: 1.8183 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8183 2023/07/24 16:07:54 - mmengine - INFO - Epoch(train) [6][400/940] lr: 1.0000e-02 eta: 1 day, 3:17:18 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.2607 loss: 1.9929 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.9929 2023/07/24 16:08:16 - mmengine - INFO - Epoch(train) [6][420/940] lr: 1.0000e-02 eta: 1 day, 3:16:54 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 3.2517 loss: 2.0105 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0105 2023/07/24 16:08:38 - mmengine - INFO - Epoch(train) [6][440/940] lr: 1.0000e-02 eta: 1 day, 3:16:30 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 3.2870 loss: 1.8041 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8041 2023/07/24 16:09:00 - mmengine - INFO - Epoch(train) [6][460/940] lr: 1.0000e-02 eta: 1 day, 3:16:07 time: 1.1030 data_time: 0.0123 memory: 15768 grad_norm: 3.2887 loss: 1.8655 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.8655 2023/07/24 16:09:22 - mmengine - INFO - Epoch(train) [6][480/940] lr: 1.0000e-02 eta: 1 day, 3:15:43 time: 1.0996 data_time: 0.0129 memory: 15768 grad_norm: 3.2997 loss: 1.7811 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7811 2023/07/24 16:09:44 - mmengine - INFO - Epoch(train) [6][500/940] lr: 1.0000e-02 eta: 1 day, 3:15:19 time: 1.0987 data_time: 0.0126 memory: 15768 grad_norm: 3.2515 loss: 1.8367 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8367 2023/07/24 16:10:06 - mmengine - INFO - Epoch(train) [6][520/940] lr: 1.0000e-02 eta: 1 day, 3:14:55 time: 1.0987 data_time: 0.0130 memory: 15768 grad_norm: 3.2940 loss: 1.7812 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7812 2023/07/24 16:10:28 - mmengine - INFO - Epoch(train) [6][540/940] lr: 1.0000e-02 eta: 1 day, 3:14:31 time: 1.0998 data_time: 0.0125 memory: 15768 grad_norm: 3.3141 loss: 1.7427 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7427 2023/07/24 16:10:50 - mmengine - INFO - Epoch(train) [6][560/940] lr: 1.0000e-02 eta: 1 day, 3:14:08 time: 1.1027 data_time: 0.0131 memory: 15768 grad_norm: 3.2745 loss: 1.8945 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8945 2023/07/24 16:11:12 - mmengine - INFO - Epoch(train) [6][580/940] lr: 1.0000e-02 eta: 1 day, 3:13:44 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.2946 loss: 1.8722 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8722 2023/07/24 16:11:34 - mmengine - INFO - Epoch(train) [6][600/940] lr: 1.0000e-02 eta: 1 day, 3:13:21 time: 1.1003 data_time: 0.0130 memory: 15768 grad_norm: 3.2679 loss: 1.6748 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6748 2023/07/24 16:11:56 - mmengine - INFO - Epoch(train) [6][620/940] lr: 1.0000e-02 eta: 1 day, 3:12:56 time: 1.0980 data_time: 0.0129 memory: 15768 grad_norm: 3.2657 loss: 1.8733 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8733 2023/07/24 16:12:18 - mmengine - INFO - Epoch(train) [6][640/940] lr: 1.0000e-02 eta: 1 day, 3:12:33 time: 1.1007 data_time: 0.0128 memory: 15768 grad_norm: 3.3225 loss: 1.7801 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7801 2023/07/24 16:12:40 - mmengine - INFO - Epoch(train) [6][660/940] lr: 1.0000e-02 eta: 1 day, 3:12:09 time: 1.0994 data_time: 0.0131 memory: 15768 grad_norm: 3.3198 loss: 1.9848 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9848 2023/07/24 16:13:02 - mmengine - INFO - Epoch(train) [6][680/940] lr: 1.0000e-02 eta: 1 day, 3:11:46 time: 1.1026 data_time: 0.0125 memory: 15768 grad_norm: 3.2537 loss: 1.8931 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8931 2023/07/24 16:13:24 - mmengine - INFO - Epoch(train) [6][700/940] lr: 1.0000e-02 eta: 1 day, 3:11:23 time: 1.0998 data_time: 0.0127 memory: 15768 grad_norm: 3.2512 loss: 1.7502 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7502 2023/07/24 16:13:46 - mmengine - INFO - Epoch(train) [6][720/940] lr: 1.0000e-02 eta: 1 day, 3:10:59 time: 1.1013 data_time: 0.0126 memory: 15768 grad_norm: 3.3160 loss: 1.7238 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7238 2023/07/24 16:14:08 - mmengine - INFO - Epoch(train) [6][740/940] lr: 1.0000e-02 eta: 1 day, 3:10:37 time: 1.1044 data_time: 0.0127 memory: 15768 grad_norm: 3.2725 loss: 1.7913 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7913 2023/07/24 16:14:30 - mmengine - INFO - Epoch(train) [6][760/940] lr: 1.0000e-02 eta: 1 day, 3:10:13 time: 1.0979 data_time: 0.0131 memory: 15768 grad_norm: 3.2663 loss: 1.5778 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5778 2023/07/24 16:14:52 - mmengine - INFO - Epoch(train) [6][780/940] lr: 1.0000e-02 eta: 1 day, 3:09:49 time: 1.1006 data_time: 0.0129 memory: 15768 grad_norm: 3.3394 loss: 2.0458 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0458 2023/07/24 16:15:14 - mmengine - INFO - Epoch(train) [6][800/940] lr: 1.0000e-02 eta: 1 day, 3:09:25 time: 1.0986 data_time: 0.0132 memory: 15768 grad_norm: 3.3073 loss: 1.9830 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9830 2023/07/24 16:15:36 - mmengine - INFO - Epoch(train) [6][820/940] lr: 1.0000e-02 eta: 1 day, 3:09:01 time: 1.0980 data_time: 0.0129 memory: 15768 grad_norm: 3.3441 loss: 2.0487 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0487 2023/07/24 16:15:58 - mmengine - INFO - Epoch(train) [6][840/940] lr: 1.0000e-02 eta: 1 day, 3:08:37 time: 1.0991 data_time: 0.0127 memory: 15768 grad_norm: 3.3145 loss: 1.8975 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8975 2023/07/24 16:16:20 - mmengine - INFO - Epoch(train) [6][860/940] lr: 1.0000e-02 eta: 1 day, 3:08:14 time: 1.1017 data_time: 0.0133 memory: 15768 grad_norm: 3.2689 loss: 1.7311 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7311 2023/07/24 16:16:42 - mmengine - INFO - Epoch(train) [6][880/940] lr: 1.0000e-02 eta: 1 day, 3:07:50 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.2939 loss: 1.6187 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6187 2023/07/24 16:17:04 - mmengine - INFO - Epoch(train) [6][900/940] lr: 1.0000e-02 eta: 1 day, 3:07:28 time: 1.1031 data_time: 0.0127 memory: 15768 grad_norm: 3.2484 loss: 1.8941 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8941 2023/07/24 16:17:26 - mmengine - INFO - Epoch(train) [6][920/940] lr: 1.0000e-02 eta: 1 day, 3:07:07 time: 1.1072 data_time: 0.0131 memory: 15768 grad_norm: 3.2572 loss: 1.8642 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8642 2023/07/24 16:17:47 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 16:17:47 - mmengine - INFO - Epoch(train) [6][940/940] lr: 1.0000e-02 eta: 1 day, 3:06:29 time: 1.0546 data_time: 0.0126 memory: 15768 grad_norm: 3.4730 loss: 2.0279 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.0279 2023/07/24 16:17:47 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/07/24 16:17:58 - mmengine - INFO - Epoch(val) [6][20/78] eta: 0:00:27 time: 0.4796 data_time: 0.3227 memory: 2147 2023/07/24 16:18:05 - mmengine - INFO - Epoch(val) [6][40/78] eta: 0:00:15 time: 0.3452 data_time: 0.1882 memory: 2147 2023/07/24 16:18:13 - mmengine - INFO - Epoch(val) [6][60/78] eta: 0:00:07 time: 0.4366 data_time: 0.2802 memory: 2147 2023/07/24 16:18:24 - mmengine - INFO - Epoch(val) [6][78/78] acc/top1: 0.5852 acc/top5: 0.8208 acc/mean1: 0.5850 data_time: 0.2375 time: 0.3913 2023/07/24 16:18:24 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_3.pth is removed 2023/07/24 16:18:24 - mmengine - INFO - The best checkpoint with 0.5852 acc/top1 at 6 epoch is saved to best_acc_top1_epoch_6.pth. 2023/07/24 16:18:50 - mmengine - INFO - Epoch(train) [7][ 20/940] lr: 1.0000e-02 eta: 1 day, 3:06:58 time: 1.2691 data_time: 0.1690 memory: 15768 grad_norm: 3.3115 loss: 1.8175 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8175 2023/07/24 16:19:12 - mmengine - INFO - Epoch(train) [7][ 40/940] lr: 1.0000e-02 eta: 1 day, 3:06:34 time: 1.0972 data_time: 0.0125 memory: 15768 grad_norm: 3.2532 loss: 1.6391 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.6391 2023/07/24 16:19:34 - mmengine - INFO - Epoch(train) [7][ 60/940] lr: 1.0000e-02 eta: 1 day, 3:06:09 time: 1.0985 data_time: 0.0127 memory: 15768 grad_norm: 3.2489 loss: 1.8926 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8926 2023/07/24 16:19:56 - mmengine - INFO - Epoch(train) [7][ 80/940] lr: 1.0000e-02 eta: 1 day, 3:05:47 time: 1.1025 data_time: 0.0144 memory: 15768 grad_norm: 3.2040 loss: 1.8708 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8708 2023/07/24 16:20:18 - mmengine - INFO - Epoch(train) [7][100/940] lr: 1.0000e-02 eta: 1 day, 3:05:23 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.3164 loss: 1.7473 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7473 2023/07/24 16:20:40 - mmengine - INFO - Epoch(train) [7][120/940] lr: 1.0000e-02 eta: 1 day, 3:05:00 time: 1.1026 data_time: 0.0130 memory: 15768 grad_norm: 3.3195 loss: 1.9025 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9025 2023/07/24 16:21:02 - mmengine - INFO - Epoch(train) [7][140/940] lr: 1.0000e-02 eta: 1 day, 3:04:36 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 3.3723 loss: 1.8149 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8149 2023/07/24 16:21:24 - mmengine - INFO - Epoch(train) [7][160/940] lr: 1.0000e-02 eta: 1 day, 3:04:13 time: 1.1001 data_time: 0.0130 memory: 15768 grad_norm: 3.2858 loss: 1.8110 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8110 2023/07/24 16:21:46 - mmengine - INFO - Epoch(train) [7][180/940] lr: 1.0000e-02 eta: 1 day, 3:03:49 time: 1.0987 data_time: 0.0130 memory: 15768 grad_norm: 3.2935 loss: 1.6346 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6346 2023/07/24 16:22:08 - mmengine - INFO - Epoch(train) [7][200/940] lr: 1.0000e-02 eta: 1 day, 3:03:25 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 3.2164 loss: 1.8420 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.8420 2023/07/24 16:22:30 - mmengine - INFO - Epoch(train) [7][220/940] lr: 1.0000e-02 eta: 1 day, 3:03:01 time: 1.0979 data_time: 0.0131 memory: 15768 grad_norm: 3.3331 loss: 1.6495 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.6495 2023/07/24 16:22:52 - mmengine - INFO - Epoch(train) [7][240/940] lr: 1.0000e-02 eta: 1 day, 3:02:37 time: 1.0994 data_time: 0.0129 memory: 15768 grad_norm: 3.2633 loss: 1.9174 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9174 2023/07/24 16:23:14 - mmengine - INFO - Epoch(train) [7][260/940] lr: 1.0000e-02 eta: 1 day, 3:02:13 time: 1.0976 data_time: 0.0129 memory: 15768 grad_norm: 3.2714 loss: 1.6838 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6838 2023/07/24 16:23:36 - mmengine - INFO - Epoch(train) [7][280/940] lr: 1.0000e-02 eta: 1 day, 3:01:50 time: 1.1022 data_time: 0.0135 memory: 15768 grad_norm: 3.3136 loss: 1.9325 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9325 2023/07/24 16:23:58 - mmengine - INFO - Epoch(train) [7][300/940] lr: 1.0000e-02 eta: 1 day, 3:01:26 time: 1.0972 data_time: 0.0126 memory: 15768 grad_norm: 3.3423 loss: 1.6833 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6833 2023/07/24 16:24:20 - mmengine - INFO - Epoch(train) [7][320/940] lr: 1.0000e-02 eta: 1 day, 3:01:02 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 3.3250 loss: 1.7375 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7375 2023/07/24 16:24:42 - mmengine - INFO - Epoch(train) [7][340/940] lr: 1.0000e-02 eta: 1 day, 3:00:39 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 3.3828 loss: 1.7820 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7820 2023/07/24 16:25:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 16:25:04 - mmengine - INFO - Epoch(train) [7][360/940] lr: 1.0000e-02 eta: 1 day, 3:00:15 time: 1.0987 data_time: 0.0128 memory: 15768 grad_norm: 3.3500 loss: 1.6423 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6423 2023/07/24 16:25:26 - mmengine - INFO - Epoch(train) [7][380/940] lr: 1.0000e-02 eta: 1 day, 2:59:51 time: 1.0985 data_time: 0.0129 memory: 15768 grad_norm: 3.3805 loss: 1.7558 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.7558 2023/07/24 16:25:48 - mmengine - INFO - Epoch(train) [7][400/940] lr: 1.0000e-02 eta: 1 day, 2:59:27 time: 1.0998 data_time: 0.0132 memory: 15768 grad_norm: 3.3534 loss: 1.7546 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7546 2023/07/24 16:26:10 - mmengine - INFO - Epoch(train) [7][420/940] lr: 1.0000e-02 eta: 1 day, 2:59:03 time: 1.0974 data_time: 0.0127 memory: 15768 grad_norm: 3.3775 loss: 1.7738 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7738 2023/07/24 16:26:32 - mmengine - INFO - Epoch(train) [7][440/940] lr: 1.0000e-02 eta: 1 day, 2:58:40 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 3.3785 loss: 1.8251 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.8251 2023/07/24 16:26:54 - mmengine - INFO - Epoch(train) [7][460/940] lr: 1.0000e-02 eta: 1 day, 2:58:16 time: 1.0972 data_time: 0.0128 memory: 15768 grad_norm: 3.3556 loss: 1.7445 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.7445 2023/07/24 16:27:16 - mmengine - INFO - Epoch(train) [7][480/940] lr: 1.0000e-02 eta: 1 day, 2:57:52 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 3.2821 loss: 1.9154 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9154 2023/07/24 16:27:38 - mmengine - INFO - Epoch(train) [7][500/940] lr: 1.0000e-02 eta: 1 day, 2:57:28 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 3.2758 loss: 1.8887 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8887 2023/07/24 16:28:00 - mmengine - INFO - Epoch(train) [7][520/940] lr: 1.0000e-02 eta: 1 day, 2:57:05 time: 1.0996 data_time: 0.0131 memory: 15768 grad_norm: 3.3480 loss: 2.0295 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.0295 2023/07/24 16:28:22 - mmengine - INFO - Epoch(train) [7][540/940] lr: 1.0000e-02 eta: 1 day, 2:56:41 time: 1.0994 data_time: 0.0130 memory: 15768 grad_norm: 3.3373 loss: 1.9048 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9048 2023/07/24 16:28:43 - mmengine - INFO - Epoch(train) [7][560/940] lr: 1.0000e-02 eta: 1 day, 2:56:17 time: 1.0959 data_time: 0.0131 memory: 15768 grad_norm: 3.3250 loss: 1.9056 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9056 2023/07/24 16:29:05 - mmengine - INFO - Epoch(train) [7][580/940] lr: 1.0000e-02 eta: 1 day, 2:55:53 time: 1.0989 data_time: 0.0133 memory: 15768 grad_norm: 3.2712 loss: 1.7738 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7738 2023/07/24 16:29:27 - mmengine - INFO - Epoch(train) [7][600/940] lr: 1.0000e-02 eta: 1 day, 2:55:29 time: 1.0989 data_time: 0.0129 memory: 15768 grad_norm: 3.3016 loss: 1.8199 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8199 2023/07/24 16:29:49 - mmengine - INFO - Epoch(train) [7][620/940] lr: 1.0000e-02 eta: 1 day, 2:55:07 time: 1.1036 data_time: 0.0132 memory: 15768 grad_norm: 3.3559 loss: 1.9164 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.9164 2023/07/24 16:30:11 - mmengine - INFO - Epoch(train) [7][640/940] lr: 1.0000e-02 eta: 1 day, 2:54:43 time: 1.0995 data_time: 0.0134 memory: 15768 grad_norm: 3.3398 loss: 1.8750 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8750 2023/07/24 16:30:34 - mmengine - INFO - Epoch(train) [7][660/940] lr: 1.0000e-02 eta: 1 day, 2:54:21 time: 1.1040 data_time: 0.0137 memory: 15768 grad_norm: 3.3196 loss: 1.8331 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8331 2023/07/24 16:30:55 - mmengine - INFO - Epoch(train) [7][680/940] lr: 1.0000e-02 eta: 1 day, 2:53:57 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 3.2432 loss: 1.8772 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8772 2023/07/24 16:31:17 - mmengine - INFO - Epoch(train) [7][700/940] lr: 1.0000e-02 eta: 1 day, 2:53:34 time: 1.0988 data_time: 0.0127 memory: 15768 grad_norm: 3.3444 loss: 1.5718 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5718 2023/07/24 16:31:39 - mmengine - INFO - Epoch(train) [7][720/940] lr: 1.0000e-02 eta: 1 day, 2:53:10 time: 1.0991 data_time: 0.0132 memory: 15768 grad_norm: 3.3011 loss: 1.8228 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8228 2023/07/24 16:32:01 - mmengine - INFO - Epoch(train) [7][740/940] lr: 1.0000e-02 eta: 1 day, 2:52:47 time: 1.1008 data_time: 0.0129 memory: 15768 grad_norm: 3.2902 loss: 2.1685 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1685 2023/07/24 16:32:23 - mmengine - INFO - Epoch(train) [7][760/940] lr: 1.0000e-02 eta: 1 day, 2:52:24 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.2876 loss: 1.6100 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6100 2023/07/24 16:32:45 - mmengine - INFO - Epoch(train) [7][780/940] lr: 1.0000e-02 eta: 1 day, 2:52:00 time: 1.1003 data_time: 0.0126 memory: 15768 grad_norm: 3.3300 loss: 1.8839 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8839 2023/07/24 16:33:07 - mmengine - INFO - Epoch(train) [7][800/940] lr: 1.0000e-02 eta: 1 day, 2:51:37 time: 1.0996 data_time: 0.0131 memory: 15768 grad_norm: 3.3654 loss: 1.8328 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8328 2023/07/24 16:33:29 - mmengine - INFO - Epoch(train) [7][820/940] lr: 1.0000e-02 eta: 1 day, 2:51:13 time: 1.0980 data_time: 0.0131 memory: 15768 grad_norm: 3.3482 loss: 1.7683 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7683 2023/07/24 16:33:51 - mmengine - INFO - Epoch(train) [7][840/940] lr: 1.0000e-02 eta: 1 day, 2:50:50 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 3.3476 loss: 1.9140 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9140 2023/07/24 16:34:13 - mmengine - INFO - Epoch(train) [7][860/940] lr: 1.0000e-02 eta: 1 day, 2:50:27 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 3.2574 loss: 1.8150 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8150 2023/07/24 16:34:35 - mmengine - INFO - Epoch(train) [7][880/940] lr: 1.0000e-02 eta: 1 day, 2:50:03 time: 1.0975 data_time: 0.0133 memory: 15768 grad_norm: 3.3270 loss: 1.6952 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.6952 2023/07/24 16:34:57 - mmengine - INFO - Epoch(train) [7][900/940] lr: 1.0000e-02 eta: 1 day, 2:49:40 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 3.2429 loss: 1.9264 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.9264 2023/07/24 16:35:19 - mmengine - INFO - Epoch(train) [7][920/940] lr: 1.0000e-02 eta: 1 day, 2:49:16 time: 1.0989 data_time: 0.0134 memory: 15768 grad_norm: 3.3629 loss: 1.8706 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8706 2023/07/24 16:35:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 16:35:40 - mmengine - INFO - Epoch(train) [7][940/940] lr: 1.0000e-02 eta: 1 day, 2:48:41 time: 1.0526 data_time: 0.0124 memory: 15768 grad_norm: 3.4614 loss: 1.8699 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8699 2023/07/24 16:35:50 - mmengine - INFO - Epoch(val) [7][20/78] eta: 0:00:28 time: 0.4831 data_time: 0.3255 memory: 2147 2023/07/24 16:35:57 - mmengine - INFO - Epoch(val) [7][40/78] eta: 0:00:15 time: 0.3368 data_time: 0.1806 memory: 2147 2023/07/24 16:36:06 - mmengine - INFO - Epoch(val) [7][60/78] eta: 0:00:07 time: 0.4361 data_time: 0.2796 memory: 2147 2023/07/24 16:36:17 - mmengine - INFO - Epoch(val) [7][78/78] acc/top1: 0.6310 acc/top5: 0.8559 acc/mean1: 0.6308 data_time: 0.2378 time: 0.3918 2023/07/24 16:36:17 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_6.pth is removed 2023/07/24 16:36:18 - mmengine - INFO - The best checkpoint with 0.6310 acc/top1 at 7 epoch is saved to best_acc_top1_epoch_7.pth. 2023/07/24 16:36:44 - mmengine - INFO - Epoch(train) [8][ 20/940] lr: 1.0000e-02 eta: 1 day, 2:49:14 time: 1.3138 data_time: 0.1541 memory: 15768 grad_norm: 3.2613 loss: 1.5756 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5756 2023/07/24 16:37:06 - mmengine - INFO - Epoch(train) [8][ 40/940] lr: 1.0000e-02 eta: 1 day, 2:48:53 time: 1.1077 data_time: 0.0130 memory: 15768 grad_norm: 3.3014 loss: 1.7225 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7225 2023/07/24 16:37:28 - mmengine - INFO - Epoch(train) [8][ 60/940] lr: 1.0000e-02 eta: 1 day, 2:48:30 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.3114 loss: 1.7214 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7214 2023/07/24 16:37:50 - mmengine - INFO - Epoch(train) [8][ 80/940] lr: 1.0000e-02 eta: 1 day, 2:48:07 time: 1.1013 data_time: 0.0134 memory: 15768 grad_norm: 3.3015 loss: 1.7813 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.7813 2023/07/24 16:38:12 - mmengine - INFO - Epoch(train) [8][100/940] lr: 1.0000e-02 eta: 1 day, 2:47:43 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.3101 loss: 1.5908 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5908 2023/07/24 16:38:34 - mmengine - INFO - Epoch(train) [8][120/940] lr: 1.0000e-02 eta: 1 day, 2:47:20 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 3.3478 loss: 1.7585 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7585 2023/07/24 16:38:56 - mmengine - INFO - Epoch(train) [8][140/940] lr: 1.0000e-02 eta: 1 day, 2:46:56 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.3441 loss: 1.6782 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6782 2023/07/24 16:39:18 - mmengine - INFO - Epoch(train) [8][160/940] lr: 1.0000e-02 eta: 1 day, 2:46:34 time: 1.1027 data_time: 0.0131 memory: 15768 grad_norm: 3.3452 loss: 1.7774 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7774 2023/07/24 16:39:40 - mmengine - INFO - Epoch(train) [8][180/940] lr: 1.0000e-02 eta: 1 day, 2:46:10 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.3175 loss: 1.6874 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6874 2023/07/24 16:40:02 - mmengine - INFO - Epoch(train) [8][200/940] lr: 1.0000e-02 eta: 1 day, 2:45:47 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.3204 loss: 1.7302 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7302 2023/07/24 16:40:24 - mmengine - INFO - Epoch(train) [8][220/940] lr: 1.0000e-02 eta: 1 day, 2:45:24 time: 1.1020 data_time: 0.0127 memory: 15768 grad_norm: 3.3003 loss: 1.6811 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6811 2023/07/24 16:40:46 - mmengine - INFO - Epoch(train) [8][240/940] lr: 1.0000e-02 eta: 1 day, 2:45:01 time: 1.0990 data_time: 0.0130 memory: 15768 grad_norm: 3.3234 loss: 1.5869 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5869 2023/07/24 16:41:09 - mmengine - INFO - Epoch(train) [8][260/940] lr: 1.0000e-02 eta: 1 day, 2:44:39 time: 1.1049 data_time: 0.0133 memory: 15768 grad_norm: 3.3563 loss: 1.7408 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7408 2023/07/24 16:41:31 - mmengine - INFO - Epoch(train) [8][280/940] lr: 1.0000e-02 eta: 1 day, 2:44:15 time: 1.0993 data_time: 0.0128 memory: 15768 grad_norm: 3.3031 loss: 1.9383 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.9383 2023/07/24 16:41:53 - mmengine - INFO - Epoch(train) [8][300/940] lr: 1.0000e-02 eta: 1 day, 2:43:52 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 3.3364 loss: 1.7217 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7217 2023/07/24 16:42:15 - mmengine - INFO - Epoch(train) [8][320/940] lr: 1.0000e-02 eta: 1 day, 2:43:30 time: 1.1027 data_time: 0.0134 memory: 15768 grad_norm: 3.3255 loss: 1.8732 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8732 2023/07/24 16:42:37 - mmengine - INFO - Epoch(train) [8][340/940] lr: 1.0000e-02 eta: 1 day, 2:43:07 time: 1.1034 data_time: 0.0132 memory: 15768 grad_norm: 3.3023 loss: 1.7487 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7487 2023/07/24 16:42:59 - mmengine - INFO - Epoch(train) [8][360/940] lr: 1.0000e-02 eta: 1 day, 2:42:44 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 3.3299 loss: 1.8647 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8647 2023/07/24 16:43:21 - mmengine - INFO - Epoch(train) [8][380/940] lr: 1.0000e-02 eta: 1 day, 2:42:21 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.3254 loss: 1.6402 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6402 2023/07/24 16:43:43 - mmengine - INFO - Epoch(train) [8][400/940] lr: 1.0000e-02 eta: 1 day, 2:41:57 time: 1.0981 data_time: 0.0130 memory: 15768 grad_norm: 3.2652 loss: 1.7001 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7001 2023/07/24 16:44:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 16:44:05 - mmengine - INFO - Epoch(train) [8][420/940] lr: 1.0000e-02 eta: 1 day, 2:41:33 time: 1.0977 data_time: 0.0129 memory: 15768 grad_norm: 3.3555 loss: 1.6654 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6654 2023/07/24 16:44:26 - mmengine - INFO - Epoch(train) [8][440/940] lr: 1.0000e-02 eta: 1 day, 2:41:09 time: 1.0974 data_time: 0.0130 memory: 15768 grad_norm: 3.3678 loss: 1.6309 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6309 2023/07/24 16:44:49 - mmengine - INFO - Epoch(train) [8][460/940] lr: 1.0000e-02 eta: 1 day, 2:40:47 time: 1.1023 data_time: 0.0134 memory: 15768 grad_norm: 3.3515 loss: 1.5048 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5048 2023/07/24 16:45:11 - mmengine - INFO - Epoch(train) [8][480/940] lr: 1.0000e-02 eta: 1 day, 2:40:23 time: 1.0986 data_time: 0.0136 memory: 15768 grad_norm: 3.3427 loss: 1.6634 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6634 2023/07/24 16:45:33 - mmengine - INFO - Epoch(train) [8][500/940] lr: 1.0000e-02 eta: 1 day, 2:40:00 time: 1.1002 data_time: 0.0132 memory: 15768 grad_norm: 3.3769 loss: 1.5706 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5706 2023/07/24 16:45:54 - mmengine - INFO - Epoch(train) [8][520/940] lr: 1.0000e-02 eta: 1 day, 2:39:36 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 3.3593 loss: 1.6208 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6208 2023/07/24 16:46:17 - mmengine - INFO - Epoch(train) [8][540/940] lr: 1.0000e-02 eta: 1 day, 2:39:14 time: 1.1041 data_time: 0.0130 memory: 15768 grad_norm: 3.3702 loss: 1.5558 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5558 2023/07/24 16:46:39 - mmengine - INFO - Epoch(train) [8][560/940] lr: 1.0000e-02 eta: 1 day, 2:38:51 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.3276 loss: 1.9630 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9630 2023/07/24 16:47:01 - mmengine - INFO - Epoch(train) [8][580/940] lr: 1.0000e-02 eta: 1 day, 2:38:28 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 3.3395 loss: 1.8185 top1_acc: 0.5625 top5_acc: 0.5625 loss_cls: 1.8185 2023/07/24 16:47:23 - mmengine - INFO - Epoch(train) [8][600/940] lr: 1.0000e-02 eta: 1 day, 2:38:04 time: 1.0985 data_time: 0.0131 memory: 15768 grad_norm: 3.4007 loss: 1.6630 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6630 2023/07/24 16:47:45 - mmengine - INFO - Epoch(train) [8][620/940] lr: 1.0000e-02 eta: 1 day, 2:37:42 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.3917 loss: 1.9011 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9011 2023/07/24 16:48:07 - mmengine - INFO - Epoch(train) [8][640/940] lr: 1.0000e-02 eta: 1 day, 2:37:18 time: 1.0985 data_time: 0.0131 memory: 15768 grad_norm: 3.3137 loss: 1.7642 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7642 2023/07/24 16:48:29 - mmengine - INFO - Epoch(train) [8][660/940] lr: 1.0000e-02 eta: 1 day, 2:36:55 time: 1.1009 data_time: 0.0132 memory: 15768 grad_norm: 3.3824 loss: 1.7664 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7664 2023/07/24 16:48:51 - mmengine - INFO - Epoch(train) [8][680/940] lr: 1.0000e-02 eta: 1 day, 2:36:32 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 3.3387 loss: 1.5215 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5215 2023/07/24 16:49:13 - mmengine - INFO - Epoch(train) [8][700/940] lr: 1.0000e-02 eta: 1 day, 2:36:09 time: 1.1006 data_time: 0.0134 memory: 15768 grad_norm: 3.3381 loss: 1.7516 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7516 2023/07/24 16:49:35 - mmengine - INFO - Epoch(train) [8][720/940] lr: 1.0000e-02 eta: 1 day, 2:35:46 time: 1.0991 data_time: 0.0135 memory: 15768 grad_norm: 3.3176 loss: 1.6403 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6403 2023/07/24 16:49:57 - mmengine - INFO - Epoch(train) [8][740/940] lr: 1.0000e-02 eta: 1 day, 2:35:22 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 3.3399 loss: 1.5756 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.5756 2023/07/24 16:50:19 - mmengine - INFO - Epoch(train) [8][760/940] lr: 1.0000e-02 eta: 1 day, 2:34:59 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.3340 loss: 1.7889 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.7889 2023/07/24 16:50:41 - mmengine - INFO - Epoch(train) [8][780/940] lr: 1.0000e-02 eta: 1 day, 2:34:36 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.3375 loss: 1.9574 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9574 2023/07/24 16:51:03 - mmengine - INFO - Epoch(train) [8][800/940] lr: 1.0000e-02 eta: 1 day, 2:34:13 time: 1.0988 data_time: 0.0134 memory: 15768 grad_norm: 3.3783 loss: 1.6683 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6683 2023/07/24 16:51:25 - mmengine - INFO - Epoch(train) [8][820/940] lr: 1.0000e-02 eta: 1 day, 2:33:51 time: 1.1029 data_time: 0.0129 memory: 15768 grad_norm: 3.3404 loss: 1.5981 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5981 2023/07/24 16:51:47 - mmengine - INFO - Epoch(train) [8][840/940] lr: 1.0000e-02 eta: 1 day, 2:33:27 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.3629 loss: 1.7209 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7209 2023/07/24 16:52:09 - mmengine - INFO - Epoch(train) [8][860/940] lr: 1.0000e-02 eta: 1 day, 2:33:04 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 3.2924 loss: 1.6827 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6827 2023/07/24 16:52:31 - mmengine - INFO - Epoch(train) [8][880/940] lr: 1.0000e-02 eta: 1 day, 2:32:41 time: 1.0995 data_time: 0.0129 memory: 15768 grad_norm: 3.3019 loss: 1.9975 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9975 2023/07/24 16:52:53 - mmengine - INFO - Epoch(train) [8][900/940] lr: 1.0000e-02 eta: 1 day, 2:32:18 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 3.3571 loss: 1.7968 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7968 2023/07/24 16:53:15 - mmengine - INFO - Epoch(train) [8][920/940] lr: 1.0000e-02 eta: 1 day, 2:31:55 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 3.3514 loss: 1.7588 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7588 2023/07/24 16:53:36 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 16:53:36 - mmengine - INFO - Epoch(train) [8][940/940] lr: 1.0000e-02 eta: 1 day, 2:31:22 time: 1.0547 data_time: 0.0126 memory: 15768 grad_norm: 3.4767 loss: 1.5734 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5734 2023/07/24 16:53:45 - mmengine - INFO - Epoch(val) [8][20/78] eta: 0:00:27 time: 0.4711 data_time: 0.3141 memory: 2147 2023/07/24 16:53:52 - mmengine - INFO - Epoch(val) [8][40/78] eta: 0:00:15 time: 0.3476 data_time: 0.1905 memory: 2147 2023/07/24 16:54:01 - mmengine - INFO - Epoch(val) [8][60/78] eta: 0:00:07 time: 0.4555 data_time: 0.2989 memory: 2147 2023/07/24 16:54:12 - mmengine - INFO - Epoch(val) [8][78/78] acc/top1: 0.6198 acc/top5: 0.8460 acc/mean1: 0.6196 data_time: 0.2427 time: 0.3967 2023/07/24 16:54:38 - mmengine - INFO - Epoch(train) [9][ 20/940] lr: 1.0000e-02 eta: 1 day, 2:31:41 time: 1.2853 data_time: 0.1373 memory: 15768 grad_norm: 3.2742 loss: 1.6989 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6989 2023/07/24 16:55:00 - mmengine - INFO - Epoch(train) [9][ 40/940] lr: 1.0000e-02 eta: 1 day, 2:31:19 time: 1.1033 data_time: 0.0131 memory: 15768 grad_norm: 3.2968 loss: 1.7655 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7655 2023/07/24 16:55:22 - mmengine - INFO - Epoch(train) [9][ 60/940] lr: 1.0000e-02 eta: 1 day, 2:30:57 time: 1.1047 data_time: 0.0126 memory: 15768 grad_norm: 3.3814 loss: 1.6038 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6038 2023/07/24 16:55:44 - mmengine - INFO - Epoch(train) [9][ 80/940] lr: 1.0000e-02 eta: 1 day, 2:30:34 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.3491 loss: 1.8179 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8179 2023/07/24 16:56:06 - mmengine - INFO - Epoch(train) [9][100/940] lr: 1.0000e-02 eta: 1 day, 2:30:11 time: 1.1024 data_time: 0.0126 memory: 15768 grad_norm: 3.3719 loss: 1.8849 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8849 2023/07/24 16:56:28 - mmengine - INFO - Epoch(train) [9][120/940] lr: 1.0000e-02 eta: 1 day, 2:29:49 time: 1.1032 data_time: 0.0135 memory: 15768 grad_norm: 3.2751 loss: 1.5470 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5470 2023/07/24 16:56:50 - mmengine - INFO - Epoch(train) [9][140/940] lr: 1.0000e-02 eta: 1 day, 2:29:26 time: 1.1016 data_time: 0.0130 memory: 15768 grad_norm: 3.3866 loss: 1.7453 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.7453 2023/07/24 16:57:12 - mmengine - INFO - Epoch(train) [9][160/940] lr: 1.0000e-02 eta: 1 day, 2:29:03 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.3256 loss: 1.6819 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6819 2023/07/24 16:57:34 - mmengine - INFO - Epoch(train) [9][180/940] lr: 1.0000e-02 eta: 1 day, 2:28:40 time: 1.0993 data_time: 0.0130 memory: 15768 grad_norm: 3.3399 loss: 1.8819 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8819 2023/07/24 16:57:56 - mmengine - INFO - Epoch(train) [9][200/940] lr: 1.0000e-02 eta: 1 day, 2:28:17 time: 1.1004 data_time: 0.0133 memory: 15768 grad_norm: 3.4173 loss: 1.6190 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6190 2023/07/24 16:58:18 - mmengine - INFO - Epoch(train) [9][220/940] lr: 1.0000e-02 eta: 1 day, 2:27:55 time: 1.1044 data_time: 0.0128 memory: 15768 grad_norm: 3.3643 loss: 1.5960 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5960 2023/07/24 16:58:40 - mmengine - INFO - Epoch(train) [9][240/940] lr: 1.0000e-02 eta: 1 day, 2:27:31 time: 1.0998 data_time: 0.0131 memory: 15768 grad_norm: 3.3851 loss: 1.6892 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.6892 2023/07/24 16:59:02 - mmengine - INFO - Epoch(train) [9][260/940] lr: 1.0000e-02 eta: 1 day, 2:27:09 time: 1.1010 data_time: 0.0132 memory: 15768 grad_norm: 3.3830 loss: 1.7283 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 1.7283 2023/07/24 16:59:24 - mmengine - INFO - Epoch(train) [9][280/940] lr: 1.0000e-02 eta: 1 day, 2:26:45 time: 1.0993 data_time: 0.0130 memory: 15768 grad_norm: 3.3863 loss: 1.7268 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7268 2023/07/24 16:59:46 - mmengine - INFO - Epoch(train) [9][300/940] lr: 1.0000e-02 eta: 1 day, 2:26:22 time: 1.0988 data_time: 0.0131 memory: 15768 grad_norm: 3.4289 loss: 1.7927 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.7927 2023/07/24 17:00:08 - mmengine - INFO - Epoch(train) [9][320/940] lr: 1.0000e-02 eta: 1 day, 2:25:59 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 3.3819 loss: 1.5487 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5487 2023/07/24 17:00:30 - mmengine - INFO - Epoch(train) [9][340/940] lr: 1.0000e-02 eta: 1 day, 2:25:36 time: 1.1003 data_time: 0.0133 memory: 15768 grad_norm: 3.3789 loss: 1.8142 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8142 2023/07/24 17:00:52 - mmengine - INFO - Epoch(train) [9][360/940] lr: 1.0000e-02 eta: 1 day, 2:25:13 time: 1.1014 data_time: 0.0133 memory: 15768 grad_norm: 3.3611 loss: 1.5026 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5026 2023/07/24 17:01:14 - mmengine - INFO - Epoch(train) [9][380/940] lr: 1.0000e-02 eta: 1 day, 2:24:50 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 3.4189 loss: 1.7799 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7799 2023/07/24 17:01:36 - mmengine - INFO - Epoch(train) [9][400/940] lr: 1.0000e-02 eta: 1 day, 2:24:28 time: 1.1030 data_time: 0.0135 memory: 15768 grad_norm: 3.3719 loss: 1.6395 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6395 2023/07/24 17:01:58 - mmengine - INFO - Epoch(train) [9][420/940] lr: 1.0000e-02 eta: 1 day, 2:24:06 time: 1.1044 data_time: 0.0137 memory: 15768 grad_norm: 3.3383 loss: 1.6774 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6774 2023/07/24 17:02:20 - mmengine - INFO - Epoch(train) [9][440/940] lr: 1.0000e-02 eta: 1 day, 2:23:43 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.3462 loss: 1.5458 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5458 2023/07/24 17:02:42 - mmengine - INFO - Epoch(train) [9][460/940] lr: 1.0000e-02 eta: 1 day, 2:23:20 time: 1.1024 data_time: 0.0129 memory: 15768 grad_norm: 3.4026 loss: 1.7441 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7441 2023/07/24 17:03:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 17:03:04 - mmengine - INFO - Epoch(train) [9][480/940] lr: 1.0000e-02 eta: 1 day, 2:22:57 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 3.3754 loss: 1.6755 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6755 2023/07/24 17:03:26 - mmengine - INFO - Epoch(train) [9][500/940] lr: 1.0000e-02 eta: 1 day, 2:22:34 time: 1.1003 data_time: 0.0130 memory: 15768 grad_norm: 3.3853 loss: 1.5903 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.5903 2023/07/24 17:03:48 - mmengine - INFO - Epoch(train) [9][520/940] lr: 1.0000e-02 eta: 1 day, 2:22:11 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 3.3337 loss: 1.7501 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7501 2023/07/24 17:04:10 - mmengine - INFO - Epoch(train) [9][540/940] lr: 1.0000e-02 eta: 1 day, 2:21:47 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 3.3600 loss: 1.7628 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7628 2023/07/24 17:04:32 - mmengine - INFO - Epoch(train) [9][560/940] lr: 1.0000e-02 eta: 1 day, 2:21:24 time: 1.0986 data_time: 0.0132 memory: 15768 grad_norm: 3.3983 loss: 1.4986 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4986 2023/07/24 17:04:54 - mmengine - INFO - Epoch(train) [9][580/940] lr: 1.0000e-02 eta: 1 day, 2:21:01 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.3567 loss: 1.6140 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6140 2023/07/24 17:05:16 - mmengine - INFO - Epoch(train) [9][600/940] lr: 1.0000e-02 eta: 1 day, 2:20:38 time: 1.0994 data_time: 0.0133 memory: 15768 grad_norm: 3.3897 loss: 1.6424 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6424 2023/07/24 17:05:38 - mmengine - INFO - Epoch(train) [9][620/940] lr: 1.0000e-02 eta: 1 day, 2:20:15 time: 1.0991 data_time: 0.0134 memory: 15768 grad_norm: 3.3345 loss: 1.7617 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7617 2023/07/24 17:06:00 - mmengine - INFO - Epoch(train) [9][640/940] lr: 1.0000e-02 eta: 1 day, 2:19:52 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 3.3916 loss: 1.7702 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7702 2023/07/24 17:06:22 - mmengine - INFO - Epoch(train) [9][660/940] lr: 1.0000e-02 eta: 1 day, 2:19:28 time: 1.0973 data_time: 0.0130 memory: 15768 grad_norm: 3.3996 loss: 1.7473 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7473 2023/07/24 17:06:44 - mmengine - INFO - Epoch(train) [9][680/940] lr: 1.0000e-02 eta: 1 day, 2:19:05 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 3.4000 loss: 1.6500 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6500 2023/07/24 17:07:06 - mmengine - INFO - Epoch(train) [9][700/940] lr: 1.0000e-02 eta: 1 day, 2:18:42 time: 1.0996 data_time: 0.0130 memory: 15768 grad_norm: 3.3780 loss: 1.6534 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6534 2023/07/24 17:07:28 - mmengine - INFO - Epoch(train) [9][720/940] lr: 1.0000e-02 eta: 1 day, 2:18:19 time: 1.1004 data_time: 0.0135 memory: 15768 grad_norm: 3.4046 loss: 1.6621 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6621 2023/07/24 17:07:50 - mmengine - INFO - Epoch(train) [9][740/940] lr: 1.0000e-02 eta: 1 day, 2:17:56 time: 1.1004 data_time: 0.0130 memory: 15768 grad_norm: 3.4424 loss: 1.7218 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7218 2023/07/24 17:08:12 - mmengine - INFO - Epoch(train) [9][760/940] lr: 1.0000e-02 eta: 1 day, 2:17:33 time: 1.0989 data_time: 0.0133 memory: 15768 grad_norm: 3.4107 loss: 1.7595 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7595 2023/07/24 17:08:34 - mmengine - INFO - Epoch(train) [9][780/940] lr: 1.0000e-02 eta: 1 day, 2:17:10 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.3923 loss: 1.5449 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5449 2023/07/24 17:08:56 - mmengine - INFO - Epoch(train) [9][800/940] lr: 1.0000e-02 eta: 1 day, 2:16:47 time: 1.0982 data_time: 0.0131 memory: 15768 grad_norm: 3.3756 loss: 1.5384 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5384 2023/07/24 17:09:18 - mmengine - INFO - Epoch(train) [9][820/940] lr: 1.0000e-02 eta: 1 day, 2:16:24 time: 1.0989 data_time: 0.0134 memory: 15768 grad_norm: 3.3640 loss: 1.4370 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4370 2023/07/24 17:09:40 - mmengine - INFO - Epoch(train) [9][840/940] lr: 1.0000e-02 eta: 1 day, 2:16:01 time: 1.0999 data_time: 0.0134 memory: 15768 grad_norm: 3.4074 loss: 1.7163 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7163 2023/07/24 17:10:02 - mmengine - INFO - Epoch(train) [9][860/940] lr: 1.0000e-02 eta: 1 day, 2:15:38 time: 1.1004 data_time: 0.0130 memory: 15768 grad_norm: 3.3885 loss: 1.7604 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7604 2023/07/24 17:10:24 - mmengine - INFO - Epoch(train) [9][880/940] lr: 1.0000e-02 eta: 1 day, 2:15:16 time: 1.1023 data_time: 0.0136 memory: 15768 grad_norm: 3.4004 loss: 1.8413 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8413 2023/07/24 17:10:46 - mmengine - INFO - Epoch(train) [9][900/940] lr: 1.0000e-02 eta: 1 day, 2:14:54 time: 1.1066 data_time: 0.0142 memory: 15768 grad_norm: 3.3973 loss: 1.5569 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5569 2023/07/24 17:11:08 - mmengine - INFO - Epoch(train) [9][920/940] lr: 1.0000e-02 eta: 1 day, 2:14:31 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 3.3713 loss: 1.6677 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6677 2023/07/24 17:11:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 17:11:29 - mmengine - INFO - Epoch(train) [9][940/940] lr: 1.0000e-02 eta: 1 day, 2:13:59 time: 1.0533 data_time: 0.0129 memory: 15768 grad_norm: 3.6179 loss: 1.6775 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6775 2023/07/24 17:11:29 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/07/24 17:11:40 - mmengine - INFO - Epoch(val) [9][20/78] eta: 0:00:27 time: 0.4805 data_time: 0.3237 memory: 2147 2023/07/24 17:11:47 - mmengine - INFO - Epoch(val) [9][40/78] eta: 0:00:15 time: 0.3496 data_time: 0.1928 memory: 2147 2023/07/24 17:11:56 - mmengine - INFO - Epoch(val) [9][60/78] eta: 0:00:07 time: 0.4494 data_time: 0.2928 memory: 2147 2023/07/24 17:12:06 - mmengine - INFO - Epoch(val) [9][78/78] acc/top1: 0.5947 acc/top5: 0.8220 acc/mean1: 0.5942 data_time: 0.2441 time: 0.3980 2023/07/24 17:12:32 - mmengine - INFO - Epoch(train) [10][ 20/940] lr: 1.0000e-02 eta: 1 day, 2:14:19 time: 1.3108 data_time: 0.1288 memory: 15768 grad_norm: 3.3015 loss: 1.5944 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5944 2023/07/24 17:12:54 - mmengine - INFO - Epoch(train) [10][ 40/940] lr: 1.0000e-02 eta: 1 day, 2:13:56 time: 1.1017 data_time: 0.0131 memory: 15768 grad_norm: 3.2503 loss: 1.5401 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5401 2023/07/24 17:13:16 - mmengine - INFO - Epoch(train) [10][ 60/940] lr: 1.0000e-02 eta: 1 day, 2:13:33 time: 1.1008 data_time: 0.0128 memory: 15768 grad_norm: 3.3905 loss: 1.6219 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6219 2023/07/24 17:13:38 - mmengine - INFO - Epoch(train) [10][ 80/940] lr: 1.0000e-02 eta: 1 day, 2:13:10 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.3866 loss: 1.5013 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5013 2023/07/24 17:14:00 - mmengine - INFO - Epoch(train) [10][100/940] lr: 1.0000e-02 eta: 1 day, 2:12:46 time: 1.0979 data_time: 0.0129 memory: 15768 grad_norm: 3.3552 loss: 1.6808 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6808 2023/07/24 17:14:22 - mmengine - INFO - Epoch(train) [10][120/940] lr: 1.0000e-02 eta: 1 day, 2:12:23 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.2872 loss: 1.5202 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5202 2023/07/24 17:14:44 - mmengine - INFO - Epoch(train) [10][140/940] lr: 1.0000e-02 eta: 1 day, 2:12:00 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 3.3637 loss: 1.5467 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5467 2023/07/24 17:15:06 - mmengine - INFO - Epoch(train) [10][160/940] lr: 1.0000e-02 eta: 1 day, 2:11:37 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.3898 loss: 1.7150 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7150 2023/07/24 17:15:28 - mmengine - INFO - Epoch(train) [10][180/940] lr: 1.0000e-02 eta: 1 day, 2:11:14 time: 1.0986 data_time: 0.0128 memory: 15768 grad_norm: 3.3719 loss: 1.7130 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7130 2023/07/24 17:15:50 - mmengine - INFO - Epoch(train) [10][200/940] lr: 1.0000e-02 eta: 1 day, 2:10:52 time: 1.1020 data_time: 0.0128 memory: 15768 grad_norm: 3.3625 loss: 1.6071 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6071 2023/07/24 17:16:12 - mmengine - INFO - Epoch(train) [10][220/940] lr: 1.0000e-02 eta: 1 day, 2:10:29 time: 1.0997 data_time: 0.0132 memory: 15768 grad_norm: 3.3224 loss: 1.5694 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.5694 2023/07/24 17:16:34 - mmengine - INFO - Epoch(train) [10][240/940] lr: 1.0000e-02 eta: 1 day, 2:10:06 time: 1.0992 data_time: 0.0137 memory: 15768 grad_norm: 3.3930 loss: 1.5064 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5064 2023/07/24 17:16:56 - mmengine - INFO - Epoch(train) [10][260/940] lr: 1.0000e-02 eta: 1 day, 2:09:43 time: 1.1026 data_time: 0.0132 memory: 15768 grad_norm: 3.3636 loss: 1.5199 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5199 2023/07/24 17:17:18 - mmengine - INFO - Epoch(train) [10][280/940] lr: 1.0000e-02 eta: 1 day, 2:09:20 time: 1.1001 data_time: 0.0136 memory: 15768 grad_norm: 3.3221 loss: 1.6950 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6950 2023/07/24 17:17:40 - mmengine - INFO - Epoch(train) [10][300/940] lr: 1.0000e-02 eta: 1 day, 2:08:57 time: 1.0981 data_time: 0.0129 memory: 15768 grad_norm: 3.3516 loss: 1.6639 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6639 2023/07/24 17:18:02 - mmengine - INFO - Epoch(train) [10][320/940] lr: 1.0000e-02 eta: 1 day, 2:08:34 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 3.3917 loss: 1.7862 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7862 2023/07/24 17:18:24 - mmengine - INFO - Epoch(train) [10][340/940] lr: 1.0000e-02 eta: 1 day, 2:08:11 time: 1.0976 data_time: 0.0132 memory: 15768 grad_norm: 3.3772 loss: 1.7143 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.7143 2023/07/24 17:18:46 - mmengine - INFO - Epoch(train) [10][360/940] lr: 1.0000e-02 eta: 1 day, 2:07:47 time: 1.0970 data_time: 0.0132 memory: 15768 grad_norm: 3.3342 loss: 1.7499 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7499 2023/07/24 17:19:08 - mmengine - INFO - Epoch(train) [10][380/940] lr: 1.0000e-02 eta: 1 day, 2:07:24 time: 1.0989 data_time: 0.0133 memory: 15768 grad_norm: 3.4573 loss: 1.5143 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5143 2023/07/24 17:19:30 - mmengine - INFO - Epoch(train) [10][400/940] lr: 1.0000e-02 eta: 1 day, 2:07:02 time: 1.1032 data_time: 0.0131 memory: 15768 grad_norm: 3.3643 loss: 1.7492 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7492 2023/07/24 17:19:52 - mmengine - INFO - Epoch(train) [10][420/940] lr: 1.0000e-02 eta: 1 day, 2:06:39 time: 1.0996 data_time: 0.0130 memory: 15768 grad_norm: 3.4061 loss: 1.6696 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6696 2023/07/24 17:20:14 - mmengine - INFO - Epoch(train) [10][440/940] lr: 1.0000e-02 eta: 1 day, 2:06:16 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.4100 loss: 1.5006 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5006 2023/07/24 17:20:36 - mmengine - INFO - Epoch(train) [10][460/940] lr: 1.0000e-02 eta: 1 day, 2:05:54 time: 1.1053 data_time: 0.0131 memory: 15768 grad_norm: 3.3854 loss: 1.7750 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7750 2023/07/24 17:20:58 - mmengine - INFO - Epoch(train) [10][480/940] lr: 1.0000e-02 eta: 1 day, 2:05:31 time: 1.1020 data_time: 0.0135 memory: 15768 grad_norm: 3.4740 loss: 1.7812 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7812 2023/07/24 17:21:20 - mmengine - INFO - Epoch(train) [10][500/940] lr: 1.0000e-02 eta: 1 day, 2:05:09 time: 1.1008 data_time: 0.0130 memory: 15768 grad_norm: 3.3996 loss: 1.6928 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6928 2023/07/24 17:21:42 - mmengine - INFO - Epoch(train) [10][520/940] lr: 1.0000e-02 eta: 1 day, 2:04:45 time: 1.0979 data_time: 0.0132 memory: 15768 grad_norm: 3.3496 loss: 1.5904 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5904 2023/07/24 17:22:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 17:22:04 - mmengine - INFO - Epoch(train) [10][540/940] lr: 1.0000e-02 eta: 1 day, 2:04:24 time: 1.1052 data_time: 0.0132 memory: 15768 grad_norm: 3.3740 loss: 1.5147 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5147 2023/07/24 17:22:26 - mmengine - INFO - Epoch(train) [10][560/940] lr: 1.0000e-02 eta: 1 day, 2:04:01 time: 1.1011 data_time: 0.0132 memory: 15768 grad_norm: 3.3457 loss: 1.7437 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7437 2023/07/24 17:22:48 - mmengine - INFO - Epoch(train) [10][580/940] lr: 1.0000e-02 eta: 1 day, 2:03:38 time: 1.1022 data_time: 0.0130 memory: 15768 grad_norm: 3.3610 loss: 1.5658 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5658 2023/07/24 17:23:10 - mmengine - INFO - Epoch(train) [10][600/940] lr: 1.0000e-02 eta: 1 day, 2:03:16 time: 1.1050 data_time: 0.0136 memory: 15768 grad_norm: 3.3824 loss: 1.5752 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5752 2023/07/24 17:23:32 - mmengine - INFO - Epoch(train) [10][620/940] lr: 1.0000e-02 eta: 1 day, 2:02:54 time: 1.1039 data_time: 0.0131 memory: 15768 grad_norm: 3.4302 loss: 1.7947 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7947 2023/07/24 17:23:55 - mmengine - INFO - Epoch(train) [10][640/940] lr: 1.0000e-02 eta: 1 day, 2:02:32 time: 1.1051 data_time: 0.0130 memory: 15768 grad_norm: 3.3566 loss: 1.6541 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6541 2023/07/24 17:24:17 - mmengine - INFO - Epoch(train) [10][660/940] lr: 1.0000e-02 eta: 1 day, 2:02:10 time: 1.1026 data_time: 0.0129 memory: 15768 grad_norm: 3.3867 loss: 1.7073 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.7073 2023/07/24 17:24:39 - mmengine - INFO - Epoch(train) [10][680/940] lr: 1.0000e-02 eta: 1 day, 2:01:48 time: 1.1043 data_time: 0.0132 memory: 15768 grad_norm: 3.4125 loss: 1.6095 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6095 2023/07/24 17:25:01 - mmengine - INFO - Epoch(train) [10][700/940] lr: 1.0000e-02 eta: 1 day, 2:01:25 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 3.4378 loss: 1.4831 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4831 2023/07/24 17:25:23 - mmengine - INFO - Epoch(train) [10][720/940] lr: 1.0000e-02 eta: 1 day, 2:01:03 time: 1.1030 data_time: 0.0126 memory: 15768 grad_norm: 3.4035 loss: 1.8137 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8137 2023/07/24 17:25:45 - mmengine - INFO - Epoch(train) [10][740/940] lr: 1.0000e-02 eta: 1 day, 2:00:40 time: 1.1008 data_time: 0.0129 memory: 15768 grad_norm: 3.4166 loss: 1.6302 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6302 2023/07/24 17:26:07 - mmengine - INFO - Epoch(train) [10][760/940] lr: 1.0000e-02 eta: 1 day, 2:00:18 time: 1.1014 data_time: 0.0128 memory: 15768 grad_norm: 3.3933 loss: 1.5814 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5814 2023/07/24 17:26:29 - mmengine - INFO - Epoch(train) [10][780/940] lr: 1.0000e-02 eta: 1 day, 1:59:55 time: 1.1022 data_time: 0.0129 memory: 15768 grad_norm: 3.3756 loss: 1.6648 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6648 2023/07/24 17:26:51 - mmengine - INFO - Epoch(train) [10][800/940] lr: 1.0000e-02 eta: 1 day, 1:59:33 time: 1.1038 data_time: 0.0129 memory: 15768 grad_norm: 3.3793 loss: 1.6793 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6793 2023/07/24 17:27:13 - mmengine - INFO - Epoch(train) [10][820/940] lr: 1.0000e-02 eta: 1 day, 1:59:10 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 3.3771 loss: 1.6973 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6973 2023/07/24 17:27:35 - mmengine - INFO - Epoch(train) [10][840/940] lr: 1.0000e-02 eta: 1 day, 1:58:47 time: 1.0998 data_time: 0.0132 memory: 15768 grad_norm: 3.3818 loss: 1.4990 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4990 2023/07/24 17:27:57 - mmengine - INFO - Epoch(train) [10][860/940] lr: 1.0000e-02 eta: 1 day, 1:58:24 time: 1.0989 data_time: 0.0127 memory: 15768 grad_norm: 3.3644 loss: 1.5440 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5440 2023/07/24 17:28:19 - mmengine - INFO - Epoch(train) [10][880/940] lr: 1.0000e-02 eta: 1 day, 1:58:01 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 3.3796 loss: 1.7199 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7199 2023/07/24 17:28:41 - mmengine - INFO - Epoch(train) [10][900/940] lr: 1.0000e-02 eta: 1 day, 1:57:38 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.4047 loss: 1.8103 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8103 2023/07/24 17:29:03 - mmengine - INFO - Epoch(train) [10][920/940] lr: 1.0000e-02 eta: 1 day, 1:57:15 time: 1.0974 data_time: 0.0131 memory: 15768 grad_norm: 3.3800 loss: 1.6152 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6152 2023/07/24 17:29:24 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 17:29:24 - mmengine - INFO - Epoch(train) [10][940/940] lr: 1.0000e-02 eta: 1 day, 1:56:45 time: 1.0567 data_time: 0.0126 memory: 15768 grad_norm: 3.5970 loss: 1.7342 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.7342 2023/07/24 17:29:34 - mmengine - INFO - Epoch(val) [10][20/78] eta: 0:00:28 time: 0.4974 data_time: 0.3403 memory: 2147 2023/07/24 17:29:41 - mmengine - INFO - Epoch(val) [10][40/78] eta: 0:00:16 time: 0.3582 data_time: 0.2014 memory: 2147 2023/07/24 17:29:50 - mmengine - INFO - Epoch(val) [10][60/78] eta: 0:00:07 time: 0.4567 data_time: 0.2995 memory: 2147 2023/07/24 17:30:00 - mmengine - INFO - Epoch(val) [10][78/78] acc/top1: 0.6343 acc/top5: 0.8565 acc/mean1: 0.6340 data_time: 0.2545 time: 0.4088 2023/07/24 17:30:00 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_7.pth is removed 2023/07/24 17:30:01 - mmengine - INFO - The best checkpoint with 0.6343 acc/top1 at 10 epoch is saved to best_acc_top1_epoch_10.pth. 2023/07/24 17:30:26 - mmengine - INFO - Epoch(train) [11][ 20/940] lr: 1.0000e-02 eta: 1 day, 1:56:45 time: 1.2323 data_time: 0.1369 memory: 15768 grad_norm: 3.3278 loss: 1.4894 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 1.4894 2023/07/24 17:30:48 - mmengine - INFO - Epoch(train) [11][ 40/940] lr: 1.0000e-02 eta: 1 day, 1:56:23 time: 1.1013 data_time: 0.0129 memory: 15768 grad_norm: 3.3681 loss: 1.5314 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.5314 2023/07/24 17:31:10 - mmengine - INFO - Epoch(train) [11][ 60/940] lr: 1.0000e-02 eta: 1 day, 1:56:00 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 3.3560 loss: 1.6191 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6191 2023/07/24 17:31:32 - mmengine - INFO - Epoch(train) [11][ 80/940] lr: 1.0000e-02 eta: 1 day, 1:55:37 time: 1.1020 data_time: 0.0128 memory: 15768 grad_norm: 3.4329 loss: 1.6679 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6679 2023/07/24 17:31:54 - mmengine - INFO - Epoch(train) [11][100/940] lr: 1.0000e-02 eta: 1 day, 1:55:14 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 3.4048 loss: 1.5178 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.5178 2023/07/24 17:32:16 - mmengine - INFO - Epoch(train) [11][120/940] lr: 1.0000e-02 eta: 1 day, 1:54:51 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.4382 loss: 1.6859 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6859 2023/07/24 17:32:38 - mmengine - INFO - Epoch(train) [11][140/940] lr: 1.0000e-02 eta: 1 day, 1:54:28 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 3.3830 loss: 1.4579 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4579 2023/07/24 17:33:00 - mmengine - INFO - Epoch(train) [11][160/940] lr: 1.0000e-02 eta: 1 day, 1:54:05 time: 1.0990 data_time: 0.0131 memory: 15768 grad_norm: 3.3874 loss: 1.4930 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4930 2023/07/24 17:33:22 - mmengine - INFO - Epoch(train) [11][180/940] lr: 1.0000e-02 eta: 1 day, 1:53:43 time: 1.1052 data_time: 0.0128 memory: 15768 grad_norm: 3.3956 loss: 1.6724 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6724 2023/07/24 17:33:44 - mmengine - INFO - Epoch(train) [11][200/940] lr: 1.0000e-02 eta: 1 day, 1:53:21 time: 1.0996 data_time: 0.0136 memory: 15768 grad_norm: 3.4198 loss: 1.5068 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5068 2023/07/24 17:34:06 - mmengine - INFO - Epoch(train) [11][220/940] lr: 1.0000e-02 eta: 1 day, 1:52:58 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.3355 loss: 1.6224 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6224 2023/07/24 17:34:28 - mmengine - INFO - Epoch(train) [11][240/940] lr: 1.0000e-02 eta: 1 day, 1:52:35 time: 1.0993 data_time: 0.0132 memory: 15768 grad_norm: 3.4399 loss: 1.5828 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5828 2023/07/24 17:34:50 - mmengine - INFO - Epoch(train) [11][260/940] lr: 1.0000e-02 eta: 1 day, 1:52:12 time: 1.0982 data_time: 0.0129 memory: 15768 grad_norm: 3.4371 loss: 1.6686 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6686 2023/07/24 17:35:12 - mmengine - INFO - Epoch(train) [11][280/940] lr: 1.0000e-02 eta: 1 day, 1:51:49 time: 1.1023 data_time: 0.0131 memory: 15768 grad_norm: 3.4089 loss: 1.4425 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.4425 2023/07/24 17:35:34 - mmengine - INFO - Epoch(train) [11][300/940] lr: 1.0000e-02 eta: 1 day, 1:51:27 time: 1.1008 data_time: 0.0130 memory: 15768 grad_norm: 3.4563 loss: 1.8035 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8035 2023/07/24 17:35:56 - mmengine - INFO - Epoch(train) [11][320/940] lr: 1.0000e-02 eta: 1 day, 1:51:03 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 3.4477 loss: 1.7046 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7046 2023/07/24 17:36:18 - mmengine - INFO - Epoch(train) [11][340/940] lr: 1.0000e-02 eta: 1 day, 1:50:41 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.3875 loss: 1.5300 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5300 2023/07/24 17:36:40 - mmengine - INFO - Epoch(train) [11][360/940] lr: 1.0000e-02 eta: 1 day, 1:50:18 time: 1.0979 data_time: 0.0132 memory: 15768 grad_norm: 3.3640 loss: 1.5084 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5084 2023/07/24 17:37:02 - mmengine - INFO - Epoch(train) [11][380/940] lr: 1.0000e-02 eta: 1 day, 1:49:55 time: 1.1016 data_time: 0.0136 memory: 15768 grad_norm: 3.4457 loss: 1.5627 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5627 2023/07/24 17:37:24 - mmengine - INFO - Epoch(train) [11][400/940] lr: 1.0000e-02 eta: 1 day, 1:49:32 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 3.4241 loss: 1.5219 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5219 2023/07/24 17:37:46 - mmengine - INFO - Epoch(train) [11][420/940] lr: 1.0000e-02 eta: 1 day, 1:49:10 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 3.4594 loss: 1.8248 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8248 2023/07/24 17:38:08 - mmengine - INFO - Epoch(train) [11][440/940] lr: 1.0000e-02 eta: 1 day, 1:48:47 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 3.4282 loss: 1.8425 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8425 2023/07/24 17:38:30 - mmengine - INFO - Epoch(train) [11][460/940] lr: 1.0000e-02 eta: 1 day, 1:48:24 time: 1.1008 data_time: 0.0130 memory: 15768 grad_norm: 3.4119 loss: 1.8051 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8051 2023/07/24 17:38:52 - mmengine - INFO - Epoch(train) [11][480/940] lr: 1.0000e-02 eta: 1 day, 1:48:01 time: 1.0990 data_time: 0.0132 memory: 15768 grad_norm: 3.3660 loss: 1.4171 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4171 2023/07/24 17:39:14 - mmengine - INFO - Epoch(train) [11][500/940] lr: 1.0000e-02 eta: 1 day, 1:47:38 time: 1.0985 data_time: 0.0130 memory: 15768 grad_norm: 3.3893 loss: 1.6737 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6737 2023/07/24 17:39:36 - mmengine - INFO - Epoch(train) [11][520/940] lr: 1.0000e-02 eta: 1 day, 1:47:15 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.3615 loss: 1.7520 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7520 2023/07/24 17:39:58 - mmengine - INFO - Epoch(train) [11][540/940] lr: 1.0000e-02 eta: 1 day, 1:46:53 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.4805 loss: 1.7752 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7752 2023/07/24 17:40:20 - mmengine - INFO - Epoch(train) [11][560/940] lr: 1.0000e-02 eta: 1 day, 1:46:30 time: 1.1021 data_time: 0.0136 memory: 15768 grad_norm: 3.4366 loss: 1.7693 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.7693 2023/07/24 17:40:42 - mmengine - INFO - Epoch(train) [11][580/940] lr: 1.0000e-02 eta: 1 day, 1:46:07 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.3887 loss: 1.6527 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6527 2023/07/24 17:41:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 17:41:04 - mmengine - INFO - Epoch(train) [11][600/940] lr: 1.0000e-02 eta: 1 day, 1:45:44 time: 1.0991 data_time: 0.0133 memory: 15768 grad_norm: 3.3435 loss: 1.5612 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5612 2023/07/24 17:41:26 - mmengine - INFO - Epoch(train) [11][620/940] lr: 1.0000e-02 eta: 1 day, 1:45:22 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.4023 loss: 1.6842 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6842 2023/07/24 17:41:48 - mmengine - INFO - Epoch(train) [11][640/940] lr: 1.0000e-02 eta: 1 day, 1:44:59 time: 1.0996 data_time: 0.0131 memory: 15768 grad_norm: 3.4175 loss: 1.6435 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.6435 2023/07/24 17:42:10 - mmengine - INFO - Epoch(train) [11][660/940] lr: 1.0000e-02 eta: 1 day, 1:44:37 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 3.4415 loss: 1.5360 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5360 2023/07/24 17:42:32 - mmengine - INFO - Epoch(train) [11][680/940] lr: 1.0000e-02 eta: 1 day, 1:44:14 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.3443 loss: 1.8267 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8267 2023/07/24 17:42:54 - mmengine - INFO - Epoch(train) [11][700/940] lr: 1.0000e-02 eta: 1 day, 1:43:51 time: 1.1006 data_time: 0.0127 memory: 15768 grad_norm: 3.4902 loss: 1.6714 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6714 2023/07/24 17:43:16 - mmengine - INFO - Epoch(train) [11][720/940] lr: 1.0000e-02 eta: 1 day, 1:43:29 time: 1.1039 data_time: 0.0128 memory: 15768 grad_norm: 3.3648 loss: 1.6088 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.6088 2023/07/24 17:43:38 - mmengine - INFO - Epoch(train) [11][740/940] lr: 1.0000e-02 eta: 1 day, 1:43:06 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 3.3699 loss: 1.5282 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5282 2023/07/24 17:44:00 - mmengine - INFO - Epoch(train) [11][760/940] lr: 1.0000e-02 eta: 1 day, 1:42:44 time: 1.1017 data_time: 0.0131 memory: 15768 grad_norm: 3.4078 loss: 1.5369 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5369 2023/07/24 17:44:22 - mmengine - INFO - Epoch(train) [11][780/940] lr: 1.0000e-02 eta: 1 day, 1:42:21 time: 1.1021 data_time: 0.0128 memory: 15768 grad_norm: 3.4349 loss: 1.4681 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4681 2023/07/24 17:44:44 - mmengine - INFO - Epoch(train) [11][800/940] lr: 1.0000e-02 eta: 1 day, 1:41:58 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 3.4574 loss: 1.5428 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5428 2023/07/24 17:45:06 - mmengine - INFO - Epoch(train) [11][820/940] lr: 1.0000e-02 eta: 1 day, 1:41:36 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.3480 loss: 1.7243 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7243 2023/07/24 17:45:28 - mmengine - INFO - Epoch(train) [11][840/940] lr: 1.0000e-02 eta: 1 day, 1:41:13 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 3.3491 loss: 1.4657 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4657 2023/07/24 17:45:50 - mmengine - INFO - Epoch(train) [11][860/940] lr: 1.0000e-02 eta: 1 day, 1:40:51 time: 1.1038 data_time: 0.0134 memory: 15768 grad_norm: 3.4591 loss: 1.5633 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5633 2023/07/24 17:46:12 - mmengine - INFO - Epoch(train) [11][880/940] lr: 1.0000e-02 eta: 1 day, 1:40:28 time: 1.1009 data_time: 0.0132 memory: 15768 grad_norm: 3.4025 loss: 1.7958 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7958 2023/07/24 17:46:34 - mmengine - INFO - Epoch(train) [11][900/940] lr: 1.0000e-02 eta: 1 day, 1:40:06 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.4391 loss: 1.8592 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 1.8592 2023/07/24 17:46:56 - mmengine - INFO - Epoch(train) [11][920/940] lr: 1.0000e-02 eta: 1 day, 1:39:43 time: 1.0998 data_time: 0.0131 memory: 15768 grad_norm: 3.3556 loss: 1.5446 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5446 2023/07/24 17:47:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 17:47:17 - mmengine - INFO - Epoch(train) [11][940/940] lr: 1.0000e-02 eta: 1 day, 1:39:13 time: 1.0531 data_time: 0.0126 memory: 15768 grad_norm: 3.5844 loss: 1.5939 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5939 2023/07/24 17:47:27 - mmengine - INFO - Epoch(val) [11][20/78] eta: 0:00:27 time: 0.4797 data_time: 0.3228 memory: 2147 2023/07/24 17:47:34 - mmengine - INFO - Epoch(val) [11][40/78] eta: 0:00:15 time: 0.3363 data_time: 0.1799 memory: 2147 2023/07/24 17:47:42 - mmengine - INFO - Epoch(val) [11][60/78] eta: 0:00:07 time: 0.4292 data_time: 0.2725 memory: 2147 2023/07/24 17:47:53 - mmengine - INFO - Epoch(val) [11][78/78] acc/top1: 0.6212 acc/top5: 0.8435 acc/mean1: 0.6209 data_time: 0.2348 time: 0.3887 2023/07/24 17:48:19 - mmengine - INFO - Epoch(train) [12][ 20/940] lr: 1.0000e-02 eta: 1 day, 1:39:22 time: 1.2974 data_time: 0.1345 memory: 15768 grad_norm: 3.3548 loss: 1.5483 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5483 2023/07/24 17:48:41 - mmengine - INFO - Epoch(train) [12][ 40/940] lr: 1.0000e-02 eta: 1 day, 1:38:59 time: 1.0992 data_time: 0.0131 memory: 15768 grad_norm: 3.3181 loss: 1.4882 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4882 2023/07/24 17:49:03 - mmengine - INFO - Epoch(train) [12][ 60/940] lr: 1.0000e-02 eta: 1 day, 1:38:37 time: 1.1038 data_time: 0.0126 memory: 15768 grad_norm: 3.3803 loss: 1.3372 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3372 2023/07/24 17:49:25 - mmengine - INFO - Epoch(train) [12][ 80/940] lr: 1.0000e-02 eta: 1 day, 1:38:14 time: 1.1017 data_time: 0.0126 memory: 15768 grad_norm: 3.4145 loss: 1.6273 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6273 2023/07/24 17:49:47 - mmengine - INFO - Epoch(train) [12][100/940] lr: 1.0000e-02 eta: 1 day, 1:37:51 time: 1.0997 data_time: 0.0127 memory: 15768 grad_norm: 3.4227 loss: 1.5389 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.5389 2023/07/24 17:50:09 - mmengine - INFO - Epoch(train) [12][120/940] lr: 1.0000e-02 eta: 1 day, 1:37:28 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 3.4541 loss: 1.5378 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5378 2023/07/24 17:50:31 - mmengine - INFO - Epoch(train) [12][140/940] lr: 1.0000e-02 eta: 1 day, 1:37:06 time: 1.1024 data_time: 0.0129 memory: 15768 grad_norm: 3.4210 loss: 1.6314 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6314 2023/07/24 17:50:53 - mmengine - INFO - Epoch(train) [12][160/940] lr: 1.0000e-02 eta: 1 day, 1:36:43 time: 1.1003 data_time: 0.0132 memory: 15768 grad_norm: 3.4066 loss: 1.4731 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.4731 2023/07/24 17:51:15 - mmengine - INFO - Epoch(train) [12][180/940] lr: 1.0000e-02 eta: 1 day, 1:36:21 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.4088 loss: 1.5142 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5142 2023/07/24 17:51:37 - mmengine - INFO - Epoch(train) [12][200/940] lr: 1.0000e-02 eta: 1 day, 1:35:58 time: 1.0989 data_time: 0.0130 memory: 15768 grad_norm: 3.4017 loss: 1.7830 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7830 2023/07/24 17:51:59 - mmengine - INFO - Epoch(train) [12][220/940] lr: 1.0000e-02 eta: 1 day, 1:35:35 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 3.4268 loss: 1.7797 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7797 2023/07/24 17:52:21 - mmengine - INFO - Epoch(train) [12][240/940] lr: 1.0000e-02 eta: 1 day, 1:35:12 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 3.4316 loss: 1.4003 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4003 2023/07/24 17:52:43 - mmengine - INFO - Epoch(train) [12][260/940] lr: 1.0000e-02 eta: 1 day, 1:34:50 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.4191 loss: 1.5404 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5404 2023/07/24 17:53:06 - mmengine - INFO - Epoch(train) [12][280/940] lr: 1.0000e-02 eta: 1 day, 1:34:27 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 3.3554 loss: 1.6290 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6290 2023/07/24 17:53:28 - mmengine - INFO - Epoch(train) [12][300/940] lr: 1.0000e-02 eta: 1 day, 1:34:05 time: 1.1016 data_time: 0.0130 memory: 15768 grad_norm: 3.4237 loss: 1.4195 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4195 2023/07/24 17:53:50 - mmengine - INFO - Epoch(train) [12][320/940] lr: 1.0000e-02 eta: 1 day, 1:33:42 time: 1.0994 data_time: 0.0129 memory: 15768 grad_norm: 3.4511 loss: 1.7021 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7021 2023/07/24 17:54:12 - mmengine - INFO - Epoch(train) [12][340/940] lr: 1.0000e-02 eta: 1 day, 1:33:19 time: 1.0990 data_time: 0.0131 memory: 15768 grad_norm: 3.4171 loss: 1.5945 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5945 2023/07/24 17:54:33 - mmengine - INFO - Epoch(train) [12][360/940] lr: 1.0000e-02 eta: 1 day, 1:32:56 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 3.3821 loss: 1.5458 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5458 2023/07/24 17:54:55 - mmengine - INFO - Epoch(train) [12][380/940] lr: 1.0000e-02 eta: 1 day, 1:32:33 time: 1.0989 data_time: 0.0130 memory: 15768 grad_norm: 3.4939 loss: 1.5314 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5314 2023/07/24 17:55:17 - mmengine - INFO - Epoch(train) [12][400/940] lr: 1.0000e-02 eta: 1 day, 1:32:10 time: 1.0974 data_time: 0.0134 memory: 15768 grad_norm: 3.4036 loss: 1.6314 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6314 2023/07/24 17:55:40 - mmengine - INFO - Epoch(train) [12][420/940] lr: 1.0000e-02 eta: 1 day, 1:31:48 time: 1.1051 data_time: 0.0129 memory: 15768 grad_norm: 3.3983 loss: 1.5488 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5488 2023/07/24 17:56:02 - mmengine - INFO - Epoch(train) [12][440/940] lr: 1.0000e-02 eta: 1 day, 1:31:26 time: 1.1014 data_time: 0.0137 memory: 15768 grad_norm: 3.4211 loss: 1.6203 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6203 2023/07/24 17:56:24 - mmengine - INFO - Epoch(train) [12][460/940] lr: 1.0000e-02 eta: 1 day, 1:31:03 time: 1.0983 data_time: 0.0130 memory: 15768 grad_norm: 3.4831 loss: 1.5134 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5134 2023/07/24 17:56:46 - mmengine - INFO - Epoch(train) [12][480/940] lr: 1.0000e-02 eta: 1 day, 1:30:40 time: 1.1001 data_time: 0.0133 memory: 15768 grad_norm: 3.4408 loss: 1.6936 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6936 2023/07/24 17:57:08 - mmengine - INFO - Epoch(train) [12][500/940] lr: 1.0000e-02 eta: 1 day, 1:30:17 time: 1.0996 data_time: 0.0129 memory: 15768 grad_norm: 3.4602 loss: 1.6399 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6399 2023/07/24 17:57:29 - mmengine - INFO - Epoch(train) [12][520/940] lr: 1.0000e-02 eta: 1 day, 1:29:54 time: 1.0980 data_time: 0.0130 memory: 15768 grad_norm: 3.4543 loss: 1.5805 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5805 2023/07/24 17:57:51 - mmengine - INFO - Epoch(train) [12][540/940] lr: 1.0000e-02 eta: 1 day, 1:29:31 time: 1.1008 data_time: 0.0130 memory: 15768 grad_norm: 3.3537 loss: 1.5965 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5965 2023/07/24 17:58:14 - mmengine - INFO - Epoch(train) [12][560/940] lr: 1.0000e-02 eta: 1 day, 1:29:09 time: 1.1029 data_time: 0.0128 memory: 15768 grad_norm: 3.4297 loss: 1.6720 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6720 2023/07/24 17:58:36 - mmengine - INFO - Epoch(train) [12][580/940] lr: 1.0000e-02 eta: 1 day, 1:28:47 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.4183 loss: 1.6805 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6805 2023/07/24 17:58:58 - mmengine - INFO - Epoch(train) [12][600/940] lr: 1.0000e-02 eta: 1 day, 1:28:24 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 3.4227 loss: 1.6560 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6560 2023/07/24 17:59:20 - mmengine - INFO - Epoch(train) [12][620/940] lr: 1.0000e-02 eta: 1 day, 1:28:01 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.3704 loss: 1.3893 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3893 2023/07/24 17:59:42 - mmengine - INFO - Epoch(train) [12][640/940] lr: 1.0000e-02 eta: 1 day, 1:27:39 time: 1.1036 data_time: 0.0137 memory: 15768 grad_norm: 3.4630 loss: 1.5259 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5259 2023/07/24 18:00:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:00:04 - mmengine - INFO - Epoch(train) [12][660/940] lr: 1.0000e-02 eta: 1 day, 1:27:16 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.4007 loss: 1.5998 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5998 2023/07/24 18:00:26 - mmengine - INFO - Epoch(train) [12][680/940] lr: 1.0000e-02 eta: 1 day, 1:26:54 time: 1.1023 data_time: 0.0133 memory: 15768 grad_norm: 3.4430 loss: 1.6380 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6380 2023/07/24 18:00:48 - mmengine - INFO - Epoch(train) [12][700/940] lr: 1.0000e-02 eta: 1 day, 1:26:32 time: 1.1032 data_time: 0.0131 memory: 15768 grad_norm: 3.4150 loss: 1.4319 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4319 2023/07/24 18:01:10 - mmengine - INFO - Epoch(train) [12][720/940] lr: 1.0000e-02 eta: 1 day, 1:26:10 time: 1.1028 data_time: 0.0144 memory: 15768 grad_norm: 3.4182 loss: 1.6126 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6126 2023/07/24 18:01:32 - mmengine - INFO - Epoch(train) [12][740/940] lr: 1.0000e-02 eta: 1 day, 1:25:47 time: 1.1024 data_time: 0.0136 memory: 15768 grad_norm: 3.4574 loss: 1.5017 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5017 2023/07/24 18:01:54 - mmengine - INFO - Epoch(train) [12][760/940] lr: 1.0000e-02 eta: 1 day, 1:25:24 time: 1.0990 data_time: 0.0133 memory: 15768 grad_norm: 3.4359 loss: 1.4695 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4695 2023/07/24 18:02:16 - mmengine - INFO - Epoch(train) [12][780/940] lr: 1.0000e-02 eta: 1 day, 1:25:02 time: 1.1007 data_time: 0.0136 memory: 15768 grad_norm: 3.4939 loss: 1.6459 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.6459 2023/07/24 18:02:38 - mmengine - INFO - Epoch(train) [12][800/940] lr: 1.0000e-02 eta: 1 day, 1:24:39 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 3.3972 loss: 1.6754 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6754 2023/07/24 18:03:00 - mmengine - INFO - Epoch(train) [12][820/940] lr: 1.0000e-02 eta: 1 day, 1:24:17 time: 1.1035 data_time: 0.0127 memory: 15768 grad_norm: 3.4748 loss: 1.3694 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3694 2023/07/24 18:03:22 - mmengine - INFO - Epoch(train) [12][840/940] lr: 1.0000e-02 eta: 1 day, 1:23:55 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 3.4654 loss: 1.6051 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6051 2023/07/24 18:03:44 - mmengine - INFO - Epoch(train) [12][860/940] lr: 1.0000e-02 eta: 1 day, 1:23:32 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 3.4695 loss: 1.5818 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5818 2023/07/24 18:04:06 - mmengine - INFO - Epoch(train) [12][880/940] lr: 1.0000e-02 eta: 1 day, 1:23:09 time: 1.1010 data_time: 0.0128 memory: 15768 grad_norm: 3.3933 loss: 1.4575 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.4575 2023/07/24 18:04:28 - mmengine - INFO - Epoch(train) [12][900/940] lr: 1.0000e-02 eta: 1 day, 1:22:47 time: 1.1018 data_time: 0.0129 memory: 15768 grad_norm: 3.4038 loss: 1.4993 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4993 2023/07/24 18:04:50 - mmengine - INFO - Epoch(train) [12][920/940] lr: 1.0000e-02 eta: 1 day, 1:22:25 time: 1.1033 data_time: 0.0131 memory: 15768 grad_norm: 3.4630 loss: 1.7094 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7094 2023/07/24 18:05:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:05:11 - mmengine - INFO - Epoch(train) [12][940/940] lr: 1.0000e-02 eta: 1 day, 1:21:55 time: 1.0548 data_time: 0.0127 memory: 15768 grad_norm: 3.5967 loss: 1.6761 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 1.6761 2023/07/24 18:05:11 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/07/24 18:05:22 - mmengine - INFO - Epoch(val) [12][20/78] eta: 0:00:27 time: 0.4821 data_time: 0.3251 memory: 2147 2023/07/24 18:05:29 - mmengine - INFO - Epoch(val) [12][40/78] eta: 0:00:15 time: 0.3347 data_time: 0.1782 memory: 2147 2023/07/24 18:05:38 - mmengine - INFO - Epoch(val) [12][60/78] eta: 0:00:07 time: 0.4449 data_time: 0.2885 memory: 2147 2023/07/24 18:05:48 - mmengine - INFO - Epoch(val) [12][78/78] acc/top1: 0.6529 acc/top5: 0.8662 acc/mean1: 0.6527 data_time: 0.2386 time: 0.3924 2023/07/24 18:05:48 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_10.pth is removed 2023/07/24 18:05:48 - mmengine - INFO - The best checkpoint with 0.6529 acc/top1 at 12 epoch is saved to best_acc_top1_epoch_12.pth. 2023/07/24 18:06:13 - mmengine - INFO - Epoch(train) [13][ 20/940] lr: 1.0000e-02 eta: 1 day, 1:21:54 time: 1.2459 data_time: 0.1442 memory: 15768 grad_norm: 3.3533 loss: 1.4199 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4199 2023/07/24 18:06:35 - mmengine - INFO - Epoch(train) [13][ 40/940] lr: 1.0000e-02 eta: 1 day, 1:21:32 time: 1.1041 data_time: 0.0127 memory: 15768 grad_norm: 3.4073 loss: 1.5403 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5403 2023/07/24 18:06:57 - mmengine - INFO - Epoch(train) [13][ 60/940] lr: 1.0000e-02 eta: 1 day, 1:21:10 time: 1.1035 data_time: 0.0128 memory: 15768 grad_norm: 3.3864 loss: 1.4269 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4269 2023/07/24 18:07:20 - mmengine - INFO - Epoch(train) [13][ 80/940] lr: 1.0000e-02 eta: 1 day, 1:20:48 time: 1.1044 data_time: 0.0126 memory: 15768 grad_norm: 3.4799 loss: 1.4545 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4545 2023/07/24 18:07:42 - mmengine - INFO - Epoch(train) [13][100/940] lr: 1.0000e-02 eta: 1 day, 1:20:26 time: 1.1030 data_time: 0.0128 memory: 15768 grad_norm: 3.4418 loss: 1.4852 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.4852 2023/07/24 18:08:04 - mmengine - INFO - Epoch(train) [13][120/940] lr: 1.0000e-02 eta: 1 day, 1:20:03 time: 1.1039 data_time: 0.0127 memory: 15768 grad_norm: 3.4084 loss: 1.5971 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5971 2023/07/24 18:08:26 - mmengine - INFO - Epoch(train) [13][140/940] lr: 1.0000e-02 eta: 1 day, 1:19:41 time: 1.1041 data_time: 0.0137 memory: 15768 grad_norm: 3.5221 loss: 1.4865 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4865 2023/07/24 18:08:48 - mmengine - INFO - Epoch(train) [13][160/940] lr: 1.0000e-02 eta: 1 day, 1:19:19 time: 1.1047 data_time: 0.0131 memory: 15768 grad_norm: 3.4589 loss: 1.4911 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4911 2023/07/24 18:09:10 - mmengine - INFO - Epoch(train) [13][180/940] lr: 1.0000e-02 eta: 1 day, 1:18:57 time: 1.1032 data_time: 0.0127 memory: 15768 grad_norm: 3.4385 loss: 1.5798 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5798 2023/07/24 18:09:32 - mmengine - INFO - Epoch(train) [13][200/940] lr: 1.0000e-02 eta: 1 day, 1:18:36 time: 1.1122 data_time: 0.0133 memory: 15768 grad_norm: 3.5136 loss: 1.4080 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4080 2023/07/24 18:09:55 - mmengine - INFO - Epoch(train) [13][220/940] lr: 1.0000e-02 eta: 1 day, 1:18:23 time: 1.1639 data_time: 0.0131 memory: 15768 grad_norm: 3.4059 loss: 1.2840 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2840 2023/07/24 18:10:19 - mmengine - INFO - Epoch(train) [13][240/940] lr: 1.0000e-02 eta: 1 day, 1:18:09 time: 1.1650 data_time: 0.0130 memory: 15768 grad_norm: 3.4469 loss: 1.5975 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5975 2023/07/24 18:10:42 - mmengine - INFO - Epoch(train) [13][260/940] lr: 1.0000e-02 eta: 1 day, 1:17:55 time: 1.1621 data_time: 0.0128 memory: 15768 grad_norm: 3.4114 loss: 1.6429 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6429 2023/07/24 18:11:05 - mmengine - INFO - Epoch(train) [13][280/940] lr: 1.0000e-02 eta: 1 day, 1:17:41 time: 1.1564 data_time: 0.0131 memory: 15768 grad_norm: 3.4888 loss: 1.5593 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5593 2023/07/24 18:11:28 - mmengine - INFO - Epoch(train) [13][300/940] lr: 1.0000e-02 eta: 1 day, 1:17:23 time: 1.1346 data_time: 0.0129 memory: 15768 grad_norm: 3.4637 loss: 1.5977 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5977 2023/07/24 18:11:50 - mmengine - INFO - Epoch(train) [13][320/940] lr: 1.0000e-02 eta: 1 day, 1:17:00 time: 1.0987 data_time: 0.0130 memory: 15768 grad_norm: 3.4480 loss: 1.6252 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6252 2023/07/24 18:12:12 - mmengine - INFO - Epoch(train) [13][340/940] lr: 1.0000e-02 eta: 1 day, 1:16:37 time: 1.0981 data_time: 0.0128 memory: 15768 grad_norm: 3.5049 loss: 1.6411 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6411 2023/07/24 18:12:34 - mmengine - INFO - Epoch(train) [13][360/940] lr: 1.0000e-02 eta: 1 day, 1:16:15 time: 1.1065 data_time: 0.0128 memory: 15768 grad_norm: 3.4880 loss: 1.5834 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5834 2023/07/24 18:12:57 - mmengine - INFO - Epoch(train) [13][380/940] lr: 1.0000e-02 eta: 1 day, 1:16:02 time: 1.1669 data_time: 0.0126 memory: 15768 grad_norm: 3.4726 loss: 1.5916 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5916 2023/07/24 18:13:20 - mmengine - INFO - Epoch(train) [13][400/940] lr: 1.0000e-02 eta: 1 day, 1:15:42 time: 1.1238 data_time: 0.0127 memory: 15768 grad_norm: 3.5004 loss: 1.5689 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5689 2023/07/24 18:13:42 - mmengine - INFO - Epoch(train) [13][420/940] lr: 1.0000e-02 eta: 1 day, 1:15:19 time: 1.0977 data_time: 0.0130 memory: 15768 grad_norm: 3.3765 loss: 1.4383 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4383 2023/07/24 18:14:04 - mmengine - INFO - Epoch(train) [13][440/940] lr: 1.0000e-02 eta: 1 day, 1:14:56 time: 1.0987 data_time: 0.0130 memory: 15768 grad_norm: 3.4741 loss: 1.6196 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.6196 2023/07/24 18:14:26 - mmengine - INFO - Epoch(train) [13][460/940] lr: 1.0000e-02 eta: 1 day, 1:14:34 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 3.4126 loss: 1.5407 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5407 2023/07/24 18:14:48 - mmengine - INFO - Epoch(train) [13][480/940] lr: 1.0000e-02 eta: 1 day, 1:14:11 time: 1.0981 data_time: 0.0130 memory: 15768 grad_norm: 3.4684 loss: 1.7439 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7439 2023/07/24 18:15:10 - mmengine - INFO - Epoch(train) [13][500/940] lr: 1.0000e-02 eta: 1 day, 1:13:48 time: 1.1001 data_time: 0.0130 memory: 15768 grad_norm: 3.4766 loss: 1.4490 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4490 2023/07/24 18:15:32 - mmengine - INFO - Epoch(train) [13][520/940] lr: 1.0000e-02 eta: 1 day, 1:13:25 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.5189 loss: 1.6214 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.6214 2023/07/24 18:15:54 - mmengine - INFO - Epoch(train) [13][540/940] lr: 1.0000e-02 eta: 1 day, 1:13:02 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.3911 loss: 1.3067 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 1.3067 2023/07/24 18:16:16 - mmengine - INFO - Epoch(train) [13][560/940] lr: 1.0000e-02 eta: 1 day, 1:12:39 time: 1.0980 data_time: 0.0130 memory: 15768 grad_norm: 3.4849 loss: 1.7641 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.7641 2023/07/24 18:16:38 - mmengine - INFO - Epoch(train) [13][580/940] lr: 1.0000e-02 eta: 1 day, 1:12:17 time: 1.0989 data_time: 0.0129 memory: 15768 grad_norm: 3.4730 loss: 1.6393 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6393 2023/07/24 18:17:00 - mmengine - INFO - Epoch(train) [13][600/940] lr: 1.0000e-02 eta: 1 day, 1:11:54 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.4451 loss: 1.6746 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6746 2023/07/24 18:17:22 - mmengine - INFO - Epoch(train) [13][620/940] lr: 1.0000e-02 eta: 1 day, 1:11:31 time: 1.1008 data_time: 0.0129 memory: 15768 grad_norm: 3.5213 loss: 1.6427 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.6427 2023/07/24 18:17:44 - mmengine - INFO - Epoch(train) [13][640/940] lr: 1.0000e-02 eta: 1 day, 1:11:09 time: 1.1012 data_time: 0.0133 memory: 15768 grad_norm: 3.5163 loss: 1.5521 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5521 2023/07/24 18:18:06 - mmengine - INFO - Epoch(train) [13][660/940] lr: 1.0000e-02 eta: 1 day, 1:10:46 time: 1.0983 data_time: 0.0129 memory: 15768 grad_norm: 3.4561 loss: 1.4771 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4771 2023/07/24 18:18:28 - mmengine - INFO - Epoch(train) [13][680/940] lr: 1.0000e-02 eta: 1 day, 1:10:23 time: 1.1015 data_time: 0.0130 memory: 15768 grad_norm: 3.4893 loss: 1.4015 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.4015 2023/07/24 18:18:50 - mmengine - INFO - Epoch(train) [13][700/940] lr: 1.0000e-02 eta: 1 day, 1:10:00 time: 1.1006 data_time: 0.0129 memory: 15768 grad_norm: 3.5381 loss: 1.6533 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6533 2023/07/24 18:19:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:19:12 - mmengine - INFO - Epoch(train) [13][720/940] lr: 1.0000e-02 eta: 1 day, 1:09:38 time: 1.1018 data_time: 0.0128 memory: 15768 grad_norm: 3.4470 loss: 1.5215 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5215 2023/07/24 18:19:34 - mmengine - INFO - Epoch(train) [13][740/940] lr: 1.0000e-02 eta: 1 day, 1:09:15 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 3.4526 loss: 1.5694 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.5694 2023/07/24 18:19:56 - mmengine - INFO - Epoch(train) [13][760/940] lr: 1.0000e-02 eta: 1 day, 1:08:53 time: 1.1026 data_time: 0.0131 memory: 15768 grad_norm: 3.4484 loss: 1.5621 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5621 2023/07/24 18:20:18 - mmengine - INFO - Epoch(train) [13][780/940] lr: 1.0000e-02 eta: 1 day, 1:08:30 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 3.4992 loss: 1.6091 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6091 2023/07/24 18:20:40 - mmengine - INFO - Epoch(train) [13][800/940] lr: 1.0000e-02 eta: 1 day, 1:08:07 time: 1.0985 data_time: 0.0132 memory: 15768 grad_norm: 3.4734 loss: 1.4640 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.4640 2023/07/24 18:21:02 - mmengine - INFO - Epoch(train) [13][820/940] lr: 1.0000e-02 eta: 1 day, 1:07:45 time: 1.1026 data_time: 0.0126 memory: 15768 grad_norm: 3.5013 loss: 1.5433 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5433 2023/07/24 18:21:24 - mmengine - INFO - Epoch(train) [13][840/940] lr: 1.0000e-02 eta: 1 day, 1:07:22 time: 1.0986 data_time: 0.0130 memory: 15768 grad_norm: 3.5316 loss: 1.6180 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6180 2023/07/24 18:21:46 - mmengine - INFO - Epoch(train) [13][860/940] lr: 1.0000e-02 eta: 1 day, 1:06:59 time: 1.1028 data_time: 0.0127 memory: 15768 grad_norm: 3.4200 loss: 1.4061 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4061 2023/07/24 18:22:08 - mmengine - INFO - Epoch(train) [13][880/940] lr: 1.0000e-02 eta: 1 day, 1:06:36 time: 1.0982 data_time: 0.0131 memory: 15768 grad_norm: 3.5179 loss: 1.6537 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6537 2023/07/24 18:22:30 - mmengine - INFO - Epoch(train) [13][900/940] lr: 1.0000e-02 eta: 1 day, 1:06:14 time: 1.1014 data_time: 0.0128 memory: 15768 grad_norm: 3.4359 loss: 1.3713 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3713 2023/07/24 18:22:52 - mmengine - INFO - Epoch(train) [13][920/940] lr: 1.0000e-02 eta: 1 day, 1:05:51 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.5010 loss: 1.5274 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5274 2023/07/24 18:23:13 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:23:13 - mmengine - INFO - Epoch(train) [13][940/940] lr: 1.0000e-02 eta: 1 day, 1:05:22 time: 1.0539 data_time: 0.0126 memory: 15768 grad_norm: 3.6988 loss: 1.7228 top1_acc: 0.0000 top5_acc: 0.7500 loss_cls: 1.7228 2023/07/24 18:23:22 - mmengine - INFO - Epoch(val) [13][20/78] eta: 0:00:28 time: 0.4834 data_time: 0.3265 memory: 2147 2023/07/24 18:23:29 - mmengine - INFO - Epoch(val) [13][40/78] eta: 0:00:15 time: 0.3438 data_time: 0.1873 memory: 2147 2023/07/24 18:23:38 - mmengine - INFO - Epoch(val) [13][60/78] eta: 0:00:07 time: 0.4287 data_time: 0.2716 memory: 2147 2023/07/24 18:23:48 - mmengine - INFO - Epoch(val) [13][78/78] acc/top1: 0.6348 acc/top5: 0.8545 acc/mean1: 0.6347 data_time: 0.2381 time: 0.3921 2023/07/24 18:24:14 - mmengine - INFO - Epoch(train) [14][ 20/940] lr: 1.0000e-02 eta: 1 day, 1:05:27 time: 1.3027 data_time: 0.1492 memory: 15768 grad_norm: 3.4670 loss: 1.4975 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4975 2023/07/24 18:24:36 - mmengine - INFO - Epoch(train) [14][ 40/940] lr: 1.0000e-02 eta: 1 day, 1:05:04 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 3.3857 loss: 1.4352 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4352 2023/07/24 18:24:59 - mmengine - INFO - Epoch(train) [14][ 60/940] lr: 1.0000e-02 eta: 1 day, 1:04:42 time: 1.1024 data_time: 0.0132 memory: 15768 grad_norm: 3.4819 loss: 1.6341 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6341 2023/07/24 18:25:21 - mmengine - INFO - Epoch(train) [14][ 80/940] lr: 1.0000e-02 eta: 1 day, 1:04:19 time: 1.0993 data_time: 0.0129 memory: 15768 grad_norm: 3.4104 loss: 1.7010 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7010 2023/07/24 18:25:43 - mmengine - INFO - Epoch(train) [14][100/940] lr: 1.0000e-02 eta: 1 day, 1:03:56 time: 1.1001 data_time: 0.0129 memory: 15768 grad_norm: 3.4970 loss: 1.4108 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4108 2023/07/24 18:26:05 - mmengine - INFO - Epoch(train) [14][120/940] lr: 1.0000e-02 eta: 1 day, 1:03:34 time: 1.1026 data_time: 0.0137 memory: 15768 grad_norm: 3.5190 loss: 1.6433 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6433 2023/07/24 18:26:27 - mmengine - INFO - Epoch(train) [14][140/940] lr: 1.0000e-02 eta: 1 day, 1:03:11 time: 1.1039 data_time: 0.0128 memory: 15768 grad_norm: 3.4924 loss: 1.4432 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4432 2023/07/24 18:26:49 - mmengine - INFO - Epoch(train) [14][160/940] lr: 1.0000e-02 eta: 1 day, 1:02:49 time: 1.1011 data_time: 0.0132 memory: 15768 grad_norm: 3.4450 loss: 1.4129 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4129 2023/07/24 18:27:11 - mmengine - INFO - Epoch(train) [14][180/940] lr: 1.0000e-02 eta: 1 day, 1:02:27 time: 1.1032 data_time: 0.0129 memory: 15768 grad_norm: 3.4654 loss: 1.5505 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5505 2023/07/24 18:27:33 - mmengine - INFO - Epoch(train) [14][200/940] lr: 1.0000e-02 eta: 1 day, 1:02:04 time: 1.1027 data_time: 0.0130 memory: 15768 grad_norm: 3.3605 loss: 1.3820 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3820 2023/07/24 18:27:55 - mmengine - INFO - Epoch(train) [14][220/940] lr: 1.0000e-02 eta: 1 day, 1:01:41 time: 1.0986 data_time: 0.0134 memory: 15768 grad_norm: 3.4133 loss: 1.6681 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6681 2023/07/24 18:28:17 - mmengine - INFO - Epoch(train) [14][240/940] lr: 1.0000e-02 eta: 1 day, 1:01:19 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 3.4233 loss: 1.5435 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5435 2023/07/24 18:28:39 - mmengine - INFO - Epoch(train) [14][260/940] lr: 1.0000e-02 eta: 1 day, 1:00:56 time: 1.0986 data_time: 0.0130 memory: 15768 grad_norm: 3.4932 loss: 1.4827 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4827 2023/07/24 18:29:01 - mmengine - INFO - Epoch(train) [14][280/940] lr: 1.0000e-02 eta: 1 day, 1:00:33 time: 1.1004 data_time: 0.0136 memory: 15768 grad_norm: 3.5064 loss: 1.6279 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6279 2023/07/24 18:29:23 - mmengine - INFO - Epoch(train) [14][300/940] lr: 1.0000e-02 eta: 1 day, 1:00:10 time: 1.0999 data_time: 0.0132 memory: 15768 grad_norm: 3.5604 loss: 1.4924 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4924 2023/07/24 18:29:45 - mmengine - INFO - Epoch(train) [14][320/940] lr: 1.0000e-02 eta: 1 day, 0:59:48 time: 1.1016 data_time: 0.0134 memory: 15768 grad_norm: 3.4829 loss: 1.6059 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6059 2023/07/24 18:30:07 - mmengine - INFO - Epoch(train) [14][340/940] lr: 1.0000e-02 eta: 1 day, 0:59:25 time: 1.1031 data_time: 0.0135 memory: 15768 grad_norm: 3.4335 loss: 1.6326 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6326 2023/07/24 18:30:29 - mmengine - INFO - Epoch(train) [14][360/940] lr: 1.0000e-02 eta: 1 day, 0:59:03 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 3.4512 loss: 1.3375 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3375 2023/07/24 18:30:51 - mmengine - INFO - Epoch(train) [14][380/940] lr: 1.0000e-02 eta: 1 day, 0:58:40 time: 1.1025 data_time: 0.0132 memory: 15768 grad_norm: 3.4547 loss: 1.5609 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5609 2023/07/24 18:31:13 - mmengine - INFO - Epoch(train) [14][400/940] lr: 1.0000e-02 eta: 1 day, 0:58:18 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 3.4764 loss: 1.5581 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.5581 2023/07/24 18:31:35 - mmengine - INFO - Epoch(train) [14][420/940] lr: 1.0000e-02 eta: 1 day, 0:57:55 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 3.4862 loss: 1.5411 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5411 2023/07/24 18:31:57 - mmengine - INFO - Epoch(train) [14][440/940] lr: 1.0000e-02 eta: 1 day, 0:57:32 time: 1.1006 data_time: 0.0137 memory: 15768 grad_norm: 3.4774 loss: 1.4352 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4352 2023/07/24 18:32:19 - mmengine - INFO - Epoch(train) [14][460/940] lr: 1.0000e-02 eta: 1 day, 0:57:10 time: 1.0988 data_time: 0.0132 memory: 15768 grad_norm: 3.4386 loss: 1.5884 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5884 2023/07/24 18:32:41 - mmengine - INFO - Epoch(train) [14][480/940] lr: 1.0000e-02 eta: 1 day, 0:56:47 time: 1.0984 data_time: 0.0129 memory: 15768 grad_norm: 3.5122 loss: 1.4972 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4972 2023/07/24 18:33:03 - mmengine - INFO - Epoch(train) [14][500/940] lr: 1.0000e-02 eta: 1 day, 0:56:24 time: 1.1019 data_time: 0.0138 memory: 15768 grad_norm: 3.4870 loss: 1.6270 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6270 2023/07/24 18:33:25 - mmengine - INFO - Epoch(train) [14][520/940] lr: 1.0000e-02 eta: 1 day, 0:56:01 time: 1.0994 data_time: 0.0133 memory: 15768 grad_norm: 3.5050 loss: 1.4872 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4872 2023/07/24 18:33:47 - mmengine - INFO - Epoch(train) [14][540/940] lr: 1.0000e-02 eta: 1 day, 0:55:39 time: 1.0994 data_time: 0.0131 memory: 15768 grad_norm: 3.3198 loss: 1.4554 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4554 2023/07/24 18:34:09 - mmengine - INFO - Epoch(train) [14][560/940] lr: 1.0000e-02 eta: 1 day, 0:55:16 time: 1.0987 data_time: 0.0133 memory: 15768 grad_norm: 3.4780 loss: 1.6271 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6271 2023/07/24 18:34:31 - mmengine - INFO - Epoch(train) [14][580/940] lr: 1.0000e-02 eta: 1 day, 0:54:53 time: 1.1016 data_time: 0.0128 memory: 15768 grad_norm: 3.5180 loss: 1.4679 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4679 2023/07/24 18:34:53 - mmengine - INFO - Epoch(train) [14][600/940] lr: 1.0000e-02 eta: 1 day, 0:54:31 time: 1.0998 data_time: 0.0131 memory: 15768 grad_norm: 3.4665 loss: 1.5151 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5151 2023/07/24 18:35:15 - mmengine - INFO - Epoch(train) [14][620/940] lr: 1.0000e-02 eta: 1 day, 0:54:08 time: 1.0987 data_time: 0.0133 memory: 15768 grad_norm: 3.5346 loss: 1.5166 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.5166 2023/07/24 18:35:37 - mmengine - INFO - Epoch(train) [14][640/940] lr: 1.0000e-02 eta: 1 day, 0:53:45 time: 1.1007 data_time: 0.0134 memory: 15768 grad_norm: 3.5645 loss: 1.7961 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7961 2023/07/24 18:35:59 - mmengine - INFO - Epoch(train) [14][660/940] lr: 1.0000e-02 eta: 1 day, 0:53:23 time: 1.1004 data_time: 0.0131 memory: 15768 grad_norm: 3.4635 loss: 1.4645 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4645 2023/07/24 18:36:21 - mmengine - INFO - Epoch(train) [14][680/940] lr: 1.0000e-02 eta: 1 day, 0:53:00 time: 1.0986 data_time: 0.0141 memory: 15768 grad_norm: 3.5026 loss: 1.6371 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6371 2023/07/24 18:36:43 - mmengine - INFO - Epoch(train) [14][700/940] lr: 1.0000e-02 eta: 1 day, 0:52:37 time: 1.0998 data_time: 0.0131 memory: 15768 grad_norm: 3.4935 loss: 1.5506 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.5506 2023/07/24 18:37:05 - mmengine - INFO - Epoch(train) [14][720/940] lr: 1.0000e-02 eta: 1 day, 0:52:15 time: 1.1037 data_time: 0.0131 memory: 15768 grad_norm: 3.4482 loss: 1.3447 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3447 2023/07/24 18:37:27 - mmengine - INFO - Epoch(train) [14][740/940] lr: 1.0000e-02 eta: 1 day, 0:51:52 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 3.4183 loss: 1.4828 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4828 2023/07/24 18:37:49 - mmengine - INFO - Epoch(train) [14][760/940] lr: 1.0000e-02 eta: 1 day, 0:51:30 time: 1.1034 data_time: 0.0134 memory: 15768 grad_norm: 3.4516 loss: 1.6929 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6929 2023/07/24 18:38:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:38:11 - mmengine - INFO - Epoch(train) [14][780/940] lr: 1.0000e-02 eta: 1 day, 0:51:08 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 3.5348 loss: 1.4161 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4161 2023/07/24 18:38:33 - mmengine - INFO - Epoch(train) [14][800/940] lr: 1.0000e-02 eta: 1 day, 0:50:45 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.5450 loss: 1.5978 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5978 2023/07/24 18:38:55 - mmengine - INFO - Epoch(train) [14][820/940] lr: 1.0000e-02 eta: 1 day, 0:50:22 time: 1.0989 data_time: 0.0133 memory: 15768 grad_norm: 3.5295 loss: 1.5123 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5123 2023/07/24 18:39:17 - mmengine - INFO - Epoch(train) [14][840/940] lr: 1.0000e-02 eta: 1 day, 0:49:59 time: 1.0976 data_time: 0.0132 memory: 15768 grad_norm: 3.4839 loss: 1.4652 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.4652 2023/07/24 18:39:39 - mmengine - INFO - Epoch(train) [14][860/940] lr: 1.0000e-02 eta: 1 day, 0:49:37 time: 1.1018 data_time: 0.0131 memory: 15768 grad_norm: 3.5021 loss: 1.6412 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.6412 2023/07/24 18:40:01 - mmengine - INFO - Epoch(train) [14][880/940] lr: 1.0000e-02 eta: 1 day, 0:49:14 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 3.4830 loss: 1.5824 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5824 2023/07/24 18:40:23 - mmengine - INFO - Epoch(train) [14][900/940] lr: 1.0000e-02 eta: 1 day, 0:48:51 time: 1.1003 data_time: 0.0131 memory: 15768 grad_norm: 3.5454 loss: 1.5653 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5653 2023/07/24 18:40:45 - mmengine - INFO - Epoch(train) [14][920/940] lr: 1.0000e-02 eta: 1 day, 0:48:29 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 3.4523 loss: 1.4613 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4613 2023/07/24 18:41:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:41:06 - mmengine - INFO - Epoch(train) [14][940/940] lr: 1.0000e-02 eta: 1 day, 0:48:01 time: 1.0572 data_time: 0.0127 memory: 15768 grad_norm: 3.7449 loss: 1.6245 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.6245 2023/07/24 18:41:16 - mmengine - INFO - Epoch(val) [14][20/78] eta: 0:00:28 time: 0.4861 data_time: 0.3287 memory: 2147 2023/07/24 18:41:23 - mmengine - INFO - Epoch(val) [14][40/78] eta: 0:00:15 time: 0.3511 data_time: 0.1940 memory: 2147 2023/07/24 18:41:32 - mmengine - INFO - Epoch(val) [14][60/78] eta: 0:00:07 time: 0.4506 data_time: 0.2933 memory: 2147 2023/07/24 18:41:42 - mmengine - INFO - Epoch(val) [14][78/78] acc/top1: 0.6661 acc/top5: 0.8749 acc/mean1: 0.6659 data_time: 0.2461 time: 0.4004 2023/07/24 18:41:42 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_12.pth is removed 2023/07/24 18:41:43 - mmengine - INFO - The best checkpoint with 0.6661 acc/top1 at 14 epoch is saved to best_acc_top1_epoch_14.pth. 2023/07/24 18:42:08 - mmengine - INFO - Epoch(train) [15][ 20/940] lr: 1.0000e-02 eta: 1 day, 0:47:56 time: 1.2461 data_time: 0.1515 memory: 15768 grad_norm: 3.4718 loss: 1.5237 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5237 2023/07/24 18:42:30 - mmengine - INFO - Epoch(train) [15][ 40/940] lr: 1.0000e-02 eta: 1 day, 0:47:34 time: 1.1022 data_time: 0.0133 memory: 15768 grad_norm: 3.4839 loss: 1.6119 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6119 2023/07/24 18:42:52 - mmengine - INFO - Epoch(train) [15][ 60/940] lr: 1.0000e-02 eta: 1 day, 0:47:11 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 3.4057 loss: 1.3088 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3088 2023/07/24 18:43:14 - mmengine - INFO - Epoch(train) [15][ 80/940] lr: 1.0000e-02 eta: 1 day, 0:46:48 time: 1.0983 data_time: 0.0129 memory: 15768 grad_norm: 3.3794 loss: 1.4982 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4982 2023/07/24 18:43:36 - mmengine - INFO - Epoch(train) [15][100/940] lr: 1.0000e-02 eta: 1 day, 0:46:26 time: 1.0993 data_time: 0.0131 memory: 15768 grad_norm: 3.5153 loss: 1.4724 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4724 2023/07/24 18:43:58 - mmengine - INFO - Epoch(train) [15][120/940] lr: 1.0000e-02 eta: 1 day, 0:46:03 time: 1.1043 data_time: 0.0132 memory: 15768 grad_norm: 3.4541 loss: 1.4869 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.4869 2023/07/24 18:44:20 - mmengine - INFO - Epoch(train) [15][140/940] lr: 1.0000e-02 eta: 1 day, 0:45:41 time: 1.1000 data_time: 0.0131 memory: 15768 grad_norm: 3.4378 loss: 1.4570 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4570 2023/07/24 18:44:42 - mmengine - INFO - Epoch(train) [15][160/940] lr: 1.0000e-02 eta: 1 day, 0:45:18 time: 1.1027 data_time: 0.0131 memory: 15768 grad_norm: 3.4584 loss: 1.5452 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5452 2023/07/24 18:45:04 - mmengine - INFO - Epoch(train) [15][180/940] lr: 1.0000e-02 eta: 1 day, 0:44:56 time: 1.1010 data_time: 0.0132 memory: 15768 grad_norm: 3.4750 loss: 1.5219 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5219 2023/07/24 18:45:26 - mmengine - INFO - Epoch(train) [15][200/940] lr: 1.0000e-02 eta: 1 day, 0:44:33 time: 1.0994 data_time: 0.0129 memory: 15768 grad_norm: 3.4646 loss: 1.4735 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4735 2023/07/24 18:45:48 - mmengine - INFO - Epoch(train) [15][220/940] lr: 1.0000e-02 eta: 1 day, 0:44:11 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 3.4716 loss: 1.4427 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4427 2023/07/24 18:46:10 - mmengine - INFO - Epoch(train) [15][240/940] lr: 1.0000e-02 eta: 1 day, 0:43:48 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 3.4631 loss: 1.2713 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2713 2023/07/24 18:46:32 - mmengine - INFO - Epoch(train) [15][260/940] lr: 1.0000e-02 eta: 1 day, 0:43:26 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.4406 loss: 1.5001 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5001 2023/07/24 18:46:54 - mmengine - INFO - Epoch(train) [15][280/940] lr: 1.0000e-02 eta: 1 day, 0:43:03 time: 1.1020 data_time: 0.0128 memory: 15768 grad_norm: 3.4731 loss: 1.5686 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5686 2023/07/24 18:47:17 - mmengine - INFO - Epoch(train) [15][300/940] lr: 1.0000e-02 eta: 1 day, 0:42:41 time: 1.1049 data_time: 0.0132 memory: 15768 grad_norm: 3.5464 loss: 1.4856 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4856 2023/07/24 18:47:39 - mmengine - INFO - Epoch(train) [15][320/940] lr: 1.0000e-02 eta: 1 day, 0:42:19 time: 1.1015 data_time: 0.0135 memory: 15768 grad_norm: 3.5107 loss: 1.4915 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4915 2023/07/24 18:48:01 - mmengine - INFO - Epoch(train) [15][340/940] lr: 1.0000e-02 eta: 1 day, 0:41:56 time: 1.1004 data_time: 0.0134 memory: 15768 grad_norm: 3.4758 loss: 1.4321 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4321 2023/07/24 18:48:23 - mmengine - INFO - Epoch(train) [15][360/940] lr: 1.0000e-02 eta: 1 day, 0:41:34 time: 1.1025 data_time: 0.0132 memory: 15768 grad_norm: 3.3636 loss: 1.5732 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5732 2023/07/24 18:48:45 - mmengine - INFO - Epoch(train) [15][380/940] lr: 1.0000e-02 eta: 1 day, 0:41:11 time: 1.0978 data_time: 0.0129 memory: 15768 grad_norm: 3.5250 loss: 1.6271 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6271 2023/07/24 18:49:07 - mmengine - INFO - Epoch(train) [15][400/940] lr: 1.0000e-02 eta: 1 day, 0:40:48 time: 1.1014 data_time: 0.0134 memory: 15768 grad_norm: 3.4759 loss: 1.4613 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4613 2023/07/24 18:49:29 - mmengine - INFO - Epoch(train) [15][420/940] lr: 1.0000e-02 eta: 1 day, 0:40:26 time: 1.0981 data_time: 0.0133 memory: 15768 grad_norm: 3.4696 loss: 1.3374 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3374 2023/07/24 18:49:51 - mmengine - INFO - Epoch(train) [15][440/940] lr: 1.0000e-02 eta: 1 day, 0:40:03 time: 1.1027 data_time: 0.0134 memory: 15768 grad_norm: 3.4928 loss: 1.5849 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5849 2023/07/24 18:50:13 - mmengine - INFO - Epoch(train) [15][460/940] lr: 1.0000e-02 eta: 1 day, 0:39:41 time: 1.1043 data_time: 0.0134 memory: 15768 grad_norm: 3.5562 loss: 1.6147 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6147 2023/07/24 18:50:35 - mmengine - INFO - Epoch(train) [15][480/940] lr: 1.0000e-02 eta: 1 day, 0:39:19 time: 1.1034 data_time: 0.0137 memory: 15768 grad_norm: 3.4998 loss: 1.5145 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5145 2023/07/24 18:50:57 - mmengine - INFO - Epoch(train) [15][500/940] lr: 1.0000e-02 eta: 1 day, 0:38:56 time: 1.1019 data_time: 0.0134 memory: 15768 grad_norm: 3.5741 loss: 1.4028 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4028 2023/07/24 18:51:19 - mmengine - INFO - Epoch(train) [15][520/940] lr: 1.0000e-02 eta: 1 day, 0:38:34 time: 1.0993 data_time: 0.0133 memory: 15768 grad_norm: 3.5361 loss: 1.4953 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4953 2023/07/24 18:51:41 - mmengine - INFO - Epoch(train) [15][540/940] lr: 1.0000e-02 eta: 1 day, 0:38:11 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 3.6142 loss: 1.7397 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7397 2023/07/24 18:52:03 - mmengine - INFO - Epoch(train) [15][560/940] lr: 1.0000e-02 eta: 1 day, 0:37:49 time: 1.1000 data_time: 0.0133 memory: 15768 grad_norm: 3.4734 loss: 1.4840 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4840 2023/07/24 18:52:25 - mmengine - INFO - Epoch(train) [15][580/940] lr: 1.0000e-02 eta: 1 day, 0:37:26 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 3.5167 loss: 1.5666 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5666 2023/07/24 18:52:47 - mmengine - INFO - Epoch(train) [15][600/940] lr: 1.0000e-02 eta: 1 day, 0:37:04 time: 1.1009 data_time: 0.0134 memory: 15768 grad_norm: 3.5193 loss: 1.9170 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9170 2023/07/24 18:53:09 - mmengine - INFO - Epoch(train) [15][620/940] lr: 1.0000e-02 eta: 1 day, 0:36:41 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 3.5089 loss: 1.4377 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4377 2023/07/24 18:53:31 - mmengine - INFO - Epoch(train) [15][640/940] lr: 1.0000e-02 eta: 1 day, 0:36:19 time: 1.1027 data_time: 0.0135 memory: 15768 grad_norm: 3.5143 loss: 1.4837 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4837 2023/07/24 18:53:53 - mmengine - INFO - Epoch(train) [15][660/940] lr: 1.0000e-02 eta: 1 day, 0:35:56 time: 1.1001 data_time: 0.0137 memory: 15768 grad_norm: 3.5333 loss: 1.3601 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3601 2023/07/24 18:54:15 - mmengine - INFO - Epoch(train) [15][680/940] lr: 1.0000e-02 eta: 1 day, 0:35:34 time: 1.1020 data_time: 0.0132 memory: 15768 grad_norm: 3.5100 loss: 1.6109 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6109 2023/07/24 18:54:37 - mmengine - INFO - Epoch(train) [15][700/940] lr: 1.0000e-02 eta: 1 day, 0:35:11 time: 1.1012 data_time: 0.0133 memory: 15768 grad_norm: 3.5533 loss: 1.4838 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4838 2023/07/24 18:54:59 - mmengine - INFO - Epoch(train) [15][720/940] lr: 1.0000e-02 eta: 1 day, 0:34:49 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 3.5105 loss: 1.4298 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4298 2023/07/24 18:55:21 - mmengine - INFO - Epoch(train) [15][740/940] lr: 1.0000e-02 eta: 1 day, 0:34:26 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 3.5657 loss: 1.5730 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5730 2023/07/24 18:55:43 - mmengine - INFO - Epoch(train) [15][760/940] lr: 1.0000e-02 eta: 1 day, 0:34:04 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.5634 loss: 1.4198 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4198 2023/07/24 18:56:05 - mmengine - INFO - Epoch(train) [15][780/940] lr: 1.0000e-02 eta: 1 day, 0:33:41 time: 1.1009 data_time: 0.0133 memory: 15768 grad_norm: 3.5425 loss: 1.7103 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7103 2023/07/24 18:56:27 - mmengine - INFO - Epoch(train) [15][800/940] lr: 1.0000e-02 eta: 1 day, 0:33:18 time: 1.0989 data_time: 0.0133 memory: 15768 grad_norm: 3.5181 loss: 1.4127 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4127 2023/07/24 18:56:49 - mmengine - INFO - Epoch(train) [15][820/940] lr: 1.0000e-02 eta: 1 day, 0:32:56 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 3.5528 loss: 1.4010 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4010 2023/07/24 18:57:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:57:11 - mmengine - INFO - Epoch(train) [15][840/940] lr: 1.0000e-02 eta: 1 day, 0:32:33 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 3.5206 loss: 1.6382 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6382 2023/07/24 18:57:33 - mmengine - INFO - Epoch(train) [15][860/940] lr: 1.0000e-02 eta: 1 day, 0:32:11 time: 1.1031 data_time: 0.0133 memory: 15768 grad_norm: 3.5445 loss: 1.7385 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7385 2023/07/24 18:57:55 - mmengine - INFO - Epoch(train) [15][880/940] lr: 1.0000e-02 eta: 1 day, 0:31:48 time: 1.0972 data_time: 0.0132 memory: 15768 grad_norm: 3.5292 loss: 1.3730 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3730 2023/07/24 18:58:17 - mmengine - INFO - Epoch(train) [15][900/940] lr: 1.0000e-02 eta: 1 day, 0:31:25 time: 1.1013 data_time: 0.0133 memory: 15768 grad_norm: 3.5613 loss: 1.5429 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5429 2023/07/24 18:58:39 - mmengine - INFO - Epoch(train) [15][920/940] lr: 1.0000e-02 eta: 1 day, 0:31:03 time: 1.1035 data_time: 0.0133 memory: 15768 grad_norm: 3.5918 loss: 1.5391 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5391 2023/07/24 18:59:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 18:59:00 - mmengine - INFO - Epoch(train) [15][940/940] lr: 1.0000e-02 eta: 1 day, 0:30:36 time: 1.0579 data_time: 0.0129 memory: 15768 grad_norm: 3.6433 loss: 1.4489 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4489 2023/07/24 18:59:00 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/07/24 18:59:12 - mmengine - INFO - Epoch(val) [15][20/78] eta: 0:00:29 time: 0.5044 data_time: 0.3469 memory: 2147 2023/07/24 18:59:18 - mmengine - INFO - Epoch(val) [15][40/78] eta: 0:00:16 time: 0.3434 data_time: 0.1868 memory: 2147 2023/07/24 18:59:27 - mmengine - INFO - Epoch(val) [15][60/78] eta: 0:00:07 time: 0.4410 data_time: 0.2846 memory: 2147 2023/07/24 18:59:37 - mmengine - INFO - Epoch(val) [15][78/78] acc/top1: 0.6479 acc/top5: 0.8623 acc/mean1: 0.6477 data_time: 0.2455 time: 0.3995 2023/07/24 19:00:03 - mmengine - INFO - Epoch(train) [16][ 20/940] lr: 1.0000e-02 eta: 1 day, 0:30:38 time: 1.3218 data_time: 0.1427 memory: 15768 grad_norm: 3.4526 loss: 1.5301 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.5301 2023/07/24 19:00:25 - mmengine - INFO - Epoch(train) [16][ 40/940] lr: 1.0000e-02 eta: 1 day, 0:30:16 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 3.4459 loss: 1.3856 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3856 2023/07/24 19:00:47 - mmengine - INFO - Epoch(train) [16][ 60/940] lr: 1.0000e-02 eta: 1 day, 0:29:53 time: 1.1027 data_time: 0.0132 memory: 15768 grad_norm: 3.5000 loss: 1.5212 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5212 2023/07/24 19:01:09 - mmengine - INFO - Epoch(train) [16][ 80/940] lr: 1.0000e-02 eta: 1 day, 0:29:31 time: 1.0994 data_time: 0.0133 memory: 15768 grad_norm: 3.5090 loss: 1.4060 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4060 2023/07/24 19:01:31 - mmengine - INFO - Epoch(train) [16][100/940] lr: 1.0000e-02 eta: 1 day, 0:29:08 time: 1.0990 data_time: 0.0133 memory: 15768 grad_norm: 3.5497 loss: 1.5256 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5256 2023/07/24 19:01:53 - mmengine - INFO - Epoch(train) [16][120/940] lr: 1.0000e-02 eta: 1 day, 0:28:45 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 3.5224 loss: 1.6261 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6261 2023/07/24 19:02:15 - mmengine - INFO - Epoch(train) [16][140/940] lr: 1.0000e-02 eta: 1 day, 0:28:23 time: 1.0991 data_time: 0.0132 memory: 15768 grad_norm: 3.4774 loss: 1.3925 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3925 2023/07/24 19:02:37 - mmengine - INFO - Epoch(train) [16][160/940] lr: 1.0000e-02 eta: 1 day, 0:28:00 time: 1.0977 data_time: 0.0133 memory: 15768 grad_norm: 3.4570 loss: 1.4526 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4526 2023/07/24 19:02:59 - mmengine - INFO - Epoch(train) [16][180/940] lr: 1.0000e-02 eta: 1 day, 0:27:37 time: 1.1001 data_time: 0.0134 memory: 15768 grad_norm: 3.5466 loss: 1.5305 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5305 2023/07/24 19:03:21 - mmengine - INFO - Epoch(train) [16][200/940] lr: 1.0000e-02 eta: 1 day, 0:27:15 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 3.5163 loss: 1.5405 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5405 2023/07/24 19:03:43 - mmengine - INFO - Epoch(train) [16][220/940] lr: 1.0000e-02 eta: 1 day, 0:26:52 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.4604 loss: 1.5932 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.5932 2023/07/24 19:04:05 - mmengine - INFO - Epoch(train) [16][240/940] lr: 1.0000e-02 eta: 1 day, 0:26:30 time: 1.0998 data_time: 0.0136 memory: 15768 grad_norm: 3.5192 loss: 1.3359 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.3359 2023/07/24 19:04:27 - mmengine - INFO - Epoch(train) [16][260/940] lr: 1.0000e-02 eta: 1 day, 0:26:07 time: 1.1021 data_time: 0.0130 memory: 15768 grad_norm: 3.5276 loss: 1.4301 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.4301 2023/07/24 19:04:49 - mmengine - INFO - Epoch(train) [16][280/940] lr: 1.0000e-02 eta: 1 day, 0:25:45 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 3.4744 loss: 1.4478 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.4478 2023/07/24 19:05:11 - mmengine - INFO - Epoch(train) [16][300/940] lr: 1.0000e-02 eta: 1 day, 0:25:22 time: 1.0989 data_time: 0.0136 memory: 15768 grad_norm: 3.5601 loss: 1.5340 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5340 2023/07/24 19:05:33 - mmengine - INFO - Epoch(train) [16][320/940] lr: 1.0000e-02 eta: 1 day, 0:24:59 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 3.5590 loss: 1.3261 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3261 2023/07/24 19:05:55 - mmengine - INFO - Epoch(train) [16][340/940] lr: 1.0000e-02 eta: 1 day, 0:24:37 time: 1.1027 data_time: 0.0132 memory: 15768 grad_norm: 3.5145 loss: 1.4933 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4933 2023/07/24 19:06:17 - mmengine - INFO - Epoch(train) [16][360/940] lr: 1.0000e-02 eta: 1 day, 0:24:15 time: 1.1023 data_time: 0.0136 memory: 15768 grad_norm: 3.5030 loss: 1.6125 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6125 2023/07/24 19:06:39 - mmengine - INFO - Epoch(train) [16][380/940] lr: 1.0000e-02 eta: 1 day, 0:23:52 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.5962 loss: 1.4719 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4719 2023/07/24 19:07:01 - mmengine - INFO - Epoch(train) [16][400/940] lr: 1.0000e-02 eta: 1 day, 0:23:30 time: 1.1037 data_time: 0.0133 memory: 15768 grad_norm: 3.5251 loss: 1.4804 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4804 2023/07/24 19:07:23 - mmengine - INFO - Epoch(train) [16][420/940] lr: 1.0000e-02 eta: 1 day, 0:23:08 time: 1.1018 data_time: 0.0134 memory: 15768 grad_norm: 3.6134 loss: 1.5599 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5599 2023/07/24 19:07:46 - mmengine - INFO - Epoch(train) [16][440/940] lr: 1.0000e-02 eta: 1 day, 0:22:45 time: 1.1023 data_time: 0.0131 memory: 15768 grad_norm: 3.5507 loss: 1.5141 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5141 2023/07/24 19:08:08 - mmengine - INFO - Epoch(train) [16][460/940] lr: 1.0000e-02 eta: 1 day, 0:22:23 time: 1.1028 data_time: 0.0132 memory: 15768 grad_norm: 3.5036 loss: 1.5283 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5283 2023/07/24 19:08:30 - mmengine - INFO - Epoch(train) [16][480/940] lr: 1.0000e-02 eta: 1 day, 0:22:00 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 3.5141 loss: 1.6279 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6279 2023/07/24 19:08:52 - mmengine - INFO - Epoch(train) [16][500/940] lr: 1.0000e-02 eta: 1 day, 0:21:38 time: 1.0994 data_time: 0.0130 memory: 15768 grad_norm: 3.6258 loss: 1.5825 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5825 2023/07/24 19:09:14 - mmengine - INFO - Epoch(train) [16][520/940] lr: 1.0000e-02 eta: 1 day, 0:21:15 time: 1.0989 data_time: 0.0133 memory: 15768 grad_norm: 3.5276 loss: 1.4450 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4450 2023/07/24 19:09:36 - mmengine - INFO - Epoch(train) [16][540/940] lr: 1.0000e-02 eta: 1 day, 0:20:52 time: 1.0988 data_time: 0.0133 memory: 15768 grad_norm: 3.5509 loss: 1.4880 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4880 2023/07/24 19:09:58 - mmengine - INFO - Epoch(train) [16][560/940] lr: 1.0000e-02 eta: 1 day, 0:20:30 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.5242 loss: 1.4063 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4063 2023/07/24 19:10:20 - mmengine - INFO - Epoch(train) [16][580/940] lr: 1.0000e-02 eta: 1 day, 0:20:08 time: 1.1014 data_time: 0.0138 memory: 15768 grad_norm: 3.4617 loss: 1.4605 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4605 2023/07/24 19:10:42 - mmengine - INFO - Epoch(train) [16][600/940] lr: 1.0000e-02 eta: 1 day, 0:19:45 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 3.5446 loss: 1.5062 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5062 2023/07/24 19:11:04 - mmengine - INFO - Epoch(train) [16][620/940] lr: 1.0000e-02 eta: 1 day, 0:19:22 time: 1.0983 data_time: 0.0133 memory: 15768 grad_norm: 3.5572 loss: 1.3910 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3910 2023/07/24 19:11:26 - mmengine - INFO - Epoch(train) [16][640/940] lr: 1.0000e-02 eta: 1 day, 0:19:00 time: 1.1000 data_time: 0.0133 memory: 15768 grad_norm: 3.5126 loss: 1.5553 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5553 2023/07/24 19:11:48 - mmengine - INFO - Epoch(train) [16][660/940] lr: 1.0000e-02 eta: 1 day, 0:18:37 time: 1.1021 data_time: 0.0132 memory: 15768 grad_norm: 3.5650 loss: 1.4744 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4744 2023/07/24 19:12:10 - mmengine - INFO - Epoch(train) [16][680/940] lr: 1.0000e-02 eta: 1 day, 0:18:15 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 3.4907 loss: 1.5758 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5758 2023/07/24 19:12:32 - mmengine - INFO - Epoch(train) [16][700/940] lr: 1.0000e-02 eta: 1 day, 0:17:52 time: 1.1013 data_time: 0.0133 memory: 15768 grad_norm: 3.5795 loss: 1.5018 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5018 2023/07/24 19:12:54 - mmengine - INFO - Epoch(train) [16][720/940] lr: 1.0000e-02 eta: 1 day, 0:17:30 time: 1.1011 data_time: 0.0134 memory: 15768 grad_norm: 3.5507 loss: 1.3881 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3881 2023/07/24 19:13:16 - mmengine - INFO - Epoch(train) [16][740/940] lr: 1.0000e-02 eta: 1 day, 0:17:07 time: 1.1025 data_time: 0.0130 memory: 15768 grad_norm: 3.5007 loss: 1.5083 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5083 2023/07/24 19:13:38 - mmengine - INFO - Epoch(train) [16][760/940] lr: 1.0000e-02 eta: 1 day, 0:16:45 time: 1.1014 data_time: 0.0130 memory: 15768 grad_norm: 3.5118 loss: 1.4463 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4463 2023/07/24 19:14:00 - mmengine - INFO - Epoch(train) [16][780/940] lr: 1.0000e-02 eta: 1 day, 0:16:23 time: 1.1019 data_time: 0.0129 memory: 15768 grad_norm: 3.5464 loss: 1.4629 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4629 2023/07/24 19:14:22 - mmengine - INFO - Epoch(train) [16][800/940] lr: 1.0000e-02 eta: 1 day, 0:16:00 time: 1.1020 data_time: 0.0133 memory: 15768 grad_norm: 3.5449 loss: 1.4882 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4882 2023/07/24 19:14:44 - mmengine - INFO - Epoch(train) [16][820/940] lr: 1.0000e-02 eta: 1 day, 0:15:38 time: 1.1029 data_time: 0.0130 memory: 15768 grad_norm: 3.5883 loss: 1.5132 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5132 2023/07/24 19:15:06 - mmengine - INFO - Epoch(train) [16][840/940] lr: 1.0000e-02 eta: 1 day, 0:15:16 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.5485 loss: 1.6186 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6186 2023/07/24 19:15:28 - mmengine - INFO - Epoch(train) [16][860/940] lr: 1.0000e-02 eta: 1 day, 0:14:53 time: 1.0985 data_time: 0.0132 memory: 15768 grad_norm: 3.5018 loss: 1.6006 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6006 2023/07/24 19:15:50 - mmengine - INFO - Epoch(train) [16][880/940] lr: 1.0000e-02 eta: 1 day, 0:14:30 time: 1.1014 data_time: 0.0132 memory: 15768 grad_norm: 3.5267 loss: 1.6198 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6198 2023/07/24 19:16:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 19:16:12 - mmengine - INFO - Epoch(train) [16][900/940] lr: 1.0000e-02 eta: 1 day, 0:14:08 time: 1.1057 data_time: 0.0129 memory: 15768 grad_norm: 3.6212 loss: 1.4270 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4270 2023/07/24 19:16:34 - mmengine - INFO - Epoch(train) [16][920/940] lr: 1.0000e-02 eta: 1 day, 0:13:46 time: 1.1001 data_time: 0.0133 memory: 15768 grad_norm: 3.5230 loss: 1.3199 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3199 2023/07/24 19:16:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 19:16:55 - mmengine - INFO - Epoch(train) [16][940/940] lr: 1.0000e-02 eta: 1 day, 0:13:19 time: 1.0547 data_time: 0.0126 memory: 15768 grad_norm: 3.7915 loss: 1.3939 top1_acc: 0.2500 top5_acc: 1.0000 loss_cls: 1.3939 2023/07/24 19:17:05 - mmengine - INFO - Epoch(val) [16][20/78] eta: 0:00:28 time: 0.4918 data_time: 0.3346 memory: 2147 2023/07/24 19:17:12 - mmengine - INFO - Epoch(val) [16][40/78] eta: 0:00:15 time: 0.3426 data_time: 0.1855 memory: 2147 2023/07/24 19:17:21 - mmengine - INFO - Epoch(val) [16][60/78] eta: 0:00:07 time: 0.4524 data_time: 0.2953 memory: 2147 2023/07/24 19:17:31 - mmengine - INFO - Epoch(val) [16][78/78] acc/top1: 0.6504 acc/top5: 0.8661 acc/mean1: 0.6503 data_time: 0.2487 time: 0.4029 2023/07/24 19:17:57 - mmengine - INFO - Epoch(train) [17][ 20/940] lr: 1.0000e-02 eta: 1 day, 0:13:16 time: 1.2872 data_time: 0.1325 memory: 15768 grad_norm: 3.5767 loss: 1.4635 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4635 2023/07/24 19:18:19 - mmengine - INFO - Epoch(train) [17][ 40/940] lr: 1.0000e-02 eta: 1 day, 0:12:54 time: 1.1067 data_time: 0.0133 memory: 15768 grad_norm: 3.4573 loss: 1.5231 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5231 2023/07/24 19:18:41 - mmengine - INFO - Epoch(train) [17][ 60/940] lr: 1.0000e-02 eta: 1 day, 0:12:31 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 3.5222 loss: 1.4016 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4016 2023/07/24 19:19:03 - mmengine - INFO - Epoch(train) [17][ 80/940] lr: 1.0000e-02 eta: 1 day, 0:12:09 time: 1.0997 data_time: 0.0133 memory: 15768 grad_norm: 3.4938 loss: 1.4622 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4622 2023/07/24 19:19:25 - mmengine - INFO - Epoch(train) [17][100/940] lr: 1.0000e-02 eta: 1 day, 0:11:46 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 3.5468 loss: 1.5258 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5258 2023/07/24 19:19:47 - mmengine - INFO - Epoch(train) [17][120/940] lr: 1.0000e-02 eta: 1 day, 0:11:24 time: 1.1001 data_time: 0.0132 memory: 15768 grad_norm: 3.4975 loss: 1.3230 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.3230 2023/07/24 19:20:09 - mmengine - INFO - Epoch(train) [17][140/940] lr: 1.0000e-02 eta: 1 day, 0:11:01 time: 1.1008 data_time: 0.0134 memory: 15768 grad_norm: 3.5265 loss: 1.2842 top1_acc: 0.3125 top5_acc: 0.9375 loss_cls: 1.2842 2023/07/24 19:20:31 - mmengine - INFO - Epoch(train) [17][160/940] lr: 1.0000e-02 eta: 1 day, 0:10:39 time: 1.0991 data_time: 0.0135 memory: 15768 grad_norm: 3.5049 loss: 1.4938 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4938 2023/07/24 19:20:53 - mmengine - INFO - Epoch(train) [17][180/940] lr: 1.0000e-02 eta: 1 day, 0:10:16 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 3.4720 loss: 1.3629 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3629 2023/07/24 19:21:15 - mmengine - INFO - Epoch(train) [17][200/940] lr: 1.0000e-02 eta: 1 day, 0:09:53 time: 1.0997 data_time: 0.0139 memory: 15768 grad_norm: 3.5839 loss: 1.3658 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3658 2023/07/24 19:21:37 - mmengine - INFO - Epoch(train) [17][220/940] lr: 1.0000e-02 eta: 1 day, 0:09:31 time: 1.1027 data_time: 0.0134 memory: 15768 grad_norm: 3.5948 loss: 1.5713 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5713 2023/07/24 19:21:59 - mmengine - INFO - Epoch(train) [17][240/940] lr: 1.0000e-02 eta: 1 day, 0:09:08 time: 1.0989 data_time: 0.0134 memory: 15768 grad_norm: 3.5712 loss: 1.4661 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4661 2023/07/24 19:22:21 - mmengine - INFO - Epoch(train) [17][260/940] lr: 1.0000e-02 eta: 1 day, 0:08:46 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 3.4961 loss: 1.4641 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4641 2023/07/24 19:22:43 - mmengine - INFO - Epoch(train) [17][280/940] lr: 1.0000e-02 eta: 1 day, 0:08:23 time: 1.1003 data_time: 0.0134 memory: 15768 grad_norm: 3.5641 loss: 1.4187 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4187 2023/07/24 19:23:05 - mmengine - INFO - Epoch(train) [17][300/940] lr: 1.0000e-02 eta: 1 day, 0:08:01 time: 1.1032 data_time: 0.0129 memory: 15768 grad_norm: 3.5374 loss: 1.4732 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4732 2023/07/24 19:23:27 - mmengine - INFO - Epoch(train) [17][320/940] lr: 1.0000e-02 eta: 1 day, 0:07:38 time: 1.0989 data_time: 0.0134 memory: 15768 grad_norm: 3.5172 loss: 1.4331 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4331 2023/07/24 19:23:49 - mmengine - INFO - Epoch(train) [17][340/940] lr: 1.0000e-02 eta: 1 day, 0:07:16 time: 1.1017 data_time: 0.0137 memory: 15768 grad_norm: 3.5412 loss: 1.4142 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4142 2023/07/24 19:24:11 - mmengine - INFO - Epoch(train) [17][360/940] lr: 1.0000e-02 eta: 1 day, 0:06:54 time: 1.1033 data_time: 0.0133 memory: 15768 grad_norm: 3.5615 loss: 1.5321 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5321 2023/07/24 19:24:33 - mmengine - INFO - Epoch(train) [17][380/940] lr: 1.0000e-02 eta: 1 day, 0:06:31 time: 1.0996 data_time: 0.0130 memory: 15768 grad_norm: 3.6220 loss: 1.6880 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.6880 2023/07/24 19:24:55 - mmengine - INFO - Epoch(train) [17][400/940] lr: 1.0000e-02 eta: 1 day, 0:06:09 time: 1.1003 data_time: 0.0135 memory: 15768 grad_norm: 3.5531 loss: 1.5460 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5460 2023/07/24 19:25:17 - mmengine - INFO - Epoch(train) [17][420/940] lr: 1.0000e-02 eta: 1 day, 0:05:46 time: 1.1016 data_time: 0.0132 memory: 15768 grad_norm: 3.5142 loss: 1.5305 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5305 2023/07/24 19:25:39 - mmengine - INFO - Epoch(train) [17][440/940] lr: 1.0000e-02 eta: 1 day, 0:05:24 time: 1.0994 data_time: 0.0131 memory: 15768 grad_norm: 3.5259 loss: 1.4927 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4927 2023/07/24 19:26:01 - mmengine - INFO - Epoch(train) [17][460/940] lr: 1.0000e-02 eta: 1 day, 0:05:01 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 3.5383 loss: 1.3729 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3729 2023/07/24 19:26:23 - mmengine - INFO - Epoch(train) [17][480/940] lr: 1.0000e-02 eta: 1 day, 0:04:39 time: 1.1011 data_time: 0.0135 memory: 15768 grad_norm: 3.5431 loss: 1.4278 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4278 2023/07/24 19:26:45 - mmengine - INFO - Epoch(train) [17][500/940] lr: 1.0000e-02 eta: 1 day, 0:04:16 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 3.4887 loss: 1.5607 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5607 2023/07/24 19:27:07 - mmengine - INFO - Epoch(train) [17][520/940] lr: 1.0000e-02 eta: 1 day, 0:03:54 time: 1.0993 data_time: 0.0135 memory: 15768 grad_norm: 3.6903 loss: 1.6555 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6555 2023/07/24 19:27:29 - mmengine - INFO - Epoch(train) [17][540/940] lr: 1.0000e-02 eta: 1 day, 0:03:31 time: 1.1022 data_time: 0.0135 memory: 15768 grad_norm: 3.5118 loss: 1.4453 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4453 2023/07/24 19:27:51 - mmengine - INFO - Epoch(train) [17][560/940] lr: 1.0000e-02 eta: 1 day, 0:03:09 time: 1.0999 data_time: 0.0135 memory: 15768 grad_norm: 3.5833 loss: 1.4874 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4874 2023/07/24 19:28:13 - mmengine - INFO - Epoch(train) [17][580/940] lr: 1.0000e-02 eta: 1 day, 0:02:46 time: 1.0984 data_time: 0.0133 memory: 15768 grad_norm: 3.6449 loss: 1.4422 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4422 2023/07/24 19:28:35 - mmengine - INFO - Epoch(train) [17][600/940] lr: 1.0000e-02 eta: 1 day, 0:02:23 time: 1.0992 data_time: 0.0136 memory: 15768 grad_norm: 3.4647 loss: 1.4472 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4472 2023/07/24 19:28:57 - mmengine - INFO - Epoch(train) [17][620/940] lr: 1.0000e-02 eta: 1 day, 0:02:01 time: 1.0992 data_time: 0.0134 memory: 15768 grad_norm: 3.6102 loss: 1.4297 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4297 2023/07/24 19:29:19 - mmengine - INFO - Epoch(train) [17][640/940] lr: 1.0000e-02 eta: 1 day, 0:01:38 time: 1.0980 data_time: 0.0129 memory: 15768 grad_norm: 3.5294 loss: 1.6038 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6038 2023/07/24 19:29:41 - mmengine - INFO - Epoch(train) [17][660/940] lr: 1.0000e-02 eta: 1 day, 0:01:16 time: 1.0994 data_time: 0.0135 memory: 15768 grad_norm: 3.5622 loss: 1.5242 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5242 2023/07/24 19:30:03 - mmengine - INFO - Epoch(train) [17][680/940] lr: 1.0000e-02 eta: 1 day, 0:00:53 time: 1.1001 data_time: 0.0128 memory: 15768 grad_norm: 3.5673 loss: 1.3886 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3886 2023/07/24 19:30:25 - mmengine - INFO - Epoch(train) [17][700/940] lr: 1.0000e-02 eta: 1 day, 0:00:30 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.6192 loss: 1.5801 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.5801 2023/07/24 19:30:47 - mmengine - INFO - Epoch(train) [17][720/940] lr: 1.0000e-02 eta: 1 day, 0:00:08 time: 1.1009 data_time: 0.0134 memory: 15768 grad_norm: 3.5955 loss: 1.5443 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5443 2023/07/24 19:31:09 - mmengine - INFO - Epoch(train) [17][740/940] lr: 1.0000e-02 eta: 23:59:45 time: 1.0988 data_time: 0.0132 memory: 15768 grad_norm: 3.5653 loss: 1.5945 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5945 2023/07/24 19:31:31 - mmengine - INFO - Epoch(train) [17][760/940] lr: 1.0000e-02 eta: 23:59:23 time: 1.0983 data_time: 0.0133 memory: 15768 grad_norm: 3.5873 loss: 1.4310 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4310 2023/07/24 19:31:53 - mmengine - INFO - Epoch(train) [17][780/940] lr: 1.0000e-02 eta: 23:59:00 time: 1.1008 data_time: 0.0131 memory: 15768 grad_norm: 3.6084 loss: 1.5976 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5976 2023/07/24 19:32:15 - mmengine - INFO - Epoch(train) [17][800/940] lr: 1.0000e-02 eta: 23:58:37 time: 1.0984 data_time: 0.0135 memory: 15768 grad_norm: 3.5691 loss: 1.6297 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6297 2023/07/24 19:32:37 - mmengine - INFO - Epoch(train) [17][820/940] lr: 1.0000e-02 eta: 23:58:15 time: 1.0972 data_time: 0.0134 memory: 15768 grad_norm: 3.5488 loss: 1.7109 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7109 2023/07/24 19:32:59 - mmengine - INFO - Epoch(train) [17][840/940] lr: 1.0000e-02 eta: 23:57:52 time: 1.0975 data_time: 0.0135 memory: 15768 grad_norm: 3.6257 loss: 1.3257 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3257 2023/07/24 19:33:21 - mmengine - INFO - Epoch(train) [17][860/940] lr: 1.0000e-02 eta: 23:57:29 time: 1.1007 data_time: 0.0132 memory: 15768 grad_norm: 3.4475 loss: 1.4530 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4530 2023/07/24 19:33:43 - mmengine - INFO - Epoch(train) [17][880/940] lr: 1.0000e-02 eta: 23:57:07 time: 1.0991 data_time: 0.0135 memory: 15768 grad_norm: 3.6104 loss: 1.6366 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6366 2023/07/24 19:34:05 - mmengine - INFO - Epoch(train) [17][900/940] lr: 1.0000e-02 eta: 23:56:44 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 3.5418 loss: 1.3954 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3954 2023/07/24 19:34:27 - mmengine - INFO - Epoch(train) [17][920/940] lr: 1.0000e-02 eta: 23:56:22 time: 1.0983 data_time: 0.0135 memory: 15768 grad_norm: 3.5414 loss: 1.3002 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3002 2023/07/24 19:34:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 19:34:48 - mmengine - INFO - Epoch(train) [17][940/940] lr: 1.0000e-02 eta: 23:55:55 time: 1.0533 data_time: 0.0129 memory: 15768 grad_norm: 3.6855 loss: 1.2967 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2967 2023/07/24 19:34:58 - mmengine - INFO - Epoch(val) [17][20/78] eta: 0:00:28 time: 0.4895 data_time: 0.3322 memory: 2147 2023/07/24 19:35:05 - mmengine - INFO - Epoch(val) [17][40/78] eta: 0:00:15 time: 0.3473 data_time: 0.1902 memory: 2147 2023/07/24 19:35:14 - mmengine - INFO - Epoch(val) [17][60/78] eta: 0:00:07 time: 0.4446 data_time: 0.2878 memory: 2147 2023/07/24 19:35:25 - mmengine - INFO - Epoch(val) [17][78/78] acc/top1: 0.6673 acc/top5: 0.8762 acc/mean1: 0.6672 data_time: 0.2451 time: 0.3993 2023/07/24 19:35:25 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_14.pth is removed 2023/07/24 19:35:26 - mmengine - INFO - The best checkpoint with 0.6673 acc/top1 at 17 epoch is saved to best_acc_top1_epoch_17.pth. 2023/07/24 19:35:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 19:35:51 - mmengine - INFO - Epoch(train) [18][ 20/940] lr: 1.0000e-02 eta: 23:55:47 time: 1.2489 data_time: 0.1473 memory: 15768 grad_norm: 3.4846 loss: 1.4351 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4351 2023/07/24 19:36:13 - mmengine - INFO - Epoch(train) [18][ 40/940] lr: 1.0000e-02 eta: 23:55:24 time: 1.1009 data_time: 0.0135 memory: 15768 grad_norm: 3.5528 loss: 1.3511 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3511 2023/07/24 19:36:35 - mmengine - INFO - Epoch(train) [18][ 60/940] lr: 1.0000e-02 eta: 23:55:02 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 3.6075 loss: 1.4346 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4346 2023/07/24 19:36:57 - mmengine - INFO - Epoch(train) [18][ 80/940] lr: 1.0000e-02 eta: 23:54:39 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 3.5482 loss: 1.4365 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4365 2023/07/24 19:37:19 - mmengine - INFO - Epoch(train) [18][100/940] lr: 1.0000e-02 eta: 23:54:17 time: 1.1019 data_time: 0.0139 memory: 15768 grad_norm: 3.5898 loss: 1.5978 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5978 2023/07/24 19:37:41 - mmengine - INFO - Epoch(train) [18][120/940] lr: 1.0000e-02 eta: 23:53:55 time: 1.1004 data_time: 0.0134 memory: 15768 grad_norm: 3.5517 loss: 1.4205 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4205 2023/07/24 19:38:03 - mmengine - INFO - Epoch(train) [18][140/940] lr: 1.0000e-02 eta: 23:53:32 time: 1.1028 data_time: 0.0134 memory: 15768 grad_norm: 3.4855 loss: 1.4174 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4174 2023/07/24 19:38:25 - mmengine - INFO - Epoch(train) [18][160/940] lr: 1.0000e-02 eta: 23:53:10 time: 1.0992 data_time: 0.0137 memory: 15768 grad_norm: 3.6156 loss: 1.3709 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3709 2023/07/24 19:38:47 - mmengine - INFO - Epoch(train) [18][180/940] lr: 1.0000e-02 eta: 23:52:47 time: 1.1009 data_time: 0.0137 memory: 15768 grad_norm: 3.5391 loss: 1.4750 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4750 2023/07/24 19:39:09 - mmengine - INFO - Epoch(train) [18][200/940] lr: 1.0000e-02 eta: 23:52:25 time: 1.0986 data_time: 0.0140 memory: 15768 grad_norm: 3.5618 loss: 1.5600 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5600 2023/07/24 19:39:31 - mmengine - INFO - Epoch(train) [18][220/940] lr: 1.0000e-02 eta: 23:52:02 time: 1.1038 data_time: 0.0139 memory: 15768 grad_norm: 3.5944 loss: 1.5791 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5791 2023/07/24 19:39:53 - mmengine - INFO - Epoch(train) [18][240/940] lr: 1.0000e-02 eta: 23:51:40 time: 1.0999 data_time: 0.0138 memory: 15768 grad_norm: 3.5348 loss: 1.3550 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3550 2023/07/24 19:40:15 - mmengine - INFO - Epoch(train) [18][260/940] lr: 1.0000e-02 eta: 23:51:18 time: 1.1045 data_time: 0.0135 memory: 15768 grad_norm: 3.5758 loss: 1.5390 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5390 2023/07/24 19:40:37 - mmengine - INFO - Epoch(train) [18][280/940] lr: 1.0000e-02 eta: 23:50:55 time: 1.1000 data_time: 0.0136 memory: 15768 grad_norm: 3.5882 loss: 1.4411 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4411 2023/07/24 19:40:59 - mmengine - INFO - Epoch(train) [18][300/940] lr: 1.0000e-02 eta: 23:50:33 time: 1.1002 data_time: 0.0137 memory: 15768 grad_norm: 3.5750 loss: 1.4197 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4197 2023/07/24 19:41:21 - mmengine - INFO - Epoch(train) [18][320/940] lr: 1.0000e-02 eta: 23:50:11 time: 1.1017 data_time: 0.0139 memory: 15768 grad_norm: 3.6233 loss: 1.2021 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2021 2023/07/24 19:41:43 - mmengine - INFO - Epoch(train) [18][340/940] lr: 1.0000e-02 eta: 23:49:48 time: 1.0986 data_time: 0.0137 memory: 15768 grad_norm: 3.6029 loss: 1.5354 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5354 2023/07/24 19:42:05 - mmengine - INFO - Epoch(train) [18][360/940] lr: 1.0000e-02 eta: 23:49:25 time: 1.0988 data_time: 0.0141 memory: 15768 grad_norm: 3.5408 loss: 1.3489 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3489 2023/07/24 19:42:27 - mmengine - INFO - Epoch(train) [18][380/940] lr: 1.0000e-02 eta: 23:49:03 time: 1.1028 data_time: 0.0132 memory: 15768 grad_norm: 3.5122 loss: 1.3861 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3861 2023/07/24 19:42:49 - mmengine - INFO - Epoch(train) [18][400/940] lr: 1.0000e-02 eta: 23:48:40 time: 1.0989 data_time: 0.0137 memory: 15768 grad_norm: 3.5723 loss: 1.3791 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3791 2023/07/24 19:43:11 - mmengine - INFO - Epoch(train) [18][420/940] lr: 1.0000e-02 eta: 23:48:18 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 3.5936 loss: 1.4402 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4402 2023/07/24 19:43:33 - mmengine - INFO - Epoch(train) [18][440/940] lr: 1.0000e-02 eta: 23:47:55 time: 1.0991 data_time: 0.0135 memory: 15768 grad_norm: 3.5277 loss: 1.5577 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5577 2023/07/24 19:43:55 - mmengine - INFO - Epoch(train) [18][460/940] lr: 1.0000e-02 eta: 23:47:33 time: 1.0993 data_time: 0.0134 memory: 15768 grad_norm: 3.5109 loss: 1.4657 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4657 2023/07/24 19:44:17 - mmengine - INFO - Epoch(train) [18][480/940] lr: 1.0000e-02 eta: 23:47:11 time: 1.1050 data_time: 0.0140 memory: 15768 grad_norm: 3.5718 loss: 1.5368 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.5368 2023/07/24 19:44:39 - mmengine - INFO - Epoch(train) [18][500/940] lr: 1.0000e-02 eta: 23:46:48 time: 1.1003 data_time: 0.0140 memory: 15768 grad_norm: 3.5943 loss: 1.4413 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4413 2023/07/24 19:45:01 - mmengine - INFO - Epoch(train) [18][520/940] lr: 1.0000e-02 eta: 23:46:26 time: 1.1015 data_time: 0.0142 memory: 15768 grad_norm: 3.5376 loss: 1.5319 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5319 2023/07/24 19:45:23 - mmengine - INFO - Epoch(train) [18][540/940] lr: 1.0000e-02 eta: 23:46:04 time: 1.1038 data_time: 0.0140 memory: 15768 grad_norm: 3.5772 loss: 1.6110 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6110 2023/07/24 19:45:45 - mmengine - INFO - Epoch(train) [18][560/940] lr: 1.0000e-02 eta: 23:45:41 time: 1.1018 data_time: 0.0134 memory: 15768 grad_norm: 3.5601 loss: 1.4483 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4483 2023/07/24 19:46:07 - mmengine - INFO - Epoch(train) [18][580/940] lr: 1.0000e-02 eta: 23:45:19 time: 1.0998 data_time: 0.0134 memory: 15768 grad_norm: 3.5578 loss: 1.6136 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6136 2023/07/24 19:46:29 - mmengine - INFO - Epoch(train) [18][600/940] lr: 1.0000e-02 eta: 23:44:56 time: 1.0995 data_time: 0.0135 memory: 15768 grad_norm: 3.5180 loss: 1.3667 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3667 2023/07/24 19:46:51 - mmengine - INFO - Epoch(train) [18][620/940] lr: 1.0000e-02 eta: 23:44:34 time: 1.1038 data_time: 0.0131 memory: 15768 grad_norm: 3.5532 loss: 1.4416 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4416 2023/07/24 19:47:13 - mmengine - INFO - Epoch(train) [18][640/940] lr: 1.0000e-02 eta: 23:44:12 time: 1.1004 data_time: 0.0140 memory: 15768 grad_norm: 3.6247 loss: 1.4358 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4358 2023/07/24 19:47:35 - mmengine - INFO - Epoch(train) [18][660/940] lr: 1.0000e-02 eta: 23:43:49 time: 1.0993 data_time: 0.0131 memory: 15768 grad_norm: 3.6299 loss: 1.4077 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.4077 2023/07/24 19:47:57 - mmengine - INFO - Epoch(train) [18][680/940] lr: 1.0000e-02 eta: 23:43:27 time: 1.0987 data_time: 0.0134 memory: 15768 grad_norm: 3.5809 loss: 1.5748 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5748 2023/07/24 19:48:19 - mmengine - INFO - Epoch(train) [18][700/940] lr: 1.0000e-02 eta: 23:43:04 time: 1.1002 data_time: 0.0134 memory: 15768 grad_norm: 3.6161 loss: 1.6602 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6602 2023/07/24 19:48:41 - mmengine - INFO - Epoch(train) [18][720/940] lr: 1.0000e-02 eta: 23:42:42 time: 1.0985 data_time: 0.0137 memory: 15768 grad_norm: 3.6186 loss: 1.4493 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4493 2023/07/24 19:49:03 - mmengine - INFO - Epoch(train) [18][740/940] lr: 1.0000e-02 eta: 23:42:19 time: 1.1025 data_time: 0.0133 memory: 15768 grad_norm: 3.5610 loss: 1.3430 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3430 2023/07/24 19:49:25 - mmengine - INFO - Epoch(train) [18][760/940] lr: 1.0000e-02 eta: 23:41:57 time: 1.0995 data_time: 0.0134 memory: 15768 grad_norm: 3.5816 loss: 1.4422 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4422 2023/07/24 19:49:47 - mmengine - INFO - Epoch(train) [18][780/940] lr: 1.0000e-02 eta: 23:41:34 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 3.5890 loss: 1.4252 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.4252 2023/07/24 19:50:09 - mmengine - INFO - Epoch(train) [18][800/940] lr: 1.0000e-02 eta: 23:41:12 time: 1.1058 data_time: 0.0135 memory: 15768 grad_norm: 3.5265 loss: 1.3396 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3396 2023/07/24 19:50:31 - mmengine - INFO - Epoch(train) [18][820/940] lr: 1.0000e-02 eta: 23:40:50 time: 1.0995 data_time: 0.0133 memory: 15768 grad_norm: 3.5956 loss: 1.4102 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4102 2023/07/24 19:50:53 - mmengine - INFO - Epoch(train) [18][840/940] lr: 1.0000e-02 eta: 23:40:28 time: 1.1019 data_time: 0.0135 memory: 15768 grad_norm: 3.5119 loss: 1.5210 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5210 2023/07/24 19:51:15 - mmengine - INFO - Epoch(train) [18][860/940] lr: 1.0000e-02 eta: 23:40:05 time: 1.1027 data_time: 0.0132 memory: 15768 grad_norm: 3.5388 loss: 1.4137 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4137 2023/07/24 19:51:37 - mmengine - INFO - Epoch(train) [18][880/940] lr: 1.0000e-02 eta: 23:39:43 time: 1.0993 data_time: 0.0135 memory: 15768 grad_norm: 3.6075 loss: 1.3422 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3422 2023/07/24 19:51:59 - mmengine - INFO - Epoch(train) [18][900/940] lr: 1.0000e-02 eta: 23:39:20 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 3.5945 loss: 1.2929 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2929 2023/07/24 19:52:21 - mmengine - INFO - Epoch(train) [18][920/940] lr: 1.0000e-02 eta: 23:38:58 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 3.6431 loss: 1.5154 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5154 2023/07/24 19:52:43 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 19:52:43 - mmengine - INFO - Epoch(train) [18][940/940] lr: 1.0000e-02 eta: 23:38:32 time: 1.0559 data_time: 0.0129 memory: 15768 grad_norm: 3.7447 loss: 1.4122 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4122 2023/07/24 19:52:43 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/07/24 19:52:53 - mmengine - INFO - Epoch(val) [18][20/78] eta: 0:00:28 time: 0.4880 data_time: 0.3305 memory: 2147 2023/07/24 19:53:00 - mmengine - INFO - Epoch(val) [18][40/78] eta: 0:00:15 time: 0.3511 data_time: 0.1939 memory: 2147 2023/07/24 19:53:10 - mmengine - INFO - Epoch(val) [18][60/78] eta: 0:00:07 time: 0.4532 data_time: 0.2964 memory: 2147 2023/07/24 19:53:18 - mmengine - INFO - Epoch(val) [18][78/78] acc/top1: 0.6590 acc/top5: 0.8695 acc/mean1: 0.6589 data_time: 0.2458 time: 0.4000 2023/07/24 19:53:44 - mmengine - INFO - Epoch(train) [19][ 20/940] lr: 1.0000e-02 eta: 23:38:27 time: 1.2914 data_time: 0.1417 memory: 15768 grad_norm: 3.5786 loss: 1.4061 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.4061 2023/07/24 19:54:06 - mmengine - INFO - Epoch(train) [19][ 40/940] lr: 1.0000e-02 eta: 23:38:04 time: 1.1001 data_time: 0.0132 memory: 15768 grad_norm: 3.5604 loss: 1.5522 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5522 2023/07/24 19:54:28 - mmengine - INFO - Epoch(train) [19][ 60/940] lr: 1.0000e-02 eta: 23:37:42 time: 1.1017 data_time: 0.0132 memory: 15768 grad_norm: 3.4864 loss: 1.4552 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4552 2023/07/24 19:54:50 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 19:54:50 - mmengine - INFO - Epoch(train) [19][ 80/940] lr: 1.0000e-02 eta: 23:37:19 time: 1.1016 data_time: 0.0136 memory: 15768 grad_norm: 3.5114 loss: 1.4583 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4583 2023/07/24 19:55:12 - mmengine - INFO - Epoch(train) [19][100/940] lr: 1.0000e-02 eta: 23:36:57 time: 1.0996 data_time: 0.0135 memory: 15768 grad_norm: 3.5818 loss: 1.4296 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.4296 2023/07/24 19:55:34 - mmengine - INFO - Epoch(train) [19][120/940] lr: 1.0000e-02 eta: 23:36:35 time: 1.1052 data_time: 0.0136 memory: 15768 grad_norm: 3.5308 loss: 1.3890 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3890 2023/07/24 19:55:56 - mmengine - INFO - Epoch(train) [19][140/940] lr: 1.0000e-02 eta: 23:36:12 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.5368 loss: 1.3387 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3387 2023/07/24 19:56:18 - mmengine - INFO - Epoch(train) [19][160/940] lr: 1.0000e-02 eta: 23:35:50 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.5499 loss: 1.4248 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4248 2023/07/24 19:56:40 - mmengine - INFO - Epoch(train) [19][180/940] lr: 1.0000e-02 eta: 23:35:28 time: 1.1046 data_time: 0.0127 memory: 15768 grad_norm: 3.6786 loss: 1.5047 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5047 2023/07/24 19:57:02 - mmengine - INFO - Epoch(train) [19][200/940] lr: 1.0000e-02 eta: 23:35:06 time: 1.1019 data_time: 0.0129 memory: 15768 grad_norm: 3.5950 loss: 1.4691 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4691 2023/07/24 19:57:24 - mmengine - INFO - Epoch(train) [19][220/940] lr: 1.0000e-02 eta: 23:34:43 time: 1.1029 data_time: 0.0140 memory: 15768 grad_norm: 3.5960 loss: 1.3470 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3470 2023/07/24 19:57:46 - mmengine - INFO - Epoch(train) [19][240/940] lr: 1.0000e-02 eta: 23:34:21 time: 1.1014 data_time: 0.0135 memory: 15768 grad_norm: 3.6842 loss: 1.3185 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3185 2023/07/24 19:58:09 - mmengine - INFO - Epoch(train) [19][260/940] lr: 1.0000e-02 eta: 23:33:59 time: 1.1014 data_time: 0.0129 memory: 15768 grad_norm: 3.5531 loss: 1.5035 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5035 2023/07/24 19:58:31 - mmengine - INFO - Epoch(train) [19][280/940] lr: 1.0000e-02 eta: 23:33:36 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 3.5628 loss: 1.3570 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3570 2023/07/24 19:58:53 - mmengine - INFO - Epoch(train) [19][300/940] lr: 1.0000e-02 eta: 23:33:14 time: 1.1014 data_time: 0.0132 memory: 15768 grad_norm: 3.5781 loss: 1.4237 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4237 2023/07/24 19:59:15 - mmengine - INFO - Epoch(train) [19][320/940] lr: 1.0000e-02 eta: 23:32:54 time: 1.1302 data_time: 0.0131 memory: 15768 grad_norm: 3.6809 loss: 1.5993 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5993 2023/07/24 19:59:37 - mmengine - INFO - Epoch(train) [19][340/940] lr: 1.0000e-02 eta: 23:32:32 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 3.6252 loss: 1.2014 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2014 2023/07/24 19:59:59 - mmengine - INFO - Epoch(train) [19][360/940] lr: 1.0000e-02 eta: 23:32:09 time: 1.0999 data_time: 0.0139 memory: 15768 grad_norm: 3.6258 loss: 1.3467 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3467 2023/07/24 20:00:21 - mmengine - INFO - Epoch(train) [19][380/940] lr: 1.0000e-02 eta: 23:31:47 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 3.6148 loss: 1.3570 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3570 2023/07/24 20:00:43 - mmengine - INFO - Epoch(train) [19][400/940] lr: 1.0000e-02 eta: 23:31:24 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 3.5933 loss: 1.4353 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4353 2023/07/24 20:01:05 - mmengine - INFO - Epoch(train) [19][420/940] lr: 1.0000e-02 eta: 23:31:02 time: 1.1053 data_time: 0.0128 memory: 15768 grad_norm: 3.6166 loss: 1.4258 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4258 2023/07/24 20:01:27 - mmengine - INFO - Epoch(train) [19][440/940] lr: 1.0000e-02 eta: 23:30:40 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 3.5747 loss: 1.4515 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4515 2023/07/24 20:01:49 - mmengine - INFO - Epoch(train) [19][460/940] lr: 1.0000e-02 eta: 23:30:17 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.5645 loss: 1.4892 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4892 2023/07/24 20:02:11 - mmengine - INFO - Epoch(train) [19][480/940] lr: 1.0000e-02 eta: 23:29:55 time: 1.1014 data_time: 0.0129 memory: 15768 grad_norm: 3.6414 loss: 1.4229 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.4229 2023/07/24 20:02:33 - mmengine - INFO - Epoch(train) [19][500/940] lr: 1.0000e-02 eta: 23:29:33 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 3.5916 loss: 1.4880 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4880 2023/07/24 20:02:55 - mmengine - INFO - Epoch(train) [19][520/940] lr: 1.0000e-02 eta: 23:29:10 time: 1.1027 data_time: 0.0132 memory: 15768 grad_norm: 3.6795 loss: 1.4609 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4609 2023/07/24 20:03:17 - mmengine - INFO - Epoch(train) [19][540/940] lr: 1.0000e-02 eta: 23:28:48 time: 1.1005 data_time: 0.0128 memory: 15768 grad_norm: 3.6000 loss: 1.4767 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4767 2023/07/24 20:03:39 - mmengine - INFO - Epoch(train) [19][560/940] lr: 1.0000e-02 eta: 23:28:26 time: 1.1016 data_time: 0.0127 memory: 15768 grad_norm: 3.5913 loss: 1.4328 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4328 2023/07/24 20:04:01 - mmengine - INFO - Epoch(train) [19][580/940] lr: 1.0000e-02 eta: 23:28:03 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 3.6346 loss: 1.4494 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.4494 2023/07/24 20:04:24 - mmengine - INFO - Epoch(train) [19][600/940] lr: 1.0000e-02 eta: 23:27:41 time: 1.1030 data_time: 0.0132 memory: 15768 grad_norm: 3.5731 loss: 1.4130 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.4130 2023/07/24 20:04:46 - mmengine - INFO - Epoch(train) [19][620/940] lr: 1.0000e-02 eta: 23:27:19 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.6257 loss: 1.4635 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4635 2023/07/24 20:05:08 - mmengine - INFO - Epoch(train) [19][640/940] lr: 1.0000e-02 eta: 23:26:56 time: 1.1021 data_time: 0.0131 memory: 15768 grad_norm: 3.6210 loss: 1.2474 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2474 2023/07/24 20:05:30 - mmengine - INFO - Epoch(train) [19][660/940] lr: 1.0000e-02 eta: 23:26:34 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 3.5222 loss: 1.5355 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.5355 2023/07/24 20:05:52 - mmengine - INFO - Epoch(train) [19][680/940] lr: 1.0000e-02 eta: 23:26:11 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.5856 loss: 1.3744 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3744 2023/07/24 20:06:14 - mmengine - INFO - Epoch(train) [19][700/940] lr: 1.0000e-02 eta: 23:25:49 time: 1.1012 data_time: 0.0132 memory: 15768 grad_norm: 3.6390 loss: 1.5823 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5823 2023/07/24 20:06:36 - mmengine - INFO - Epoch(train) [19][720/940] lr: 1.0000e-02 eta: 23:25:27 time: 1.1014 data_time: 0.0130 memory: 15768 grad_norm: 3.6222 loss: 1.5229 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5229 2023/07/24 20:06:58 - mmengine - INFO - Epoch(train) [19][740/940] lr: 1.0000e-02 eta: 23:25:04 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 3.6163 loss: 1.6155 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6155 2023/07/24 20:07:20 - mmengine - INFO - Epoch(train) [19][760/940] lr: 1.0000e-02 eta: 23:24:42 time: 1.1000 data_time: 0.0131 memory: 15768 grad_norm: 3.5559 loss: 1.4795 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4795 2023/07/24 20:07:42 - mmengine - INFO - Epoch(train) [19][780/940] lr: 1.0000e-02 eta: 23:24:19 time: 1.1019 data_time: 0.0132 memory: 15768 grad_norm: 3.5067 loss: 1.4566 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4566 2023/07/24 20:08:04 - mmengine - INFO - Epoch(train) [19][800/940] lr: 1.0000e-02 eta: 23:23:57 time: 1.1001 data_time: 0.0134 memory: 15768 grad_norm: 3.5673 loss: 1.4386 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4386 2023/07/24 20:08:26 - mmengine - INFO - Epoch(train) [19][820/940] lr: 1.0000e-02 eta: 23:23:35 time: 1.1041 data_time: 0.0136 memory: 15768 grad_norm: 3.6838 loss: 1.4925 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4925 2023/07/24 20:08:48 - mmengine - INFO - Epoch(train) [19][840/940] lr: 1.0000e-02 eta: 23:23:13 time: 1.1025 data_time: 0.0139 memory: 15768 grad_norm: 3.6039 loss: 1.3805 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3805 2023/07/24 20:09:10 - mmengine - INFO - Epoch(train) [19][860/940] lr: 1.0000e-02 eta: 23:22:50 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 3.6131 loss: 1.5117 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5117 2023/07/24 20:09:32 - mmengine - INFO - Epoch(train) [19][880/940] lr: 1.0000e-02 eta: 23:22:28 time: 1.1048 data_time: 0.0131 memory: 15768 grad_norm: 3.6500 loss: 1.5734 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5734 2023/07/24 20:09:54 - mmengine - INFO - Epoch(train) [19][900/940] lr: 1.0000e-02 eta: 23:22:06 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 3.6242 loss: 1.3783 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3783 2023/07/24 20:10:16 - mmengine - INFO - Epoch(train) [19][920/940] lr: 1.0000e-02 eta: 23:21:43 time: 1.1002 data_time: 0.0129 memory: 15768 grad_norm: 3.6170 loss: 1.4465 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4465 2023/07/24 20:10:37 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 20:10:37 - mmengine - INFO - Epoch(train) [19][940/940] lr: 1.0000e-02 eta: 23:21:17 time: 1.0596 data_time: 0.0128 memory: 15768 grad_norm: 3.7855 loss: 1.3851 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.3851 2023/07/24 20:10:47 - mmengine - INFO - Epoch(val) [19][20/78] eta: 0:00:28 time: 0.4859 data_time: 0.3290 memory: 2147 2023/07/24 20:10:54 - mmengine - INFO - Epoch(val) [19][40/78] eta: 0:00:15 time: 0.3444 data_time: 0.1877 memory: 2147 2023/07/24 20:11:02 - mmengine - INFO - Epoch(val) [19][60/78] eta: 0:00:07 time: 0.4383 data_time: 0.2814 memory: 2147 2023/07/24 20:11:14 - mmengine - INFO - Epoch(val) [19][78/78] acc/top1: 0.6536 acc/top5: 0.8674 acc/mean1: 0.6535 data_time: 0.2413 time: 0.3954 2023/07/24 20:11:39 - mmengine - INFO - Epoch(train) [20][ 20/940] lr: 1.0000e-02 eta: 23:21:11 time: 1.2824 data_time: 0.1474 memory: 15768 grad_norm: 3.5728 loss: 1.4758 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4758 2023/07/24 20:12:01 - mmengine - INFO - Epoch(train) [20][ 40/940] lr: 1.0000e-02 eta: 23:20:48 time: 1.1014 data_time: 0.0133 memory: 15768 grad_norm: 3.5606 loss: 1.5950 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5950 2023/07/24 20:12:23 - mmengine - INFO - Epoch(train) [20][ 60/940] lr: 1.0000e-02 eta: 23:20:26 time: 1.1042 data_time: 0.0130 memory: 15768 grad_norm: 3.6119 loss: 1.3647 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3647 2023/07/24 20:12:45 - mmengine - INFO - Epoch(train) [20][ 80/940] lr: 1.0000e-02 eta: 23:20:04 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.6159 loss: 1.4648 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4648 2023/07/24 20:13:07 - mmengine - INFO - Epoch(train) [20][100/940] lr: 1.0000e-02 eta: 23:19:41 time: 1.1026 data_time: 0.0131 memory: 15768 grad_norm: 3.5707 loss: 1.3561 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3561 2023/07/24 20:13:29 - mmengine - INFO - Epoch(train) [20][120/940] lr: 1.0000e-02 eta: 23:19:19 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.5515 loss: 1.2689 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2689 2023/07/24 20:13:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 20:13:51 - mmengine - INFO - Epoch(train) [20][140/940] lr: 1.0000e-02 eta: 23:18:56 time: 1.0977 data_time: 0.0130 memory: 15768 grad_norm: 3.6074 loss: 1.4306 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4306 2023/07/24 20:14:13 - mmengine - INFO - Epoch(train) [20][160/940] lr: 1.0000e-02 eta: 23:18:34 time: 1.0996 data_time: 0.0129 memory: 15768 grad_norm: 3.7028 loss: 1.2690 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2690 2023/07/24 20:14:35 - mmengine - INFO - Epoch(train) [20][180/940] lr: 1.0000e-02 eta: 23:18:11 time: 1.0992 data_time: 0.0129 memory: 15768 grad_norm: 3.5958 loss: 1.3485 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3485 2023/07/24 20:14:57 - mmengine - INFO - Epoch(train) [20][200/940] lr: 1.0000e-02 eta: 23:17:49 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 3.6416 loss: 1.4074 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4074 2023/07/24 20:15:19 - mmengine - INFO - Epoch(train) [20][220/940] lr: 1.0000e-02 eta: 23:17:26 time: 1.1024 data_time: 0.0131 memory: 15768 grad_norm: 3.6149 loss: 1.2594 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2594 2023/07/24 20:15:41 - mmengine - INFO - Epoch(train) [20][240/940] lr: 1.0000e-02 eta: 23:17:04 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 3.5571 loss: 1.2855 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2855 2023/07/24 20:16:03 - mmengine - INFO - Epoch(train) [20][260/940] lr: 1.0000e-02 eta: 23:16:42 time: 1.1013 data_time: 0.0129 memory: 15768 grad_norm: 3.5819 loss: 1.3562 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3562 2023/07/24 20:16:25 - mmengine - INFO - Epoch(train) [20][280/940] lr: 1.0000e-02 eta: 23:16:19 time: 1.1022 data_time: 0.0133 memory: 15768 grad_norm: 3.5952 loss: 1.2010 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2010 2023/07/24 20:16:47 - mmengine - INFO - Epoch(train) [20][300/940] lr: 1.0000e-02 eta: 23:15:57 time: 1.1004 data_time: 0.0130 memory: 15768 grad_norm: 3.5698 loss: 1.4305 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4305 2023/07/24 20:17:09 - mmengine - INFO - Epoch(train) [20][320/940] lr: 1.0000e-02 eta: 23:15:34 time: 1.1001 data_time: 0.0132 memory: 15768 grad_norm: 3.5708 loss: 1.4461 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.4461 2023/07/24 20:17:32 - mmengine - INFO - Epoch(train) [20][340/940] lr: 1.0000e-02 eta: 23:15:12 time: 1.1015 data_time: 0.0131 memory: 15768 grad_norm: 3.5999 loss: 1.4501 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4501 2023/07/24 20:17:54 - mmengine - INFO - Epoch(train) [20][360/940] lr: 1.0000e-02 eta: 23:14:50 time: 1.1014 data_time: 0.0132 memory: 15768 grad_norm: 3.5796 loss: 1.4395 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4395 2023/07/24 20:18:16 - mmengine - INFO - Epoch(train) [20][380/940] lr: 1.0000e-02 eta: 23:14:27 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.7055 loss: 1.2922 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2922 2023/07/24 20:18:38 - mmengine - INFO - Epoch(train) [20][400/940] lr: 1.0000e-02 eta: 23:14:05 time: 1.1005 data_time: 0.0134 memory: 15768 grad_norm: 3.6143 loss: 1.3997 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3997 2023/07/24 20:19:00 - mmengine - INFO - Epoch(train) [20][420/940] lr: 1.0000e-02 eta: 23:13:42 time: 1.1007 data_time: 0.0136 memory: 15768 grad_norm: 3.6465 loss: 1.4661 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4661 2023/07/24 20:19:22 - mmengine - INFO - Epoch(train) [20][440/940] lr: 1.0000e-02 eta: 23:13:20 time: 1.1031 data_time: 0.0132 memory: 15768 grad_norm: 3.7091 loss: 1.4845 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4845 2023/07/24 20:19:44 - mmengine - INFO - Epoch(train) [20][460/940] lr: 1.0000e-02 eta: 23:12:58 time: 1.1003 data_time: 0.0132 memory: 15768 grad_norm: 3.6708 loss: 1.3476 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3476 2023/07/24 20:20:06 - mmengine - INFO - Epoch(train) [20][480/940] lr: 1.0000e-02 eta: 23:12:35 time: 1.0992 data_time: 0.0135 memory: 15768 grad_norm: 3.7153 loss: 1.4753 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4753 2023/07/24 20:20:28 - mmengine - INFO - Epoch(train) [20][500/940] lr: 1.0000e-02 eta: 23:12:13 time: 1.1008 data_time: 0.0131 memory: 15768 grad_norm: 3.5349 loss: 1.2939 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2939 2023/07/24 20:20:50 - mmengine - INFO - Epoch(train) [20][520/940] lr: 1.0000e-02 eta: 23:11:50 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 3.7226 loss: 1.6435 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6435 2023/07/24 20:21:12 - mmengine - INFO - Epoch(train) [20][540/940] lr: 1.0000e-02 eta: 23:11:28 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 3.6548 loss: 1.3809 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3809 2023/07/24 20:21:34 - mmengine - INFO - Epoch(train) [20][560/940] lr: 1.0000e-02 eta: 23:11:06 time: 1.1041 data_time: 0.0134 memory: 15768 grad_norm: 3.6275 loss: 1.5777 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5777 2023/07/24 20:21:56 - mmengine - INFO - Epoch(train) [20][580/940] lr: 1.0000e-02 eta: 23:10:44 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.6181 loss: 1.3791 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3791 2023/07/24 20:22:18 - mmengine - INFO - Epoch(train) [20][600/940] lr: 1.0000e-02 eta: 23:10:21 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 3.6024 loss: 1.4665 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.4665 2023/07/24 20:22:40 - mmengine - INFO - Epoch(train) [20][620/940] lr: 1.0000e-02 eta: 23:09:59 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 3.6462 loss: 1.5815 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5815 2023/07/24 20:23:02 - mmengine - INFO - Epoch(train) [20][640/940] lr: 1.0000e-02 eta: 23:09:36 time: 1.1004 data_time: 0.0131 memory: 15768 grad_norm: 3.6182 loss: 1.3303 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3303 2023/07/24 20:23:24 - mmengine - INFO - Epoch(train) [20][660/940] lr: 1.0000e-02 eta: 23:09:14 time: 1.1014 data_time: 0.0135 memory: 15768 grad_norm: 3.5434 loss: 1.3886 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3886 2023/07/24 20:23:46 - mmengine - INFO - Epoch(train) [20][680/940] lr: 1.0000e-02 eta: 23:08:52 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 3.6507 loss: 1.4196 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4196 2023/07/24 20:24:08 - mmengine - INFO - Epoch(train) [20][700/940] lr: 1.0000e-02 eta: 23:08:29 time: 1.0997 data_time: 0.0134 memory: 15768 grad_norm: 3.5829 loss: 1.3393 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3393 2023/07/24 20:24:30 - mmengine - INFO - Epoch(train) [20][720/940] lr: 1.0000e-02 eta: 23:08:07 time: 1.0994 data_time: 0.0135 memory: 15768 grad_norm: 3.6204 loss: 1.4006 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4006 2023/07/24 20:24:52 - mmengine - INFO - Epoch(train) [20][740/940] lr: 1.0000e-02 eta: 23:07:44 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 3.6410 loss: 1.5302 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5302 2023/07/24 20:25:14 - mmengine - INFO - Epoch(train) [20][760/940] lr: 1.0000e-02 eta: 23:07:22 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 3.5776 loss: 1.2542 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2542 2023/07/24 20:25:36 - mmengine - INFO - Epoch(train) [20][780/940] lr: 1.0000e-02 eta: 23:06:59 time: 1.1020 data_time: 0.0132 memory: 15768 grad_norm: 3.5722 loss: 1.3123 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3123 2023/07/24 20:25:58 - mmengine - INFO - Epoch(train) [20][800/940] lr: 1.0000e-02 eta: 23:06:37 time: 1.0987 data_time: 0.0131 memory: 15768 grad_norm: 3.6020 loss: 1.3423 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3423 2023/07/24 20:26:20 - mmengine - INFO - Epoch(train) [20][820/940] lr: 1.0000e-02 eta: 23:06:15 time: 1.1011 data_time: 0.0132 memory: 15768 grad_norm: 3.6045 loss: 1.3586 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3586 2023/07/24 20:26:42 - mmengine - INFO - Epoch(train) [20][840/940] lr: 1.0000e-02 eta: 23:05:52 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.6682 loss: 1.3685 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3685 2023/07/24 20:27:04 - mmengine - INFO - Epoch(train) [20][860/940] lr: 1.0000e-02 eta: 23:05:30 time: 1.1030 data_time: 0.0128 memory: 15768 grad_norm: 3.6364 loss: 1.4197 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4197 2023/07/24 20:27:26 - mmengine - INFO - Epoch(train) [20][880/940] lr: 1.0000e-02 eta: 23:05:08 time: 1.1012 data_time: 0.0131 memory: 15768 grad_norm: 3.6099 loss: 1.2673 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2673 2023/07/24 20:27:48 - mmengine - INFO - Epoch(train) [20][900/940] lr: 1.0000e-02 eta: 23:04:45 time: 1.1035 data_time: 0.0130 memory: 15768 grad_norm: 3.6927 loss: 1.4907 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4907 2023/07/24 20:28:10 - mmengine - INFO - Epoch(train) [20][920/940] lr: 1.0000e-02 eta: 23:04:23 time: 1.1023 data_time: 0.0127 memory: 15768 grad_norm: 3.6103 loss: 1.4681 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4681 2023/07/24 20:28:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 20:28:31 - mmengine - INFO - Epoch(train) [20][940/940] lr: 1.0000e-02 eta: 23:03:57 time: 1.0564 data_time: 0.0125 memory: 15768 grad_norm: 3.7250 loss: 1.4550 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4550 2023/07/24 20:28:41 - mmengine - INFO - Epoch(val) [20][20/78] eta: 0:00:28 time: 0.4829 data_time: 0.3256 memory: 2147 2023/07/24 20:28:48 - mmengine - INFO - Epoch(val) [20][40/78] eta: 0:00:15 time: 0.3381 data_time: 0.1808 memory: 2147 2023/07/24 20:28:56 - mmengine - INFO - Epoch(val) [20][60/78] eta: 0:00:07 time: 0.4427 data_time: 0.2861 memory: 2147 2023/07/24 20:29:07 - mmengine - INFO - Epoch(val) [20][78/78] acc/top1: 0.6677 acc/top5: 0.8756 acc/mean1: 0.6675 data_time: 0.2409 time: 0.3951 2023/07/24 20:29:07 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_17.pth is removed 2023/07/24 20:29:08 - mmengine - INFO - The best checkpoint with 0.6677 acc/top1 at 20 epoch is saved to best_acc_top1_epoch_20.pth. 2023/07/24 20:29:33 - mmengine - INFO - Epoch(train) [21][ 20/940] lr: 1.0000e-02 eta: 23:03:48 time: 1.2584 data_time: 0.1523 memory: 15768 grad_norm: 3.5720 loss: 1.5778 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5778 2023/07/24 20:29:55 - mmengine - INFO - Epoch(train) [21][ 40/940] lr: 1.0000e-02 eta: 23:03:25 time: 1.1027 data_time: 0.0131 memory: 15768 grad_norm: 3.5304 loss: 1.3219 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3219 2023/07/24 20:30:17 - mmengine - INFO - Epoch(train) [21][ 60/940] lr: 1.0000e-02 eta: 23:03:03 time: 1.1014 data_time: 0.0131 memory: 15768 grad_norm: 3.6117 loss: 1.4206 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4206 2023/07/24 20:30:39 - mmengine - INFO - Epoch(train) [21][ 80/940] lr: 1.0000e-02 eta: 23:02:41 time: 1.1020 data_time: 0.0130 memory: 15768 grad_norm: 3.5681 loss: 1.3798 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3798 2023/07/24 20:31:01 - mmengine - INFO - Epoch(train) [21][100/940] lr: 1.0000e-02 eta: 23:02:19 time: 1.1029 data_time: 0.0131 memory: 15768 grad_norm: 3.6602 loss: 1.3352 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3352 2023/07/24 20:31:23 - mmengine - INFO - Epoch(train) [21][120/940] lr: 1.0000e-02 eta: 23:01:56 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 3.6289 loss: 1.4542 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4542 2023/07/24 20:31:45 - mmengine - INFO - Epoch(train) [21][140/940] lr: 1.0000e-02 eta: 23:01:34 time: 1.1030 data_time: 0.0137 memory: 15768 grad_norm: 3.6789 loss: 1.4308 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4308 2023/07/24 20:32:08 - mmengine - INFO - Epoch(train) [21][160/940] lr: 1.0000e-02 eta: 23:01:15 time: 1.1473 data_time: 0.0134 memory: 15768 grad_norm: 3.6225 loss: 1.3378 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3378 2023/07/24 20:32:31 - mmengine - INFO - Epoch(train) [21][180/940] lr: 1.0000e-02 eta: 23:00:55 time: 1.1278 data_time: 0.0129 memory: 15768 grad_norm: 3.5944 loss: 1.3654 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3654 2023/07/24 20:32:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 20:32:53 - mmengine - INFO - Epoch(train) [21][200/940] lr: 1.0000e-02 eta: 23:00:33 time: 1.1025 data_time: 0.0130 memory: 15768 grad_norm: 3.5918 loss: 1.2353 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2353 2023/07/24 20:33:15 - mmengine - INFO - Epoch(train) [21][220/940] lr: 1.0000e-02 eta: 23:00:10 time: 1.1008 data_time: 0.0130 memory: 15768 grad_norm: 3.6223 loss: 1.4095 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4095 2023/07/24 20:33:37 - mmengine - INFO - Epoch(train) [21][240/940] lr: 1.0000e-02 eta: 22:59:48 time: 1.1027 data_time: 0.0131 memory: 15768 grad_norm: 3.5713 loss: 1.4182 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.4182 2023/07/24 20:33:59 - mmengine - INFO - Epoch(train) [21][260/940] lr: 1.0000e-02 eta: 22:59:26 time: 1.1028 data_time: 0.0124 memory: 15768 grad_norm: 3.6217 loss: 1.4794 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.4794 2023/07/24 20:34:21 - mmengine - INFO - Epoch(train) [21][280/940] lr: 1.0000e-02 eta: 22:59:04 time: 1.0993 data_time: 0.0131 memory: 15768 grad_norm: 3.6181 loss: 1.4280 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4280 2023/07/24 20:34:43 - mmengine - INFO - Epoch(train) [21][300/940] lr: 1.0000e-02 eta: 22:58:41 time: 1.1026 data_time: 0.0132 memory: 15768 grad_norm: 3.5986 loss: 1.5165 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5165 2023/07/24 20:35:05 - mmengine - INFO - Epoch(train) [21][320/940] lr: 1.0000e-02 eta: 22:58:19 time: 1.1067 data_time: 0.0132 memory: 15768 grad_norm: 3.5583 loss: 1.4277 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4277 2023/07/24 20:35:27 - mmengine - INFO - Epoch(train) [21][340/940] lr: 1.0000e-02 eta: 22:57:57 time: 1.0992 data_time: 0.0127 memory: 15768 grad_norm: 3.5394 loss: 1.3643 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3643 2023/07/24 20:35:49 - mmengine - INFO - Epoch(train) [21][360/940] lr: 1.0000e-02 eta: 22:57:34 time: 1.0998 data_time: 0.0131 memory: 15768 grad_norm: 3.5934 loss: 1.4999 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.4999 2023/07/24 20:36:11 - mmengine - INFO - Epoch(train) [21][380/940] lr: 1.0000e-02 eta: 22:57:12 time: 1.1022 data_time: 0.0127 memory: 15768 grad_norm: 3.6405 loss: 1.4959 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4959 2023/07/24 20:36:33 - mmengine - INFO - Epoch(train) [21][400/940] lr: 1.0000e-02 eta: 22:56:50 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 3.6299 loss: 1.2618 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2618 2023/07/24 20:36:55 - mmengine - INFO - Epoch(train) [21][420/940] lr: 1.0000e-02 eta: 22:56:27 time: 1.1004 data_time: 0.0131 memory: 15768 grad_norm: 3.6517 loss: 1.4315 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4315 2023/07/24 20:37:17 - mmengine - INFO - Epoch(train) [21][440/940] lr: 1.0000e-02 eta: 22:56:05 time: 1.1012 data_time: 0.0135 memory: 15768 grad_norm: 3.6317 loss: 1.4257 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4257 2023/07/24 20:37:39 - mmengine - INFO - Epoch(train) [21][460/940] lr: 1.0000e-02 eta: 22:55:43 time: 1.1024 data_time: 0.0127 memory: 15768 grad_norm: 3.6382 loss: 1.3891 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3891 2023/07/24 20:38:01 - mmengine - INFO - Epoch(train) [21][480/940] lr: 1.0000e-02 eta: 22:55:20 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 3.6707 loss: 1.3592 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3592 2023/07/24 20:38:23 - mmengine - INFO - Epoch(train) [21][500/940] lr: 1.0000e-02 eta: 22:54:58 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 3.6169 loss: 1.3279 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3279 2023/07/24 20:38:45 - mmengine - INFO - Epoch(train) [21][520/940] lr: 1.0000e-02 eta: 22:54:36 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 3.5665 loss: 1.4805 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4805 2023/07/24 20:39:07 - mmengine - INFO - Epoch(train) [21][540/940] lr: 1.0000e-02 eta: 22:54:13 time: 1.1005 data_time: 0.0134 memory: 15768 grad_norm: 3.5883 loss: 1.2107 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2107 2023/07/24 20:39:30 - mmengine - INFO - Epoch(train) [21][560/940] lr: 1.0000e-02 eta: 22:53:51 time: 1.1007 data_time: 0.0132 memory: 15768 grad_norm: 3.6412 loss: 1.4198 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.4198 2023/07/24 20:39:52 - mmengine - INFO - Epoch(train) [21][580/940] lr: 1.0000e-02 eta: 22:53:29 time: 1.1008 data_time: 0.0132 memory: 15768 grad_norm: 3.7190 loss: 1.4300 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4300 2023/07/24 20:40:14 - mmengine - INFO - Epoch(train) [21][600/940] lr: 1.0000e-02 eta: 22:53:06 time: 1.1018 data_time: 0.0130 memory: 15768 grad_norm: 3.6441 loss: 1.2433 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2433 2023/07/24 20:40:36 - mmengine - INFO - Epoch(train) [21][620/940] lr: 1.0000e-02 eta: 22:52:44 time: 1.1001 data_time: 0.0129 memory: 15768 grad_norm: 3.6733 loss: 1.5112 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5112 2023/07/24 20:40:58 - mmengine - INFO - Epoch(train) [21][640/940] lr: 1.0000e-02 eta: 22:52:22 time: 1.1028 data_time: 0.0131 memory: 15768 grad_norm: 3.5803 loss: 1.4808 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4808 2023/07/24 20:41:20 - mmengine - INFO - Epoch(train) [21][660/940] lr: 1.0000e-02 eta: 22:51:59 time: 1.0984 data_time: 0.0129 memory: 15768 grad_norm: 3.6321 loss: 1.3551 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3551 2023/07/24 20:41:42 - mmengine - INFO - Epoch(train) [21][680/940] lr: 1.0000e-02 eta: 22:51:37 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.6712 loss: 1.3713 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3713 2023/07/24 20:42:04 - mmengine - INFO - Epoch(train) [21][700/940] lr: 1.0000e-02 eta: 22:51:15 time: 1.1041 data_time: 0.0131 memory: 15768 grad_norm: 3.6310 loss: 1.4017 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.4017 2023/07/24 20:42:26 - mmengine - INFO - Epoch(train) [21][720/940] lr: 1.0000e-02 eta: 22:50:52 time: 1.1014 data_time: 0.0129 memory: 15768 grad_norm: 3.6508 loss: 1.3800 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3800 2023/07/24 20:42:48 - mmengine - INFO - Epoch(train) [21][740/940] lr: 1.0000e-02 eta: 22:50:30 time: 1.1041 data_time: 0.0130 memory: 15768 grad_norm: 3.7204 loss: 1.4603 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4603 2023/07/24 20:43:10 - mmengine - INFO - Epoch(train) [21][760/940] lr: 1.0000e-02 eta: 22:50:08 time: 1.1021 data_time: 0.0132 memory: 15768 grad_norm: 3.6659 loss: 1.3902 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3902 2023/07/24 20:43:32 - mmengine - INFO - Epoch(train) [21][780/940] lr: 1.0000e-02 eta: 22:49:46 time: 1.1041 data_time: 0.0129 memory: 15768 grad_norm: 3.5963 loss: 1.3252 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3252 2023/07/24 20:43:54 - mmengine - INFO - Epoch(train) [21][800/940] lr: 1.0000e-02 eta: 22:49:24 time: 1.1003 data_time: 0.0138 memory: 15768 grad_norm: 3.6692 loss: 1.3288 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3288 2023/07/24 20:44:16 - mmengine - INFO - Epoch(train) [21][820/940] lr: 1.0000e-02 eta: 22:49:01 time: 1.1041 data_time: 0.0129 memory: 15768 grad_norm: 3.6389 loss: 1.3136 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3136 2023/07/24 20:44:38 - mmengine - INFO - Epoch(train) [21][840/940] lr: 1.0000e-02 eta: 22:48:39 time: 1.1005 data_time: 0.0125 memory: 15768 grad_norm: 3.6801 loss: 1.3749 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3749 2023/07/24 20:45:00 - mmengine - INFO - Epoch(train) [21][860/940] lr: 1.0000e-02 eta: 22:48:17 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 3.6068 loss: 1.3662 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3662 2023/07/24 20:45:22 - mmengine - INFO - Epoch(train) [21][880/940] lr: 1.0000e-02 eta: 22:47:54 time: 1.1011 data_time: 0.0139 memory: 15768 grad_norm: 3.6220 loss: 1.4282 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4282 2023/07/24 20:45:44 - mmengine - INFO - Epoch(train) [21][900/940] lr: 1.0000e-02 eta: 22:47:32 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 3.6216 loss: 1.3073 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3073 2023/07/24 20:46:06 - mmengine - INFO - Epoch(train) [21][920/940] lr: 1.0000e-02 eta: 22:47:10 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.6793 loss: 1.2961 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2961 2023/07/24 20:46:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 20:46:27 - mmengine - INFO - Epoch(train) [21][940/940] lr: 1.0000e-02 eta: 22:46:44 time: 1.0549 data_time: 0.0124 memory: 15768 grad_norm: 3.8396 loss: 1.4408 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4408 2023/07/24 20:46:27 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/07/24 20:46:38 - mmengine - INFO - Epoch(val) [21][20/78] eta: 0:00:28 time: 0.4881 data_time: 0.3306 memory: 2147 2023/07/24 20:46:45 - mmengine - INFO - Epoch(val) [21][40/78] eta: 0:00:16 time: 0.3543 data_time: 0.1975 memory: 2147 2023/07/24 20:46:54 - mmengine - INFO - Epoch(val) [21][60/78] eta: 0:00:07 time: 0.4301 data_time: 0.2731 memory: 2147 2023/07/24 20:47:03 - mmengine - INFO - Epoch(val) [21][78/78] acc/top1: 0.6432 acc/top5: 0.8595 acc/mean1: 0.6429 data_time: 0.2400 time: 0.3941 2023/07/24 20:47:29 - mmengine - INFO - Epoch(train) [22][ 20/940] lr: 1.0000e-02 eta: 22:46:36 time: 1.2917 data_time: 0.1254 memory: 15768 grad_norm: 3.6406 loss: 1.4723 top1_acc: 0.4375 top5_acc: 1.0000 loss_cls: 1.4723 2023/07/24 20:47:51 - mmengine - INFO - Epoch(train) [22][ 40/940] lr: 1.0000e-02 eta: 22:46:14 time: 1.1014 data_time: 0.0130 memory: 15768 grad_norm: 3.6625 loss: 1.2929 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2929 2023/07/24 20:48:13 - mmengine - INFO - Epoch(train) [22][ 60/940] lr: 1.0000e-02 eta: 22:45:51 time: 1.1034 data_time: 0.0134 memory: 15768 grad_norm: 3.6089 loss: 1.3380 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3380 2023/07/24 20:48:35 - mmengine - INFO - Epoch(train) [22][ 80/940] lr: 1.0000e-02 eta: 22:45:29 time: 1.1047 data_time: 0.0128 memory: 15768 grad_norm: 3.5620 loss: 1.2938 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2938 2023/07/24 20:48:57 - mmengine - INFO - Epoch(train) [22][100/940] lr: 1.0000e-02 eta: 22:45:07 time: 1.0999 data_time: 0.0126 memory: 15768 grad_norm: 3.6294 loss: 1.4959 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4959 2023/07/24 20:49:19 - mmengine - INFO - Epoch(train) [22][120/940] lr: 1.0000e-02 eta: 22:44:45 time: 1.1012 data_time: 0.0133 memory: 15768 grad_norm: 3.6218 loss: 1.6083 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6083 2023/07/24 20:49:41 - mmengine - INFO - Epoch(train) [22][140/940] lr: 1.0000e-02 eta: 22:44:22 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 3.6067 loss: 1.2491 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2491 2023/07/24 20:50:03 - mmengine - INFO - Epoch(train) [22][160/940] lr: 1.0000e-02 eta: 22:44:00 time: 1.1054 data_time: 0.0134 memory: 15768 grad_norm: 3.6299 loss: 1.4381 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4381 2023/07/24 20:50:25 - mmengine - INFO - Epoch(train) [22][180/940] lr: 1.0000e-02 eta: 22:43:38 time: 1.0998 data_time: 0.0135 memory: 15768 grad_norm: 3.6350 loss: 1.3398 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3398 2023/07/24 20:50:47 - mmengine - INFO - Epoch(train) [22][200/940] lr: 1.0000e-02 eta: 22:43:16 time: 1.1025 data_time: 0.0133 memory: 15768 grad_norm: 3.6886 loss: 1.4165 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4165 2023/07/24 20:51:09 - mmengine - INFO - Epoch(train) [22][220/940] lr: 1.0000e-02 eta: 22:42:53 time: 1.0998 data_time: 0.0128 memory: 15768 grad_norm: 3.6922 loss: 1.5742 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.5742 2023/07/24 20:51:32 - mmengine - INFO - Epoch(train) [22][240/940] lr: 1.0000e-02 eta: 22:42:31 time: 1.1038 data_time: 0.0128 memory: 15768 grad_norm: 3.6312 loss: 1.4642 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.4642 2023/07/24 20:51:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 20:51:54 - mmengine - INFO - Epoch(train) [22][260/940] lr: 1.0000e-02 eta: 22:42:09 time: 1.1014 data_time: 0.0129 memory: 15768 grad_norm: 3.6571 loss: 1.2884 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2884 2023/07/24 20:52:16 - mmengine - INFO - Epoch(train) [22][280/940] lr: 1.0000e-02 eta: 22:41:47 time: 1.1043 data_time: 0.0133 memory: 15768 grad_norm: 3.6299 loss: 1.4770 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4770 2023/07/24 20:52:38 - mmengine - INFO - Epoch(train) [22][300/940] lr: 1.0000e-02 eta: 22:41:24 time: 1.0993 data_time: 0.0128 memory: 15768 grad_norm: 3.7107 loss: 1.1470 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1470 2023/07/24 20:53:00 - mmengine - INFO - Epoch(train) [22][320/940] lr: 1.0000e-02 eta: 22:41:03 time: 1.1153 data_time: 0.0134 memory: 15768 grad_norm: 3.7622 loss: 1.4255 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4255 2023/07/24 20:53:22 - mmengine - INFO - Epoch(train) [22][340/940] lr: 1.0000e-02 eta: 22:40:40 time: 1.0993 data_time: 0.0130 memory: 15768 grad_norm: 3.7982 loss: 1.2817 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2817 2023/07/24 20:53:44 - mmengine - INFO - Epoch(train) [22][360/940] lr: 1.0000e-02 eta: 22:40:18 time: 1.1034 data_time: 0.0127 memory: 15768 grad_norm: 3.6702 loss: 1.5275 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5275 2023/07/24 20:54:06 - mmengine - INFO - Epoch(train) [22][380/940] lr: 1.0000e-02 eta: 22:39:56 time: 1.1016 data_time: 0.0125 memory: 15768 grad_norm: 3.6322 loss: 1.4831 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4831 2023/07/24 20:54:28 - mmengine - INFO - Epoch(train) [22][400/940] lr: 1.0000e-02 eta: 22:39:33 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 3.6735 loss: 1.3182 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3182 2023/07/24 20:54:50 - mmengine - INFO - Epoch(train) [22][420/940] lr: 1.0000e-02 eta: 22:39:11 time: 1.1022 data_time: 0.0133 memory: 15768 grad_norm: 3.6408 loss: 1.2999 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2999 2023/07/24 20:55:12 - mmengine - INFO - Epoch(train) [22][440/940] lr: 1.0000e-02 eta: 22:38:49 time: 1.1018 data_time: 0.0130 memory: 15768 grad_norm: 3.6836 loss: 1.2787 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2787 2023/07/24 20:55:34 - mmengine - INFO - Epoch(train) [22][460/940] lr: 1.0000e-02 eta: 22:38:27 time: 1.1032 data_time: 0.0133 memory: 15768 grad_norm: 3.6756 loss: 1.4726 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4726 2023/07/24 20:55:56 - mmengine - INFO - Epoch(train) [22][480/940] lr: 1.0000e-02 eta: 22:38:04 time: 1.0994 data_time: 0.0133 memory: 15768 grad_norm: 3.6960 loss: 1.3922 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3922 2023/07/24 20:56:18 - mmengine - INFO - Epoch(train) [22][500/940] lr: 1.0000e-02 eta: 22:37:42 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 3.6528 loss: 1.3336 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3336 2023/07/24 20:56:40 - mmengine - INFO - Epoch(train) [22][520/940] lr: 1.0000e-02 eta: 22:37:20 time: 1.1034 data_time: 0.0131 memory: 15768 grad_norm: 3.6924 loss: 1.4675 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4675 2023/07/24 20:57:02 - mmengine - INFO - Epoch(train) [22][540/940] lr: 1.0000e-02 eta: 22:36:57 time: 1.1014 data_time: 0.0132 memory: 15768 grad_norm: 3.6679 loss: 1.3805 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3805 2023/07/24 20:57:24 - mmengine - INFO - Epoch(train) [22][560/940] lr: 1.0000e-02 eta: 22:36:35 time: 1.1011 data_time: 0.0135 memory: 15768 grad_norm: 3.6274 loss: 1.2125 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2125 2023/07/24 20:57:46 - mmengine - INFO - Epoch(train) [22][580/940] lr: 1.0000e-02 eta: 22:36:13 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.6411 loss: 1.3813 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3813 2023/07/24 20:58:08 - mmengine - INFO - Epoch(train) [22][600/940] lr: 1.0000e-02 eta: 22:35:50 time: 1.0978 data_time: 0.0131 memory: 15768 grad_norm: 3.6103 loss: 1.3982 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3982 2023/07/24 20:58:30 - mmengine - INFO - Epoch(train) [22][620/940] lr: 1.0000e-02 eta: 22:35:28 time: 1.1020 data_time: 0.0130 memory: 15768 grad_norm: 3.6466 loss: 1.4604 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4604 2023/07/24 20:58:52 - mmengine - INFO - Epoch(train) [22][640/940] lr: 1.0000e-02 eta: 22:35:06 time: 1.1006 data_time: 0.0132 memory: 15768 grad_norm: 3.6619 loss: 1.3591 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3591 2023/07/24 20:59:14 - mmengine - INFO - Epoch(train) [22][660/940] lr: 1.0000e-02 eta: 22:34:44 time: 1.1044 data_time: 0.0127 memory: 15768 grad_norm: 3.6744 loss: 1.4425 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4425 2023/07/24 20:59:36 - mmengine - INFO - Epoch(train) [22][680/940] lr: 1.0000e-02 eta: 22:34:21 time: 1.1006 data_time: 0.0134 memory: 15768 grad_norm: 3.6026 loss: 1.3561 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3561 2023/07/24 20:59:58 - mmengine - INFO - Epoch(train) [22][700/940] lr: 1.0000e-02 eta: 22:33:59 time: 1.1024 data_time: 0.0135 memory: 15768 grad_norm: 3.6933 loss: 1.3262 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3262 2023/07/24 21:00:20 - mmengine - INFO - Epoch(train) [22][720/940] lr: 1.0000e-02 eta: 22:33:37 time: 1.1005 data_time: 0.0128 memory: 15768 grad_norm: 3.6693 loss: 1.3610 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3610 2023/07/24 21:00:42 - mmengine - INFO - Epoch(train) [22][740/940] lr: 1.0000e-02 eta: 22:33:14 time: 1.0994 data_time: 0.0127 memory: 15768 grad_norm: 3.6639 loss: 1.5133 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5133 2023/07/24 21:01:04 - mmengine - INFO - Epoch(train) [22][760/940] lr: 1.0000e-02 eta: 22:32:52 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 3.6511 loss: 1.2833 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2833 2023/07/24 21:01:27 - mmengine - INFO - Epoch(train) [22][780/940] lr: 1.0000e-02 eta: 22:32:29 time: 1.1016 data_time: 0.0134 memory: 15768 grad_norm: 3.6954 loss: 1.3889 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3889 2023/07/24 21:01:48 - mmengine - INFO - Epoch(train) [22][800/940] lr: 1.0000e-02 eta: 22:32:07 time: 1.0986 data_time: 0.0134 memory: 15768 grad_norm: 3.6204 loss: 1.3997 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3997 2023/07/24 21:02:10 - mmengine - INFO - Epoch(train) [22][820/940] lr: 1.0000e-02 eta: 22:31:45 time: 1.1001 data_time: 0.0135 memory: 15768 grad_norm: 3.6033 loss: 1.4334 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4334 2023/07/24 21:02:33 - mmengine - INFO - Epoch(train) [22][840/940] lr: 1.0000e-02 eta: 22:31:22 time: 1.1025 data_time: 0.0131 memory: 15768 grad_norm: 3.6952 loss: 1.6908 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6908 2023/07/24 21:02:55 - mmengine - INFO - Epoch(train) [22][860/940] lr: 1.0000e-02 eta: 22:31:00 time: 1.1018 data_time: 0.0130 memory: 15768 grad_norm: 3.5804 loss: 1.3882 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3882 2023/07/24 21:03:17 - mmengine - INFO - Epoch(train) [22][880/940] lr: 1.0000e-02 eta: 22:30:38 time: 1.1004 data_time: 0.0135 memory: 15768 grad_norm: 3.7073 loss: 1.4258 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4258 2023/07/24 21:03:39 - mmengine - INFO - Epoch(train) [22][900/940] lr: 1.0000e-02 eta: 22:30:15 time: 1.1017 data_time: 0.0131 memory: 15768 grad_norm: 3.6937 loss: 1.3073 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3073 2023/07/24 21:04:01 - mmengine - INFO - Epoch(train) [22][920/940] lr: 1.0000e-02 eta: 22:29:53 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 3.6918 loss: 1.5062 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5062 2023/07/24 21:04:22 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 21:04:22 - mmengine - INFO - Epoch(train) [22][940/940] lr: 1.0000e-02 eta: 22:29:27 time: 1.0544 data_time: 0.0127 memory: 15768 grad_norm: 3.8415 loss: 1.3797 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.3797 2023/07/24 21:04:31 - mmengine - INFO - Epoch(val) [22][20/78] eta: 0:00:28 time: 0.4847 data_time: 0.3272 memory: 2147 2023/07/24 21:04:39 - mmengine - INFO - Epoch(val) [22][40/78] eta: 0:00:15 time: 0.3538 data_time: 0.1969 memory: 2147 2023/07/24 21:04:47 - mmengine - INFO - Epoch(val) [22][60/78] eta: 0:00:07 time: 0.4361 data_time: 0.2796 memory: 2147 2023/07/24 21:04:58 - mmengine - INFO - Epoch(val) [22][78/78] acc/top1: 0.6644 acc/top5: 0.8758 acc/mean1: 0.6644 data_time: 0.2432 time: 0.3973 2023/07/24 21:05:24 - mmengine - INFO - Epoch(train) [23][ 20/940] lr: 1.0000e-02 eta: 22:29:19 time: 1.3022 data_time: 0.1203 memory: 15768 grad_norm: 3.6028 loss: 1.3348 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3348 2023/07/24 21:05:47 - mmengine - INFO - Epoch(train) [23][ 40/940] lr: 1.0000e-02 eta: 22:28:57 time: 1.1056 data_time: 0.0129 memory: 15768 grad_norm: 3.6105 loss: 1.3726 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.3726 2023/07/24 21:06:09 - mmengine - INFO - Epoch(train) [23][ 60/940] lr: 1.0000e-02 eta: 22:28:35 time: 1.1031 data_time: 0.0131 memory: 15768 grad_norm: 3.5979 loss: 1.2742 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2742 2023/07/24 21:06:31 - mmengine - INFO - Epoch(train) [23][ 80/940] lr: 1.0000e-02 eta: 22:28:13 time: 1.1009 data_time: 0.0129 memory: 15768 grad_norm: 3.7090 loss: 1.4375 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4375 2023/07/24 21:06:53 - mmengine - INFO - Epoch(train) [23][100/940] lr: 1.0000e-02 eta: 22:27:51 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 3.7149 loss: 1.4071 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.4071 2023/07/24 21:07:15 - mmengine - INFO - Epoch(train) [23][120/940] lr: 1.0000e-02 eta: 22:27:28 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 3.6913 loss: 1.2849 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2849 2023/07/24 21:07:37 - mmengine - INFO - Epoch(train) [23][140/940] lr: 1.0000e-02 eta: 22:27:06 time: 1.1020 data_time: 0.0133 memory: 15768 grad_norm: 3.6394 loss: 1.3765 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3765 2023/07/24 21:07:59 - mmengine - INFO - Epoch(train) [23][160/940] lr: 1.0000e-02 eta: 22:26:43 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.6774 loss: 1.1772 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1772 2023/07/24 21:08:21 - mmengine - INFO - Epoch(train) [23][180/940] lr: 1.0000e-02 eta: 22:26:21 time: 1.1033 data_time: 0.0128 memory: 15768 grad_norm: 3.5729 loss: 1.3832 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3832 2023/07/24 21:08:43 - mmengine - INFO - Epoch(train) [23][200/940] lr: 1.0000e-02 eta: 22:25:59 time: 1.1008 data_time: 0.0126 memory: 15768 grad_norm: 3.7124 loss: 1.5372 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5372 2023/07/24 21:09:05 - mmengine - INFO - Epoch(train) [23][220/940] lr: 1.0000e-02 eta: 22:25:36 time: 1.0992 data_time: 0.0131 memory: 15768 grad_norm: 3.6082 loss: 1.3202 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3202 2023/07/24 21:09:27 - mmengine - INFO - Epoch(train) [23][240/940] lr: 1.0000e-02 eta: 22:25:14 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 3.6768 loss: 1.5919 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5919 2023/07/24 21:09:49 - mmengine - INFO - Epoch(train) [23][260/940] lr: 1.0000e-02 eta: 22:24:51 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 3.6772 loss: 1.3413 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3413 2023/07/24 21:10:11 - mmengine - INFO - Epoch(train) [23][280/940] lr: 1.0000e-02 eta: 22:24:29 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.7128 loss: 1.3188 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3188 2023/07/24 21:10:33 - mmengine - INFO - Epoch(train) [23][300/940] lr: 1.0000e-02 eta: 22:24:07 time: 1.1043 data_time: 0.0135 memory: 15768 grad_norm: 3.6768 loss: 1.3231 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3231 2023/07/24 21:10:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 21:10:55 - mmengine - INFO - Epoch(train) [23][320/940] lr: 1.0000e-02 eta: 22:23:45 time: 1.1003 data_time: 0.0136 memory: 15768 grad_norm: 3.6688 loss: 1.4726 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4726 2023/07/24 21:11:17 - mmengine - INFO - Epoch(train) [23][340/940] lr: 1.0000e-02 eta: 22:23:22 time: 1.1019 data_time: 0.0131 memory: 15768 grad_norm: 3.7799 loss: 1.4114 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4114 2023/07/24 21:11:39 - mmengine - INFO - Epoch(train) [23][360/940] lr: 1.0000e-02 eta: 22:23:00 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 3.7036 loss: 1.5764 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5764 2023/07/24 21:12:01 - mmengine - INFO - Epoch(train) [23][380/940] lr: 1.0000e-02 eta: 22:22:38 time: 1.1051 data_time: 0.0128 memory: 15768 grad_norm: 3.7056 loss: 1.6620 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6620 2023/07/24 21:12:23 - mmengine - INFO - Epoch(train) [23][400/940] lr: 1.0000e-02 eta: 22:22:16 time: 1.1018 data_time: 0.0127 memory: 15768 grad_norm: 3.6464 loss: 1.2461 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2461 2023/07/24 21:12:45 - mmengine - INFO - Epoch(train) [23][420/940] lr: 1.0000e-02 eta: 22:21:54 time: 1.1036 data_time: 0.0129 memory: 15768 grad_norm: 3.6482 loss: 1.2908 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2908 2023/07/24 21:13:07 - mmengine - INFO - Epoch(train) [23][440/940] lr: 1.0000e-02 eta: 22:21:31 time: 1.1031 data_time: 0.0131 memory: 15768 grad_norm: 3.6043 loss: 1.2138 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2138 2023/07/24 21:13:29 - mmengine - INFO - Epoch(train) [23][460/940] lr: 1.0000e-02 eta: 22:21:09 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 3.6586 loss: 1.2888 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2888 2023/07/24 21:13:51 - mmengine - INFO - Epoch(train) [23][480/940] lr: 1.0000e-02 eta: 22:20:47 time: 1.1015 data_time: 0.0135 memory: 15768 grad_norm: 3.7793 loss: 1.3228 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3228 2023/07/24 21:14:13 - mmengine - INFO - Epoch(train) [23][500/940] lr: 1.0000e-02 eta: 22:20:24 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.7180 loss: 1.7713 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7713 2023/07/24 21:14:35 - mmengine - INFO - Epoch(train) [23][520/940] lr: 1.0000e-02 eta: 22:20:02 time: 1.1000 data_time: 0.0131 memory: 15768 grad_norm: 3.6484 loss: 1.4628 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4628 2023/07/24 21:14:57 - mmengine - INFO - Epoch(train) [23][540/940] lr: 1.0000e-02 eta: 22:19:40 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.7565 loss: 1.5509 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5509 2023/07/24 21:15:19 - mmengine - INFO - Epoch(train) [23][560/940] lr: 1.0000e-02 eta: 22:19:17 time: 1.1024 data_time: 0.0132 memory: 15768 grad_norm: 3.6317 loss: 1.2569 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2569 2023/07/24 21:15:41 - mmengine - INFO - Epoch(train) [23][580/940] lr: 1.0000e-02 eta: 22:18:55 time: 1.1000 data_time: 0.0133 memory: 15768 grad_norm: 3.6442 loss: 1.1343 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1343 2023/07/24 21:16:03 - mmengine - INFO - Epoch(train) [23][600/940] lr: 1.0000e-02 eta: 22:18:33 time: 1.1028 data_time: 0.0127 memory: 15768 grad_norm: 3.7603 loss: 1.5786 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5786 2023/07/24 21:16:25 - mmengine - INFO - Epoch(train) [23][620/940] lr: 1.0000e-02 eta: 22:18:11 time: 1.1011 data_time: 0.0132 memory: 15768 grad_norm: 3.6322 loss: 1.2880 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2880 2023/07/24 21:16:47 - mmengine - INFO - Epoch(train) [23][640/940] lr: 1.0000e-02 eta: 22:17:48 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 3.6349 loss: 1.4505 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4505 2023/07/24 21:17:09 - mmengine - INFO - Epoch(train) [23][660/940] lr: 1.0000e-02 eta: 22:17:26 time: 1.1039 data_time: 0.0134 memory: 15768 grad_norm: 3.6424 loss: 1.2706 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2706 2023/07/24 21:17:32 - mmengine - INFO - Epoch(train) [23][680/940] lr: 1.0000e-02 eta: 22:17:04 time: 1.1037 data_time: 0.0130 memory: 15768 grad_norm: 3.7444 loss: 1.6640 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6640 2023/07/24 21:17:54 - mmengine - INFO - Epoch(train) [23][700/940] lr: 1.0000e-02 eta: 22:16:42 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 3.7305 loss: 1.3772 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3772 2023/07/24 21:18:16 - mmengine - INFO - Epoch(train) [23][720/940] lr: 1.0000e-02 eta: 22:16:19 time: 1.1023 data_time: 0.0134 memory: 15768 grad_norm: 3.6861 loss: 1.3185 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3185 2023/07/24 21:18:38 - mmengine - INFO - Epoch(train) [23][740/940] lr: 1.0000e-02 eta: 22:15:57 time: 1.1025 data_time: 0.0127 memory: 15768 grad_norm: 3.6471 loss: 1.1965 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1965 2023/07/24 21:19:00 - mmengine - INFO - Epoch(train) [23][760/940] lr: 1.0000e-02 eta: 22:15:35 time: 1.1000 data_time: 0.0134 memory: 15768 grad_norm: 3.7948 loss: 1.4626 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4626 2023/07/24 21:19:22 - mmengine - INFO - Epoch(train) [23][780/940] lr: 1.0000e-02 eta: 22:15:13 time: 1.1036 data_time: 0.0121 memory: 15768 grad_norm: 3.6651 loss: 1.3362 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3362 2023/07/24 21:19:44 - mmengine - INFO - Epoch(train) [23][800/940] lr: 1.0000e-02 eta: 22:14:51 time: 1.1054 data_time: 0.0131 memory: 15768 grad_norm: 3.6141 loss: 1.3406 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3406 2023/07/24 21:20:06 - mmengine - INFO - Epoch(train) [23][820/940] lr: 1.0000e-02 eta: 22:14:28 time: 1.1025 data_time: 0.0128 memory: 15768 grad_norm: 3.7306 loss: 1.2623 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2623 2023/07/24 21:20:28 - mmengine - INFO - Epoch(train) [23][840/940] lr: 1.0000e-02 eta: 22:14:06 time: 1.1009 data_time: 0.0128 memory: 15768 grad_norm: 3.7321 loss: 1.4644 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4644 2023/07/24 21:20:50 - mmengine - INFO - Epoch(train) [23][860/940] lr: 1.0000e-02 eta: 22:13:44 time: 1.1023 data_time: 0.0126 memory: 15768 grad_norm: 3.6637 loss: 1.3345 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3345 2023/07/24 21:21:12 - mmengine - INFO - Epoch(train) [23][880/940] lr: 1.0000e-02 eta: 22:13:21 time: 1.1001 data_time: 0.0128 memory: 15768 grad_norm: 3.6786 loss: 1.2891 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2891 2023/07/24 21:21:34 - mmengine - INFO - Epoch(train) [23][900/940] lr: 1.0000e-02 eta: 22:12:59 time: 1.1017 data_time: 0.0133 memory: 15768 grad_norm: 3.7369 loss: 1.3903 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3903 2023/07/24 21:21:56 - mmengine - INFO - Epoch(train) [23][920/940] lr: 1.0000e-02 eta: 22:12:37 time: 1.1020 data_time: 0.0132 memory: 15768 grad_norm: 3.7188 loss: 1.5823 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5823 2023/07/24 21:22:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 21:22:17 - mmengine - INFO - Epoch(train) [23][940/940] lr: 1.0000e-02 eta: 22:12:12 time: 1.0555 data_time: 0.0123 memory: 15768 grad_norm: 3.9248 loss: 1.4681 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4681 2023/07/24 21:22:27 - mmengine - INFO - Epoch(val) [23][20/78] eta: 0:00:27 time: 0.4746 data_time: 0.3176 memory: 2147 2023/07/24 21:22:34 - mmengine - INFO - Epoch(val) [23][40/78] eta: 0:00:15 time: 0.3487 data_time: 0.1920 memory: 2147 2023/07/24 21:22:42 - mmengine - INFO - Epoch(val) [23][60/78] eta: 0:00:07 time: 0.4375 data_time: 0.2809 memory: 2147 2023/07/24 21:22:54 - mmengine - INFO - Epoch(val) [23][78/78] acc/top1: 0.6559 acc/top5: 0.8681 acc/mean1: 0.6557 data_time: 0.2385 time: 0.3926 2023/07/24 21:23:20 - mmengine - INFO - Epoch(train) [24][ 20/940] lr: 1.0000e-02 eta: 22:12:02 time: 1.2962 data_time: 0.1471 memory: 15768 grad_norm: 3.5960 loss: 1.1519 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1519 2023/07/24 21:23:42 - mmengine - INFO - Epoch(train) [24][ 40/940] lr: 1.0000e-02 eta: 22:11:40 time: 1.1001 data_time: 0.0126 memory: 15768 grad_norm: 3.6667 loss: 1.2215 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2215 2023/07/24 21:24:04 - mmengine - INFO - Epoch(train) [24][ 60/940] lr: 1.0000e-02 eta: 22:11:18 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 3.6110 loss: 1.4362 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4362 2023/07/24 21:24:27 - mmengine - INFO - Epoch(train) [24][ 80/940] lr: 1.0000e-02 eta: 22:10:56 time: 1.1052 data_time: 0.0127 memory: 15768 grad_norm: 3.6649 loss: 1.2659 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2659 2023/07/24 21:24:49 - mmengine - INFO - Epoch(train) [24][100/940] lr: 1.0000e-02 eta: 22:10:33 time: 1.1015 data_time: 0.0128 memory: 15768 grad_norm: 3.6803 loss: 1.4265 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4265 2023/07/24 21:25:11 - mmengine - INFO - Epoch(train) [24][120/940] lr: 1.0000e-02 eta: 22:10:11 time: 1.1033 data_time: 0.0126 memory: 15768 grad_norm: 3.6428 loss: 1.5745 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.5745 2023/07/24 21:25:33 - mmengine - INFO - Epoch(train) [24][140/940] lr: 1.0000e-02 eta: 22:09:49 time: 1.1046 data_time: 0.0127 memory: 15768 grad_norm: 3.6755 loss: 1.3763 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3763 2023/07/24 21:25:55 - mmengine - INFO - Epoch(train) [24][160/940] lr: 1.0000e-02 eta: 22:09:27 time: 1.1002 data_time: 0.0127 memory: 15768 grad_norm: 3.6498 loss: 1.4506 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4506 2023/07/24 21:26:17 - mmengine - INFO - Epoch(train) [24][180/940] lr: 1.0000e-02 eta: 22:09:04 time: 1.1000 data_time: 0.0127 memory: 15768 grad_norm: 3.6920 loss: 1.4149 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4149 2023/07/24 21:26:39 - mmengine - INFO - Epoch(train) [24][200/940] lr: 1.0000e-02 eta: 22:08:42 time: 1.1039 data_time: 0.0127 memory: 15768 grad_norm: 3.6824 loss: 1.3500 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3500 2023/07/24 21:27:01 - mmengine - INFO - Epoch(train) [24][220/940] lr: 1.0000e-02 eta: 22:08:20 time: 1.1005 data_time: 0.0128 memory: 15768 grad_norm: 3.6971 loss: 1.6039 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6039 2023/07/24 21:27:23 - mmengine - INFO - Epoch(train) [24][240/940] lr: 1.0000e-02 eta: 22:07:58 time: 1.1020 data_time: 0.0125 memory: 15768 grad_norm: 3.5976 loss: 1.3953 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3953 2023/07/24 21:27:45 - mmengine - INFO - Epoch(train) [24][260/940] lr: 1.0000e-02 eta: 22:07:35 time: 1.1019 data_time: 0.0127 memory: 15768 grad_norm: 3.5891 loss: 1.4094 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4094 2023/07/24 21:28:07 - mmengine - INFO - Epoch(train) [24][280/940] lr: 1.0000e-02 eta: 22:07:13 time: 1.1020 data_time: 0.0126 memory: 15768 grad_norm: 3.7052 loss: 1.2033 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2033 2023/07/24 21:28:29 - mmengine - INFO - Epoch(train) [24][300/940] lr: 1.0000e-02 eta: 22:06:51 time: 1.1011 data_time: 0.0126 memory: 15768 grad_norm: 3.6779 loss: 1.3592 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3592 2023/07/24 21:28:51 - mmengine - INFO - Epoch(train) [24][320/940] lr: 1.0000e-02 eta: 22:06:29 time: 1.1039 data_time: 0.0126 memory: 15768 grad_norm: 3.7732 loss: 1.3884 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3884 2023/07/24 21:29:13 - mmengine - INFO - Epoch(train) [24][340/940] lr: 1.0000e-02 eta: 22:06:06 time: 1.1017 data_time: 0.0126 memory: 15768 grad_norm: 3.7224 loss: 1.3444 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3444 2023/07/24 21:29:35 - mmengine - INFO - Epoch(train) [24][360/940] lr: 1.0000e-02 eta: 22:05:44 time: 1.0998 data_time: 0.0126 memory: 15768 grad_norm: 3.6579 loss: 1.4452 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4452 2023/07/24 21:29:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 21:29:57 - mmengine - INFO - Epoch(train) [24][380/940] lr: 1.0000e-02 eta: 22:05:22 time: 1.1036 data_time: 0.0126 memory: 15768 grad_norm: 3.6406 loss: 1.3645 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3645 2023/07/24 21:30:19 - mmengine - INFO - Epoch(train) [24][400/940] lr: 1.0000e-02 eta: 22:04:59 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 3.7958 loss: 1.1815 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1815 2023/07/24 21:30:41 - mmengine - INFO - Epoch(train) [24][420/940] lr: 1.0000e-02 eta: 22:04:37 time: 1.1046 data_time: 0.0134 memory: 15768 grad_norm: 3.7524 loss: 1.3751 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3751 2023/07/24 21:31:03 - mmengine - INFO - Epoch(train) [24][440/940] lr: 1.0000e-02 eta: 22:04:15 time: 1.1008 data_time: 0.0128 memory: 15768 grad_norm: 3.6942 loss: 1.2352 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2352 2023/07/24 21:31:25 - mmengine - INFO - Epoch(train) [24][460/940] lr: 1.0000e-02 eta: 22:03:53 time: 1.0999 data_time: 0.0127 memory: 15768 grad_norm: 3.7200 loss: 1.2817 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2817 2023/07/24 21:31:47 - mmengine - INFO - Epoch(train) [24][480/940] lr: 1.0000e-02 eta: 22:03:30 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 3.7064 loss: 1.3911 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.3911 2023/07/24 21:32:09 - mmengine - INFO - Epoch(train) [24][500/940] lr: 1.0000e-02 eta: 22:03:08 time: 1.1021 data_time: 0.0133 memory: 15768 grad_norm: 3.8605 loss: 1.5980 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5980 2023/07/24 21:32:31 - mmengine - INFO - Epoch(train) [24][520/940] lr: 1.0000e-02 eta: 22:02:46 time: 1.1015 data_time: 0.0134 memory: 15768 grad_norm: 3.7578 loss: 1.3309 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3309 2023/07/24 21:32:53 - mmengine - INFO - Epoch(train) [24][540/940] lr: 1.0000e-02 eta: 22:02:23 time: 1.0999 data_time: 0.0136 memory: 15768 grad_norm: 3.7736 loss: 1.2789 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2789 2023/07/24 21:33:15 - mmengine - INFO - Epoch(train) [24][560/940] lr: 1.0000e-02 eta: 22:02:01 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 3.7730 loss: 1.3449 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.3449 2023/07/24 21:33:37 - mmengine - INFO - Epoch(train) [24][580/940] lr: 1.0000e-02 eta: 22:01:39 time: 1.1029 data_time: 0.0131 memory: 15768 grad_norm: 3.7517 loss: 1.4602 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4602 2023/07/24 21:34:00 - mmengine - INFO - Epoch(train) [24][600/940] lr: 1.0000e-02 eta: 22:01:17 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 3.6818 loss: 1.4506 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4506 2023/07/24 21:34:22 - mmengine - INFO - Epoch(train) [24][620/940] lr: 1.0000e-02 eta: 22:00:54 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 3.7042 loss: 1.4391 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4391 2023/07/24 21:34:44 - mmengine - INFO - Epoch(train) [24][640/940] lr: 1.0000e-02 eta: 22:00:32 time: 1.1009 data_time: 0.0134 memory: 15768 grad_norm: 3.6944 loss: 1.5230 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5230 2023/07/24 21:35:06 - mmengine - INFO - Epoch(train) [24][660/940] lr: 1.0000e-02 eta: 22:00:10 time: 1.1021 data_time: 0.0138 memory: 15768 grad_norm: 3.7337 loss: 1.3482 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3482 2023/07/24 21:35:28 - mmengine - INFO - Epoch(train) [24][680/940] lr: 1.0000e-02 eta: 21:59:47 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 3.6806 loss: 1.3810 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3810 2023/07/24 21:35:50 - mmengine - INFO - Epoch(train) [24][700/940] lr: 1.0000e-02 eta: 21:59:25 time: 1.1002 data_time: 0.0135 memory: 15768 grad_norm: 3.6865 loss: 1.3830 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3830 2023/07/24 21:36:12 - mmengine - INFO - Epoch(train) [24][720/940] lr: 1.0000e-02 eta: 21:59:03 time: 1.1007 data_time: 0.0134 memory: 15768 grad_norm: 3.7102 loss: 1.1963 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1963 2023/07/24 21:36:34 - mmengine - INFO - Epoch(train) [24][740/940] lr: 1.0000e-02 eta: 21:58:41 time: 1.1039 data_time: 0.0129 memory: 15768 grad_norm: 3.7673 loss: 1.2491 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2491 2023/07/24 21:36:56 - mmengine - INFO - Epoch(train) [24][760/940] lr: 1.0000e-02 eta: 21:58:18 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.7280 loss: 1.5119 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5119 2023/07/24 21:37:18 - mmengine - INFO - Epoch(train) [24][780/940] lr: 1.0000e-02 eta: 21:57:56 time: 1.1004 data_time: 0.0126 memory: 15768 grad_norm: 3.7431 loss: 1.3937 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3937 2023/07/24 21:37:40 - mmengine - INFO - Epoch(train) [24][800/940] lr: 1.0000e-02 eta: 21:57:34 time: 1.1001 data_time: 0.0126 memory: 15768 grad_norm: 3.7026 loss: 1.4070 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4070 2023/07/24 21:38:02 - mmengine - INFO - Epoch(train) [24][820/940] lr: 1.0000e-02 eta: 21:57:11 time: 1.1047 data_time: 0.0133 memory: 15768 grad_norm: 3.7045 loss: 1.3121 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3121 2023/07/24 21:38:24 - mmengine - INFO - Epoch(train) [24][840/940] lr: 1.0000e-02 eta: 21:56:49 time: 1.1026 data_time: 0.0128 memory: 15768 grad_norm: 3.7059 loss: 1.2932 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2932 2023/07/24 21:38:46 - mmengine - INFO - Epoch(train) [24][860/940] lr: 1.0000e-02 eta: 21:56:27 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.7566 loss: 1.3678 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3678 2023/07/24 21:39:08 - mmengine - INFO - Epoch(train) [24][880/940] lr: 1.0000e-02 eta: 21:56:05 time: 1.1019 data_time: 0.0133 memory: 15768 grad_norm: 3.6760 loss: 1.3534 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3534 2023/07/24 21:39:30 - mmengine - INFO - Epoch(train) [24][900/940] lr: 1.0000e-02 eta: 21:55:42 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 3.7944 loss: 1.4054 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4054 2023/07/24 21:39:52 - mmengine - INFO - Epoch(train) [24][920/940] lr: 1.0000e-02 eta: 21:55:20 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.7380 loss: 1.4219 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4219 2023/07/24 21:40:13 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 21:40:13 - mmengine - INFO - Epoch(train) [24][940/940] lr: 1.0000e-02 eta: 21:54:55 time: 1.0567 data_time: 0.0124 memory: 15768 grad_norm: 3.8974 loss: 1.4845 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.4845 2023/07/24 21:40:13 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/07/24 21:40:24 - mmengine - INFO - Epoch(val) [24][20/78] eta: 0:00:27 time: 0.4809 data_time: 0.3239 memory: 2147 2023/07/24 21:40:31 - mmengine - INFO - Epoch(val) [24][40/78] eta: 0:00:15 time: 0.3430 data_time: 0.1863 memory: 2147 2023/07/24 21:40:40 - mmengine - INFO - Epoch(val) [24][60/78] eta: 0:00:07 time: 0.4469 data_time: 0.2906 memory: 2147 2023/07/24 21:40:49 - mmengine - INFO - Epoch(val) [24][78/78] acc/top1: 0.6531 acc/top5: 0.8713 acc/mean1: 0.6529 data_time: 0.2400 time: 0.3938 2023/07/24 21:41:15 - mmengine - INFO - Epoch(train) [25][ 20/940] lr: 1.0000e-02 eta: 21:54:45 time: 1.2930 data_time: 0.1373 memory: 15768 grad_norm: 3.5943 loss: 1.3176 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3176 2023/07/24 21:41:37 - mmengine - INFO - Epoch(train) [25][ 40/940] lr: 1.0000e-02 eta: 21:54:23 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.6306 loss: 1.2443 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2443 2023/07/24 21:41:59 - mmengine - INFO - Epoch(train) [25][ 60/940] lr: 1.0000e-02 eta: 21:54:00 time: 1.1044 data_time: 0.0130 memory: 15768 grad_norm: 3.7362 loss: 1.4798 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4798 2023/07/24 21:42:21 - mmengine - INFO - Epoch(train) [25][ 80/940] lr: 1.0000e-02 eta: 21:53:38 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 3.6627 loss: 1.2206 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2206 2023/07/24 21:42:43 - mmengine - INFO - Epoch(train) [25][100/940] lr: 1.0000e-02 eta: 21:53:16 time: 1.1021 data_time: 0.0127 memory: 15768 grad_norm: 3.6724 loss: 1.3224 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3224 2023/07/24 21:43:06 - mmengine - INFO - Epoch(train) [25][120/940] lr: 1.0000e-02 eta: 21:52:56 time: 1.1373 data_time: 0.0130 memory: 15768 grad_norm: 3.6338 loss: 1.5074 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5074 2023/07/24 21:43:29 - mmengine - INFO - Epoch(train) [25][140/940] lr: 1.0000e-02 eta: 21:52:37 time: 1.1576 data_time: 0.0128 memory: 15768 grad_norm: 3.6827 loss: 1.2779 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2779 2023/07/24 21:43:52 - mmengine - INFO - Epoch(train) [25][160/940] lr: 1.0000e-02 eta: 21:52:19 time: 1.1644 data_time: 0.0122 memory: 15768 grad_norm: 3.7059 loss: 1.3418 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3418 2023/07/24 21:44:14 - mmengine - INFO - Epoch(train) [25][180/940] lr: 1.0000e-02 eta: 21:51:57 time: 1.1018 data_time: 0.0127 memory: 15768 grad_norm: 3.6916 loss: 1.2908 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2908 2023/07/24 21:44:37 - mmengine - INFO - Epoch(train) [25][200/940] lr: 1.0000e-02 eta: 21:51:34 time: 1.1017 data_time: 0.0127 memory: 15768 grad_norm: 3.6623 loss: 1.2648 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2648 2023/07/24 21:44:59 - mmengine - INFO - Epoch(train) [25][220/940] lr: 1.0000e-02 eta: 21:51:12 time: 1.1006 data_time: 0.0132 memory: 15768 grad_norm: 3.6688 loss: 1.3097 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3097 2023/07/24 21:45:21 - mmengine - INFO - Epoch(train) [25][240/940] lr: 1.0000e-02 eta: 21:50:50 time: 1.1022 data_time: 0.0129 memory: 15768 grad_norm: 3.6842 loss: 1.3298 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.3298 2023/07/24 21:45:43 - mmengine - INFO - Epoch(train) [25][260/940] lr: 1.0000e-02 eta: 21:50:27 time: 1.0993 data_time: 0.0128 memory: 15768 grad_norm: 3.6799 loss: 1.3032 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3032 2023/07/24 21:46:05 - mmengine - INFO - Epoch(train) [25][280/940] lr: 1.0000e-02 eta: 21:50:05 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 3.6882 loss: 1.4108 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4108 2023/07/24 21:46:27 - mmengine - INFO - Epoch(train) [25][300/940] lr: 1.0000e-02 eta: 21:49:43 time: 1.0993 data_time: 0.0129 memory: 15768 grad_norm: 3.6676 loss: 1.4583 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4583 2023/07/24 21:46:49 - mmengine - INFO - Epoch(train) [25][320/940] lr: 1.0000e-02 eta: 21:49:20 time: 1.1037 data_time: 0.0128 memory: 15768 grad_norm: 3.7176 loss: 1.3844 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3844 2023/07/24 21:47:11 - mmengine - INFO - Epoch(train) [25][340/940] lr: 1.0000e-02 eta: 21:48:58 time: 1.1005 data_time: 0.0127 memory: 15768 grad_norm: 3.7647 loss: 1.4728 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4728 2023/07/24 21:47:33 - mmengine - INFO - Epoch(train) [25][360/940] lr: 1.0000e-02 eta: 21:48:36 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 3.7685 loss: 1.3108 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3108 2023/07/24 21:47:55 - mmengine - INFO - Epoch(train) [25][380/940] lr: 1.0000e-02 eta: 21:48:13 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.7602 loss: 1.3875 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3875 2023/07/24 21:48:17 - mmengine - INFO - Epoch(train) [25][400/940] lr: 1.0000e-02 eta: 21:47:51 time: 1.1016 data_time: 0.0130 memory: 15768 grad_norm: 3.6902 loss: 1.2959 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2959 2023/07/24 21:48:39 - mmengine - INFO - Epoch(train) [25][420/940] lr: 1.0000e-02 eta: 21:47:29 time: 1.1039 data_time: 0.0133 memory: 15768 grad_norm: 3.6500 loss: 1.4035 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4035 2023/07/24 21:49:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 21:49:01 - mmengine - INFO - Epoch(train) [25][440/940] lr: 1.0000e-02 eta: 21:47:07 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 3.7129 loss: 1.4862 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4862 2023/07/24 21:49:23 - mmengine - INFO - Epoch(train) [25][460/940] lr: 1.0000e-02 eta: 21:46:44 time: 1.1027 data_time: 0.0129 memory: 15768 grad_norm: 3.7092 loss: 1.3751 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3751 2023/07/24 21:49:45 - mmengine - INFO - Epoch(train) [25][480/940] lr: 1.0000e-02 eta: 21:46:22 time: 1.1029 data_time: 0.0127 memory: 15768 grad_norm: 3.7348 loss: 1.2762 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2762 2023/07/24 21:50:07 - mmengine - INFO - Epoch(train) [25][500/940] lr: 1.0000e-02 eta: 21:46:00 time: 1.1041 data_time: 0.0128 memory: 15768 grad_norm: 3.7487 loss: 1.4644 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4644 2023/07/24 21:50:29 - mmengine - INFO - Epoch(train) [25][520/940] lr: 1.0000e-02 eta: 21:45:38 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.7805 loss: 1.3350 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3350 2023/07/24 21:50:51 - mmengine - INFO - Epoch(train) [25][540/940] lr: 1.0000e-02 eta: 21:45:15 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.7051 loss: 1.4191 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4191 2023/07/24 21:51:13 - mmengine - INFO - Epoch(train) [25][560/940] lr: 1.0000e-02 eta: 21:44:53 time: 1.1022 data_time: 0.0131 memory: 15768 grad_norm: 3.7689 loss: 1.3881 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.3881 2023/07/24 21:51:35 - mmengine - INFO - Epoch(train) [25][580/940] lr: 1.0000e-02 eta: 21:44:31 time: 1.0994 data_time: 0.0131 memory: 15768 grad_norm: 3.7063 loss: 1.4115 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4115 2023/07/24 21:51:57 - mmengine - INFO - Epoch(train) [25][600/940] lr: 1.0000e-02 eta: 21:44:09 time: 1.1048 data_time: 0.0132 memory: 15768 grad_norm: 3.7146 loss: 1.4133 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4133 2023/07/24 21:52:19 - mmengine - INFO - Epoch(train) [25][620/940] lr: 1.0000e-02 eta: 21:43:46 time: 1.1025 data_time: 0.0131 memory: 15768 grad_norm: 3.7574 loss: 1.4477 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4477 2023/07/24 21:52:41 - mmengine - INFO - Epoch(train) [25][640/940] lr: 1.0000e-02 eta: 21:43:24 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 3.6485 loss: 1.4080 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4080 2023/07/24 21:53:03 - mmengine - INFO - Epoch(train) [25][660/940] lr: 1.0000e-02 eta: 21:43:02 time: 1.1024 data_time: 0.0133 memory: 15768 grad_norm: 3.6196 loss: 1.2380 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2380 2023/07/24 21:53:25 - mmengine - INFO - Epoch(train) [25][680/940] lr: 1.0000e-02 eta: 21:42:39 time: 1.1008 data_time: 0.0131 memory: 15768 grad_norm: 3.6061 loss: 1.3583 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3583 2023/07/24 21:53:47 - mmengine - INFO - Epoch(train) [25][700/940] lr: 1.0000e-02 eta: 21:42:17 time: 1.1005 data_time: 0.0127 memory: 15768 grad_norm: 3.6890 loss: 1.3077 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3077 2023/07/24 21:54:09 - mmengine - INFO - Epoch(train) [25][720/940] lr: 1.0000e-02 eta: 21:41:55 time: 1.1013 data_time: 0.0133 memory: 15768 grad_norm: 3.6829 loss: 1.2173 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2173 2023/07/24 21:54:31 - mmengine - INFO - Epoch(train) [25][740/940] lr: 1.0000e-02 eta: 21:41:32 time: 1.1000 data_time: 0.0134 memory: 15768 grad_norm: 3.6775 loss: 1.2477 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2477 2023/07/24 21:54:53 - mmengine - INFO - Epoch(train) [25][760/940] lr: 1.0000e-02 eta: 21:41:10 time: 1.1026 data_time: 0.0128 memory: 15768 grad_norm: 3.6642 loss: 1.2687 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2687 2023/07/24 21:55:15 - mmengine - INFO - Epoch(train) [25][780/940] lr: 1.0000e-02 eta: 21:40:48 time: 1.1004 data_time: 0.0126 memory: 15768 grad_norm: 3.6586 loss: 1.2760 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2760 2023/07/24 21:55:37 - mmengine - INFO - Epoch(train) [25][800/940] lr: 1.0000e-02 eta: 21:40:26 time: 1.1036 data_time: 0.0129 memory: 15768 grad_norm: 3.7972 loss: 1.3480 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3480 2023/07/24 21:55:59 - mmengine - INFO - Epoch(train) [25][820/940] lr: 1.0000e-02 eta: 21:40:03 time: 1.1014 data_time: 0.0126 memory: 15768 grad_norm: 3.7446 loss: 1.2715 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2715 2023/07/24 21:56:21 - mmengine - INFO - Epoch(train) [25][840/940] lr: 1.0000e-02 eta: 21:39:41 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 3.7498 loss: 1.2623 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2623 2023/07/24 21:56:44 - mmengine - INFO - Epoch(train) [25][860/940] lr: 1.0000e-02 eta: 21:39:19 time: 1.1023 data_time: 0.0129 memory: 15768 grad_norm: 3.7594 loss: 1.2815 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2815 2023/07/24 21:57:06 - mmengine - INFO - Epoch(train) [25][880/940] lr: 1.0000e-02 eta: 21:38:57 time: 1.1020 data_time: 0.0127 memory: 15768 grad_norm: 3.7845 loss: 1.4913 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4913 2023/07/24 21:57:28 - mmengine - INFO - Epoch(train) [25][900/940] lr: 1.0000e-02 eta: 21:38:34 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 3.7416 loss: 1.2337 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2337 2023/07/24 21:57:50 - mmengine - INFO - Epoch(train) [25][920/940] lr: 1.0000e-02 eta: 21:38:12 time: 1.0996 data_time: 0.0130 memory: 15768 grad_norm: 3.6400 loss: 1.2569 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2569 2023/07/24 21:58:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 21:58:11 - mmengine - INFO - Epoch(train) [25][940/940] lr: 1.0000e-02 eta: 21:37:47 time: 1.0549 data_time: 0.0124 memory: 15768 grad_norm: 3.9124 loss: 1.5290 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 1.5290 2023/07/24 21:58:21 - mmengine - INFO - Epoch(val) [25][20/78] eta: 0:00:28 time: 0.4913 data_time: 0.3340 memory: 2147 2023/07/24 21:58:27 - mmengine - INFO - Epoch(val) [25][40/78] eta: 0:00:15 time: 0.3435 data_time: 0.1870 memory: 2147 2023/07/24 21:58:36 - mmengine - INFO - Epoch(val) [25][60/78] eta: 0:00:07 time: 0.4427 data_time: 0.2862 memory: 2147 2023/07/24 21:58:47 - mmengine - INFO - Epoch(val) [25][78/78] acc/top1: 0.6677 acc/top5: 0.8738 acc/mean1: 0.6674 data_time: 0.2457 time: 0.3996 2023/07/24 21:59:13 - mmengine - INFO - Epoch(train) [26][ 20/940] lr: 1.0000e-02 eta: 21:37:36 time: 1.2986 data_time: 0.1448 memory: 15768 grad_norm: 3.6949 loss: 1.4421 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4421 2023/07/24 21:59:35 - mmengine - INFO - Epoch(train) [26][ 40/940] lr: 1.0000e-02 eta: 21:37:14 time: 1.1007 data_time: 0.0132 memory: 15768 grad_norm: 3.6900 loss: 1.3649 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3649 2023/07/24 21:59:57 - mmengine - INFO - Epoch(train) [26][ 60/940] lr: 1.0000e-02 eta: 21:36:52 time: 1.1027 data_time: 0.0132 memory: 15768 grad_norm: 3.7576 loss: 1.4601 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.4601 2023/07/24 22:00:19 - mmengine - INFO - Epoch(train) [26][ 80/940] lr: 1.0000e-02 eta: 21:36:30 time: 1.1015 data_time: 0.0130 memory: 15768 grad_norm: 3.6493 loss: 1.3016 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.3016 2023/07/24 22:00:41 - mmengine - INFO - Epoch(train) [26][100/940] lr: 1.0000e-02 eta: 21:36:07 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.7046 loss: 1.4590 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4590 2023/07/24 22:01:03 - mmengine - INFO - Epoch(train) [26][120/940] lr: 1.0000e-02 eta: 21:35:45 time: 1.1007 data_time: 0.0128 memory: 15768 grad_norm: 3.6949 loss: 1.2750 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2750 2023/07/24 22:01:25 - mmengine - INFO - Epoch(train) [26][140/940] lr: 1.0000e-02 eta: 21:35:23 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.7232 loss: 1.1640 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1640 2023/07/24 22:01:47 - mmengine - INFO - Epoch(train) [26][160/940] lr: 1.0000e-02 eta: 21:35:00 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.7648 loss: 1.4236 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4236 2023/07/24 22:02:09 - mmengine - INFO - Epoch(train) [26][180/940] lr: 1.0000e-02 eta: 21:34:38 time: 1.1025 data_time: 0.0126 memory: 15768 grad_norm: 3.7329 loss: 1.5433 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5433 2023/07/24 22:02:31 - mmengine - INFO - Epoch(train) [26][200/940] lr: 1.0000e-02 eta: 21:34:16 time: 1.0986 data_time: 0.0127 memory: 15768 grad_norm: 3.7327 loss: 1.2507 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2507 2023/07/24 22:02:53 - mmengine - INFO - Epoch(train) [26][220/940] lr: 1.0000e-02 eta: 21:33:53 time: 1.1021 data_time: 0.0131 memory: 15768 grad_norm: 3.7245 loss: 1.3881 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3881 2023/07/24 22:03:15 - mmengine - INFO - Epoch(train) [26][240/940] lr: 1.0000e-02 eta: 21:33:31 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.7042 loss: 1.2795 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2795 2023/07/24 22:03:37 - mmengine - INFO - Epoch(train) [26][260/940] lr: 1.0000e-02 eta: 21:33:09 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.7592 loss: 1.1300 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1300 2023/07/24 22:03:59 - mmengine - INFO - Epoch(train) [26][280/940] lr: 1.0000e-02 eta: 21:32:46 time: 1.1014 data_time: 0.0130 memory: 15768 grad_norm: 3.7138 loss: 1.3273 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3273 2023/07/24 22:04:21 - mmengine - INFO - Epoch(train) [26][300/940] lr: 1.0000e-02 eta: 21:32:24 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 3.7422 loss: 1.2797 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2797 2023/07/24 22:04:43 - mmengine - INFO - Epoch(train) [26][320/940] lr: 1.0000e-02 eta: 21:32:02 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 3.6572 loss: 1.4496 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4496 2023/07/24 22:05:05 - mmengine - INFO - Epoch(train) [26][340/940] lr: 1.0000e-02 eta: 21:31:39 time: 1.0989 data_time: 0.0129 memory: 15768 grad_norm: 3.7546 loss: 1.3367 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3367 2023/07/24 22:05:27 - mmengine - INFO - Epoch(train) [26][360/940] lr: 1.0000e-02 eta: 21:31:17 time: 1.1014 data_time: 0.0130 memory: 15768 grad_norm: 3.7176 loss: 1.4517 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4517 2023/07/24 22:05:49 - mmengine - INFO - Epoch(train) [26][380/940] lr: 1.0000e-02 eta: 21:30:55 time: 1.1024 data_time: 0.0133 memory: 15768 grad_norm: 3.7727 loss: 1.4883 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.4883 2023/07/24 22:06:11 - mmengine - INFO - Epoch(train) [26][400/940] lr: 1.0000e-02 eta: 21:30:33 time: 1.1036 data_time: 0.0133 memory: 15768 grad_norm: 3.7542 loss: 1.2061 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2061 2023/07/24 22:06:33 - mmengine - INFO - Epoch(train) [26][420/940] lr: 1.0000e-02 eta: 21:30:10 time: 1.1013 data_time: 0.0128 memory: 15768 grad_norm: 3.6995 loss: 1.3111 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3111 2023/07/24 22:06:55 - mmengine - INFO - Epoch(train) [26][440/940] lr: 1.0000e-02 eta: 21:29:48 time: 1.1001 data_time: 0.0126 memory: 15768 grad_norm: 3.7506 loss: 1.3048 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3048 2023/07/24 22:07:17 - mmengine - INFO - Epoch(train) [26][460/940] lr: 1.0000e-02 eta: 21:29:26 time: 1.1001 data_time: 0.0138 memory: 15768 grad_norm: 3.7948 loss: 1.4381 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4381 2023/07/24 22:07:39 - mmengine - INFO - Epoch(train) [26][480/940] lr: 1.0000e-02 eta: 21:29:03 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 3.6956 loss: 1.4115 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4115 2023/07/24 22:08:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 22:08:01 - mmengine - INFO - Epoch(train) [26][500/940] lr: 1.0000e-02 eta: 21:28:41 time: 1.1016 data_time: 0.0127 memory: 15768 grad_norm: 3.6969 loss: 1.1495 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1495 2023/07/24 22:08:23 - mmengine - INFO - Epoch(train) [26][520/940] lr: 1.0000e-02 eta: 21:28:19 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 3.7209 loss: 1.3399 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3399 2023/07/24 22:08:45 - mmengine - INFO - Epoch(train) [26][540/940] lr: 1.0000e-02 eta: 21:27:57 time: 1.1027 data_time: 0.0129 memory: 15768 grad_norm: 3.7643 loss: 1.3251 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3251 2023/07/24 22:09:07 - mmengine - INFO - Epoch(train) [26][560/940] lr: 1.0000e-02 eta: 21:27:34 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.7774 loss: 1.3282 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3282 2023/07/24 22:09:29 - mmengine - INFO - Epoch(train) [26][580/940] lr: 1.0000e-02 eta: 21:27:12 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 3.6956 loss: 1.4478 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4478 2023/07/24 22:09:51 - mmengine - INFO - Epoch(train) [26][600/940] lr: 1.0000e-02 eta: 21:26:50 time: 1.1005 data_time: 0.0128 memory: 15768 grad_norm: 3.7194 loss: 1.3794 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3794 2023/07/24 22:10:13 - mmengine - INFO - Epoch(train) [26][620/940] lr: 1.0000e-02 eta: 21:26:27 time: 1.1007 data_time: 0.0133 memory: 15768 grad_norm: 3.8378 loss: 1.5290 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5290 2023/07/24 22:10:35 - mmengine - INFO - Epoch(train) [26][640/940] lr: 1.0000e-02 eta: 21:26:05 time: 1.1024 data_time: 0.0132 memory: 15768 grad_norm: 3.7309 loss: 1.1379 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1379 2023/07/24 22:10:57 - mmengine - INFO - Epoch(train) [26][660/940] lr: 1.0000e-02 eta: 21:25:43 time: 1.1019 data_time: 0.0134 memory: 15768 grad_norm: 3.7788 loss: 1.3809 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3809 2023/07/24 22:11:19 - mmengine - INFO - Epoch(train) [26][680/940] lr: 1.0000e-02 eta: 21:25:21 time: 1.1015 data_time: 0.0133 memory: 15768 grad_norm: 3.8824 loss: 1.2519 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2519 2023/07/24 22:11:41 - mmengine - INFO - Epoch(train) [26][700/940] lr: 1.0000e-02 eta: 21:24:58 time: 1.1030 data_time: 0.0133 memory: 15768 grad_norm: 3.7672 loss: 1.3668 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.3668 2023/07/24 22:12:03 - mmengine - INFO - Epoch(train) [26][720/940] lr: 1.0000e-02 eta: 21:24:36 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.7256 loss: 1.1913 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1913 2023/07/24 22:12:26 - mmengine - INFO - Epoch(train) [26][740/940] lr: 1.0000e-02 eta: 21:24:14 time: 1.1027 data_time: 0.0130 memory: 15768 grad_norm: 3.7844 loss: 1.5356 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5356 2023/07/24 22:12:48 - mmengine - INFO - Epoch(train) [26][760/940] lr: 1.0000e-02 eta: 21:23:52 time: 1.1016 data_time: 0.0132 memory: 15768 grad_norm: 3.8144 loss: 1.3445 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3445 2023/07/24 22:13:10 - mmengine - INFO - Epoch(train) [26][780/940] lr: 1.0000e-02 eta: 21:23:30 time: 1.1056 data_time: 0.0126 memory: 15768 grad_norm: 3.7252 loss: 1.1204 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1204 2023/07/24 22:13:32 - mmengine - INFO - Epoch(train) [26][800/940] lr: 1.0000e-02 eta: 21:23:07 time: 1.0976 data_time: 0.0131 memory: 15768 grad_norm: 3.7578 loss: 1.2164 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2164 2023/07/24 22:13:54 - mmengine - INFO - Epoch(train) [26][820/940] lr: 1.0000e-02 eta: 21:22:45 time: 1.0997 data_time: 0.0133 memory: 15768 grad_norm: 3.7086 loss: 1.3434 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3434 2023/07/24 22:14:16 - mmengine - INFO - Epoch(train) [26][840/940] lr: 1.0000e-02 eta: 21:22:22 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.7672 loss: 1.3736 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3736 2023/07/24 22:14:38 - mmengine - INFO - Epoch(train) [26][860/940] lr: 1.0000e-02 eta: 21:22:00 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.8192 loss: 1.2979 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2979 2023/07/24 22:15:00 - mmengine - INFO - Epoch(train) [26][880/940] lr: 1.0000e-02 eta: 21:21:38 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 3.6680 loss: 1.3603 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3603 2023/07/24 22:15:22 - mmengine - INFO - Epoch(train) [26][900/940] lr: 1.0000e-02 eta: 21:21:16 time: 1.1025 data_time: 0.0131 memory: 15768 grad_norm: 3.7740 loss: 1.2665 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2665 2023/07/24 22:15:44 - mmengine - INFO - Epoch(train) [26][920/940] lr: 1.0000e-02 eta: 21:20:53 time: 1.1018 data_time: 0.0135 memory: 15768 grad_norm: 3.7721 loss: 1.3830 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3830 2023/07/24 22:16:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 22:16:05 - mmengine - INFO - Epoch(train) [26][940/940] lr: 1.0000e-02 eta: 21:20:28 time: 1.0536 data_time: 0.0130 memory: 15768 grad_norm: 3.9658 loss: 1.5366 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.5366 2023/07/24 22:16:15 - mmengine - INFO - Epoch(val) [26][20/78] eta: 0:00:28 time: 0.4894 data_time: 0.3321 memory: 2147 2023/07/24 22:16:22 - mmengine - INFO - Epoch(val) [26][40/78] eta: 0:00:15 time: 0.3451 data_time: 0.1881 memory: 2147 2023/07/24 22:16:30 - mmengine - INFO - Epoch(val) [26][60/78] eta: 0:00:07 time: 0.4267 data_time: 0.2700 memory: 2147 2023/07/24 22:16:41 - mmengine - INFO - Epoch(val) [26][78/78] acc/top1: 0.6726 acc/top5: 0.8795 acc/mean1: 0.6724 data_time: 0.2407 time: 0.3949 2023/07/24 22:16:41 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_20.pth is removed 2023/07/24 22:16:42 - mmengine - INFO - The best checkpoint with 0.6726 acc/top1 at 26 epoch is saved to best_acc_top1_epoch_26.pth. 2023/07/24 22:17:07 - mmengine - INFO - Epoch(train) [27][ 20/940] lr: 1.0000e-02 eta: 21:20:16 time: 1.2688 data_time: 0.1742 memory: 15768 grad_norm: 3.7130 loss: 1.2761 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2761 2023/07/24 22:17:29 - mmengine - INFO - Epoch(train) [27][ 40/940] lr: 1.0000e-02 eta: 21:19:54 time: 1.1045 data_time: 0.0130 memory: 15768 grad_norm: 3.7468 loss: 1.2838 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2838 2023/07/24 22:17:51 - mmengine - INFO - Epoch(train) [27][ 60/940] lr: 1.0000e-02 eta: 21:19:31 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.7328 loss: 1.4206 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4206 2023/07/24 22:18:13 - mmengine - INFO - Epoch(train) [27][ 80/940] lr: 1.0000e-02 eta: 21:19:09 time: 1.0996 data_time: 0.0130 memory: 15768 grad_norm: 3.6916 loss: 1.3511 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3511 2023/07/24 22:18:35 - mmengine - INFO - Epoch(train) [27][100/940] lr: 1.0000e-02 eta: 21:18:46 time: 1.0978 data_time: 0.0133 memory: 15768 grad_norm: 3.8528 loss: 1.2997 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2997 2023/07/24 22:18:57 - mmengine - INFO - Epoch(train) [27][120/940] lr: 1.0000e-02 eta: 21:18:24 time: 1.0989 data_time: 0.0130 memory: 15768 grad_norm: 3.7384 loss: 1.3801 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3801 2023/07/24 22:19:19 - mmengine - INFO - Epoch(train) [27][140/940] lr: 1.0000e-02 eta: 21:18:02 time: 1.1002 data_time: 0.0132 memory: 15768 grad_norm: 3.7066 loss: 1.1142 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1142 2023/07/24 22:19:41 - mmengine - INFO - Epoch(train) [27][160/940] lr: 1.0000e-02 eta: 21:17:39 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.6703 loss: 1.3277 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3277 2023/07/24 22:20:03 - mmengine - INFO - Epoch(train) [27][180/940] lr: 1.0000e-02 eta: 21:17:17 time: 1.1017 data_time: 0.0131 memory: 15768 grad_norm: 3.7729 loss: 1.2326 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2326 2023/07/24 22:20:25 - mmengine - INFO - Epoch(train) [27][200/940] lr: 1.0000e-02 eta: 21:16:55 time: 1.0993 data_time: 0.0131 memory: 15768 grad_norm: 3.8329 loss: 1.2632 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2632 2023/07/24 22:20:47 - mmengine - INFO - Epoch(train) [27][220/940] lr: 1.0000e-02 eta: 21:16:32 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 3.7742 loss: 1.3801 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3801 2023/07/24 22:21:09 - mmengine - INFO - Epoch(train) [27][240/940] lr: 1.0000e-02 eta: 21:16:10 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.7164 loss: 1.2299 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2299 2023/07/24 22:21:31 - mmengine - INFO - Epoch(train) [27][260/940] lr: 1.0000e-02 eta: 21:15:48 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 3.7285 loss: 1.3129 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3129 2023/07/24 22:21:53 - mmengine - INFO - Epoch(train) [27][280/940] lr: 1.0000e-02 eta: 21:15:25 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 3.6620 loss: 1.2553 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2553 2023/07/24 22:22:15 - mmengine - INFO - Epoch(train) [27][300/940] lr: 1.0000e-02 eta: 21:15:03 time: 1.0978 data_time: 0.0130 memory: 15768 grad_norm: 3.7628 loss: 1.2865 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2865 2023/07/24 22:22:37 - mmengine - INFO - Epoch(train) [27][320/940] lr: 1.0000e-02 eta: 21:14:40 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 3.7872 loss: 1.4286 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4286 2023/07/24 22:22:59 - mmengine - INFO - Epoch(train) [27][340/940] lr: 1.0000e-02 eta: 21:14:18 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.7941 loss: 1.3485 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3485 2023/07/24 22:23:21 - mmengine - INFO - Epoch(train) [27][360/940] lr: 1.0000e-02 eta: 21:13:56 time: 1.1000 data_time: 0.0133 memory: 15768 grad_norm: 3.7518 loss: 1.3209 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3209 2023/07/24 22:23:43 - mmengine - INFO - Epoch(train) [27][380/940] lr: 1.0000e-02 eta: 21:13:33 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.7173 loss: 1.3296 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3296 2023/07/24 22:24:05 - mmengine - INFO - Epoch(train) [27][400/940] lr: 1.0000e-02 eta: 21:13:11 time: 1.1025 data_time: 0.0132 memory: 15768 grad_norm: 3.6977 loss: 1.3171 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3171 2023/07/24 22:24:27 - mmengine - INFO - Epoch(train) [27][420/940] lr: 1.0000e-02 eta: 21:12:49 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 3.7841 loss: 1.3309 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3309 2023/07/24 22:24:49 - mmengine - INFO - Epoch(train) [27][440/940] lr: 1.0000e-02 eta: 21:12:27 time: 1.1017 data_time: 0.0130 memory: 15768 grad_norm: 3.7359 loss: 1.2605 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2605 2023/07/24 22:25:11 - mmengine - INFO - Epoch(train) [27][460/940] lr: 1.0000e-02 eta: 21:12:04 time: 1.1010 data_time: 0.0129 memory: 15768 grad_norm: 3.8369 loss: 1.5136 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5136 2023/07/24 22:25:33 - mmengine - INFO - Epoch(train) [27][480/940] lr: 1.0000e-02 eta: 21:11:42 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.7680 loss: 1.1823 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1823 2023/07/24 22:25:55 - mmengine - INFO - Epoch(train) [27][500/940] lr: 1.0000e-02 eta: 21:11:20 time: 1.0993 data_time: 0.0130 memory: 15768 grad_norm: 3.7714 loss: 1.0998 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0998 2023/07/24 22:26:17 - mmengine - INFO - Epoch(train) [27][520/940] lr: 1.0000e-02 eta: 21:10:57 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 3.7642 loss: 1.2918 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2918 2023/07/24 22:26:39 - mmengine - INFO - Epoch(train) [27][540/940] lr: 1.0000e-02 eta: 21:10:35 time: 1.1045 data_time: 0.0130 memory: 15768 grad_norm: 3.8174 loss: 1.4774 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4774 2023/07/24 22:27:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 22:27:01 - mmengine - INFO - Epoch(train) [27][560/940] lr: 1.0000e-02 eta: 21:10:13 time: 1.1004 data_time: 0.0133 memory: 15768 grad_norm: 3.7573 loss: 1.6061 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6061 2023/07/24 22:27:23 - mmengine - INFO - Epoch(train) [27][580/940] lr: 1.0000e-02 eta: 21:09:51 time: 1.1022 data_time: 0.0131 memory: 15768 grad_norm: 3.7857 loss: 1.2300 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2300 2023/07/24 22:27:46 - mmengine - INFO - Epoch(train) [27][600/940] lr: 1.0000e-02 eta: 21:09:29 time: 1.1048 data_time: 0.0129 memory: 15768 grad_norm: 3.7253 loss: 1.3429 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3429 2023/07/24 22:28:08 - mmengine - INFO - Epoch(train) [27][620/940] lr: 1.0000e-02 eta: 21:09:06 time: 1.1014 data_time: 0.0131 memory: 15768 grad_norm: 3.7979 loss: 1.2343 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2343 2023/07/24 22:28:30 - mmengine - INFO - Epoch(train) [27][640/940] lr: 1.0000e-02 eta: 21:08:44 time: 1.1011 data_time: 0.0139 memory: 15768 grad_norm: 3.7518 loss: 1.4156 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4156 2023/07/24 22:28:52 - mmengine - INFO - Epoch(train) [27][660/940] lr: 1.0000e-02 eta: 21:08:22 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 3.6979 loss: 1.2996 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2996 2023/07/24 22:29:14 - mmengine - INFO - Epoch(train) [27][680/940] lr: 1.0000e-02 eta: 21:08:00 time: 1.1030 data_time: 0.0131 memory: 15768 grad_norm: 3.6861 loss: 1.1910 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.1910 2023/07/24 22:29:36 - mmengine - INFO - Epoch(train) [27][700/940] lr: 1.0000e-02 eta: 21:07:37 time: 1.0987 data_time: 0.0126 memory: 15768 grad_norm: 3.7320 loss: 1.3174 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3174 2023/07/24 22:29:58 - mmengine - INFO - Epoch(train) [27][720/940] lr: 1.0000e-02 eta: 21:07:15 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 3.7048 loss: 1.3782 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3782 2023/07/24 22:30:20 - mmengine - INFO - Epoch(train) [27][740/940] lr: 1.0000e-02 eta: 21:06:53 time: 1.1083 data_time: 0.0127 memory: 15768 grad_norm: 3.7763 loss: 1.3132 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3132 2023/07/24 22:30:42 - mmengine - INFO - Epoch(train) [27][760/940] lr: 1.0000e-02 eta: 21:06:31 time: 1.0997 data_time: 0.0132 memory: 15768 grad_norm: 3.7848 loss: 1.2836 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2836 2023/07/24 22:31:04 - mmengine - INFO - Epoch(train) [27][780/940] lr: 1.0000e-02 eta: 21:06:08 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 3.8066 loss: 1.2962 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2962 2023/07/24 22:31:26 - mmengine - INFO - Epoch(train) [27][800/940] lr: 1.0000e-02 eta: 21:05:46 time: 1.1027 data_time: 0.0129 memory: 15768 grad_norm: 3.7636 loss: 1.2746 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2746 2023/07/24 22:31:48 - mmengine - INFO - Epoch(train) [27][820/940] lr: 1.0000e-02 eta: 21:05:24 time: 1.1062 data_time: 0.0134 memory: 15768 grad_norm: 3.7860 loss: 1.2310 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2310 2023/07/24 22:32:10 - mmengine - INFO - Epoch(train) [27][840/940] lr: 1.0000e-02 eta: 21:05:02 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 3.7073 loss: 1.1809 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1809 2023/07/24 22:32:32 - mmengine - INFO - Epoch(train) [27][860/940] lr: 1.0000e-02 eta: 21:04:40 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.7399 loss: 1.5201 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5201 2023/07/24 22:32:54 - mmengine - INFO - Epoch(train) [27][880/940] lr: 1.0000e-02 eta: 21:04:17 time: 1.1026 data_time: 0.0129 memory: 15768 grad_norm: 3.7969 loss: 1.2945 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2945 2023/07/24 22:33:16 - mmengine - INFO - Epoch(train) [27][900/940] lr: 1.0000e-02 eta: 21:03:55 time: 1.1017 data_time: 0.0129 memory: 15768 grad_norm: 3.8614 loss: 1.5604 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5604 2023/07/24 22:33:38 - mmengine - INFO - Epoch(train) [27][920/940] lr: 1.0000e-02 eta: 21:03:33 time: 1.1032 data_time: 0.0130 memory: 15768 grad_norm: 3.7870 loss: 1.4043 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4043 2023/07/24 22:33:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 22:33:59 - mmengine - INFO - Epoch(train) [27][940/940] lr: 1.0000e-02 eta: 21:03:08 time: 1.0579 data_time: 0.0130 memory: 15768 grad_norm: 3.9743 loss: 1.3068 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3068 2023/07/24 22:33:59 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/07/24 22:34:10 - mmengine - INFO - Epoch(val) [27][20/78] eta: 0:00:27 time: 0.4758 data_time: 0.3192 memory: 2147 2023/07/24 22:34:17 - mmengine - INFO - Epoch(val) [27][40/78] eta: 0:00:15 time: 0.3447 data_time: 0.1879 memory: 2147 2023/07/24 22:34:26 - mmengine - INFO - Epoch(val) [27][60/78] eta: 0:00:07 time: 0.4312 data_time: 0.2745 memory: 2147 2023/07/24 22:34:35 - mmengine - INFO - Epoch(val) [27][78/78] acc/top1: 0.6581 acc/top5: 0.8705 acc/mean1: 0.6580 data_time: 0.2345 time: 0.3884 2023/07/24 22:35:01 - mmengine - INFO - Epoch(train) [28][ 20/940] lr: 1.0000e-02 eta: 21:02:56 time: 1.2922 data_time: 0.1528 memory: 15768 grad_norm: 3.7669 loss: 1.2419 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2419 2023/07/24 22:35:23 - mmengine - INFO - Epoch(train) [28][ 40/940] lr: 1.0000e-02 eta: 21:02:34 time: 1.1045 data_time: 0.0129 memory: 15768 grad_norm: 3.6983 loss: 1.3658 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3658 2023/07/24 22:35:45 - mmengine - INFO - Epoch(train) [28][ 60/940] lr: 1.0000e-02 eta: 21:02:12 time: 1.1030 data_time: 0.0129 memory: 15768 grad_norm: 3.7683 loss: 1.3981 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3981 2023/07/24 22:36:07 - mmengine - INFO - Epoch(train) [28][ 80/940] lr: 1.0000e-02 eta: 21:01:50 time: 1.1018 data_time: 0.0127 memory: 15768 grad_norm: 3.7034 loss: 1.3914 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3914 2023/07/24 22:36:29 - mmengine - INFO - Epoch(train) [28][100/940] lr: 1.0000e-02 eta: 21:01:28 time: 1.1025 data_time: 0.0128 memory: 15768 grad_norm: 3.8030 loss: 1.3514 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3514 2023/07/24 22:36:51 - mmengine - INFO - Epoch(train) [28][120/940] lr: 1.0000e-02 eta: 21:01:05 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 3.7479 loss: 1.3503 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3503 2023/07/24 22:37:13 - mmengine - INFO - Epoch(train) [28][140/940] lr: 1.0000e-02 eta: 21:00:43 time: 1.0999 data_time: 0.0126 memory: 15768 grad_norm: 3.7109 loss: 1.2609 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2609 2023/07/24 22:37:35 - mmengine - INFO - Epoch(train) [28][160/940] lr: 1.0000e-02 eta: 21:00:21 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.7315 loss: 1.2321 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2321 2023/07/24 22:37:57 - mmengine - INFO - Epoch(train) [28][180/940] lr: 1.0000e-02 eta: 20:59:58 time: 1.1008 data_time: 0.0126 memory: 15768 grad_norm: 3.7421 loss: 1.3825 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3825 2023/07/24 22:38:19 - mmengine - INFO - Epoch(train) [28][200/940] lr: 1.0000e-02 eta: 20:59:36 time: 1.0989 data_time: 0.0128 memory: 15768 grad_norm: 3.7455 loss: 1.2636 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2636 2023/07/24 22:38:42 - mmengine - INFO - Epoch(train) [28][220/940] lr: 1.0000e-02 eta: 20:59:14 time: 1.1029 data_time: 0.0125 memory: 15768 grad_norm: 3.7800 loss: 1.4129 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.4129 2023/07/24 22:39:04 - mmengine - INFO - Epoch(train) [28][240/940] lr: 1.0000e-02 eta: 20:58:52 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 3.8152 loss: 1.5705 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5705 2023/07/24 22:39:26 - mmengine - INFO - Epoch(train) [28][260/940] lr: 1.0000e-02 eta: 20:58:29 time: 1.1015 data_time: 0.0130 memory: 15768 grad_norm: 3.7245 loss: 1.4566 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4566 2023/07/24 22:39:48 - mmengine - INFO - Epoch(train) [28][280/940] lr: 1.0000e-02 eta: 20:58:07 time: 1.1021 data_time: 0.0129 memory: 15768 grad_norm: 3.7638 loss: 1.3607 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3607 2023/07/24 22:40:10 - mmengine - INFO - Epoch(train) [28][300/940] lr: 1.0000e-02 eta: 20:57:45 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.7886 loss: 1.3582 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3582 2023/07/24 22:40:32 - mmengine - INFO - Epoch(train) [28][320/940] lr: 1.0000e-02 eta: 20:57:22 time: 1.0982 data_time: 0.0128 memory: 15768 grad_norm: 3.8180 loss: 1.2177 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2177 2023/07/24 22:40:54 - mmengine - INFO - Epoch(train) [28][340/940] lr: 1.0000e-02 eta: 20:57:00 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.7511 loss: 1.2551 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2551 2023/07/24 22:41:16 - mmengine - INFO - Epoch(train) [28][360/940] lr: 1.0000e-02 eta: 20:56:38 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.7826 loss: 1.4129 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.4129 2023/07/24 22:41:38 - mmengine - INFO - Epoch(train) [28][380/940] lr: 1.0000e-02 eta: 20:56:15 time: 1.1017 data_time: 0.0124 memory: 15768 grad_norm: 3.7925 loss: 1.1922 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1922 2023/07/24 22:42:00 - mmengine - INFO - Epoch(train) [28][400/940] lr: 1.0000e-02 eta: 20:55:53 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 3.8469 loss: 1.3514 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3514 2023/07/24 22:42:22 - mmengine - INFO - Epoch(train) [28][420/940] lr: 1.0000e-02 eta: 20:55:31 time: 1.1015 data_time: 0.0126 memory: 15768 grad_norm: 3.8307 loss: 1.2674 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2674 2023/07/24 22:42:44 - mmengine - INFO - Epoch(train) [28][440/940] lr: 1.0000e-02 eta: 20:55:09 time: 1.1028 data_time: 0.0128 memory: 15768 grad_norm: 3.7162 loss: 1.1772 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1772 2023/07/24 22:43:06 - mmengine - INFO - Epoch(train) [28][460/940] lr: 1.0000e-02 eta: 20:54:47 time: 1.1042 data_time: 0.0125 memory: 15768 grad_norm: 3.7494 loss: 1.3149 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3149 2023/07/24 22:43:28 - mmengine - INFO - Epoch(train) [28][480/940] lr: 1.0000e-02 eta: 20:54:24 time: 1.1029 data_time: 0.0126 memory: 15768 grad_norm: 3.8010 loss: 1.2482 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2482 2023/07/24 22:43:50 - mmengine - INFO - Epoch(train) [28][500/940] lr: 1.0000e-02 eta: 20:54:02 time: 1.0983 data_time: 0.0130 memory: 15768 grad_norm: 3.8654 loss: 1.4430 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4430 2023/07/24 22:44:12 - mmengine - INFO - Epoch(train) [28][520/940] lr: 1.0000e-02 eta: 20:53:40 time: 1.1010 data_time: 0.0128 memory: 15768 grad_norm: 3.7399 loss: 1.3306 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3306 2023/07/24 22:44:34 - mmengine - INFO - Epoch(train) [28][540/940] lr: 1.0000e-02 eta: 20:53:17 time: 1.0989 data_time: 0.0127 memory: 15768 grad_norm: 3.7167 loss: 1.1467 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1467 2023/07/24 22:44:56 - mmengine - INFO - Epoch(train) [28][560/940] lr: 1.0000e-02 eta: 20:52:55 time: 1.1049 data_time: 0.0130 memory: 15768 grad_norm: 3.7426 loss: 1.2252 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2252 2023/07/24 22:45:18 - mmengine - INFO - Epoch(train) [28][580/940] lr: 1.0000e-02 eta: 20:52:33 time: 1.1013 data_time: 0.0127 memory: 15768 grad_norm: 3.7235 loss: 1.2752 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2752 2023/07/24 22:45:40 - mmengine - INFO - Epoch(train) [28][600/940] lr: 1.0000e-02 eta: 20:52:11 time: 1.1051 data_time: 0.0134 memory: 15768 grad_norm: 3.7771 loss: 1.3667 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3667 2023/07/24 22:46:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 22:46:02 - mmengine - INFO - Epoch(train) [28][620/940] lr: 1.0000e-02 eta: 20:51:49 time: 1.1000 data_time: 0.0131 memory: 15768 grad_norm: 3.7187 loss: 1.3483 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3483 2023/07/24 22:46:24 - mmengine - INFO - Epoch(train) [28][640/940] lr: 1.0000e-02 eta: 20:51:26 time: 1.1011 data_time: 0.0125 memory: 15768 grad_norm: 3.8495 loss: 1.3465 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3465 2023/07/24 22:46:46 - mmengine - INFO - Epoch(train) [28][660/940] lr: 1.0000e-02 eta: 20:51:04 time: 1.1040 data_time: 0.0128 memory: 15768 grad_norm: 3.7687 loss: 1.1892 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1892 2023/07/24 22:47:08 - mmengine - INFO - Epoch(train) [28][680/940] lr: 1.0000e-02 eta: 20:50:42 time: 1.0986 data_time: 0.0126 memory: 15768 grad_norm: 3.8989 loss: 1.3344 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3344 2023/07/24 22:47:30 - mmengine - INFO - Epoch(train) [28][700/940] lr: 1.0000e-02 eta: 20:50:19 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.7550 loss: 1.2108 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2108 2023/07/24 22:47:52 - mmengine - INFO - Epoch(train) [28][720/940] lr: 1.0000e-02 eta: 20:49:57 time: 1.0980 data_time: 0.0128 memory: 15768 grad_norm: 3.8415 loss: 1.3312 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3312 2023/07/24 22:48:14 - mmengine - INFO - Epoch(train) [28][740/940] lr: 1.0000e-02 eta: 20:49:35 time: 1.0990 data_time: 0.0128 memory: 15768 grad_norm: 3.7813 loss: 1.3312 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3312 2023/07/24 22:48:36 - mmengine - INFO - Epoch(train) [28][760/940] lr: 1.0000e-02 eta: 20:49:13 time: 1.1047 data_time: 0.0128 memory: 15768 grad_norm: 3.7611 loss: 1.1958 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1958 2023/07/24 22:48:58 - mmengine - INFO - Epoch(train) [28][780/940] lr: 1.0000e-02 eta: 20:48:50 time: 1.1015 data_time: 0.0128 memory: 15768 grad_norm: 3.8590 loss: 1.3830 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3830 2023/07/24 22:49:20 - mmengine - INFO - Epoch(train) [28][800/940] lr: 1.0000e-02 eta: 20:48:28 time: 1.0995 data_time: 0.0129 memory: 15768 grad_norm: 3.8136 loss: 1.2654 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2654 2023/07/24 22:49:42 - mmengine - INFO - Epoch(train) [28][820/940] lr: 1.0000e-02 eta: 20:48:06 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 3.8013 loss: 1.3974 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3974 2023/07/24 22:50:04 - mmengine - INFO - Epoch(train) [28][840/940] lr: 1.0000e-02 eta: 20:47:43 time: 1.0988 data_time: 0.0131 memory: 15768 grad_norm: 3.7499 loss: 1.2242 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2242 2023/07/24 22:50:26 - mmengine - INFO - Epoch(train) [28][860/940] lr: 1.0000e-02 eta: 20:47:21 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 3.8318 loss: 1.3688 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3688 2023/07/24 22:50:48 - mmengine - INFO - Epoch(train) [28][880/940] lr: 1.0000e-02 eta: 20:46:59 time: 1.1024 data_time: 0.0132 memory: 15768 grad_norm: 3.8281 loss: 1.3228 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3228 2023/07/24 22:51:10 - mmengine - INFO - Epoch(train) [28][900/940] lr: 1.0000e-02 eta: 20:46:36 time: 1.1014 data_time: 0.0131 memory: 15768 grad_norm: 3.8752 loss: 1.1468 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1468 2023/07/24 22:51:32 - mmengine - INFO - Epoch(train) [28][920/940] lr: 1.0000e-02 eta: 20:46:14 time: 1.0993 data_time: 0.0131 memory: 15768 grad_norm: 3.8784 loss: 1.6001 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6001 2023/07/24 22:51:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 22:51:53 - mmengine - INFO - Epoch(train) [28][940/940] lr: 1.0000e-02 eta: 20:45:50 time: 1.0596 data_time: 0.0141 memory: 15768 grad_norm: 4.0177 loss: 1.3716 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3716 2023/07/24 22:52:03 - mmengine - INFO - Epoch(val) [28][20/78] eta: 0:00:28 time: 0.4854 data_time: 0.3280 memory: 2147 2023/07/24 22:52:10 - mmengine - INFO - Epoch(val) [28][40/78] eta: 0:00:15 time: 0.3417 data_time: 0.1849 memory: 2147 2023/07/24 22:52:19 - mmengine - INFO - Epoch(val) [28][60/78] eta: 0:00:07 time: 0.4468 data_time: 0.2900 memory: 2147 2023/07/24 22:52:30 - mmengine - INFO - Epoch(val) [28][78/78] acc/top1: 0.6589 acc/top5: 0.8693 acc/mean1: 0.6588 data_time: 0.2419 time: 0.3961 2023/07/24 22:52:55 - mmengine - INFO - Epoch(train) [29][ 20/940] lr: 1.0000e-02 eta: 20:45:37 time: 1.2849 data_time: 0.1385 memory: 15768 grad_norm: 3.7491 loss: 1.3272 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3272 2023/07/24 22:53:17 - mmengine - INFO - Epoch(train) [29][ 40/940] lr: 1.0000e-02 eta: 20:45:15 time: 1.1003 data_time: 0.0142 memory: 15768 grad_norm: 3.7593 loss: 1.3074 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3074 2023/07/24 22:53:39 - mmengine - INFO - Epoch(train) [29][ 60/940] lr: 1.0000e-02 eta: 20:44:52 time: 1.1013 data_time: 0.0134 memory: 15768 grad_norm: 3.7534 loss: 1.3100 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3100 2023/07/24 22:54:01 - mmengine - INFO - Epoch(train) [29][ 80/940] lr: 1.0000e-02 eta: 20:44:30 time: 1.1033 data_time: 0.0132 memory: 15768 grad_norm: 3.8364 loss: 1.3551 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3551 2023/07/24 22:54:23 - mmengine - INFO - Epoch(train) [29][100/940] lr: 1.0000e-02 eta: 20:44:08 time: 1.1032 data_time: 0.0133 memory: 15768 grad_norm: 3.7098 loss: 1.1741 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1741 2023/07/24 22:54:45 - mmengine - INFO - Epoch(train) [29][120/940] lr: 1.0000e-02 eta: 20:43:46 time: 1.0999 data_time: 0.0127 memory: 15768 grad_norm: 3.7361 loss: 1.1444 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1444 2023/07/24 22:55:08 - mmengine - INFO - Epoch(train) [29][140/940] lr: 1.0000e-02 eta: 20:43:24 time: 1.1036 data_time: 0.0126 memory: 15768 grad_norm: 3.7718 loss: 1.1569 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1569 2023/07/24 22:55:30 - mmengine - INFO - Epoch(train) [29][160/940] lr: 1.0000e-02 eta: 20:43:01 time: 1.1029 data_time: 0.0131 memory: 15768 grad_norm: 3.7606 loss: 1.2027 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2027 2023/07/24 22:55:52 - mmengine - INFO - Epoch(train) [29][180/940] lr: 1.0000e-02 eta: 20:42:39 time: 1.1054 data_time: 0.0130 memory: 15768 grad_norm: 3.8386 loss: 1.5179 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5179 2023/07/24 22:56:14 - mmengine - INFO - Epoch(train) [29][200/940] lr: 1.0000e-02 eta: 20:42:17 time: 1.1029 data_time: 0.0129 memory: 15768 grad_norm: 3.7391 loss: 1.2632 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2632 2023/07/24 22:56:36 - mmengine - INFO - Epoch(train) [29][220/940] lr: 1.0000e-02 eta: 20:41:55 time: 1.1025 data_time: 0.0128 memory: 15768 grad_norm: 3.8053 loss: 1.2518 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2518 2023/07/24 22:56:58 - mmengine - INFO - Epoch(train) [29][240/940] lr: 1.0000e-02 eta: 20:41:33 time: 1.1024 data_time: 0.0135 memory: 15768 grad_norm: 3.7959 loss: 1.2631 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2631 2023/07/24 22:57:20 - mmengine - INFO - Epoch(train) [29][260/940] lr: 1.0000e-02 eta: 20:41:10 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 3.7795 loss: 1.2731 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2731 2023/07/24 22:57:42 - mmengine - INFO - Epoch(train) [29][280/940] lr: 1.0000e-02 eta: 20:40:48 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 3.7257 loss: 1.0315 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0315 2023/07/24 22:58:04 - mmengine - INFO - Epoch(train) [29][300/940] lr: 1.0000e-02 eta: 20:40:26 time: 1.1014 data_time: 0.0125 memory: 15768 grad_norm: 3.9080 loss: 1.4691 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4691 2023/07/24 22:58:26 - mmengine - INFO - Epoch(train) [29][320/940] lr: 1.0000e-02 eta: 20:40:04 time: 1.1031 data_time: 0.0126 memory: 15768 grad_norm: 3.8182 loss: 1.2212 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2212 2023/07/24 22:58:48 - mmengine - INFO - Epoch(train) [29][340/940] lr: 1.0000e-02 eta: 20:39:41 time: 1.1002 data_time: 0.0126 memory: 15768 grad_norm: 3.7588 loss: 1.1863 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1863 2023/07/24 22:59:10 - mmengine - INFO - Epoch(train) [29][360/940] lr: 1.0000e-02 eta: 20:39:19 time: 1.1024 data_time: 0.0132 memory: 15768 grad_norm: 3.8085 loss: 1.4031 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.4031 2023/07/24 22:59:32 - mmengine - INFO - Epoch(train) [29][380/940] lr: 1.0000e-02 eta: 20:38:57 time: 1.1000 data_time: 0.0125 memory: 15768 grad_norm: 3.7534 loss: 1.2975 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2975 2023/07/24 22:59:54 - mmengine - INFO - Epoch(train) [29][400/940] lr: 1.0000e-02 eta: 20:38:35 time: 1.1022 data_time: 0.0130 memory: 15768 grad_norm: 3.8338 loss: 1.2618 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2618 2023/07/24 23:00:16 - mmengine - INFO - Epoch(train) [29][420/940] lr: 1.0000e-02 eta: 20:38:12 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.7881 loss: 1.4113 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4113 2023/07/24 23:00:38 - mmengine - INFO - Epoch(train) [29][440/940] lr: 1.0000e-02 eta: 20:37:50 time: 1.1031 data_time: 0.0132 memory: 15768 grad_norm: 3.7976 loss: 1.4794 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4794 2023/07/24 23:01:00 - mmengine - INFO - Epoch(train) [29][460/940] lr: 1.0000e-02 eta: 20:37:28 time: 1.1041 data_time: 0.0125 memory: 15768 grad_norm: 3.8009 loss: 1.3330 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3330 2023/07/24 23:01:22 - mmengine - INFO - Epoch(train) [29][480/940] lr: 1.0000e-02 eta: 20:37:06 time: 1.0985 data_time: 0.0130 memory: 15768 grad_norm: 3.8852 loss: 1.5099 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5099 2023/07/24 23:01:44 - mmengine - INFO - Epoch(train) [29][500/940] lr: 1.0000e-02 eta: 20:36:43 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 3.8296 loss: 1.2673 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2673 2023/07/24 23:02:06 - mmengine - INFO - Epoch(train) [29][520/940] lr: 1.0000e-02 eta: 20:36:21 time: 1.1018 data_time: 0.0133 memory: 15768 grad_norm: 3.8229 loss: 1.2930 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2930 2023/07/24 23:02:28 - mmengine - INFO - Epoch(train) [29][540/940] lr: 1.0000e-02 eta: 20:35:59 time: 1.0991 data_time: 0.0128 memory: 15768 grad_norm: 3.7579 loss: 1.1550 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1550 2023/07/24 23:02:50 - mmengine - INFO - Epoch(train) [29][560/940] lr: 1.0000e-02 eta: 20:35:37 time: 1.1041 data_time: 0.0131 memory: 15768 grad_norm: 3.7175 loss: 1.2390 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2390 2023/07/24 23:03:12 - mmengine - INFO - Epoch(train) [29][580/940] lr: 1.0000e-02 eta: 20:35:14 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.7980 loss: 1.3555 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3555 2023/07/24 23:03:34 - mmengine - INFO - Epoch(train) [29][600/940] lr: 1.0000e-02 eta: 20:34:52 time: 1.1021 data_time: 0.0132 memory: 15768 grad_norm: 3.7454 loss: 1.2970 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2970 2023/07/24 23:03:56 - mmengine - INFO - Epoch(train) [29][620/940] lr: 1.0000e-02 eta: 20:34:30 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 3.7762 loss: 1.3516 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3516 2023/07/24 23:04:18 - mmengine - INFO - Epoch(train) [29][640/940] lr: 1.0000e-02 eta: 20:34:08 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 3.7136 loss: 1.1840 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1840 2023/07/24 23:04:40 - mmengine - INFO - Epoch(train) [29][660/940] lr: 1.0000e-02 eta: 20:33:45 time: 1.1043 data_time: 0.0129 memory: 15768 grad_norm: 3.8271 loss: 1.2689 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2689 2023/07/24 23:05:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 23:05:02 - mmengine - INFO - Epoch(train) [29][680/940] lr: 1.0000e-02 eta: 20:33:23 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 3.8690 loss: 1.2316 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2316 2023/07/24 23:05:25 - mmengine - INFO - Epoch(train) [29][700/940] lr: 1.0000e-02 eta: 20:33:01 time: 1.1033 data_time: 0.0126 memory: 15768 grad_norm: 3.8460 loss: 1.3542 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3542 2023/07/24 23:05:47 - mmengine - INFO - Epoch(train) [29][720/940] lr: 1.0000e-02 eta: 20:32:39 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 3.7872 loss: 1.2615 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2615 2023/07/24 23:06:09 - mmengine - INFO - Epoch(train) [29][740/940] lr: 1.0000e-02 eta: 20:32:16 time: 1.1033 data_time: 0.0132 memory: 15768 grad_norm: 3.8310 loss: 1.3707 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3707 2023/07/24 23:06:31 - mmengine - INFO - Epoch(train) [29][760/940] lr: 1.0000e-02 eta: 20:31:54 time: 1.1021 data_time: 0.0129 memory: 15768 grad_norm: 3.8120 loss: 1.3059 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3059 2023/07/24 23:06:53 - mmengine - INFO - Epoch(train) [29][780/940] lr: 1.0000e-02 eta: 20:31:32 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.7819 loss: 1.2141 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2141 2023/07/24 23:07:15 - mmengine - INFO - Epoch(train) [29][800/940] lr: 1.0000e-02 eta: 20:31:10 time: 1.1024 data_time: 0.0127 memory: 15768 grad_norm: 3.8978 loss: 1.5567 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5567 2023/07/24 23:07:37 - mmengine - INFO - Epoch(train) [29][820/940] lr: 1.0000e-02 eta: 20:30:47 time: 1.0996 data_time: 0.0127 memory: 15768 grad_norm: 3.8252 loss: 1.3522 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3522 2023/07/24 23:07:59 - mmengine - INFO - Epoch(train) [29][840/940] lr: 1.0000e-02 eta: 20:30:25 time: 1.0967 data_time: 0.0129 memory: 15768 grad_norm: 3.8190 loss: 1.4647 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4647 2023/07/24 23:08:21 - mmengine - INFO - Epoch(train) [29][860/940] lr: 1.0000e-02 eta: 20:30:03 time: 1.1029 data_time: 0.0134 memory: 15768 grad_norm: 3.7515 loss: 1.1139 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1139 2023/07/24 23:08:43 - mmengine - INFO - Epoch(train) [29][880/940] lr: 1.0000e-02 eta: 20:29:40 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.8721 loss: 1.3260 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3260 2023/07/24 23:09:05 - mmengine - INFO - Epoch(train) [29][900/940] lr: 1.0000e-02 eta: 20:29:18 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 3.7947 loss: 1.2878 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2878 2023/07/24 23:09:27 - mmengine - INFO - Epoch(train) [29][920/940] lr: 1.0000e-02 eta: 20:28:56 time: 1.0989 data_time: 0.0128 memory: 15768 grad_norm: 3.7479 loss: 1.4377 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4377 2023/07/24 23:09:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 23:09:48 - mmengine - INFO - Epoch(train) [29][940/940] lr: 1.0000e-02 eta: 20:28:31 time: 1.0545 data_time: 0.0124 memory: 15768 grad_norm: 3.9316 loss: 1.4232 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4232 2023/07/24 23:09:57 - mmengine - INFO - Epoch(val) [29][20/78] eta: 0:00:28 time: 0.4848 data_time: 0.3281 memory: 2147 2023/07/24 23:10:04 - mmengine - INFO - Epoch(val) [29][40/78] eta: 0:00:15 time: 0.3369 data_time: 0.1804 memory: 2147 2023/07/24 23:10:13 - mmengine - INFO - Epoch(val) [29][60/78] eta: 0:00:07 time: 0.4310 data_time: 0.2746 memory: 2147 2023/07/24 23:10:24 - mmengine - INFO - Epoch(val) [29][78/78] acc/top1: 0.6708 acc/top5: 0.8754 acc/mean1: 0.6709 data_time: 0.2369 time: 0.3907 2023/07/24 23:10:50 - mmengine - INFO - Epoch(train) [30][ 20/940] lr: 1.0000e-02 eta: 20:28:19 time: 1.2998 data_time: 0.1502 memory: 15768 grad_norm: 3.7209 loss: 1.3272 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3272 2023/07/24 23:11:12 - mmengine - INFO - Epoch(train) [30][ 40/940] lr: 1.0000e-02 eta: 20:27:56 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 3.8493 loss: 1.2292 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2292 2023/07/24 23:11:34 - mmengine - INFO - Epoch(train) [30][ 60/940] lr: 1.0000e-02 eta: 20:27:34 time: 1.1037 data_time: 0.0131 memory: 15768 grad_norm: 3.7960 loss: 1.2398 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2398 2023/07/24 23:11:56 - mmengine - INFO - Epoch(train) [30][ 80/940] lr: 1.0000e-02 eta: 20:27:12 time: 1.1010 data_time: 0.0128 memory: 15768 grad_norm: 3.7370 loss: 1.3031 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3031 2023/07/24 23:12:18 - mmengine - INFO - Epoch(train) [30][100/940] lr: 1.0000e-02 eta: 20:26:50 time: 1.1024 data_time: 0.0127 memory: 15768 grad_norm: 3.7324 loss: 1.2692 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2692 2023/07/24 23:12:40 - mmengine - INFO - Epoch(train) [30][120/940] lr: 1.0000e-02 eta: 20:26:28 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.8507 loss: 1.2026 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2026 2023/07/24 23:13:02 - mmengine - INFO - Epoch(train) [30][140/940] lr: 1.0000e-02 eta: 20:26:05 time: 1.1003 data_time: 0.0132 memory: 15768 grad_norm: 3.8077 loss: 1.3529 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3529 2023/07/24 23:13:24 - mmengine - INFO - Epoch(train) [30][160/940] lr: 1.0000e-02 eta: 20:25:43 time: 1.1041 data_time: 0.0132 memory: 15768 grad_norm: 3.7403 loss: 1.1470 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1470 2023/07/24 23:13:46 - mmengine - INFO - Epoch(train) [30][180/940] lr: 1.0000e-02 eta: 20:25:21 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.8009 loss: 1.1923 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1923 2023/07/24 23:14:08 - mmengine - INFO - Epoch(train) [30][200/940] lr: 1.0000e-02 eta: 20:24:59 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 3.7579 loss: 1.2033 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2033 2023/07/24 23:14:30 - mmengine - INFO - Epoch(train) [30][220/940] lr: 1.0000e-02 eta: 20:24:36 time: 1.1016 data_time: 0.0130 memory: 15768 grad_norm: 3.9975 loss: 1.2411 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2411 2023/07/24 23:14:52 - mmengine - INFO - Epoch(train) [30][240/940] lr: 1.0000e-02 eta: 20:24:14 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.8107 loss: 1.2750 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2750 2023/07/24 23:15:14 - mmengine - INFO - Epoch(train) [30][260/940] lr: 1.0000e-02 eta: 20:23:52 time: 1.1013 data_time: 0.0129 memory: 15768 grad_norm: 3.8684 loss: 1.2080 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2080 2023/07/24 23:15:36 - mmengine - INFO - Epoch(train) [30][280/940] lr: 1.0000e-02 eta: 20:23:30 time: 1.1008 data_time: 0.0129 memory: 15768 grad_norm: 3.8743 loss: 1.2577 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2577 2023/07/24 23:15:58 - mmengine - INFO - Epoch(train) [30][300/940] lr: 1.0000e-02 eta: 20:23:07 time: 1.0981 data_time: 0.0129 memory: 15768 grad_norm: 3.8077 loss: 1.3115 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3115 2023/07/24 23:16:20 - mmengine - INFO - Epoch(train) [30][320/940] lr: 1.0000e-02 eta: 20:22:45 time: 1.1012 data_time: 0.0132 memory: 15768 grad_norm: 3.8041 loss: 1.0920 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0920 2023/07/24 23:16:42 - mmengine - INFO - Epoch(train) [30][340/940] lr: 1.0000e-02 eta: 20:22:23 time: 1.1009 data_time: 0.0128 memory: 15768 grad_norm: 3.7909 loss: 1.4690 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4690 2023/07/24 23:17:04 - mmengine - INFO - Epoch(train) [30][360/940] lr: 1.0000e-02 eta: 20:22:00 time: 1.0991 data_time: 0.0128 memory: 15768 grad_norm: 3.8230 loss: 1.3705 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3705 2023/07/24 23:17:26 - mmengine - INFO - Epoch(train) [30][380/940] lr: 1.0000e-02 eta: 20:21:38 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 3.9119 loss: 1.4351 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4351 2023/07/24 23:17:48 - mmengine - INFO - Epoch(train) [30][400/940] lr: 1.0000e-02 eta: 20:21:16 time: 1.0983 data_time: 0.0128 memory: 15768 grad_norm: 3.8102 loss: 1.1273 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1273 2023/07/24 23:18:10 - mmengine - INFO - Epoch(train) [30][420/940] lr: 1.0000e-02 eta: 20:20:53 time: 1.1019 data_time: 0.0128 memory: 15768 grad_norm: 3.7725 loss: 1.2599 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2599 2023/07/24 23:18:33 - mmengine - INFO - Epoch(train) [30][440/940] lr: 1.0000e-02 eta: 20:20:32 time: 1.1122 data_time: 0.0126 memory: 15768 grad_norm: 3.8457 loss: 1.2824 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2824 2023/07/24 23:18:56 - mmengine - INFO - Epoch(train) [30][460/940] lr: 1.0000e-02 eta: 20:20:13 time: 1.1675 data_time: 0.0128 memory: 15768 grad_norm: 3.8373 loss: 1.3672 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3672 2023/07/24 23:19:19 - mmengine - INFO - Epoch(train) [30][480/940] lr: 1.0000e-02 eta: 20:19:54 time: 1.1741 data_time: 0.0129 memory: 15768 grad_norm: 3.7803 loss: 1.2394 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2394 2023/07/24 23:19:43 - mmengine - INFO - Epoch(train) [30][500/940] lr: 1.0000e-02 eta: 20:19:35 time: 1.1695 data_time: 0.0138 memory: 15768 grad_norm: 3.7823 loss: 1.3196 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3196 2023/07/24 23:20:06 - mmengine - INFO - Epoch(train) [30][520/940] lr: 1.0000e-02 eta: 20:19:16 time: 1.1627 data_time: 0.0125 memory: 15768 grad_norm: 3.8328 loss: 1.2686 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2686 2023/07/24 23:20:29 - mmengine - INFO - Epoch(train) [30][540/940] lr: 1.0000e-02 eta: 20:18:56 time: 1.1657 data_time: 0.0126 memory: 15768 grad_norm: 3.8244 loss: 1.3375 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3375 2023/07/24 23:20:53 - mmengine - INFO - Epoch(train) [30][560/940] lr: 1.0000e-02 eta: 20:18:37 time: 1.1670 data_time: 0.0125 memory: 15768 grad_norm: 3.7641 loss: 1.0858 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0858 2023/07/24 23:21:15 - mmengine - INFO - Epoch(train) [30][580/940] lr: 1.0000e-02 eta: 20:18:15 time: 1.1061 data_time: 0.0124 memory: 15768 grad_norm: 3.8466 loss: 1.2691 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2691 2023/07/24 23:21:37 - mmengine - INFO - Epoch(train) [30][600/940] lr: 1.0000e-02 eta: 20:17:53 time: 1.1028 data_time: 0.0130 memory: 15768 grad_norm: 3.8812 loss: 1.2882 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2882 2023/07/24 23:21:59 - mmengine - INFO - Epoch(train) [30][620/940] lr: 1.0000e-02 eta: 20:17:31 time: 1.1028 data_time: 0.0129 memory: 15768 grad_norm: 3.8591 loss: 1.5543 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5543 2023/07/24 23:22:21 - mmengine - INFO - Epoch(train) [30][640/940] lr: 1.0000e-02 eta: 20:17:09 time: 1.1030 data_time: 0.0131 memory: 15768 grad_norm: 3.8287 loss: 1.3913 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3913 2023/07/24 23:22:43 - mmengine - INFO - Epoch(train) [30][660/940] lr: 1.0000e-02 eta: 20:16:47 time: 1.1038 data_time: 0.0134 memory: 15768 grad_norm: 3.7782 loss: 1.1690 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1690 2023/07/24 23:23:05 - mmengine - INFO - Epoch(train) [30][680/940] lr: 1.0000e-02 eta: 20:16:24 time: 1.1029 data_time: 0.0131 memory: 15768 grad_norm: 3.8476 loss: 1.1949 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1949 2023/07/24 23:23:27 - mmengine - INFO - Epoch(train) [30][700/940] lr: 1.0000e-02 eta: 20:16:02 time: 1.1024 data_time: 0.0131 memory: 15768 grad_norm: 3.9273 loss: 1.4387 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4387 2023/07/24 23:23:49 - mmengine - INFO - Epoch(train) [30][720/940] lr: 1.0000e-02 eta: 20:15:40 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.8636 loss: 1.3859 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3859 2023/07/24 23:24:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 23:24:11 - mmengine - INFO - Epoch(train) [30][740/940] lr: 1.0000e-02 eta: 20:15:18 time: 1.1040 data_time: 0.0127 memory: 15768 grad_norm: 3.9082 loss: 1.4165 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4165 2023/07/24 23:24:33 - mmengine - INFO - Epoch(train) [30][760/940] lr: 1.0000e-02 eta: 20:14:55 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 3.8784 loss: 1.4169 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4169 2023/07/24 23:24:55 - mmengine - INFO - Epoch(train) [30][780/940] lr: 1.0000e-02 eta: 20:14:33 time: 1.1003 data_time: 0.0129 memory: 15768 grad_norm: 3.7489 loss: 1.3814 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3814 2023/07/24 23:25:17 - mmengine - INFO - Epoch(train) [30][800/940] lr: 1.0000e-02 eta: 20:14:11 time: 1.0996 data_time: 0.0129 memory: 15768 grad_norm: 3.8018 loss: 1.2795 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2795 2023/07/24 23:25:40 - mmengine - INFO - Epoch(train) [30][820/940] lr: 1.0000e-02 eta: 20:13:49 time: 1.1071 data_time: 0.0127 memory: 15768 grad_norm: 3.9345 loss: 1.2966 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2966 2023/07/24 23:26:02 - mmengine - INFO - Epoch(train) [30][840/940] lr: 1.0000e-02 eta: 20:13:26 time: 1.1001 data_time: 0.0132 memory: 15768 grad_norm: 3.7770 loss: 1.2530 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2530 2023/07/24 23:26:23 - mmengine - INFO - Epoch(train) [30][860/940] lr: 1.0000e-02 eta: 20:13:04 time: 1.0988 data_time: 0.0126 memory: 15768 grad_norm: 3.8150 loss: 1.3260 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3260 2023/07/24 23:26:46 - mmengine - INFO - Epoch(train) [30][880/940] lr: 1.0000e-02 eta: 20:12:42 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 3.8139 loss: 1.3813 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3813 2023/07/24 23:27:08 - mmengine - INFO - Epoch(train) [30][900/940] lr: 1.0000e-02 eta: 20:12:20 time: 1.1039 data_time: 0.0127 memory: 15768 grad_norm: 3.8728 loss: 1.3241 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3241 2023/07/24 23:27:30 - mmengine - INFO - Epoch(train) [30][920/940] lr: 1.0000e-02 eta: 20:11:57 time: 1.1014 data_time: 0.0135 memory: 15768 grad_norm: 3.8027 loss: 1.3176 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3176 2023/07/24 23:27:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 23:27:51 - mmengine - INFO - Epoch(train) [30][940/940] lr: 1.0000e-02 eta: 20:11:33 time: 1.0558 data_time: 0.0123 memory: 15768 grad_norm: 4.0197 loss: 1.2203 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2203 2023/07/24 23:27:51 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/07/24 23:28:02 - mmengine - INFO - Epoch(val) [30][20/78] eta: 0:00:28 time: 0.4844 data_time: 0.3273 memory: 2147 2023/07/24 23:28:08 - mmengine - INFO - Epoch(val) [30][40/78] eta: 0:00:15 time: 0.3435 data_time: 0.1863 memory: 2147 2023/07/24 23:28:17 - mmengine - INFO - Epoch(val) [30][60/78] eta: 0:00:07 time: 0.4502 data_time: 0.2931 memory: 2147 2023/07/24 23:28:26 - mmengine - INFO - Epoch(val) [30][78/78] acc/top1: 0.6675 acc/top5: 0.8751 acc/mean1: 0.6675 data_time: 0.2419 time: 0.3961 2023/07/24 23:28:53 - mmengine - INFO - Epoch(train) [31][ 20/940] lr: 1.0000e-02 eta: 20:11:20 time: 1.3016 data_time: 0.1557 memory: 15768 grad_norm: 3.7183 loss: 1.2843 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2843 2023/07/24 23:29:14 - mmengine - INFO - Epoch(train) [31][ 40/940] lr: 1.0000e-02 eta: 20:10:58 time: 1.0979 data_time: 0.0129 memory: 15768 grad_norm: 3.7728 loss: 1.1382 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1382 2023/07/24 23:29:36 - mmengine - INFO - Epoch(train) [31][ 60/940] lr: 1.0000e-02 eta: 20:10:35 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.7258 loss: 1.1477 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1477 2023/07/24 23:29:58 - mmengine - INFO - Epoch(train) [31][ 80/940] lr: 1.0000e-02 eta: 20:10:13 time: 1.0996 data_time: 0.0129 memory: 15768 grad_norm: 3.8092 loss: 1.1518 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1518 2023/07/24 23:30:20 - mmengine - INFO - Epoch(train) [31][100/940] lr: 1.0000e-02 eta: 20:09:51 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 3.7575 loss: 1.1465 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1465 2023/07/24 23:30:43 - mmengine - INFO - Epoch(train) [31][120/940] lr: 1.0000e-02 eta: 20:09:29 time: 1.1039 data_time: 0.0126 memory: 15768 grad_norm: 3.8248 loss: 1.1814 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1814 2023/07/24 23:31:05 - mmengine - INFO - Epoch(train) [31][140/940] lr: 1.0000e-02 eta: 20:09:06 time: 1.0994 data_time: 0.0126 memory: 15768 grad_norm: 3.7671 loss: 1.3111 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3111 2023/07/24 23:31:27 - mmengine - INFO - Epoch(train) [31][160/940] lr: 1.0000e-02 eta: 20:08:44 time: 1.1033 data_time: 0.0126 memory: 15768 grad_norm: 3.7545 loss: 1.2617 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2617 2023/07/24 23:31:49 - mmengine - INFO - Epoch(train) [31][180/940] lr: 1.0000e-02 eta: 20:08:22 time: 1.1009 data_time: 0.0126 memory: 15768 grad_norm: 3.8003 loss: 1.0995 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0995 2023/07/24 23:32:11 - mmengine - INFO - Epoch(train) [31][200/940] lr: 1.0000e-02 eta: 20:08:00 time: 1.1058 data_time: 0.0132 memory: 15768 grad_norm: 3.8215 loss: 1.3442 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3442 2023/07/24 23:32:33 - mmengine - INFO - Epoch(train) [31][220/940] lr: 1.0000e-02 eta: 20:07:38 time: 1.1025 data_time: 0.0124 memory: 15768 grad_norm: 3.8400 loss: 1.1127 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1127 2023/07/24 23:32:55 - mmengine - INFO - Epoch(train) [31][240/940] lr: 1.0000e-02 eta: 20:07:15 time: 1.1017 data_time: 0.0132 memory: 15768 grad_norm: 3.7939 loss: 1.2238 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2238 2023/07/24 23:33:17 - mmengine - INFO - Epoch(train) [31][260/940] lr: 1.0000e-02 eta: 20:06:53 time: 1.1014 data_time: 0.0126 memory: 15768 grad_norm: 3.8925 loss: 1.2295 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2295 2023/07/24 23:33:39 - mmengine - INFO - Epoch(train) [31][280/940] lr: 1.0000e-02 eta: 20:06:31 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.9280 loss: 1.3526 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3526 2023/07/24 23:34:01 - mmengine - INFO - Epoch(train) [31][300/940] lr: 1.0000e-02 eta: 20:06:09 time: 1.1034 data_time: 0.0132 memory: 15768 grad_norm: 3.7963 loss: 1.3666 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3666 2023/07/24 23:34:23 - mmengine - INFO - Epoch(train) [31][320/940] lr: 1.0000e-02 eta: 20:05:46 time: 1.1020 data_time: 0.0134 memory: 15768 grad_norm: 3.8161 loss: 1.0599 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0599 2023/07/24 23:34:45 - mmengine - INFO - Epoch(train) [31][340/940] lr: 1.0000e-02 eta: 20:05:24 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 3.9178 loss: 1.4369 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4369 2023/07/24 23:35:07 - mmengine - INFO - Epoch(train) [31][360/940] lr: 1.0000e-02 eta: 20:05:02 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 3.8070 loss: 1.3559 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3559 2023/07/24 23:35:29 - mmengine - INFO - Epoch(train) [31][380/940] lr: 1.0000e-02 eta: 20:04:39 time: 1.0993 data_time: 0.0125 memory: 15768 grad_norm: 3.8490 loss: 1.3574 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3574 2023/07/24 23:35:51 - mmengine - INFO - Epoch(train) [31][400/940] lr: 1.0000e-02 eta: 20:04:17 time: 1.0999 data_time: 0.0128 memory: 15768 grad_norm: 3.7722 loss: 1.2146 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2146 2023/07/24 23:36:13 - mmengine - INFO - Epoch(train) [31][420/940] lr: 1.0000e-02 eta: 20:03:55 time: 1.0977 data_time: 0.0126 memory: 15768 grad_norm: 3.8410 loss: 1.1930 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1930 2023/07/24 23:36:35 - mmengine - INFO - Epoch(train) [31][440/940] lr: 1.0000e-02 eta: 20:03:32 time: 1.0995 data_time: 0.0127 memory: 15768 grad_norm: 3.8920 loss: 1.4477 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4477 2023/07/24 23:36:57 - mmengine - INFO - Epoch(train) [31][460/940] lr: 1.0000e-02 eta: 20:03:10 time: 1.1019 data_time: 0.0127 memory: 15768 grad_norm: 3.8086 loss: 1.2249 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2249 2023/07/24 23:37:19 - mmengine - INFO - Epoch(train) [31][480/940] lr: 1.0000e-02 eta: 20:02:48 time: 1.1039 data_time: 0.0129 memory: 15768 grad_norm: 3.9057 loss: 1.3218 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3218 2023/07/24 23:37:41 - mmengine - INFO - Epoch(train) [31][500/940] lr: 1.0000e-02 eta: 20:02:26 time: 1.1028 data_time: 0.0130 memory: 15768 grad_norm: 3.7488 loss: 1.3200 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3200 2023/07/24 23:38:03 - mmengine - INFO - Epoch(train) [31][520/940] lr: 1.0000e-02 eta: 20:02:03 time: 1.0993 data_time: 0.0133 memory: 15768 grad_norm: 3.8416 loss: 1.3059 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3059 2023/07/24 23:38:25 - mmengine - INFO - Epoch(train) [31][540/940] lr: 1.0000e-02 eta: 20:01:41 time: 1.1042 data_time: 0.0129 memory: 15768 grad_norm: 3.8523 loss: 1.2599 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2599 2023/07/24 23:38:47 - mmengine - INFO - Epoch(train) [31][560/940] lr: 1.0000e-02 eta: 20:01:19 time: 1.1073 data_time: 0.0129 memory: 15768 grad_norm: 3.8309 loss: 1.3143 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3143 2023/07/24 23:39:09 - mmengine - INFO - Epoch(train) [31][580/940] lr: 1.0000e-02 eta: 20:00:57 time: 1.0996 data_time: 0.0127 memory: 15768 grad_norm: 3.8324 loss: 1.1319 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1319 2023/07/24 23:39:31 - mmengine - INFO - Epoch(train) [31][600/940] lr: 1.0000e-02 eta: 20:00:35 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 3.9922 loss: 1.2963 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2963 2023/07/24 23:39:53 - mmengine - INFO - Epoch(train) [31][620/940] lr: 1.0000e-02 eta: 20:00:12 time: 1.1003 data_time: 0.0126 memory: 15768 grad_norm: 3.9375 loss: 1.2492 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2492 2023/07/24 23:40:15 - mmengine - INFO - Epoch(train) [31][640/940] lr: 1.0000e-02 eta: 19:59:50 time: 1.1002 data_time: 0.0129 memory: 15768 grad_norm: 3.7800 loss: 1.2968 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2968 2023/07/24 23:40:37 - mmengine - INFO - Epoch(train) [31][660/940] lr: 1.0000e-02 eta: 19:59:28 time: 1.1002 data_time: 0.0127 memory: 15768 grad_norm: 3.9359 loss: 1.3107 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3107 2023/07/24 23:40:59 - mmengine - INFO - Epoch(train) [31][680/940] lr: 1.0000e-02 eta: 19:59:06 time: 1.1017 data_time: 0.0130 memory: 15768 grad_norm: 3.8580 loss: 1.3994 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.3994 2023/07/24 23:41:21 - mmengine - INFO - Epoch(train) [31][700/940] lr: 1.0000e-02 eta: 19:58:43 time: 1.1011 data_time: 0.0126 memory: 15768 grad_norm: 3.8059 loss: 1.0396 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0396 2023/07/24 23:41:44 - mmengine - INFO - Epoch(train) [31][720/940] lr: 1.0000e-02 eta: 19:58:21 time: 1.1023 data_time: 0.0128 memory: 15768 grad_norm: 3.8562 loss: 1.2653 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2653 2023/07/24 23:42:06 - mmengine - INFO - Epoch(train) [31][740/940] lr: 1.0000e-02 eta: 19:57:59 time: 1.1016 data_time: 0.0128 memory: 15768 grad_norm: 3.8211 loss: 1.4159 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4159 2023/07/24 23:42:28 - mmengine - INFO - Epoch(train) [31][760/940] lr: 1.0000e-02 eta: 19:57:37 time: 1.1001 data_time: 0.0127 memory: 15768 grad_norm: 3.9314 loss: 1.4104 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4104 2023/07/24 23:42:50 - mmengine - INFO - Epoch(train) [31][780/940] lr: 1.0000e-02 eta: 19:57:14 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.8937 loss: 1.3543 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3543 2023/07/24 23:43:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 23:43:12 - mmengine - INFO - Epoch(train) [31][800/940] lr: 1.0000e-02 eta: 19:56:52 time: 1.1016 data_time: 0.0130 memory: 15768 grad_norm: 3.8042 loss: 1.2597 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2597 2023/07/24 23:43:34 - mmengine - INFO - Epoch(train) [31][820/940] lr: 1.0000e-02 eta: 19:56:30 time: 1.1028 data_time: 0.0128 memory: 15768 grad_norm: 3.9046 loss: 1.3290 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3290 2023/07/24 23:43:56 - mmengine - INFO - Epoch(train) [31][840/940] lr: 1.0000e-02 eta: 19:56:08 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.8560 loss: 1.2816 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2816 2023/07/24 23:44:18 - mmengine - INFO - Epoch(train) [31][860/940] lr: 1.0000e-02 eta: 19:55:46 time: 1.1055 data_time: 0.0127 memory: 15768 grad_norm: 3.7952 loss: 1.3485 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3485 2023/07/24 23:44:40 - mmengine - INFO - Epoch(train) [31][880/940] lr: 1.0000e-02 eta: 19:55:23 time: 1.1010 data_time: 0.0132 memory: 15768 grad_norm: 3.8548 loss: 1.2364 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.2364 2023/07/24 23:45:02 - mmengine - INFO - Epoch(train) [31][900/940] lr: 1.0000e-02 eta: 19:55:01 time: 1.1013 data_time: 0.0128 memory: 15768 grad_norm: 3.8148 loss: 1.3857 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3857 2023/07/24 23:45:24 - mmengine - INFO - Epoch(train) [31][920/940] lr: 1.0000e-02 eta: 19:54:39 time: 1.1000 data_time: 0.0134 memory: 15768 grad_norm: 3.8214 loss: 1.3923 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.3923 2023/07/24 23:45:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/24 23:45:45 - mmengine - INFO - Epoch(train) [31][940/940] lr: 1.0000e-02 eta: 19:54:15 time: 1.0582 data_time: 0.0124 memory: 15768 grad_norm: 4.0937 loss: 1.1479 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1479 2023/07/24 23:45:55 - mmengine - INFO - Epoch(val) [31][20/78] eta: 0:00:28 time: 0.4872 data_time: 0.3302 memory: 2147 2023/07/24 23:46:02 - mmengine - INFO - Epoch(val) [31][40/78] eta: 0:00:15 time: 0.3473 data_time: 0.1904 memory: 2147 2023/07/24 23:46:11 - mmengine - INFO - Epoch(val) [31][60/78] eta: 0:00:07 time: 0.4553 data_time: 0.2983 memory: 2147 2023/07/24 23:46:21 - mmengine - INFO - Epoch(val) [31][78/78] acc/top1: 0.6567 acc/top5: 0.8652 acc/mean1: 0.6565 data_time: 0.2477 time: 0.4018 2023/07/24 23:46:47 - mmengine - INFO - Epoch(train) [32][ 20/940] lr: 1.0000e-02 eta: 19:54:02 time: 1.3205 data_time: 0.1491 memory: 15768 grad_norm: 3.8856 loss: 1.3767 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3767 2023/07/24 23:47:09 - mmengine - INFO - Epoch(train) [32][ 40/940] lr: 1.0000e-02 eta: 19:53:40 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.8143 loss: 1.3368 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3368 2023/07/24 23:47:31 - mmengine - INFO - Epoch(train) [32][ 60/940] lr: 1.0000e-02 eta: 19:53:17 time: 1.0997 data_time: 0.0125 memory: 15768 grad_norm: 3.7997 loss: 1.1259 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1259 2023/07/24 23:47:53 - mmengine - INFO - Epoch(train) [32][ 80/940] lr: 1.0000e-02 eta: 19:52:55 time: 1.1019 data_time: 0.0127 memory: 15768 grad_norm: 3.8696 loss: 1.1396 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1396 2023/07/24 23:48:15 - mmengine - INFO - Epoch(train) [32][100/940] lr: 1.0000e-02 eta: 19:52:33 time: 1.1000 data_time: 0.0127 memory: 15768 grad_norm: 3.8555 loss: 1.2270 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2270 2023/07/24 23:48:37 - mmengine - INFO - Epoch(train) [32][120/940] lr: 1.0000e-02 eta: 19:52:11 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.8298 loss: 1.1358 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1358 2023/07/24 23:48:59 - mmengine - INFO - Epoch(train) [32][140/940] lr: 1.0000e-02 eta: 19:51:48 time: 1.1002 data_time: 0.0129 memory: 15768 grad_norm: 3.7657 loss: 1.2009 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2009 2023/07/24 23:49:21 - mmengine - INFO - Epoch(train) [32][160/940] lr: 1.0000e-02 eta: 19:51:26 time: 1.0972 data_time: 0.0131 memory: 15768 grad_norm: 3.8608 loss: 1.2495 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2495 2023/07/24 23:49:43 - mmengine - INFO - Epoch(train) [32][180/940] lr: 1.0000e-02 eta: 19:51:03 time: 1.0989 data_time: 0.0129 memory: 15768 grad_norm: 3.8162 loss: 1.1697 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.1697 2023/07/24 23:50:05 - mmengine - INFO - Epoch(train) [32][200/940] lr: 1.0000e-02 eta: 19:50:41 time: 1.1022 data_time: 0.0130 memory: 15768 grad_norm: 3.8506 loss: 1.1981 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1981 2023/07/24 23:50:27 - mmengine - INFO - Epoch(train) [32][220/940] lr: 1.0000e-02 eta: 19:50:19 time: 1.0994 data_time: 0.0130 memory: 15768 grad_norm: 3.8097 loss: 1.2768 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2768 2023/07/24 23:50:49 - mmengine - INFO - Epoch(train) [32][240/940] lr: 1.0000e-02 eta: 19:49:57 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 3.8629 loss: 1.1541 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1541 2023/07/24 23:51:11 - mmengine - INFO - Epoch(train) [32][260/940] lr: 1.0000e-02 eta: 19:49:34 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 3.7405 loss: 1.3545 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3545 2023/07/24 23:51:33 - mmengine - INFO - Epoch(train) [32][280/940] lr: 1.0000e-02 eta: 19:49:12 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 3.9206 loss: 1.2421 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2421 2023/07/24 23:51:55 - mmengine - INFO - Epoch(train) [32][300/940] lr: 1.0000e-02 eta: 19:48:50 time: 1.1023 data_time: 0.0128 memory: 15768 grad_norm: 3.9065 loss: 1.3216 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3216 2023/07/24 23:52:17 - mmengine - INFO - Epoch(train) [32][320/940] lr: 1.0000e-02 eta: 19:48:27 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.8160 loss: 1.2964 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2964 2023/07/24 23:52:40 - mmengine - INFO - Epoch(train) [32][340/940] lr: 1.0000e-02 eta: 19:48:05 time: 1.1028 data_time: 0.0127 memory: 15768 grad_norm: 3.8546 loss: 1.2425 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2425 2023/07/24 23:53:01 - mmengine - INFO - Epoch(train) [32][360/940] lr: 1.0000e-02 eta: 19:47:43 time: 1.0994 data_time: 0.0133 memory: 15768 grad_norm: 3.8125 loss: 1.2450 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2450 2023/07/24 23:53:24 - mmengine - INFO - Epoch(train) [32][380/940] lr: 1.0000e-02 eta: 19:47:21 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 3.8590 loss: 1.3069 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3069 2023/07/24 23:53:46 - mmengine - INFO - Epoch(train) [32][400/940] lr: 1.0000e-02 eta: 19:46:58 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 3.8875 loss: 1.3292 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3292 2023/07/24 23:54:08 - mmengine - INFO - Epoch(train) [32][420/940] lr: 1.0000e-02 eta: 19:46:36 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.7868 loss: 1.3194 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3194 2023/07/24 23:54:30 - mmengine - INFO - Epoch(train) [32][440/940] lr: 1.0000e-02 eta: 19:46:14 time: 1.1024 data_time: 0.0130 memory: 15768 grad_norm: 3.8292 loss: 1.1555 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1555 2023/07/24 23:54:52 - mmengine - INFO - Epoch(train) [32][460/940] lr: 1.0000e-02 eta: 19:45:52 time: 1.1030 data_time: 0.0130 memory: 15768 grad_norm: 3.8028 loss: 1.3888 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3888 2023/07/24 23:55:14 - mmengine - INFO - Epoch(train) [32][480/940] lr: 1.0000e-02 eta: 19:45:29 time: 1.1004 data_time: 0.0127 memory: 15768 grad_norm: 3.8753 loss: 1.3528 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3528 2023/07/24 23:55:36 - mmengine - INFO - Epoch(train) [32][500/940] lr: 1.0000e-02 eta: 19:45:07 time: 1.0992 data_time: 0.0128 memory: 15768 grad_norm: 3.7896 loss: 1.4005 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4005 2023/07/24 23:55:58 - mmengine - INFO - Epoch(train) [32][520/940] lr: 1.0000e-02 eta: 19:44:45 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.9707 loss: 1.4018 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4018 2023/07/24 23:56:20 - mmengine - INFO - Epoch(train) [32][540/940] lr: 1.0000e-02 eta: 19:44:23 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.7714 loss: 1.3145 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3145 2023/07/24 23:56:42 - mmengine - INFO - Epoch(train) [32][560/940] lr: 1.0000e-02 eta: 19:44:00 time: 1.1007 data_time: 0.0125 memory: 15768 grad_norm: 3.7810 loss: 1.1237 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1237 2023/07/24 23:57:04 - mmengine - INFO - Epoch(train) [32][580/940] lr: 1.0000e-02 eta: 19:43:38 time: 1.1002 data_time: 0.0127 memory: 15768 grad_norm: 3.7647 loss: 1.0853 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0853 2023/07/24 23:57:26 - mmengine - INFO - Epoch(train) [32][600/940] lr: 1.0000e-02 eta: 19:43:16 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.7654 loss: 1.1571 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1571 2023/07/24 23:57:48 - mmengine - INFO - Epoch(train) [32][620/940] lr: 1.0000e-02 eta: 19:42:53 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.7764 loss: 1.0636 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0636 2023/07/24 23:58:10 - mmengine - INFO - Epoch(train) [32][640/940] lr: 1.0000e-02 eta: 19:42:31 time: 1.1013 data_time: 0.0130 memory: 15768 grad_norm: 3.8353 loss: 1.4944 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4944 2023/07/24 23:58:32 - mmengine - INFO - Epoch(train) [32][660/940] lr: 1.0000e-02 eta: 19:42:09 time: 1.1029 data_time: 0.0125 memory: 15768 grad_norm: 3.7946 loss: 1.1594 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1594 2023/07/24 23:58:54 - mmengine - INFO - Epoch(train) [32][680/940] lr: 1.0000e-02 eta: 19:41:47 time: 1.1056 data_time: 0.0133 memory: 15768 grad_norm: 3.8374 loss: 1.3601 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3601 2023/07/24 23:59:16 - mmengine - INFO - Epoch(train) [32][700/940] lr: 1.0000e-02 eta: 19:41:25 time: 1.1011 data_time: 0.0127 memory: 15768 grad_norm: 3.8487 loss: 1.2878 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2878 2023/07/24 23:59:38 - mmengine - INFO - Epoch(train) [32][720/940] lr: 1.0000e-02 eta: 19:41:02 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.8692 loss: 1.3005 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3005 2023/07/25 00:00:00 - mmengine - INFO - Epoch(train) [32][740/940] lr: 1.0000e-02 eta: 19:40:40 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 3.9484 loss: 1.6649 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6649 2023/07/25 00:00:22 - mmengine - INFO - Epoch(train) [32][760/940] lr: 1.0000e-02 eta: 19:40:18 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.9243 loss: 1.3202 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3202 2023/07/25 00:00:44 - mmengine - INFO - Epoch(train) [32][780/940] lr: 1.0000e-02 eta: 19:39:56 time: 1.1018 data_time: 0.0130 memory: 15768 grad_norm: 3.8736 loss: 1.3357 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3357 2023/07/25 00:01:06 - mmengine - INFO - Epoch(train) [32][800/940] lr: 1.0000e-02 eta: 19:39:33 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.7807 loss: 1.3040 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3040 2023/07/25 00:01:28 - mmengine - INFO - Epoch(train) [32][820/940] lr: 1.0000e-02 eta: 19:39:11 time: 1.0981 data_time: 0.0126 memory: 15768 grad_norm: 3.8306 loss: 1.2131 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2131 2023/07/25 00:01:50 - mmengine - INFO - Epoch(train) [32][840/940] lr: 1.0000e-02 eta: 19:38:49 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 3.8453 loss: 1.2396 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2396 2023/07/25 00:02:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:02:12 - mmengine - INFO - Epoch(train) [32][860/940] lr: 1.0000e-02 eta: 19:38:26 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 3.8907 loss: 1.2932 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2932 2023/07/25 00:02:34 - mmengine - INFO - Epoch(train) [32][880/940] lr: 1.0000e-02 eta: 19:38:04 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.8331 loss: 1.2697 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2697 2023/07/25 00:02:56 - mmengine - INFO - Epoch(train) [32][900/940] lr: 1.0000e-02 eta: 19:37:42 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.8938 loss: 1.1607 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1607 2023/07/25 00:03:18 - mmengine - INFO - Epoch(train) [32][920/940] lr: 1.0000e-02 eta: 19:37:19 time: 1.1024 data_time: 0.0131 memory: 15768 grad_norm: 3.8452 loss: 1.2845 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2845 2023/07/25 00:03:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:03:39 - mmengine - INFO - Epoch(train) [32][940/940] lr: 1.0000e-02 eta: 19:36:55 time: 1.0570 data_time: 0.0123 memory: 15768 grad_norm: 4.1063 loss: 1.2847 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2847 2023/07/25 00:03:49 - mmengine - INFO - Epoch(val) [32][20/78] eta: 0:00:28 time: 0.4921 data_time: 0.3346 memory: 2147 2023/07/25 00:03:56 - mmengine - INFO - Epoch(val) [32][40/78] eta: 0:00:16 time: 0.3610 data_time: 0.2039 memory: 2147 2023/07/25 00:04:06 - mmengine - INFO - Epoch(val) [32][60/78] eta: 0:00:07 time: 0.4659 data_time: 0.3092 memory: 2147 2023/07/25 00:04:16 - mmengine - INFO - Epoch(val) [32][78/78] acc/top1: 0.6603 acc/top5: 0.8703 acc/mean1: 0.6602 data_time: 0.2535 time: 0.4079 2023/07/25 00:04:41 - mmengine - INFO - Epoch(train) [33][ 20/940] lr: 1.0000e-02 eta: 19:36:40 time: 1.2757 data_time: 0.1426 memory: 15768 grad_norm: 3.6995 loss: 1.2602 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2602 2023/07/25 00:05:04 - mmengine - INFO - Epoch(train) [33][ 40/940] lr: 1.0000e-02 eta: 19:36:18 time: 1.1047 data_time: 0.0135 memory: 15768 grad_norm: 3.7600 loss: 1.4566 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.4566 2023/07/25 00:05:26 - mmengine - INFO - Epoch(train) [33][ 60/940] lr: 1.0000e-02 eta: 19:35:56 time: 1.1010 data_time: 0.0128 memory: 15768 grad_norm: 3.7448 loss: 1.2089 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2089 2023/07/25 00:05:48 - mmengine - INFO - Epoch(train) [33][ 80/940] lr: 1.0000e-02 eta: 19:35:34 time: 1.1042 data_time: 0.0131 memory: 15768 grad_norm: 3.7899 loss: 1.2648 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2648 2023/07/25 00:06:10 - mmengine - INFO - Epoch(train) [33][100/940] lr: 1.0000e-02 eta: 19:35:12 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.8508 loss: 1.2557 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2557 2023/07/25 00:06:32 - mmengine - INFO - Epoch(train) [33][120/940] lr: 1.0000e-02 eta: 19:34:49 time: 1.1010 data_time: 0.0127 memory: 15768 grad_norm: 3.7476 loss: 1.2934 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2934 2023/07/25 00:06:54 - mmengine - INFO - Epoch(train) [33][140/940] lr: 1.0000e-02 eta: 19:34:27 time: 1.1019 data_time: 0.0131 memory: 15768 grad_norm: 3.8989 loss: 1.1024 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1024 2023/07/25 00:07:16 - mmengine - INFO - Epoch(train) [33][160/940] lr: 1.0000e-02 eta: 19:34:05 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.8725 loss: 1.2107 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2107 2023/07/25 00:07:38 - mmengine - INFO - Epoch(train) [33][180/940] lr: 1.0000e-02 eta: 19:33:43 time: 1.1027 data_time: 0.0128 memory: 15768 grad_norm: 3.8802 loss: 1.2845 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2845 2023/07/25 00:08:00 - mmengine - INFO - Epoch(train) [33][200/940] lr: 1.0000e-02 eta: 19:33:20 time: 1.1005 data_time: 0.0126 memory: 15768 grad_norm: 3.8566 loss: 1.3758 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3758 2023/07/25 00:08:22 - mmengine - INFO - Epoch(train) [33][220/940] lr: 1.0000e-02 eta: 19:32:58 time: 1.0986 data_time: 0.0130 memory: 15768 grad_norm: 3.8072 loss: 1.2997 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2997 2023/07/25 00:08:44 - mmengine - INFO - Epoch(train) [33][240/940] lr: 1.0000e-02 eta: 19:32:36 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.8328 loss: 1.3341 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3341 2023/07/25 00:09:06 - mmengine - INFO - Epoch(train) [33][260/940] lr: 1.0000e-02 eta: 19:32:14 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.8232 loss: 1.1465 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1465 2023/07/25 00:09:28 - mmengine - INFO - Epoch(train) [33][280/940] lr: 1.0000e-02 eta: 19:31:51 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.8557 loss: 1.3729 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3729 2023/07/25 00:09:50 - mmengine - INFO - Epoch(train) [33][300/940] lr: 1.0000e-02 eta: 19:31:29 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.9285 loss: 1.1503 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1503 2023/07/25 00:10:12 - mmengine - INFO - Epoch(train) [33][320/940] lr: 1.0000e-02 eta: 19:31:07 time: 1.1013 data_time: 0.0127 memory: 15768 grad_norm: 3.9204 loss: 1.1585 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1585 2023/07/25 00:10:34 - mmengine - INFO - Epoch(train) [33][340/940] lr: 1.0000e-02 eta: 19:30:45 time: 1.1018 data_time: 0.0129 memory: 15768 grad_norm: 3.8457 loss: 1.2348 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2348 2023/07/25 00:10:56 - mmengine - INFO - Epoch(train) [33][360/940] lr: 1.0000e-02 eta: 19:30:22 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.9176 loss: 1.3145 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3145 2023/07/25 00:11:18 - mmengine - INFO - Epoch(train) [33][380/940] lr: 1.0000e-02 eta: 19:30:00 time: 1.0991 data_time: 0.0127 memory: 15768 grad_norm: 3.8277 loss: 1.2333 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2333 2023/07/25 00:11:40 - mmengine - INFO - Epoch(train) [33][400/940] lr: 1.0000e-02 eta: 19:29:38 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 3.8761 loss: 1.2805 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2805 2023/07/25 00:12:02 - mmengine - INFO - Epoch(train) [33][420/940] lr: 1.0000e-02 eta: 19:29:15 time: 1.0998 data_time: 0.0126 memory: 15768 grad_norm: 3.9259 loss: 1.2883 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2883 2023/07/25 00:12:24 - mmengine - INFO - Epoch(train) [33][440/940] lr: 1.0000e-02 eta: 19:28:53 time: 1.1018 data_time: 0.0131 memory: 15768 grad_norm: 3.9096 loss: 1.2147 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2147 2023/07/25 00:12:46 - mmengine - INFO - Epoch(train) [33][460/940] lr: 1.0000e-02 eta: 19:28:31 time: 1.1036 data_time: 0.0127 memory: 15768 grad_norm: 3.7976 loss: 1.0451 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0451 2023/07/25 00:13:08 - mmengine - INFO - Epoch(train) [33][480/940] lr: 1.0000e-02 eta: 19:28:09 time: 1.1013 data_time: 0.0127 memory: 15768 grad_norm: 3.8894 loss: 1.2142 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2142 2023/07/25 00:13:30 - mmengine - INFO - Epoch(train) [33][500/940] lr: 1.0000e-02 eta: 19:27:46 time: 1.1003 data_time: 0.0127 memory: 15768 grad_norm: 3.9642 loss: 1.2182 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2182 2023/07/25 00:13:52 - mmengine - INFO - Epoch(train) [33][520/940] lr: 1.0000e-02 eta: 19:27:24 time: 1.1030 data_time: 0.0126 memory: 15768 grad_norm: 3.7967 loss: 1.3182 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3182 2023/07/25 00:14:14 - mmengine - INFO - Epoch(train) [33][540/940] lr: 1.0000e-02 eta: 19:27:02 time: 1.1015 data_time: 0.0127 memory: 15768 grad_norm: 3.9043 loss: 1.2190 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2190 2023/07/25 00:14:36 - mmengine - INFO - Epoch(train) [33][560/940] lr: 1.0000e-02 eta: 19:26:40 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 3.8727 loss: 1.3109 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3109 2023/07/25 00:14:58 - mmengine - INFO - Epoch(train) [33][580/940] lr: 1.0000e-02 eta: 19:26:18 time: 1.1035 data_time: 0.0129 memory: 15768 grad_norm: 3.8183 loss: 1.4296 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4296 2023/07/25 00:15:20 - mmengine - INFO - Epoch(train) [33][600/940] lr: 1.0000e-02 eta: 19:25:55 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 3.8824 loss: 1.1588 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1588 2023/07/25 00:15:42 - mmengine - INFO - Epoch(train) [33][620/940] lr: 1.0000e-02 eta: 19:25:33 time: 1.0982 data_time: 0.0127 memory: 15768 grad_norm: 3.8726 loss: 1.2330 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2330 2023/07/25 00:16:04 - mmengine - INFO - Epoch(train) [33][640/940] lr: 1.0000e-02 eta: 19:25:11 time: 1.1026 data_time: 0.0135 memory: 15768 grad_norm: 3.7846 loss: 1.4840 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4840 2023/07/25 00:16:26 - mmengine - INFO - Epoch(train) [33][660/940] lr: 1.0000e-02 eta: 19:24:49 time: 1.1031 data_time: 0.0132 memory: 15768 grad_norm: 3.8813 loss: 1.2392 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2392 2023/07/25 00:16:48 - mmengine - INFO - Epoch(train) [33][680/940] lr: 1.0000e-02 eta: 19:24:26 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.7854 loss: 1.2853 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2853 2023/07/25 00:17:10 - mmengine - INFO - Epoch(train) [33][700/940] lr: 1.0000e-02 eta: 19:24:04 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.8781 loss: 1.3155 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3155 2023/07/25 00:17:32 - mmengine - INFO - Epoch(train) [33][720/940] lr: 1.0000e-02 eta: 19:23:42 time: 1.0981 data_time: 0.0128 memory: 15768 grad_norm: 3.8877 loss: 1.3857 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3857 2023/07/25 00:17:54 - mmengine - INFO - Epoch(train) [33][740/940] lr: 1.0000e-02 eta: 19:23:19 time: 1.1001 data_time: 0.0130 memory: 15768 grad_norm: 3.9001 loss: 1.3588 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.3588 2023/07/25 00:18:16 - mmengine - INFO - Epoch(train) [33][760/940] lr: 1.0000e-02 eta: 19:22:57 time: 1.1025 data_time: 0.0129 memory: 15768 grad_norm: 3.8404 loss: 1.1558 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1558 2023/07/25 00:18:38 - mmengine - INFO - Epoch(train) [33][780/940] lr: 1.0000e-02 eta: 19:22:35 time: 1.1015 data_time: 0.0128 memory: 15768 grad_norm: 3.8168 loss: 1.0805 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0805 2023/07/25 00:19:00 - mmengine - INFO - Epoch(train) [33][800/940] lr: 1.0000e-02 eta: 19:22:13 time: 1.1024 data_time: 0.0130 memory: 15768 grad_norm: 3.7942 loss: 1.2577 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2577 2023/07/25 00:19:22 - mmengine - INFO - Epoch(train) [33][820/940] lr: 1.0000e-02 eta: 19:21:50 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.8615 loss: 1.3380 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3380 2023/07/25 00:19:44 - mmengine - INFO - Epoch(train) [33][840/940] lr: 1.0000e-02 eta: 19:21:28 time: 1.0993 data_time: 0.0130 memory: 15768 grad_norm: 3.9092 loss: 1.2922 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2922 2023/07/25 00:20:06 - mmengine - INFO - Epoch(train) [33][860/940] lr: 1.0000e-02 eta: 19:21:06 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 3.8442 loss: 1.2720 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2720 2023/07/25 00:20:28 - mmengine - INFO - Epoch(train) [33][880/940] lr: 1.0000e-02 eta: 19:20:44 time: 1.1019 data_time: 0.0131 memory: 15768 grad_norm: 3.9521 loss: 1.3694 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3694 2023/07/25 00:20:50 - mmengine - INFO - Epoch(train) [33][900/940] lr: 1.0000e-02 eta: 19:20:21 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 3.8728 loss: 1.2571 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2571 2023/07/25 00:21:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:21:12 - mmengine - INFO - Epoch(train) [33][920/940] lr: 1.0000e-02 eta: 19:19:59 time: 1.0998 data_time: 0.0132 memory: 15768 grad_norm: 3.8838 loss: 1.2814 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2814 2023/07/25 00:21:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:21:34 - mmengine - INFO - Epoch(train) [33][940/940] lr: 1.0000e-02 eta: 19:19:35 time: 1.0532 data_time: 0.0125 memory: 15768 grad_norm: 4.0703 loss: 1.4776 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4776 2023/07/25 00:21:34 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/07/25 00:21:45 - mmengine - INFO - Epoch(val) [33][20/78] eta: 0:00:28 time: 0.4991 data_time: 0.3419 memory: 2147 2023/07/25 00:21:52 - mmengine - INFO - Epoch(val) [33][40/78] eta: 0:00:15 time: 0.3414 data_time: 0.1845 memory: 2147 2023/07/25 00:22:01 - mmengine - INFO - Epoch(val) [33][60/78] eta: 0:00:07 time: 0.4682 data_time: 0.3115 memory: 2147 2023/07/25 00:22:10 - mmengine - INFO - Epoch(val) [33][78/78] acc/top1: 0.6309 acc/top5: 0.8456 acc/mean1: 0.6307 data_time: 0.2494 time: 0.4035 2023/07/25 00:22:36 - mmengine - INFO - Epoch(train) [34][ 20/940] lr: 1.0000e-02 eta: 19:19:21 time: 1.2981 data_time: 0.1501 memory: 15768 grad_norm: 3.8200 loss: 1.1815 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1815 2023/07/25 00:22:58 - mmengine - INFO - Epoch(train) [34][ 40/940] lr: 1.0000e-02 eta: 19:18:58 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.7302 loss: 1.0951 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0951 2023/07/25 00:23:20 - mmengine - INFO - Epoch(train) [34][ 60/940] lr: 1.0000e-02 eta: 19:18:36 time: 1.1033 data_time: 0.0129 memory: 15768 grad_norm: 3.9019 loss: 1.2945 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2945 2023/07/25 00:23:42 - mmengine - INFO - Epoch(train) [34][ 80/940] lr: 1.0000e-02 eta: 19:18:14 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.9641 loss: 1.1225 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1225 2023/07/25 00:24:04 - mmengine - INFO - Epoch(train) [34][100/940] lr: 1.0000e-02 eta: 19:17:52 time: 1.1007 data_time: 0.0128 memory: 15768 grad_norm: 3.8289 loss: 1.2630 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2630 2023/07/25 00:24:26 - mmengine - INFO - Epoch(train) [34][120/940] lr: 1.0000e-02 eta: 19:17:30 time: 1.1028 data_time: 0.0128 memory: 15768 grad_norm: 3.8460 loss: 1.1253 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1253 2023/07/25 00:24:48 - mmengine - INFO - Epoch(train) [34][140/940] lr: 1.0000e-02 eta: 19:17:07 time: 1.1002 data_time: 0.0126 memory: 15768 grad_norm: 3.7915 loss: 1.2136 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2136 2023/07/25 00:25:10 - mmengine - INFO - Epoch(train) [34][160/940] lr: 1.0000e-02 eta: 19:16:45 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 3.8130 loss: 1.3548 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3548 2023/07/25 00:25:32 - mmengine - INFO - Epoch(train) [34][180/940] lr: 1.0000e-02 eta: 19:16:23 time: 1.1031 data_time: 0.0128 memory: 15768 grad_norm: 3.8526 loss: 1.1711 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1711 2023/07/25 00:25:54 - mmengine - INFO - Epoch(train) [34][200/940] lr: 1.0000e-02 eta: 19:16:01 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.9881 loss: 1.2735 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2735 2023/07/25 00:26:16 - mmengine - INFO - Epoch(train) [34][220/940] lr: 1.0000e-02 eta: 19:15:38 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.9728 loss: 1.3026 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3026 2023/07/25 00:26:38 - mmengine - INFO - Epoch(train) [34][240/940] lr: 1.0000e-02 eta: 19:15:16 time: 1.1025 data_time: 0.0126 memory: 15768 grad_norm: 3.8868 loss: 1.2284 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2284 2023/07/25 00:27:01 - mmengine - INFO - Epoch(train) [34][260/940] lr: 1.0000e-02 eta: 19:14:55 time: 1.1348 data_time: 0.0127 memory: 15768 grad_norm: 3.9550 loss: 1.1854 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1854 2023/07/25 00:27:23 - mmengine - INFO - Epoch(train) [34][280/940] lr: 1.0000e-02 eta: 19:14:33 time: 1.1024 data_time: 0.0125 memory: 15768 grad_norm: 3.8162 loss: 1.2512 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2512 2023/07/25 00:27:45 - mmengine - INFO - Epoch(train) [34][300/940] lr: 1.0000e-02 eta: 19:14:11 time: 1.1018 data_time: 0.0128 memory: 15768 grad_norm: 3.7700 loss: 1.3630 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3630 2023/07/25 00:28:07 - mmengine - INFO - Epoch(train) [34][320/940] lr: 1.0000e-02 eta: 19:13:49 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.7633 loss: 1.0395 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0395 2023/07/25 00:28:29 - mmengine - INFO - Epoch(train) [34][340/940] lr: 1.0000e-02 eta: 19:13:26 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 3.8709 loss: 1.2653 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2653 2023/07/25 00:28:51 - mmengine - INFO - Epoch(train) [34][360/940] lr: 1.0000e-02 eta: 19:13:04 time: 1.0978 data_time: 0.0127 memory: 15768 grad_norm: 3.9397 loss: 1.2785 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2785 2023/07/25 00:29:13 - mmengine - INFO - Epoch(train) [34][380/940] lr: 1.0000e-02 eta: 19:12:42 time: 1.0985 data_time: 0.0131 memory: 15768 grad_norm: 3.9184 loss: 1.2920 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2920 2023/07/25 00:29:35 - mmengine - INFO - Epoch(train) [34][400/940] lr: 1.0000e-02 eta: 19:12:20 time: 1.1024 data_time: 0.0128 memory: 15768 grad_norm: 3.9098 loss: 1.3746 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3746 2023/07/25 00:29:57 - mmengine - INFO - Epoch(train) [34][420/940] lr: 1.0000e-02 eta: 19:11:57 time: 1.0981 data_time: 0.0126 memory: 15768 grad_norm: 3.8823 loss: 1.2160 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2160 2023/07/25 00:30:19 - mmengine - INFO - Epoch(train) [34][440/940] lr: 1.0000e-02 eta: 19:11:35 time: 1.0977 data_time: 0.0128 memory: 15768 grad_norm: 3.9236 loss: 1.2964 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2964 2023/07/25 00:30:41 - mmengine - INFO - Epoch(train) [34][460/940] lr: 1.0000e-02 eta: 19:11:12 time: 1.1003 data_time: 0.0130 memory: 15768 grad_norm: 3.8598 loss: 1.1232 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1232 2023/07/25 00:31:03 - mmengine - INFO - Epoch(train) [34][480/940] lr: 1.0000e-02 eta: 19:10:50 time: 1.1035 data_time: 0.0127 memory: 15768 grad_norm: 4.0109 loss: 1.2743 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2743 2023/07/25 00:31:25 - mmengine - INFO - Epoch(train) [34][500/940] lr: 1.0000e-02 eta: 19:10:28 time: 1.0990 data_time: 0.0127 memory: 15768 grad_norm: 3.8593 loss: 1.1983 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1983 2023/07/25 00:31:47 - mmengine - INFO - Epoch(train) [34][520/940] lr: 1.0000e-02 eta: 19:10:06 time: 1.1040 data_time: 0.0128 memory: 15768 grad_norm: 3.8717 loss: 1.1464 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1464 2023/07/25 00:32:09 - mmengine - INFO - Epoch(train) [34][540/940] lr: 1.0000e-02 eta: 19:09:44 time: 1.1007 data_time: 0.0128 memory: 15768 grad_norm: 3.9997 loss: 1.1148 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1148 2023/07/25 00:32:31 - mmengine - INFO - Epoch(train) [34][560/940] lr: 1.0000e-02 eta: 19:09:21 time: 1.1000 data_time: 0.0125 memory: 15768 grad_norm: 3.9481 loss: 1.2905 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2905 2023/07/25 00:32:53 - mmengine - INFO - Epoch(train) [34][580/940] lr: 1.0000e-02 eta: 19:08:59 time: 1.1058 data_time: 0.0127 memory: 15768 grad_norm: 3.9507 loss: 1.3393 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3393 2023/07/25 00:33:15 - mmengine - INFO - Epoch(train) [34][600/940] lr: 1.0000e-02 eta: 19:08:37 time: 1.1020 data_time: 0.0128 memory: 15768 grad_norm: 3.8759 loss: 1.2593 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2593 2023/07/25 00:33:37 - mmengine - INFO - Epoch(train) [34][620/940] lr: 1.0000e-02 eta: 19:08:15 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.8584 loss: 1.1844 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1844 2023/07/25 00:33:59 - mmengine - INFO - Epoch(train) [34][640/940] lr: 1.0000e-02 eta: 19:07:53 time: 1.1027 data_time: 0.0129 memory: 15768 grad_norm: 3.9802 loss: 1.3050 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3050 2023/07/25 00:34:21 - mmengine - INFO - Epoch(train) [34][660/940] lr: 1.0000e-02 eta: 19:07:30 time: 1.1025 data_time: 0.0136 memory: 15768 grad_norm: 4.0082 loss: 1.2221 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2221 2023/07/25 00:34:43 - mmengine - INFO - Epoch(train) [34][680/940] lr: 1.0000e-02 eta: 19:07:08 time: 1.0974 data_time: 0.0128 memory: 15768 grad_norm: 3.8678 loss: 1.3666 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3666 2023/07/25 00:35:05 - mmengine - INFO - Epoch(train) [34][700/940] lr: 1.0000e-02 eta: 19:06:46 time: 1.0989 data_time: 0.0126 memory: 15768 grad_norm: 3.9078 loss: 1.2918 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2918 2023/07/25 00:35:27 - mmengine - INFO - Epoch(train) [34][720/940] lr: 1.0000e-02 eta: 19:06:23 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.9155 loss: 1.3769 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3769 2023/07/25 00:35:49 - mmengine - INFO - Epoch(train) [34][740/940] lr: 1.0000e-02 eta: 19:06:01 time: 1.1000 data_time: 0.0127 memory: 15768 grad_norm: 3.9882 loss: 1.3179 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3179 2023/07/25 00:36:11 - mmengine - INFO - Epoch(train) [34][760/940] lr: 1.0000e-02 eta: 19:05:39 time: 1.0993 data_time: 0.0131 memory: 15768 grad_norm: 3.9560 loss: 1.3506 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3506 2023/07/25 00:36:33 - mmengine - INFO - Epoch(train) [34][780/940] lr: 1.0000e-02 eta: 19:05:17 time: 1.1010 data_time: 0.0127 memory: 15768 grad_norm: 3.8519 loss: 1.2231 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2231 2023/07/25 00:36:55 - mmengine - INFO - Epoch(train) [34][800/940] lr: 1.0000e-02 eta: 19:04:54 time: 1.1029 data_time: 0.0124 memory: 15768 grad_norm: 3.9227 loss: 1.4425 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4425 2023/07/25 00:37:17 - mmengine - INFO - Epoch(train) [34][820/940] lr: 1.0000e-02 eta: 19:04:32 time: 1.1012 data_time: 0.0127 memory: 15768 grad_norm: 3.9303 loss: 1.3150 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.3150 2023/07/25 00:37:39 - mmengine - INFO - Epoch(train) [34][840/940] lr: 1.0000e-02 eta: 19:04:10 time: 1.1006 data_time: 0.0127 memory: 15768 grad_norm: 3.8322 loss: 1.2481 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2481 2023/07/25 00:38:01 - mmengine - INFO - Epoch(train) [34][860/940] lr: 1.0000e-02 eta: 19:03:48 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.8909 loss: 1.3021 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3021 2023/07/25 00:38:23 - mmengine - INFO - Epoch(train) [34][880/940] lr: 1.0000e-02 eta: 19:03:25 time: 1.1024 data_time: 0.0128 memory: 15768 grad_norm: 3.9457 loss: 1.2812 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2812 2023/07/25 00:38:45 - mmengine - INFO - Epoch(train) [34][900/940] lr: 1.0000e-02 eta: 19:03:03 time: 1.1010 data_time: 0.0129 memory: 15768 grad_norm: 3.8881 loss: 1.4702 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4702 2023/07/25 00:39:07 - mmengine - INFO - Epoch(train) [34][920/940] lr: 1.0000e-02 eta: 19:02:41 time: 1.0985 data_time: 0.0130 memory: 15768 grad_norm: 3.9576 loss: 1.2125 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2125 2023/07/25 00:39:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:39:29 - mmengine - INFO - Epoch(train) [34][940/940] lr: 1.0000e-02 eta: 19:02:17 time: 1.0569 data_time: 0.0125 memory: 15768 grad_norm: 4.0910 loss: 1.4164 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.4164 2023/07/25 00:39:38 - mmengine - INFO - Epoch(val) [34][20/78] eta: 0:00:28 time: 0.4897 data_time: 0.3326 memory: 2147 2023/07/25 00:39:45 - mmengine - INFO - Epoch(val) [34][40/78] eta: 0:00:15 time: 0.3374 data_time: 0.1807 memory: 2147 2023/07/25 00:39:54 - mmengine - INFO - Epoch(val) [34][60/78] eta: 0:00:07 time: 0.4568 data_time: 0.3000 memory: 2147 2023/07/25 00:40:05 - mmengine - INFO - Epoch(val) [34][78/78] acc/top1: 0.6392 acc/top5: 0.8530 acc/mean1: 0.6390 data_time: 0.2461 time: 0.4003 2023/07/25 00:40:31 - mmengine - INFO - Epoch(train) [35][ 20/940] lr: 1.0000e-02 eta: 19:02:02 time: 1.2854 data_time: 0.1388 memory: 15768 grad_norm: 3.8664 loss: 1.4051 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4051 2023/07/25 00:40:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:40:53 - mmengine - INFO - Epoch(train) [35][ 40/940] lr: 1.0000e-02 eta: 19:01:40 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 3.9147 loss: 1.1219 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1219 2023/07/25 00:41:15 - mmengine - INFO - Epoch(train) [35][ 60/940] lr: 1.0000e-02 eta: 19:01:17 time: 1.1014 data_time: 0.0125 memory: 15768 grad_norm: 3.8883 loss: 1.2568 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2568 2023/07/25 00:41:37 - mmengine - INFO - Epoch(train) [35][ 80/940] lr: 1.0000e-02 eta: 19:00:55 time: 1.1019 data_time: 0.0128 memory: 15768 grad_norm: 3.7816 loss: 1.2746 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2746 2023/07/25 00:41:59 - mmengine - INFO - Epoch(train) [35][100/940] lr: 1.0000e-02 eta: 19:00:33 time: 1.0991 data_time: 0.0125 memory: 15768 grad_norm: 3.8178 loss: 1.1800 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1800 2023/07/25 00:42:21 - mmengine - INFO - Epoch(train) [35][120/940] lr: 1.0000e-02 eta: 19:00:11 time: 1.1017 data_time: 0.0133 memory: 15768 grad_norm: 3.8464 loss: 1.1141 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1141 2023/07/25 00:42:43 - mmengine - INFO - Epoch(train) [35][140/940] lr: 1.0000e-02 eta: 18:59:48 time: 1.0985 data_time: 0.0124 memory: 15768 grad_norm: 3.8876 loss: 1.2535 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2535 2023/07/25 00:43:05 - mmengine - INFO - Epoch(train) [35][160/940] lr: 1.0000e-02 eta: 18:59:26 time: 1.1060 data_time: 0.0123 memory: 15768 grad_norm: 3.9540 loss: 1.4299 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4299 2023/07/25 00:43:27 - mmengine - INFO - Epoch(train) [35][180/940] lr: 1.0000e-02 eta: 18:59:04 time: 1.0986 data_time: 0.0126 memory: 15768 grad_norm: 3.8245 loss: 1.0629 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0629 2023/07/25 00:43:49 - mmengine - INFO - Epoch(train) [35][200/940] lr: 1.0000e-02 eta: 18:58:42 time: 1.1011 data_time: 0.0124 memory: 15768 grad_norm: 3.8143 loss: 1.2144 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2144 2023/07/25 00:44:11 - mmengine - INFO - Epoch(train) [35][220/940] lr: 1.0000e-02 eta: 18:58:19 time: 1.1026 data_time: 0.0125 memory: 15768 grad_norm: 3.9671 loss: 1.4170 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4170 2023/07/25 00:44:33 - mmengine - INFO - Epoch(train) [35][240/940] lr: 1.0000e-02 eta: 18:57:57 time: 1.1041 data_time: 0.0126 memory: 15768 grad_norm: 3.8679 loss: 1.1580 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1580 2023/07/25 00:44:55 - mmengine - INFO - Epoch(train) [35][260/940] lr: 1.0000e-02 eta: 18:57:35 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 3.7985 loss: 1.0663 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0663 2023/07/25 00:45:17 - mmengine - INFO - Epoch(train) [35][280/940] lr: 1.0000e-02 eta: 18:57:13 time: 1.1013 data_time: 0.0134 memory: 15768 grad_norm: 3.8927 loss: 1.3716 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3716 2023/07/25 00:45:39 - mmengine - INFO - Epoch(train) [35][300/940] lr: 1.0000e-02 eta: 18:56:51 time: 1.1025 data_time: 0.0127 memory: 15768 grad_norm: 3.9036 loss: 1.3539 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3539 2023/07/25 00:46:01 - mmengine - INFO - Epoch(train) [35][320/940] lr: 1.0000e-02 eta: 18:56:28 time: 1.1003 data_time: 0.0126 memory: 15768 grad_norm: 3.9891 loss: 1.2709 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2709 2023/07/25 00:46:23 - mmengine - INFO - Epoch(train) [35][340/940] lr: 1.0000e-02 eta: 18:56:06 time: 1.1005 data_time: 0.0127 memory: 15768 grad_norm: 3.9015 loss: 1.2244 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2244 2023/07/25 00:46:45 - mmengine - INFO - Epoch(train) [35][360/940] lr: 1.0000e-02 eta: 18:55:44 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 3.8036 loss: 1.2551 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2551 2023/07/25 00:47:07 - mmengine - INFO - Epoch(train) [35][380/940] lr: 1.0000e-02 eta: 18:55:22 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.8893 loss: 1.1767 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1767 2023/07/25 00:47:30 - mmengine - INFO - Epoch(train) [35][400/940] lr: 1.0000e-02 eta: 18:54:59 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 3.9631 loss: 1.2003 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2003 2023/07/25 00:47:52 - mmengine - INFO - Epoch(train) [35][420/940] lr: 1.0000e-02 eta: 18:54:37 time: 1.1015 data_time: 0.0126 memory: 15768 grad_norm: 4.0080 loss: 1.3270 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3270 2023/07/25 00:48:14 - mmengine - INFO - Epoch(train) [35][440/940] lr: 1.0000e-02 eta: 18:54:15 time: 1.1007 data_time: 0.0126 memory: 15768 grad_norm: 3.8361 loss: 1.3888 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3888 2023/07/25 00:48:36 - mmengine - INFO - Epoch(train) [35][460/940] lr: 1.0000e-02 eta: 18:53:53 time: 1.0986 data_time: 0.0127 memory: 15768 grad_norm: 3.9509 loss: 1.2424 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2424 2023/07/25 00:48:58 - mmengine - INFO - Epoch(train) [35][480/940] lr: 1.0000e-02 eta: 18:53:30 time: 1.1019 data_time: 0.0132 memory: 15768 grad_norm: 3.8969 loss: 1.4193 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4193 2023/07/25 00:49:20 - mmengine - INFO - Epoch(train) [35][500/940] lr: 1.0000e-02 eta: 18:53:08 time: 1.1003 data_time: 0.0126 memory: 15768 grad_norm: 3.9292 loss: 1.2617 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2617 2023/07/25 00:49:42 - mmengine - INFO - Epoch(train) [35][520/940] lr: 1.0000e-02 eta: 18:52:46 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.9229 loss: 1.1998 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1998 2023/07/25 00:50:04 - mmengine - INFO - Epoch(train) [35][540/940] lr: 1.0000e-02 eta: 18:52:24 time: 1.0994 data_time: 0.0128 memory: 15768 grad_norm: 3.9751 loss: 1.3058 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3058 2023/07/25 00:50:26 - mmengine - INFO - Epoch(train) [35][560/940] lr: 1.0000e-02 eta: 18:52:01 time: 1.0990 data_time: 0.0131 memory: 15768 grad_norm: 3.9089 loss: 1.3737 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3737 2023/07/25 00:50:48 - mmengine - INFO - Epoch(train) [35][580/940] lr: 1.0000e-02 eta: 18:51:39 time: 1.0983 data_time: 0.0129 memory: 15768 grad_norm: 3.9262 loss: 1.1961 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1961 2023/07/25 00:51:10 - mmengine - INFO - Epoch(train) [35][600/940] lr: 1.0000e-02 eta: 18:51:17 time: 1.1005 data_time: 0.0127 memory: 15768 grad_norm: 3.8913 loss: 1.1714 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1714 2023/07/25 00:51:32 - mmengine - INFO - Epoch(train) [35][620/940] lr: 1.0000e-02 eta: 18:50:55 time: 1.1063 data_time: 0.0126 memory: 15768 grad_norm: 4.0222 loss: 1.4462 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4462 2023/07/25 00:51:54 - mmengine - INFO - Epoch(train) [35][640/940] lr: 1.0000e-02 eta: 18:50:32 time: 1.1008 data_time: 0.0135 memory: 15768 grad_norm: 3.8640 loss: 1.1897 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1897 2023/07/25 00:52:16 - mmengine - INFO - Epoch(train) [35][660/940] lr: 1.0000e-02 eta: 18:50:10 time: 1.1025 data_time: 0.0129 memory: 15768 grad_norm: 3.8893 loss: 1.2795 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2795 2023/07/25 00:52:38 - mmengine - INFO - Epoch(train) [35][680/940] lr: 1.0000e-02 eta: 18:49:48 time: 1.0988 data_time: 0.0134 memory: 15768 grad_norm: 3.8941 loss: 1.0894 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0894 2023/07/25 00:53:00 - mmengine - INFO - Epoch(train) [35][700/940] lr: 1.0000e-02 eta: 18:49:26 time: 1.0986 data_time: 0.0127 memory: 15768 grad_norm: 3.9771 loss: 1.4757 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4757 2023/07/25 00:53:22 - mmengine - INFO - Epoch(train) [35][720/940] lr: 1.0000e-02 eta: 18:49:03 time: 1.1030 data_time: 0.0135 memory: 15768 grad_norm: 3.9373 loss: 1.3550 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3550 2023/07/25 00:53:44 - mmengine - INFO - Epoch(train) [35][740/940] lr: 1.0000e-02 eta: 18:48:41 time: 1.1041 data_time: 0.0123 memory: 15768 grad_norm: 3.9630 loss: 1.2486 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2486 2023/07/25 00:54:06 - mmengine - INFO - Epoch(train) [35][760/940] lr: 1.0000e-02 eta: 18:48:19 time: 1.1019 data_time: 0.0129 memory: 15768 grad_norm: 3.8998 loss: 1.1968 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1968 2023/07/25 00:54:28 - mmengine - INFO - Epoch(train) [35][780/940] lr: 1.0000e-02 eta: 18:47:57 time: 1.1017 data_time: 0.0125 memory: 15768 grad_norm: 3.9378 loss: 1.2914 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2914 2023/07/25 00:54:50 - mmengine - INFO - Epoch(train) [35][800/940] lr: 1.0000e-02 eta: 18:47:35 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.9481 loss: 1.2996 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2996 2023/07/25 00:55:12 - mmengine - INFO - Epoch(train) [35][820/940] lr: 1.0000e-02 eta: 18:47:12 time: 1.1018 data_time: 0.0125 memory: 15768 grad_norm: 4.0081 loss: 1.3946 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3946 2023/07/25 00:55:34 - mmengine - INFO - Epoch(train) [35][840/940] lr: 1.0000e-02 eta: 18:46:50 time: 1.0985 data_time: 0.0127 memory: 15768 grad_norm: 3.9494 loss: 1.2618 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2618 2023/07/25 00:55:56 - mmengine - INFO - Epoch(train) [35][860/940] lr: 1.0000e-02 eta: 18:46:28 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 3.9844 loss: 1.3464 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.3464 2023/07/25 00:56:18 - mmengine - INFO - Epoch(train) [35][880/940] lr: 1.0000e-02 eta: 18:46:06 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 3.9663 loss: 1.3964 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3964 2023/07/25 00:56:40 - mmengine - INFO - Epoch(train) [35][900/940] lr: 1.0000e-02 eta: 18:45:43 time: 1.1016 data_time: 0.0130 memory: 15768 grad_norm: 3.9676 loss: 1.2888 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2888 2023/07/25 00:57:02 - mmengine - INFO - Epoch(train) [35][920/940] lr: 1.0000e-02 eta: 18:45:21 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.8668 loss: 1.1844 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1844 2023/07/25 00:57:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:57:23 - mmengine - INFO - Epoch(train) [35][940/940] lr: 1.0000e-02 eta: 18:44:57 time: 1.0553 data_time: 0.0126 memory: 15768 grad_norm: 4.0306 loss: 1.5012 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5012 2023/07/25 00:57:33 - mmengine - INFO - Epoch(val) [35][20/78] eta: 0:00:28 time: 0.4933 data_time: 0.3360 memory: 2147 2023/07/25 00:57:40 - mmengine - INFO - Epoch(val) [35][40/78] eta: 0:00:16 time: 0.3570 data_time: 0.2002 memory: 2147 2023/07/25 00:57:49 - mmengine - INFO - Epoch(val) [35][60/78] eta: 0:00:07 time: 0.4382 data_time: 0.2814 memory: 2147 2023/07/25 00:57:59 - mmengine - INFO - Epoch(val) [35][78/78] acc/top1: 0.6563 acc/top5: 0.8646 acc/mean1: 0.6562 data_time: 0.2459 time: 0.4000 2023/07/25 00:58:25 - mmengine - INFO - Epoch(train) [36][ 20/940] lr: 1.0000e-02 eta: 18:44:42 time: 1.2874 data_time: 0.1590 memory: 15768 grad_norm: 3.7729 loss: 1.2941 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2941 2023/07/25 00:58:47 - mmengine - INFO - Epoch(train) [36][ 40/940] lr: 1.0000e-02 eta: 18:44:20 time: 1.0999 data_time: 0.0128 memory: 15768 grad_norm: 3.8230 loss: 1.2567 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2567 2023/07/25 00:59:09 - mmengine - INFO - Epoch(train) [36][ 60/940] lr: 1.0000e-02 eta: 18:43:57 time: 1.1007 data_time: 0.0125 memory: 15768 grad_norm: 3.8154 loss: 1.2578 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2578 2023/07/25 00:59:31 - mmengine - INFO - Epoch(train) [36][ 80/940] lr: 1.0000e-02 eta: 18:43:35 time: 1.0999 data_time: 0.0128 memory: 15768 grad_norm: 3.8436 loss: 1.0763 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0763 2023/07/25 00:59:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 00:59:53 - mmengine - INFO - Epoch(train) [36][100/940] lr: 1.0000e-02 eta: 18:43:13 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 3.9025 loss: 1.1534 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1534 2023/07/25 01:00:15 - mmengine - INFO - Epoch(train) [36][120/940] lr: 1.0000e-02 eta: 18:42:51 time: 1.0988 data_time: 0.0132 memory: 15768 grad_norm: 3.8972 loss: 1.2900 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2900 2023/07/25 01:00:37 - mmengine - INFO - Epoch(train) [36][140/940] lr: 1.0000e-02 eta: 18:42:28 time: 1.1021 data_time: 0.0127 memory: 15768 grad_norm: 3.9138 loss: 1.0889 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0889 2023/07/25 01:00:59 - mmengine - INFO - Epoch(train) [36][160/940] lr: 1.0000e-02 eta: 18:42:06 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.8619 loss: 1.1258 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1258 2023/07/25 01:01:21 - mmengine - INFO - Epoch(train) [36][180/940] lr: 1.0000e-02 eta: 18:41:44 time: 1.1025 data_time: 0.0128 memory: 15768 grad_norm: 3.9061 loss: 1.3733 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.3733 2023/07/25 01:01:43 - mmengine - INFO - Epoch(train) [36][200/940] lr: 1.0000e-02 eta: 18:41:22 time: 1.1013 data_time: 0.0128 memory: 15768 grad_norm: 3.9524 loss: 1.2580 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2580 2023/07/25 01:02:05 - mmengine - INFO - Epoch(train) [36][220/940] lr: 1.0000e-02 eta: 18:40:59 time: 1.0997 data_time: 0.0128 memory: 15768 grad_norm: 3.8077 loss: 1.3143 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3143 2023/07/25 01:02:28 - mmengine - INFO - Epoch(train) [36][240/940] lr: 1.0000e-02 eta: 18:40:37 time: 1.1066 data_time: 0.0125 memory: 15768 grad_norm: 3.8835 loss: 1.2960 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2960 2023/07/25 01:02:50 - mmengine - INFO - Epoch(train) [36][260/940] lr: 1.0000e-02 eta: 18:40:15 time: 1.1008 data_time: 0.0125 memory: 15768 grad_norm: 3.7960 loss: 1.2849 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2849 2023/07/25 01:03:12 - mmengine - INFO - Epoch(train) [36][280/940] lr: 1.0000e-02 eta: 18:39:53 time: 1.1033 data_time: 0.0128 memory: 15768 grad_norm: 3.9744 loss: 1.3738 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3738 2023/07/25 01:03:34 - mmengine - INFO - Epoch(train) [36][300/940] lr: 1.0000e-02 eta: 18:39:31 time: 1.1018 data_time: 0.0126 memory: 15768 grad_norm: 3.8972 loss: 1.2381 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2381 2023/07/25 01:03:56 - mmengine - INFO - Epoch(train) [36][320/940] lr: 1.0000e-02 eta: 18:39:09 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.8528 loss: 1.1078 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1078 2023/07/25 01:04:18 - mmengine - INFO - Epoch(train) [36][340/940] lr: 1.0000e-02 eta: 18:38:46 time: 1.0997 data_time: 0.0128 memory: 15768 grad_norm: 4.0336 loss: 1.3214 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3214 2023/07/25 01:04:40 - mmengine - INFO - Epoch(train) [36][360/940] lr: 1.0000e-02 eta: 18:38:24 time: 1.1030 data_time: 0.0127 memory: 15768 grad_norm: 4.0595 loss: 1.3510 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3510 2023/07/25 01:05:02 - mmengine - INFO - Epoch(train) [36][380/940] lr: 1.0000e-02 eta: 18:38:02 time: 1.1010 data_time: 0.0129 memory: 15768 grad_norm: 3.9571 loss: 1.1240 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1240 2023/07/25 01:05:24 - mmengine - INFO - Epoch(train) [36][400/940] lr: 1.0000e-02 eta: 18:37:40 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 3.9247 loss: 1.1942 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1942 2023/07/25 01:05:46 - mmengine - INFO - Epoch(train) [36][420/940] lr: 1.0000e-02 eta: 18:37:17 time: 1.1014 data_time: 0.0127 memory: 15768 grad_norm: 3.8546 loss: 1.1628 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1628 2023/07/25 01:06:08 - mmengine - INFO - Epoch(train) [36][440/940] lr: 1.0000e-02 eta: 18:36:55 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 3.9536 loss: 1.3938 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3938 2023/07/25 01:06:30 - mmengine - INFO - Epoch(train) [36][460/940] lr: 1.0000e-02 eta: 18:36:33 time: 1.1001 data_time: 0.0129 memory: 15768 grad_norm: 3.9481 loss: 1.2395 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2395 2023/07/25 01:06:52 - mmengine - INFO - Epoch(train) [36][480/940] lr: 1.0000e-02 eta: 18:36:11 time: 1.1032 data_time: 0.0129 memory: 15768 grad_norm: 3.9168 loss: 1.1571 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1571 2023/07/25 01:07:14 - mmengine - INFO - Epoch(train) [36][500/940] lr: 1.0000e-02 eta: 18:35:48 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 3.9808 loss: 1.2890 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2890 2023/07/25 01:07:36 - mmengine - INFO - Epoch(train) [36][520/940] lr: 1.0000e-02 eta: 18:35:26 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 3.9327 loss: 1.3922 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3922 2023/07/25 01:07:58 - mmengine - INFO - Epoch(train) [36][540/940] lr: 1.0000e-02 eta: 18:35:04 time: 1.1016 data_time: 0.0129 memory: 15768 grad_norm: 3.9524 loss: 1.2098 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2098 2023/07/25 01:08:20 - mmengine - INFO - Epoch(train) [36][560/940] lr: 1.0000e-02 eta: 18:34:42 time: 1.0985 data_time: 0.0131 memory: 15768 grad_norm: 3.8685 loss: 1.1066 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1066 2023/07/25 01:08:42 - mmengine - INFO - Epoch(train) [36][580/940] lr: 1.0000e-02 eta: 18:34:19 time: 1.1012 data_time: 0.0127 memory: 15768 grad_norm: 3.9656 loss: 1.0785 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0785 2023/07/25 01:09:04 - mmengine - INFO - Epoch(train) [36][600/940] lr: 1.0000e-02 eta: 18:33:57 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.9726 loss: 1.2823 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2823 2023/07/25 01:09:26 - mmengine - INFO - Epoch(train) [36][620/940] lr: 1.0000e-02 eta: 18:33:35 time: 1.1011 data_time: 0.0126 memory: 15768 grad_norm: 3.8920 loss: 1.1017 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1017 2023/07/25 01:09:48 - mmengine - INFO - Epoch(train) [36][640/940] lr: 1.0000e-02 eta: 18:33:13 time: 1.1002 data_time: 0.0129 memory: 15768 grad_norm: 3.9640 loss: 1.2678 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2678 2023/07/25 01:10:10 - mmengine - INFO - Epoch(train) [36][660/940] lr: 1.0000e-02 eta: 18:32:51 time: 1.1038 data_time: 0.0129 memory: 15768 grad_norm: 3.9709 loss: 1.1273 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1273 2023/07/25 01:10:32 - mmengine - INFO - Epoch(train) [36][680/940] lr: 1.0000e-02 eta: 18:32:28 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 3.9447 loss: 1.1736 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1736 2023/07/25 01:10:54 - mmengine - INFO - Epoch(train) [36][700/940] lr: 1.0000e-02 eta: 18:32:06 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 4.0004 loss: 1.3242 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3242 2023/07/25 01:11:16 - mmengine - INFO - Epoch(train) [36][720/940] lr: 1.0000e-02 eta: 18:31:44 time: 1.1021 data_time: 0.0139 memory: 15768 grad_norm: 3.9565 loss: 1.2398 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2398 2023/07/25 01:11:38 - mmengine - INFO - Epoch(train) [36][740/940] lr: 1.0000e-02 eta: 18:31:22 time: 1.1034 data_time: 0.0135 memory: 15768 grad_norm: 3.9196 loss: 1.0867 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0867 2023/07/25 01:12:00 - mmengine - INFO - Epoch(train) [36][760/940] lr: 1.0000e-02 eta: 18:30:59 time: 1.0998 data_time: 0.0127 memory: 15768 grad_norm: 3.9410 loss: 1.1322 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1322 2023/07/25 01:12:22 - mmengine - INFO - Epoch(train) [36][780/940] lr: 1.0000e-02 eta: 18:30:37 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 3.9920 loss: 1.4307 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4307 2023/07/25 01:12:44 - mmengine - INFO - Epoch(train) [36][800/940] lr: 1.0000e-02 eta: 18:30:15 time: 1.1008 data_time: 0.0131 memory: 15768 grad_norm: 3.8706 loss: 1.2130 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2130 2023/07/25 01:13:06 - mmengine - INFO - Epoch(train) [36][820/940] lr: 1.0000e-02 eta: 18:29:53 time: 1.0988 data_time: 0.0128 memory: 15768 grad_norm: 3.9289 loss: 1.2427 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2427 2023/07/25 01:13:28 - mmengine - INFO - Epoch(train) [36][840/940] lr: 1.0000e-02 eta: 18:29:30 time: 1.1022 data_time: 0.0128 memory: 15768 grad_norm: 3.9092 loss: 1.0532 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0532 2023/07/25 01:13:50 - mmengine - INFO - Epoch(train) [36][860/940] lr: 1.0000e-02 eta: 18:29:08 time: 1.1027 data_time: 0.0126 memory: 15768 grad_norm: 4.0537 loss: 1.1751 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1751 2023/07/25 01:14:12 - mmengine - INFO - Epoch(train) [36][880/940] lr: 1.0000e-02 eta: 18:28:46 time: 1.1001 data_time: 0.0133 memory: 15768 grad_norm: 3.9718 loss: 1.2603 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.2603 2023/07/25 01:14:34 - mmengine - INFO - Epoch(train) [36][900/940] lr: 1.0000e-02 eta: 18:28:24 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 3.9503 loss: 1.2699 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2699 2023/07/25 01:14:56 - mmengine - INFO - Epoch(train) [36][920/940] lr: 1.0000e-02 eta: 18:28:02 time: 1.1011 data_time: 0.0131 memory: 15768 grad_norm: 3.9897 loss: 1.2046 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2046 2023/07/25 01:15:18 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 01:15:18 - mmengine - INFO - Epoch(train) [36][940/940] lr: 1.0000e-02 eta: 18:27:39 time: 1.1052 data_time: 0.0122 memory: 15768 grad_norm: 4.1406 loss: 1.3862 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.3862 2023/07/25 01:15:18 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/07/25 01:15:29 - mmengine - INFO - Epoch(val) [36][20/78] eta: 0:00:27 time: 0.4777 data_time: 0.3206 memory: 2147 2023/07/25 01:15:36 - mmengine - INFO - Epoch(val) [36][40/78] eta: 0:00:15 time: 0.3612 data_time: 0.2041 memory: 2147 2023/07/25 01:15:45 - mmengine - INFO - Epoch(val) [36][60/78] eta: 0:00:07 time: 0.4374 data_time: 0.2808 memory: 2147 2023/07/25 01:15:54 - mmengine - INFO - Epoch(val) [36][78/78] acc/top1: 0.6703 acc/top5: 0.8766 acc/mean1: 0.6702 data_time: 0.2395 time: 0.3936 2023/07/25 01:16:20 - mmengine - INFO - Epoch(train) [37][ 20/940] lr: 1.0000e-02 eta: 18:27:24 time: 1.3051 data_time: 0.1515 memory: 15768 grad_norm: 3.8714 loss: 1.2366 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2366 2023/07/25 01:16:42 - mmengine - INFO - Epoch(train) [37][ 40/940] lr: 1.0000e-02 eta: 18:27:02 time: 1.1000 data_time: 0.0127 memory: 15768 grad_norm: 3.8575 loss: 1.4053 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4053 2023/07/25 01:17:04 - mmengine - INFO - Epoch(train) [37][ 60/940] lr: 1.0000e-02 eta: 18:26:40 time: 1.1006 data_time: 0.0126 memory: 15768 grad_norm: 3.8947 loss: 1.0772 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0772 2023/07/25 01:17:26 - mmengine - INFO - Epoch(train) [37][ 80/940] lr: 1.0000e-02 eta: 18:26:18 time: 1.1033 data_time: 0.0134 memory: 15768 grad_norm: 3.9190 loss: 1.1064 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1064 2023/07/25 01:17:48 - mmengine - INFO - Epoch(train) [37][100/940] lr: 1.0000e-02 eta: 18:25:56 time: 1.1009 data_time: 0.0133 memory: 15768 grad_norm: 3.9428 loss: 1.2000 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2000 2023/07/25 01:18:10 - mmengine - INFO - Epoch(train) [37][120/940] lr: 1.0000e-02 eta: 18:25:33 time: 1.0979 data_time: 0.0128 memory: 15768 grad_norm: 3.7933 loss: 1.1369 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1369 2023/07/25 01:18:32 - mmengine - INFO - Epoch(train) [37][140/940] lr: 1.0000e-02 eta: 18:25:11 time: 1.1040 data_time: 0.0128 memory: 15768 grad_norm: 3.9296 loss: 1.3592 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3592 2023/07/25 01:18:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 01:18:55 - mmengine - INFO - Epoch(train) [37][160/940] lr: 1.0000e-02 eta: 18:24:49 time: 1.1008 data_time: 0.0126 memory: 15768 grad_norm: 3.9819 loss: 1.1962 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1962 2023/07/25 01:19:17 - mmengine - INFO - Epoch(train) [37][180/940] lr: 1.0000e-02 eta: 18:24:27 time: 1.1025 data_time: 0.0123 memory: 15768 grad_norm: 4.0006 loss: 1.2964 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2964 2023/07/25 01:19:39 - mmengine - INFO - Epoch(train) [37][200/940] lr: 1.0000e-02 eta: 18:24:04 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 3.8641 loss: 1.0947 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0947 2023/07/25 01:20:01 - mmengine - INFO - Epoch(train) [37][220/940] lr: 1.0000e-02 eta: 18:23:42 time: 1.1022 data_time: 0.0127 memory: 15768 grad_norm: 3.9822 loss: 1.3741 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3741 2023/07/25 01:20:23 - mmengine - INFO - Epoch(train) [37][240/940] lr: 1.0000e-02 eta: 18:23:20 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 4.0034 loss: 1.1642 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1642 2023/07/25 01:20:45 - mmengine - INFO - Epoch(train) [37][260/940] lr: 1.0000e-02 eta: 18:22:58 time: 1.0975 data_time: 0.0129 memory: 15768 grad_norm: 3.8581 loss: 1.1713 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1713 2023/07/25 01:21:07 - mmengine - INFO - Epoch(train) [37][280/940] lr: 1.0000e-02 eta: 18:22:35 time: 1.0998 data_time: 0.0128 memory: 15768 grad_norm: 3.9227 loss: 1.1252 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1252 2023/07/25 01:21:29 - mmengine - INFO - Epoch(train) [37][300/940] lr: 1.0000e-02 eta: 18:22:13 time: 1.1029 data_time: 0.0123 memory: 15768 grad_norm: 3.9604 loss: 1.2018 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2018 2023/07/25 01:21:51 - mmengine - INFO - Epoch(train) [37][320/940] lr: 1.0000e-02 eta: 18:21:51 time: 1.1038 data_time: 0.0129 memory: 15768 grad_norm: 3.9089 loss: 1.1611 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1611 2023/07/25 01:22:13 - mmengine - INFO - Epoch(train) [37][340/940] lr: 1.0000e-02 eta: 18:21:29 time: 1.1002 data_time: 0.0129 memory: 15768 grad_norm: 3.9709 loss: 1.2450 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2450 2023/07/25 01:22:35 - mmengine - INFO - Epoch(train) [37][360/940] lr: 1.0000e-02 eta: 18:21:06 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 3.8534 loss: 1.2293 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2293 2023/07/25 01:22:57 - mmengine - INFO - Epoch(train) [37][380/940] lr: 1.0000e-02 eta: 18:20:44 time: 1.1001 data_time: 0.0126 memory: 15768 grad_norm: 3.9689 loss: 1.2697 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2697 2023/07/25 01:23:19 - mmengine - INFO - Epoch(train) [37][400/940] lr: 1.0000e-02 eta: 18:20:22 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 3.9257 loss: 1.2674 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2674 2023/07/25 01:23:41 - mmengine - INFO - Epoch(train) [37][420/940] lr: 1.0000e-02 eta: 18:20:00 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 3.8238 loss: 1.1754 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1754 2023/07/25 01:24:03 - mmengine - INFO - Epoch(train) [37][440/940] lr: 1.0000e-02 eta: 18:19:38 time: 1.1014 data_time: 0.0130 memory: 15768 grad_norm: 4.0095 loss: 1.4142 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4142 2023/07/25 01:24:25 - mmengine - INFO - Epoch(train) [37][460/940] lr: 1.0000e-02 eta: 18:19:15 time: 1.1007 data_time: 0.0125 memory: 15768 grad_norm: 4.0430 loss: 1.4774 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4774 2023/07/25 01:24:47 - mmengine - INFO - Epoch(train) [37][480/940] lr: 1.0000e-02 eta: 18:18:53 time: 1.1004 data_time: 0.0124 memory: 15768 grad_norm: 3.9236 loss: 1.2852 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2852 2023/07/25 01:25:09 - mmengine - INFO - Epoch(train) [37][500/940] lr: 1.0000e-02 eta: 18:18:31 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 3.9709 loss: 1.0694 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0694 2023/07/25 01:25:31 - mmengine - INFO - Epoch(train) [37][520/940] lr: 1.0000e-02 eta: 18:18:09 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.8779 loss: 1.2498 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2498 2023/07/25 01:25:53 - mmengine - INFO - Epoch(train) [37][540/940] lr: 1.0000e-02 eta: 18:17:46 time: 1.1015 data_time: 0.0130 memory: 15768 grad_norm: 4.0050 loss: 1.0814 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0814 2023/07/25 01:26:15 - mmengine - INFO - Epoch(train) [37][560/940] lr: 1.0000e-02 eta: 18:17:24 time: 1.1012 data_time: 0.0134 memory: 15768 grad_norm: 3.9818 loss: 1.3124 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3124 2023/07/25 01:26:37 - mmengine - INFO - Epoch(train) [37][580/940] lr: 1.0000e-02 eta: 18:17:02 time: 1.1015 data_time: 0.0127 memory: 15768 grad_norm: 3.9396 loss: 1.1116 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1116 2023/07/25 01:26:59 - mmengine - INFO - Epoch(train) [37][600/940] lr: 1.0000e-02 eta: 18:16:40 time: 1.0997 data_time: 0.0133 memory: 15768 grad_norm: 4.0072 loss: 1.2223 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2223 2023/07/25 01:27:21 - mmengine - INFO - Epoch(train) [37][620/940] lr: 1.0000e-02 eta: 18:16:17 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 3.9781 loss: 1.1607 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1607 2023/07/25 01:27:43 - mmengine - INFO - Epoch(train) [37][640/940] lr: 1.0000e-02 eta: 18:15:55 time: 1.0990 data_time: 0.0132 memory: 15768 grad_norm: 3.9168 loss: 1.3167 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3167 2023/07/25 01:28:05 - mmengine - INFO - Epoch(train) [37][660/940] lr: 1.0000e-02 eta: 18:15:33 time: 1.0983 data_time: 0.0126 memory: 15768 grad_norm: 3.9103 loss: 1.1805 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1805 2023/07/25 01:28:27 - mmengine - INFO - Epoch(train) [37][680/940] lr: 1.0000e-02 eta: 18:15:11 time: 1.1031 data_time: 0.0130 memory: 15768 grad_norm: 3.9952 loss: 1.2010 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2010 2023/07/25 01:28:49 - mmengine - INFO - Epoch(train) [37][700/940] lr: 1.0000e-02 eta: 18:14:48 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.9089 loss: 1.2461 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2461 2023/07/25 01:29:11 - mmengine - INFO - Epoch(train) [37][720/940] lr: 1.0000e-02 eta: 18:14:26 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.9445 loss: 1.2324 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2324 2023/07/25 01:29:33 - mmengine - INFO - Epoch(train) [37][740/940] lr: 1.0000e-02 eta: 18:14:04 time: 1.0976 data_time: 0.0130 memory: 15768 grad_norm: 3.9063 loss: 1.1369 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1369 2023/07/25 01:29:55 - mmengine - INFO - Epoch(train) [37][760/940] lr: 1.0000e-02 eta: 18:13:41 time: 1.0972 data_time: 0.0129 memory: 15768 grad_norm: 3.8429 loss: 1.2844 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2844 2023/07/25 01:30:17 - mmengine - INFO - Epoch(train) [37][780/940] lr: 1.0000e-02 eta: 18:13:19 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.9532 loss: 1.2109 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2109 2023/07/25 01:30:39 - mmengine - INFO - Epoch(train) [37][800/940] lr: 1.0000e-02 eta: 18:12:57 time: 1.1009 data_time: 0.0128 memory: 15768 grad_norm: 3.9323 loss: 1.2720 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2720 2023/07/25 01:31:01 - mmengine - INFO - Epoch(train) [37][820/940] lr: 1.0000e-02 eta: 18:12:35 time: 1.1026 data_time: 0.0128 memory: 15768 grad_norm: 3.8888 loss: 1.1361 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1361 2023/07/25 01:31:23 - mmengine - INFO - Epoch(train) [37][840/940] lr: 1.0000e-02 eta: 18:12:12 time: 1.0986 data_time: 0.0130 memory: 15768 grad_norm: 3.9761 loss: 1.1722 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1722 2023/07/25 01:31:45 - mmengine - INFO - Epoch(train) [37][860/940] lr: 1.0000e-02 eta: 18:11:50 time: 1.1008 data_time: 0.0131 memory: 15768 grad_norm: 3.9513 loss: 1.2123 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2123 2023/07/25 01:32:07 - mmengine - INFO - Epoch(train) [37][880/940] lr: 1.0000e-02 eta: 18:11:28 time: 1.0986 data_time: 0.0130 memory: 15768 grad_norm: 4.0505 loss: 1.1733 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1733 2023/07/25 01:32:29 - mmengine - INFO - Epoch(train) [37][900/940] lr: 1.0000e-02 eta: 18:11:06 time: 1.0980 data_time: 0.0129 memory: 15768 grad_norm: 3.9955 loss: 1.2116 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2116 2023/07/25 01:32:51 - mmengine - INFO - Epoch(train) [37][920/940] lr: 1.0000e-02 eta: 18:10:43 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 3.9902 loss: 1.1860 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1860 2023/07/25 01:33:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 01:33:12 - mmengine - INFO - Epoch(train) [37][940/940] lr: 1.0000e-02 eta: 18:10:19 time: 1.0541 data_time: 0.0125 memory: 15768 grad_norm: 4.2924 loss: 1.1919 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1919 2023/07/25 01:33:21 - mmengine - INFO - Epoch(val) [37][20/78] eta: 0:00:27 time: 0.4742 data_time: 0.3174 memory: 2147 2023/07/25 01:33:28 - mmengine - INFO - Epoch(val) [37][40/78] eta: 0:00:15 time: 0.3457 data_time: 0.1887 memory: 2147 2023/07/25 01:33:37 - mmengine - INFO - Epoch(val) [37][60/78] eta: 0:00:07 time: 0.4375 data_time: 0.2806 memory: 2147 2023/07/25 01:33:48 - mmengine - INFO - Epoch(val) [37][78/78] acc/top1: 0.6671 acc/top5: 0.8743 acc/mean1: 0.6670 data_time: 0.2392 time: 0.3933 2023/07/25 01:34:15 - mmengine - INFO - Epoch(train) [38][ 20/940] lr: 1.0000e-02 eta: 18:10:05 time: 1.3343 data_time: 0.1345 memory: 15768 grad_norm: 3.8815 loss: 1.2792 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2792 2023/07/25 01:34:38 - mmengine - INFO - Epoch(train) [38][ 40/940] lr: 1.0000e-02 eta: 18:09:45 time: 1.1627 data_time: 0.0128 memory: 15768 grad_norm: 3.8070 loss: 1.1055 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1055 2023/07/25 01:35:01 - mmengine - INFO - Epoch(train) [38][ 60/940] lr: 1.0000e-02 eta: 18:09:25 time: 1.1613 data_time: 0.0127 memory: 15768 grad_norm: 3.9083 loss: 1.2798 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.2798 2023/07/25 01:35:24 - mmengine - INFO - Epoch(train) [38][ 80/940] lr: 1.0000e-02 eta: 18:09:05 time: 1.1569 data_time: 0.0128 memory: 15768 grad_norm: 3.8517 loss: 1.0743 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0743 2023/07/25 01:35:48 - mmengine - INFO - Epoch(train) [38][100/940] lr: 1.0000e-02 eta: 18:08:44 time: 1.1629 data_time: 0.0129 memory: 15768 grad_norm: 3.8947 loss: 1.2555 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2555 2023/07/25 01:36:11 - mmengine - INFO - Epoch(train) [38][120/940] lr: 1.0000e-02 eta: 18:08:24 time: 1.1651 data_time: 0.0128 memory: 15768 grad_norm: 3.9032 loss: 1.2586 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2586 2023/07/25 01:36:34 - mmengine - INFO - Epoch(train) [38][140/940] lr: 1.0000e-02 eta: 18:08:03 time: 1.1360 data_time: 0.0127 memory: 15768 grad_norm: 3.8919 loss: 1.0500 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0500 2023/07/25 01:36:56 - mmengine - INFO - Epoch(train) [38][160/940] lr: 1.0000e-02 eta: 18:07:41 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.9281 loss: 1.1888 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1888 2023/07/25 01:37:18 - mmengine - INFO - Epoch(train) [38][180/940] lr: 1.0000e-02 eta: 18:07:19 time: 1.0999 data_time: 0.0127 memory: 15768 grad_norm: 3.8861 loss: 1.1003 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1003 2023/07/25 01:37:40 - mmengine - INFO - Epoch(train) [38][200/940] lr: 1.0000e-02 eta: 18:06:56 time: 1.0993 data_time: 0.0126 memory: 15768 grad_norm: 3.8692 loss: 1.1910 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1910 2023/07/25 01:38:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 01:38:02 - mmengine - INFO - Epoch(train) [38][220/940] lr: 1.0000e-02 eta: 18:06:34 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 4.0279 loss: 1.2631 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2631 2023/07/25 01:38:24 - mmengine - INFO - Epoch(train) [38][240/940] lr: 1.0000e-02 eta: 18:06:12 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.9687 loss: 1.1659 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1659 2023/07/25 01:38:46 - mmengine - INFO - Epoch(train) [38][260/940] lr: 1.0000e-02 eta: 18:05:50 time: 1.0996 data_time: 0.0127 memory: 15768 grad_norm: 3.9447 loss: 1.2215 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2215 2023/07/25 01:39:08 - mmengine - INFO - Epoch(train) [38][280/940] lr: 1.0000e-02 eta: 18:05:28 time: 1.1020 data_time: 0.0131 memory: 15768 grad_norm: 3.9250 loss: 1.2263 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2263 2023/07/25 01:39:30 - mmengine - INFO - Epoch(train) [38][300/940] lr: 1.0000e-02 eta: 18:05:05 time: 1.1013 data_time: 0.0128 memory: 15768 grad_norm: 3.9016 loss: 1.3111 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3111 2023/07/25 01:39:52 - mmengine - INFO - Epoch(train) [38][320/940] lr: 1.0000e-02 eta: 18:04:43 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 4.0055 loss: 1.1752 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1752 2023/07/25 01:40:14 - mmengine - INFO - Epoch(train) [38][340/940] lr: 1.0000e-02 eta: 18:04:21 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 4.0344 loss: 1.1866 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1866 2023/07/25 01:40:36 - mmengine - INFO - Epoch(train) [38][360/940] lr: 1.0000e-02 eta: 18:03:59 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 4.0416 loss: 1.2150 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2150 2023/07/25 01:40:58 - mmengine - INFO - Epoch(train) [38][380/940] lr: 1.0000e-02 eta: 18:03:36 time: 1.0985 data_time: 0.0128 memory: 15768 grad_norm: 3.9667 loss: 1.1990 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1990 2023/07/25 01:41:20 - mmengine - INFO - Epoch(train) [38][400/940] lr: 1.0000e-02 eta: 18:03:14 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.9442 loss: 1.2258 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2258 2023/07/25 01:41:42 - mmengine - INFO - Epoch(train) [38][420/940] lr: 1.0000e-02 eta: 18:02:52 time: 1.1042 data_time: 0.0127 memory: 15768 grad_norm: 3.8948 loss: 1.2274 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2274 2023/07/25 01:42:04 - mmengine - INFO - Epoch(train) [38][440/940] lr: 1.0000e-02 eta: 18:02:30 time: 1.1008 data_time: 0.0132 memory: 15768 grad_norm: 4.0152 loss: 1.2013 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2013 2023/07/25 01:42:26 - mmengine - INFO - Epoch(train) [38][460/940] lr: 1.0000e-02 eta: 18:02:07 time: 1.1018 data_time: 0.0128 memory: 15768 grad_norm: 4.0600 loss: 1.2292 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2292 2023/07/25 01:42:48 - mmengine - INFO - Epoch(train) [38][480/940] lr: 1.0000e-02 eta: 18:01:45 time: 1.0989 data_time: 0.0126 memory: 15768 grad_norm: 3.9463 loss: 1.2110 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2110 2023/07/25 01:43:10 - mmengine - INFO - Epoch(train) [38][500/940] lr: 1.0000e-02 eta: 18:01:23 time: 1.1020 data_time: 0.0127 memory: 15768 grad_norm: 4.0097 loss: 1.2535 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2535 2023/07/25 01:43:32 - mmengine - INFO - Epoch(train) [38][520/940] lr: 1.0000e-02 eta: 18:01:01 time: 1.1020 data_time: 0.0126 memory: 15768 grad_norm: 3.9732 loss: 1.0948 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0948 2023/07/25 01:43:54 - mmengine - INFO - Epoch(train) [38][540/940] lr: 1.0000e-02 eta: 18:00:39 time: 1.1037 data_time: 0.0128 memory: 15768 grad_norm: 3.9650 loss: 1.2290 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2290 2023/07/25 01:44:16 - mmengine - INFO - Epoch(train) [38][560/940] lr: 1.0000e-02 eta: 18:00:16 time: 1.1018 data_time: 0.0126 memory: 15768 grad_norm: 3.9501 loss: 1.1489 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1489 2023/07/25 01:44:38 - mmengine - INFO - Epoch(train) [38][580/940] lr: 1.0000e-02 eta: 17:59:54 time: 1.0985 data_time: 0.0130 memory: 15768 grad_norm: 3.9555 loss: 1.0574 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0574 2023/07/25 01:45:00 - mmengine - INFO - Epoch(train) [38][600/940] lr: 1.0000e-02 eta: 17:59:32 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.8978 loss: 1.2174 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2174 2023/07/25 01:45:22 - mmengine - INFO - Epoch(train) [38][620/940] lr: 1.0000e-02 eta: 17:59:10 time: 1.1009 data_time: 0.0127 memory: 15768 grad_norm: 4.0555 loss: 1.3654 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3654 2023/07/25 01:45:44 - mmengine - INFO - Epoch(train) [38][640/940] lr: 1.0000e-02 eta: 17:58:47 time: 1.1026 data_time: 0.0131 memory: 15768 grad_norm: 3.9712 loss: 1.2361 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2361 2023/07/25 01:46:06 - mmengine - INFO - Epoch(train) [38][660/940] lr: 1.0000e-02 eta: 17:58:25 time: 1.1021 data_time: 0.0127 memory: 15768 grad_norm: 3.9435 loss: 1.4228 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4228 2023/07/25 01:46:28 - mmengine - INFO - Epoch(train) [38][680/940] lr: 1.0000e-02 eta: 17:58:03 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 3.9925 loss: 1.2461 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2461 2023/07/25 01:46:50 - mmengine - INFO - Epoch(train) [38][700/940] lr: 1.0000e-02 eta: 17:57:41 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.9676 loss: 1.2324 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2324 2023/07/25 01:47:12 - mmengine - INFO - Epoch(train) [38][720/940] lr: 1.0000e-02 eta: 17:57:19 time: 1.1023 data_time: 0.0129 memory: 15768 grad_norm: 3.9935 loss: 1.3833 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3833 2023/07/25 01:47:34 - mmengine - INFO - Epoch(train) [38][740/940] lr: 1.0000e-02 eta: 17:56:56 time: 1.1003 data_time: 0.0130 memory: 15768 grad_norm: 3.9418 loss: 1.0915 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0915 2023/07/25 01:47:56 - mmengine - INFO - Epoch(train) [38][760/940] lr: 1.0000e-02 eta: 17:56:34 time: 1.0996 data_time: 0.0129 memory: 15768 grad_norm: 4.0402 loss: 1.1119 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1119 2023/07/25 01:48:18 - mmengine - INFO - Epoch(train) [38][780/940] lr: 1.0000e-02 eta: 17:56:12 time: 1.0988 data_time: 0.0127 memory: 15768 grad_norm: 3.9245 loss: 1.2739 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2739 2023/07/25 01:48:40 - mmengine - INFO - Epoch(train) [38][800/940] lr: 1.0000e-02 eta: 17:55:50 time: 1.1021 data_time: 0.0128 memory: 15768 grad_norm: 4.0425 loss: 1.3100 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3100 2023/07/25 01:49:02 - mmengine - INFO - Epoch(train) [38][820/940] lr: 1.0000e-02 eta: 17:55:27 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 3.9817 loss: 1.1654 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1654 2023/07/25 01:49:24 - mmengine - INFO - Epoch(train) [38][840/940] lr: 1.0000e-02 eta: 17:55:05 time: 1.1005 data_time: 0.0129 memory: 15768 grad_norm: 3.9645 loss: 1.3142 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.3142 2023/07/25 01:49:46 - mmengine - INFO - Epoch(train) [38][860/940] lr: 1.0000e-02 eta: 17:54:43 time: 1.1009 data_time: 0.0129 memory: 15768 grad_norm: 3.9208 loss: 1.1261 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1261 2023/07/25 01:50:08 - mmengine - INFO - Epoch(train) [38][880/940] lr: 1.0000e-02 eta: 17:54:21 time: 1.0995 data_time: 0.0133 memory: 15768 grad_norm: 3.9870 loss: 1.2372 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2372 2023/07/25 01:50:30 - mmengine - INFO - Epoch(train) [38][900/940] lr: 1.0000e-02 eta: 17:53:58 time: 1.1013 data_time: 0.0130 memory: 15768 grad_norm: 3.9833 loss: 1.2964 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2964 2023/07/25 01:50:52 - mmengine - INFO - Epoch(train) [38][920/940] lr: 1.0000e-02 eta: 17:53:36 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.9823 loss: 1.2206 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2206 2023/07/25 01:51:15 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 01:51:15 - mmengine - INFO - Epoch(train) [38][940/940] lr: 1.0000e-02 eta: 17:53:14 time: 1.1168 data_time: 0.0124 memory: 15768 grad_norm: 4.1851 loss: 1.3565 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.3565 2023/07/25 01:51:24 - mmengine - INFO - Epoch(val) [38][20/78] eta: 0:00:28 time: 0.4846 data_time: 0.3271 memory: 2147 2023/07/25 01:51:31 - mmengine - INFO - Epoch(val) [38][40/78] eta: 0:00:15 time: 0.3477 data_time: 0.1908 memory: 2147 2023/07/25 01:51:40 - mmengine - INFO - Epoch(val) [38][60/78] eta: 0:00:07 time: 0.4403 data_time: 0.2835 memory: 2147 2023/07/25 01:51:52 - mmengine - INFO - Epoch(val) [38][78/78] acc/top1: 0.6698 acc/top5: 0.8773 acc/mean1: 0.6697 data_time: 0.2434 time: 0.3976 2023/07/25 01:52:18 - mmengine - INFO - Epoch(train) [39][ 20/940] lr: 1.0000e-02 eta: 17:52:59 time: 1.3200 data_time: 0.1717 memory: 15768 grad_norm: 3.8157 loss: 1.1231 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1231 2023/07/25 01:52:42 - mmengine - INFO - Epoch(train) [39][ 40/940] lr: 1.0000e-02 eta: 17:52:40 time: 1.1774 data_time: 0.0127 memory: 15768 grad_norm: 3.9712 loss: 1.2682 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2682 2023/07/25 01:53:05 - mmengine - INFO - Epoch(train) [39][ 60/940] lr: 1.0000e-02 eta: 17:52:19 time: 1.1610 data_time: 0.0130 memory: 15768 grad_norm: 3.8814 loss: 1.2557 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2557 2023/07/25 01:53:28 - mmengine - INFO - Epoch(train) [39][ 80/940] lr: 1.0000e-02 eta: 17:51:59 time: 1.1637 data_time: 0.0129 memory: 15768 grad_norm: 3.8925 loss: 1.2675 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2675 2023/07/25 01:53:52 - mmengine - INFO - Epoch(train) [39][100/940] lr: 1.0000e-02 eta: 17:51:39 time: 1.1643 data_time: 0.0125 memory: 15768 grad_norm: 3.9169 loss: 1.2001 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2001 2023/07/25 01:54:15 - mmengine - INFO - Epoch(train) [39][120/940] lr: 1.0000e-02 eta: 17:51:19 time: 1.1644 data_time: 0.0125 memory: 15768 grad_norm: 3.9284 loss: 1.1033 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1033 2023/07/25 01:54:38 - mmengine - INFO - Epoch(train) [39][140/940] lr: 1.0000e-02 eta: 17:50:58 time: 1.1380 data_time: 0.0124 memory: 15768 grad_norm: 4.0020 loss: 1.3030 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3030 2023/07/25 01:55:00 - mmengine - INFO - Epoch(train) [39][160/940] lr: 1.0000e-02 eta: 17:50:35 time: 1.0994 data_time: 0.0128 memory: 15768 grad_norm: 3.9020 loss: 1.2920 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2920 2023/07/25 01:55:22 - mmengine - INFO - Epoch(train) [39][180/940] lr: 1.0000e-02 eta: 17:50:13 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 3.8484 loss: 1.0525 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0525 2023/07/25 01:55:44 - mmengine - INFO - Epoch(train) [39][200/940] lr: 1.0000e-02 eta: 17:49:51 time: 1.0985 data_time: 0.0128 memory: 15768 grad_norm: 3.9898 loss: 1.1727 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1727 2023/07/25 01:56:06 - mmengine - INFO - Epoch(train) [39][220/940] lr: 1.0000e-02 eta: 17:49:29 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.9448 loss: 1.1837 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1837 2023/07/25 01:56:28 - mmengine - INFO - Epoch(train) [39][240/940] lr: 1.0000e-02 eta: 17:49:06 time: 1.1044 data_time: 0.0134 memory: 15768 grad_norm: 3.9912 loss: 1.2137 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2137 2023/07/25 01:56:50 - mmengine - INFO - Epoch(train) [39][260/940] lr: 1.0000e-02 eta: 17:48:44 time: 1.1017 data_time: 0.0135 memory: 15768 grad_norm: 3.9536 loss: 1.1815 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1815 2023/07/25 01:57:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 01:57:12 - mmengine - INFO - Epoch(train) [39][280/940] lr: 1.0000e-02 eta: 17:48:22 time: 1.1044 data_time: 0.0131 memory: 15768 grad_norm: 3.9545 loss: 1.1931 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1931 2023/07/25 01:57:34 - mmengine - INFO - Epoch(train) [39][300/940] lr: 1.0000e-02 eta: 17:48:00 time: 1.1020 data_time: 0.0130 memory: 15768 grad_norm: 3.9460 loss: 1.2945 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2945 2023/07/25 01:57:56 - mmengine - INFO - Epoch(train) [39][320/940] lr: 1.0000e-02 eta: 17:47:38 time: 1.1043 data_time: 0.0130 memory: 15768 grad_norm: 3.9499 loss: 1.0337 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0337 2023/07/25 01:58:18 - mmengine - INFO - Epoch(train) [39][340/940] lr: 1.0000e-02 eta: 17:47:15 time: 1.1022 data_time: 0.0129 memory: 15768 grad_norm: 4.0399 loss: 1.3811 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3811 2023/07/25 01:58:40 - mmengine - INFO - Epoch(train) [39][360/940] lr: 1.0000e-02 eta: 17:46:53 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 4.0616 loss: 1.4720 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4720 2023/07/25 01:59:02 - mmengine - INFO - Epoch(train) [39][380/940] lr: 1.0000e-02 eta: 17:46:31 time: 1.1007 data_time: 0.0129 memory: 15768 grad_norm: 3.9849 loss: 1.3345 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3345 2023/07/25 01:59:24 - mmengine - INFO - Epoch(train) [39][400/940] lr: 1.0000e-02 eta: 17:46:09 time: 1.0999 data_time: 0.0128 memory: 15768 grad_norm: 4.0011 loss: 1.2247 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2247 2023/07/25 01:59:46 - mmengine - INFO - Epoch(train) [39][420/940] lr: 1.0000e-02 eta: 17:45:47 time: 1.1027 data_time: 0.0127 memory: 15768 grad_norm: 3.9910 loss: 1.1687 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1687 2023/07/25 02:00:08 - mmengine - INFO - Epoch(train) [39][440/940] lr: 1.0000e-02 eta: 17:45:24 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 3.9351 loss: 1.0165 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0165 2023/07/25 02:00:30 - mmengine - INFO - Epoch(train) [39][460/940] lr: 1.0000e-02 eta: 17:45:02 time: 1.1020 data_time: 0.0128 memory: 15768 grad_norm: 3.9933 loss: 1.1463 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1463 2023/07/25 02:00:52 - mmengine - INFO - Epoch(train) [39][480/940] lr: 1.0000e-02 eta: 17:44:40 time: 1.0987 data_time: 0.0125 memory: 15768 grad_norm: 4.0537 loss: 1.2736 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2736 2023/07/25 02:01:14 - mmengine - INFO - Epoch(train) [39][500/940] lr: 1.0000e-02 eta: 17:44:18 time: 1.1013 data_time: 0.0130 memory: 15768 grad_norm: 3.9963 loss: 1.1888 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1888 2023/07/25 02:01:36 - mmengine - INFO - Epoch(train) [39][520/940] lr: 1.0000e-02 eta: 17:43:55 time: 1.1027 data_time: 0.0135 memory: 15768 grad_norm: 4.0233 loss: 1.2986 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2986 2023/07/25 02:01:58 - mmengine - INFO - Epoch(train) [39][540/940] lr: 1.0000e-02 eta: 17:43:33 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 4.0313 loss: 1.2636 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2636 2023/07/25 02:02:20 - mmengine - INFO - Epoch(train) [39][560/940] lr: 1.0000e-02 eta: 17:43:11 time: 1.0990 data_time: 0.0131 memory: 15768 grad_norm: 3.9344 loss: 1.0929 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0929 2023/07/25 02:02:42 - mmengine - INFO - Epoch(train) [39][580/940] lr: 1.0000e-02 eta: 17:42:49 time: 1.1042 data_time: 0.0124 memory: 15768 grad_norm: 3.9720 loss: 1.2756 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2756 2023/07/25 02:03:04 - mmengine - INFO - Epoch(train) [39][600/940] lr: 1.0000e-02 eta: 17:42:26 time: 1.0970 data_time: 0.0128 memory: 15768 grad_norm: 3.9948 loss: 1.0319 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0319 2023/07/25 02:03:26 - mmengine - INFO - Epoch(train) [39][620/940] lr: 1.0000e-02 eta: 17:42:04 time: 1.0992 data_time: 0.0126 memory: 15768 grad_norm: 4.0124 loss: 1.1656 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1656 2023/07/25 02:03:48 - mmengine - INFO - Epoch(train) [39][640/940] lr: 1.0000e-02 eta: 17:41:42 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 3.9454 loss: 1.1676 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1676 2023/07/25 02:04:10 - mmengine - INFO - Epoch(train) [39][660/940] lr: 1.0000e-02 eta: 17:41:20 time: 1.1032 data_time: 0.0125 memory: 15768 grad_norm: 3.9532 loss: 1.1647 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1647 2023/07/25 02:04:32 - mmengine - INFO - Epoch(train) [39][680/940] lr: 1.0000e-02 eta: 17:40:57 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 3.9308 loss: 1.2109 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2109 2023/07/25 02:04:54 - mmengine - INFO - Epoch(train) [39][700/940] lr: 1.0000e-02 eta: 17:40:35 time: 1.1047 data_time: 0.0129 memory: 15768 grad_norm: 4.0478 loss: 1.2738 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2738 2023/07/25 02:05:16 - mmengine - INFO - Epoch(train) [39][720/940] lr: 1.0000e-02 eta: 17:40:13 time: 1.1024 data_time: 0.0135 memory: 15768 grad_norm: 4.0632 loss: 1.2001 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2001 2023/07/25 02:05:38 - mmengine - INFO - Epoch(train) [39][740/940] lr: 1.0000e-02 eta: 17:39:51 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.9804 loss: 1.2343 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2343 2023/07/25 02:06:00 - mmengine - INFO - Epoch(train) [39][760/940] lr: 1.0000e-02 eta: 17:39:28 time: 1.0986 data_time: 0.0130 memory: 15768 grad_norm: 3.9741 loss: 0.9895 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9895 2023/07/25 02:06:22 - mmengine - INFO - Epoch(train) [39][780/940] lr: 1.0000e-02 eta: 17:39:06 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 3.9475 loss: 1.1121 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1121 2023/07/25 02:06:44 - mmengine - INFO - Epoch(train) [39][800/940] lr: 1.0000e-02 eta: 17:38:44 time: 1.1012 data_time: 0.0131 memory: 15768 grad_norm: 3.9789 loss: 1.3696 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3696 2023/07/25 02:07:06 - mmengine - INFO - Epoch(train) [39][820/940] lr: 1.0000e-02 eta: 17:38:22 time: 1.1030 data_time: 0.0129 memory: 15768 grad_norm: 3.9302 loss: 1.1463 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1463 2023/07/25 02:07:29 - mmengine - INFO - Epoch(train) [39][840/940] lr: 1.0000e-02 eta: 17:38:00 time: 1.1028 data_time: 0.0134 memory: 15768 grad_norm: 4.0252 loss: 1.2002 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2002 2023/07/25 02:07:51 - mmengine - INFO - Epoch(train) [39][860/940] lr: 1.0000e-02 eta: 17:37:37 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 4.0758 loss: 1.0945 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0945 2023/07/25 02:08:13 - mmengine - INFO - Epoch(train) [39][880/940] lr: 1.0000e-02 eta: 17:37:15 time: 1.1003 data_time: 0.0131 memory: 15768 grad_norm: 3.9727 loss: 1.1197 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1197 2023/07/25 02:08:35 - mmengine - INFO - Epoch(train) [39][900/940] lr: 1.0000e-02 eta: 17:36:53 time: 1.0982 data_time: 0.0128 memory: 15768 grad_norm: 4.0229 loss: 1.1230 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1230 2023/07/25 02:08:57 - mmengine - INFO - Epoch(train) [39][920/940] lr: 1.0000e-02 eta: 17:36:31 time: 1.1001 data_time: 0.0130 memory: 15768 grad_norm: 4.0083 loss: 1.0585 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0585 2023/07/25 02:09:18 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 02:09:18 - mmengine - INFO - Epoch(train) [39][940/940] lr: 1.0000e-02 eta: 17:36:07 time: 1.0543 data_time: 0.0126 memory: 15768 grad_norm: 4.1469 loss: 1.1493 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1493 2023/07/25 02:09:18 - mmengine - INFO - Saving checkpoint at 39 epochs 2023/07/25 02:09:29 - mmengine - INFO - Epoch(val) [39][20/78] eta: 0:00:28 time: 0.4946 data_time: 0.3370 memory: 2147 2023/07/25 02:09:36 - mmengine - INFO - Epoch(val) [39][40/78] eta: 0:00:16 time: 0.3501 data_time: 0.1935 memory: 2147 2023/07/25 02:09:45 - mmengine - INFO - Epoch(val) [39][60/78] eta: 0:00:07 time: 0.4444 data_time: 0.2879 memory: 2147 2023/07/25 02:09:53 - mmengine - INFO - Epoch(val) [39][78/78] acc/top1: 0.6429 acc/top5: 0.8549 acc/mean1: 0.6427 data_time: 0.2432 time: 0.3973 2023/07/25 02:10:19 - mmengine - INFO - Epoch(train) [40][ 20/940] lr: 1.0000e-02 eta: 17:35:50 time: 1.2838 data_time: 0.1518 memory: 15768 grad_norm: 3.8897 loss: 1.0985 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0985 2023/07/25 02:10:41 - mmengine - INFO - Epoch(train) [40][ 40/940] lr: 1.0000e-02 eta: 17:35:28 time: 1.0990 data_time: 0.0126 memory: 15768 grad_norm: 3.8798 loss: 1.0531 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0531 2023/07/25 02:11:03 - mmengine - INFO - Epoch(train) [40][ 60/940] lr: 1.0000e-02 eta: 17:35:06 time: 1.0987 data_time: 0.0125 memory: 15768 grad_norm: 3.7874 loss: 1.2614 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2614 2023/07/25 02:11:25 - mmengine - INFO - Epoch(train) [40][ 80/940] lr: 1.0000e-02 eta: 17:34:43 time: 1.1006 data_time: 0.0129 memory: 15768 grad_norm: 3.8663 loss: 1.0506 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0506 2023/07/25 02:11:47 - mmengine - INFO - Epoch(train) [40][100/940] lr: 1.0000e-02 eta: 17:34:21 time: 1.1002 data_time: 0.0127 memory: 15768 grad_norm: 3.9597 loss: 1.3260 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3260 2023/07/25 02:12:09 - mmengine - INFO - Epoch(train) [40][120/940] lr: 1.0000e-02 eta: 17:33:59 time: 1.1010 data_time: 0.0124 memory: 15768 grad_norm: 3.9846 loss: 1.2545 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2545 2023/07/25 02:12:31 - mmengine - INFO - Epoch(train) [40][140/940] lr: 1.0000e-02 eta: 17:33:37 time: 1.1034 data_time: 0.0126 memory: 15768 grad_norm: 4.0042 loss: 1.1941 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1941 2023/07/25 02:12:53 - mmengine - INFO - Epoch(train) [40][160/940] lr: 1.0000e-02 eta: 17:33:14 time: 1.0980 data_time: 0.0128 memory: 15768 grad_norm: 4.0279 loss: 1.1201 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1201 2023/07/25 02:13:15 - mmengine - INFO - Epoch(train) [40][180/940] lr: 1.0000e-02 eta: 17:32:52 time: 1.1031 data_time: 0.0129 memory: 15768 grad_norm: 3.9536 loss: 1.2403 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2403 2023/07/25 02:13:37 - mmengine - INFO - Epoch(train) [40][200/940] lr: 1.0000e-02 eta: 17:32:30 time: 1.0980 data_time: 0.0128 memory: 15768 grad_norm: 3.9506 loss: 1.1052 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1052 2023/07/25 02:13:59 - mmengine - INFO - Epoch(train) [40][220/940] lr: 1.0000e-02 eta: 17:32:08 time: 1.1004 data_time: 0.0127 memory: 15768 grad_norm: 3.9662 loss: 1.1416 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1416 2023/07/25 02:14:21 - mmengine - INFO - Epoch(train) [40][240/940] lr: 1.0000e-02 eta: 17:31:45 time: 1.0993 data_time: 0.0128 memory: 15768 grad_norm: 4.0472 loss: 1.3236 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3236 2023/07/25 02:14:43 - mmengine - INFO - Epoch(train) [40][260/940] lr: 1.0000e-02 eta: 17:31:23 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 3.9065 loss: 1.0960 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0960 2023/07/25 02:15:05 - mmengine - INFO - Epoch(train) [40][280/940] lr: 1.0000e-02 eta: 17:31:01 time: 1.1027 data_time: 0.0130 memory: 15768 grad_norm: 4.0448 loss: 1.2786 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2786 2023/07/25 02:15:27 - mmengine - INFO - Epoch(train) [40][300/940] lr: 1.0000e-02 eta: 17:30:39 time: 1.0995 data_time: 0.0129 memory: 15768 grad_norm: 3.9974 loss: 1.2221 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2221 2023/07/25 02:15:49 - mmengine - INFO - Epoch(train) [40][320/940] lr: 1.0000e-02 eta: 17:30:16 time: 1.0987 data_time: 0.0128 memory: 15768 grad_norm: 4.0208 loss: 1.1024 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1024 2023/07/25 02:16:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 02:16:11 - mmengine - INFO - Epoch(train) [40][340/940] lr: 1.0000e-02 eta: 17:29:54 time: 1.0981 data_time: 0.0128 memory: 15768 grad_norm: 4.0120 loss: 1.0493 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0493 2023/07/25 02:16:33 - mmengine - INFO - Epoch(train) [40][360/940] lr: 1.0000e-02 eta: 17:29:32 time: 1.0974 data_time: 0.0131 memory: 15768 grad_norm: 4.0044 loss: 1.1824 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1824 2023/07/25 02:16:55 - mmengine - INFO - Epoch(train) [40][380/940] lr: 1.0000e-02 eta: 17:29:09 time: 1.0989 data_time: 0.0129 memory: 15768 grad_norm: 4.0236 loss: 1.1557 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1557 2023/07/25 02:17:17 - mmengine - INFO - Epoch(train) [40][400/940] lr: 1.0000e-02 eta: 17:28:47 time: 1.0980 data_time: 0.0131 memory: 15768 grad_norm: 4.0744 loss: 1.3776 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3776 2023/07/25 02:17:39 - mmengine - INFO - Epoch(train) [40][420/940] lr: 1.0000e-02 eta: 17:28:25 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 4.0906 loss: 1.2847 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2847 2023/07/25 02:18:01 - mmengine - INFO - Epoch(train) [40][440/940] lr: 1.0000e-02 eta: 17:28:03 time: 1.1005 data_time: 0.0127 memory: 15768 grad_norm: 3.9875 loss: 1.1323 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1323 2023/07/25 02:18:23 - mmengine - INFO - Epoch(train) [40][460/940] lr: 1.0000e-02 eta: 17:27:40 time: 1.1020 data_time: 0.0128 memory: 15768 grad_norm: 4.0716 loss: 1.2371 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2371 2023/07/25 02:18:45 - mmengine - INFO - Epoch(train) [40][480/940] lr: 1.0000e-02 eta: 17:27:18 time: 1.0998 data_time: 0.0127 memory: 15768 grad_norm: 3.9949 loss: 1.2231 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2231 2023/07/25 02:19:07 - mmengine - INFO - Epoch(train) [40][500/940] lr: 1.0000e-02 eta: 17:26:56 time: 1.0994 data_time: 0.0129 memory: 15768 grad_norm: 3.9709 loss: 1.2079 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2079 2023/07/25 02:19:29 - mmengine - INFO - Epoch(train) [40][520/940] lr: 1.0000e-02 eta: 17:26:34 time: 1.1010 data_time: 0.0128 memory: 15768 grad_norm: 3.9860 loss: 1.3864 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3864 2023/07/25 02:19:51 - mmengine - INFO - Epoch(train) [40][540/940] lr: 1.0000e-02 eta: 17:26:11 time: 1.1008 data_time: 0.0126 memory: 15768 grad_norm: 3.9068 loss: 1.1787 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1787 2023/07/25 02:20:13 - mmengine - INFO - Epoch(train) [40][560/940] lr: 1.0000e-02 eta: 17:25:49 time: 1.1003 data_time: 0.0132 memory: 15768 grad_norm: 3.9332 loss: 0.9616 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9616 2023/07/25 02:20:35 - mmengine - INFO - Epoch(train) [40][580/940] lr: 1.0000e-02 eta: 17:25:27 time: 1.1061 data_time: 0.0128 memory: 15768 grad_norm: 3.9549 loss: 1.0565 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0565 2023/07/25 02:20:57 - mmengine - INFO - Epoch(train) [40][600/940] lr: 1.0000e-02 eta: 17:25:05 time: 1.0987 data_time: 0.0130 memory: 15768 grad_norm: 3.9944 loss: 1.2969 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2969 2023/07/25 02:21:19 - mmengine - INFO - Epoch(train) [40][620/940] lr: 1.0000e-02 eta: 17:24:43 time: 1.1047 data_time: 0.0127 memory: 15768 grad_norm: 4.0337 loss: 1.1789 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1789 2023/07/25 02:21:41 - mmengine - INFO - Epoch(train) [40][640/940] lr: 1.0000e-02 eta: 17:24:20 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.9881 loss: 1.3527 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3527 2023/07/25 02:22:03 - mmengine - INFO - Epoch(train) [40][660/940] lr: 1.0000e-02 eta: 17:23:58 time: 1.1018 data_time: 0.0124 memory: 15768 grad_norm: 3.8831 loss: 1.2433 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2433 2023/07/25 02:22:25 - mmengine - INFO - Epoch(train) [40][680/940] lr: 1.0000e-02 eta: 17:23:36 time: 1.0980 data_time: 0.0127 memory: 15768 grad_norm: 3.9891 loss: 1.2026 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2026 2023/07/25 02:22:47 - mmengine - INFO - Epoch(train) [40][700/940] lr: 1.0000e-02 eta: 17:23:14 time: 1.1001 data_time: 0.0128 memory: 15768 grad_norm: 3.9771 loss: 1.2611 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2611 2023/07/25 02:23:09 - mmengine - INFO - Epoch(train) [40][720/940] lr: 1.0000e-02 eta: 17:22:52 time: 1.1060 data_time: 0.0129 memory: 15768 grad_norm: 4.0068 loss: 1.3573 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.3573 2023/07/25 02:23:31 - mmengine - INFO - Epoch(train) [40][740/940] lr: 1.0000e-02 eta: 17:22:29 time: 1.1018 data_time: 0.0138 memory: 15768 grad_norm: 3.9984 loss: 1.2906 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2906 2023/07/25 02:23:54 - mmengine - INFO - Epoch(train) [40][760/940] lr: 1.0000e-02 eta: 17:22:07 time: 1.1037 data_time: 0.0131 memory: 15768 grad_norm: 4.0547 loss: 1.1893 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1893 2023/07/25 02:24:16 - mmengine - INFO - Epoch(train) [40][780/940] lr: 1.0000e-02 eta: 17:21:45 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.8954 loss: 1.3418 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3418 2023/07/25 02:24:38 - mmengine - INFO - Epoch(train) [40][800/940] lr: 1.0000e-02 eta: 17:21:24 time: 1.1353 data_time: 0.0126 memory: 15768 grad_norm: 3.9603 loss: 1.2565 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2565 2023/07/25 02:25:00 - mmengine - INFO - Epoch(train) [40][820/940] lr: 1.0000e-02 eta: 17:21:02 time: 1.1118 data_time: 0.0127 memory: 15768 grad_norm: 4.0915 loss: 1.2535 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2535 2023/07/25 02:25:23 - mmengine - INFO - Epoch(train) [40][840/940] lr: 1.0000e-02 eta: 17:20:40 time: 1.1033 data_time: 0.0133 memory: 15768 grad_norm: 4.0082 loss: 1.2840 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2840 2023/07/25 02:25:45 - mmengine - INFO - Epoch(train) [40][860/940] lr: 1.0000e-02 eta: 17:20:17 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 4.0549 loss: 1.3344 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3344 2023/07/25 02:26:07 - mmengine - INFO - Epoch(train) [40][880/940] lr: 1.0000e-02 eta: 17:19:55 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 3.9580 loss: 1.2901 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2901 2023/07/25 02:26:29 - mmengine - INFO - Epoch(train) [40][900/940] lr: 1.0000e-02 eta: 17:19:33 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 4.1146 loss: 1.2735 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2735 2023/07/25 02:26:51 - mmengine - INFO - Epoch(train) [40][920/940] lr: 1.0000e-02 eta: 17:19:11 time: 1.1027 data_time: 0.0132 memory: 15768 grad_norm: 3.9374 loss: 1.1667 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1667 2023/07/25 02:27:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 02:27:12 - mmengine - INFO - Epoch(train) [40][940/940] lr: 1.0000e-02 eta: 17:18:47 time: 1.0541 data_time: 0.0126 memory: 15768 grad_norm: 4.1744 loss: 1.2750 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2750 2023/07/25 02:27:21 - mmengine - INFO - Epoch(val) [40][20/78] eta: 0:00:28 time: 0.4839 data_time: 0.3268 memory: 2147 2023/07/25 02:27:28 - mmengine - INFO - Epoch(val) [40][40/78] eta: 0:00:15 time: 0.3400 data_time: 0.1832 memory: 2147 2023/07/25 02:27:37 - mmengine - INFO - Epoch(val) [40][60/78] eta: 0:00:07 time: 0.4300 data_time: 0.2731 memory: 2147 2023/07/25 02:27:49 - mmengine - INFO - Epoch(val) [40][78/78] acc/top1: 0.6695 acc/top5: 0.8751 acc/mean1: 0.6693 data_time: 0.2367 time: 0.3909 2023/07/25 02:28:15 - mmengine - INFO - Epoch(train) [41][ 20/940] lr: 1.0000e-03 eta: 17:18:31 time: 1.2993 data_time: 0.1391 memory: 15768 grad_norm: 3.8600 loss: 1.2087 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2087 2023/07/25 02:28:37 - mmengine - INFO - Epoch(train) [41][ 40/940] lr: 1.0000e-03 eta: 17:18:09 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 3.7963 loss: 1.0850 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0850 2023/07/25 02:28:59 - mmengine - INFO - Epoch(train) [41][ 60/940] lr: 1.0000e-03 eta: 17:17:46 time: 1.1017 data_time: 0.0128 memory: 15768 grad_norm: 3.6878 loss: 1.2042 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2042 2023/07/25 02:29:21 - mmengine - INFO - Epoch(train) [41][ 80/940] lr: 1.0000e-03 eta: 17:17:24 time: 1.0995 data_time: 0.0127 memory: 15768 grad_norm: 3.7502 loss: 1.2264 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2264 2023/07/25 02:29:43 - mmengine - INFO - Epoch(train) [41][100/940] lr: 1.0000e-03 eta: 17:17:02 time: 1.0995 data_time: 0.0129 memory: 15768 grad_norm: 3.8193 loss: 1.2011 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2011 2023/07/25 02:30:05 - mmengine - INFO - Epoch(train) [41][120/940] lr: 1.0000e-03 eta: 17:16:40 time: 1.1027 data_time: 0.0130 memory: 15768 grad_norm: 3.7691 loss: 1.2060 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2060 2023/07/25 02:30:27 - mmengine - INFO - Epoch(train) [41][140/940] lr: 1.0000e-03 eta: 17:16:17 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 3.7349 loss: 1.1689 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1689 2023/07/25 02:30:49 - mmengine - INFO - Epoch(train) [41][160/940] lr: 1.0000e-03 eta: 17:15:55 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 3.7335 loss: 1.1559 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1559 2023/07/25 02:31:11 - mmengine - INFO - Epoch(train) [41][180/940] lr: 1.0000e-03 eta: 17:15:33 time: 1.1006 data_time: 0.0129 memory: 15768 grad_norm: 3.6734 loss: 0.9925 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9925 2023/07/25 02:31:33 - mmengine - INFO - Epoch(train) [41][200/940] lr: 1.0000e-03 eta: 17:15:11 time: 1.0984 data_time: 0.0129 memory: 15768 grad_norm: 3.7434 loss: 1.2158 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2158 2023/07/25 02:31:55 - mmengine - INFO - Epoch(train) [41][220/940] lr: 1.0000e-03 eta: 17:14:48 time: 1.1003 data_time: 0.0129 memory: 15768 grad_norm: 3.7306 loss: 1.1432 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1432 2023/07/25 02:32:17 - mmengine - INFO - Epoch(train) [41][240/940] lr: 1.0000e-03 eta: 17:14:26 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.6611 loss: 1.1628 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1628 2023/07/25 02:32:39 - mmengine - INFO - Epoch(train) [41][260/940] lr: 1.0000e-03 eta: 17:14:04 time: 1.0984 data_time: 0.0127 memory: 15768 grad_norm: 3.7586 loss: 1.2602 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2602 2023/07/25 02:33:01 - mmengine - INFO - Epoch(train) [41][280/940] lr: 1.0000e-03 eta: 17:13:42 time: 1.0996 data_time: 0.0131 memory: 15768 grad_norm: 3.7607 loss: 1.0814 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0814 2023/07/25 02:33:23 - mmengine - INFO - Epoch(train) [41][300/940] lr: 1.0000e-03 eta: 17:13:19 time: 1.1023 data_time: 0.0127 memory: 15768 grad_norm: 3.7930 loss: 1.2741 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2741 2023/07/25 02:33:45 - mmengine - INFO - Epoch(train) [41][320/940] lr: 1.0000e-03 eta: 17:12:57 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.6717 loss: 1.1819 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1819 2023/07/25 02:34:07 - mmengine - INFO - Epoch(train) [41][340/940] lr: 1.0000e-03 eta: 17:12:35 time: 1.1044 data_time: 0.0128 memory: 15768 grad_norm: 3.6414 loss: 1.0761 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0761 2023/07/25 02:34:29 - mmengine - INFO - Epoch(train) [41][360/940] lr: 1.0000e-03 eta: 17:12:13 time: 1.1020 data_time: 0.0130 memory: 15768 grad_norm: 3.6763 loss: 1.1433 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1433 2023/07/25 02:34:51 - mmengine - INFO - Epoch(train) [41][380/940] lr: 1.0000e-03 eta: 17:11:51 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 3.7684 loss: 1.0716 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0716 2023/07/25 02:35:13 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 02:35:13 - mmengine - INFO - Epoch(train) [41][400/940] lr: 1.0000e-03 eta: 17:11:28 time: 1.0983 data_time: 0.0133 memory: 15768 grad_norm: 3.7869 loss: 1.0562 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0562 2023/07/25 02:35:35 - mmengine - INFO - Epoch(train) [41][420/940] lr: 1.0000e-03 eta: 17:11:06 time: 1.1003 data_time: 0.0131 memory: 15768 grad_norm: 3.7295 loss: 1.0436 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0436 2023/07/25 02:35:57 - mmengine - INFO - Epoch(train) [41][440/940] lr: 1.0000e-03 eta: 17:10:44 time: 1.0984 data_time: 0.0131 memory: 15768 grad_norm: 3.7882 loss: 0.9801 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9801 2023/07/25 02:36:19 - mmengine - INFO - Epoch(train) [41][460/940] lr: 1.0000e-03 eta: 17:10:22 time: 1.1013 data_time: 0.0126 memory: 15768 grad_norm: 3.8104 loss: 1.1588 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1588 2023/07/25 02:36:41 - mmengine - INFO - Epoch(train) [41][480/940] lr: 1.0000e-03 eta: 17:09:59 time: 1.0997 data_time: 0.0130 memory: 15768 grad_norm: 3.7233 loss: 0.8691 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8691 2023/07/25 02:37:03 - mmengine - INFO - Epoch(train) [41][500/940] lr: 1.0000e-03 eta: 17:09:37 time: 1.1003 data_time: 0.0131 memory: 15768 grad_norm: 3.6710 loss: 0.9373 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9373 2023/07/25 02:37:25 - mmengine - INFO - Epoch(train) [41][520/940] lr: 1.0000e-03 eta: 17:09:15 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.6977 loss: 1.1426 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1426 2023/07/25 02:37:47 - mmengine - INFO - Epoch(train) [41][540/940] lr: 1.0000e-03 eta: 17:08:53 time: 1.0998 data_time: 0.0132 memory: 15768 grad_norm: 3.7458 loss: 1.0952 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0952 2023/07/25 02:38:09 - mmengine - INFO - Epoch(train) [41][560/940] lr: 1.0000e-03 eta: 17:08:30 time: 1.1015 data_time: 0.0130 memory: 15768 grad_norm: 3.7994 loss: 1.1165 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1165 2023/07/25 02:38:31 - mmengine - INFO - Epoch(train) [41][580/940] lr: 1.0000e-03 eta: 17:08:08 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 3.7185 loss: 1.2262 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2262 2023/07/25 02:38:53 - mmengine - INFO - Epoch(train) [41][600/940] lr: 1.0000e-03 eta: 17:07:46 time: 1.0990 data_time: 0.0130 memory: 15768 grad_norm: 3.6930 loss: 0.9472 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9472 2023/07/25 02:39:15 - mmengine - INFO - Epoch(train) [41][620/940] lr: 1.0000e-03 eta: 17:07:24 time: 1.1017 data_time: 0.0129 memory: 15768 grad_norm: 3.7345 loss: 0.9362 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9362 2023/07/25 02:39:37 - mmengine - INFO - Epoch(train) [41][640/940] lr: 1.0000e-03 eta: 17:07:01 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 3.7219 loss: 0.9831 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9831 2023/07/25 02:39:59 - mmengine - INFO - Epoch(train) [41][660/940] lr: 1.0000e-03 eta: 17:06:39 time: 1.1038 data_time: 0.0128 memory: 15768 grad_norm: 3.6923 loss: 0.9459 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9459 2023/07/25 02:40:21 - mmengine - INFO - Epoch(train) [41][680/940] lr: 1.0000e-03 eta: 17:06:17 time: 1.1004 data_time: 0.0129 memory: 15768 grad_norm: 3.7504 loss: 1.0397 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0397 2023/07/25 02:40:43 - mmengine - INFO - Epoch(train) [41][700/940] lr: 1.0000e-03 eta: 17:05:55 time: 1.1023 data_time: 0.0132 memory: 15768 grad_norm: 3.7734 loss: 1.0249 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0249 2023/07/25 02:41:05 - mmengine - INFO - Epoch(train) [41][720/940] lr: 1.0000e-03 eta: 17:05:33 time: 1.1021 data_time: 0.0130 memory: 15768 grad_norm: 3.7440 loss: 1.0385 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0385 2023/07/25 02:41:27 - mmengine - INFO - Epoch(train) [41][740/940] lr: 1.0000e-03 eta: 17:05:10 time: 1.0994 data_time: 0.0129 memory: 15768 grad_norm: 3.8541 loss: 1.0487 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0487 2023/07/25 02:41:49 - mmengine - INFO - Epoch(train) [41][760/940] lr: 1.0000e-03 eta: 17:04:48 time: 1.1016 data_time: 0.0130 memory: 15768 grad_norm: 3.6910 loss: 1.0768 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0768 2023/07/25 02:42:11 - mmengine - INFO - Epoch(train) [41][780/940] lr: 1.0000e-03 eta: 17:04:26 time: 1.0997 data_time: 0.0126 memory: 15768 grad_norm: 3.6837 loss: 1.1625 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1625 2023/07/25 02:42:33 - mmengine - INFO - Epoch(train) [41][800/940] lr: 1.0000e-03 eta: 17:04:04 time: 1.1021 data_time: 0.0128 memory: 15768 grad_norm: 3.6598 loss: 1.0042 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0042 2023/07/25 02:42:55 - mmengine - INFO - Epoch(train) [41][820/940] lr: 1.0000e-03 eta: 17:03:41 time: 1.0996 data_time: 0.0128 memory: 15768 grad_norm: 3.7325 loss: 1.0954 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0954 2023/07/25 02:43:17 - mmengine - INFO - Epoch(train) [41][840/940] lr: 1.0000e-03 eta: 17:03:19 time: 1.1038 data_time: 0.0126 memory: 15768 grad_norm: 3.7074 loss: 0.9919 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9919 2023/07/25 02:43:39 - mmengine - INFO - Epoch(train) [41][860/940] lr: 1.0000e-03 eta: 17:02:57 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 3.7703 loss: 1.0279 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0279 2023/07/25 02:44:01 - mmengine - INFO - Epoch(train) [41][880/940] lr: 1.0000e-03 eta: 17:02:35 time: 1.0992 data_time: 0.0131 memory: 15768 grad_norm: 3.7941 loss: 1.2237 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2237 2023/07/25 02:44:23 - mmengine - INFO - Epoch(train) [41][900/940] lr: 1.0000e-03 eta: 17:02:13 time: 1.1023 data_time: 0.0125 memory: 15768 grad_norm: 3.7717 loss: 1.0835 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0835 2023/07/25 02:44:45 - mmengine - INFO - Epoch(train) [41][920/940] lr: 1.0000e-03 eta: 17:01:50 time: 1.1005 data_time: 0.0128 memory: 15768 grad_norm: 3.7519 loss: 1.1322 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1322 2023/07/25 02:45:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 02:45:06 - mmengine - INFO - Epoch(train) [41][940/940] lr: 1.0000e-03 eta: 17:01:27 time: 1.0569 data_time: 0.0125 memory: 15768 grad_norm: 4.0352 loss: 1.1980 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1980 2023/07/25 02:45:16 - mmengine - INFO - Epoch(val) [41][20/78] eta: 0:00:27 time: 0.4755 data_time: 0.3182 memory: 2147 2023/07/25 02:45:23 - mmengine - INFO - Epoch(val) [41][40/78] eta: 0:00:15 time: 0.3503 data_time: 0.1931 memory: 2147 2023/07/25 02:45:32 - mmengine - INFO - Epoch(val) [41][60/78] eta: 0:00:07 time: 0.4553 data_time: 0.2987 memory: 2147 2023/07/25 02:45:42 - mmengine - INFO - Epoch(val) [41][78/78] acc/top1: 0.7038 acc/top5: 0.8973 acc/mean1: 0.7037 data_time: 0.2440 time: 0.3981 2023/07/25 02:45:42 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_26.pth is removed 2023/07/25 02:45:43 - mmengine - INFO - The best checkpoint with 0.7038 acc/top1 at 41 epoch is saved to best_acc_top1_epoch_41.pth. 2023/07/25 02:46:08 - mmengine - INFO - Epoch(train) [42][ 20/940] lr: 1.0000e-03 eta: 17:01:09 time: 1.2429 data_time: 0.1429 memory: 15768 grad_norm: 3.7918 loss: 1.0727 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0727 2023/07/25 02:46:31 - mmengine - INFO - Epoch(train) [42][ 40/940] lr: 1.0000e-03 eta: 17:00:48 time: 1.1533 data_time: 0.0128 memory: 15768 grad_norm: 3.7146 loss: 0.9364 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9364 2023/07/25 02:46:54 - mmengine - INFO - Epoch(train) [42][ 60/940] lr: 1.0000e-03 eta: 17:00:28 time: 1.1610 data_time: 0.0126 memory: 15768 grad_norm: 3.7327 loss: 1.1063 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1063 2023/07/25 02:47:18 - mmengine - INFO - Epoch(train) [42][ 80/940] lr: 1.0000e-03 eta: 17:00:07 time: 1.1665 data_time: 0.0130 memory: 15768 grad_norm: 3.7541 loss: 0.9925 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9925 2023/07/25 02:47:41 - mmengine - INFO - Epoch(train) [42][100/940] lr: 1.0000e-03 eta: 16:59:47 time: 1.1618 data_time: 0.0129 memory: 15768 grad_norm: 3.7251 loss: 0.8605 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8605 2023/07/25 02:48:03 - mmengine - INFO - Epoch(train) [42][120/940] lr: 1.0000e-03 eta: 16:59:25 time: 1.1181 data_time: 0.0129 memory: 15768 grad_norm: 3.7356 loss: 1.1681 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1681 2023/07/25 02:48:25 - mmengine - INFO - Epoch(train) [42][140/940] lr: 1.0000e-03 eta: 16:59:03 time: 1.1012 data_time: 0.0129 memory: 15768 grad_norm: 3.8240 loss: 0.9932 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9932 2023/07/25 02:48:47 - mmengine - INFO - Epoch(train) [42][160/940] lr: 1.0000e-03 eta: 16:58:40 time: 1.0984 data_time: 0.0129 memory: 15768 grad_norm: 3.7374 loss: 0.9035 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9035 2023/07/25 02:49:09 - mmengine - INFO - Epoch(train) [42][180/940] lr: 1.0000e-03 eta: 16:58:18 time: 1.1013 data_time: 0.0130 memory: 15768 grad_norm: 3.7389 loss: 0.9810 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9810 2023/07/25 02:49:31 - mmengine - INFO - Epoch(train) [42][200/940] lr: 1.0000e-03 eta: 16:57:56 time: 1.0977 data_time: 0.0133 memory: 15768 grad_norm: 3.7015 loss: 1.1783 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1783 2023/07/25 02:49:53 - mmengine - INFO - Epoch(train) [42][220/940] lr: 1.0000e-03 eta: 16:57:34 time: 1.0998 data_time: 0.0128 memory: 15768 grad_norm: 3.7387 loss: 1.0741 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0741 2023/07/25 02:50:15 - mmengine - INFO - Epoch(train) [42][240/940] lr: 1.0000e-03 eta: 16:57:11 time: 1.0979 data_time: 0.0128 memory: 15768 grad_norm: 3.6679 loss: 0.9949 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9949 2023/07/25 02:50:37 - mmengine - INFO - Epoch(train) [42][260/940] lr: 1.0000e-03 eta: 16:56:49 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 3.7539 loss: 0.9386 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9386 2023/07/25 02:50:59 - mmengine - INFO - Epoch(train) [42][280/940] lr: 1.0000e-03 eta: 16:56:27 time: 1.1023 data_time: 0.0132 memory: 15768 grad_norm: 3.7499 loss: 1.0498 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0498 2023/07/25 02:51:21 - mmengine - INFO - Epoch(train) [42][300/940] lr: 1.0000e-03 eta: 16:56:05 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.7723 loss: 1.0396 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0396 2023/07/25 02:51:43 - mmengine - INFO - Epoch(train) [42][320/940] lr: 1.0000e-03 eta: 16:55:42 time: 1.1024 data_time: 0.0129 memory: 15768 grad_norm: 3.7478 loss: 1.2442 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2442 2023/07/25 02:52:05 - mmengine - INFO - Epoch(train) [42][340/940] lr: 1.0000e-03 eta: 16:55:20 time: 1.0993 data_time: 0.0128 memory: 15768 grad_norm: 3.7868 loss: 1.1822 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1822 2023/07/25 02:52:27 - mmengine - INFO - Epoch(train) [42][360/940] lr: 1.0000e-03 eta: 16:54:58 time: 1.0993 data_time: 0.0132 memory: 15768 grad_norm: 3.8074 loss: 0.9498 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9498 2023/07/25 02:52:49 - mmengine - INFO - Epoch(train) [42][380/940] lr: 1.0000e-03 eta: 16:54:36 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 3.7796 loss: 0.9264 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9264 2023/07/25 02:53:11 - mmengine - INFO - Epoch(train) [42][400/940] lr: 1.0000e-03 eta: 16:54:13 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.8637 loss: 0.9278 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9278 2023/07/25 02:53:33 - mmengine - INFO - Epoch(train) [42][420/940] lr: 1.0000e-03 eta: 16:53:51 time: 1.0997 data_time: 0.0128 memory: 15768 grad_norm: 3.8123 loss: 0.9314 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9314 2023/07/25 02:53:55 - mmengine - INFO - Epoch(train) [42][440/940] lr: 1.0000e-03 eta: 16:53:29 time: 1.1047 data_time: 0.0123 memory: 15768 grad_norm: 3.7106 loss: 1.0512 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0512 2023/07/25 02:54:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 02:54:17 - mmengine - INFO - Epoch(train) [42][460/940] lr: 1.0000e-03 eta: 16:53:07 time: 1.0985 data_time: 0.0128 memory: 15768 grad_norm: 3.7425 loss: 0.9259 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9259 2023/07/25 02:54:40 - mmengine - INFO - Epoch(train) [42][480/940] lr: 1.0000e-03 eta: 16:52:46 time: 1.1401 data_time: 0.0130 memory: 15768 grad_norm: 3.6921 loss: 1.1463 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1463 2023/07/25 02:55:04 - mmengine - INFO - Epoch(train) [42][500/940] lr: 1.0000e-03 eta: 16:52:25 time: 1.1618 data_time: 0.0129 memory: 15768 grad_norm: 3.7558 loss: 0.9106 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9106 2023/07/25 02:55:26 - mmengine - INFO - Epoch(train) [42][520/940] lr: 1.0000e-03 eta: 16:52:03 time: 1.1064 data_time: 0.0129 memory: 15768 grad_norm: 3.6892 loss: 1.0548 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0548 2023/07/25 02:55:48 - mmengine - INFO - Epoch(train) [42][540/940] lr: 1.0000e-03 eta: 16:51:41 time: 1.1023 data_time: 0.0129 memory: 15768 grad_norm: 3.7894 loss: 1.0043 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0043 2023/07/25 02:56:10 - mmengine - INFO - Epoch(train) [42][560/940] lr: 1.0000e-03 eta: 16:51:19 time: 1.1020 data_time: 0.0131 memory: 15768 grad_norm: 3.8494 loss: 1.0865 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0865 2023/07/25 02:56:32 - mmengine - INFO - Epoch(train) [42][580/940] lr: 1.0000e-03 eta: 16:50:56 time: 1.1025 data_time: 0.0130 memory: 15768 grad_norm: 3.8001 loss: 1.1643 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1643 2023/07/25 02:56:54 - mmengine - INFO - Epoch(train) [42][600/940] lr: 1.0000e-03 eta: 16:50:34 time: 1.0985 data_time: 0.0130 memory: 15768 grad_norm: 3.7736 loss: 1.0444 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0444 2023/07/25 02:57:16 - mmengine - INFO - Epoch(train) [42][620/940] lr: 1.0000e-03 eta: 16:50:12 time: 1.0980 data_time: 0.0129 memory: 15768 grad_norm: 3.7019 loss: 1.0187 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0187 2023/07/25 02:57:38 - mmengine - INFO - Epoch(train) [42][640/940] lr: 1.0000e-03 eta: 16:49:50 time: 1.1004 data_time: 0.0133 memory: 15768 grad_norm: 3.7867 loss: 0.9714 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9714 2023/07/25 02:58:00 - mmengine - INFO - Epoch(train) [42][660/940] lr: 1.0000e-03 eta: 16:49:27 time: 1.0980 data_time: 0.0132 memory: 15768 grad_norm: 3.7618 loss: 0.9868 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9868 2023/07/25 02:58:22 - mmengine - INFO - Epoch(train) [42][680/940] lr: 1.0000e-03 eta: 16:49:05 time: 1.1011 data_time: 0.0132 memory: 15768 grad_norm: 3.7608 loss: 1.1328 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1328 2023/07/25 02:58:44 - mmengine - INFO - Epoch(train) [42][700/940] lr: 1.0000e-03 eta: 16:48:43 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.8506 loss: 1.1303 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1303 2023/07/25 02:59:06 - mmengine - INFO - Epoch(train) [42][720/940] lr: 1.0000e-03 eta: 16:48:21 time: 1.1035 data_time: 0.0135 memory: 15768 grad_norm: 3.8446 loss: 0.9997 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9997 2023/07/25 02:59:28 - mmengine - INFO - Epoch(train) [42][740/940] lr: 1.0000e-03 eta: 16:47:58 time: 1.0979 data_time: 0.0133 memory: 15768 grad_norm: 3.8004 loss: 1.0602 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0602 2023/07/25 02:59:50 - mmengine - INFO - Epoch(train) [42][760/940] lr: 1.0000e-03 eta: 16:47:36 time: 1.0979 data_time: 0.0131 memory: 15768 grad_norm: 3.7082 loss: 0.9101 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9101 2023/07/25 03:00:12 - mmengine - INFO - Epoch(train) [42][780/940] lr: 1.0000e-03 eta: 16:47:14 time: 1.1011 data_time: 0.0124 memory: 15768 grad_norm: 3.8193 loss: 1.0544 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0544 2023/07/25 03:00:34 - mmengine - INFO - Epoch(train) [42][800/940] lr: 1.0000e-03 eta: 16:46:52 time: 1.1006 data_time: 0.0128 memory: 15768 grad_norm: 3.7879 loss: 1.0654 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0654 2023/07/25 03:00:56 - mmengine - INFO - Epoch(train) [42][820/940] lr: 1.0000e-03 eta: 16:46:29 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 3.8430 loss: 0.8695 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8695 2023/07/25 03:01:18 - mmengine - INFO - Epoch(train) [42][840/940] lr: 1.0000e-03 eta: 16:46:07 time: 1.1036 data_time: 0.0130 memory: 15768 grad_norm: 3.7241 loss: 1.1110 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1110 2023/07/25 03:01:40 - mmengine - INFO - Epoch(train) [42][860/940] lr: 1.0000e-03 eta: 16:45:45 time: 1.0994 data_time: 0.0130 memory: 15768 grad_norm: 3.8989 loss: 1.0181 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0181 2023/07/25 03:02:02 - mmengine - INFO - Epoch(train) [42][880/940] lr: 1.0000e-03 eta: 16:45:23 time: 1.1015 data_time: 0.0135 memory: 15768 grad_norm: 3.7798 loss: 0.8972 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8972 2023/07/25 03:02:24 - mmengine - INFO - Epoch(train) [42][900/940] lr: 1.0000e-03 eta: 16:45:00 time: 1.0990 data_time: 0.0128 memory: 15768 grad_norm: 3.7268 loss: 0.9872 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9872 2023/07/25 03:02:46 - mmengine - INFO - Epoch(train) [42][920/940] lr: 1.0000e-03 eta: 16:44:38 time: 1.1064 data_time: 0.0134 memory: 15768 grad_norm: 3.8883 loss: 1.0084 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0084 2023/07/25 03:03:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 03:03:07 - mmengine - INFO - Epoch(train) [42][940/940] lr: 1.0000e-03 eta: 16:44:15 time: 1.0535 data_time: 0.0131 memory: 15768 grad_norm: 4.1010 loss: 1.1490 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.1490 2023/07/25 03:03:07 - mmengine - INFO - Saving checkpoint at 42 epochs 2023/07/25 03:03:18 - mmengine - INFO - Epoch(val) [42][20/78] eta: 0:00:28 time: 0.4853 data_time: 0.3276 memory: 2147 2023/07/25 03:03:25 - mmengine - INFO - Epoch(val) [42][40/78] eta: 0:00:15 time: 0.3396 data_time: 0.1828 memory: 2147 2023/07/25 03:03:33 - mmengine - INFO - Epoch(val) [42][60/78] eta: 0:00:07 time: 0.4277 data_time: 0.2708 memory: 2147 2023/07/25 03:03:44 - mmengine - INFO - Epoch(val) [42][78/78] acc/top1: 0.7069 acc/top5: 0.8982 acc/mean1: 0.7068 data_time: 0.2344 time: 0.3886 2023/07/25 03:03:44 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_41.pth is removed 2023/07/25 03:03:45 - mmengine - INFO - The best checkpoint with 0.7069 acc/top1 at 42 epoch is saved to best_acc_top1_epoch_42.pth. 2023/07/25 03:04:10 - mmengine - INFO - Epoch(train) [43][ 20/940] lr: 1.0000e-03 eta: 16:43:57 time: 1.2490 data_time: 0.1506 memory: 15768 grad_norm: 3.7736 loss: 1.0178 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0178 2023/07/25 03:04:32 - mmengine - INFO - Epoch(train) [43][ 40/940] lr: 1.0000e-03 eta: 16:43:34 time: 1.0994 data_time: 0.0133 memory: 15768 grad_norm: 3.7015 loss: 1.0898 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0898 2023/07/25 03:04:54 - mmengine - INFO - Epoch(train) [43][ 60/940] lr: 1.0000e-03 eta: 16:43:12 time: 1.1009 data_time: 0.0129 memory: 15768 grad_norm: 3.8736 loss: 1.0921 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0921 2023/07/25 03:05:16 - mmengine - INFO - Epoch(train) [43][ 80/940] lr: 1.0000e-03 eta: 16:42:50 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 3.8243 loss: 0.9602 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9602 2023/07/25 03:05:38 - mmengine - INFO - Epoch(train) [43][100/940] lr: 1.0000e-03 eta: 16:42:28 time: 1.1057 data_time: 0.0132 memory: 15768 grad_norm: 3.6714 loss: 1.0086 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0086 2023/07/25 03:06:00 - mmengine - INFO - Epoch(train) [43][120/940] lr: 1.0000e-03 eta: 16:42:06 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 3.7702 loss: 1.0462 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0462 2023/07/25 03:06:22 - mmengine - INFO - Epoch(train) [43][140/940] lr: 1.0000e-03 eta: 16:41:43 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.8663 loss: 1.1244 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1244 2023/07/25 03:06:44 - mmengine - INFO - Epoch(train) [43][160/940] lr: 1.0000e-03 eta: 16:41:21 time: 1.0995 data_time: 0.0136 memory: 15768 grad_norm: 3.7437 loss: 1.0113 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0113 2023/07/25 03:07:06 - mmengine - INFO - Epoch(train) [43][180/940] lr: 1.0000e-03 eta: 16:40:59 time: 1.1042 data_time: 0.0135 memory: 15768 grad_norm: 3.7974 loss: 1.1365 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1365 2023/07/25 03:07:28 - mmengine - INFO - Epoch(train) [43][200/940] lr: 1.0000e-03 eta: 16:40:37 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.8062 loss: 0.9275 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9275 2023/07/25 03:07:50 - mmengine - INFO - Epoch(train) [43][220/940] lr: 1.0000e-03 eta: 16:40:15 time: 1.1004 data_time: 0.0127 memory: 15768 grad_norm: 3.8253 loss: 0.9753 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9753 2023/07/25 03:08:12 - mmengine - INFO - Epoch(train) [43][240/940] lr: 1.0000e-03 eta: 16:39:52 time: 1.1011 data_time: 0.0135 memory: 15768 grad_norm: 3.7637 loss: 0.8939 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8939 2023/07/25 03:08:34 - mmengine - INFO - Epoch(train) [43][260/940] lr: 1.0000e-03 eta: 16:39:30 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 3.7951 loss: 1.0034 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0034 2023/07/25 03:08:56 - mmengine - INFO - Epoch(train) [43][280/940] lr: 1.0000e-03 eta: 16:39:08 time: 1.1008 data_time: 0.0133 memory: 15768 grad_norm: 3.7598 loss: 1.0044 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0044 2023/07/25 03:09:18 - mmengine - INFO - Epoch(train) [43][300/940] lr: 1.0000e-03 eta: 16:38:46 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 3.8880 loss: 1.1216 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1216 2023/07/25 03:09:40 - mmengine - INFO - Epoch(train) [43][320/940] lr: 1.0000e-03 eta: 16:38:23 time: 1.0991 data_time: 0.0135 memory: 15768 grad_norm: 3.7477 loss: 0.9366 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9366 2023/07/25 03:10:02 - mmengine - INFO - Epoch(train) [43][340/940] lr: 1.0000e-03 eta: 16:38:01 time: 1.0988 data_time: 0.0133 memory: 15768 grad_norm: 3.7692 loss: 1.0101 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0101 2023/07/25 03:10:24 - mmengine - INFO - Epoch(train) [43][360/940] lr: 1.0000e-03 eta: 16:37:39 time: 1.1030 data_time: 0.0133 memory: 15768 grad_norm: 3.7965 loss: 1.0496 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0496 2023/07/25 03:10:46 - mmengine - INFO - Epoch(train) [43][380/940] lr: 1.0000e-03 eta: 16:37:17 time: 1.0991 data_time: 0.0132 memory: 15768 grad_norm: 3.8534 loss: 1.0203 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0203 2023/07/25 03:11:08 - mmengine - INFO - Epoch(train) [43][400/940] lr: 1.0000e-03 eta: 16:36:54 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 3.9072 loss: 0.9954 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9954 2023/07/25 03:11:30 - mmengine - INFO - Epoch(train) [43][420/940] lr: 1.0000e-03 eta: 16:36:32 time: 1.1033 data_time: 0.0136 memory: 15768 grad_norm: 3.8531 loss: 1.0483 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0483 2023/07/25 03:11:52 - mmengine - INFO - Epoch(train) [43][440/940] lr: 1.0000e-03 eta: 16:36:10 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 3.8720 loss: 0.9837 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9837 2023/07/25 03:12:14 - mmengine - INFO - Epoch(train) [43][460/940] lr: 1.0000e-03 eta: 16:35:48 time: 1.1006 data_time: 0.0132 memory: 15768 grad_norm: 3.8724 loss: 0.9508 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9508 2023/07/25 03:12:36 - mmengine - INFO - Epoch(train) [43][480/940] lr: 1.0000e-03 eta: 16:35:25 time: 1.0982 data_time: 0.0132 memory: 15768 grad_norm: 3.7941 loss: 1.0617 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0617 2023/07/25 03:12:58 - mmengine - INFO - Epoch(train) [43][500/940] lr: 1.0000e-03 eta: 16:35:03 time: 1.1001 data_time: 0.0133 memory: 15768 grad_norm: 3.8457 loss: 0.8488 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8488 2023/07/25 03:13:20 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 03:13:20 - mmengine - INFO - Epoch(train) [43][520/940] lr: 1.0000e-03 eta: 16:34:41 time: 1.1019 data_time: 0.0132 memory: 15768 grad_norm: 3.8362 loss: 0.9951 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9951 2023/07/25 03:13:42 - mmengine - INFO - Epoch(train) [43][540/940] lr: 1.0000e-03 eta: 16:34:19 time: 1.0993 data_time: 0.0133 memory: 15768 grad_norm: 3.8721 loss: 0.9555 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9555 2023/07/25 03:14:04 - mmengine - INFO - Epoch(train) [43][560/940] lr: 1.0000e-03 eta: 16:33:56 time: 1.0979 data_time: 0.0134 memory: 15768 grad_norm: 3.8032 loss: 1.0530 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0530 2023/07/25 03:14:26 - mmengine - INFO - Epoch(train) [43][580/940] lr: 1.0000e-03 eta: 16:33:34 time: 1.0989 data_time: 0.0135 memory: 15768 grad_norm: 3.7385 loss: 0.9162 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9162 2023/07/25 03:14:48 - mmengine - INFO - Epoch(train) [43][600/940] lr: 1.0000e-03 eta: 16:33:12 time: 1.1056 data_time: 0.0134 memory: 15768 grad_norm: 3.6926 loss: 1.0161 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0161 2023/07/25 03:15:10 - mmengine - INFO - Epoch(train) [43][620/940] lr: 1.0000e-03 eta: 16:32:50 time: 1.1018 data_time: 0.0133 memory: 15768 grad_norm: 3.8672 loss: 1.0382 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0382 2023/07/25 03:15:32 - mmengine - INFO - Epoch(train) [43][640/940] lr: 1.0000e-03 eta: 16:32:28 time: 1.1044 data_time: 0.0130 memory: 15768 grad_norm: 3.7715 loss: 0.9819 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9819 2023/07/25 03:15:54 - mmengine - INFO - Epoch(train) [43][660/940] lr: 1.0000e-03 eta: 16:32:06 time: 1.1031 data_time: 0.0134 memory: 15768 grad_norm: 3.8335 loss: 1.1036 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1036 2023/07/25 03:16:16 - mmengine - INFO - Epoch(train) [43][680/940] lr: 1.0000e-03 eta: 16:31:43 time: 1.0983 data_time: 0.0133 memory: 15768 grad_norm: 3.8449 loss: 0.9251 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9251 2023/07/25 03:16:38 - mmengine - INFO - Epoch(train) [43][700/940] lr: 1.0000e-03 eta: 16:31:21 time: 1.0992 data_time: 0.0134 memory: 15768 grad_norm: 3.7946 loss: 0.9428 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9428 2023/07/25 03:17:00 - mmengine - INFO - Epoch(train) [43][720/940] lr: 1.0000e-03 eta: 16:30:59 time: 1.1019 data_time: 0.0129 memory: 15768 grad_norm: 3.8104 loss: 0.8632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8632 2023/07/25 03:17:22 - mmengine - INFO - Epoch(train) [43][740/940] lr: 1.0000e-03 eta: 16:30:37 time: 1.0991 data_time: 0.0132 memory: 15768 grad_norm: 3.7115 loss: 0.9279 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9279 2023/07/25 03:17:44 - mmengine - INFO - Epoch(train) [43][760/940] lr: 1.0000e-03 eta: 16:30:14 time: 1.1021 data_time: 0.0130 memory: 15768 grad_norm: 3.9061 loss: 1.0454 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0454 2023/07/25 03:18:06 - mmengine - INFO - Epoch(train) [43][780/940] lr: 1.0000e-03 eta: 16:29:52 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 3.9100 loss: 1.0562 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0562 2023/07/25 03:18:28 - mmengine - INFO - Epoch(train) [43][800/940] lr: 1.0000e-03 eta: 16:29:30 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 3.8091 loss: 1.0680 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0680 2023/07/25 03:18:50 - mmengine - INFO - Epoch(train) [43][820/940] lr: 1.0000e-03 eta: 16:29:08 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 3.8622 loss: 0.9211 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9211 2023/07/25 03:19:12 - mmengine - INFO - Epoch(train) [43][840/940] lr: 1.0000e-03 eta: 16:28:45 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 3.7778 loss: 0.8424 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8424 2023/07/25 03:19:34 - mmengine - INFO - Epoch(train) [43][860/940] lr: 1.0000e-03 eta: 16:28:23 time: 1.1035 data_time: 0.0132 memory: 15768 grad_norm: 3.8579 loss: 1.0094 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0094 2023/07/25 03:19:56 - mmengine - INFO - Epoch(train) [43][880/940] lr: 1.0000e-03 eta: 16:28:01 time: 1.0979 data_time: 0.0130 memory: 15768 grad_norm: 3.9300 loss: 1.1112 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1112 2023/07/25 03:20:18 - mmengine - INFO - Epoch(train) [43][900/940] lr: 1.0000e-03 eta: 16:27:39 time: 1.0986 data_time: 0.0129 memory: 15768 grad_norm: 3.8686 loss: 0.8921 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8921 2023/07/25 03:20:40 - mmengine - INFO - Epoch(train) [43][920/940] lr: 1.0000e-03 eta: 16:27:16 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 3.7530 loss: 0.9566 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9566 2023/07/25 03:21:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 03:21:01 - mmengine - INFO - Epoch(train) [43][940/940] lr: 1.0000e-03 eta: 16:26:53 time: 1.0523 data_time: 0.0128 memory: 15768 grad_norm: 4.0556 loss: 1.0333 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0333 2023/07/25 03:21:11 - mmengine - INFO - Epoch(val) [43][20/78] eta: 0:00:27 time: 0.4786 data_time: 0.3212 memory: 2147 2023/07/25 03:21:18 - mmengine - INFO - Epoch(val) [43][40/78] eta: 0:00:15 time: 0.3543 data_time: 0.1975 memory: 2147 2023/07/25 03:21:26 - mmengine - INFO - Epoch(val) [43][60/78] eta: 0:00:07 time: 0.4274 data_time: 0.2707 memory: 2147 2023/07/25 03:21:38 - mmengine - INFO - Epoch(val) [43][78/78] acc/top1: 0.7110 acc/top5: 0.8979 acc/mean1: 0.7109 data_time: 0.2396 time: 0.3937 2023/07/25 03:21:38 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_42.pth is removed 2023/07/25 03:21:39 - mmengine - INFO - The best checkpoint with 0.7110 acc/top1 at 43 epoch is saved to best_acc_top1_epoch_43.pth. 2023/07/25 03:22:04 - mmengine - INFO - Epoch(train) [44][ 20/940] lr: 1.0000e-03 eta: 16:26:35 time: 1.2785 data_time: 0.1855 memory: 15768 grad_norm: 3.8765 loss: 1.0087 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0087 2023/07/25 03:22:26 - mmengine - INFO - Epoch(train) [44][ 40/940] lr: 1.0000e-03 eta: 16:26:13 time: 1.1031 data_time: 0.0138 memory: 15768 grad_norm: 3.8431 loss: 1.0824 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0824 2023/07/25 03:22:48 - mmengine - INFO - Epoch(train) [44][ 60/940] lr: 1.0000e-03 eta: 16:25:51 time: 1.0978 data_time: 0.0132 memory: 15768 grad_norm: 3.8394 loss: 1.1372 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1372 2023/07/25 03:23:10 - mmengine - INFO - Epoch(train) [44][ 80/940] lr: 1.0000e-03 eta: 16:25:29 time: 1.0997 data_time: 0.0132 memory: 15768 grad_norm: 3.9346 loss: 1.0693 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0693 2023/07/25 03:23:32 - mmengine - INFO - Epoch(train) [44][100/940] lr: 1.0000e-03 eta: 16:25:06 time: 1.0989 data_time: 0.0130 memory: 15768 grad_norm: 3.7739 loss: 0.9069 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9069 2023/07/25 03:23:54 - mmengine - INFO - Epoch(train) [44][120/940] lr: 1.0000e-03 eta: 16:24:44 time: 1.1026 data_time: 0.0132 memory: 15768 grad_norm: 3.7560 loss: 0.9703 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9703 2023/07/25 03:24:16 - mmengine - INFO - Epoch(train) [44][140/940] lr: 1.0000e-03 eta: 16:24:22 time: 1.1018 data_time: 0.0139 memory: 15768 grad_norm: 3.7768 loss: 0.9473 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9473 2023/07/25 03:24:38 - mmengine - INFO - Epoch(train) [44][160/940] lr: 1.0000e-03 eta: 16:24:00 time: 1.1007 data_time: 0.0132 memory: 15768 grad_norm: 3.7869 loss: 1.0114 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0114 2023/07/25 03:25:00 - mmengine - INFO - Epoch(train) [44][180/940] lr: 1.0000e-03 eta: 16:23:38 time: 1.0994 data_time: 0.0130 memory: 15768 grad_norm: 3.8863 loss: 0.9720 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.9720 2023/07/25 03:25:22 - mmengine - INFO - Epoch(train) [44][200/940] lr: 1.0000e-03 eta: 16:23:15 time: 1.1003 data_time: 0.0130 memory: 15768 grad_norm: 3.8349 loss: 0.8704 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8704 2023/07/25 03:25:44 - mmengine - INFO - Epoch(train) [44][220/940] lr: 1.0000e-03 eta: 16:22:53 time: 1.0979 data_time: 0.0132 memory: 15768 grad_norm: 3.8252 loss: 0.9798 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9798 2023/07/25 03:26:06 - mmengine - INFO - Epoch(train) [44][240/940] lr: 1.0000e-03 eta: 16:22:31 time: 1.0988 data_time: 0.0133 memory: 15768 grad_norm: 3.8110 loss: 0.8598 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8598 2023/07/25 03:26:28 - mmengine - INFO - Epoch(train) [44][260/940] lr: 1.0000e-03 eta: 16:22:08 time: 1.0989 data_time: 0.0135 memory: 15768 grad_norm: 3.8334 loss: 1.0261 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0261 2023/07/25 03:26:50 - mmengine - INFO - Epoch(train) [44][280/940] lr: 1.0000e-03 eta: 16:21:46 time: 1.1008 data_time: 0.0133 memory: 15768 grad_norm: 3.8358 loss: 1.0590 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0590 2023/07/25 03:27:12 - mmengine - INFO - Epoch(train) [44][300/940] lr: 1.0000e-03 eta: 16:21:24 time: 1.0995 data_time: 0.0134 memory: 15768 grad_norm: 3.8154 loss: 1.0496 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0496 2023/07/25 03:27:34 - mmengine - INFO - Epoch(train) [44][320/940] lr: 1.0000e-03 eta: 16:21:02 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 3.8297 loss: 0.9957 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9957 2023/07/25 03:27:56 - mmengine - INFO - Epoch(train) [44][340/940] lr: 1.0000e-03 eta: 16:20:40 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 3.8907 loss: 0.7710 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7710 2023/07/25 03:28:18 - mmengine - INFO - Epoch(train) [44][360/940] lr: 1.0000e-03 eta: 16:20:17 time: 1.1024 data_time: 0.0130 memory: 15768 grad_norm: 3.8747 loss: 0.8702 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8702 2023/07/25 03:28:40 - mmengine - INFO - Epoch(train) [44][380/940] lr: 1.0000e-03 eta: 16:19:55 time: 1.0979 data_time: 0.0132 memory: 15768 grad_norm: 3.8403 loss: 0.8592 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.8592 2023/07/25 03:29:02 - mmengine - INFO - Epoch(train) [44][400/940] lr: 1.0000e-03 eta: 16:19:33 time: 1.0980 data_time: 0.0133 memory: 15768 grad_norm: 3.9186 loss: 1.0150 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0150 2023/07/25 03:29:24 - mmengine - INFO - Epoch(train) [44][420/940] lr: 1.0000e-03 eta: 16:19:11 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 3.8960 loss: 0.8891 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8891 2023/07/25 03:29:46 - mmengine - INFO - Epoch(train) [44][440/940] lr: 1.0000e-03 eta: 16:18:48 time: 1.0987 data_time: 0.0137 memory: 15768 grad_norm: 3.8710 loss: 1.1218 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1218 2023/07/25 03:30:08 - mmengine - INFO - Epoch(train) [44][460/940] lr: 1.0000e-03 eta: 16:18:26 time: 1.0984 data_time: 0.0135 memory: 15768 grad_norm: 3.9139 loss: 1.0399 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0399 2023/07/25 03:30:30 - mmengine - INFO - Epoch(train) [44][480/940] lr: 1.0000e-03 eta: 16:18:04 time: 1.1001 data_time: 0.0133 memory: 15768 grad_norm: 3.8196 loss: 1.0381 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0381 2023/07/25 03:30:52 - mmengine - INFO - Epoch(train) [44][500/940] lr: 1.0000e-03 eta: 16:17:42 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 3.7732 loss: 0.8897 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8897 2023/07/25 03:31:14 - mmengine - INFO - Epoch(train) [44][520/940] lr: 1.0000e-03 eta: 16:17:19 time: 1.1009 data_time: 0.0134 memory: 15768 grad_norm: 3.8898 loss: 0.9794 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9794 2023/07/25 03:31:36 - mmengine - INFO - Epoch(train) [44][540/940] lr: 1.0000e-03 eta: 16:16:57 time: 1.1042 data_time: 0.0131 memory: 15768 grad_norm: 3.8353 loss: 1.1118 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1118 2023/07/25 03:31:58 - mmengine - INFO - Epoch(train) [44][560/940] lr: 1.0000e-03 eta: 16:16:35 time: 1.1003 data_time: 0.0129 memory: 15768 grad_norm: 3.7580 loss: 0.9439 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9439 2023/07/25 03:32:20 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 03:32:20 - mmengine - INFO - Epoch(train) [44][580/940] lr: 1.0000e-03 eta: 16:16:13 time: 1.1037 data_time: 0.0131 memory: 15768 grad_norm: 3.9007 loss: 1.2015 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2015 2023/07/25 03:32:43 - mmengine - INFO - Epoch(train) [44][600/940] lr: 1.0000e-03 eta: 16:15:51 time: 1.1290 data_time: 0.0136 memory: 15768 grad_norm: 3.7733 loss: 0.8727 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8727 2023/07/25 03:33:06 - mmengine - INFO - Epoch(train) [44][620/940] lr: 1.0000e-03 eta: 16:15:31 time: 1.1632 data_time: 0.0131 memory: 15768 grad_norm: 3.9560 loss: 0.9415 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9415 2023/07/25 03:33:29 - mmengine - INFO - Epoch(train) [44][640/940] lr: 1.0000e-03 eta: 16:15:09 time: 1.1095 data_time: 0.0132 memory: 15768 grad_norm: 3.8101 loss: 1.0438 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0438 2023/07/25 03:33:51 - mmengine - INFO - Epoch(train) [44][660/940] lr: 1.0000e-03 eta: 16:14:47 time: 1.1012 data_time: 0.0128 memory: 15768 grad_norm: 3.8135 loss: 0.9460 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9460 2023/07/25 03:34:13 - mmengine - INFO - Epoch(train) [44][680/940] lr: 1.0000e-03 eta: 16:14:24 time: 1.1016 data_time: 0.0135 memory: 15768 grad_norm: 3.9272 loss: 1.0220 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0220 2023/07/25 03:34:35 - mmengine - INFO - Epoch(train) [44][700/940] lr: 1.0000e-03 eta: 16:14:02 time: 1.1027 data_time: 0.0130 memory: 15768 grad_norm: 3.9261 loss: 1.0243 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0243 2023/07/25 03:34:57 - mmengine - INFO - Epoch(train) [44][720/940] lr: 1.0000e-03 eta: 16:13:40 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 3.9057 loss: 0.9524 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9524 2023/07/25 03:35:19 - mmengine - INFO - Epoch(train) [44][740/940] lr: 1.0000e-03 eta: 16:13:18 time: 1.0992 data_time: 0.0131 memory: 15768 grad_norm: 3.9452 loss: 1.1018 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1018 2023/07/25 03:35:41 - mmengine - INFO - Epoch(train) [44][760/940] lr: 1.0000e-03 eta: 16:12:55 time: 1.1024 data_time: 0.0134 memory: 15768 grad_norm: 3.8924 loss: 0.9912 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9912 2023/07/25 03:36:03 - mmengine - INFO - Epoch(train) [44][780/940] lr: 1.0000e-03 eta: 16:12:33 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 3.8402 loss: 1.0202 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0202 2023/07/25 03:36:25 - mmengine - INFO - Epoch(train) [44][800/940] lr: 1.0000e-03 eta: 16:12:11 time: 1.0986 data_time: 0.0129 memory: 15768 grad_norm: 3.8853 loss: 0.9281 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9281 2023/07/25 03:36:47 - mmengine - INFO - Epoch(train) [44][820/940] lr: 1.0000e-03 eta: 16:11:49 time: 1.0983 data_time: 0.0128 memory: 15768 grad_norm: 3.9449 loss: 1.1344 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1344 2023/07/25 03:37:09 - mmengine - INFO - Epoch(train) [44][840/940] lr: 1.0000e-03 eta: 16:11:26 time: 1.0987 data_time: 0.0129 memory: 15768 grad_norm: 3.8272 loss: 0.8639 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8639 2023/07/25 03:37:31 - mmengine - INFO - Epoch(train) [44][860/940] lr: 1.0000e-03 eta: 16:11:04 time: 1.1003 data_time: 0.0133 memory: 15768 grad_norm: 3.8537 loss: 1.2275 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2275 2023/07/25 03:37:53 - mmengine - INFO - Epoch(train) [44][880/940] lr: 1.0000e-03 eta: 16:10:42 time: 1.0999 data_time: 0.0126 memory: 15768 grad_norm: 3.9546 loss: 1.2326 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2326 2023/07/25 03:38:15 - mmengine - INFO - Epoch(train) [44][900/940] lr: 1.0000e-03 eta: 16:10:20 time: 1.1236 data_time: 0.0133 memory: 15768 grad_norm: 3.9378 loss: 1.0286 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0286 2023/07/25 03:38:38 - mmengine - INFO - Epoch(train) [44][920/940] lr: 1.0000e-03 eta: 16:10:00 time: 1.1603 data_time: 0.0134 memory: 15768 grad_norm: 3.8938 loss: 0.8528 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8528 2023/07/25 03:39:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 03:39:01 - mmengine - INFO - Epoch(train) [44][940/940] lr: 1.0000e-03 eta: 16:09:38 time: 1.1187 data_time: 0.0120 memory: 15768 grad_norm: 4.1790 loss: 1.0689 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0689 2023/07/25 03:39:10 - mmengine - INFO - Epoch(val) [44][20/78] eta: 0:00:27 time: 0.4719 data_time: 0.3143 memory: 2147 2023/07/25 03:39:17 - mmengine - INFO - Epoch(val) [44][40/78] eta: 0:00:15 time: 0.3381 data_time: 0.1809 memory: 2147 2023/07/25 03:39:26 - mmengine - INFO - Epoch(val) [44][60/78] eta: 0:00:07 time: 0.4323 data_time: 0.2756 memory: 2147 2023/07/25 03:39:36 - mmengine - INFO - Epoch(val) [44][78/78] acc/top1: 0.7115 acc/top5: 0.8963 acc/mean1: 0.7114 data_time: 0.2340 time: 0.3883 2023/07/25 03:39:36 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_43.pth is removed 2023/07/25 03:39:37 - mmengine - INFO - The best checkpoint with 0.7115 acc/top1 at 44 epoch is saved to best_acc_top1_epoch_44.pth. 2023/07/25 03:40:02 - mmengine - INFO - Epoch(train) [45][ 20/940] lr: 1.0000e-03 eta: 16:09:19 time: 1.2486 data_time: 0.1500 memory: 15768 grad_norm: 3.8413 loss: 0.9728 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9728 2023/07/25 03:40:24 - mmengine - INFO - Epoch(train) [45][ 40/940] lr: 1.0000e-03 eta: 16:08:57 time: 1.1037 data_time: 0.0137 memory: 15768 grad_norm: 3.8517 loss: 0.9621 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9621 2023/07/25 03:40:46 - mmengine - INFO - Epoch(train) [45][ 60/940] lr: 1.0000e-03 eta: 16:08:35 time: 1.1011 data_time: 0.0131 memory: 15768 grad_norm: 3.8863 loss: 0.8678 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8678 2023/07/25 03:41:09 - mmengine - INFO - Epoch(train) [45][ 80/940] lr: 1.0000e-03 eta: 16:08:13 time: 1.1017 data_time: 0.0133 memory: 15768 grad_norm: 3.8416 loss: 0.9510 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9510 2023/07/25 03:41:30 - mmengine - INFO - Epoch(train) [45][100/940] lr: 1.0000e-03 eta: 16:07:51 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 3.7924 loss: 0.9771 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9771 2023/07/25 03:41:53 - mmengine - INFO - Epoch(train) [45][120/940] lr: 1.0000e-03 eta: 16:07:28 time: 1.1027 data_time: 0.0133 memory: 15768 grad_norm: 3.8804 loss: 1.0374 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0374 2023/07/25 03:42:15 - mmengine - INFO - Epoch(train) [45][140/940] lr: 1.0000e-03 eta: 16:07:06 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 3.8278 loss: 1.0203 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0203 2023/07/25 03:42:37 - mmengine - INFO - Epoch(train) [45][160/940] lr: 1.0000e-03 eta: 16:06:44 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 3.8771 loss: 1.0272 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0272 2023/07/25 03:42:59 - mmengine - INFO - Epoch(train) [45][180/940] lr: 1.0000e-03 eta: 16:06:22 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.9381 loss: 0.9726 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.9726 2023/07/25 03:43:21 - mmengine - INFO - Epoch(train) [45][200/940] lr: 1.0000e-03 eta: 16:06:00 time: 1.1036 data_time: 0.0131 memory: 15768 grad_norm: 3.8528 loss: 0.9405 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9405 2023/07/25 03:43:43 - mmengine - INFO - Epoch(train) [45][220/940] lr: 1.0000e-03 eta: 16:05:37 time: 1.1060 data_time: 0.0133 memory: 15768 grad_norm: 3.8768 loss: 0.9773 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9773 2023/07/25 03:44:05 - mmengine - INFO - Epoch(train) [45][240/940] lr: 1.0000e-03 eta: 16:05:15 time: 1.1047 data_time: 0.0133 memory: 15768 grad_norm: 3.8055 loss: 0.9492 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9492 2023/07/25 03:44:27 - mmengine - INFO - Epoch(train) [45][260/940] lr: 1.0000e-03 eta: 16:04:53 time: 1.1046 data_time: 0.0137 memory: 15768 grad_norm: 3.8709 loss: 0.9270 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9270 2023/07/25 03:44:49 - mmengine - INFO - Epoch(train) [45][280/940] lr: 1.0000e-03 eta: 16:04:31 time: 1.1036 data_time: 0.0129 memory: 15768 grad_norm: 3.8687 loss: 0.8949 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8949 2023/07/25 03:45:11 - mmengine - INFO - Epoch(train) [45][300/940] lr: 1.0000e-03 eta: 16:04:09 time: 1.1018 data_time: 0.0133 memory: 15768 grad_norm: 3.8548 loss: 1.1146 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1146 2023/07/25 03:45:33 - mmengine - INFO - Epoch(train) [45][320/940] lr: 1.0000e-03 eta: 16:03:47 time: 1.1046 data_time: 0.0133 memory: 15768 grad_norm: 3.8965 loss: 0.9974 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9974 2023/07/25 03:45:55 - mmengine - INFO - Epoch(train) [45][340/940] lr: 1.0000e-03 eta: 16:03:25 time: 1.1056 data_time: 0.0131 memory: 15768 grad_norm: 3.9627 loss: 1.1176 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1176 2023/07/25 03:46:17 - mmengine - INFO - Epoch(train) [45][360/940] lr: 1.0000e-03 eta: 16:03:02 time: 1.1022 data_time: 0.0133 memory: 15768 grad_norm: 3.8828 loss: 1.0405 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0405 2023/07/25 03:46:39 - mmengine - INFO - Epoch(train) [45][380/940] lr: 1.0000e-03 eta: 16:02:40 time: 1.0981 data_time: 0.0133 memory: 15768 grad_norm: 3.8823 loss: 1.0463 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0463 2023/07/25 03:47:01 - mmengine - INFO - Epoch(train) [45][400/940] lr: 1.0000e-03 eta: 16:02:18 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 3.8490 loss: 0.7820 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7820 2023/07/25 03:47:23 - mmengine - INFO - Epoch(train) [45][420/940] lr: 1.0000e-03 eta: 16:01:56 time: 1.1012 data_time: 0.0134 memory: 15768 grad_norm: 4.0482 loss: 0.9931 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9931 2023/07/25 03:47:45 - mmengine - INFO - Epoch(train) [45][440/940] lr: 1.0000e-03 eta: 16:01:34 time: 1.1062 data_time: 0.0130 memory: 15768 grad_norm: 3.8283 loss: 1.0820 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.0820 2023/07/25 03:48:07 - mmengine - INFO - Epoch(train) [45][460/940] lr: 1.0000e-03 eta: 16:01:11 time: 1.1028 data_time: 0.0130 memory: 15768 grad_norm: 3.8840 loss: 1.0007 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0007 2023/07/25 03:48:29 - mmengine - INFO - Epoch(train) [45][480/940] lr: 1.0000e-03 eta: 16:00:49 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 3.9680 loss: 0.9235 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9235 2023/07/25 03:48:51 - mmengine - INFO - Epoch(train) [45][500/940] lr: 1.0000e-03 eta: 16:00:27 time: 1.0999 data_time: 0.0135 memory: 15768 grad_norm: 4.0416 loss: 0.9249 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9249 2023/07/25 03:49:13 - mmengine - INFO - Epoch(train) [45][520/940] lr: 1.0000e-03 eta: 16:00:05 time: 1.0994 data_time: 0.0135 memory: 15768 grad_norm: 3.9620 loss: 1.0124 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0124 2023/07/25 03:49:35 - mmengine - INFO - Epoch(train) [45][540/940] lr: 1.0000e-03 eta: 15:59:42 time: 1.0990 data_time: 0.0135 memory: 15768 grad_norm: 3.8999 loss: 1.0379 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0379 2023/07/25 03:49:57 - mmengine - INFO - Epoch(train) [45][560/940] lr: 1.0000e-03 eta: 15:59:20 time: 1.0994 data_time: 0.0138 memory: 15768 grad_norm: 3.8602 loss: 0.9066 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9066 2023/07/25 03:50:19 - mmengine - INFO - Epoch(train) [45][580/940] lr: 1.0000e-03 eta: 15:58:58 time: 1.1035 data_time: 0.0130 memory: 15768 grad_norm: 3.8905 loss: 0.8776 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8776 2023/07/25 03:50:41 - mmengine - INFO - Epoch(train) [45][600/940] lr: 1.0000e-03 eta: 15:58:36 time: 1.0978 data_time: 0.0134 memory: 15768 grad_norm: 3.8765 loss: 1.0903 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0903 2023/07/25 03:51:03 - mmengine - INFO - Epoch(train) [45][620/940] lr: 1.0000e-03 eta: 15:58:14 time: 1.1004 data_time: 0.0134 memory: 15768 grad_norm: 3.8165 loss: 0.8794 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8794 2023/07/25 03:51:25 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 03:51:25 - mmengine - INFO - Epoch(train) [45][640/940] lr: 1.0000e-03 eta: 15:57:51 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 3.9590 loss: 0.9558 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9558 2023/07/25 03:51:47 - mmengine - INFO - Epoch(train) [45][660/940] lr: 1.0000e-03 eta: 15:57:29 time: 1.1009 data_time: 0.0135 memory: 15768 grad_norm: 3.8560 loss: 0.9640 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9640 2023/07/25 03:52:09 - mmengine - INFO - Epoch(train) [45][680/940] lr: 1.0000e-03 eta: 15:57:07 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 4.0010 loss: 0.9410 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9410 2023/07/25 03:52:31 - mmengine - INFO - Epoch(train) [45][700/940] lr: 1.0000e-03 eta: 15:56:45 time: 1.1029 data_time: 0.0133 memory: 15768 grad_norm: 3.9659 loss: 1.0592 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0592 2023/07/25 03:52:54 - mmengine - INFO - Epoch(train) [45][720/940] lr: 1.0000e-03 eta: 15:56:22 time: 1.1031 data_time: 0.0136 memory: 15768 grad_norm: 3.9676 loss: 0.8596 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8596 2023/07/25 03:53:16 - mmengine - INFO - Epoch(train) [45][740/940] lr: 1.0000e-03 eta: 15:56:00 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 3.9801 loss: 1.1910 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1910 2023/07/25 03:53:38 - mmengine - INFO - Epoch(train) [45][760/940] lr: 1.0000e-03 eta: 15:55:38 time: 1.0998 data_time: 0.0131 memory: 15768 grad_norm: 3.8597 loss: 0.8835 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8835 2023/07/25 03:54:00 - mmengine - INFO - Epoch(train) [45][780/940] lr: 1.0000e-03 eta: 15:55:16 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 3.9008 loss: 0.8829 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8829 2023/07/25 03:54:22 - mmengine - INFO - Epoch(train) [45][800/940] lr: 1.0000e-03 eta: 15:54:54 time: 1.1014 data_time: 0.0133 memory: 15768 grad_norm: 3.9444 loss: 1.0822 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0822 2023/07/25 03:54:44 - mmengine - INFO - Epoch(train) [45][820/940] lr: 1.0000e-03 eta: 15:54:31 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 3.8687 loss: 0.9957 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9957 2023/07/25 03:55:06 - mmengine - INFO - Epoch(train) [45][840/940] lr: 1.0000e-03 eta: 15:54:09 time: 1.1005 data_time: 0.0128 memory: 15768 grad_norm: 3.9772 loss: 0.8581 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8581 2023/07/25 03:55:28 - mmengine - INFO - Epoch(train) [45][860/940] lr: 1.0000e-03 eta: 15:53:47 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 3.9653 loss: 1.0632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0632 2023/07/25 03:55:50 - mmengine - INFO - Epoch(train) [45][880/940] lr: 1.0000e-03 eta: 15:53:25 time: 1.0982 data_time: 0.0132 memory: 15768 grad_norm: 3.9277 loss: 0.9485 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9485 2023/07/25 03:56:12 - mmengine - INFO - Epoch(train) [45][900/940] lr: 1.0000e-03 eta: 15:53:02 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 4.0710 loss: 1.1044 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1044 2023/07/25 03:56:34 - mmengine - INFO - Epoch(train) [45][920/940] lr: 1.0000e-03 eta: 15:52:41 time: 1.1193 data_time: 0.0133 memory: 15768 grad_norm: 3.9811 loss: 1.0440 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0440 2023/07/25 03:56:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 03:56:56 - mmengine - INFO - Epoch(train) [45][940/940] lr: 1.0000e-03 eta: 15:52:19 time: 1.1146 data_time: 0.0128 memory: 15768 grad_norm: 4.2596 loss: 1.1074 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1074 2023/07/25 03:56:56 - mmengine - INFO - Saving checkpoint at 45 epochs 2023/07/25 03:57:07 - mmengine - INFO - Epoch(val) [45][20/78] eta: 0:00:27 time: 0.4785 data_time: 0.3215 memory: 2147 2023/07/25 03:57:14 - mmengine - INFO - Epoch(val) [45][40/78] eta: 0:00:15 time: 0.3306 data_time: 0.1741 memory: 2147 2023/07/25 03:57:22 - mmengine - INFO - Epoch(val) [45][60/78] eta: 0:00:07 time: 0.4376 data_time: 0.2803 memory: 2147 2023/07/25 03:57:32 - mmengine - INFO - Epoch(val) [45][78/78] acc/top1: 0.7127 acc/top5: 0.8984 acc/mean1: 0.7126 data_time: 0.2330 time: 0.3871 2023/07/25 03:57:32 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_44.pth is removed 2023/07/25 03:57:33 - mmengine - INFO - The best checkpoint with 0.7127 acc/top1 at 45 epoch is saved to best_acc_top1_epoch_45.pth. 2023/07/25 03:57:59 - mmengine - INFO - Epoch(train) [46][ 20/940] lr: 1.0000e-03 eta: 15:52:01 time: 1.2629 data_time: 0.1668 memory: 15768 grad_norm: 3.9337 loss: 0.9472 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9472 2023/07/25 03:58:21 - mmengine - INFO - Epoch(train) [46][ 40/940] lr: 1.0000e-03 eta: 15:51:38 time: 1.1006 data_time: 0.0134 memory: 15768 grad_norm: 3.8533 loss: 0.9698 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9698 2023/07/25 03:58:43 - mmengine - INFO - Epoch(train) [46][ 60/940] lr: 1.0000e-03 eta: 15:51:16 time: 1.0985 data_time: 0.0135 memory: 15768 grad_norm: 3.9159 loss: 1.0129 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0129 2023/07/25 03:59:05 - mmengine - INFO - Epoch(train) [46][ 80/940] lr: 1.0000e-03 eta: 15:50:54 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 3.9712 loss: 0.9627 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9627 2023/07/25 03:59:27 - mmengine - INFO - Epoch(train) [46][100/940] lr: 1.0000e-03 eta: 15:50:32 time: 1.0988 data_time: 0.0133 memory: 15768 grad_norm: 3.9675 loss: 1.0111 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0111 2023/07/25 03:59:49 - mmengine - INFO - Epoch(train) [46][120/940] lr: 1.0000e-03 eta: 15:50:09 time: 1.0991 data_time: 0.0134 memory: 15768 grad_norm: 3.9882 loss: 0.9179 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9179 2023/07/25 04:00:11 - mmengine - INFO - Epoch(train) [46][140/940] lr: 1.0000e-03 eta: 15:49:47 time: 1.1008 data_time: 0.0133 memory: 15768 grad_norm: 3.8340 loss: 0.9976 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9976 2023/07/25 04:00:33 - mmengine - INFO - Epoch(train) [46][160/940] lr: 1.0000e-03 eta: 15:49:25 time: 1.1005 data_time: 0.0140 memory: 15768 grad_norm: 3.9204 loss: 1.1117 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1117 2023/07/25 04:00:55 - mmengine - INFO - Epoch(train) [46][180/940] lr: 1.0000e-03 eta: 15:49:03 time: 1.1016 data_time: 0.0132 memory: 15768 grad_norm: 3.9816 loss: 0.7840 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7840 2023/07/25 04:01:17 - mmengine - INFO - Epoch(train) [46][200/940] lr: 1.0000e-03 eta: 15:48:40 time: 1.1008 data_time: 0.0129 memory: 15768 grad_norm: 4.0682 loss: 0.9991 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9991 2023/07/25 04:01:39 - mmengine - INFO - Epoch(train) [46][220/940] lr: 1.0000e-03 eta: 15:48:18 time: 1.1011 data_time: 0.0134 memory: 15768 grad_norm: 3.8995 loss: 0.9689 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9689 2023/07/25 04:02:01 - mmengine - INFO - Epoch(train) [46][240/940] lr: 1.0000e-03 eta: 15:47:56 time: 1.0991 data_time: 0.0133 memory: 15768 grad_norm: 3.8724 loss: 0.8218 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8218 2023/07/25 04:02:23 - mmengine - INFO - Epoch(train) [46][260/940] lr: 1.0000e-03 eta: 15:47:34 time: 1.1004 data_time: 0.0135 memory: 15768 grad_norm: 3.9375 loss: 0.8887 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8887 2023/07/25 04:02:45 - mmengine - INFO - Epoch(train) [46][280/940] lr: 1.0000e-03 eta: 15:47:12 time: 1.0984 data_time: 0.0133 memory: 15768 grad_norm: 3.8838 loss: 0.8433 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8433 2023/07/25 04:03:07 - mmengine - INFO - Epoch(train) [46][300/940] lr: 1.0000e-03 eta: 15:46:49 time: 1.1029 data_time: 0.0133 memory: 15768 grad_norm: 3.8520 loss: 1.0458 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0458 2023/07/25 04:03:29 - mmengine - INFO - Epoch(train) [46][320/940] lr: 1.0000e-03 eta: 15:46:27 time: 1.1004 data_time: 0.0131 memory: 15768 grad_norm: 3.9710 loss: 0.8875 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8875 2023/07/25 04:03:51 - mmengine - INFO - Epoch(train) [46][340/940] lr: 1.0000e-03 eta: 15:46:05 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 3.8799 loss: 1.0016 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.0016 2023/07/25 04:04:13 - mmengine - INFO - Epoch(train) [46][360/940] lr: 1.0000e-03 eta: 15:45:43 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 3.8533 loss: 0.8794 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8794 2023/07/25 04:04:35 - mmengine - INFO - Epoch(train) [46][380/940] lr: 1.0000e-03 eta: 15:45:21 time: 1.1019 data_time: 0.0132 memory: 15768 grad_norm: 3.9510 loss: 0.9556 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9556 2023/07/25 04:04:57 - mmengine - INFO - Epoch(train) [46][400/940] lr: 1.0000e-03 eta: 15:44:58 time: 1.1053 data_time: 0.0141 memory: 15768 grad_norm: 3.8943 loss: 0.9118 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9118 2023/07/25 04:05:19 - mmengine - INFO - Epoch(train) [46][420/940] lr: 1.0000e-03 eta: 15:44:36 time: 1.1015 data_time: 0.0133 memory: 15768 grad_norm: 3.9407 loss: 1.0860 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0860 2023/07/25 04:05:41 - mmengine - INFO - Epoch(train) [46][440/940] lr: 1.0000e-03 eta: 15:44:14 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 4.0117 loss: 0.9263 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9263 2023/07/25 04:06:03 - mmengine - INFO - Epoch(train) [46][460/940] lr: 1.0000e-03 eta: 15:43:52 time: 1.0996 data_time: 0.0130 memory: 15768 grad_norm: 3.9273 loss: 1.0236 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0236 2023/07/25 04:06:25 - mmengine - INFO - Epoch(train) [46][480/940] lr: 1.0000e-03 eta: 15:43:30 time: 1.1032 data_time: 0.0132 memory: 15768 grad_norm: 3.9081 loss: 0.8558 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8558 2023/07/25 04:06:47 - mmengine - INFO - Epoch(train) [46][500/940] lr: 1.0000e-03 eta: 15:43:07 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 3.9967 loss: 0.9632 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9632 2023/07/25 04:07:09 - mmengine - INFO - Epoch(train) [46][520/940] lr: 1.0000e-03 eta: 15:42:45 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 3.9800 loss: 0.8950 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8950 2023/07/25 04:07:31 - mmengine - INFO - Epoch(train) [46][540/940] lr: 1.0000e-03 eta: 15:42:23 time: 1.1029 data_time: 0.0128 memory: 15768 grad_norm: 4.0473 loss: 0.9231 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9231 2023/07/25 04:07:53 - mmengine - INFO - Epoch(train) [46][560/940] lr: 1.0000e-03 eta: 15:42:01 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 3.9101 loss: 1.0239 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0239 2023/07/25 04:08:15 - mmengine - INFO - Epoch(train) [46][580/940] lr: 1.0000e-03 eta: 15:41:39 time: 1.1009 data_time: 0.0128 memory: 15768 grad_norm: 4.0143 loss: 0.9214 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9214 2023/07/25 04:08:37 - mmengine - INFO - Epoch(train) [46][600/940] lr: 1.0000e-03 eta: 15:41:16 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 4.0134 loss: 0.9798 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9798 2023/07/25 04:08:59 - mmengine - INFO - Epoch(train) [46][620/940] lr: 1.0000e-03 eta: 15:40:54 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 3.9385 loss: 1.0344 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0344 2023/07/25 04:09:21 - mmengine - INFO - Epoch(train) [46][640/940] lr: 1.0000e-03 eta: 15:40:32 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 3.9353 loss: 1.0292 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0292 2023/07/25 04:09:43 - mmengine - INFO - Epoch(train) [46][660/940] lr: 1.0000e-03 eta: 15:40:10 time: 1.1005 data_time: 0.0137 memory: 15768 grad_norm: 3.9411 loss: 1.0562 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0562 2023/07/25 04:10:05 - mmengine - INFO - Epoch(train) [46][680/940] lr: 1.0000e-03 eta: 15:39:47 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 3.8870 loss: 0.9177 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9177 2023/07/25 04:10:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 04:10:27 - mmengine - INFO - Epoch(train) [46][700/940] lr: 1.0000e-03 eta: 15:39:25 time: 1.1010 data_time: 0.0132 memory: 15768 grad_norm: 3.8542 loss: 1.0425 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0425 2023/07/25 04:10:49 - mmengine - INFO - Epoch(train) [46][720/940] lr: 1.0000e-03 eta: 15:39:03 time: 1.1013 data_time: 0.0133 memory: 15768 grad_norm: 3.9315 loss: 0.9946 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9946 2023/07/25 04:11:11 - mmengine - INFO - Epoch(train) [46][740/940] lr: 1.0000e-03 eta: 15:38:41 time: 1.1010 data_time: 0.0132 memory: 15768 grad_norm: 3.9557 loss: 0.9557 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9557 2023/07/25 04:11:33 - mmengine - INFO - Epoch(train) [46][760/940] lr: 1.0000e-03 eta: 15:38:19 time: 1.1039 data_time: 0.0131 memory: 15768 grad_norm: 3.9536 loss: 0.9356 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9356 2023/07/25 04:11:55 - mmengine - INFO - Epoch(train) [46][780/940] lr: 1.0000e-03 eta: 15:37:56 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 3.9784 loss: 0.9514 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9514 2023/07/25 04:12:17 - mmengine - INFO - Epoch(train) [46][800/940] lr: 1.0000e-03 eta: 15:37:34 time: 1.1005 data_time: 0.0136 memory: 15768 grad_norm: 3.8671 loss: 0.8725 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8725 2023/07/25 04:12:39 - mmengine - INFO - Epoch(train) [46][820/940] lr: 1.0000e-03 eta: 15:37:12 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 3.9886 loss: 0.9222 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9222 2023/07/25 04:13:01 - mmengine - INFO - Epoch(train) [46][840/940] lr: 1.0000e-03 eta: 15:36:50 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 4.0205 loss: 1.0917 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0917 2023/07/25 04:13:23 - mmengine - INFO - Epoch(train) [46][860/940] lr: 1.0000e-03 eta: 15:36:28 time: 1.0976 data_time: 0.0132 memory: 15768 grad_norm: 3.9055 loss: 0.9410 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9410 2023/07/25 04:13:46 - mmengine - INFO - Epoch(train) [46][880/940] lr: 1.0000e-03 eta: 15:36:06 time: 1.1289 data_time: 0.0133 memory: 15768 grad_norm: 3.9536 loss: 0.9945 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9945 2023/07/25 04:14:08 - mmengine - INFO - Epoch(train) [46][900/940] lr: 1.0000e-03 eta: 15:35:44 time: 1.1029 data_time: 0.0138 memory: 15768 grad_norm: 3.9481 loss: 0.8583 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8583 2023/07/25 04:14:30 - mmengine - INFO - Epoch(train) [46][920/940] lr: 1.0000e-03 eta: 15:35:22 time: 1.0995 data_time: 0.0134 memory: 15768 grad_norm: 3.9101 loss: 0.9722 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9722 2023/07/25 04:14:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 04:14:51 - mmengine - INFO - Epoch(train) [46][940/940] lr: 1.0000e-03 eta: 15:34:58 time: 1.0581 data_time: 0.0125 memory: 15768 grad_norm: 4.2770 loss: 0.9646 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9646 2023/07/25 04:15:01 - mmengine - INFO - Epoch(val) [46][20/78] eta: 0:00:28 time: 0.4931 data_time: 0.3353 memory: 2147 2023/07/25 04:15:08 - mmengine - INFO - Epoch(val) [46][40/78] eta: 0:00:15 time: 0.3342 data_time: 0.1776 memory: 2147 2023/07/25 04:15:16 - mmengine - INFO - Epoch(val) [46][60/78] eta: 0:00:07 time: 0.4387 data_time: 0.2820 memory: 2147 2023/07/25 04:15:27 - mmengine - INFO - Epoch(val) [46][78/78] acc/top1: 0.7122 acc/top5: 0.8980 acc/mean1: 0.7121 data_time: 0.2398 time: 0.3940 2023/07/25 04:15:53 - mmengine - INFO - Epoch(train) [47][ 20/940] lr: 1.0000e-03 eta: 15:34:41 time: 1.2932 data_time: 0.1831 memory: 15768 grad_norm: 3.9543 loss: 0.9146 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9146 2023/07/25 04:16:15 - mmengine - INFO - Epoch(train) [47][ 40/940] lr: 1.0000e-03 eta: 15:34:18 time: 1.0975 data_time: 0.0132 memory: 15768 grad_norm: 3.9444 loss: 0.9585 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9585 2023/07/25 04:16:37 - mmengine - INFO - Epoch(train) [47][ 60/940] lr: 1.0000e-03 eta: 15:33:56 time: 1.1004 data_time: 0.0131 memory: 15768 grad_norm: 3.8198 loss: 1.0079 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0079 2023/07/25 04:16:59 - mmengine - INFO - Epoch(train) [47][ 80/940] lr: 1.0000e-03 eta: 15:33:34 time: 1.0996 data_time: 0.0133 memory: 15768 grad_norm: 4.0187 loss: 1.0632 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0632 2023/07/25 04:17:21 - mmengine - INFO - Epoch(train) [47][100/940] lr: 1.0000e-03 eta: 15:33:12 time: 1.1040 data_time: 0.0128 memory: 15768 grad_norm: 3.9126 loss: 1.0192 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0192 2023/07/25 04:17:43 - mmengine - INFO - Epoch(train) [47][120/940] lr: 1.0000e-03 eta: 15:32:50 time: 1.0992 data_time: 0.0132 memory: 15768 grad_norm: 3.9584 loss: 0.8935 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8935 2023/07/25 04:18:05 - mmengine - INFO - Epoch(train) [47][140/940] lr: 1.0000e-03 eta: 15:32:27 time: 1.0993 data_time: 0.0135 memory: 15768 grad_norm: 3.9915 loss: 0.8329 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8329 2023/07/25 04:18:27 - mmengine - INFO - Epoch(train) [47][160/940] lr: 1.0000e-03 eta: 15:32:05 time: 1.0985 data_time: 0.0130 memory: 15768 grad_norm: 3.9339 loss: 0.8491 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8491 2023/07/25 04:18:49 - mmengine - INFO - Epoch(train) [47][180/940] lr: 1.0000e-03 eta: 15:31:43 time: 1.1001 data_time: 0.0128 memory: 15768 grad_norm: 3.9140 loss: 1.0267 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0267 2023/07/25 04:19:11 - mmengine - INFO - Epoch(train) [47][200/940] lr: 1.0000e-03 eta: 15:31:21 time: 1.1039 data_time: 0.0129 memory: 15768 grad_norm: 4.0343 loss: 0.9660 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9660 2023/07/25 04:19:33 - mmengine - INFO - Epoch(train) [47][220/940] lr: 1.0000e-03 eta: 15:30:58 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 3.8997 loss: 0.9845 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9845 2023/07/25 04:19:55 - mmengine - INFO - Epoch(train) [47][240/940] lr: 1.0000e-03 eta: 15:30:36 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 4.0185 loss: 0.8150 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8150 2023/07/25 04:20:17 - mmengine - INFO - Epoch(train) [47][260/940] lr: 1.0000e-03 eta: 15:30:14 time: 1.0984 data_time: 0.0131 memory: 15768 grad_norm: 3.9895 loss: 0.9746 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9746 2023/07/25 04:20:39 - mmengine - INFO - Epoch(train) [47][280/940] lr: 1.0000e-03 eta: 15:29:52 time: 1.0982 data_time: 0.0128 memory: 15768 grad_norm: 4.0240 loss: 1.0979 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0979 2023/07/25 04:21:01 - mmengine - INFO - Epoch(train) [47][300/940] lr: 1.0000e-03 eta: 15:29:29 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 3.9181 loss: 0.9879 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9879 2023/07/25 04:21:23 - mmengine - INFO - Epoch(train) [47][320/940] lr: 1.0000e-03 eta: 15:29:07 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 3.8764 loss: 0.8299 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8299 2023/07/25 04:21:45 - mmengine - INFO - Epoch(train) [47][340/940] lr: 1.0000e-03 eta: 15:28:45 time: 1.0981 data_time: 0.0128 memory: 15768 grad_norm: 3.9874 loss: 0.9846 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9846 2023/07/25 04:22:07 - mmengine - INFO - Epoch(train) [47][360/940] lr: 1.0000e-03 eta: 15:28:23 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 3.8921 loss: 0.9449 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9449 2023/07/25 04:22:29 - mmengine - INFO - Epoch(train) [47][380/940] lr: 1.0000e-03 eta: 15:28:00 time: 1.0991 data_time: 0.0127 memory: 15768 grad_norm: 4.0238 loss: 0.8771 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8771 2023/07/25 04:22:51 - mmengine - INFO - Epoch(train) [47][400/940] lr: 1.0000e-03 eta: 15:27:38 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 3.9529 loss: 1.0787 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0787 2023/07/25 04:23:13 - mmengine - INFO - Epoch(train) [47][420/940] lr: 1.0000e-03 eta: 15:27:16 time: 1.0994 data_time: 0.0128 memory: 15768 grad_norm: 3.9637 loss: 0.8145 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8145 2023/07/25 04:23:35 - mmengine - INFO - Epoch(train) [47][440/940] lr: 1.0000e-03 eta: 15:26:54 time: 1.1010 data_time: 0.0127 memory: 15768 grad_norm: 3.9390 loss: 1.0969 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0969 2023/07/25 04:23:57 - mmengine - INFO - Epoch(train) [47][460/940] lr: 1.0000e-03 eta: 15:26:32 time: 1.1002 data_time: 0.0132 memory: 15768 grad_norm: 3.9877 loss: 0.9359 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9359 2023/07/25 04:24:19 - mmengine - INFO - Epoch(train) [47][480/940] lr: 1.0000e-03 eta: 15:26:09 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 3.9510 loss: 0.9516 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.9516 2023/07/25 04:24:41 - mmengine - INFO - Epoch(train) [47][500/940] lr: 1.0000e-03 eta: 15:25:47 time: 1.0984 data_time: 0.0131 memory: 15768 grad_norm: 3.9922 loss: 0.9734 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9734 2023/07/25 04:25:03 - mmengine - INFO - Epoch(train) [47][520/940] lr: 1.0000e-03 eta: 15:25:25 time: 1.1023 data_time: 0.0139 memory: 15768 grad_norm: 4.0229 loss: 1.1052 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1052 2023/07/25 04:25:25 - mmengine - INFO - Epoch(train) [47][540/940] lr: 1.0000e-03 eta: 15:25:03 time: 1.1013 data_time: 0.0138 memory: 15768 grad_norm: 3.9347 loss: 1.0427 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0427 2023/07/25 04:25:47 - mmengine - INFO - Epoch(train) [47][560/940] lr: 1.0000e-03 eta: 15:24:41 time: 1.1030 data_time: 0.0132 memory: 15768 grad_norm: 4.0462 loss: 1.0392 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0392 2023/07/25 04:26:09 - mmengine - INFO - Epoch(train) [47][580/940] lr: 1.0000e-03 eta: 15:24:18 time: 1.1032 data_time: 0.0130 memory: 15768 grad_norm: 4.0191 loss: 1.0148 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0148 2023/07/25 04:26:31 - mmengine - INFO - Epoch(train) [47][600/940] lr: 1.0000e-03 eta: 15:23:56 time: 1.1037 data_time: 0.0133 memory: 15768 grad_norm: 4.0309 loss: 0.9932 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9932 2023/07/25 04:26:53 - mmengine - INFO - Epoch(train) [47][620/940] lr: 1.0000e-03 eta: 15:23:34 time: 1.1031 data_time: 0.0133 memory: 15768 grad_norm: 3.9420 loss: 0.9125 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9125 2023/07/25 04:27:15 - mmengine - INFO - Epoch(train) [47][640/940] lr: 1.0000e-03 eta: 15:23:12 time: 1.0983 data_time: 0.0135 memory: 15768 grad_norm: 3.9771 loss: 0.8906 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8906 2023/07/25 04:27:37 - mmengine - INFO - Epoch(train) [47][660/940] lr: 1.0000e-03 eta: 15:22:50 time: 1.1060 data_time: 0.0133 memory: 15768 grad_norm: 4.0326 loss: 1.1560 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1560 2023/07/25 04:27:59 - mmengine - INFO - Epoch(train) [47][680/940] lr: 1.0000e-03 eta: 15:22:28 time: 1.0973 data_time: 0.0133 memory: 15768 grad_norm: 3.9644 loss: 0.8252 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8252 2023/07/25 04:28:21 - mmengine - INFO - Epoch(train) [47][700/940] lr: 1.0000e-03 eta: 15:22:05 time: 1.0982 data_time: 0.0135 memory: 15768 grad_norm: 4.0231 loss: 0.8957 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8957 2023/07/25 04:28:43 - mmengine - INFO - Epoch(train) [47][720/940] lr: 1.0000e-03 eta: 15:21:43 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 4.0221 loss: 0.8510 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8510 2023/07/25 04:29:05 - mmengine - INFO - Epoch(train) [47][740/940] lr: 1.0000e-03 eta: 15:21:21 time: 1.1006 data_time: 0.0132 memory: 15768 grad_norm: 3.9954 loss: 0.8929 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8929 2023/07/25 04:29:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 04:29:27 - mmengine - INFO - Epoch(train) [47][760/940] lr: 1.0000e-03 eta: 15:20:59 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 3.9128 loss: 0.8427 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8427 2023/07/25 04:29:49 - mmengine - INFO - Epoch(train) [47][780/940] lr: 1.0000e-03 eta: 15:20:36 time: 1.0971 data_time: 0.0131 memory: 15768 grad_norm: 4.0463 loss: 0.9549 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9549 2023/07/25 04:30:11 - mmengine - INFO - Epoch(train) [47][800/940] lr: 1.0000e-03 eta: 15:20:14 time: 1.0995 data_time: 0.0126 memory: 15768 grad_norm: 3.9277 loss: 0.9437 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9437 2023/07/25 04:30:33 - mmengine - INFO - Epoch(train) [47][820/940] lr: 1.0000e-03 eta: 15:19:52 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 3.9972 loss: 1.1696 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1696 2023/07/25 04:30:55 - mmengine - INFO - Epoch(train) [47][840/940] lr: 1.0000e-03 eta: 15:19:30 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 4.0156 loss: 0.9181 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9181 2023/07/25 04:31:17 - mmengine - INFO - Epoch(train) [47][860/940] lr: 1.0000e-03 eta: 15:19:07 time: 1.0989 data_time: 0.0128 memory: 15768 grad_norm: 4.0063 loss: 0.8623 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8623 2023/07/25 04:31:39 - mmengine - INFO - Epoch(train) [47][880/940] lr: 1.0000e-03 eta: 15:18:45 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 4.0302 loss: 0.9759 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9759 2023/07/25 04:32:01 - mmengine - INFO - Epoch(train) [47][900/940] lr: 1.0000e-03 eta: 15:18:23 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 4.0973 loss: 0.8076 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8076 2023/07/25 04:32:23 - mmengine - INFO - Epoch(train) [47][920/940] lr: 1.0000e-03 eta: 15:18:01 time: 1.1028 data_time: 0.0131 memory: 15768 grad_norm: 4.0626 loss: 0.9684 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9684 2023/07/25 04:32:44 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 04:32:44 - mmengine - INFO - Epoch(train) [47][940/940] lr: 1.0000e-03 eta: 15:17:37 time: 1.0536 data_time: 0.0126 memory: 15768 grad_norm: 4.2733 loss: 0.9759 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 0.9759 2023/07/25 04:32:54 - mmengine - INFO - Epoch(val) [47][20/78] eta: 0:00:28 time: 0.4874 data_time: 0.3297 memory: 2147 2023/07/25 04:33:01 - mmengine - INFO - Epoch(val) [47][40/78] eta: 0:00:16 time: 0.3599 data_time: 0.2028 memory: 2147 2023/07/25 04:33:10 - mmengine - INFO - Epoch(val) [47][60/78] eta: 0:00:07 time: 0.4488 data_time: 0.2920 memory: 2147 2023/07/25 04:33:20 - mmengine - INFO - Epoch(val) [47][78/78] acc/top1: 0.7135 acc/top5: 0.8985 acc/mean1: 0.7134 data_time: 0.2464 time: 0.4007 2023/07/25 04:33:20 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_45.pth is removed 2023/07/25 04:33:21 - mmengine - INFO - The best checkpoint with 0.7135 acc/top1 at 47 epoch is saved to best_acc_top1_epoch_47.pth. 2023/07/25 04:33:46 - mmengine - INFO - Epoch(train) [48][ 20/940] lr: 1.0000e-03 eta: 15:17:19 time: 1.2467 data_time: 0.1399 memory: 15768 grad_norm: 3.9973 loss: 1.0207 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0207 2023/07/25 04:34:08 - mmengine - INFO - Epoch(train) [48][ 40/940] lr: 1.0000e-03 eta: 15:16:56 time: 1.1046 data_time: 0.0132 memory: 15768 grad_norm: 3.9214 loss: 0.8511 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8511 2023/07/25 04:34:31 - mmengine - INFO - Epoch(train) [48][ 60/940] lr: 1.0000e-03 eta: 15:16:35 time: 1.1577 data_time: 0.0129 memory: 15768 grad_norm: 3.9762 loss: 0.8550 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8550 2023/07/25 04:34:54 - mmengine - INFO - Epoch(train) [48][ 80/940] lr: 1.0000e-03 eta: 15:16:15 time: 1.1670 data_time: 0.0129 memory: 15768 grad_norm: 3.9400 loss: 0.9724 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9724 2023/07/25 04:35:18 - mmengine - INFO - Epoch(train) [48][100/940] lr: 1.0000e-03 eta: 15:15:54 time: 1.1661 data_time: 0.0130 memory: 15768 grad_norm: 3.8478 loss: 0.7735 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7735 2023/07/25 04:35:41 - mmengine - INFO - Epoch(train) [48][120/940] lr: 1.0000e-03 eta: 15:15:33 time: 1.1620 data_time: 0.0134 memory: 15768 grad_norm: 3.9242 loss: 0.8571 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8571 2023/07/25 04:36:04 - mmengine - INFO - Epoch(train) [48][140/940] lr: 1.0000e-03 eta: 15:15:12 time: 1.1657 data_time: 0.0131 memory: 15768 grad_norm: 3.9445 loss: 1.0840 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0840 2023/07/25 04:36:27 - mmengine - INFO - Epoch(train) [48][160/940] lr: 1.0000e-03 eta: 15:14:52 time: 1.1673 data_time: 0.0131 memory: 15768 grad_norm: 3.9794 loss: 0.8677 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8677 2023/07/25 04:36:50 - mmengine - INFO - Epoch(train) [48][180/940] lr: 1.0000e-03 eta: 15:14:31 time: 1.1479 data_time: 0.0133 memory: 15768 grad_norm: 3.9749 loss: 1.0228 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0228 2023/07/25 04:37:12 - mmengine - INFO - Epoch(train) [48][200/940] lr: 1.0000e-03 eta: 15:14:08 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 4.0284 loss: 0.8703 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8703 2023/07/25 04:37:34 - mmengine - INFO - Epoch(train) [48][220/940] lr: 1.0000e-03 eta: 15:13:46 time: 1.0998 data_time: 0.0134 memory: 15768 grad_norm: 4.0081 loss: 0.9299 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9299 2023/07/25 04:37:56 - mmengine - INFO - Epoch(train) [48][240/940] lr: 1.0000e-03 eta: 15:13:24 time: 1.1030 data_time: 0.0131 memory: 15768 grad_norm: 3.9985 loss: 0.9076 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9076 2023/07/25 04:38:18 - mmengine - INFO - Epoch(train) [48][260/940] lr: 1.0000e-03 eta: 15:13:02 time: 1.1013 data_time: 0.0130 memory: 15768 grad_norm: 4.0780 loss: 0.9176 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9176 2023/07/25 04:38:40 - mmengine - INFO - Epoch(train) [48][280/940] lr: 1.0000e-03 eta: 15:12:39 time: 1.0991 data_time: 0.0134 memory: 15768 grad_norm: 3.8625 loss: 0.9746 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9746 2023/07/25 04:39:03 - mmengine - INFO - Epoch(train) [48][300/940] lr: 1.0000e-03 eta: 15:12:17 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 4.0296 loss: 1.0318 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0318 2023/07/25 04:39:25 - mmengine - INFO - Epoch(train) [48][320/940] lr: 1.0000e-03 eta: 15:11:55 time: 1.1019 data_time: 0.0136 memory: 15768 grad_norm: 3.9610 loss: 0.8411 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8411 2023/07/25 04:39:47 - mmengine - INFO - Epoch(train) [48][340/940] lr: 1.0000e-03 eta: 15:11:33 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 3.9510 loss: 0.9616 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9616 2023/07/25 04:40:09 - mmengine - INFO - Epoch(train) [48][360/940] lr: 1.0000e-03 eta: 15:11:11 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 3.9521 loss: 0.8297 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8297 2023/07/25 04:40:30 - mmengine - INFO - Epoch(train) [48][380/940] lr: 1.0000e-03 eta: 15:10:48 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 3.9721 loss: 1.0301 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0301 2023/07/25 04:40:52 - mmengine - INFO - Epoch(train) [48][400/940] lr: 1.0000e-03 eta: 15:10:26 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 4.0194 loss: 0.9096 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9096 2023/07/25 04:41:15 - mmengine - INFO - Epoch(train) [48][420/940] lr: 1.0000e-03 eta: 15:10:04 time: 1.1019 data_time: 0.0135 memory: 15768 grad_norm: 4.0114 loss: 1.1021 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1021 2023/07/25 04:41:37 - mmengine - INFO - Epoch(train) [48][440/940] lr: 1.0000e-03 eta: 15:09:42 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 4.0409 loss: 0.8867 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8867 2023/07/25 04:41:59 - mmengine - INFO - Epoch(train) [48][460/940] lr: 1.0000e-03 eta: 15:09:20 time: 1.1022 data_time: 0.0136 memory: 15768 grad_norm: 3.9687 loss: 1.0394 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0394 2023/07/25 04:42:21 - mmengine - INFO - Epoch(train) [48][480/940] lr: 1.0000e-03 eta: 15:08:57 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 4.0394 loss: 0.9477 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9477 2023/07/25 04:42:43 - mmengine - INFO - Epoch(train) [48][500/940] lr: 1.0000e-03 eta: 15:08:35 time: 1.0991 data_time: 0.0135 memory: 15768 grad_norm: 4.0457 loss: 1.0151 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0151 2023/07/25 04:43:05 - mmengine - INFO - Epoch(train) [48][520/940] lr: 1.0000e-03 eta: 15:08:13 time: 1.1088 data_time: 0.0134 memory: 15768 grad_norm: 3.9794 loss: 0.7720 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7720 2023/07/25 04:43:28 - mmengine - INFO - Epoch(train) [48][540/940] lr: 1.0000e-03 eta: 15:07:52 time: 1.1618 data_time: 0.0131 memory: 15768 grad_norm: 3.9753 loss: 0.8197 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8197 2023/07/25 04:43:51 - mmengine - INFO - Epoch(train) [48][560/940] lr: 1.0000e-03 eta: 15:07:31 time: 1.1615 data_time: 0.0134 memory: 15768 grad_norm: 4.1417 loss: 1.2311 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2311 2023/07/25 04:44:15 - mmengine - INFO - Epoch(train) [48][580/940] lr: 1.0000e-03 eta: 15:07:11 time: 1.1686 data_time: 0.0132 memory: 15768 grad_norm: 4.0505 loss: 1.1063 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1063 2023/07/25 04:44:38 - mmengine - INFO - Epoch(train) [48][600/940] lr: 1.0000e-03 eta: 15:06:50 time: 1.1656 data_time: 0.0132 memory: 15768 grad_norm: 4.0032 loss: 0.9723 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9723 2023/07/25 04:45:01 - mmengine - INFO - Epoch(train) [48][620/940] lr: 1.0000e-03 eta: 15:06:29 time: 1.1631 data_time: 0.0131 memory: 15768 grad_norm: 3.9954 loss: 0.9910 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9910 2023/07/25 04:45:23 - mmengine - INFO - Epoch(train) [48][640/940] lr: 1.0000e-03 eta: 15:06:07 time: 1.1028 data_time: 0.0134 memory: 15768 grad_norm: 3.9524 loss: 0.8115 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8115 2023/07/25 04:45:45 - mmengine - INFO - Epoch(train) [48][660/940] lr: 1.0000e-03 eta: 15:05:45 time: 1.1004 data_time: 0.0133 memory: 15768 grad_norm: 4.0051 loss: 0.9435 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9435 2023/07/25 04:46:07 - mmengine - INFO - Epoch(train) [48][680/940] lr: 1.0000e-03 eta: 15:05:22 time: 1.1037 data_time: 0.0128 memory: 15768 grad_norm: 3.9872 loss: 0.9039 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9039 2023/07/25 04:46:29 - mmengine - INFO - Epoch(train) [48][700/940] lr: 1.0000e-03 eta: 15:05:00 time: 1.1028 data_time: 0.0131 memory: 15768 grad_norm: 4.0103 loss: 1.0218 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0218 2023/07/25 04:46:51 - mmengine - INFO - Epoch(train) [48][720/940] lr: 1.0000e-03 eta: 15:04:38 time: 1.1023 data_time: 0.0132 memory: 15768 grad_norm: 4.0433 loss: 0.7923 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7923 2023/07/25 04:47:13 - mmengine - INFO - Epoch(train) [48][740/940] lr: 1.0000e-03 eta: 15:04:16 time: 1.1016 data_time: 0.0132 memory: 15768 grad_norm: 4.0223 loss: 0.8499 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8499 2023/07/25 04:47:35 - mmengine - INFO - Epoch(train) [48][760/940] lr: 1.0000e-03 eta: 15:03:54 time: 1.0998 data_time: 0.0131 memory: 15768 grad_norm: 4.0318 loss: 0.9091 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9091 2023/07/25 04:47:58 - mmengine - INFO - Epoch(train) [48][780/940] lr: 1.0000e-03 eta: 15:03:32 time: 1.1045 data_time: 0.0128 memory: 15768 grad_norm: 4.0821 loss: 0.9910 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9910 2023/07/25 04:48:20 - mmengine - INFO - Epoch(train) [48][800/940] lr: 1.0000e-03 eta: 15:03:09 time: 1.0971 data_time: 0.0134 memory: 15768 grad_norm: 3.9316 loss: 0.7894 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7894 2023/07/25 04:48:42 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 04:48:42 - mmengine - INFO - Epoch(train) [48][820/940] lr: 1.0000e-03 eta: 15:02:47 time: 1.1003 data_time: 0.0132 memory: 15768 grad_norm: 4.0019 loss: 0.8650 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8650 2023/07/25 04:49:04 - mmengine - INFO - Epoch(train) [48][840/940] lr: 1.0000e-03 eta: 15:02:25 time: 1.1028 data_time: 0.0135 memory: 15768 grad_norm: 4.0772 loss: 0.9026 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9026 2023/07/25 04:49:26 - mmengine - INFO - Epoch(train) [48][860/940] lr: 1.0000e-03 eta: 15:02:03 time: 1.1028 data_time: 0.0133 memory: 15768 grad_norm: 4.0944 loss: 0.9141 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9141 2023/07/25 04:49:48 - mmengine - INFO - Epoch(train) [48][880/940] lr: 1.0000e-03 eta: 15:01:40 time: 1.0981 data_time: 0.0134 memory: 15768 grad_norm: 4.0131 loss: 0.7960 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7960 2023/07/25 04:50:10 - mmengine - INFO - Epoch(train) [48][900/940] lr: 1.0000e-03 eta: 15:01:18 time: 1.0974 data_time: 0.0130 memory: 15768 grad_norm: 3.9936 loss: 0.9301 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9301 2023/07/25 04:50:32 - mmengine - INFO - Epoch(train) [48][920/940] lr: 1.0000e-03 eta: 15:00:56 time: 1.0997 data_time: 0.0132 memory: 15768 grad_norm: 4.0819 loss: 0.8688 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8688 2023/07/25 04:50:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 04:50:53 - mmengine - INFO - Epoch(train) [48][940/940] lr: 1.0000e-03 eta: 15:00:33 time: 1.0535 data_time: 0.0129 memory: 15768 grad_norm: 4.3728 loss: 0.9648 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 0.9648 2023/07/25 04:50:53 - mmengine - INFO - Saving checkpoint at 48 epochs 2023/07/25 04:51:04 - mmengine - INFO - Epoch(val) [48][20/78] eta: 0:00:28 time: 0.4892 data_time: 0.3314 memory: 2147 2023/07/25 04:51:10 - mmengine - INFO - Epoch(val) [48][40/78] eta: 0:00:15 time: 0.3213 data_time: 0.1645 memory: 2147 2023/07/25 04:51:18 - mmengine - INFO - Epoch(val) [48][60/78] eta: 0:00:07 time: 0.4201 data_time: 0.2632 memory: 2147 2023/07/25 04:51:28 - mmengine - INFO - Epoch(val) [48][78/78] acc/top1: 0.7119 acc/top5: 0.8990 acc/mean1: 0.7118 data_time: 0.2298 time: 0.3840 2023/07/25 04:51:54 - mmengine - INFO - Epoch(train) [49][ 20/940] lr: 1.0000e-03 eta: 15:00:14 time: 1.2844 data_time: 0.1575 memory: 15768 grad_norm: 3.9795 loss: 0.9615 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9615 2023/07/25 04:52:16 - mmengine - INFO - Epoch(train) [49][ 40/940] lr: 1.0000e-03 eta: 14:59:52 time: 1.1013 data_time: 0.0137 memory: 15768 grad_norm: 4.0381 loss: 0.9539 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9539 2023/07/25 04:52:38 - mmengine - INFO - Epoch(train) [49][ 60/940] lr: 1.0000e-03 eta: 14:59:30 time: 1.1022 data_time: 0.0134 memory: 15768 grad_norm: 4.0307 loss: 0.8447 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8447 2023/07/25 04:53:00 - mmengine - INFO - Epoch(train) [49][ 80/940] lr: 1.0000e-03 eta: 14:59:08 time: 1.1058 data_time: 0.0133 memory: 15768 grad_norm: 4.0220 loss: 0.9986 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9986 2023/07/25 04:53:22 - mmengine - INFO - Epoch(train) [49][100/940] lr: 1.0000e-03 eta: 14:58:46 time: 1.0982 data_time: 0.0129 memory: 15768 grad_norm: 3.9773 loss: 0.9713 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9713 2023/07/25 04:53:44 - mmengine - INFO - Epoch(train) [49][120/940] lr: 1.0000e-03 eta: 14:58:23 time: 1.1012 data_time: 0.0137 memory: 15768 grad_norm: 3.9771 loss: 0.9337 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9337 2023/07/25 04:54:06 - mmengine - INFO - Epoch(train) [49][140/940] lr: 1.0000e-03 eta: 14:58:01 time: 1.1007 data_time: 0.0128 memory: 15768 grad_norm: 4.0395 loss: 0.9715 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9715 2023/07/25 04:54:28 - mmengine - INFO - Epoch(train) [49][160/940] lr: 1.0000e-03 eta: 14:57:39 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 4.0037 loss: 0.9135 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9135 2023/07/25 04:54:50 - mmengine - INFO - Epoch(train) [49][180/940] lr: 1.0000e-03 eta: 14:57:17 time: 1.1035 data_time: 0.0133 memory: 15768 grad_norm: 3.9717 loss: 0.9844 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9844 2023/07/25 04:55:12 - mmengine - INFO - Epoch(train) [49][200/940] lr: 1.0000e-03 eta: 14:56:55 time: 1.1036 data_time: 0.0130 memory: 15768 grad_norm: 4.0341 loss: 1.0419 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0419 2023/07/25 04:55:34 - mmengine - INFO - Epoch(train) [49][220/940] lr: 1.0000e-03 eta: 14:56:33 time: 1.1028 data_time: 0.0135 memory: 15768 grad_norm: 4.0668 loss: 1.0058 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0058 2023/07/25 04:55:56 - mmengine - INFO - Epoch(train) [49][240/940] lr: 1.0000e-03 eta: 14:56:10 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.0021 loss: 0.9688 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9688 2023/07/25 04:56:19 - mmengine - INFO - Epoch(train) [49][260/940] lr: 1.0000e-03 eta: 14:55:48 time: 1.1024 data_time: 0.0129 memory: 15768 grad_norm: 3.9816 loss: 1.0146 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.0146 2023/07/25 04:56:41 - mmengine - INFO - Epoch(train) [49][280/940] lr: 1.0000e-03 eta: 14:55:26 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 4.0074 loss: 0.9461 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9461 2023/07/25 04:57:03 - mmengine - INFO - Epoch(train) [49][300/940] lr: 1.0000e-03 eta: 14:55:04 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 3.9423 loss: 0.9910 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9910 2023/07/25 04:57:25 - mmengine - INFO - Epoch(train) [49][320/940] lr: 1.0000e-03 eta: 14:54:42 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 4.1219 loss: 1.0633 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0633 2023/07/25 04:57:47 - mmengine - INFO - Epoch(train) [49][340/940] lr: 1.0000e-03 eta: 14:54:19 time: 1.1033 data_time: 0.0131 memory: 15768 grad_norm: 4.1185 loss: 0.8872 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8872 2023/07/25 04:58:09 - mmengine - INFO - Epoch(train) [49][360/940] lr: 1.0000e-03 eta: 14:53:57 time: 1.1009 data_time: 0.0133 memory: 15768 grad_norm: 3.9897 loss: 0.9590 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9590 2023/07/25 04:58:31 - mmengine - INFO - Epoch(train) [49][380/940] lr: 1.0000e-03 eta: 14:53:35 time: 1.1036 data_time: 0.0133 memory: 15768 grad_norm: 3.9318 loss: 0.9766 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9766 2023/07/25 04:58:53 - mmengine - INFO - Epoch(train) [49][400/940] lr: 1.0000e-03 eta: 14:53:13 time: 1.1020 data_time: 0.0132 memory: 15768 grad_norm: 4.0918 loss: 0.9313 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9313 2023/07/25 04:59:15 - mmengine - INFO - Epoch(train) [49][420/940] lr: 1.0000e-03 eta: 14:52:51 time: 1.1018 data_time: 0.0134 memory: 15768 grad_norm: 4.0436 loss: 0.9612 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9612 2023/07/25 04:59:37 - mmengine - INFO - Epoch(train) [49][440/940] lr: 1.0000e-03 eta: 14:52:28 time: 1.1037 data_time: 0.0132 memory: 15768 grad_norm: 4.0571 loss: 0.9430 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9430 2023/07/25 04:59:59 - mmengine - INFO - Epoch(train) [49][460/940] lr: 1.0000e-03 eta: 14:52:06 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 4.0006 loss: 1.0306 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0306 2023/07/25 05:00:21 - mmengine - INFO - Epoch(train) [49][480/940] lr: 1.0000e-03 eta: 14:51:44 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 4.0655 loss: 0.9224 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9224 2023/07/25 05:00:43 - mmengine - INFO - Epoch(train) [49][500/940] lr: 1.0000e-03 eta: 14:51:22 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.0382 loss: 0.9529 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9529 2023/07/25 05:01:05 - mmengine - INFO - Epoch(train) [49][520/940] lr: 1.0000e-03 eta: 14:51:00 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.0432 loss: 0.8005 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8005 2023/07/25 05:01:27 - mmengine - INFO - Epoch(train) [49][540/940] lr: 1.0000e-03 eta: 14:50:38 time: 1.1052 data_time: 0.0133 memory: 15768 grad_norm: 4.0701 loss: 0.8628 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8628 2023/07/25 05:01:49 - mmengine - INFO - Epoch(train) [49][560/940] lr: 1.0000e-03 eta: 14:50:15 time: 1.1003 data_time: 0.0134 memory: 15768 grad_norm: 3.9894 loss: 0.9346 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9346 2023/07/25 05:02:11 - mmengine - INFO - Epoch(train) [49][580/940] lr: 1.0000e-03 eta: 14:49:53 time: 1.0998 data_time: 0.0132 memory: 15768 grad_norm: 4.0046 loss: 0.8688 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8688 2023/07/25 05:02:33 - mmengine - INFO - Epoch(train) [49][600/940] lr: 1.0000e-03 eta: 14:49:31 time: 1.1023 data_time: 0.0135 memory: 15768 grad_norm: 4.0174 loss: 1.1183 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1183 2023/07/25 05:02:55 - mmengine - INFO - Epoch(train) [49][620/940] lr: 1.0000e-03 eta: 14:49:09 time: 1.1017 data_time: 0.0130 memory: 15768 grad_norm: 4.0699 loss: 0.9466 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9466 2023/07/25 05:03:17 - mmengine - INFO - Epoch(train) [49][640/940] lr: 1.0000e-03 eta: 14:48:46 time: 1.1005 data_time: 0.0134 memory: 15768 grad_norm: 4.0663 loss: 0.8122 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8122 2023/07/25 05:03:39 - mmengine - INFO - Epoch(train) [49][660/940] lr: 1.0000e-03 eta: 14:48:24 time: 1.1004 data_time: 0.0135 memory: 15768 grad_norm: 3.9774 loss: 0.8423 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8423 2023/07/25 05:04:01 - mmengine - INFO - Epoch(train) [49][680/940] lr: 1.0000e-03 eta: 14:48:02 time: 1.0995 data_time: 0.0134 memory: 15768 grad_norm: 4.0675 loss: 0.8289 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8289 2023/07/25 05:04:23 - mmengine - INFO - Epoch(train) [49][700/940] lr: 1.0000e-03 eta: 14:47:40 time: 1.0995 data_time: 0.0135 memory: 15768 grad_norm: 4.0242 loss: 0.9938 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9938 2023/07/25 05:04:45 - mmengine - INFO - Epoch(train) [49][720/940] lr: 1.0000e-03 eta: 14:47:18 time: 1.0989 data_time: 0.0134 memory: 15768 grad_norm: 3.9634 loss: 0.8850 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8850 2023/07/25 05:05:07 - mmengine - INFO - Epoch(train) [49][740/940] lr: 1.0000e-03 eta: 14:46:55 time: 1.1051 data_time: 0.0133 memory: 15768 grad_norm: 4.0636 loss: 0.8950 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8950 2023/07/25 05:05:29 - mmengine - INFO - Epoch(train) [49][760/940] lr: 1.0000e-03 eta: 14:46:33 time: 1.0987 data_time: 0.0133 memory: 15768 grad_norm: 4.0064 loss: 0.8106 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8106 2023/07/25 05:05:51 - mmengine - INFO - Epoch(train) [49][780/940] lr: 1.0000e-03 eta: 14:46:11 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 4.1200 loss: 0.9363 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9363 2023/07/25 05:06:13 - mmengine - INFO - Epoch(train) [49][800/940] lr: 1.0000e-03 eta: 14:45:49 time: 1.1029 data_time: 0.0132 memory: 15768 grad_norm: 4.1112 loss: 0.9513 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9513 2023/07/25 05:06:35 - mmengine - INFO - Epoch(train) [49][820/940] lr: 1.0000e-03 eta: 14:45:27 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 4.1066 loss: 0.9466 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9466 2023/07/25 05:06:57 - mmengine - INFO - Epoch(train) [49][840/940] lr: 1.0000e-03 eta: 14:45:04 time: 1.1033 data_time: 0.0132 memory: 15768 grad_norm: 4.0333 loss: 1.0449 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0449 2023/07/25 05:07:19 - mmengine - INFO - Epoch(train) [49][860/940] lr: 1.0000e-03 eta: 14:44:42 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 4.0565 loss: 1.0230 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0230 2023/07/25 05:07:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 05:07:41 - mmengine - INFO - Epoch(train) [49][880/940] lr: 1.0000e-03 eta: 14:44:20 time: 1.1009 data_time: 0.0135 memory: 15768 grad_norm: 4.1040 loss: 0.8680 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8680 2023/07/25 05:08:03 - mmengine - INFO - Epoch(train) [49][900/940] lr: 1.0000e-03 eta: 14:43:58 time: 1.1026 data_time: 0.0133 memory: 15768 grad_norm: 3.9058 loss: 0.9077 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9077 2023/07/25 05:08:25 - mmengine - INFO - Epoch(train) [49][920/940] lr: 1.0000e-03 eta: 14:43:36 time: 1.0982 data_time: 0.0132 memory: 15768 grad_norm: 4.1136 loss: 1.0495 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0495 2023/07/25 05:08:46 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 05:08:46 - mmengine - INFO - Epoch(train) [49][940/940] lr: 1.0000e-03 eta: 14:43:12 time: 1.0537 data_time: 0.0127 memory: 15768 grad_norm: 4.2963 loss: 1.0386 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0386 2023/07/25 05:08:56 - mmengine - INFO - Epoch(val) [49][20/78] eta: 0:00:28 time: 0.4874 data_time: 0.3294 memory: 2147 2023/07/25 05:09:03 - mmengine - INFO - Epoch(val) [49][40/78] eta: 0:00:15 time: 0.3396 data_time: 0.1822 memory: 2147 2023/07/25 05:09:12 - mmengine - INFO - Epoch(val) [49][60/78] eta: 0:00:07 time: 0.4300 data_time: 0.2733 memory: 2147 2023/07/25 05:09:23 - mmengine - INFO - Epoch(val) [49][78/78] acc/top1: 0.7128 acc/top5: 0.8998 acc/mean1: 0.7127 data_time: 0.2375 time: 0.3919 2023/07/25 05:09:49 - mmengine - INFO - Epoch(train) [50][ 20/940] lr: 1.0000e-03 eta: 14:42:55 time: 1.3362 data_time: 0.1718 memory: 15768 grad_norm: 4.1055 loss: 1.0532 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0532 2023/07/25 05:10:13 - mmengine - INFO - Epoch(train) [50][ 40/940] lr: 1.0000e-03 eta: 14:42:34 time: 1.1637 data_time: 0.0140 memory: 15768 grad_norm: 3.9989 loss: 0.8542 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8542 2023/07/25 05:10:36 - mmengine - INFO - Epoch(train) [50][ 60/940] lr: 1.0000e-03 eta: 14:42:13 time: 1.1638 data_time: 0.0132 memory: 15768 grad_norm: 4.0361 loss: 0.8676 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8676 2023/07/25 05:10:59 - mmengine - INFO - Epoch(train) [50][ 80/940] lr: 1.0000e-03 eta: 14:41:52 time: 1.1678 data_time: 0.0127 memory: 15768 grad_norm: 4.0123 loss: 0.8753 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8753 2023/07/25 05:11:21 - mmengine - INFO - Epoch(train) [50][100/940] lr: 1.0000e-03 eta: 14:41:31 time: 1.1139 data_time: 0.0132 memory: 15768 grad_norm: 4.0537 loss: 1.0192 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0192 2023/07/25 05:11:44 - mmengine - INFO - Epoch(train) [50][120/940] lr: 1.0000e-03 eta: 14:41:08 time: 1.1059 data_time: 0.0136 memory: 15768 grad_norm: 4.0028 loss: 1.0136 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0136 2023/07/25 05:12:06 - mmengine - INFO - Epoch(train) [50][140/940] lr: 1.0000e-03 eta: 14:40:46 time: 1.0996 data_time: 0.0133 memory: 15768 grad_norm: 3.9826 loss: 0.9852 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9852 2023/07/25 05:12:28 - mmengine - INFO - Epoch(train) [50][160/940] lr: 1.0000e-03 eta: 14:40:24 time: 1.0988 data_time: 0.0132 memory: 15768 grad_norm: 4.0016 loss: 0.9996 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9996 2023/07/25 05:12:50 - mmengine - INFO - Epoch(train) [50][180/940] lr: 1.0000e-03 eta: 14:40:02 time: 1.1024 data_time: 0.0133 memory: 15768 grad_norm: 3.9787 loss: 0.8877 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8877 2023/07/25 05:13:12 - mmengine - INFO - Epoch(train) [50][200/940] lr: 1.0000e-03 eta: 14:39:40 time: 1.1034 data_time: 0.0131 memory: 15768 grad_norm: 3.9702 loss: 0.9041 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9041 2023/07/25 05:13:34 - mmengine - INFO - Epoch(train) [50][220/940] lr: 1.0000e-03 eta: 14:39:17 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 4.0399 loss: 0.9899 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9899 2023/07/25 05:13:56 - mmengine - INFO - Epoch(train) [50][240/940] lr: 1.0000e-03 eta: 14:38:55 time: 1.0986 data_time: 0.0134 memory: 15768 grad_norm: 4.0803 loss: 0.9327 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9327 2023/07/25 05:14:18 - mmengine - INFO - Epoch(train) [50][260/940] lr: 1.0000e-03 eta: 14:38:33 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 4.0167 loss: 0.9533 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9533 2023/07/25 05:14:40 - mmengine - INFO - Epoch(train) [50][280/940] lr: 1.0000e-03 eta: 14:38:11 time: 1.0981 data_time: 0.0134 memory: 15768 grad_norm: 4.0253 loss: 0.7558 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7558 2023/07/25 05:15:02 - mmengine - INFO - Epoch(train) [50][300/940] lr: 1.0000e-03 eta: 14:37:48 time: 1.0988 data_time: 0.0127 memory: 15768 grad_norm: 4.1753 loss: 0.9883 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9883 2023/07/25 05:15:24 - mmengine - INFO - Epoch(train) [50][320/940] lr: 1.0000e-03 eta: 14:37:26 time: 1.0984 data_time: 0.0129 memory: 15768 grad_norm: 4.0191 loss: 0.8843 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8843 2023/07/25 05:15:46 - mmengine - INFO - Epoch(train) [50][340/940] lr: 1.0000e-03 eta: 14:37:04 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 4.0963 loss: 0.8837 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8837 2023/07/25 05:16:08 - mmengine - INFO - Epoch(train) [50][360/940] lr: 1.0000e-03 eta: 14:36:42 time: 1.1049 data_time: 0.0133 memory: 15768 grad_norm: 4.1193 loss: 0.9419 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9419 2023/07/25 05:16:30 - mmengine - INFO - Epoch(train) [50][380/940] lr: 1.0000e-03 eta: 14:36:20 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 4.0661 loss: 0.7637 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7637 2023/07/25 05:16:52 - mmengine - INFO - Epoch(train) [50][400/940] lr: 1.0000e-03 eta: 14:35:57 time: 1.1008 data_time: 0.0133 memory: 15768 grad_norm: 4.0861 loss: 1.0977 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0977 2023/07/25 05:17:14 - mmengine - INFO - Epoch(train) [50][420/940] lr: 1.0000e-03 eta: 14:35:35 time: 1.0982 data_time: 0.0131 memory: 15768 grad_norm: 4.0934 loss: 0.8794 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8794 2023/07/25 05:17:36 - mmengine - INFO - Epoch(train) [50][440/940] lr: 1.0000e-03 eta: 14:35:13 time: 1.0999 data_time: 0.0135 memory: 15768 grad_norm: 4.0215 loss: 1.0294 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0294 2023/07/25 05:17:58 - mmengine - INFO - Epoch(train) [50][460/940] lr: 1.0000e-03 eta: 14:34:51 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 4.0583 loss: 0.9993 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9993 2023/07/25 05:18:20 - mmengine - INFO - Epoch(train) [50][480/940] lr: 1.0000e-03 eta: 14:34:28 time: 1.0992 data_time: 0.0128 memory: 15768 grad_norm: 4.0411 loss: 0.8086 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8086 2023/07/25 05:18:42 - mmengine - INFO - Epoch(train) [50][500/940] lr: 1.0000e-03 eta: 14:34:06 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 4.0967 loss: 1.0497 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0497 2023/07/25 05:19:04 - mmengine - INFO - Epoch(train) [50][520/940] lr: 1.0000e-03 eta: 14:33:44 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.0760 loss: 0.9477 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9477 2023/07/25 05:19:26 - mmengine - INFO - Epoch(train) [50][540/940] lr: 1.0000e-03 eta: 14:33:22 time: 1.0992 data_time: 0.0132 memory: 15768 grad_norm: 4.1363 loss: 0.9986 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9986 2023/07/25 05:19:47 - mmengine - INFO - Epoch(train) [50][560/940] lr: 1.0000e-03 eta: 14:32:59 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 3.9791 loss: 0.8725 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8725 2023/07/25 05:20:10 - mmengine - INFO - Epoch(train) [50][580/940] lr: 1.0000e-03 eta: 14:32:37 time: 1.1004 data_time: 0.0131 memory: 15768 grad_norm: 3.9701 loss: 0.8120 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8120 2023/07/25 05:20:32 - mmengine - INFO - Epoch(train) [50][600/940] lr: 1.0000e-03 eta: 14:32:15 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 4.0822 loss: 0.9001 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9001 2023/07/25 05:20:53 - mmengine - INFO - Epoch(train) [50][620/940] lr: 1.0000e-03 eta: 14:31:53 time: 1.0991 data_time: 0.0128 memory: 15768 grad_norm: 4.0958 loss: 1.1378 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1378 2023/07/25 05:21:16 - mmengine - INFO - Epoch(train) [50][640/940] lr: 1.0000e-03 eta: 14:31:30 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 4.0909 loss: 0.8597 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8597 2023/07/25 05:21:38 - mmengine - INFO - Epoch(train) [50][660/940] lr: 1.0000e-03 eta: 14:31:08 time: 1.1031 data_time: 0.0133 memory: 15768 grad_norm: 4.1110 loss: 0.9198 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9198 2023/07/25 05:22:00 - mmengine - INFO - Epoch(train) [50][680/940] lr: 1.0000e-03 eta: 14:30:46 time: 1.1027 data_time: 0.0129 memory: 15768 grad_norm: 4.1004 loss: 1.0134 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0134 2023/07/25 05:22:22 - mmengine - INFO - Epoch(train) [50][700/940] lr: 1.0000e-03 eta: 14:30:24 time: 1.1002 data_time: 0.0132 memory: 15768 grad_norm: 4.1540 loss: 0.8318 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8318 2023/07/25 05:22:44 - mmengine - INFO - Epoch(train) [50][720/940] lr: 1.0000e-03 eta: 14:30:02 time: 1.1037 data_time: 0.0132 memory: 15768 grad_norm: 4.0303 loss: 1.0770 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0770 2023/07/25 05:23:06 - mmengine - INFO - Epoch(train) [50][740/940] lr: 1.0000e-03 eta: 14:29:40 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 4.0893 loss: 0.9993 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9993 2023/07/25 05:23:28 - mmengine - INFO - Epoch(train) [50][760/940] lr: 1.0000e-03 eta: 14:29:17 time: 1.1005 data_time: 0.0134 memory: 15768 grad_norm: 4.0056 loss: 0.9354 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9354 2023/07/25 05:23:50 - mmengine - INFO - Epoch(train) [50][780/940] lr: 1.0000e-03 eta: 14:28:55 time: 1.0994 data_time: 0.0138 memory: 15768 grad_norm: 4.0708 loss: 0.9931 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9931 2023/07/25 05:24:12 - mmengine - INFO - Epoch(train) [50][800/940] lr: 1.0000e-03 eta: 14:28:33 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 4.0491 loss: 0.9335 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9335 2023/07/25 05:24:34 - mmengine - INFO - Epoch(train) [50][820/940] lr: 1.0000e-03 eta: 14:28:11 time: 1.0986 data_time: 0.0129 memory: 15768 grad_norm: 4.0314 loss: 0.9435 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9435 2023/07/25 05:24:56 - mmengine - INFO - Epoch(train) [50][840/940] lr: 1.0000e-03 eta: 14:27:49 time: 1.1029 data_time: 0.0132 memory: 15768 grad_norm: 4.1024 loss: 0.7423 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7423 2023/07/25 05:25:18 - mmengine - INFO - Epoch(train) [50][860/940] lr: 1.0000e-03 eta: 14:27:26 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 4.0925 loss: 1.0834 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0834 2023/07/25 05:25:40 - mmengine - INFO - Epoch(train) [50][880/940] lr: 1.0000e-03 eta: 14:27:04 time: 1.0994 data_time: 0.0131 memory: 15768 grad_norm: 4.0715 loss: 0.9190 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9190 2023/07/25 05:26:02 - mmengine - INFO - Epoch(train) [50][900/940] lr: 1.0000e-03 eta: 14:26:42 time: 1.1013 data_time: 0.0131 memory: 15768 grad_norm: 4.0238 loss: 0.8523 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8523 2023/07/25 05:26:24 - mmengine - INFO - Epoch(train) [50][920/940] lr: 1.0000e-03 eta: 14:26:20 time: 1.1000 data_time: 0.0135 memory: 15768 grad_norm: 4.0605 loss: 0.9520 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9520 2023/07/25 05:26:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 05:26:45 - mmengine - INFO - Epoch(train) [50][940/940] lr: 1.0000e-03 eta: 14:25:56 time: 1.0545 data_time: 0.0123 memory: 15768 grad_norm: 4.3486 loss: 1.0262 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0262 2023/07/25 05:26:55 - mmengine - INFO - Epoch(val) [50][20/78] eta: 0:00:27 time: 0.4824 data_time: 0.3245 memory: 2147 2023/07/25 05:27:02 - mmengine - INFO - Epoch(val) [50][40/78] eta: 0:00:15 time: 0.3525 data_time: 0.1960 memory: 2147 2023/07/25 05:27:10 - mmengine - INFO - Epoch(val) [50][60/78] eta: 0:00:07 time: 0.4308 data_time: 0.2735 memory: 2147 2023/07/25 05:27:21 - mmengine - INFO - Epoch(val) [50][78/78] acc/top1: 0.7129 acc/top5: 0.8994 acc/mean1: 0.7129 data_time: 0.2403 time: 0.3947 2023/07/25 05:27:47 - mmengine - INFO - Epoch(train) [51][ 20/940] lr: 1.0000e-03 eta: 14:25:38 time: 1.2960 data_time: 0.1544 memory: 15768 grad_norm: 4.0494 loss: 1.0822 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0822 2023/07/25 05:28:09 - mmengine - INFO - Epoch(train) [51][ 40/940] lr: 1.0000e-03 eta: 14:25:16 time: 1.1001 data_time: 0.0128 memory: 15768 grad_norm: 4.0724 loss: 1.0302 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0302 2023/07/25 05:28:31 - mmengine - INFO - Epoch(train) [51][ 60/940] lr: 1.0000e-03 eta: 14:24:54 time: 1.1030 data_time: 0.0130 memory: 15768 grad_norm: 4.0810 loss: 1.0560 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0560 2023/07/25 05:28:53 - mmengine - INFO - Epoch(train) [51][ 80/940] lr: 1.0000e-03 eta: 14:24:32 time: 1.1014 data_time: 0.0128 memory: 15768 grad_norm: 3.9800 loss: 0.9340 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9340 2023/07/25 05:29:15 - mmengine - INFO - Epoch(train) [51][100/940] lr: 1.0000e-03 eta: 14:24:09 time: 1.0984 data_time: 0.0132 memory: 15768 grad_norm: 4.1171 loss: 0.9998 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9998 2023/07/25 05:29:37 - mmengine - INFO - Epoch(train) [51][120/940] lr: 1.0000e-03 eta: 14:23:47 time: 1.0978 data_time: 0.0135 memory: 15768 grad_norm: 3.9992 loss: 0.8994 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8994 2023/07/25 05:29:59 - mmengine - INFO - Epoch(train) [51][140/940] lr: 1.0000e-03 eta: 14:23:25 time: 1.1029 data_time: 0.0131 memory: 15768 grad_norm: 3.9951 loss: 1.0281 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0281 2023/07/25 05:30:21 - mmengine - INFO - Epoch(train) [51][160/940] lr: 1.0000e-03 eta: 14:23:03 time: 1.0986 data_time: 0.0132 memory: 15768 grad_norm: 4.0657 loss: 0.8265 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8265 2023/07/25 05:30:43 - mmengine - INFO - Epoch(train) [51][180/940] lr: 1.0000e-03 eta: 14:22:40 time: 1.0984 data_time: 0.0133 memory: 15768 grad_norm: 3.9866 loss: 0.8467 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8467 2023/07/25 05:31:05 - mmengine - INFO - Epoch(train) [51][200/940] lr: 1.0000e-03 eta: 14:22:18 time: 1.0985 data_time: 0.0133 memory: 15768 grad_norm: 4.0790 loss: 1.0631 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0631 2023/07/25 05:31:27 - mmengine - INFO - Epoch(train) [51][220/940] lr: 1.0000e-03 eta: 14:21:56 time: 1.1008 data_time: 0.0130 memory: 15768 grad_norm: 4.0302 loss: 0.8426 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8426 2023/07/25 05:31:49 - mmengine - INFO - Epoch(train) [51][240/940] lr: 1.0000e-03 eta: 14:21:34 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 4.0536 loss: 0.8381 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8381 2023/07/25 05:32:11 - mmengine - INFO - Epoch(train) [51][260/940] lr: 1.0000e-03 eta: 14:21:12 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 4.0506 loss: 0.9521 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9521 2023/07/25 05:32:33 - mmengine - INFO - Epoch(train) [51][280/940] lr: 1.0000e-03 eta: 14:20:49 time: 1.1054 data_time: 0.0131 memory: 15768 grad_norm: 4.0334 loss: 0.8925 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8925 2023/07/25 05:32:55 - mmengine - INFO - Epoch(train) [51][300/940] lr: 1.0000e-03 eta: 14:20:27 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 4.0985 loss: 0.8840 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8840 2023/07/25 05:33:17 - mmengine - INFO - Epoch(train) [51][320/940] lr: 1.0000e-03 eta: 14:20:05 time: 1.1014 data_time: 0.0132 memory: 15768 grad_norm: 4.0193 loss: 1.0119 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0119 2023/07/25 05:33:39 - mmengine - INFO - Epoch(train) [51][340/940] lr: 1.0000e-03 eta: 14:19:43 time: 1.1039 data_time: 0.0132 memory: 15768 grad_norm: 4.0510 loss: 1.0564 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0564 2023/07/25 05:34:01 - mmengine - INFO - Epoch(train) [51][360/940] lr: 1.0000e-03 eta: 14:19:21 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 4.0802 loss: 0.9559 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9559 2023/07/25 05:34:23 - mmengine - INFO - Epoch(train) [51][380/940] lr: 1.0000e-03 eta: 14:18:58 time: 1.0977 data_time: 0.0132 memory: 15768 grad_norm: 4.0407 loss: 0.8681 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8681 2023/07/25 05:34:45 - mmengine - INFO - Epoch(train) [51][400/940] lr: 1.0000e-03 eta: 14:18:36 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 4.0431 loss: 0.9669 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.9669 2023/07/25 05:35:07 - mmengine - INFO - Epoch(train) [51][420/940] lr: 1.0000e-03 eta: 14:18:14 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 4.1442 loss: 0.9814 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.9814 2023/07/25 05:35:29 - mmengine - INFO - Epoch(train) [51][440/940] lr: 1.0000e-03 eta: 14:17:52 time: 1.1191 data_time: 0.0133 memory: 15768 grad_norm: 4.0092 loss: 0.8425 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8425 2023/07/25 05:35:52 - mmengine - INFO - Epoch(train) [51][460/940] lr: 1.0000e-03 eta: 14:17:31 time: 1.1486 data_time: 0.0129 memory: 15768 grad_norm: 4.1479 loss: 0.8613 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8613 2023/07/25 05:36:14 - mmengine - INFO - Epoch(train) [51][480/940] lr: 1.0000e-03 eta: 14:17:09 time: 1.1039 data_time: 0.0131 memory: 15768 grad_norm: 4.0325 loss: 0.9685 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9685 2023/07/25 05:36:36 - mmengine - INFO - Epoch(train) [51][500/940] lr: 1.0000e-03 eta: 14:16:46 time: 1.0984 data_time: 0.0135 memory: 15768 grad_norm: 4.0956 loss: 0.9002 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9002 2023/07/25 05:36:58 - mmengine - INFO - Epoch(train) [51][520/940] lr: 1.0000e-03 eta: 14:16:24 time: 1.0985 data_time: 0.0133 memory: 15768 grad_norm: 4.0724 loss: 0.8989 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8989 2023/07/25 05:37:20 - mmengine - INFO - Epoch(train) [51][540/940] lr: 1.0000e-03 eta: 14:16:02 time: 1.0976 data_time: 0.0132 memory: 15768 grad_norm: 4.0528 loss: 0.9560 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9560 2023/07/25 05:37:42 - mmengine - INFO - Epoch(train) [51][560/940] lr: 1.0000e-03 eta: 14:15:40 time: 1.0999 data_time: 0.0136 memory: 15768 grad_norm: 3.9595 loss: 0.9814 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9814 2023/07/25 05:38:04 - mmengine - INFO - Epoch(train) [51][580/940] lr: 1.0000e-03 eta: 14:15:18 time: 1.1040 data_time: 0.0138 memory: 15768 grad_norm: 4.0599 loss: 1.0029 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0029 2023/07/25 05:38:26 - mmengine - INFO - Epoch(train) [51][600/940] lr: 1.0000e-03 eta: 14:14:55 time: 1.1011 data_time: 0.0131 memory: 15768 grad_norm: 3.9550 loss: 0.7534 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7534 2023/07/25 05:38:48 - mmengine - INFO - Epoch(train) [51][620/940] lr: 1.0000e-03 eta: 14:14:33 time: 1.1018 data_time: 0.0131 memory: 15768 grad_norm: 4.1195 loss: 0.7879 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7879 2023/07/25 05:39:10 - mmengine - INFO - Epoch(train) [51][640/940] lr: 1.0000e-03 eta: 14:14:11 time: 1.1016 data_time: 0.0132 memory: 15768 grad_norm: 4.0671 loss: 1.0693 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0693 2023/07/25 05:39:32 - mmengine - INFO - Epoch(train) [51][660/940] lr: 1.0000e-03 eta: 14:13:49 time: 1.1013 data_time: 0.0129 memory: 15768 grad_norm: 4.1410 loss: 0.9044 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9044 2023/07/25 05:39:54 - mmengine - INFO - Epoch(train) [51][680/940] lr: 1.0000e-03 eta: 14:13:27 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 4.1344 loss: 0.9415 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9415 2023/07/25 05:40:16 - mmengine - INFO - Epoch(train) [51][700/940] lr: 1.0000e-03 eta: 14:13:04 time: 1.0984 data_time: 0.0130 memory: 15768 grad_norm: 4.1165 loss: 0.9043 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9043 2023/07/25 05:40:38 - mmengine - INFO - Epoch(train) [51][720/940] lr: 1.0000e-03 eta: 14:12:42 time: 1.0989 data_time: 0.0130 memory: 15768 grad_norm: 4.0501 loss: 1.1298 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1298 2023/07/25 05:41:00 - mmengine - INFO - Epoch(train) [51][740/940] lr: 1.0000e-03 eta: 14:12:20 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 4.0846 loss: 0.8019 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8019 2023/07/25 05:41:22 - mmengine - INFO - Epoch(train) [51][760/940] lr: 1.0000e-03 eta: 14:11:58 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 4.1358 loss: 0.8940 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8940 2023/07/25 05:41:44 - mmengine - INFO - Epoch(train) [51][780/940] lr: 1.0000e-03 eta: 14:11:35 time: 1.0998 data_time: 0.0132 memory: 15768 grad_norm: 4.1319 loss: 0.9995 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9995 2023/07/25 05:42:06 - mmengine - INFO - Epoch(train) [51][800/940] lr: 1.0000e-03 eta: 14:11:13 time: 1.1020 data_time: 0.0129 memory: 15768 grad_norm: 4.1784 loss: 1.1084 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1084 2023/07/25 05:42:28 - mmengine - INFO - Epoch(train) [51][820/940] lr: 1.0000e-03 eta: 14:10:51 time: 1.1029 data_time: 0.0132 memory: 15768 grad_norm: 4.0401 loss: 0.8717 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8717 2023/07/25 05:42:50 - mmengine - INFO - Epoch(train) [51][840/940] lr: 1.0000e-03 eta: 14:10:29 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 3.9513 loss: 0.7638 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7638 2023/07/25 05:43:12 - mmengine - INFO - Epoch(train) [51][860/940] lr: 1.0000e-03 eta: 14:10:07 time: 1.1002 data_time: 0.0128 memory: 15768 grad_norm: 3.9746 loss: 0.8772 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8772 2023/07/25 05:43:34 - mmengine - INFO - Epoch(train) [51][880/940] lr: 1.0000e-03 eta: 14:09:44 time: 1.0993 data_time: 0.0133 memory: 15768 grad_norm: 4.1298 loss: 0.8992 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8992 2023/07/25 05:43:56 - mmengine - INFO - Epoch(train) [51][900/940] lr: 1.0000e-03 eta: 14:09:22 time: 1.1012 data_time: 0.0132 memory: 15768 grad_norm: 4.0463 loss: 0.9730 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9730 2023/07/25 05:44:18 - mmengine - INFO - Epoch(train) [51][920/940] lr: 1.0000e-03 eta: 14:09:00 time: 1.0999 data_time: 0.0136 memory: 15768 grad_norm: 4.2080 loss: 0.8104 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8104 2023/07/25 05:44:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 05:44:39 - mmengine - INFO - Epoch(train) [51][940/940] lr: 1.0000e-03 eta: 14:08:37 time: 1.0530 data_time: 0.0131 memory: 15768 grad_norm: 4.3680 loss: 0.9343 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 0.9343 2023/07/25 05:44:39 - mmengine - INFO - Saving checkpoint at 51 epochs 2023/07/25 05:44:50 - mmengine - INFO - Epoch(val) [51][20/78] eta: 0:00:28 time: 0.4866 data_time: 0.3289 memory: 2147 2023/07/25 05:44:57 - mmengine - INFO - Epoch(val) [51][40/78] eta: 0:00:15 time: 0.3511 data_time: 0.1939 memory: 2147 2023/07/25 05:45:06 - mmengine - INFO - Epoch(val) [51][60/78] eta: 0:00:07 time: 0.4388 data_time: 0.2814 memory: 2147 2023/07/25 05:45:16 - mmengine - INFO - Epoch(val) [51][78/78] acc/top1: 0.7109 acc/top5: 0.8975 acc/mean1: 0.7108 data_time: 0.2422 time: 0.3969 2023/07/25 05:45:42 - mmengine - INFO - Epoch(train) [52][ 20/940] lr: 1.0000e-03 eta: 14:08:18 time: 1.2823 data_time: 0.1578 memory: 15768 grad_norm: 4.1688 loss: 0.8890 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8890 2023/07/25 05:46:04 - mmengine - INFO - Epoch(train) [52][ 40/940] lr: 1.0000e-03 eta: 14:07:56 time: 1.1019 data_time: 0.0132 memory: 15768 grad_norm: 4.0117 loss: 0.9488 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9488 2023/07/25 05:46:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 05:46:26 - mmengine - INFO - Epoch(train) [52][ 60/940] lr: 1.0000e-03 eta: 14:07:34 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 4.1403 loss: 0.8070 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8070 2023/07/25 05:46:48 - mmengine - INFO - Epoch(train) [52][ 80/940] lr: 1.0000e-03 eta: 14:07:12 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 4.0932 loss: 0.7599 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7599 2023/07/25 05:47:10 - mmengine - INFO - Epoch(train) [52][100/940] lr: 1.0000e-03 eta: 14:06:49 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.1093 loss: 0.9662 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9662 2023/07/25 05:47:32 - mmengine - INFO - Epoch(train) [52][120/940] lr: 1.0000e-03 eta: 14:06:27 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 4.0838 loss: 0.8408 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8408 2023/07/25 05:47:54 - mmengine - INFO - Epoch(train) [52][140/940] lr: 1.0000e-03 eta: 14:06:05 time: 1.0991 data_time: 0.0135 memory: 15768 grad_norm: 4.1105 loss: 0.8970 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8970 2023/07/25 05:48:16 - mmengine - INFO - Epoch(train) [52][160/940] lr: 1.0000e-03 eta: 14:05:43 time: 1.1059 data_time: 0.0126 memory: 15768 grad_norm: 4.2003 loss: 0.8866 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8866 2023/07/25 05:48:38 - mmengine - INFO - Epoch(train) [52][180/940] lr: 1.0000e-03 eta: 14:05:21 time: 1.1033 data_time: 0.0134 memory: 15768 grad_norm: 4.0494 loss: 0.9944 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9944 2023/07/25 05:49:00 - mmengine - INFO - Epoch(train) [52][200/940] lr: 1.0000e-03 eta: 14:04:58 time: 1.1001 data_time: 0.0135 memory: 15768 grad_norm: 4.0885 loss: 0.8955 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8955 2023/07/25 05:49:22 - mmengine - INFO - Epoch(train) [52][220/940] lr: 1.0000e-03 eta: 14:04:36 time: 1.1044 data_time: 0.0130 memory: 15768 grad_norm: 4.1486 loss: 0.8793 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8793 2023/07/25 05:49:44 - mmengine - INFO - Epoch(train) [52][240/940] lr: 1.0000e-03 eta: 14:04:14 time: 1.1003 data_time: 0.0136 memory: 15768 grad_norm: 4.0823 loss: 0.9134 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9134 2023/07/25 05:50:06 - mmengine - INFO - Epoch(train) [52][260/940] lr: 1.0000e-03 eta: 14:03:52 time: 1.1019 data_time: 0.0134 memory: 15768 grad_norm: 4.0593 loss: 0.9413 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9413 2023/07/25 05:50:28 - mmengine - INFO - Epoch(train) [52][280/940] lr: 1.0000e-03 eta: 14:03:30 time: 1.0990 data_time: 0.0131 memory: 15768 grad_norm: 4.0568 loss: 1.0936 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0936 2023/07/25 05:50:50 - mmengine - INFO - Epoch(train) [52][300/940] lr: 1.0000e-03 eta: 14:03:08 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 4.1641 loss: 0.8660 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8660 2023/07/25 05:51:12 - mmengine - INFO - Epoch(train) [52][320/940] lr: 1.0000e-03 eta: 14:02:45 time: 1.1031 data_time: 0.0139 memory: 15768 grad_norm: 4.0508 loss: 0.9471 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9471 2023/07/25 05:51:34 - mmengine - INFO - Epoch(train) [52][340/940] lr: 1.0000e-03 eta: 14:02:23 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 4.1453 loss: 0.7941 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7941 2023/07/25 05:51:56 - mmengine - INFO - Epoch(train) [52][360/940] lr: 1.0000e-03 eta: 14:02:01 time: 1.0978 data_time: 0.0134 memory: 15768 grad_norm: 4.0505 loss: 0.9509 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9509 2023/07/25 05:52:18 - mmengine - INFO - Epoch(train) [52][380/940] lr: 1.0000e-03 eta: 14:01:39 time: 1.1024 data_time: 0.0135 memory: 15768 grad_norm: 3.9843 loss: 0.8707 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8707 2023/07/25 05:52:40 - mmengine - INFO - Epoch(train) [52][400/940] lr: 1.0000e-03 eta: 14:01:16 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 4.0797 loss: 0.8882 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8882 2023/07/25 05:53:02 - mmengine - INFO - Epoch(train) [52][420/940] lr: 1.0000e-03 eta: 14:00:54 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 4.1621 loss: 0.9449 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9449 2023/07/25 05:53:24 - mmengine - INFO - Epoch(train) [52][440/940] lr: 1.0000e-03 eta: 14:00:32 time: 1.0996 data_time: 0.0134 memory: 15768 grad_norm: 4.1113 loss: 0.9149 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9149 2023/07/25 05:53:46 - mmengine - INFO - Epoch(train) [52][460/940] lr: 1.0000e-03 eta: 14:00:10 time: 1.0992 data_time: 0.0129 memory: 15768 grad_norm: 4.1873 loss: 0.9928 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9928 2023/07/25 05:54:08 - mmengine - INFO - Epoch(train) [52][480/940] lr: 1.0000e-03 eta: 13:59:48 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 4.1026 loss: 0.9666 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9666 2023/07/25 05:54:30 - mmengine - INFO - Epoch(train) [52][500/940] lr: 1.0000e-03 eta: 13:59:25 time: 1.0981 data_time: 0.0131 memory: 15768 grad_norm: 4.0632 loss: 1.0343 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0343 2023/07/25 05:54:52 - mmengine - INFO - Epoch(train) [52][520/940] lr: 1.0000e-03 eta: 13:59:03 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 4.0517 loss: 1.0245 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0245 2023/07/25 05:55:14 - mmengine - INFO - Epoch(train) [52][540/940] lr: 1.0000e-03 eta: 13:58:41 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 4.1002 loss: 1.0546 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0546 2023/07/25 05:55:36 - mmengine - INFO - Epoch(train) [52][560/940] lr: 1.0000e-03 eta: 13:58:19 time: 1.1004 data_time: 0.0131 memory: 15768 grad_norm: 4.1292 loss: 0.8485 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8485 2023/07/25 05:55:58 - mmengine - INFO - Epoch(train) [52][580/940] lr: 1.0000e-03 eta: 13:57:56 time: 1.0979 data_time: 0.0130 memory: 15768 grad_norm: 4.0643 loss: 0.8032 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8032 2023/07/25 05:56:20 - mmengine - INFO - Epoch(train) [52][600/940] lr: 1.0000e-03 eta: 13:57:34 time: 1.1040 data_time: 0.0132 memory: 15768 grad_norm: 4.1306 loss: 0.9387 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9387 2023/07/25 05:56:42 - mmengine - INFO - Epoch(train) [52][620/940] lr: 1.0000e-03 eta: 13:57:12 time: 1.1029 data_time: 0.0131 memory: 15768 grad_norm: 4.1070 loss: 0.9400 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9400 2023/07/25 05:57:04 - mmengine - INFO - Epoch(train) [52][640/940] lr: 1.0000e-03 eta: 13:56:50 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 4.0843 loss: 0.7816 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7816 2023/07/25 05:57:26 - mmengine - INFO - Epoch(train) [52][660/940] lr: 1.0000e-03 eta: 13:56:28 time: 1.0994 data_time: 0.0130 memory: 15768 grad_norm: 4.1556 loss: 0.9894 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9894 2023/07/25 05:57:48 - mmengine - INFO - Epoch(train) [52][680/940] lr: 1.0000e-03 eta: 13:56:05 time: 1.0981 data_time: 0.0136 memory: 15768 grad_norm: 4.0880 loss: 0.8073 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8073 2023/07/25 05:58:10 - mmengine - INFO - Epoch(train) [52][700/940] lr: 1.0000e-03 eta: 13:55:43 time: 1.1028 data_time: 0.0132 memory: 15768 grad_norm: 4.1051 loss: 0.9627 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9627 2023/07/25 05:58:32 - mmengine - INFO - Epoch(train) [52][720/940] lr: 1.0000e-03 eta: 13:55:21 time: 1.0980 data_time: 0.0131 memory: 15768 grad_norm: 4.0511 loss: 0.8326 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8326 2023/07/25 05:58:54 - mmengine - INFO - Epoch(train) [52][740/940] lr: 1.0000e-03 eta: 13:54:59 time: 1.1035 data_time: 0.0130 memory: 15768 grad_norm: 4.0733 loss: 1.1389 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1389 2023/07/25 05:59:16 - mmengine - INFO - Epoch(train) [52][760/940] lr: 1.0000e-03 eta: 13:54:37 time: 1.1015 data_time: 0.0133 memory: 15768 grad_norm: 4.0786 loss: 1.0315 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0315 2023/07/25 05:59:38 - mmengine - INFO - Epoch(train) [52][780/940] lr: 1.0000e-03 eta: 13:54:15 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 3.9913 loss: 0.7318 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7318 2023/07/25 06:00:00 - mmengine - INFO - Epoch(train) [52][800/940] lr: 1.0000e-03 eta: 13:53:52 time: 1.0996 data_time: 0.0133 memory: 15768 grad_norm: 4.2081 loss: 0.9662 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9662 2023/07/25 06:00:22 - mmengine - INFO - Epoch(train) [52][820/940] lr: 1.0000e-03 eta: 13:53:30 time: 1.1037 data_time: 0.0132 memory: 15768 grad_norm: 4.1718 loss: 0.7933 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7933 2023/07/25 06:00:44 - mmengine - INFO - Epoch(train) [52][840/940] lr: 1.0000e-03 eta: 13:53:08 time: 1.1015 data_time: 0.0129 memory: 15768 grad_norm: 4.2641 loss: 0.8687 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8687 2023/07/25 06:01:06 - mmengine - INFO - Epoch(train) [52][860/940] lr: 1.0000e-03 eta: 13:52:46 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 4.0934 loss: 0.9645 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9645 2023/07/25 06:01:28 - mmengine - INFO - Epoch(train) [52][880/940] lr: 1.0000e-03 eta: 13:52:24 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 4.1944 loss: 0.7734 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7734 2023/07/25 06:01:50 - mmengine - INFO - Epoch(train) [52][900/940] lr: 1.0000e-03 eta: 13:52:01 time: 1.0993 data_time: 0.0127 memory: 15768 grad_norm: 4.0960 loss: 0.9170 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 0.9170 2023/07/25 06:02:12 - mmengine - INFO - Epoch(train) [52][920/940] lr: 1.0000e-03 eta: 13:51:39 time: 1.0981 data_time: 0.0132 memory: 15768 grad_norm: 4.2220 loss: 0.9398 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9398 2023/07/25 06:02:33 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 06:02:33 - mmengine - INFO - Epoch(train) [52][940/940] lr: 1.0000e-03 eta: 13:51:16 time: 1.0535 data_time: 0.0128 memory: 15768 grad_norm: 4.4315 loss: 0.8593 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8593 2023/07/25 06:02:43 - mmengine - INFO - Epoch(val) [52][20/78] eta: 0:00:28 time: 0.4953 data_time: 0.3379 memory: 2147 2023/07/25 06:02:50 - mmengine - INFO - Epoch(val) [52][40/78] eta: 0:00:15 time: 0.3392 data_time: 0.1821 memory: 2147 2023/07/25 06:02:59 - mmengine - INFO - Epoch(val) [52][60/78] eta: 0:00:07 time: 0.4368 data_time: 0.2803 memory: 2147 2023/07/25 06:03:09 - mmengine - INFO - Epoch(val) [52][78/78] acc/top1: 0.7119 acc/top5: 0.8992 acc/mean1: 0.7118 data_time: 0.2415 time: 0.3956 2023/07/25 06:03:35 - mmengine - INFO - Epoch(train) [53][ 20/940] lr: 1.0000e-03 eta: 13:50:58 time: 1.3137 data_time: 0.1783 memory: 15768 grad_norm: 4.1818 loss: 0.9673 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9673 2023/07/25 06:03:57 - mmengine - INFO - Epoch(train) [53][ 40/940] lr: 1.0000e-03 eta: 13:50:36 time: 1.0998 data_time: 0.0134 memory: 15768 grad_norm: 4.1346 loss: 1.0236 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0236 2023/07/25 06:04:19 - mmengine - INFO - Epoch(train) [53][ 60/940] lr: 1.0000e-03 eta: 13:50:13 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 4.1491 loss: 0.8694 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8694 2023/07/25 06:04:41 - mmengine - INFO - Epoch(train) [53][ 80/940] lr: 1.0000e-03 eta: 13:49:51 time: 1.1017 data_time: 0.0132 memory: 15768 grad_norm: 4.0912 loss: 0.9275 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9275 2023/07/25 06:05:03 - mmengine - INFO - Epoch(train) [53][100/940] lr: 1.0000e-03 eta: 13:49:29 time: 1.1040 data_time: 0.0132 memory: 15768 grad_norm: 4.1312 loss: 0.9250 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9250 2023/07/25 06:05:25 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 06:05:25 - mmengine - INFO - Epoch(train) [53][120/940] lr: 1.0000e-03 eta: 13:49:07 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 4.1008 loss: 1.0046 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0046 2023/07/25 06:05:47 - mmengine - INFO - Epoch(train) [53][140/940] lr: 1.0000e-03 eta: 13:48:45 time: 1.0993 data_time: 0.0128 memory: 15768 grad_norm: 4.0841 loss: 0.9395 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9395 2023/07/25 06:06:09 - mmengine - INFO - Epoch(train) [53][160/940] lr: 1.0000e-03 eta: 13:48:22 time: 1.0993 data_time: 0.0130 memory: 15768 grad_norm: 4.0766 loss: 0.9628 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 0.9628 2023/07/25 06:06:31 - mmengine - INFO - Epoch(train) [53][180/940] lr: 1.0000e-03 eta: 13:48:00 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 4.1726 loss: 1.0368 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0368 2023/07/25 06:06:53 - mmengine - INFO - Epoch(train) [53][200/940] lr: 1.0000e-03 eta: 13:47:38 time: 1.1011 data_time: 0.0134 memory: 15768 grad_norm: 3.9931 loss: 0.9232 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9232 2023/07/25 06:07:15 - mmengine - INFO - Epoch(train) [53][220/940] lr: 1.0000e-03 eta: 13:47:16 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 4.1133 loss: 0.9905 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9905 2023/07/25 06:07:37 - mmengine - INFO - Epoch(train) [53][240/940] lr: 1.0000e-03 eta: 13:46:54 time: 1.1009 data_time: 0.0132 memory: 15768 grad_norm: 4.1395 loss: 0.8973 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8973 2023/07/25 06:07:59 - mmengine - INFO - Epoch(train) [53][260/940] lr: 1.0000e-03 eta: 13:46:31 time: 1.1017 data_time: 0.0136 memory: 15768 grad_norm: 4.1789 loss: 0.9325 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9325 2023/07/25 06:08:21 - mmengine - INFO - Epoch(train) [53][280/940] lr: 1.0000e-03 eta: 13:46:09 time: 1.0980 data_time: 0.0134 memory: 15768 grad_norm: 4.1288 loss: 0.8908 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8908 2023/07/25 06:08:43 - mmengine - INFO - Epoch(train) [53][300/940] lr: 1.0000e-03 eta: 13:45:47 time: 1.1002 data_time: 0.0134 memory: 15768 grad_norm: 4.1279 loss: 0.8699 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8699 2023/07/25 06:09:05 - mmengine - INFO - Epoch(train) [53][320/940] lr: 1.0000e-03 eta: 13:45:25 time: 1.1051 data_time: 0.0133 memory: 15768 grad_norm: 4.1100 loss: 0.9932 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9932 2023/07/25 06:09:27 - mmengine - INFO - Epoch(train) [53][340/940] lr: 1.0000e-03 eta: 13:45:03 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 4.1762 loss: 1.0668 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0668 2023/07/25 06:09:49 - mmengine - INFO - Epoch(train) [53][360/940] lr: 1.0000e-03 eta: 13:44:40 time: 1.0987 data_time: 0.0134 memory: 15768 grad_norm: 4.0934 loss: 0.9432 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9432 2023/07/25 06:10:11 - mmengine - INFO - Epoch(train) [53][380/940] lr: 1.0000e-03 eta: 13:44:18 time: 1.1025 data_time: 0.0131 memory: 15768 grad_norm: 4.1190 loss: 0.6932 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6932 2023/07/25 06:10:33 - mmengine - INFO - Epoch(train) [53][400/940] lr: 1.0000e-03 eta: 13:43:56 time: 1.0973 data_time: 0.0133 memory: 15768 grad_norm: 4.1531 loss: 1.0820 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0820 2023/07/25 06:10:56 - mmengine - INFO - Epoch(train) [53][420/940] lr: 1.0000e-03 eta: 13:43:35 time: 1.1483 data_time: 0.0134 memory: 15768 grad_norm: 4.1116 loss: 0.8640 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8640 2023/07/25 06:11:19 - mmengine - INFO - Epoch(train) [53][440/940] lr: 1.0000e-03 eta: 13:43:14 time: 1.1651 data_time: 0.0128 memory: 15768 grad_norm: 4.1061 loss: 0.9905 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9905 2023/07/25 06:11:42 - mmengine - INFO - Epoch(train) [53][460/940] lr: 1.0000e-03 eta: 13:42:52 time: 1.1064 data_time: 0.0129 memory: 15768 grad_norm: 4.1383 loss: 1.0428 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0428 2023/07/25 06:12:04 - mmengine - INFO - Epoch(train) [53][480/940] lr: 1.0000e-03 eta: 13:42:29 time: 1.0990 data_time: 0.0129 memory: 15768 grad_norm: 4.1074 loss: 0.8617 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8617 2023/07/25 06:12:26 - mmengine - INFO - Epoch(train) [53][500/940] lr: 1.0000e-03 eta: 13:42:07 time: 1.1012 data_time: 0.0131 memory: 15768 grad_norm: 4.1948 loss: 0.8842 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8842 2023/07/25 06:12:48 - mmengine - INFO - Epoch(train) [53][520/940] lr: 1.0000e-03 eta: 13:41:45 time: 1.1011 data_time: 0.0128 memory: 15768 grad_norm: 4.1962 loss: 0.8762 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8762 2023/07/25 06:13:10 - mmengine - INFO - Epoch(train) [53][540/940] lr: 1.0000e-03 eta: 13:41:23 time: 1.1000 data_time: 0.0131 memory: 15768 grad_norm: 4.0687 loss: 1.0105 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0105 2023/07/25 06:13:32 - mmengine - INFO - Epoch(train) [53][560/940] lr: 1.0000e-03 eta: 13:41:00 time: 1.0980 data_time: 0.0136 memory: 15768 grad_norm: 4.1851 loss: 0.7702 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7702 2023/07/25 06:13:54 - mmengine - INFO - Epoch(train) [53][580/940] lr: 1.0000e-03 eta: 13:40:38 time: 1.1011 data_time: 0.0134 memory: 15768 grad_norm: 4.1804 loss: 0.8322 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8322 2023/07/25 06:14:16 - mmengine - INFO - Epoch(train) [53][600/940] lr: 1.0000e-03 eta: 13:40:16 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 4.0581 loss: 0.8914 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8914 2023/07/25 06:14:38 - mmengine - INFO - Epoch(train) [53][620/940] lr: 1.0000e-03 eta: 13:39:54 time: 1.1081 data_time: 0.0128 memory: 15768 grad_norm: 4.0653 loss: 0.8799 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8799 2023/07/25 06:15:00 - mmengine - INFO - Epoch(train) [53][640/940] lr: 1.0000e-03 eta: 13:39:32 time: 1.1003 data_time: 0.0127 memory: 15768 grad_norm: 4.1138 loss: 0.8231 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8231 2023/07/25 06:15:22 - mmengine - INFO - Epoch(train) [53][660/940] lr: 1.0000e-03 eta: 13:39:10 time: 1.1032 data_time: 0.0127 memory: 15768 grad_norm: 4.1802 loss: 0.9564 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9564 2023/07/25 06:15:44 - mmengine - INFO - Epoch(train) [53][680/940] lr: 1.0000e-03 eta: 13:38:47 time: 1.1036 data_time: 0.0132 memory: 15768 grad_norm: 4.1914 loss: 0.9772 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9772 2023/07/25 06:16:06 - mmengine - INFO - Epoch(train) [53][700/940] lr: 1.0000e-03 eta: 13:38:25 time: 1.1016 data_time: 0.0127 memory: 15768 grad_norm: 4.0487 loss: 0.9317 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9317 2023/07/25 06:16:28 - mmengine - INFO - Epoch(train) [53][720/940] lr: 1.0000e-03 eta: 13:38:03 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 4.1416 loss: 0.8984 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8984 2023/07/25 06:16:50 - mmengine - INFO - Epoch(train) [53][740/940] lr: 1.0000e-03 eta: 13:37:41 time: 1.1003 data_time: 0.0130 memory: 15768 grad_norm: 4.1751 loss: 1.0238 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0238 2023/07/25 06:17:12 - mmengine - INFO - Epoch(train) [53][760/940] lr: 1.0000e-03 eta: 13:37:19 time: 1.1041 data_time: 0.0131 memory: 15768 grad_norm: 4.0838 loss: 0.8270 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8270 2023/07/25 06:17:34 - mmengine - INFO - Epoch(train) [53][780/940] lr: 1.0000e-03 eta: 13:36:57 time: 1.0994 data_time: 0.0131 memory: 15768 grad_norm: 4.0965 loss: 0.8732 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8732 2023/07/25 06:17:56 - mmengine - INFO - Epoch(train) [53][800/940] lr: 1.0000e-03 eta: 13:36:34 time: 1.1011 data_time: 0.0126 memory: 15768 grad_norm: 4.2072 loss: 0.9538 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9538 2023/07/25 06:18:18 - mmengine - INFO - Epoch(train) [53][820/940] lr: 1.0000e-03 eta: 13:36:12 time: 1.1020 data_time: 0.0128 memory: 15768 grad_norm: 4.1290 loss: 0.8379 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8379 2023/07/25 06:18:40 - mmengine - INFO - Epoch(train) [53][840/940] lr: 1.0000e-03 eta: 13:35:50 time: 1.1014 data_time: 0.0127 memory: 15768 grad_norm: 4.1829 loss: 0.9315 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9315 2023/07/25 06:19:02 - mmengine - INFO - Epoch(train) [53][860/940] lr: 1.0000e-03 eta: 13:35:28 time: 1.1006 data_time: 0.0134 memory: 15768 grad_norm: 4.1616 loss: 0.9414 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9414 2023/07/25 06:19:24 - mmengine - INFO - Epoch(train) [53][880/940] lr: 1.0000e-03 eta: 13:35:06 time: 1.1037 data_time: 0.0130 memory: 15768 grad_norm: 4.1463 loss: 0.8915 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8915 2023/07/25 06:19:46 - mmengine - INFO - Epoch(train) [53][900/940] lr: 1.0000e-03 eta: 13:34:44 time: 1.1021 data_time: 0.0131 memory: 15768 grad_norm: 4.2200 loss: 0.9051 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9051 2023/07/25 06:20:08 - mmengine - INFO - Epoch(train) [53][920/940] lr: 1.0000e-03 eta: 13:34:21 time: 1.1022 data_time: 0.0130 memory: 15768 grad_norm: 4.2636 loss: 0.9041 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9041 2023/07/25 06:20:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 06:20:29 - mmengine - INFO - Epoch(train) [53][940/940] lr: 1.0000e-03 eta: 13:33:58 time: 1.0537 data_time: 0.0128 memory: 15768 grad_norm: 4.2896 loss: 0.9426 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9426 2023/07/25 06:20:39 - mmengine - INFO - Epoch(val) [53][20/78] eta: 0:00:27 time: 0.4762 data_time: 0.3193 memory: 2147 2023/07/25 06:20:46 - mmengine - INFO - Epoch(val) [53][40/78] eta: 0:00:15 time: 0.3589 data_time: 0.2022 memory: 2147 2023/07/25 06:20:55 - mmengine - INFO - Epoch(val) [53][60/78] eta: 0:00:07 time: 0.4397 data_time: 0.2826 memory: 2147 2023/07/25 06:21:06 - mmengine - INFO - Epoch(val) [53][78/78] acc/top1: 0.7110 acc/top5: 0.8995 acc/mean1: 0.7109 data_time: 0.2435 time: 0.3976 2023/07/25 06:21:31 - mmengine - INFO - Epoch(train) [54][ 20/940] lr: 1.0000e-03 eta: 13:33:39 time: 1.2834 data_time: 0.1505 memory: 15768 grad_norm: 4.1047 loss: 0.9042 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9042 2023/07/25 06:21:53 - mmengine - INFO - Epoch(train) [54][ 40/940] lr: 1.0000e-03 eta: 13:33:17 time: 1.1032 data_time: 0.0131 memory: 15768 grad_norm: 4.1913 loss: 0.8258 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8258 2023/07/25 06:22:15 - mmengine - INFO - Epoch(train) [54][ 60/940] lr: 1.0000e-03 eta: 13:32:55 time: 1.0986 data_time: 0.0128 memory: 15768 grad_norm: 4.2431 loss: 0.9068 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9068 2023/07/25 06:22:37 - mmengine - INFO - Epoch(train) [54][ 80/940] lr: 1.0000e-03 eta: 13:32:33 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 4.1773 loss: 0.8686 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8686 2023/07/25 06:22:59 - mmengine - INFO - Epoch(train) [54][100/940] lr: 1.0000e-03 eta: 13:32:11 time: 1.0995 data_time: 0.0134 memory: 15768 grad_norm: 4.1807 loss: 0.9834 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9834 2023/07/25 06:23:21 - mmengine - INFO - Epoch(train) [54][120/940] lr: 1.0000e-03 eta: 13:31:48 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 4.1047 loss: 0.8501 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8501 2023/07/25 06:23:43 - mmengine - INFO - Epoch(train) [54][140/940] lr: 1.0000e-03 eta: 13:31:26 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 4.2343 loss: 0.9458 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9458 2023/07/25 06:24:05 - mmengine - INFO - Epoch(train) [54][160/940] lr: 1.0000e-03 eta: 13:31:04 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 4.2978 loss: 1.0471 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0471 2023/07/25 06:24:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 06:24:27 - mmengine - INFO - Epoch(train) [54][180/940] lr: 1.0000e-03 eta: 13:30:42 time: 1.1023 data_time: 0.0128 memory: 15768 grad_norm: 4.0478 loss: 0.8767 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8767 2023/07/25 06:24:49 - mmengine - INFO - Epoch(train) [54][200/940] lr: 1.0000e-03 eta: 13:30:20 time: 1.0999 data_time: 0.0128 memory: 15768 grad_norm: 4.0999 loss: 0.9116 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9116 2023/07/25 06:25:11 - mmengine - INFO - Epoch(train) [54][220/940] lr: 1.0000e-03 eta: 13:29:57 time: 1.1013 data_time: 0.0127 memory: 15768 grad_norm: 4.2876 loss: 0.9952 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9952 2023/07/25 06:25:33 - mmengine - INFO - Epoch(train) [54][240/940] lr: 1.0000e-03 eta: 13:29:35 time: 1.1053 data_time: 0.0131 memory: 15768 grad_norm: 4.1150 loss: 0.8252 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8252 2023/07/25 06:25:55 - mmengine - INFO - Epoch(train) [54][260/940] lr: 1.0000e-03 eta: 13:29:13 time: 1.0986 data_time: 0.0128 memory: 15768 grad_norm: 4.2007 loss: 0.8748 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8748 2023/07/25 06:26:17 - mmengine - INFO - Epoch(train) [54][280/940] lr: 1.0000e-03 eta: 13:28:51 time: 1.1002 data_time: 0.0129 memory: 15768 grad_norm: 4.0763 loss: 0.8427 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8427 2023/07/25 06:26:39 - mmengine - INFO - Epoch(train) [54][300/940] lr: 1.0000e-03 eta: 13:28:29 time: 1.1034 data_time: 0.0129 memory: 15768 grad_norm: 4.2306 loss: 0.9751 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9751 2023/07/25 06:27:02 - mmengine - INFO - Epoch(train) [54][320/940] lr: 1.0000e-03 eta: 13:28:07 time: 1.1022 data_time: 0.0130 memory: 15768 grad_norm: 4.0381 loss: 0.8807 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8807 2023/07/25 06:27:24 - mmengine - INFO - Epoch(train) [54][340/940] lr: 1.0000e-03 eta: 13:27:44 time: 1.1034 data_time: 0.0129 memory: 15768 grad_norm: 4.1155 loss: 0.8623 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8623 2023/07/25 06:27:46 - mmengine - INFO - Epoch(train) [54][360/940] lr: 1.0000e-03 eta: 13:27:22 time: 1.0985 data_time: 0.0129 memory: 15768 grad_norm: 4.1462 loss: 0.8308 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8308 2023/07/25 06:28:08 - mmengine - INFO - Epoch(train) [54][380/940] lr: 1.0000e-03 eta: 13:27:00 time: 1.0973 data_time: 0.0129 memory: 15768 grad_norm: 4.3090 loss: 0.8863 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8863 2023/07/25 06:28:29 - mmengine - INFO - Epoch(train) [54][400/940] lr: 1.0000e-03 eta: 13:26:38 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.0981 loss: 1.0392 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0392 2023/07/25 06:28:52 - mmengine - INFO - Epoch(train) [54][420/940] lr: 1.0000e-03 eta: 13:26:15 time: 1.1012 data_time: 0.0131 memory: 15768 grad_norm: 4.1666 loss: 0.9283 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9283 2023/07/25 06:29:14 - mmengine - INFO - Epoch(train) [54][440/940] lr: 1.0000e-03 eta: 13:25:53 time: 1.0990 data_time: 0.0129 memory: 15768 grad_norm: 4.2709 loss: 0.8730 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8730 2023/07/25 06:29:36 - mmengine - INFO - Epoch(train) [54][460/940] lr: 1.0000e-03 eta: 13:25:31 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 4.1306 loss: 0.8795 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8795 2023/07/25 06:29:57 - mmengine - INFO - Epoch(train) [54][480/940] lr: 1.0000e-03 eta: 13:25:09 time: 1.0992 data_time: 0.0126 memory: 15768 grad_norm: 4.1491 loss: 0.8646 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8646 2023/07/25 06:30:19 - mmengine - INFO - Epoch(train) [54][500/940] lr: 1.0000e-03 eta: 13:24:47 time: 1.0999 data_time: 0.0128 memory: 15768 grad_norm: 4.1790 loss: 0.8600 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8600 2023/07/25 06:30:42 - mmengine - INFO - Epoch(train) [54][520/940] lr: 1.0000e-03 eta: 13:24:24 time: 1.1018 data_time: 0.0134 memory: 15768 grad_norm: 4.1324 loss: 0.8809 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8809 2023/07/25 06:31:04 - mmengine - INFO - Epoch(train) [54][540/940] lr: 1.0000e-03 eta: 13:24:02 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 4.1443 loss: 0.8717 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8717 2023/07/25 06:31:26 - mmengine - INFO - Epoch(train) [54][560/940] lr: 1.0000e-03 eta: 13:23:40 time: 1.0987 data_time: 0.0129 memory: 15768 grad_norm: 4.1091 loss: 0.7779 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7779 2023/07/25 06:31:48 - mmengine - INFO - Epoch(train) [54][580/940] lr: 1.0000e-03 eta: 13:23:18 time: 1.1028 data_time: 0.0132 memory: 15768 grad_norm: 4.0226 loss: 0.7640 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.7640 2023/07/25 06:32:10 - mmengine - INFO - Epoch(train) [54][600/940] lr: 1.0000e-03 eta: 13:22:56 time: 1.0993 data_time: 0.0127 memory: 15768 grad_norm: 4.1924 loss: 1.0570 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0570 2023/07/25 06:32:32 - mmengine - INFO - Epoch(train) [54][620/940] lr: 1.0000e-03 eta: 13:22:33 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 4.1777 loss: 0.7736 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7736 2023/07/25 06:32:54 - mmengine - INFO - Epoch(train) [54][640/940] lr: 1.0000e-03 eta: 13:22:11 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 4.1578 loss: 0.8935 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8935 2023/07/25 06:33:16 - mmengine - INFO - Epoch(train) [54][660/940] lr: 1.0000e-03 eta: 13:21:49 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 4.1356 loss: 0.8749 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8749 2023/07/25 06:33:38 - mmengine - INFO - Epoch(train) [54][680/940] lr: 1.0000e-03 eta: 13:21:27 time: 1.1018 data_time: 0.0130 memory: 15768 grad_norm: 4.1658 loss: 0.8327 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8327 2023/07/25 06:34:00 - mmengine - INFO - Epoch(train) [54][700/940] lr: 1.0000e-03 eta: 13:21:05 time: 1.1006 data_time: 0.0133 memory: 15768 grad_norm: 4.1267 loss: 0.9072 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9072 2023/07/25 06:34:22 - mmengine - INFO - Epoch(train) [54][720/940] lr: 1.0000e-03 eta: 13:20:42 time: 1.0976 data_time: 0.0129 memory: 15768 grad_norm: 4.2064 loss: 0.9223 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9223 2023/07/25 06:34:44 - mmengine - INFO - Epoch(train) [54][740/940] lr: 1.0000e-03 eta: 13:20:20 time: 1.1029 data_time: 0.0126 memory: 15768 grad_norm: 4.0838 loss: 0.8812 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8812 2023/07/25 06:35:06 - mmengine - INFO - Epoch(train) [54][760/940] lr: 1.0000e-03 eta: 13:19:58 time: 1.1001 data_time: 0.0130 memory: 15768 grad_norm: 4.0544 loss: 0.8308 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8308 2023/07/25 06:35:28 - mmengine - INFO - Epoch(train) [54][780/940] lr: 1.0000e-03 eta: 13:19:36 time: 1.0981 data_time: 0.0131 memory: 15768 grad_norm: 4.1358 loss: 0.8093 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8093 2023/07/25 06:35:50 - mmengine - INFO - Epoch(train) [54][800/940] lr: 1.0000e-03 eta: 13:19:14 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 4.1956 loss: 0.8251 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8251 2023/07/25 06:36:12 - mmengine - INFO - Epoch(train) [54][820/940] lr: 1.0000e-03 eta: 13:18:51 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 4.2145 loss: 0.9097 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9097 2023/07/25 06:36:34 - mmengine - INFO - Epoch(train) [54][840/940] lr: 1.0000e-03 eta: 13:18:29 time: 1.1019 data_time: 0.0135 memory: 15768 grad_norm: 4.1581 loss: 0.7740 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7740 2023/07/25 06:36:56 - mmengine - INFO - Epoch(train) [54][860/940] lr: 1.0000e-03 eta: 13:18:07 time: 1.1011 data_time: 0.0130 memory: 15768 grad_norm: 4.1727 loss: 0.8759 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8759 2023/07/25 06:37:18 - mmengine - INFO - Epoch(train) [54][880/940] lr: 1.0000e-03 eta: 13:17:45 time: 1.1039 data_time: 0.0130 memory: 15768 grad_norm: 4.2549 loss: 0.8442 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8442 2023/07/25 06:37:40 - mmengine - INFO - Epoch(train) [54][900/940] lr: 1.0000e-03 eta: 13:17:23 time: 1.0989 data_time: 0.0128 memory: 15768 grad_norm: 4.2548 loss: 0.9537 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9537 2023/07/25 06:38:02 - mmengine - INFO - Epoch(train) [54][920/940] lr: 1.0000e-03 eta: 13:17:01 time: 1.0972 data_time: 0.0131 memory: 15768 grad_norm: 4.1029 loss: 0.9192 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9192 2023/07/25 06:38:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 06:38:23 - mmengine - INFO - Epoch(train) [54][940/940] lr: 1.0000e-03 eta: 13:16:38 time: 1.0550 data_time: 0.0128 memory: 15768 grad_norm: 4.5064 loss: 0.9545 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9545 2023/07/25 06:38:23 - mmengine - INFO - Saving checkpoint at 54 epochs 2023/07/25 06:38:34 - mmengine - INFO - Epoch(val) [54][20/78] eta: 0:00:28 time: 0.4844 data_time: 0.3267 memory: 2147 2023/07/25 06:38:41 - mmengine - INFO - Epoch(val) [54][40/78] eta: 0:00:16 time: 0.3607 data_time: 0.2040 memory: 2147 2023/07/25 06:38:50 - mmengine - INFO - Epoch(val) [54][60/78] eta: 0:00:07 time: 0.4390 data_time: 0.2823 memory: 2147 2023/07/25 06:38:59 - mmengine - INFO - Epoch(val) [54][78/78] acc/top1: 0.7125 acc/top5: 0.8986 acc/mean1: 0.7124 data_time: 0.2414 time: 0.3956 2023/07/25 06:39:25 - mmengine - INFO - Epoch(train) [55][ 20/940] lr: 1.0000e-03 eta: 13:16:19 time: 1.2880 data_time: 0.1642 memory: 15768 grad_norm: 4.2185 loss: 0.8438 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8438 2023/07/25 06:39:47 - mmengine - INFO - Epoch(train) [55][ 40/940] lr: 1.0000e-03 eta: 13:15:56 time: 1.1007 data_time: 0.0132 memory: 15768 grad_norm: 4.0668 loss: 0.9613 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9613 2023/07/25 06:40:09 - mmengine - INFO - Epoch(train) [55][ 60/940] lr: 1.0000e-03 eta: 13:15:34 time: 1.1009 data_time: 0.0132 memory: 15768 grad_norm: 4.1858 loss: 1.0636 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0636 2023/07/25 06:40:31 - mmengine - INFO - Epoch(train) [55][ 80/940] lr: 1.0000e-03 eta: 13:15:12 time: 1.1035 data_time: 0.0129 memory: 15768 grad_norm: 4.1354 loss: 1.0509 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0509 2023/07/25 06:40:53 - mmengine - INFO - Epoch(train) [55][100/940] lr: 1.0000e-03 eta: 13:14:50 time: 1.1009 data_time: 0.0135 memory: 15768 grad_norm: 4.2110 loss: 0.7875 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7875 2023/07/25 06:41:15 - mmengine - INFO - Epoch(train) [55][120/940] lr: 1.0000e-03 eta: 13:14:28 time: 1.0992 data_time: 0.0130 memory: 15768 grad_norm: 4.1405 loss: 0.7702 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7702 2023/07/25 06:41:37 - mmengine - INFO - Epoch(train) [55][140/940] lr: 1.0000e-03 eta: 13:14:05 time: 1.1007 data_time: 0.0130 memory: 15768 grad_norm: 4.1289 loss: 0.9954 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9954 2023/07/25 06:41:59 - mmengine - INFO - Epoch(train) [55][160/940] lr: 1.0000e-03 eta: 13:13:43 time: 1.1000 data_time: 0.0133 memory: 15768 grad_norm: 4.1294 loss: 0.8534 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8534 2023/07/25 06:42:21 - mmengine - INFO - Epoch(train) [55][180/940] lr: 1.0000e-03 eta: 13:13:21 time: 1.1018 data_time: 0.0130 memory: 15768 grad_norm: 4.1257 loss: 0.9102 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9102 2023/07/25 06:42:43 - mmengine - INFO - Epoch(train) [55][200/940] lr: 1.0000e-03 eta: 13:12:59 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 4.1310 loss: 0.9514 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9514 2023/07/25 06:43:05 - mmengine - INFO - Epoch(train) [55][220/940] lr: 1.0000e-03 eta: 13:12:37 time: 1.1021 data_time: 0.0129 memory: 15768 grad_norm: 4.2522 loss: 0.8739 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8739 2023/07/25 06:43:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 06:43:27 - mmengine - INFO - Epoch(train) [55][240/940] lr: 1.0000e-03 eta: 13:12:14 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 4.1932 loss: 0.8974 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8974 2023/07/25 06:43:49 - mmengine - INFO - Epoch(train) [55][260/940] lr: 1.0000e-03 eta: 13:11:52 time: 1.0981 data_time: 0.0131 memory: 15768 grad_norm: 4.1822 loss: 1.0625 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0625 2023/07/25 06:44:11 - mmengine - INFO - Epoch(train) [55][280/940] lr: 1.0000e-03 eta: 13:11:30 time: 1.1043 data_time: 0.0145 memory: 15768 grad_norm: 4.1815 loss: 0.7969 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7969 2023/07/25 06:44:33 - mmengine - INFO - Epoch(train) [55][300/940] lr: 1.0000e-03 eta: 13:11:08 time: 1.1029 data_time: 0.0128 memory: 15768 grad_norm: 4.1780 loss: 0.9222 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9222 2023/07/25 06:44:55 - mmengine - INFO - Epoch(train) [55][320/940] lr: 1.0000e-03 eta: 13:10:46 time: 1.1001 data_time: 0.0129 memory: 15768 grad_norm: 4.0942 loss: 0.9720 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9720 2023/07/25 06:45:17 - mmengine - INFO - Epoch(train) [55][340/940] lr: 1.0000e-03 eta: 13:10:24 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 4.3019 loss: 0.8369 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8369 2023/07/25 06:45:39 - mmengine - INFO - Epoch(train) [55][360/940] lr: 1.0000e-03 eta: 13:10:01 time: 1.0987 data_time: 0.0133 memory: 15768 grad_norm: 4.2648 loss: 0.8653 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8653 2023/07/25 06:46:01 - mmengine - INFO - Epoch(train) [55][380/940] lr: 1.0000e-03 eta: 13:09:39 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 4.1438 loss: 0.8823 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8823 2023/07/25 06:46:23 - mmengine - INFO - Epoch(train) [55][400/940] lr: 1.0000e-03 eta: 13:09:17 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 4.2225 loss: 0.9311 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9311 2023/07/25 06:46:45 - mmengine - INFO - Epoch(train) [55][420/940] lr: 1.0000e-03 eta: 13:08:55 time: 1.1038 data_time: 0.0131 memory: 15768 grad_norm: 4.1399 loss: 0.7983 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7983 2023/07/25 06:47:07 - mmengine - INFO - Epoch(train) [55][440/940] lr: 1.0000e-03 eta: 13:08:33 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 4.2024 loss: 0.8607 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8607 2023/07/25 06:47:29 - mmengine - INFO - Epoch(train) [55][460/940] lr: 1.0000e-03 eta: 13:08:10 time: 1.1013 data_time: 0.0135 memory: 15768 grad_norm: 4.2096 loss: 0.8969 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8969 2023/07/25 06:47:51 - mmengine - INFO - Epoch(train) [55][480/940] lr: 1.0000e-03 eta: 13:07:48 time: 1.1008 data_time: 0.0132 memory: 15768 grad_norm: 4.1965 loss: 0.8273 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8273 2023/07/25 06:48:14 - mmengine - INFO - Epoch(train) [55][500/940] lr: 1.0000e-03 eta: 13:07:26 time: 1.1017 data_time: 0.0133 memory: 15768 grad_norm: 4.2264 loss: 0.8638 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8638 2023/07/25 06:48:36 - mmengine - INFO - Epoch(train) [55][520/940] lr: 1.0000e-03 eta: 13:07:04 time: 1.0991 data_time: 0.0134 memory: 15768 grad_norm: 4.2197 loss: 0.7637 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7637 2023/07/25 06:48:57 - mmengine - INFO - Epoch(train) [55][540/940] lr: 1.0000e-03 eta: 13:06:42 time: 1.0984 data_time: 0.0129 memory: 15768 grad_norm: 4.2563 loss: 1.0324 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0324 2023/07/25 06:49:19 - mmengine - INFO - Epoch(train) [55][560/940] lr: 1.0000e-03 eta: 13:06:19 time: 1.0986 data_time: 0.0131 memory: 15768 grad_norm: 4.2111 loss: 0.8681 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8681 2023/07/25 06:49:41 - mmengine - INFO - Epoch(train) [55][580/940] lr: 1.0000e-03 eta: 13:05:57 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 4.2205 loss: 0.9459 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9459 2023/07/25 06:50:04 - mmengine - INFO - Epoch(train) [55][600/940] lr: 1.0000e-03 eta: 13:05:35 time: 1.1034 data_time: 0.0133 memory: 15768 grad_norm: 4.1685 loss: 1.0711 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0711 2023/07/25 06:50:26 - mmengine - INFO - Epoch(train) [55][620/940] lr: 1.0000e-03 eta: 13:05:13 time: 1.1005 data_time: 0.0127 memory: 15768 grad_norm: 4.1329 loss: 0.8897 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8897 2023/07/25 06:50:48 - mmengine - INFO - Epoch(train) [55][640/940] lr: 1.0000e-03 eta: 13:04:51 time: 1.1015 data_time: 0.0131 memory: 15768 grad_norm: 4.1382 loss: 0.7770 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7770 2023/07/25 06:51:10 - mmengine - INFO - Epoch(train) [55][660/940] lr: 1.0000e-03 eta: 13:04:28 time: 1.0988 data_time: 0.0127 memory: 15768 grad_norm: 4.2127 loss: 0.8451 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8451 2023/07/25 06:51:32 - mmengine - INFO - Epoch(train) [55][680/940] lr: 1.0000e-03 eta: 13:04:06 time: 1.0992 data_time: 0.0128 memory: 15768 grad_norm: 4.1519 loss: 0.8485 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8485 2023/07/25 06:51:54 - mmengine - INFO - Epoch(train) [55][700/940] lr: 1.0000e-03 eta: 13:03:44 time: 1.0997 data_time: 0.0133 memory: 15768 grad_norm: 4.0709 loss: 0.9156 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9156 2023/07/25 06:52:16 - mmengine - INFO - Epoch(train) [55][720/940] lr: 1.0000e-03 eta: 13:03:22 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 4.0902 loss: 0.8544 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8544 2023/07/25 06:52:38 - mmengine - INFO - Epoch(train) [55][740/940] lr: 1.0000e-03 eta: 13:03:00 time: 1.1067 data_time: 0.0123 memory: 15768 grad_norm: 4.2209 loss: 1.0084 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0084 2023/07/25 06:53:00 - mmengine - INFO - Epoch(train) [55][760/940] lr: 1.0000e-03 eta: 13:02:38 time: 1.0985 data_time: 0.0129 memory: 15768 grad_norm: 4.2345 loss: 0.8142 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8142 2023/07/25 06:53:22 - mmengine - INFO - Epoch(train) [55][780/940] lr: 1.0000e-03 eta: 13:02:15 time: 1.1001 data_time: 0.0128 memory: 15768 grad_norm: 4.1894 loss: 0.8618 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8618 2023/07/25 06:53:44 - mmengine - INFO - Epoch(train) [55][800/940] lr: 1.0000e-03 eta: 13:01:53 time: 1.1061 data_time: 0.0133 memory: 15768 grad_norm: 4.1731 loss: 0.9111 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9111 2023/07/25 06:54:06 - mmengine - INFO - Epoch(train) [55][820/940] lr: 1.0000e-03 eta: 13:01:31 time: 1.0966 data_time: 0.0129 memory: 15768 grad_norm: 4.1972 loss: 0.9499 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9499 2023/07/25 06:54:28 - mmengine - INFO - Epoch(train) [55][840/940] lr: 1.0000e-03 eta: 13:01:09 time: 1.0980 data_time: 0.0133 memory: 15768 grad_norm: 4.2155 loss: 0.8527 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8527 2023/07/25 06:54:50 - mmengine - INFO - Epoch(train) [55][860/940] lr: 1.0000e-03 eta: 13:00:47 time: 1.1030 data_time: 0.0129 memory: 15768 grad_norm: 4.2104 loss: 0.9150 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9150 2023/07/25 06:55:12 - mmengine - INFO - Epoch(train) [55][880/940] lr: 1.0000e-03 eta: 13:00:24 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 4.1937 loss: 0.9952 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9952 2023/07/25 06:55:34 - mmengine - INFO - Epoch(train) [55][900/940] lr: 1.0000e-03 eta: 13:00:02 time: 1.0992 data_time: 0.0129 memory: 15768 grad_norm: 4.1857 loss: 0.9618 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9618 2023/07/25 06:55:56 - mmengine - INFO - Epoch(train) [55][920/940] lr: 1.0000e-03 eta: 12:59:40 time: 1.0977 data_time: 0.0134 memory: 15768 grad_norm: 4.1769 loss: 1.0542 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0542 2023/07/25 06:56:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 06:56:17 - mmengine - INFO - Epoch(train) [55][940/940] lr: 1.0000e-03 eta: 12:59:17 time: 1.0524 data_time: 0.0127 memory: 15768 grad_norm: 4.4473 loss: 1.0142 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0142 2023/07/25 06:56:27 - mmengine - INFO - Epoch(val) [55][20/78] eta: 0:00:28 time: 0.4957 data_time: 0.3379 memory: 2147 2023/07/25 06:56:34 - mmengine - INFO - Epoch(val) [55][40/78] eta: 0:00:16 time: 0.3580 data_time: 0.2005 memory: 2147 2023/07/25 06:56:43 - mmengine - INFO - Epoch(val) [55][60/78] eta: 0:00:07 time: 0.4389 data_time: 0.2817 memory: 2147 2023/07/25 06:56:53 - mmengine - INFO - Epoch(val) [55][78/78] acc/top1: 0.7114 acc/top5: 0.8970 acc/mean1: 0.7113 data_time: 0.2460 time: 0.4006 2023/07/25 06:57:19 - mmengine - INFO - Epoch(train) [56][ 20/940] lr: 1.0000e-03 eta: 12:58:58 time: 1.2935 data_time: 0.1667 memory: 15768 grad_norm: 4.0971 loss: 0.8794 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 0.8794 2023/07/25 06:57:41 - mmengine - INFO - Epoch(train) [56][ 40/940] lr: 1.0000e-03 eta: 12:58:36 time: 1.1007 data_time: 0.0133 memory: 15768 grad_norm: 4.1887 loss: 1.0112 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0112 2023/07/25 06:58:03 - mmengine - INFO - Epoch(train) [56][ 60/940] lr: 1.0000e-03 eta: 12:58:14 time: 1.0990 data_time: 0.0131 memory: 15768 grad_norm: 4.2576 loss: 1.0316 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0316 2023/07/25 06:58:25 - mmengine - INFO - Epoch(train) [56][ 80/940] lr: 1.0000e-03 eta: 12:57:51 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 4.1620 loss: 0.9675 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9675 2023/07/25 06:58:47 - mmengine - INFO - Epoch(train) [56][100/940] lr: 1.0000e-03 eta: 12:57:29 time: 1.1095 data_time: 0.0134 memory: 15768 grad_norm: 4.1150 loss: 0.8148 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8148 2023/07/25 06:59:09 - mmengine - INFO - Epoch(train) [56][120/940] lr: 1.0000e-03 eta: 12:57:07 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 4.1306 loss: 0.8317 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8317 2023/07/25 06:59:31 - mmengine - INFO - Epoch(train) [56][140/940] lr: 1.0000e-03 eta: 12:56:45 time: 1.0979 data_time: 0.0131 memory: 15768 grad_norm: 4.0947 loss: 0.9543 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9543 2023/07/25 06:59:53 - mmengine - INFO - Epoch(train) [56][160/940] lr: 1.0000e-03 eta: 12:56:23 time: 1.1046 data_time: 0.0132 memory: 15768 grad_norm: 4.1262 loss: 0.7459 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7459 2023/07/25 07:00:15 - mmengine - INFO - Epoch(train) [56][180/940] lr: 1.0000e-03 eta: 12:56:01 time: 1.0999 data_time: 0.0132 memory: 15768 grad_norm: 4.0937 loss: 0.9645 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9645 2023/07/25 07:00:37 - mmengine - INFO - Epoch(train) [56][200/940] lr: 1.0000e-03 eta: 12:55:38 time: 1.0988 data_time: 0.0134 memory: 15768 grad_norm: 4.1521 loss: 0.8185 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8185 2023/07/25 07:00:59 - mmengine - INFO - Epoch(train) [56][220/940] lr: 1.0000e-03 eta: 12:55:16 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 4.1907 loss: 0.9466 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9466 2023/07/25 07:01:21 - mmengine - INFO - Epoch(train) [56][240/940] lr: 1.0000e-03 eta: 12:54:54 time: 1.1011 data_time: 0.0131 memory: 15768 grad_norm: 4.1296 loss: 0.9456 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9456 2023/07/25 07:01:43 - mmengine - INFO - Epoch(train) [56][260/940] lr: 1.0000e-03 eta: 12:54:32 time: 1.0972 data_time: 0.0133 memory: 15768 grad_norm: 4.1523 loss: 0.9371 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9371 2023/07/25 07:02:05 - mmengine - INFO - Epoch(train) [56][280/940] lr: 1.0000e-03 eta: 12:54:10 time: 1.1025 data_time: 0.0129 memory: 15768 grad_norm: 4.2969 loss: 1.0383 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0383 2023/07/25 07:02:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 07:02:27 - mmengine - INFO - Epoch(train) [56][300/940] lr: 1.0000e-03 eta: 12:53:47 time: 1.1018 data_time: 0.0134 memory: 15768 grad_norm: 4.2015 loss: 0.7319 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7319 2023/07/25 07:02:49 - mmengine - INFO - Epoch(train) [56][320/940] lr: 1.0000e-03 eta: 12:53:25 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 4.1792 loss: 0.7622 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7622 2023/07/25 07:03:11 - mmengine - INFO - Epoch(train) [56][340/940] lr: 1.0000e-03 eta: 12:53:03 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 4.1718 loss: 0.7687 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7687 2023/07/25 07:03:33 - mmengine - INFO - Epoch(train) [56][360/940] lr: 1.0000e-03 eta: 12:52:41 time: 1.0988 data_time: 0.0129 memory: 15768 grad_norm: 4.1304 loss: 0.7910 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.7910 2023/07/25 07:03:55 - mmengine - INFO - Epoch(train) [56][380/940] lr: 1.0000e-03 eta: 12:52:19 time: 1.0993 data_time: 0.0132 memory: 15768 grad_norm: 4.2525 loss: 0.9427 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9427 2023/07/25 07:04:17 - mmengine - INFO - Epoch(train) [56][400/940] lr: 1.0000e-03 eta: 12:51:57 time: 1.1046 data_time: 0.0141 memory: 15768 grad_norm: 4.2771 loss: 0.9421 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9421 2023/07/25 07:04:39 - mmengine - INFO - Epoch(train) [56][420/940] lr: 1.0000e-03 eta: 12:51:34 time: 1.0998 data_time: 0.0136 memory: 15768 grad_norm: 4.2290 loss: 0.7787 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7787 2023/07/25 07:05:01 - mmengine - INFO - Epoch(train) [56][440/940] lr: 1.0000e-03 eta: 12:51:12 time: 1.1000 data_time: 0.0134 memory: 15768 grad_norm: 4.1629 loss: 1.0036 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0036 2023/07/25 07:05:23 - mmengine - INFO - Epoch(train) [56][460/940] lr: 1.0000e-03 eta: 12:50:50 time: 1.1218 data_time: 0.0135 memory: 15768 grad_norm: 4.2172 loss: 0.7572 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7572 2023/07/25 07:05:45 - mmengine - INFO - Epoch(train) [56][480/940] lr: 1.0000e-03 eta: 12:50:28 time: 1.1006 data_time: 0.0129 memory: 15768 grad_norm: 4.0827 loss: 0.8567 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8567 2023/07/25 07:06:07 - mmengine - INFO - Epoch(train) [56][500/940] lr: 1.0000e-03 eta: 12:50:06 time: 1.1025 data_time: 0.0127 memory: 15768 grad_norm: 4.2114 loss: 0.8678 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8678 2023/07/25 07:06:29 - mmengine - INFO - Epoch(train) [56][520/940] lr: 1.0000e-03 eta: 12:49:44 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 4.2726 loss: 1.0104 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0104 2023/07/25 07:06:51 - mmengine - INFO - Epoch(train) [56][540/940] lr: 1.0000e-03 eta: 12:49:22 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 4.2147 loss: 0.9049 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9049 2023/07/25 07:07:13 - mmengine - INFO - Epoch(train) [56][560/940] lr: 1.0000e-03 eta: 12:48:59 time: 1.1022 data_time: 0.0133 memory: 15768 grad_norm: 4.2335 loss: 0.7174 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7174 2023/07/25 07:07:35 - mmengine - INFO - Epoch(train) [56][580/940] lr: 1.0000e-03 eta: 12:48:37 time: 1.0971 data_time: 0.0136 memory: 15768 grad_norm: 4.2230 loss: 0.8905 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8905 2023/07/25 07:07:57 - mmengine - INFO - Epoch(train) [56][600/940] lr: 1.0000e-03 eta: 12:48:15 time: 1.0991 data_time: 0.0133 memory: 15768 grad_norm: 4.1779 loss: 0.9176 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9176 2023/07/25 07:08:19 - mmengine - INFO - Epoch(train) [56][620/940] lr: 1.0000e-03 eta: 12:47:53 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 4.1651 loss: 0.9242 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9242 2023/07/25 07:08:41 - mmengine - INFO - Epoch(train) [56][640/940] lr: 1.0000e-03 eta: 12:47:31 time: 1.1006 data_time: 0.0132 memory: 15768 grad_norm: 4.2162 loss: 0.9634 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9634 2023/07/25 07:09:03 - mmengine - INFO - Epoch(train) [56][660/940] lr: 1.0000e-03 eta: 12:47:08 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 4.2318 loss: 0.8944 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8944 2023/07/25 07:09:25 - mmengine - INFO - Epoch(train) [56][680/940] lr: 1.0000e-03 eta: 12:46:46 time: 1.0997 data_time: 0.0131 memory: 15768 grad_norm: 4.2984 loss: 1.0181 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0181 2023/07/25 07:09:47 - mmengine - INFO - Epoch(train) [56][700/940] lr: 1.0000e-03 eta: 12:46:24 time: 1.0977 data_time: 0.0133 memory: 15768 grad_norm: 4.2289 loss: 0.8783 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8783 2023/07/25 07:10:09 - mmengine - INFO - Epoch(train) [56][720/940] lr: 1.0000e-03 eta: 12:46:02 time: 1.0981 data_time: 0.0133 memory: 15768 grad_norm: 4.1800 loss: 0.9621 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9621 2023/07/25 07:10:31 - mmengine - INFO - Epoch(train) [56][740/940] lr: 1.0000e-03 eta: 12:45:39 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 4.2105 loss: 0.8691 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8691 2023/07/25 07:10:53 - mmengine - INFO - Epoch(train) [56][760/940] lr: 1.0000e-03 eta: 12:45:17 time: 1.1030 data_time: 0.0133 memory: 15768 grad_norm: 4.4212 loss: 0.8334 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.8334 2023/07/25 07:11:15 - mmengine - INFO - Epoch(train) [56][780/940] lr: 1.0000e-03 eta: 12:44:55 time: 1.0999 data_time: 0.0137 memory: 15768 grad_norm: 4.2097 loss: 0.9806 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9806 2023/07/25 07:11:37 - mmengine - INFO - Epoch(train) [56][800/940] lr: 1.0000e-03 eta: 12:44:33 time: 1.1033 data_time: 0.0132 memory: 15768 grad_norm: 4.2099 loss: 0.9054 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9054 2023/07/25 07:11:59 - mmengine - INFO - Epoch(train) [56][820/940] lr: 1.0000e-03 eta: 12:44:11 time: 1.0991 data_time: 0.0132 memory: 15768 grad_norm: 4.2465 loss: 0.8154 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8154 2023/07/25 07:12:21 - mmengine - INFO - Epoch(train) [56][840/940] lr: 1.0000e-03 eta: 12:43:49 time: 1.0984 data_time: 0.0135 memory: 15768 grad_norm: 4.1131 loss: 0.8395 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8395 2023/07/25 07:12:43 - mmengine - INFO - Epoch(train) [56][860/940] lr: 1.0000e-03 eta: 12:43:26 time: 1.0993 data_time: 0.0134 memory: 15768 grad_norm: 4.2647 loss: 0.9406 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 0.9406 2023/07/25 07:13:05 - mmengine - INFO - Epoch(train) [56][880/940] lr: 1.0000e-03 eta: 12:43:04 time: 1.0991 data_time: 0.0132 memory: 15768 grad_norm: 4.3057 loss: 0.9517 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9517 2023/07/25 07:13:27 - mmengine - INFO - Epoch(train) [56][900/940] lr: 1.0000e-03 eta: 12:42:42 time: 1.0992 data_time: 0.0142 memory: 15768 grad_norm: 4.2239 loss: 0.9230 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9230 2023/07/25 07:13:49 - mmengine - INFO - Epoch(train) [56][920/940] lr: 1.0000e-03 eta: 12:42:20 time: 1.1009 data_time: 0.0142 memory: 15768 grad_norm: 4.1577 loss: 0.9141 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9141 2023/07/25 07:14:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 07:14:11 - mmengine - INFO - Epoch(train) [56][940/940] lr: 1.0000e-03 eta: 12:41:57 time: 1.0583 data_time: 0.0142 memory: 15768 grad_norm: 4.5259 loss: 0.8660 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8660 2023/07/25 07:14:20 - mmengine - INFO - Epoch(val) [56][20/78] eta: 0:00:28 time: 0.4895 data_time: 0.3322 memory: 2147 2023/07/25 07:14:27 - mmengine - INFO - Epoch(val) [56][40/78] eta: 0:00:16 time: 0.3535 data_time: 0.1963 memory: 2147 2023/07/25 07:14:36 - mmengine - INFO - Epoch(val) [56][60/78] eta: 0:00:07 time: 0.4397 data_time: 0.2826 memory: 2147 2023/07/25 07:14:46 - mmengine - INFO - Epoch(val) [56][78/78] acc/top1: 0.7123 acc/top5: 0.8978 acc/mean1: 0.7122 data_time: 0.2459 time: 0.4002 2023/07/25 07:15:12 - mmengine - INFO - Epoch(train) [57][ 20/940] lr: 1.0000e-03 eta: 12:41:38 time: 1.2859 data_time: 0.1374 memory: 15768 grad_norm: 4.2085 loss: 0.9409 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9409 2023/07/25 07:15:34 - mmengine - INFO - Epoch(train) [57][ 40/940] lr: 1.0000e-03 eta: 12:41:15 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.1118 loss: 0.8750 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8750 2023/07/25 07:15:56 - mmengine - INFO - Epoch(train) [57][ 60/940] lr: 1.0000e-03 eta: 12:40:53 time: 1.1035 data_time: 0.0142 memory: 15768 grad_norm: 4.1145 loss: 0.9413 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.9413 2023/07/25 07:16:18 - mmengine - INFO - Epoch(train) [57][ 80/940] lr: 1.0000e-03 eta: 12:40:31 time: 1.0993 data_time: 0.0142 memory: 15768 grad_norm: 4.2085 loss: 1.0301 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0301 2023/07/25 07:16:40 - mmengine - INFO - Epoch(train) [57][100/940] lr: 1.0000e-03 eta: 12:40:09 time: 1.0990 data_time: 0.0139 memory: 15768 grad_norm: 4.2150 loss: 0.8631 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8631 2023/07/25 07:17:02 - mmengine - INFO - Epoch(train) [57][120/940] lr: 1.0000e-03 eta: 12:39:47 time: 1.0987 data_time: 0.0142 memory: 15768 grad_norm: 4.2242 loss: 0.8747 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8747 2023/07/25 07:17:24 - mmengine - INFO - Epoch(train) [57][140/940] lr: 1.0000e-03 eta: 12:39:25 time: 1.1011 data_time: 0.0141 memory: 15768 grad_norm: 4.1619 loss: 0.8225 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8225 2023/07/25 07:17:46 - mmengine - INFO - Epoch(train) [57][160/940] lr: 1.0000e-03 eta: 12:39:02 time: 1.1032 data_time: 0.0145 memory: 15768 grad_norm: 4.2793 loss: 0.8151 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8151 2023/07/25 07:18:08 - mmengine - INFO - Epoch(train) [57][180/940] lr: 1.0000e-03 eta: 12:38:40 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.1501 loss: 0.8992 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8992 2023/07/25 07:18:30 - mmengine - INFO - Epoch(train) [57][200/940] lr: 1.0000e-03 eta: 12:38:18 time: 1.1034 data_time: 0.0138 memory: 15768 grad_norm: 4.3403 loss: 0.9226 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9226 2023/07/25 07:18:52 - mmengine - INFO - Epoch(train) [57][220/940] lr: 1.0000e-03 eta: 12:37:56 time: 1.1000 data_time: 0.0144 memory: 15768 grad_norm: 4.2261 loss: 0.7321 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7321 2023/07/25 07:19:14 - mmengine - INFO - Epoch(train) [57][240/940] lr: 1.0000e-03 eta: 12:37:34 time: 1.0999 data_time: 0.0140 memory: 15768 grad_norm: 4.1632 loss: 0.8279 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8279 2023/07/25 07:19:36 - mmengine - INFO - Epoch(train) [57][260/940] lr: 1.0000e-03 eta: 12:37:11 time: 1.0994 data_time: 0.0138 memory: 15768 grad_norm: 4.2452 loss: 0.9387 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9387 2023/07/25 07:19:58 - mmengine - INFO - Epoch(train) [57][280/940] lr: 1.0000e-03 eta: 12:36:49 time: 1.1008 data_time: 0.0138 memory: 15768 grad_norm: 4.3065 loss: 0.8438 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8438 2023/07/25 07:20:20 - mmengine - INFO - Epoch(train) [57][300/940] lr: 1.0000e-03 eta: 12:36:27 time: 1.0989 data_time: 0.0142 memory: 15768 grad_norm: 4.2620 loss: 0.9157 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9157 2023/07/25 07:20:42 - mmengine - INFO - Epoch(train) [57][320/940] lr: 1.0000e-03 eta: 12:36:05 time: 1.1017 data_time: 0.0144 memory: 15768 grad_norm: 4.2538 loss: 0.9577 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9577 2023/07/25 07:21:04 - mmengine - INFO - Epoch(train) [57][340/940] lr: 1.0000e-03 eta: 12:35:43 time: 1.1028 data_time: 0.0136 memory: 15768 grad_norm: 4.2005 loss: 0.9542 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9542 2023/07/25 07:21:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 07:21:26 - mmengine - INFO - Epoch(train) [57][360/940] lr: 1.0000e-03 eta: 12:35:21 time: 1.1021 data_time: 0.0142 memory: 15768 grad_norm: 4.1955 loss: 0.8211 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8211 2023/07/25 07:21:48 - mmengine - INFO - Epoch(train) [57][380/940] lr: 1.0000e-03 eta: 12:34:58 time: 1.1019 data_time: 0.0137 memory: 15768 grad_norm: 4.2076 loss: 1.0487 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0487 2023/07/25 07:22:10 - mmengine - INFO - Epoch(train) [57][400/940] lr: 1.0000e-03 eta: 12:34:36 time: 1.0995 data_time: 0.0144 memory: 15768 grad_norm: 4.2247 loss: 0.8363 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8363 2023/07/25 07:22:32 - mmengine - INFO - Epoch(train) [57][420/940] lr: 1.0000e-03 eta: 12:34:14 time: 1.1034 data_time: 0.0136 memory: 15768 grad_norm: 4.1768 loss: 0.8297 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8297 2023/07/25 07:22:54 - mmengine - INFO - Epoch(train) [57][440/940] lr: 1.0000e-03 eta: 12:33:52 time: 1.1013 data_time: 0.0138 memory: 15768 grad_norm: 4.2101 loss: 0.8742 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8742 2023/07/25 07:23:17 - mmengine - INFO - Epoch(train) [57][460/940] lr: 1.0000e-03 eta: 12:33:30 time: 1.1026 data_time: 0.0141 memory: 15768 grad_norm: 4.3278 loss: 1.0129 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0129 2023/07/25 07:23:39 - mmengine - INFO - Epoch(train) [57][480/940] lr: 1.0000e-03 eta: 12:33:08 time: 1.1002 data_time: 0.0141 memory: 15768 grad_norm: 4.1994 loss: 1.0741 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0741 2023/07/25 07:24:01 - mmengine - INFO - Epoch(train) [57][500/940] lr: 1.0000e-03 eta: 12:32:45 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.2436 loss: 0.8647 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8647 2023/07/25 07:24:23 - mmengine - INFO - Epoch(train) [57][520/940] lr: 1.0000e-03 eta: 12:32:23 time: 1.1025 data_time: 0.0142 memory: 15768 grad_norm: 4.2398 loss: 1.0678 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0678 2023/07/25 07:24:45 - mmengine - INFO - Epoch(train) [57][540/940] lr: 1.0000e-03 eta: 12:32:01 time: 1.1047 data_time: 0.0139 memory: 15768 grad_norm: 4.2466 loss: 0.7950 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7950 2023/07/25 07:25:07 - mmengine - INFO - Epoch(train) [57][560/940] lr: 1.0000e-03 eta: 12:31:39 time: 1.0982 data_time: 0.0140 memory: 15768 grad_norm: 4.2193 loss: 0.8667 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8667 2023/07/25 07:25:29 - mmengine - INFO - Epoch(train) [57][580/940] lr: 1.0000e-03 eta: 12:31:17 time: 1.1022 data_time: 0.0141 memory: 15768 grad_norm: 4.1331 loss: 0.9591 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9591 2023/07/25 07:25:51 - mmengine - INFO - Epoch(train) [57][600/940] lr: 1.0000e-03 eta: 12:30:55 time: 1.0999 data_time: 0.0142 memory: 15768 grad_norm: 4.2934 loss: 0.9524 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9524 2023/07/25 07:26:13 - mmengine - INFO - Epoch(train) [57][620/940] lr: 1.0000e-03 eta: 12:30:32 time: 1.1006 data_time: 0.0142 memory: 15768 grad_norm: 4.1890 loss: 0.8876 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8876 2023/07/25 07:26:35 - mmengine - INFO - Epoch(train) [57][640/940] lr: 1.0000e-03 eta: 12:30:10 time: 1.1022 data_time: 0.0142 memory: 15768 grad_norm: 4.1627 loss: 0.8906 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8906 2023/07/25 07:26:58 - mmengine - INFO - Epoch(train) [57][660/940] lr: 1.0000e-03 eta: 12:29:49 time: 1.1464 data_time: 0.0141 memory: 15768 grad_norm: 4.1527 loss: 0.8931 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8931 2023/07/25 07:27:20 - mmengine - INFO - Epoch(train) [57][680/940] lr: 1.0000e-03 eta: 12:29:27 time: 1.1093 data_time: 0.0142 memory: 15768 grad_norm: 4.2345 loss: 0.9785 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9785 2023/07/25 07:27:42 - mmengine - INFO - Epoch(train) [57][700/940] lr: 1.0000e-03 eta: 12:29:05 time: 1.1041 data_time: 0.0141 memory: 15768 grad_norm: 4.2062 loss: 0.8570 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8570 2023/07/25 07:28:04 - mmengine - INFO - Epoch(train) [57][720/940] lr: 1.0000e-03 eta: 12:28:42 time: 1.0985 data_time: 0.0137 memory: 15768 grad_norm: 4.1729 loss: 0.9074 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9074 2023/07/25 07:28:26 - mmengine - INFO - Epoch(train) [57][740/940] lr: 1.0000e-03 eta: 12:28:20 time: 1.1029 data_time: 0.0141 memory: 15768 grad_norm: 4.2366 loss: 0.9163 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9163 2023/07/25 07:28:48 - mmengine - INFO - Epoch(train) [57][760/940] lr: 1.0000e-03 eta: 12:27:58 time: 1.1013 data_time: 0.0141 memory: 15768 grad_norm: 4.2006 loss: 0.8705 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8705 2023/07/25 07:29:10 - mmengine - INFO - Epoch(train) [57][780/940] lr: 1.0000e-03 eta: 12:27:36 time: 1.1033 data_time: 0.0139 memory: 15768 grad_norm: 4.2226 loss: 0.8057 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8057 2023/07/25 07:29:32 - mmengine - INFO - Epoch(train) [57][800/940] lr: 1.0000e-03 eta: 12:27:14 time: 1.1005 data_time: 0.0140 memory: 15768 grad_norm: 4.2127 loss: 0.9726 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9726 2023/07/25 07:29:54 - mmengine - INFO - Epoch(train) [57][820/940] lr: 1.0000e-03 eta: 12:26:52 time: 1.0982 data_time: 0.0141 memory: 15768 grad_norm: 4.2020 loss: 0.9909 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9909 2023/07/25 07:30:16 - mmengine - INFO - Epoch(train) [57][840/940] lr: 1.0000e-03 eta: 12:26:29 time: 1.0982 data_time: 0.0141 memory: 15768 grad_norm: 4.2259 loss: 0.8654 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8654 2023/07/25 07:30:38 - mmengine - INFO - Epoch(train) [57][860/940] lr: 1.0000e-03 eta: 12:26:07 time: 1.0999 data_time: 0.0140 memory: 15768 grad_norm: 4.2724 loss: 0.8796 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8796 2023/07/25 07:31:00 - mmengine - INFO - Epoch(train) [57][880/940] lr: 1.0000e-03 eta: 12:25:45 time: 1.1012 data_time: 0.0138 memory: 15768 grad_norm: 4.3101 loss: 0.8632 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8632 2023/07/25 07:31:22 - mmengine - INFO - Epoch(train) [57][900/940] lr: 1.0000e-03 eta: 12:25:23 time: 1.1037 data_time: 0.0140 memory: 15768 grad_norm: 4.2159 loss: 0.8519 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8519 2023/07/25 07:31:44 - mmengine - INFO - Epoch(train) [57][920/940] lr: 1.0000e-03 eta: 12:25:01 time: 1.1014 data_time: 0.0140 memory: 15768 grad_norm: 4.1345 loss: 0.8919 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8919 2023/07/25 07:32:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 07:32:05 - mmengine - INFO - Epoch(train) [57][940/940] lr: 1.0000e-03 eta: 12:24:38 time: 1.0620 data_time: 0.0141 memory: 15768 grad_norm: 4.4596 loss: 1.0466 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0466 2023/07/25 07:32:05 - mmengine - INFO - Saving checkpoint at 57 epochs 2023/07/25 07:32:16 - mmengine - INFO - Epoch(val) [57][20/78] eta: 0:00:27 time: 0.4780 data_time: 0.3207 memory: 2147 2023/07/25 07:32:23 - mmengine - INFO - Epoch(val) [57][40/78] eta: 0:00:15 time: 0.3505 data_time: 0.1933 memory: 2147 2023/07/25 07:32:32 - mmengine - INFO - Epoch(val) [57][60/78] eta: 0:00:07 time: 0.4337 data_time: 0.2771 memory: 2147 2023/07/25 07:32:41 - mmengine - INFO - Epoch(val) [57][78/78] acc/top1: 0.7110 acc/top5: 0.8989 acc/mean1: 0.7109 data_time: 0.2375 time: 0.3917 2023/07/25 07:33:08 - mmengine - INFO - Epoch(train) [58][ 20/940] lr: 1.0000e-03 eta: 12:24:19 time: 1.3518 data_time: 0.1461 memory: 15768 grad_norm: 4.3050 loss: 0.8826 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8826 2023/07/25 07:33:32 - mmengine - INFO - Epoch(train) [58][ 40/940] lr: 1.0000e-03 eta: 12:23:58 time: 1.1684 data_time: 0.0144 memory: 15768 grad_norm: 4.2076 loss: 0.8174 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8174 2023/07/25 07:33:55 - mmengine - INFO - Epoch(train) [58][ 60/940] lr: 1.0000e-03 eta: 12:23:37 time: 1.1720 data_time: 0.0137 memory: 15768 grad_norm: 4.2329 loss: 1.1201 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1201 2023/07/25 07:34:18 - mmengine - INFO - Epoch(train) [58][ 80/940] lr: 1.0000e-03 eta: 12:23:16 time: 1.1689 data_time: 0.0144 memory: 15768 grad_norm: 4.2114 loss: 0.7166 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7166 2023/07/25 07:34:42 - mmengine - INFO - Epoch(train) [58][100/940] lr: 1.0000e-03 eta: 12:22:55 time: 1.1622 data_time: 0.0134 memory: 15768 grad_norm: 4.2092 loss: 1.0264 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0264 2023/07/25 07:35:05 - mmengine - INFO - Epoch(train) [58][120/940] lr: 1.0000e-03 eta: 12:22:34 time: 1.1620 data_time: 0.0138 memory: 15768 grad_norm: 4.1865 loss: 0.8508 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8508 2023/07/25 07:35:28 - mmengine - INFO - Epoch(train) [58][140/940] lr: 1.0000e-03 eta: 12:22:12 time: 1.1348 data_time: 0.0141 memory: 15768 grad_norm: 4.2435 loss: 0.9833 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9833 2023/07/25 07:35:50 - mmengine - INFO - Epoch(train) [58][160/940] lr: 1.0000e-03 eta: 12:21:50 time: 1.0995 data_time: 0.0140 memory: 15768 grad_norm: 4.3235 loss: 0.8828 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8828 2023/07/25 07:36:12 - mmengine - INFO - Epoch(train) [58][180/940] lr: 1.0000e-03 eta: 12:21:28 time: 1.1041 data_time: 0.0136 memory: 15768 grad_norm: 4.1811 loss: 0.8691 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8691 2023/07/25 07:36:34 - mmengine - INFO - Epoch(train) [58][200/940] lr: 1.0000e-03 eta: 12:21:05 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.2332 loss: 0.8237 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8237 2023/07/25 07:36:56 - mmengine - INFO - Epoch(train) [58][220/940] lr: 1.0000e-03 eta: 12:20:43 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.1364 loss: 0.8091 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 0.8091 2023/07/25 07:37:18 - mmengine - INFO - Epoch(train) [58][240/940] lr: 1.0000e-03 eta: 12:20:21 time: 1.0981 data_time: 0.0143 memory: 15768 grad_norm: 4.2571 loss: 0.7764 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7764 2023/07/25 07:37:40 - mmengine - INFO - Epoch(train) [58][260/940] lr: 1.0000e-03 eta: 12:19:59 time: 1.0982 data_time: 0.0141 memory: 15768 grad_norm: 4.1938 loss: 0.8354 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8354 2023/07/25 07:38:02 - mmengine - INFO - Epoch(train) [58][280/940] lr: 1.0000e-03 eta: 12:19:37 time: 1.1008 data_time: 0.0142 memory: 15768 grad_norm: 4.2761 loss: 0.9105 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9105 2023/07/25 07:38:24 - mmengine - INFO - Epoch(train) [58][300/940] lr: 1.0000e-03 eta: 12:19:14 time: 1.0986 data_time: 0.0139 memory: 15768 grad_norm: 4.2172 loss: 0.9673 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9673 2023/07/25 07:38:46 - mmengine - INFO - Epoch(train) [58][320/940] lr: 1.0000e-03 eta: 12:18:52 time: 1.1008 data_time: 0.0140 memory: 15768 grad_norm: 4.0902 loss: 0.7404 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7404 2023/07/25 07:39:08 - mmengine - INFO - Epoch(train) [58][340/940] lr: 1.0000e-03 eta: 12:18:30 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.1544 loss: 0.8875 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8875 2023/07/25 07:39:30 - mmengine - INFO - Epoch(train) [58][360/940] lr: 1.0000e-03 eta: 12:18:08 time: 1.1001 data_time: 0.0135 memory: 15768 grad_norm: 4.3173 loss: 1.0478 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0478 2023/07/25 07:39:52 - mmengine - INFO - Epoch(train) [58][380/940] lr: 1.0000e-03 eta: 12:17:46 time: 1.1001 data_time: 0.0139 memory: 15768 grad_norm: 4.3044 loss: 1.0619 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0619 2023/07/25 07:40:14 - mmengine - INFO - Epoch(train) [58][400/940] lr: 1.0000e-03 eta: 12:17:23 time: 1.1002 data_time: 0.0141 memory: 15768 grad_norm: 4.2169 loss: 0.8525 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8525 2023/07/25 07:40:36 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 07:40:36 - mmengine - INFO - Epoch(train) [58][420/940] lr: 1.0000e-03 eta: 12:17:01 time: 1.1013 data_time: 0.0139 memory: 15768 grad_norm: 4.3215 loss: 0.8379 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8379 2023/07/25 07:40:58 - mmengine - INFO - Epoch(train) [58][440/940] lr: 1.0000e-03 eta: 12:16:39 time: 1.1010 data_time: 0.0142 memory: 15768 grad_norm: 4.2385 loss: 0.8730 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8730 2023/07/25 07:41:20 - mmengine - INFO - Epoch(train) [58][460/940] lr: 1.0000e-03 eta: 12:16:17 time: 1.1007 data_time: 0.0136 memory: 15768 grad_norm: 4.2469 loss: 0.8430 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8430 2023/07/25 07:41:42 - mmengine - INFO - Epoch(train) [58][480/940] lr: 1.0000e-03 eta: 12:15:55 time: 1.1004 data_time: 0.0137 memory: 15768 grad_norm: 4.2459 loss: 0.9158 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9158 2023/07/25 07:42:04 - mmengine - INFO - Epoch(train) [58][500/940] lr: 1.0000e-03 eta: 12:15:33 time: 1.1016 data_time: 0.0141 memory: 15768 grad_norm: 4.2150 loss: 0.8710 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8710 2023/07/25 07:42:26 - mmengine - INFO - Epoch(train) [58][520/940] lr: 1.0000e-03 eta: 12:15:10 time: 1.0990 data_time: 0.0138 memory: 15768 grad_norm: 4.2354 loss: 0.8597 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8597 2023/07/25 07:42:48 - mmengine - INFO - Epoch(train) [58][540/940] lr: 1.0000e-03 eta: 12:14:48 time: 1.0999 data_time: 0.0141 memory: 15768 grad_norm: 4.2998 loss: 0.9641 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9641 2023/07/25 07:43:10 - mmengine - INFO - Epoch(train) [58][560/940] lr: 1.0000e-03 eta: 12:14:26 time: 1.1044 data_time: 0.0141 memory: 15768 grad_norm: 4.3027 loss: 0.8834 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8834 2023/07/25 07:43:32 - mmengine - INFO - Epoch(train) [58][580/940] lr: 1.0000e-03 eta: 12:14:04 time: 1.1000 data_time: 0.0141 memory: 15768 grad_norm: 4.2806 loss: 0.8729 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8729 2023/07/25 07:43:54 - mmengine - INFO - Epoch(train) [58][600/940] lr: 1.0000e-03 eta: 12:13:42 time: 1.1006 data_time: 0.0143 memory: 15768 grad_norm: 4.2820 loss: 0.9647 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9647 2023/07/25 07:44:16 - mmengine - INFO - Epoch(train) [58][620/940] lr: 1.0000e-03 eta: 12:13:19 time: 1.0988 data_time: 0.0142 memory: 15768 grad_norm: 4.1862 loss: 0.9402 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9402 2023/07/25 07:44:38 - mmengine - INFO - Epoch(train) [58][640/940] lr: 1.0000e-03 eta: 12:12:57 time: 1.0994 data_time: 0.0148 memory: 15768 grad_norm: 4.2151 loss: 0.8476 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8476 2023/07/25 07:45:00 - mmengine - INFO - Epoch(train) [58][660/940] lr: 1.0000e-03 eta: 12:12:35 time: 1.0993 data_time: 0.0141 memory: 15768 grad_norm: 4.3103 loss: 0.8999 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8999 2023/07/25 07:45:22 - mmengine - INFO - Epoch(train) [58][680/940] lr: 1.0000e-03 eta: 12:12:13 time: 1.1003 data_time: 0.0143 memory: 15768 grad_norm: 4.2425 loss: 0.8639 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8639 2023/07/25 07:45:44 - mmengine - INFO - Epoch(train) [58][700/940] lr: 1.0000e-03 eta: 12:11:51 time: 1.0977 data_time: 0.0142 memory: 15768 grad_norm: 4.2030 loss: 0.6859 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6859 2023/07/25 07:46:06 - mmengine - INFO - Epoch(train) [58][720/940] lr: 1.0000e-03 eta: 12:11:28 time: 1.0988 data_time: 0.0143 memory: 15768 grad_norm: 4.2815 loss: 0.8281 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8281 2023/07/25 07:46:28 - mmengine - INFO - Epoch(train) [58][740/940] lr: 1.0000e-03 eta: 12:11:06 time: 1.1007 data_time: 0.0142 memory: 15768 grad_norm: 4.2668 loss: 0.8569 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8569 2023/07/25 07:46:50 - mmengine - INFO - Epoch(train) [58][760/940] lr: 1.0000e-03 eta: 12:10:44 time: 1.1022 data_time: 0.0138 memory: 15768 grad_norm: 4.3404 loss: 0.9811 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9811 2023/07/25 07:47:12 - mmengine - INFO - Epoch(train) [58][780/940] lr: 1.0000e-03 eta: 12:10:22 time: 1.1032 data_time: 0.0137 memory: 15768 grad_norm: 4.2998 loss: 0.8841 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8841 2023/07/25 07:47:34 - mmengine - INFO - Epoch(train) [58][800/940] lr: 1.0000e-03 eta: 12:10:00 time: 1.1010 data_time: 0.0146 memory: 15768 grad_norm: 4.3110 loss: 0.8493 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8493 2023/07/25 07:47:56 - mmengine - INFO - Epoch(train) [58][820/940] lr: 1.0000e-03 eta: 12:09:37 time: 1.0982 data_time: 0.0140 memory: 15768 grad_norm: 4.2462 loss: 0.8690 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8690 2023/07/25 07:48:18 - mmengine - INFO - Epoch(train) [58][840/940] lr: 1.0000e-03 eta: 12:09:15 time: 1.1000 data_time: 0.0143 memory: 15768 grad_norm: 4.2593 loss: 0.8585 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8585 2023/07/25 07:48:40 - mmengine - INFO - Epoch(train) [58][860/940] lr: 1.0000e-03 eta: 12:08:53 time: 1.0989 data_time: 0.0141 memory: 15768 grad_norm: 4.2689 loss: 0.8961 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8961 2023/07/25 07:49:02 - mmengine - INFO - Epoch(train) [58][880/940] lr: 1.0000e-03 eta: 12:08:31 time: 1.1006 data_time: 0.0143 memory: 15768 grad_norm: 4.3964 loss: 0.9809 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9809 2023/07/25 07:49:24 - mmengine - INFO - Epoch(train) [58][900/940] lr: 1.0000e-03 eta: 12:08:09 time: 1.1064 data_time: 0.0138 memory: 15768 grad_norm: 4.2306 loss: 0.8381 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8381 2023/07/25 07:49:46 - mmengine - INFO - Epoch(train) [58][920/940] lr: 1.0000e-03 eta: 12:07:47 time: 1.0994 data_time: 0.0143 memory: 15768 grad_norm: 4.2488 loss: 0.8652 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8652 2023/07/25 07:50:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 07:50:07 - mmengine - INFO - Epoch(train) [58][940/940] lr: 1.0000e-03 eta: 12:07:24 time: 1.0549 data_time: 0.0137 memory: 15768 grad_norm: 4.4985 loss: 0.9185 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9185 2023/07/25 07:50:17 - mmengine - INFO - Epoch(val) [58][20/78] eta: 0:00:28 time: 0.4892 data_time: 0.3318 memory: 2147 2023/07/25 07:50:24 - mmengine - INFO - Epoch(val) [58][40/78] eta: 0:00:16 time: 0.3573 data_time: 0.1999 memory: 2147 2023/07/25 07:50:33 - mmengine - INFO - Epoch(val) [58][60/78] eta: 0:00:07 time: 0.4352 data_time: 0.2784 memory: 2147 2023/07/25 07:50:43 - mmengine - INFO - Epoch(val) [58][78/78] acc/top1: 0.7137 acc/top5: 0.8988 acc/mean1: 0.7136 data_time: 0.2447 time: 0.3990 2023/07/25 07:50:43 - mmengine - INFO - The previous best checkpoint /mnt/data/mmact/lilin/Repos/mmaction2/work_dirs/support_mobileone/tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_47.pth is removed 2023/07/25 07:50:44 - mmengine - INFO - The best checkpoint with 0.7137 acc/top1 at 58 epoch is saved to best_acc_top1_epoch_58.pth. 2023/07/25 07:51:10 - mmengine - INFO - Epoch(train) [59][ 20/940] lr: 1.0000e-03 eta: 12:07:04 time: 1.2613 data_time: 0.1652 memory: 15768 grad_norm: 4.1209 loss: 0.8605 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8605 2023/07/25 07:51:32 - mmengine - INFO - Epoch(train) [59][ 40/940] lr: 1.0000e-03 eta: 12:06:42 time: 1.1025 data_time: 0.0144 memory: 15768 grad_norm: 4.2593 loss: 0.8609 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8609 2023/07/25 07:51:54 - mmengine - INFO - Epoch(train) [59][ 60/940] lr: 1.0000e-03 eta: 12:06:20 time: 1.1014 data_time: 0.0144 memory: 15768 grad_norm: 4.2582 loss: 0.8443 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8443 2023/07/25 07:52:16 - mmengine - INFO - Epoch(train) [59][ 80/940] lr: 1.0000e-03 eta: 12:05:57 time: 1.0996 data_time: 0.0140 memory: 15768 grad_norm: 4.2283 loss: 0.9213 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9213 2023/07/25 07:52:38 - mmengine - INFO - Epoch(train) [59][100/940] lr: 1.0000e-03 eta: 12:05:35 time: 1.1000 data_time: 0.0143 memory: 15768 grad_norm: 4.2553 loss: 0.9915 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9915 2023/07/25 07:53:00 - mmengine - INFO - Epoch(train) [59][120/940] lr: 1.0000e-03 eta: 12:05:13 time: 1.1009 data_time: 0.0143 memory: 15768 grad_norm: 4.1963 loss: 0.7637 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7637 2023/07/25 07:53:22 - mmengine - INFO - Epoch(train) [59][140/940] lr: 1.0000e-03 eta: 12:04:51 time: 1.1019 data_time: 0.0139 memory: 15768 grad_norm: 4.3990 loss: 0.9050 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9050 2023/07/25 07:53:44 - mmengine - INFO - Epoch(train) [59][160/940] lr: 1.0000e-03 eta: 12:04:29 time: 1.0990 data_time: 0.0141 memory: 15768 grad_norm: 4.4035 loss: 0.8118 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8118 2023/07/25 07:54:06 - mmengine - INFO - Epoch(train) [59][180/940] lr: 1.0000e-03 eta: 12:04:06 time: 1.0991 data_time: 0.0139 memory: 15768 grad_norm: 4.1752 loss: 0.8797 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8797 2023/07/25 07:54:28 - mmengine - INFO - Epoch(train) [59][200/940] lr: 1.0000e-03 eta: 12:03:44 time: 1.0989 data_time: 0.0141 memory: 15768 grad_norm: 4.2970 loss: 0.9441 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9441 2023/07/25 07:54:50 - mmengine - INFO - Epoch(train) [59][220/940] lr: 1.0000e-03 eta: 12:03:22 time: 1.0979 data_time: 0.0145 memory: 15768 grad_norm: 4.2533 loss: 0.9979 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9979 2023/07/25 07:55:12 - mmengine - INFO - Epoch(train) [59][240/940] lr: 1.0000e-03 eta: 12:03:00 time: 1.1027 data_time: 0.0143 memory: 15768 grad_norm: 4.1995 loss: 0.8272 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8272 2023/07/25 07:55:34 - mmengine - INFO - Epoch(train) [59][260/940] lr: 1.0000e-03 eta: 12:02:38 time: 1.0972 data_time: 0.0141 memory: 15768 grad_norm: 4.3155 loss: 0.9085 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9085 2023/07/25 07:55:56 - mmengine - INFO - Epoch(train) [59][280/940] lr: 1.0000e-03 eta: 12:02:15 time: 1.1014 data_time: 0.0142 memory: 15768 grad_norm: 4.2161 loss: 1.0853 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0853 2023/07/25 07:56:18 - mmengine - INFO - Epoch(train) [59][300/940] lr: 1.0000e-03 eta: 12:01:53 time: 1.1014 data_time: 0.0136 memory: 15768 grad_norm: 4.3765 loss: 0.8545 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8545 2023/07/25 07:56:40 - mmengine - INFO - Epoch(train) [59][320/940] lr: 1.0000e-03 eta: 12:01:31 time: 1.1005 data_time: 0.0140 memory: 15768 grad_norm: 4.2966 loss: 0.9201 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9201 2023/07/25 07:57:02 - mmengine - INFO - Epoch(train) [59][340/940] lr: 1.0000e-03 eta: 12:01:09 time: 1.1026 data_time: 0.0144 memory: 15768 grad_norm: 4.2464 loss: 0.9787 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.9787 2023/07/25 07:57:24 - mmengine - INFO - Epoch(train) [59][360/940] lr: 1.0000e-03 eta: 12:00:47 time: 1.0992 data_time: 0.0144 memory: 15768 grad_norm: 4.2773 loss: 0.8948 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8948 2023/07/25 07:57:46 - mmengine - INFO - Epoch(train) [59][380/940] lr: 1.0000e-03 eta: 12:00:25 time: 1.0996 data_time: 0.0140 memory: 15768 grad_norm: 4.4304 loss: 0.8265 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8265 2023/07/25 07:58:08 - mmengine - INFO - Epoch(train) [59][400/940] lr: 1.0000e-03 eta: 12:00:02 time: 1.0988 data_time: 0.0142 memory: 15768 grad_norm: 4.2672 loss: 0.9128 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9128 2023/07/25 07:58:30 - mmengine - INFO - Epoch(train) [59][420/940] lr: 1.0000e-03 eta: 11:59:40 time: 1.1033 data_time: 0.0139 memory: 15768 grad_norm: 4.3386 loss: 0.9134 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9134 2023/07/25 07:58:52 - mmengine - INFO - Epoch(train) [59][440/940] lr: 1.0000e-03 eta: 11:59:18 time: 1.1031 data_time: 0.0140 memory: 15768 grad_norm: 4.3866 loss: 0.9280 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9280 2023/07/25 07:59:14 - mmengine - INFO - Epoch(train) [59][460/940] lr: 1.0000e-03 eta: 11:58:56 time: 1.0991 data_time: 0.0139 memory: 15768 grad_norm: 4.1766 loss: 0.9015 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9015 2023/07/25 07:59:36 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 07:59:36 - mmengine - INFO - Epoch(train) [59][480/940] lr: 1.0000e-03 eta: 11:58:34 time: 1.1025 data_time: 0.0133 memory: 15768 grad_norm: 4.3617 loss: 0.9822 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9822 2023/07/25 07:59:58 - mmengine - INFO - Epoch(train) [59][500/940] lr: 1.0000e-03 eta: 11:58:12 time: 1.0998 data_time: 0.0142 memory: 15768 grad_norm: 4.2372 loss: 0.7713 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7713 2023/07/25 08:00:20 - mmengine - INFO - Epoch(train) [59][520/940] lr: 1.0000e-03 eta: 11:57:49 time: 1.0989 data_time: 0.0139 memory: 15768 grad_norm: 4.2333 loss: 0.8722 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8722 2023/07/25 08:00:42 - mmengine - INFO - Epoch(train) [59][540/940] lr: 1.0000e-03 eta: 11:57:27 time: 1.0988 data_time: 0.0145 memory: 15768 grad_norm: 4.2594 loss: 0.9085 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9085 2023/07/25 08:01:04 - mmengine - INFO - Epoch(train) [59][560/940] lr: 1.0000e-03 eta: 11:57:05 time: 1.1013 data_time: 0.0135 memory: 15768 grad_norm: 4.2935 loss: 1.0058 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0058 2023/07/25 08:01:26 - mmengine - INFO - Epoch(train) [59][580/940] lr: 1.0000e-03 eta: 11:56:43 time: 1.1036 data_time: 0.0132 memory: 15768 grad_norm: 4.1434 loss: 0.8876 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8876 2023/07/25 08:01:48 - mmengine - INFO - Epoch(train) [59][600/940] lr: 1.0000e-03 eta: 11:56:21 time: 1.0989 data_time: 0.0139 memory: 15768 grad_norm: 4.2709 loss: 0.8771 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.8771 2023/07/25 08:02:10 - mmengine - INFO - Epoch(train) [59][620/940] lr: 1.0000e-03 eta: 11:55:58 time: 1.0990 data_time: 0.0140 memory: 15768 grad_norm: 4.2200 loss: 0.8484 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8484 2023/07/25 08:02:32 - mmengine - INFO - Epoch(train) [59][640/940] lr: 1.0000e-03 eta: 11:55:36 time: 1.0999 data_time: 0.0145 memory: 15768 grad_norm: 4.2615 loss: 1.0553 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0553 2023/07/25 08:02:54 - mmengine - INFO - Epoch(train) [59][660/940] lr: 1.0000e-03 eta: 11:55:14 time: 1.0999 data_time: 0.0137 memory: 15768 grad_norm: 4.2871 loss: 0.9042 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.9042 2023/07/25 08:03:16 - mmengine - INFO - Epoch(train) [59][680/940] lr: 1.0000e-03 eta: 11:54:52 time: 1.1025 data_time: 0.0144 memory: 15768 grad_norm: 4.2762 loss: 0.9815 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9815 2023/07/25 08:03:38 - mmengine - INFO - Epoch(train) [59][700/940] lr: 1.0000e-03 eta: 11:54:30 time: 1.1015 data_time: 0.0142 memory: 15768 grad_norm: 4.2676 loss: 0.8931 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8931 2023/07/25 08:04:00 - mmengine - INFO - Epoch(train) [59][720/940] lr: 1.0000e-03 eta: 11:54:08 time: 1.1009 data_time: 0.0143 memory: 15768 grad_norm: 4.1675 loss: 0.9296 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9296 2023/07/25 08:04:22 - mmengine - INFO - Epoch(train) [59][740/940] lr: 1.0000e-03 eta: 11:53:45 time: 1.1040 data_time: 0.0147 memory: 15768 grad_norm: 4.3223 loss: 0.8754 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8754 2023/07/25 08:04:44 - mmengine - INFO - Epoch(train) [59][760/940] lr: 1.0000e-03 eta: 11:53:23 time: 1.1009 data_time: 0.0143 memory: 15768 grad_norm: 4.2485 loss: 0.8656 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8656 2023/07/25 08:05:06 - mmengine - INFO - Epoch(train) [59][780/940] lr: 1.0000e-03 eta: 11:53:01 time: 1.1004 data_time: 0.0142 memory: 15768 grad_norm: 4.2400 loss: 0.8329 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8329 2023/07/25 08:05:28 - mmengine - INFO - Epoch(train) [59][800/940] lr: 1.0000e-03 eta: 11:52:39 time: 1.1015 data_time: 0.0140 memory: 15768 grad_norm: 4.3670 loss: 0.8571 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8571 2023/07/25 08:05:50 - mmengine - INFO - Epoch(train) [59][820/940] lr: 1.0000e-03 eta: 11:52:17 time: 1.1022 data_time: 0.0140 memory: 15768 grad_norm: 4.2679 loss: 0.7836 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7836 2023/07/25 08:06:12 - mmengine - INFO - Epoch(train) [59][840/940] lr: 1.0000e-03 eta: 11:51:55 time: 1.1016 data_time: 0.0146 memory: 15768 grad_norm: 4.3269 loss: 0.9738 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9738 2023/07/25 08:06:34 - mmengine - INFO - Epoch(train) [59][860/940] lr: 1.0000e-03 eta: 11:51:32 time: 1.0987 data_time: 0.0139 memory: 15768 grad_norm: 4.2156 loss: 1.0341 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0341 2023/07/25 08:06:56 - mmengine - INFO - Epoch(train) [59][880/940] lr: 1.0000e-03 eta: 11:51:10 time: 1.1011 data_time: 0.0141 memory: 15768 grad_norm: 4.2108 loss: 0.8504 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8504 2023/07/25 08:07:18 - mmengine - INFO - Epoch(train) [59][900/940] lr: 1.0000e-03 eta: 11:50:48 time: 1.1010 data_time: 0.0144 memory: 15768 grad_norm: 4.1935 loss: 0.8958 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8958 2023/07/25 08:07:40 - mmengine - INFO - Epoch(train) [59][920/940] lr: 1.0000e-03 eta: 11:50:26 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.3380 loss: 1.0653 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0653 2023/07/25 08:08:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 08:08:01 - mmengine - INFO - Epoch(train) [59][940/940] lr: 1.0000e-03 eta: 11:50:03 time: 1.0542 data_time: 0.0140 memory: 15768 grad_norm: 4.5263 loss: 0.8802 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 0.8802 2023/07/25 08:08:11 - mmengine - INFO - Epoch(val) [59][20/78] eta: 0:00:27 time: 0.4821 data_time: 0.3245 memory: 2147 2023/07/25 08:08:18 - mmengine - INFO - Epoch(val) [59][40/78] eta: 0:00:15 time: 0.3414 data_time: 0.1840 memory: 2147 2023/07/25 08:08:27 - mmengine - INFO - Epoch(val) [59][60/78] eta: 0:00:07 time: 0.4466 data_time: 0.2892 memory: 2147 2023/07/25 08:08:37 - mmengine - INFO - Epoch(val) [59][78/78] acc/top1: 0.7110 acc/top5: 0.8988 acc/mean1: 0.7109 data_time: 0.2408 time: 0.3954 2023/07/25 08:09:03 - mmengine - INFO - Epoch(train) [60][ 20/940] lr: 1.0000e-03 eta: 11:49:44 time: 1.3022 data_time: 0.1475 memory: 15768 grad_norm: 4.2639 loss: 0.8601 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8601 2023/07/25 08:09:25 - mmengine - INFO - Epoch(train) [60][ 40/940] lr: 1.0000e-03 eta: 11:49:22 time: 1.0997 data_time: 0.0143 memory: 15768 grad_norm: 4.2859 loss: 0.7995 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7995 2023/07/25 08:09:47 - mmengine - INFO - Epoch(train) [60][ 60/940] lr: 1.0000e-03 eta: 11:48:59 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.2675 loss: 0.9535 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9535 2023/07/25 08:10:09 - mmengine - INFO - Epoch(train) [60][ 80/940] lr: 1.0000e-03 eta: 11:48:37 time: 1.1063 data_time: 0.0142 memory: 15768 grad_norm: 4.3222 loss: 0.8264 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8264 2023/07/25 08:10:31 - mmengine - INFO - Epoch(train) [60][100/940] lr: 1.0000e-03 eta: 11:48:15 time: 1.0990 data_time: 0.0141 memory: 15768 grad_norm: 4.3375 loss: 0.8807 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8807 2023/07/25 08:10:53 - mmengine - INFO - Epoch(train) [60][120/940] lr: 1.0000e-03 eta: 11:47:53 time: 1.0987 data_time: 0.0141 memory: 15768 grad_norm: 4.2406 loss: 0.9328 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9328 2023/07/25 08:11:15 - mmengine - INFO - Epoch(train) [60][140/940] lr: 1.0000e-03 eta: 11:47:31 time: 1.0982 data_time: 0.0141 memory: 15768 grad_norm: 4.2101 loss: 0.8331 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8331 2023/07/25 08:11:37 - mmengine - INFO - Epoch(train) [60][160/940] lr: 1.0000e-03 eta: 11:47:08 time: 1.1007 data_time: 0.0144 memory: 15768 grad_norm: 4.3240 loss: 0.8596 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8596 2023/07/25 08:11:59 - mmengine - INFO - Epoch(train) [60][180/940] lr: 1.0000e-03 eta: 11:46:46 time: 1.1020 data_time: 0.0142 memory: 15768 grad_norm: 4.2582 loss: 0.9181 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9181 2023/07/25 08:12:21 - mmengine - INFO - Epoch(train) [60][200/940] lr: 1.0000e-03 eta: 11:46:24 time: 1.0994 data_time: 0.0141 memory: 15768 grad_norm: 4.2220 loss: 1.0118 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0118 2023/07/25 08:12:43 - mmengine - INFO - Epoch(train) [60][220/940] lr: 1.0000e-03 eta: 11:46:02 time: 1.0965 data_time: 0.0140 memory: 15768 grad_norm: 4.3260 loss: 0.8886 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8886 2023/07/25 08:13:05 - mmengine - INFO - Epoch(train) [60][240/940] lr: 1.0000e-03 eta: 11:45:40 time: 1.0983 data_time: 0.0144 memory: 15768 grad_norm: 4.2845 loss: 0.8452 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8452 2023/07/25 08:13:27 - mmengine - INFO - Epoch(train) [60][260/940] lr: 1.0000e-03 eta: 11:45:17 time: 1.0998 data_time: 0.0138 memory: 15768 grad_norm: 4.1814 loss: 0.6991 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6991 2023/07/25 08:13:49 - mmengine - INFO - Epoch(train) [60][280/940] lr: 1.0000e-03 eta: 11:44:55 time: 1.1018 data_time: 0.0144 memory: 15768 grad_norm: 4.2832 loss: 0.9005 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9005 2023/07/25 08:14:11 - mmengine - INFO - Epoch(train) [60][300/940] lr: 1.0000e-03 eta: 11:44:33 time: 1.1004 data_time: 0.0141 memory: 15768 grad_norm: 4.1512 loss: 0.9101 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9101 2023/07/25 08:14:33 - mmengine - INFO - Epoch(train) [60][320/940] lr: 1.0000e-03 eta: 11:44:11 time: 1.0983 data_time: 0.0144 memory: 15768 grad_norm: 4.2914 loss: 0.7903 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7903 2023/07/25 08:14:55 - mmengine - INFO - Epoch(train) [60][340/940] lr: 1.0000e-03 eta: 11:43:49 time: 1.0985 data_time: 0.0142 memory: 15768 grad_norm: 4.2549 loss: 0.8358 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8358 2023/07/25 08:15:17 - mmengine - INFO - Epoch(train) [60][360/940] lr: 1.0000e-03 eta: 11:43:27 time: 1.1028 data_time: 0.0137 memory: 15768 grad_norm: 4.2452 loss: 1.0014 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0014 2023/07/25 08:15:39 - mmengine - INFO - Epoch(train) [60][380/940] lr: 1.0000e-03 eta: 11:43:04 time: 1.0978 data_time: 0.0139 memory: 15768 grad_norm: 4.2970 loss: 0.8739 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8739 2023/07/25 08:16:01 - mmengine - INFO - Epoch(train) [60][400/940] lr: 1.0000e-03 eta: 11:42:42 time: 1.0994 data_time: 0.0141 memory: 15768 grad_norm: 4.2575 loss: 0.8464 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8464 2023/07/25 08:16:23 - mmengine - INFO - Epoch(train) [60][420/940] lr: 1.0000e-03 eta: 11:42:20 time: 1.1038 data_time: 0.0133 memory: 15768 grad_norm: 4.2504 loss: 0.8378 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8378 2023/07/25 08:16:45 - mmengine - INFO - Epoch(train) [60][440/940] lr: 1.0000e-03 eta: 11:41:58 time: 1.1011 data_time: 0.0137 memory: 15768 grad_norm: 4.3052 loss: 1.1235 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1235 2023/07/25 08:17:07 - mmengine - INFO - Epoch(train) [60][460/940] lr: 1.0000e-03 eta: 11:41:36 time: 1.1008 data_time: 0.0139 memory: 15768 grad_norm: 4.2789 loss: 0.7917 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7917 2023/07/25 08:17:29 - mmengine - INFO - Epoch(train) [60][480/940] lr: 1.0000e-03 eta: 11:41:13 time: 1.1013 data_time: 0.0133 memory: 15768 grad_norm: 4.3113 loss: 0.8238 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8238 2023/07/25 08:17:51 - mmengine - INFO - Epoch(train) [60][500/940] lr: 1.0000e-03 eta: 11:40:51 time: 1.1021 data_time: 0.0138 memory: 15768 grad_norm: 4.3292 loss: 0.9211 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9211 2023/07/25 08:18:13 - mmengine - INFO - Epoch(train) [60][520/940] lr: 1.0000e-03 eta: 11:40:29 time: 1.1069 data_time: 0.0141 memory: 15768 grad_norm: 4.1842 loss: 0.9400 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9400 2023/07/25 08:18:35 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 08:18:35 - mmengine - INFO - Epoch(train) [60][540/940] lr: 1.0000e-03 eta: 11:40:07 time: 1.0999 data_time: 0.0141 memory: 15768 grad_norm: 4.2407 loss: 0.8014 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8014 2023/07/25 08:18:57 - mmengine - INFO - Epoch(train) [60][560/940] lr: 1.0000e-03 eta: 11:39:45 time: 1.1004 data_time: 0.0141 memory: 15768 grad_norm: 4.2871 loss: 0.9444 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9444 2023/07/25 08:19:19 - mmengine - INFO - Epoch(train) [60][580/940] lr: 1.0000e-03 eta: 11:39:23 time: 1.1016 data_time: 0.0139 memory: 15768 grad_norm: 4.2221 loss: 0.8794 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8794 2023/07/25 08:19:41 - mmengine - INFO - Epoch(train) [60][600/940] lr: 1.0000e-03 eta: 11:39:01 time: 1.1003 data_time: 0.0135 memory: 15768 grad_norm: 4.2767 loss: 0.9977 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9977 2023/07/25 08:20:03 - mmengine - INFO - Epoch(train) [60][620/940] lr: 1.0000e-03 eta: 11:38:38 time: 1.1005 data_time: 0.0140 memory: 15768 grad_norm: 4.3631 loss: 0.9166 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9166 2023/07/25 08:20:26 - mmengine - INFO - Epoch(train) [60][640/940] lr: 1.0000e-03 eta: 11:38:17 time: 1.1480 data_time: 0.0143 memory: 15768 grad_norm: 4.2642 loss: 0.9170 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9170 2023/07/25 08:20:49 - mmengine - INFO - Epoch(train) [60][660/940] lr: 1.0000e-03 eta: 11:37:55 time: 1.1613 data_time: 0.0138 memory: 15768 grad_norm: 4.2728 loss: 0.7964 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7964 2023/07/25 08:21:13 - mmengine - INFO - Epoch(train) [60][680/940] lr: 1.0000e-03 eta: 11:37:34 time: 1.1578 data_time: 0.0151 memory: 15768 grad_norm: 4.4953 loss: 0.9188 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9188 2023/07/25 08:21:35 - mmengine - INFO - Epoch(train) [60][700/940] lr: 1.0000e-03 eta: 11:37:13 time: 1.1456 data_time: 0.0141 memory: 15768 grad_norm: 4.3995 loss: 0.9657 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9657 2023/07/25 08:21:57 - mmengine - INFO - Epoch(train) [60][720/940] lr: 1.0000e-03 eta: 11:36:50 time: 1.1022 data_time: 0.0147 memory: 15768 grad_norm: 4.3059 loss: 0.8706 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8706 2023/07/25 08:22:20 - mmengine - INFO - Epoch(train) [60][740/940] lr: 1.0000e-03 eta: 11:36:28 time: 1.1025 data_time: 0.0143 memory: 15768 grad_norm: 4.2409 loss: 0.7886 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7886 2023/07/25 08:22:41 - mmengine - INFO - Epoch(train) [60][760/940] lr: 1.0000e-03 eta: 11:36:06 time: 1.0985 data_time: 0.0143 memory: 15768 grad_norm: 4.3011 loss: 0.7860 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7860 2023/07/25 08:23:03 - mmengine - INFO - Epoch(train) [60][780/940] lr: 1.0000e-03 eta: 11:35:44 time: 1.1002 data_time: 0.0147 memory: 15768 grad_norm: 4.3682 loss: 0.7228 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7228 2023/07/25 08:23:26 - mmengine - INFO - Epoch(train) [60][800/940] lr: 1.0000e-03 eta: 11:35:22 time: 1.1007 data_time: 0.0138 memory: 15768 grad_norm: 4.2858 loss: 0.8657 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8657 2023/07/25 08:23:48 - mmengine - INFO - Epoch(train) [60][820/940] lr: 1.0000e-03 eta: 11:34:59 time: 1.1026 data_time: 0.0143 memory: 15768 grad_norm: 4.3351 loss: 0.9589 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9589 2023/07/25 08:24:10 - mmengine - INFO - Epoch(train) [60][840/940] lr: 1.0000e-03 eta: 11:34:37 time: 1.0984 data_time: 0.0138 memory: 15768 grad_norm: 4.3602 loss: 0.9416 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9416 2023/07/25 08:24:32 - mmengine - INFO - Epoch(train) [60][860/940] lr: 1.0000e-03 eta: 11:34:15 time: 1.1035 data_time: 0.0141 memory: 15768 grad_norm: 4.3398 loss: 0.9891 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9891 2023/07/25 08:24:54 - mmengine - INFO - Epoch(train) [60][880/940] lr: 1.0000e-03 eta: 11:33:53 time: 1.0994 data_time: 0.0141 memory: 15768 grad_norm: 4.3986 loss: 0.8612 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8612 2023/07/25 08:25:16 - mmengine - INFO - Epoch(train) [60][900/940] lr: 1.0000e-03 eta: 11:33:31 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.3314 loss: 1.0626 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0626 2023/07/25 08:25:38 - mmengine - INFO - Epoch(train) [60][920/940] lr: 1.0000e-03 eta: 11:33:09 time: 1.0984 data_time: 0.0141 memory: 15768 grad_norm: 4.2851 loss: 0.8159 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8159 2023/07/25 08:25:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 08:25:59 - mmengine - INFO - Epoch(train) [60][940/940] lr: 1.0000e-03 eta: 11:32:46 time: 1.0554 data_time: 0.0141 memory: 15768 grad_norm: 4.5054 loss: 0.8379 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8379 2023/07/25 08:25:59 - mmengine - INFO - Saving checkpoint at 60 epochs 2023/07/25 08:26:10 - mmengine - INFO - Epoch(val) [60][20/78] eta: 0:00:28 time: 0.4836 data_time: 0.3268 memory: 2147 2023/07/25 08:26:17 - mmengine - INFO - Epoch(val) [60][40/78] eta: 0:00:15 time: 0.3562 data_time: 0.1994 memory: 2147 2023/07/25 08:26:25 - mmengine - INFO - Epoch(val) [60][60/78] eta: 0:00:07 time: 0.4339 data_time: 0.2772 memory: 2147 2023/07/25 08:26:35 - mmengine - INFO - Epoch(val) [60][78/78] acc/top1: 0.7123 acc/top5: 0.8989 acc/mean1: 0.7122 data_time: 0.2396 time: 0.3935 2023/07/25 08:27:01 - mmengine - INFO - Epoch(train) [61][ 20/940] lr: 1.0000e-03 eta: 11:32:26 time: 1.2883 data_time: 0.1551 memory: 15768 grad_norm: 4.3411 loss: 1.0438 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0438 2023/07/25 08:27:23 - mmengine - INFO - Epoch(train) [61][ 40/940] lr: 1.0000e-03 eta: 11:32:04 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.2205 loss: 0.9189 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9189 2023/07/25 08:27:45 - mmengine - INFO - Epoch(train) [61][ 60/940] lr: 1.0000e-03 eta: 11:31:42 time: 1.1010 data_time: 0.0138 memory: 15768 grad_norm: 4.3262 loss: 0.8294 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8294 2023/07/25 08:28:07 - mmengine - INFO - Epoch(train) [61][ 80/940] lr: 1.0000e-03 eta: 11:31:20 time: 1.1022 data_time: 0.0141 memory: 15768 grad_norm: 4.2055 loss: 0.7822 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7822 2023/07/25 08:28:29 - mmengine - INFO - Epoch(train) [61][100/940] lr: 1.0000e-03 eta: 11:30:57 time: 1.1002 data_time: 0.0142 memory: 15768 grad_norm: 4.3634 loss: 1.0298 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0298 2023/07/25 08:28:51 - mmengine - INFO - Epoch(train) [61][120/940] lr: 1.0000e-03 eta: 11:30:35 time: 1.1030 data_time: 0.0139 memory: 15768 grad_norm: 4.1384 loss: 0.8370 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8370 2023/07/25 08:29:13 - mmengine - INFO - Epoch(train) [61][140/940] lr: 1.0000e-03 eta: 11:30:13 time: 1.0990 data_time: 0.0142 memory: 15768 grad_norm: 4.3589 loss: 0.9295 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9295 2023/07/25 08:29:35 - mmengine - INFO - Epoch(train) [61][160/940] lr: 1.0000e-03 eta: 11:29:51 time: 1.1035 data_time: 0.0140 memory: 15768 grad_norm: 4.2082 loss: 0.9182 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9182 2023/07/25 08:29:57 - mmengine - INFO - Epoch(train) [61][180/940] lr: 1.0000e-03 eta: 11:29:29 time: 1.1033 data_time: 0.0143 memory: 15768 grad_norm: 4.2860 loss: 0.8510 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8510 2023/07/25 08:30:19 - mmengine - INFO - Epoch(train) [61][200/940] lr: 1.0000e-03 eta: 11:29:07 time: 1.1039 data_time: 0.0143 memory: 15768 grad_norm: 4.3233 loss: 0.8100 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8100 2023/07/25 08:30:41 - mmengine - INFO - Epoch(train) [61][220/940] lr: 1.0000e-03 eta: 11:28:44 time: 1.1003 data_time: 0.0144 memory: 15768 grad_norm: 4.2934 loss: 0.7268 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7268 2023/07/25 08:31:03 - mmengine - INFO - Epoch(train) [61][240/940] lr: 1.0000e-03 eta: 11:28:22 time: 1.1000 data_time: 0.0141 memory: 15768 grad_norm: 4.3611 loss: 0.8349 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8349 2023/07/25 08:31:25 - mmengine - INFO - Epoch(train) [61][260/940] lr: 1.0000e-03 eta: 11:28:00 time: 1.1006 data_time: 0.0144 memory: 15768 grad_norm: 4.3040 loss: 1.0020 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0020 2023/07/25 08:31:47 - mmengine - INFO - Epoch(train) [61][280/940] lr: 1.0000e-03 eta: 11:27:38 time: 1.1006 data_time: 0.0142 memory: 15768 grad_norm: 4.3196 loss: 0.8265 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8265 2023/07/25 08:32:09 - mmengine - INFO - Epoch(train) [61][300/940] lr: 1.0000e-03 eta: 11:27:16 time: 1.1002 data_time: 0.0143 memory: 15768 grad_norm: 4.2490 loss: 0.8007 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8007 2023/07/25 08:32:31 - mmengine - INFO - Epoch(train) [61][320/940] lr: 1.0000e-03 eta: 11:26:54 time: 1.1009 data_time: 0.0141 memory: 15768 grad_norm: 4.3083 loss: 0.9765 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9765 2023/07/25 08:32:53 - mmengine - INFO - Epoch(train) [61][340/940] lr: 1.0000e-03 eta: 11:26:31 time: 1.1002 data_time: 0.0141 memory: 15768 grad_norm: 4.3401 loss: 0.9092 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9092 2023/07/25 08:33:15 - mmengine - INFO - Epoch(train) [61][360/940] lr: 1.0000e-03 eta: 11:26:09 time: 1.0988 data_time: 0.0138 memory: 15768 grad_norm: 4.3360 loss: 0.8606 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8606 2023/07/25 08:33:37 - mmengine - INFO - Epoch(train) [61][380/940] lr: 1.0000e-03 eta: 11:25:47 time: 1.1013 data_time: 0.0137 memory: 15768 grad_norm: 4.3446 loss: 1.0240 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0240 2023/07/25 08:34:00 - mmengine - INFO - Epoch(train) [61][400/940] lr: 1.0000e-03 eta: 11:25:25 time: 1.1028 data_time: 0.0146 memory: 15768 grad_norm: 4.3590 loss: 0.8803 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8803 2023/07/25 08:34:22 - mmengine - INFO - Epoch(train) [61][420/940] lr: 1.0000e-03 eta: 11:25:03 time: 1.1033 data_time: 0.0139 memory: 15768 grad_norm: 4.4131 loss: 1.0166 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0166 2023/07/25 08:34:44 - mmengine - INFO - Epoch(train) [61][440/940] lr: 1.0000e-03 eta: 11:24:41 time: 1.1001 data_time: 0.0141 memory: 15768 grad_norm: 4.3238 loss: 0.8625 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8625 2023/07/25 08:35:06 - mmengine - INFO - Epoch(train) [61][460/940] lr: 1.0000e-03 eta: 11:24:18 time: 1.1007 data_time: 0.0138 memory: 15768 grad_norm: 4.4191 loss: 0.8200 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8200 2023/07/25 08:35:28 - mmengine - INFO - Epoch(train) [61][480/940] lr: 1.0000e-03 eta: 11:23:56 time: 1.1010 data_time: 0.0140 memory: 15768 grad_norm: 4.4111 loss: 0.8462 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8462 2023/07/25 08:35:50 - mmengine - INFO - Epoch(train) [61][500/940] lr: 1.0000e-03 eta: 11:23:34 time: 1.0978 data_time: 0.0139 memory: 15768 grad_norm: 4.3934 loss: 0.9613 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9613 2023/07/25 08:36:12 - mmengine - INFO - Epoch(train) [61][520/940] lr: 1.0000e-03 eta: 11:23:12 time: 1.0974 data_time: 0.0137 memory: 15768 grad_norm: 4.4834 loss: 0.9613 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9613 2023/07/25 08:36:34 - mmengine - INFO - Epoch(train) [61][540/940] lr: 1.0000e-03 eta: 11:22:50 time: 1.0997 data_time: 0.0138 memory: 15768 grad_norm: 4.3209 loss: 0.9392 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9392 2023/07/25 08:36:56 - mmengine - INFO - Epoch(train) [61][560/940] lr: 1.0000e-03 eta: 11:22:27 time: 1.1005 data_time: 0.0143 memory: 15768 grad_norm: 4.2081 loss: 0.8298 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8298 2023/07/25 08:37:18 - mmengine - INFO - Epoch(train) [61][580/940] lr: 1.0000e-03 eta: 11:22:05 time: 1.1013 data_time: 0.0141 memory: 15768 grad_norm: 4.2118 loss: 0.8605 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8605 2023/07/25 08:37:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 08:37:40 - mmengine - INFO - Epoch(train) [61][600/940] lr: 1.0000e-03 eta: 11:21:43 time: 1.0993 data_time: 0.0142 memory: 15768 grad_norm: 4.2501 loss: 0.8842 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8842 2023/07/25 08:38:02 - mmengine - INFO - Epoch(train) [61][620/940] lr: 1.0000e-03 eta: 11:21:21 time: 1.1002 data_time: 0.0139 memory: 15768 grad_norm: 4.3323 loss: 0.8663 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8663 2023/07/25 08:38:24 - mmengine - INFO - Epoch(train) [61][640/940] lr: 1.0000e-03 eta: 11:20:59 time: 1.0988 data_time: 0.0141 memory: 15768 grad_norm: 4.2744 loss: 0.9060 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9060 2023/07/25 08:38:46 - mmengine - INFO - Epoch(train) [61][660/940] lr: 1.0000e-03 eta: 11:20:37 time: 1.1014 data_time: 0.0144 memory: 15768 grad_norm: 4.4448 loss: 0.9080 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9080 2023/07/25 08:39:08 - mmengine - INFO - Epoch(train) [61][680/940] lr: 1.0000e-03 eta: 11:20:14 time: 1.1006 data_time: 0.0139 memory: 15768 grad_norm: 4.2556 loss: 0.9079 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9079 2023/07/25 08:39:30 - mmengine - INFO - Epoch(train) [61][700/940] lr: 1.0000e-03 eta: 11:19:52 time: 1.0983 data_time: 0.0141 memory: 15768 grad_norm: 4.3106 loss: 0.7465 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.7465 2023/07/25 08:39:52 - mmengine - INFO - Epoch(train) [61][720/940] lr: 1.0000e-03 eta: 11:19:30 time: 1.0992 data_time: 0.0142 memory: 15768 grad_norm: 4.2897 loss: 0.7892 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7892 2023/07/25 08:40:14 - mmengine - INFO - Epoch(train) [61][740/940] lr: 1.0000e-03 eta: 11:19:08 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.3461 loss: 1.0048 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0048 2023/07/25 08:40:36 - mmengine - INFO - Epoch(train) [61][760/940] lr: 1.0000e-03 eta: 11:18:46 time: 1.0978 data_time: 0.0141 memory: 15768 grad_norm: 4.2613 loss: 0.8919 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8919 2023/07/25 08:40:58 - mmengine - INFO - Epoch(train) [61][780/940] lr: 1.0000e-03 eta: 11:18:23 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 4.4586 loss: 0.8398 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8398 2023/07/25 08:41:20 - mmengine - INFO - Epoch(train) [61][800/940] lr: 1.0000e-03 eta: 11:18:01 time: 1.1142 data_time: 0.0141 memory: 15768 grad_norm: 4.3124 loss: 0.9699 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9699 2023/07/25 08:41:43 - mmengine - INFO - Epoch(train) [61][820/940] lr: 1.0000e-03 eta: 11:17:40 time: 1.1656 data_time: 0.0138 memory: 15768 grad_norm: 4.3744 loss: 0.7544 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7544 2023/07/25 08:42:07 - mmengine - INFO - Epoch(train) [61][840/940] lr: 1.0000e-03 eta: 11:17:19 time: 1.1682 data_time: 0.0134 memory: 15768 grad_norm: 4.3469 loss: 0.7768 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7768 2023/07/25 08:42:30 - mmengine - INFO - Epoch(train) [61][860/940] lr: 1.0000e-03 eta: 11:16:57 time: 1.1661 data_time: 0.0140 memory: 15768 grad_norm: 4.3514 loss: 0.8495 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8495 2023/07/25 08:42:52 - mmengine - INFO - Epoch(train) [61][880/940] lr: 1.0000e-03 eta: 11:16:35 time: 1.0986 data_time: 0.0140 memory: 15768 grad_norm: 4.3356 loss: 0.7873 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7873 2023/07/25 08:43:14 - mmengine - INFO - Epoch(train) [61][900/940] lr: 1.0000e-03 eta: 11:16:13 time: 1.0996 data_time: 0.0139 memory: 15768 grad_norm: 4.2710 loss: 0.6464 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6464 2023/07/25 08:43:36 - mmengine - INFO - Epoch(train) [61][920/940] lr: 1.0000e-03 eta: 11:15:51 time: 1.1052 data_time: 0.0138 memory: 15768 grad_norm: 4.3507 loss: 0.8831 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8831 2023/07/25 08:43:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 08:43:57 - mmengine - INFO - Epoch(train) [61][940/940] lr: 1.0000e-03 eta: 11:15:28 time: 1.0531 data_time: 0.0138 memory: 15768 grad_norm: 4.5819 loss: 0.7029 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7029 2023/07/25 08:44:07 - mmengine - INFO - Epoch(val) [61][20/78] eta: 0:00:28 time: 0.4900 data_time: 0.3320 memory: 2147 2023/07/25 08:44:14 - mmengine - INFO - Epoch(val) [61][40/78] eta: 0:00:15 time: 0.3444 data_time: 0.1874 memory: 2147 2023/07/25 08:44:23 - mmengine - INFO - Epoch(val) [61][60/78] eta: 0:00:07 time: 0.4451 data_time: 0.2886 memory: 2147 2023/07/25 08:44:32 - mmengine - INFO - Epoch(val) [61][78/78] acc/top1: 0.7129 acc/top5: 0.8987 acc/mean1: 0.7128 data_time: 0.2472 time: 0.4014 2023/07/25 08:44:58 - mmengine - INFO - Epoch(train) [62][ 20/940] lr: 1.0000e-03 eta: 11:15:08 time: 1.2832 data_time: 0.1580 memory: 15768 grad_norm: 4.3552 loss: 0.8793 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8793 2023/07/25 08:45:20 - mmengine - INFO - Epoch(train) [62][ 40/940] lr: 1.0000e-03 eta: 11:14:46 time: 1.1023 data_time: 0.0137 memory: 15768 grad_norm: 4.3668 loss: 0.7914 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7914 2023/07/25 08:45:42 - mmengine - INFO - Epoch(train) [62][ 60/940] lr: 1.0000e-03 eta: 11:14:24 time: 1.1011 data_time: 0.0141 memory: 15768 grad_norm: 4.2246 loss: 0.6961 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6961 2023/07/25 08:46:04 - mmengine - INFO - Epoch(train) [62][ 80/940] lr: 1.0000e-03 eta: 11:14:02 time: 1.1046 data_time: 0.0139 memory: 15768 grad_norm: 4.3207 loss: 0.8833 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8833 2023/07/25 08:46:26 - mmengine - INFO - Epoch(train) [62][100/940] lr: 1.0000e-03 eta: 11:13:40 time: 1.0996 data_time: 0.0141 memory: 15768 grad_norm: 4.3457 loss: 0.7642 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7642 2023/07/25 08:46:48 - mmengine - INFO - Epoch(train) [62][120/940] lr: 1.0000e-03 eta: 11:13:18 time: 1.1001 data_time: 0.0140 memory: 15768 grad_norm: 4.3562 loss: 0.9091 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9091 2023/07/25 08:47:10 - mmengine - INFO - Epoch(train) [62][140/940] lr: 1.0000e-03 eta: 11:12:55 time: 1.1031 data_time: 0.0138 memory: 15768 grad_norm: 4.4830 loss: 0.9319 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9319 2023/07/25 08:47:32 - mmengine - INFO - Epoch(train) [62][160/940] lr: 1.0000e-03 eta: 11:12:33 time: 1.1026 data_time: 0.0140 memory: 15768 grad_norm: 4.3383 loss: 0.7316 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7316 2023/07/25 08:47:54 - mmengine - INFO - Epoch(train) [62][180/940] lr: 1.0000e-03 eta: 11:12:11 time: 1.0979 data_time: 0.0141 memory: 15768 grad_norm: 4.2915 loss: 0.9165 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9165 2023/07/25 08:48:16 - mmengine - INFO - Epoch(train) [62][200/940] lr: 1.0000e-03 eta: 11:11:49 time: 1.1015 data_time: 0.0140 memory: 15768 grad_norm: 4.2809 loss: 0.9646 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9646 2023/07/25 08:48:38 - mmengine - INFO - Epoch(train) [62][220/940] lr: 1.0000e-03 eta: 11:11:27 time: 1.1045 data_time: 0.0145 memory: 15768 grad_norm: 4.2796 loss: 0.8195 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8195 2023/07/25 08:49:00 - mmengine - INFO - Epoch(train) [62][240/940] lr: 1.0000e-03 eta: 11:11:05 time: 1.1019 data_time: 0.0142 memory: 15768 grad_norm: 4.4318 loss: 1.0202 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0202 2023/07/25 08:49:22 - mmengine - INFO - Epoch(train) [62][260/940] lr: 1.0000e-03 eta: 11:10:42 time: 1.0991 data_time: 0.0138 memory: 15768 grad_norm: 4.3656 loss: 0.8673 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8673 2023/07/25 08:49:44 - mmengine - INFO - Epoch(train) [62][280/940] lr: 1.0000e-03 eta: 11:10:20 time: 1.1014 data_time: 0.0140 memory: 15768 grad_norm: 4.4166 loss: 0.8162 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8162 2023/07/25 08:50:06 - mmengine - INFO - Epoch(train) [62][300/940] lr: 1.0000e-03 eta: 11:09:58 time: 1.0994 data_time: 0.0143 memory: 15768 grad_norm: 4.3967 loss: 1.1080 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1080 2023/07/25 08:50:28 - mmengine - INFO - Epoch(train) [62][320/940] lr: 1.0000e-03 eta: 11:09:36 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.3371 loss: 0.9312 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9312 2023/07/25 08:50:50 - mmengine - INFO - Epoch(train) [62][340/940] lr: 1.0000e-03 eta: 11:09:14 time: 1.0987 data_time: 0.0139 memory: 15768 grad_norm: 4.3742 loss: 1.1429 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1429 2023/07/25 08:51:12 - mmengine - INFO - Epoch(train) [62][360/940] lr: 1.0000e-03 eta: 11:08:51 time: 1.0985 data_time: 0.0143 memory: 15768 grad_norm: 4.4550 loss: 0.8596 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8596 2023/07/25 08:51:34 - mmengine - INFO - Epoch(train) [62][380/940] lr: 1.0000e-03 eta: 11:08:29 time: 1.1003 data_time: 0.0142 memory: 15768 grad_norm: 4.3530 loss: 0.9011 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9011 2023/07/25 08:51:56 - mmengine - INFO - Epoch(train) [62][400/940] lr: 1.0000e-03 eta: 11:08:07 time: 1.0969 data_time: 0.0143 memory: 15768 grad_norm: 4.2163 loss: 0.9728 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9728 2023/07/25 08:52:18 - mmengine - INFO - Epoch(train) [62][420/940] lr: 1.0000e-03 eta: 11:07:45 time: 1.0991 data_time: 0.0140 memory: 15768 grad_norm: 4.3347 loss: 0.9262 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9262 2023/07/25 08:52:40 - mmengine - INFO - Epoch(train) [62][440/940] lr: 1.0000e-03 eta: 11:07:23 time: 1.1003 data_time: 0.0138 memory: 15768 grad_norm: 4.3624 loss: 1.0568 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0568 2023/07/25 08:53:02 - mmengine - INFO - Epoch(train) [62][460/940] lr: 1.0000e-03 eta: 11:07:01 time: 1.1000 data_time: 0.0142 memory: 15768 grad_norm: 4.3804 loss: 0.8916 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8916 2023/07/25 08:53:24 - mmengine - INFO - Epoch(train) [62][480/940] lr: 1.0000e-03 eta: 11:06:38 time: 1.1008 data_time: 0.0143 memory: 15768 grad_norm: 4.3520 loss: 0.9847 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9847 2023/07/25 08:53:46 - mmengine - INFO - Epoch(train) [62][500/940] lr: 1.0000e-03 eta: 11:06:16 time: 1.0993 data_time: 0.0139 memory: 15768 grad_norm: 4.3947 loss: 1.0640 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0640 2023/07/25 08:54:08 - mmengine - INFO - Epoch(train) [62][520/940] lr: 1.0000e-03 eta: 11:05:54 time: 1.1012 data_time: 0.0142 memory: 15768 grad_norm: 4.3230 loss: 0.8876 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8876 2023/07/25 08:54:30 - mmengine - INFO - Epoch(train) [62][540/940] lr: 1.0000e-03 eta: 11:05:32 time: 1.1027 data_time: 0.0138 memory: 15768 grad_norm: 4.4637 loss: 0.8468 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8468 2023/07/25 08:54:52 - mmengine - INFO - Epoch(train) [62][560/940] lr: 1.0000e-03 eta: 11:05:10 time: 1.1054 data_time: 0.0131 memory: 15768 grad_norm: 4.3684 loss: 0.8796 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8796 2023/07/25 08:55:14 - mmengine - INFO - Epoch(train) [62][580/940] lr: 1.0000e-03 eta: 11:04:48 time: 1.0993 data_time: 0.0141 memory: 15768 grad_norm: 4.3050 loss: 1.0264 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0264 2023/07/25 08:55:37 - mmengine - INFO - Epoch(train) [62][600/940] lr: 1.0000e-03 eta: 11:04:25 time: 1.1033 data_time: 0.0141 memory: 15768 grad_norm: 4.3963 loss: 0.9392 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9392 2023/07/25 08:55:59 - mmengine - INFO - Epoch(train) [62][620/940] lr: 1.0000e-03 eta: 11:04:03 time: 1.1019 data_time: 0.0139 memory: 15768 grad_norm: 4.3591 loss: 0.8575 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.8575 2023/07/25 08:56:21 - mmengine - INFO - Epoch(train) [62][640/940] lr: 1.0000e-03 eta: 11:03:41 time: 1.0995 data_time: 0.0140 memory: 15768 grad_norm: 4.3456 loss: 0.9139 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9139 2023/07/25 08:56:43 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 08:56:43 - mmengine - INFO - Epoch(train) [62][660/940] lr: 1.0000e-03 eta: 11:03:19 time: 1.0982 data_time: 0.0137 memory: 15768 grad_norm: 4.4883 loss: 0.8421 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8421 2023/07/25 08:57:05 - mmengine - INFO - Epoch(train) [62][680/940] lr: 1.0000e-03 eta: 11:02:57 time: 1.1009 data_time: 0.0134 memory: 15768 grad_norm: 4.4755 loss: 0.9047 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9047 2023/07/25 08:57:27 - mmengine - INFO - Epoch(train) [62][700/940] lr: 1.0000e-03 eta: 11:02:35 time: 1.1009 data_time: 0.0138 memory: 15768 grad_norm: 4.2052 loss: 0.9767 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9767 2023/07/25 08:57:49 - mmengine - INFO - Epoch(train) [62][720/940] lr: 1.0000e-03 eta: 11:02:12 time: 1.0983 data_time: 0.0137 memory: 15768 grad_norm: 4.3543 loss: 0.8875 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8875 2023/07/25 08:58:11 - mmengine - INFO - Epoch(train) [62][740/940] lr: 1.0000e-03 eta: 11:01:50 time: 1.1000 data_time: 0.0142 memory: 15768 grad_norm: 4.4585 loss: 0.9661 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9661 2023/07/25 08:58:33 - mmengine - INFO - Epoch(train) [62][760/940] lr: 1.0000e-03 eta: 11:01:28 time: 1.1012 data_time: 0.0140 memory: 15768 grad_norm: 4.3778 loss: 1.0102 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0102 2023/07/25 08:58:55 - mmengine - INFO - Epoch(train) [62][780/940] lr: 1.0000e-03 eta: 11:01:06 time: 1.1027 data_time: 0.0141 memory: 15768 grad_norm: 4.3764 loss: 0.9480 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9480 2023/07/25 08:59:17 - mmengine - INFO - Epoch(train) [62][800/940] lr: 1.0000e-03 eta: 11:00:44 time: 1.0996 data_time: 0.0139 memory: 15768 grad_norm: 4.2722 loss: 0.7722 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7722 2023/07/25 08:59:39 - mmengine - INFO - Epoch(train) [62][820/940] lr: 1.0000e-03 eta: 11:00:21 time: 1.0981 data_time: 0.0142 memory: 15768 grad_norm: 4.3666 loss: 0.7811 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7811 2023/07/25 09:00:01 - mmengine - INFO - Epoch(train) [62][840/940] lr: 1.0000e-03 eta: 10:59:59 time: 1.1015 data_time: 0.0135 memory: 15768 grad_norm: 4.3746 loss: 0.9353 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9353 2023/07/25 09:00:23 - mmengine - INFO - Epoch(train) [62][860/940] lr: 1.0000e-03 eta: 10:59:37 time: 1.0978 data_time: 0.0139 memory: 15768 grad_norm: 4.3380 loss: 0.9055 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9055 2023/07/25 09:00:45 - mmengine - INFO - Epoch(train) [62][880/940] lr: 1.0000e-03 eta: 10:59:15 time: 1.1063 data_time: 0.0142 memory: 15768 grad_norm: 4.4135 loss: 1.0151 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0151 2023/07/25 09:01:07 - mmengine - INFO - Epoch(train) [62][900/940] lr: 1.0000e-03 eta: 10:58:53 time: 1.1006 data_time: 0.0139 memory: 15768 grad_norm: 4.2348 loss: 0.8347 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8347 2023/07/25 09:01:29 - mmengine - INFO - Epoch(train) [62][920/940] lr: 1.0000e-03 eta: 10:58:31 time: 1.0979 data_time: 0.0142 memory: 15768 grad_norm: 4.3381 loss: 0.8322 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8322 2023/07/25 09:01:50 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 09:01:50 - mmengine - INFO - Epoch(train) [62][940/940] lr: 1.0000e-03 eta: 10:58:08 time: 1.0543 data_time: 0.0139 memory: 15768 grad_norm: 4.6713 loss: 0.9202 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9202 2023/07/25 09:01:59 - mmengine - INFO - Epoch(val) [62][20/78] eta: 0:00:27 time: 0.4817 data_time: 0.3239 memory: 2147 2023/07/25 09:02:07 - mmengine - INFO - Epoch(val) [62][40/78] eta: 0:00:15 time: 0.3602 data_time: 0.2030 memory: 2147 2023/07/25 09:02:15 - mmengine - INFO - Epoch(val) [62][60/78] eta: 0:00:07 time: 0.4198 data_time: 0.2630 memory: 2147 2023/07/25 09:02:25 - mmengine - INFO - Epoch(val) [62][78/78] acc/top1: 0.7128 acc/top5: 0.8982 acc/mean1: 0.7127 data_time: 0.2377 time: 0.3921 2023/07/25 09:02:52 - mmengine - INFO - Epoch(train) [63][ 20/940] lr: 1.0000e-03 eta: 10:57:49 time: 1.3443 data_time: 0.1542 memory: 15768 grad_norm: 4.3727 loss: 0.6943 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6943 2023/07/25 09:03:16 - mmengine - INFO - Epoch(train) [63][ 40/940] lr: 1.0000e-03 eta: 10:57:27 time: 1.1672 data_time: 0.0141 memory: 15768 grad_norm: 4.3248 loss: 0.8869 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8869 2023/07/25 09:03:38 - mmengine - INFO - Epoch(train) [63][ 60/940] lr: 1.0000e-03 eta: 10:57:05 time: 1.1094 data_time: 0.0141 memory: 15768 grad_norm: 4.3647 loss: 0.7948 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7948 2023/07/25 09:04:00 - mmengine - INFO - Epoch(train) [63][ 80/940] lr: 1.0000e-03 eta: 10:56:43 time: 1.0997 data_time: 0.0140 memory: 15768 grad_norm: 4.2524 loss: 0.9388 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9388 2023/07/25 09:04:22 - mmengine - INFO - Epoch(train) [63][100/940] lr: 1.0000e-03 eta: 10:56:21 time: 1.1009 data_time: 0.0135 memory: 15768 grad_norm: 4.3248 loss: 0.9343 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9343 2023/07/25 09:04:44 - mmengine - INFO - Epoch(train) [63][120/940] lr: 1.0000e-03 eta: 10:55:59 time: 1.1012 data_time: 0.0141 memory: 15768 grad_norm: 4.3420 loss: 0.9078 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9078 2023/07/25 09:05:06 - mmengine - INFO - Epoch(train) [63][140/940] lr: 1.0000e-03 eta: 10:55:37 time: 1.0967 data_time: 0.0142 memory: 15768 grad_norm: 4.4133 loss: 0.7845 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7845 2023/07/25 09:05:28 - mmengine - INFO - Epoch(train) [63][160/940] lr: 1.0000e-03 eta: 10:55:14 time: 1.1000 data_time: 0.0131 memory: 15768 grad_norm: 4.3288 loss: 0.8001 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8001 2023/07/25 09:05:50 - mmengine - INFO - Epoch(train) [63][180/940] lr: 1.0000e-03 eta: 10:54:52 time: 1.1029 data_time: 0.0136 memory: 15768 grad_norm: 4.3571 loss: 0.7855 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7855 2023/07/25 09:06:12 - mmengine - INFO - Epoch(train) [63][200/940] lr: 1.0000e-03 eta: 10:54:30 time: 1.1027 data_time: 0.0136 memory: 15768 grad_norm: 4.2186 loss: 0.8889 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8889 2023/07/25 09:06:34 - mmengine - INFO - Epoch(train) [63][220/940] lr: 1.0000e-03 eta: 10:54:08 time: 1.0978 data_time: 0.0137 memory: 15768 grad_norm: 4.3629 loss: 0.7859 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7859 2023/07/25 09:06:56 - mmengine - INFO - Epoch(train) [63][240/940] lr: 1.0000e-03 eta: 10:53:46 time: 1.0994 data_time: 0.0140 memory: 15768 grad_norm: 4.3682 loss: 0.9115 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9115 2023/07/25 09:07:18 - mmengine - INFO - Epoch(train) [63][260/940] lr: 1.0000e-03 eta: 10:53:23 time: 1.0981 data_time: 0.0138 memory: 15768 grad_norm: 4.4070 loss: 0.8373 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8373 2023/07/25 09:07:40 - mmengine - INFO - Epoch(train) [63][280/940] lr: 1.0000e-03 eta: 10:53:01 time: 1.0987 data_time: 0.0139 memory: 15768 grad_norm: 4.4269 loss: 0.9572 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9572 2023/07/25 09:08:02 - mmengine - INFO - Epoch(train) [63][300/940] lr: 1.0000e-03 eta: 10:52:39 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.3725 loss: 1.0590 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0590 2023/07/25 09:08:24 - mmengine - INFO - Epoch(train) [63][320/940] lr: 1.0000e-03 eta: 10:52:17 time: 1.1027 data_time: 0.0142 memory: 15768 grad_norm: 4.3215 loss: 0.8409 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8409 2023/07/25 09:08:46 - mmengine - INFO - Epoch(train) [63][340/940] lr: 1.0000e-03 eta: 10:51:55 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.3470 loss: 0.9798 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9798 2023/07/25 09:09:08 - mmengine - INFO - Epoch(train) [63][360/940] lr: 1.0000e-03 eta: 10:51:33 time: 1.1013 data_time: 0.0143 memory: 15768 grad_norm: 4.2105 loss: 0.7401 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7401 2023/07/25 09:09:30 - mmengine - INFO - Epoch(train) [63][380/940] lr: 1.0000e-03 eta: 10:51:10 time: 1.0989 data_time: 0.0137 memory: 15768 grad_norm: 4.3217 loss: 0.9597 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9597 2023/07/25 09:09:52 - mmengine - INFO - Epoch(train) [63][400/940] lr: 1.0000e-03 eta: 10:50:48 time: 1.1017 data_time: 0.0142 memory: 15768 grad_norm: 4.2932 loss: 0.9171 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9171 2023/07/25 09:10:14 - mmengine - INFO - Epoch(train) [63][420/940] lr: 1.0000e-03 eta: 10:50:26 time: 1.1006 data_time: 0.0142 memory: 15768 grad_norm: 4.4604 loss: 0.7755 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7755 2023/07/25 09:10:36 - mmengine - INFO - Epoch(train) [63][440/940] lr: 1.0000e-03 eta: 10:50:04 time: 1.0999 data_time: 0.0141 memory: 15768 grad_norm: 4.3267 loss: 0.7799 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7799 2023/07/25 09:10:58 - mmengine - INFO - Epoch(train) [63][460/940] lr: 1.0000e-03 eta: 10:49:42 time: 1.1012 data_time: 0.0137 memory: 15768 grad_norm: 4.3819 loss: 0.8324 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8324 2023/07/25 09:11:20 - mmengine - INFO - Epoch(train) [63][480/940] lr: 1.0000e-03 eta: 10:49:20 time: 1.1016 data_time: 0.0139 memory: 15768 grad_norm: 4.4240 loss: 0.7943 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7943 2023/07/25 09:11:42 - mmengine - INFO - Epoch(train) [63][500/940] lr: 1.0000e-03 eta: 10:48:57 time: 1.1008 data_time: 0.0137 memory: 15768 grad_norm: 4.2447 loss: 0.9489 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9489 2023/07/25 09:12:04 - mmengine - INFO - Epoch(train) [63][520/940] lr: 1.0000e-03 eta: 10:48:35 time: 1.0979 data_time: 0.0141 memory: 15768 grad_norm: 4.3099 loss: 0.8098 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8098 2023/07/25 09:12:26 - mmengine - INFO - Epoch(train) [63][540/940] lr: 1.0000e-03 eta: 10:48:13 time: 1.0986 data_time: 0.0143 memory: 15768 grad_norm: 4.3247 loss: 0.8472 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8472 2023/07/25 09:12:48 - mmengine - INFO - Epoch(train) [63][560/940] lr: 1.0000e-03 eta: 10:47:51 time: 1.0986 data_time: 0.0142 memory: 15768 grad_norm: 4.3195 loss: 0.8865 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8865 2023/07/25 09:13:10 - mmengine - INFO - Epoch(train) [63][580/940] lr: 1.0000e-03 eta: 10:47:29 time: 1.0980 data_time: 0.0142 memory: 15768 grad_norm: 4.2992 loss: 0.8211 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8211 2023/07/25 09:13:32 - mmengine - INFO - Epoch(train) [63][600/940] lr: 1.0000e-03 eta: 10:47:07 time: 1.1028 data_time: 0.0142 memory: 15768 grad_norm: 4.4551 loss: 0.8365 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8365 2023/07/25 09:13:54 - mmengine - INFO - Epoch(train) [63][620/940] lr: 1.0000e-03 eta: 10:46:44 time: 1.1017 data_time: 0.0142 memory: 15768 grad_norm: 4.3881 loss: 0.8458 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8458 2023/07/25 09:14:16 - mmengine - INFO - Epoch(train) [63][640/940] lr: 1.0000e-03 eta: 10:46:22 time: 1.1004 data_time: 0.0141 memory: 15768 grad_norm: 4.3254 loss: 0.8133 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8133 2023/07/25 09:14:38 - mmengine - INFO - Epoch(train) [63][660/940] lr: 1.0000e-03 eta: 10:46:00 time: 1.1022 data_time: 0.0141 memory: 15768 grad_norm: 4.4213 loss: 0.9661 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9661 2023/07/25 09:15:00 - mmengine - INFO - Epoch(train) [63][680/940] lr: 1.0000e-03 eta: 10:45:38 time: 1.1026 data_time: 0.0144 memory: 15768 grad_norm: 4.3933 loss: 0.9279 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9279 2023/07/25 09:15:22 - mmengine - INFO - Epoch(train) [63][700/940] lr: 1.0000e-03 eta: 10:45:16 time: 1.1028 data_time: 0.0137 memory: 15768 grad_norm: 4.5515 loss: 0.7678 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7678 2023/07/25 09:15:44 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 09:15:44 - mmengine - INFO - Epoch(train) [63][720/940] lr: 1.0000e-03 eta: 10:44:54 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.3013 loss: 0.7843 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7843 2023/07/25 09:16:06 - mmengine - INFO - Epoch(train) [63][740/940] lr: 1.0000e-03 eta: 10:44:31 time: 1.0991 data_time: 0.0142 memory: 15768 grad_norm: 4.5287 loss: 0.9995 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9995 2023/07/25 09:16:28 - mmengine - INFO - Epoch(train) [63][760/940] lr: 1.0000e-03 eta: 10:44:09 time: 1.0985 data_time: 0.0143 memory: 15768 grad_norm: 4.4694 loss: 0.7283 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7283 2023/07/25 09:16:50 - mmengine - INFO - Epoch(train) [63][780/940] lr: 1.0000e-03 eta: 10:43:47 time: 1.1035 data_time: 0.0139 memory: 15768 grad_norm: 4.4432 loss: 0.9262 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9262 2023/07/25 09:17:12 - mmengine - INFO - Epoch(train) [63][800/940] lr: 1.0000e-03 eta: 10:43:25 time: 1.0995 data_time: 0.0139 memory: 15768 grad_norm: 4.4274 loss: 0.8364 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8364 2023/07/25 09:17:34 - mmengine - INFO - Epoch(train) [63][820/940] lr: 1.0000e-03 eta: 10:43:03 time: 1.1007 data_time: 0.0140 memory: 15768 grad_norm: 4.3880 loss: 0.8356 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8356 2023/07/25 09:17:56 - mmengine - INFO - Epoch(train) [63][840/940] lr: 1.0000e-03 eta: 10:42:40 time: 1.0990 data_time: 0.0143 memory: 15768 grad_norm: 4.5480 loss: 0.9603 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9603 2023/07/25 09:18:18 - mmengine - INFO - Epoch(train) [63][860/940] lr: 1.0000e-03 eta: 10:42:18 time: 1.1004 data_time: 0.0136 memory: 15768 grad_norm: 4.4034 loss: 0.8910 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8910 2023/07/25 09:18:40 - mmengine - INFO - Epoch(train) [63][880/940] lr: 1.0000e-03 eta: 10:41:56 time: 1.0997 data_time: 0.0139 memory: 15768 grad_norm: 4.4123 loss: 0.8814 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8814 2023/07/25 09:19:02 - mmengine - INFO - Epoch(train) [63][900/940] lr: 1.0000e-03 eta: 10:41:34 time: 1.0977 data_time: 0.0138 memory: 15768 grad_norm: 4.4289 loss: 0.8473 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8473 2023/07/25 09:19:24 - mmengine - INFO - Epoch(train) [63][920/940] lr: 1.0000e-03 eta: 10:41:12 time: 1.0991 data_time: 0.0143 memory: 15768 grad_norm: 4.3797 loss: 0.8060 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8060 2023/07/25 09:19:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 09:19:45 - mmengine - INFO - Epoch(train) [63][940/940] lr: 1.0000e-03 eta: 10:40:49 time: 1.0538 data_time: 0.0138 memory: 15768 grad_norm: 4.6414 loss: 0.9792 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9792 2023/07/25 09:19:45 - mmengine - INFO - Saving checkpoint at 63 epochs 2023/07/25 09:19:56 - mmengine - INFO - Epoch(val) [63][20/78] eta: 0:00:28 time: 0.4888 data_time: 0.3314 memory: 2147 2023/07/25 09:20:02 - mmengine - INFO - Epoch(val) [63][40/78] eta: 0:00:15 time: 0.3262 data_time: 0.1698 memory: 2147 2023/07/25 09:20:11 - mmengine - INFO - Epoch(val) [63][60/78] eta: 0:00:07 time: 0.4379 data_time: 0.2812 memory: 2147 2023/07/25 09:20:21 - mmengine - INFO - Epoch(val) [63][78/78] acc/top1: 0.7110 acc/top5: 0.8991 acc/mean1: 0.7108 data_time: 0.2370 time: 0.3910 2023/07/25 09:20:46 - mmengine - INFO - Epoch(train) [64][ 20/940] lr: 1.0000e-03 eta: 10:40:29 time: 1.2847 data_time: 0.1605 memory: 15768 grad_norm: 4.3143 loss: 0.9235 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9235 2023/07/25 09:21:08 - mmengine - INFO - Epoch(train) [64][ 40/940] lr: 1.0000e-03 eta: 10:40:07 time: 1.1003 data_time: 0.0137 memory: 15768 grad_norm: 4.3659 loss: 0.8530 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8530 2023/07/25 09:21:30 - mmengine - INFO - Epoch(train) [64][ 60/940] lr: 1.0000e-03 eta: 10:39:45 time: 1.1010 data_time: 0.0145 memory: 15768 grad_norm: 4.5046 loss: 1.1147 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1147 2023/07/25 09:21:52 - mmengine - INFO - Epoch(train) [64][ 80/940] lr: 1.0000e-03 eta: 10:39:23 time: 1.1003 data_time: 0.0136 memory: 15768 grad_norm: 4.3654 loss: 0.8635 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8635 2023/07/25 09:22:14 - mmengine - INFO - Epoch(train) [64][100/940] lr: 1.0000e-03 eta: 10:39:00 time: 1.0989 data_time: 0.0142 memory: 15768 grad_norm: 4.4570 loss: 1.0171 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0171 2023/07/25 09:22:36 - mmengine - INFO - Epoch(train) [64][120/940] lr: 1.0000e-03 eta: 10:38:38 time: 1.1030 data_time: 0.0138 memory: 15768 grad_norm: 4.4379 loss: 0.8539 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8539 2023/07/25 09:22:59 - mmengine - INFO - Epoch(train) [64][140/940] lr: 1.0000e-03 eta: 10:38:16 time: 1.1025 data_time: 0.0143 memory: 15768 grad_norm: 4.2359 loss: 0.8049 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8049 2023/07/25 09:23:21 - mmengine - INFO - Epoch(train) [64][160/940] lr: 1.0000e-03 eta: 10:37:54 time: 1.0983 data_time: 0.0140 memory: 15768 grad_norm: 4.2925 loss: 0.8988 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8988 2023/07/25 09:23:43 - mmengine - INFO - Epoch(train) [64][180/940] lr: 1.0000e-03 eta: 10:37:32 time: 1.0996 data_time: 0.0139 memory: 15768 grad_norm: 4.3158 loss: 0.9307 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9307 2023/07/25 09:24:05 - mmengine - INFO - Epoch(train) [64][200/940] lr: 1.0000e-03 eta: 10:37:10 time: 1.1039 data_time: 0.0141 memory: 15768 grad_norm: 4.3148 loss: 0.7329 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7329 2023/07/25 09:24:27 - mmengine - INFO - Epoch(train) [64][220/940] lr: 1.0000e-03 eta: 10:36:47 time: 1.1007 data_time: 0.0136 memory: 15768 grad_norm: 4.2800 loss: 0.8384 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8384 2023/07/25 09:24:49 - mmengine - INFO - Epoch(train) [64][240/940] lr: 1.0000e-03 eta: 10:36:25 time: 1.1034 data_time: 0.0145 memory: 15768 grad_norm: 4.2992 loss: 0.8426 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8426 2023/07/25 09:25:11 - mmengine - INFO - Epoch(train) [64][260/940] lr: 1.0000e-03 eta: 10:36:03 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 4.3060 loss: 0.7751 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7751 2023/07/25 09:25:33 - mmengine - INFO - Epoch(train) [64][280/940] lr: 1.0000e-03 eta: 10:35:41 time: 1.1027 data_time: 0.0141 memory: 15768 grad_norm: 4.3673 loss: 0.9291 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9291 2023/07/25 09:25:55 - mmengine - INFO - Epoch(train) [64][300/940] lr: 1.0000e-03 eta: 10:35:19 time: 1.0996 data_time: 0.0136 memory: 15768 grad_norm: 4.3817 loss: 0.8551 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8551 2023/07/25 09:26:17 - mmengine - INFO - Epoch(train) [64][320/940] lr: 1.0000e-03 eta: 10:34:57 time: 1.1031 data_time: 0.0145 memory: 15768 grad_norm: 4.4456 loss: 0.8641 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8641 2023/07/25 09:26:39 - mmengine - INFO - Epoch(train) [64][340/940] lr: 1.0000e-03 eta: 10:34:34 time: 1.1012 data_time: 0.0138 memory: 15768 grad_norm: 4.3590 loss: 0.8427 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.8427 2023/07/25 09:27:01 - mmengine - INFO - Epoch(train) [64][360/940] lr: 1.0000e-03 eta: 10:34:12 time: 1.1018 data_time: 0.0140 memory: 15768 grad_norm: 4.3935 loss: 0.8186 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8186 2023/07/25 09:27:23 - mmengine - INFO - Epoch(train) [64][380/940] lr: 1.0000e-03 eta: 10:33:50 time: 1.1046 data_time: 0.0140 memory: 15768 grad_norm: 4.4188 loss: 0.8426 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8426 2023/07/25 09:27:45 - mmengine - INFO - Epoch(train) [64][400/940] lr: 1.0000e-03 eta: 10:33:28 time: 1.1037 data_time: 0.0130 memory: 15768 grad_norm: 4.4086 loss: 0.8887 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8887 2023/07/25 09:28:07 - mmengine - INFO - Epoch(train) [64][420/940] lr: 1.0000e-03 eta: 10:33:06 time: 1.1004 data_time: 0.0142 memory: 15768 grad_norm: 4.4225 loss: 0.9612 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9612 2023/07/25 09:28:29 - mmengine - INFO - Epoch(train) [64][440/940] lr: 1.0000e-03 eta: 10:32:44 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.4782 loss: 0.9676 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9676 2023/07/25 09:28:51 - mmengine - INFO - Epoch(train) [64][460/940] lr: 1.0000e-03 eta: 10:32:22 time: 1.1028 data_time: 0.0136 memory: 15768 grad_norm: 4.4527 loss: 0.8303 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8303 2023/07/25 09:29:13 - mmengine - INFO - Epoch(train) [64][480/940] lr: 1.0000e-03 eta: 10:31:59 time: 1.1005 data_time: 0.0141 memory: 15768 grad_norm: 4.5129 loss: 0.7811 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7811 2023/07/25 09:29:35 - mmengine - INFO - Epoch(train) [64][500/940] lr: 1.0000e-03 eta: 10:31:37 time: 1.1018 data_time: 0.0135 memory: 15768 grad_norm: 4.4175 loss: 0.7775 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7775 2023/07/25 09:29:57 - mmengine - INFO - Epoch(train) [64][520/940] lr: 1.0000e-03 eta: 10:31:15 time: 1.1015 data_time: 0.0147 memory: 15768 grad_norm: 4.4246 loss: 0.8228 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8228 2023/07/25 09:30:19 - mmengine - INFO - Epoch(train) [64][540/940] lr: 1.0000e-03 eta: 10:30:53 time: 1.1007 data_time: 0.0138 memory: 15768 grad_norm: 4.2623 loss: 0.8063 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8063 2023/07/25 09:30:41 - mmengine - INFO - Epoch(train) [64][560/940] lr: 1.0000e-03 eta: 10:30:31 time: 1.1002 data_time: 0.0141 memory: 15768 grad_norm: 4.4389 loss: 0.8761 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.8761 2023/07/25 09:31:03 - mmengine - INFO - Epoch(train) [64][580/940] lr: 1.0000e-03 eta: 10:30:09 time: 1.1020 data_time: 0.0139 memory: 15768 grad_norm: 4.3738 loss: 0.9273 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9273 2023/07/25 09:31:25 - mmengine - INFO - Epoch(train) [64][600/940] lr: 1.0000e-03 eta: 10:29:46 time: 1.0989 data_time: 0.0144 memory: 15768 grad_norm: 4.3719 loss: 0.8880 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8880 2023/07/25 09:31:47 - mmengine - INFO - Epoch(train) [64][620/940] lr: 1.0000e-03 eta: 10:29:24 time: 1.1041 data_time: 0.0138 memory: 15768 grad_norm: 4.3591 loss: 0.7818 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.7818 2023/07/25 09:32:09 - mmengine - INFO - Epoch(train) [64][640/940] lr: 1.0000e-03 eta: 10:29:02 time: 1.0998 data_time: 0.0136 memory: 15768 grad_norm: 4.4011 loss: 0.8695 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8695 2023/07/25 09:32:31 - mmengine - INFO - Epoch(train) [64][660/940] lr: 1.0000e-03 eta: 10:28:40 time: 1.0997 data_time: 0.0142 memory: 15768 grad_norm: 4.4485 loss: 0.8895 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8895 2023/07/25 09:32:53 - mmengine - INFO - Epoch(train) [64][680/940] lr: 1.0000e-03 eta: 10:28:18 time: 1.0976 data_time: 0.0143 memory: 15768 grad_norm: 4.3248 loss: 0.7989 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7989 2023/07/25 09:33:15 - mmengine - INFO - Epoch(train) [64][700/940] lr: 1.0000e-03 eta: 10:27:56 time: 1.0986 data_time: 0.0145 memory: 15768 grad_norm: 4.5124 loss: 0.8628 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8628 2023/07/25 09:33:37 - mmengine - INFO - Epoch(train) [64][720/940] lr: 1.0000e-03 eta: 10:27:33 time: 1.1008 data_time: 0.0139 memory: 15768 grad_norm: 4.4893 loss: 0.8332 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8332 2023/07/25 09:33:59 - mmengine - INFO - Epoch(train) [64][740/940] lr: 1.0000e-03 eta: 10:27:11 time: 1.1024 data_time: 0.0142 memory: 15768 grad_norm: 4.4201 loss: 0.9442 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9442 2023/07/25 09:34:21 - mmengine - INFO - Epoch(train) [64][760/940] lr: 1.0000e-03 eta: 10:26:49 time: 1.1027 data_time: 0.0141 memory: 15768 grad_norm: 4.4909 loss: 0.9230 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9230 2023/07/25 09:34:43 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 09:34:43 - mmengine - INFO - Epoch(train) [64][780/940] lr: 1.0000e-03 eta: 10:26:27 time: 1.1012 data_time: 0.0139 memory: 15768 grad_norm: 4.4553 loss: 0.8762 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8762 2023/07/25 09:35:05 - mmengine - INFO - Epoch(train) [64][800/940] lr: 1.0000e-03 eta: 10:26:05 time: 1.0998 data_time: 0.0140 memory: 15768 grad_norm: 4.3532 loss: 1.0651 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0651 2023/07/25 09:35:27 - mmengine - INFO - Epoch(train) [64][820/940] lr: 1.0000e-03 eta: 10:25:43 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.4468 loss: 1.0300 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0300 2023/07/25 09:35:49 - mmengine - INFO - Epoch(train) [64][840/940] lr: 1.0000e-03 eta: 10:25:20 time: 1.1009 data_time: 0.0143 memory: 15768 grad_norm: 4.3931 loss: 0.9584 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9584 2023/07/25 09:36:12 - mmengine - INFO - Epoch(train) [64][860/940] lr: 1.0000e-03 eta: 10:24:58 time: 1.1038 data_time: 0.0142 memory: 15768 grad_norm: 4.4717 loss: 0.9208 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9208 2023/07/25 09:36:34 - mmengine - INFO - Epoch(train) [64][880/940] lr: 1.0000e-03 eta: 10:24:36 time: 1.1068 data_time: 0.0140 memory: 15768 grad_norm: 4.3505 loss: 0.9675 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9675 2023/07/25 09:36:56 - mmengine - INFO - Epoch(train) [64][900/940] lr: 1.0000e-03 eta: 10:24:14 time: 1.1015 data_time: 0.0138 memory: 15768 grad_norm: 4.2947 loss: 0.7819 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7819 2023/07/25 09:37:18 - mmengine - INFO - Epoch(train) [64][920/940] lr: 1.0000e-03 eta: 10:23:52 time: 1.1025 data_time: 0.0139 memory: 15768 grad_norm: 4.4723 loss: 0.9118 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9118 2023/07/25 09:37:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 09:37:39 - mmengine - INFO - Epoch(train) [64][940/940] lr: 1.0000e-03 eta: 10:23:29 time: 1.0562 data_time: 0.0135 memory: 15768 grad_norm: 4.7278 loss: 0.8163 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.8163 2023/07/25 09:37:49 - mmengine - INFO - Epoch(val) [64][20/78] eta: 0:00:27 time: 0.4824 data_time: 0.3253 memory: 2147 2023/07/25 09:37:56 - mmengine - INFO - Epoch(val) [64][40/78] eta: 0:00:15 time: 0.3494 data_time: 0.1931 memory: 2147 2023/07/25 09:38:04 - mmengine - INFO - Epoch(val) [64][60/78] eta: 0:00:07 time: 0.4293 data_time: 0.2725 memory: 2147 2023/07/25 09:38:15 - mmengine - INFO - Epoch(val) [64][78/78] acc/top1: 0.7125 acc/top5: 0.8995 acc/mean1: 0.7125 data_time: 0.2382 time: 0.3920 2023/07/25 09:38:41 - mmengine - INFO - Epoch(train) [65][ 20/940] lr: 1.0000e-03 eta: 10:23:09 time: 1.2926 data_time: 0.1551 memory: 15768 grad_norm: 4.4301 loss: 0.7853 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7853 2023/07/25 09:39:03 - mmengine - INFO - Epoch(train) [65][ 40/940] lr: 1.0000e-03 eta: 10:22:47 time: 1.1026 data_time: 0.0138 memory: 15768 grad_norm: 4.3445 loss: 0.9032 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9032 2023/07/25 09:39:25 - mmengine - INFO - Epoch(train) [65][ 60/940] lr: 1.0000e-03 eta: 10:22:25 time: 1.1009 data_time: 0.0140 memory: 15768 grad_norm: 4.3945 loss: 0.8089 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8089 2023/07/25 09:39:47 - mmengine - INFO - Epoch(train) [65][ 80/940] lr: 1.0000e-03 eta: 10:22:03 time: 1.0994 data_time: 0.0137 memory: 15768 grad_norm: 4.3473 loss: 0.8879 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8879 2023/07/25 09:40:09 - mmengine - INFO - Epoch(train) [65][100/940] lr: 1.0000e-03 eta: 10:21:41 time: 1.1035 data_time: 0.0133 memory: 15768 grad_norm: 4.2767 loss: 0.8031 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8031 2023/07/25 09:40:31 - mmengine - INFO - Epoch(train) [65][120/940] lr: 1.0000e-03 eta: 10:21:18 time: 1.1001 data_time: 0.0142 memory: 15768 grad_norm: 4.4331 loss: 0.8310 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8310 2023/07/25 09:40:53 - mmengine - INFO - Epoch(train) [65][140/940] lr: 1.0000e-03 eta: 10:20:56 time: 1.1020 data_time: 0.0140 memory: 15768 grad_norm: 4.2859 loss: 0.9078 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9078 2023/07/25 09:41:15 - mmengine - INFO - Epoch(train) [65][160/940] lr: 1.0000e-03 eta: 10:20:34 time: 1.1024 data_time: 0.0142 memory: 15768 grad_norm: 4.4600 loss: 1.0041 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0041 2023/07/25 09:41:37 - mmengine - INFO - Epoch(train) [65][180/940] lr: 1.0000e-03 eta: 10:20:12 time: 1.1022 data_time: 0.0138 memory: 15768 grad_norm: 4.3517 loss: 0.9620 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9620 2023/07/25 09:41:59 - mmengine - INFO - Epoch(train) [65][200/940] lr: 1.0000e-03 eta: 10:19:50 time: 1.0983 data_time: 0.0142 memory: 15768 grad_norm: 4.4557 loss: 0.7462 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7462 2023/07/25 09:42:21 - mmengine - INFO - Epoch(train) [65][220/940] lr: 1.0000e-03 eta: 10:19:28 time: 1.0998 data_time: 0.0138 memory: 15768 grad_norm: 4.4088 loss: 0.8545 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8545 2023/07/25 09:42:43 - mmengine - INFO - Epoch(train) [65][240/940] lr: 1.0000e-03 eta: 10:19:05 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.4431 loss: 0.9879 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9879 2023/07/25 09:43:05 - mmengine - INFO - Epoch(train) [65][260/940] lr: 1.0000e-03 eta: 10:18:43 time: 1.0989 data_time: 0.0139 memory: 15768 grad_norm: 4.4239 loss: 0.9681 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9681 2023/07/25 09:43:27 - mmengine - INFO - Epoch(train) [65][280/940] lr: 1.0000e-03 eta: 10:18:21 time: 1.0978 data_time: 0.0143 memory: 15768 grad_norm: 4.2143 loss: 0.8245 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8245 2023/07/25 09:43:49 - mmengine - INFO - Epoch(train) [65][300/940] lr: 1.0000e-03 eta: 10:17:59 time: 1.1005 data_time: 0.0141 memory: 15768 grad_norm: 4.3974 loss: 0.7751 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7751 2023/07/25 09:44:11 - mmengine - INFO - Epoch(train) [65][320/940] lr: 1.0000e-03 eta: 10:17:37 time: 1.1003 data_time: 0.0143 memory: 15768 grad_norm: 4.2823 loss: 0.9805 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9805 2023/07/25 09:44:33 - mmengine - INFO - Epoch(train) [65][340/940] lr: 1.0000e-03 eta: 10:17:15 time: 1.1039 data_time: 0.0137 memory: 15768 grad_norm: 4.5432 loss: 0.8573 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8573 2023/07/25 09:44:55 - mmengine - INFO - Epoch(train) [65][360/940] lr: 1.0000e-03 eta: 10:16:52 time: 1.1038 data_time: 0.0128 memory: 15768 grad_norm: 4.4141 loss: 0.7677 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7677 2023/07/25 09:45:17 - mmengine - INFO - Epoch(train) [65][380/940] lr: 1.0000e-03 eta: 10:16:30 time: 1.1001 data_time: 0.0142 memory: 15768 grad_norm: 4.4152 loss: 0.8028 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8028 2023/07/25 09:45:39 - mmengine - INFO - Epoch(train) [65][400/940] lr: 1.0000e-03 eta: 10:16:08 time: 1.1034 data_time: 0.0141 memory: 15768 grad_norm: 4.3632 loss: 0.8471 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8471 2023/07/25 09:46:01 - mmengine - INFO - Epoch(train) [65][420/940] lr: 1.0000e-03 eta: 10:15:46 time: 1.1004 data_time: 0.0142 memory: 15768 grad_norm: 4.3475 loss: 0.8873 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8873 2023/07/25 09:46:23 - mmengine - INFO - Epoch(train) [65][440/940] lr: 1.0000e-03 eta: 10:15:24 time: 1.1013 data_time: 0.0142 memory: 15768 grad_norm: 4.4864 loss: 0.8607 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8607 2023/07/25 09:46:45 - mmengine - INFO - Epoch(train) [65][460/940] lr: 1.0000e-03 eta: 10:15:02 time: 1.1030 data_time: 0.0142 memory: 15768 grad_norm: 4.4037 loss: 0.7704 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7704 2023/07/25 09:47:07 - mmengine - INFO - Epoch(train) [65][480/940] lr: 1.0000e-03 eta: 10:14:40 time: 1.1062 data_time: 0.0144 memory: 15768 grad_norm: 4.4228 loss: 0.8290 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8290 2023/07/25 09:47:29 - mmengine - INFO - Epoch(train) [65][500/940] lr: 1.0000e-03 eta: 10:14:17 time: 1.1007 data_time: 0.0141 memory: 15768 grad_norm: 4.3862 loss: 0.8116 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8116 2023/07/25 09:47:51 - mmengine - INFO - Epoch(train) [65][520/940] lr: 1.0000e-03 eta: 10:13:55 time: 1.1018 data_time: 0.0139 memory: 15768 grad_norm: 4.4786 loss: 0.8443 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8443 2023/07/25 09:48:14 - mmengine - INFO - Epoch(train) [65][540/940] lr: 1.0000e-03 eta: 10:13:33 time: 1.1015 data_time: 0.0139 memory: 15768 grad_norm: 4.4328 loss: 0.8152 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8152 2023/07/25 09:48:36 - mmengine - INFO - Epoch(train) [65][560/940] lr: 1.0000e-03 eta: 10:13:11 time: 1.1008 data_time: 0.0143 memory: 15768 grad_norm: 4.3587 loss: 0.8681 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8681 2023/07/25 09:48:58 - mmengine - INFO - Epoch(train) [65][580/940] lr: 1.0000e-03 eta: 10:12:49 time: 1.0999 data_time: 0.0138 memory: 15768 grad_norm: 4.4307 loss: 0.8541 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8541 2023/07/25 09:49:19 - mmengine - INFO - Epoch(train) [65][600/940] lr: 1.0000e-03 eta: 10:12:27 time: 1.0972 data_time: 0.0143 memory: 15768 grad_norm: 4.4027 loss: 0.6927 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6927 2023/07/25 09:49:42 - mmengine - INFO - Epoch(train) [65][620/940] lr: 1.0000e-03 eta: 10:12:04 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.3910 loss: 0.9890 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9890 2023/07/25 09:50:04 - mmengine - INFO - Epoch(train) [65][640/940] lr: 1.0000e-03 eta: 10:11:42 time: 1.1022 data_time: 0.0138 memory: 15768 grad_norm: 4.3473 loss: 0.7993 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7993 2023/07/25 09:50:26 - mmengine - INFO - Epoch(train) [65][660/940] lr: 1.0000e-03 eta: 10:11:20 time: 1.1005 data_time: 0.0137 memory: 15768 grad_norm: 4.3337 loss: 0.8219 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8219 2023/07/25 09:50:48 - mmengine - INFO - Epoch(train) [65][680/940] lr: 1.0000e-03 eta: 10:10:58 time: 1.1006 data_time: 0.0138 memory: 15768 grad_norm: 4.4516 loss: 0.8736 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8736 2023/07/25 09:51:10 - mmengine - INFO - Epoch(train) [65][700/940] lr: 1.0000e-03 eta: 10:10:36 time: 1.1034 data_time: 0.0143 memory: 15768 grad_norm: 4.5292 loss: 0.8758 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8758 2023/07/25 09:51:32 - mmengine - INFO - Epoch(train) [65][720/940] lr: 1.0000e-03 eta: 10:10:14 time: 1.0995 data_time: 0.0137 memory: 15768 grad_norm: 4.4601 loss: 0.9781 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9781 2023/07/25 09:51:54 - mmengine - INFO - Epoch(train) [65][740/940] lr: 1.0000e-03 eta: 10:09:51 time: 1.0991 data_time: 0.0140 memory: 15768 grad_norm: 4.3616 loss: 0.7862 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7862 2023/07/25 09:52:16 - mmengine - INFO - Epoch(train) [65][760/940] lr: 1.0000e-03 eta: 10:09:29 time: 1.1012 data_time: 0.0144 memory: 15768 grad_norm: 4.4199 loss: 0.9603 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.9603 2023/07/25 09:52:38 - mmengine - INFO - Epoch(train) [65][780/940] lr: 1.0000e-03 eta: 10:09:07 time: 1.1021 data_time: 0.0141 memory: 15768 grad_norm: 4.4545 loss: 0.9176 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9176 2023/07/25 09:53:00 - mmengine - INFO - Epoch(train) [65][800/940] lr: 1.0000e-03 eta: 10:08:45 time: 1.1014 data_time: 0.0142 memory: 15768 grad_norm: 4.5134 loss: 0.7654 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7654 2023/07/25 09:53:22 - mmengine - INFO - Epoch(train) [65][820/940] lr: 1.0000e-03 eta: 10:08:23 time: 1.1029 data_time: 0.0139 memory: 15768 grad_norm: 4.4438 loss: 0.8798 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8798 2023/07/25 09:53:44 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 09:53:44 - mmengine - INFO - Epoch(train) [65][840/940] lr: 1.0000e-03 eta: 10:08:01 time: 1.1019 data_time: 0.0136 memory: 15768 grad_norm: 4.3095 loss: 0.9116 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9116 2023/07/25 09:54:06 - mmengine - INFO - Epoch(train) [65][860/940] lr: 1.0000e-03 eta: 10:07:39 time: 1.1030 data_time: 0.0134 memory: 15768 grad_norm: 4.4031 loss: 0.9573 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9573 2023/07/25 09:54:28 - mmengine - INFO - Epoch(train) [65][880/940] lr: 1.0000e-03 eta: 10:07:16 time: 1.1004 data_time: 0.0140 memory: 15768 grad_norm: 4.3533 loss: 0.9443 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9443 2023/07/25 09:54:50 - mmengine - INFO - Epoch(train) [65][900/940] lr: 1.0000e-03 eta: 10:06:54 time: 1.1021 data_time: 0.0140 memory: 15768 grad_norm: 4.3925 loss: 0.7719 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7719 2023/07/25 09:55:12 - mmengine - INFO - Epoch(train) [65][920/940] lr: 1.0000e-03 eta: 10:06:32 time: 1.1010 data_time: 0.0144 memory: 15768 grad_norm: 4.4196 loss: 0.8138 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8138 2023/07/25 09:55:33 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 09:55:33 - mmengine - INFO - Epoch(train) [65][940/940] lr: 1.0000e-03 eta: 10:06:09 time: 1.0534 data_time: 0.0136 memory: 15768 grad_norm: 4.6840 loss: 1.0026 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.0026 2023/07/25 09:55:42 - mmengine - INFO - Epoch(val) [65][20/78] eta: 0:00:27 time: 0.4716 data_time: 0.3144 memory: 2147 2023/07/25 09:55:49 - mmengine - INFO - Epoch(val) [65][40/78] eta: 0:00:15 time: 0.3398 data_time: 0.1831 memory: 2147 2023/07/25 09:55:58 - mmengine - INFO - Epoch(val) [65][60/78] eta: 0:00:07 time: 0.4291 data_time: 0.2724 memory: 2147 2023/07/25 09:56:10 - mmengine - INFO - Epoch(val) [65][78/78] acc/top1: 0.7123 acc/top5: 0.8980 acc/mean1: 0.7122 data_time: 0.2348 time: 0.3889 2023/07/25 09:56:35 - mmengine - INFO - Epoch(train) [66][ 20/940] lr: 1.0000e-03 eta: 10:05:49 time: 1.2880 data_time: 0.1497 memory: 15768 grad_norm: 4.3325 loss: 0.7726 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7726 2023/07/25 09:56:57 - mmengine - INFO - Epoch(train) [66][ 40/940] lr: 1.0000e-03 eta: 10:05:27 time: 1.1040 data_time: 0.0143 memory: 15768 grad_norm: 4.3606 loss: 1.0041 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0041 2023/07/25 09:57:19 - mmengine - INFO - Epoch(train) [66][ 60/940] lr: 1.0000e-03 eta: 10:05:05 time: 1.1027 data_time: 0.0141 memory: 15768 grad_norm: 4.4105 loss: 0.8784 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8784 2023/07/25 09:57:42 - mmengine - INFO - Epoch(train) [66][ 80/940] lr: 1.0000e-03 eta: 10:04:43 time: 1.1016 data_time: 0.0142 memory: 15768 grad_norm: 4.2327 loss: 0.7472 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7472 2023/07/25 09:58:04 - mmengine - INFO - Epoch(train) [66][100/940] lr: 1.0000e-03 eta: 10:04:21 time: 1.1007 data_time: 0.0141 memory: 15768 grad_norm: 4.3828 loss: 0.9171 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9171 2023/07/25 09:58:26 - mmengine - INFO - Epoch(train) [66][120/940] lr: 1.0000e-03 eta: 10:03:59 time: 1.1000 data_time: 0.0141 memory: 15768 grad_norm: 4.4312 loss: 0.9214 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9214 2023/07/25 09:58:48 - mmengine - INFO - Epoch(train) [66][140/940] lr: 1.0000e-03 eta: 10:03:36 time: 1.1018 data_time: 0.0141 memory: 15768 grad_norm: 4.3699 loss: 0.9169 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9169 2023/07/25 09:59:10 - mmengine - INFO - Epoch(train) [66][160/940] lr: 1.0000e-03 eta: 10:03:14 time: 1.1024 data_time: 0.0144 memory: 15768 grad_norm: 4.4026 loss: 0.9362 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9362 2023/07/25 09:59:32 - mmengine - INFO - Epoch(train) [66][180/940] lr: 1.0000e-03 eta: 10:02:52 time: 1.1001 data_time: 0.0137 memory: 15768 grad_norm: 4.4394 loss: 0.8471 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8471 2023/07/25 09:59:54 - mmengine - INFO - Epoch(train) [66][200/940] lr: 1.0000e-03 eta: 10:02:30 time: 1.0997 data_time: 0.0143 memory: 15768 grad_norm: 4.3891 loss: 0.8510 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8510 2023/07/25 10:00:16 - mmengine - INFO - Epoch(train) [66][220/940] lr: 1.0000e-03 eta: 10:02:08 time: 1.0986 data_time: 0.0141 memory: 15768 grad_norm: 4.3820 loss: 0.9785 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9785 2023/07/25 10:00:38 - mmengine - INFO - Epoch(train) [66][240/940] lr: 1.0000e-03 eta: 10:01:46 time: 1.1010 data_time: 0.0146 memory: 15768 grad_norm: 4.3655 loss: 0.7982 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7982 2023/07/25 10:01:00 - mmengine - INFO - Epoch(train) [66][260/940] lr: 1.0000e-03 eta: 10:01:23 time: 1.1024 data_time: 0.0145 memory: 15768 grad_norm: 4.5354 loss: 0.9538 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9538 2023/07/25 10:01:22 - mmengine - INFO - Epoch(train) [66][280/940] lr: 1.0000e-03 eta: 10:01:01 time: 1.0991 data_time: 0.0143 memory: 15768 grad_norm: 4.3874 loss: 0.7465 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7465 2023/07/25 10:01:44 - mmengine - INFO - Epoch(train) [66][300/940] lr: 1.0000e-03 eta: 10:00:39 time: 1.1047 data_time: 0.0139 memory: 15768 grad_norm: 4.3264 loss: 0.8103 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8103 2023/07/25 10:02:06 - mmengine - INFO - Epoch(train) [66][320/940] lr: 1.0000e-03 eta: 10:00:17 time: 1.0999 data_time: 0.0144 memory: 15768 grad_norm: 4.3856 loss: 0.8565 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8565 2023/07/25 10:02:28 - mmengine - INFO - Epoch(train) [66][340/940] lr: 1.0000e-03 eta: 9:59:55 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.4137 loss: 0.8923 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8923 2023/07/25 10:02:50 - mmengine - INFO - Epoch(train) [66][360/940] lr: 1.0000e-03 eta: 9:59:33 time: 1.1024 data_time: 0.0144 memory: 15768 grad_norm: 4.3580 loss: 0.8398 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8398 2023/07/25 10:03:12 - mmengine - INFO - Epoch(train) [66][380/940] lr: 1.0000e-03 eta: 9:59:10 time: 1.0994 data_time: 0.0143 memory: 15768 grad_norm: 4.3656 loss: 0.7694 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7694 2023/07/25 10:03:34 - mmengine - INFO - Epoch(train) [66][400/940] lr: 1.0000e-03 eta: 9:58:48 time: 1.1037 data_time: 0.0144 memory: 15768 grad_norm: 4.4797 loss: 0.7048 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7048 2023/07/25 10:03:56 - mmengine - INFO - Epoch(train) [66][420/940] lr: 1.0000e-03 eta: 9:58:26 time: 1.1010 data_time: 0.0142 memory: 15768 grad_norm: 4.4271 loss: 0.8944 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8944 2023/07/25 10:04:18 - mmengine - INFO - Epoch(train) [66][440/940] lr: 1.0000e-03 eta: 9:58:04 time: 1.1010 data_time: 0.0148 memory: 15768 grad_norm: 4.4714 loss: 0.8214 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8214 2023/07/25 10:04:40 - mmengine - INFO - Epoch(train) [66][460/940] lr: 1.0000e-03 eta: 9:57:42 time: 1.1012 data_time: 0.0143 memory: 15768 grad_norm: 4.3948 loss: 0.9364 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9364 2023/07/25 10:05:02 - mmengine - INFO - Epoch(train) [66][480/940] lr: 1.0000e-03 eta: 9:57:20 time: 1.1014 data_time: 0.0143 memory: 15768 grad_norm: 4.3656 loss: 0.8475 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8475 2023/07/25 10:05:24 - mmengine - INFO - Epoch(train) [66][500/940] lr: 1.0000e-03 eta: 9:56:58 time: 1.1016 data_time: 0.0140 memory: 15768 grad_norm: 4.5641 loss: 0.7997 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7997 2023/07/25 10:05:46 - mmengine - INFO - Epoch(train) [66][520/940] lr: 1.0000e-03 eta: 9:56:35 time: 1.1020 data_time: 0.0144 memory: 15768 grad_norm: 4.4285 loss: 0.7957 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7957 2023/07/25 10:06:08 - mmengine - INFO - Epoch(train) [66][540/940] lr: 1.0000e-03 eta: 9:56:13 time: 1.1020 data_time: 0.0140 memory: 15768 grad_norm: 4.4611 loss: 0.7946 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7946 2023/07/25 10:06:30 - mmengine - INFO - Epoch(train) [66][560/940] lr: 1.0000e-03 eta: 9:55:51 time: 1.1017 data_time: 0.0145 memory: 15768 grad_norm: 4.3751 loss: 0.8249 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8249 2023/07/25 10:06:52 - mmengine - INFO - Epoch(train) [66][580/940] lr: 1.0000e-03 eta: 9:55:29 time: 1.1057 data_time: 0.0141 memory: 15768 grad_norm: 4.4577 loss: 0.9220 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9220 2023/07/25 10:07:14 - mmengine - INFO - Epoch(train) [66][600/940] lr: 1.0000e-03 eta: 9:55:07 time: 1.0989 data_time: 0.0145 memory: 15768 grad_norm: 4.4149 loss: 0.8479 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8479 2023/07/25 10:07:36 - mmengine - INFO - Epoch(train) [66][620/940] lr: 1.0000e-03 eta: 9:54:45 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.4719 loss: 0.8923 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8923 2023/07/25 10:07:58 - mmengine - INFO - Epoch(train) [66][640/940] lr: 1.0000e-03 eta: 9:54:23 time: 1.0994 data_time: 0.0142 memory: 15768 grad_norm: 4.3858 loss: 0.7717 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7717 2023/07/25 10:08:20 - mmengine - INFO - Epoch(train) [66][660/940] lr: 1.0000e-03 eta: 9:54:00 time: 1.1027 data_time: 0.0142 memory: 15768 grad_norm: 4.4322 loss: 0.7853 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7853 2023/07/25 10:08:42 - mmengine - INFO - Epoch(train) [66][680/940] lr: 1.0000e-03 eta: 9:53:38 time: 1.0997 data_time: 0.0140 memory: 15768 grad_norm: 4.4154 loss: 0.7990 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7990 2023/07/25 10:09:04 - mmengine - INFO - Epoch(train) [66][700/940] lr: 1.0000e-03 eta: 9:53:16 time: 1.0975 data_time: 0.0141 memory: 15768 grad_norm: 4.4266 loss: 0.9032 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9032 2023/07/25 10:09:26 - mmengine - INFO - Epoch(train) [66][720/940] lr: 1.0000e-03 eta: 9:52:54 time: 1.0986 data_time: 0.0143 memory: 15768 grad_norm: 4.4696 loss: 0.7718 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7718 2023/07/25 10:09:48 - mmengine - INFO - Epoch(train) [66][740/940] lr: 1.0000e-03 eta: 9:52:32 time: 1.1002 data_time: 0.0144 memory: 15768 grad_norm: 4.4734 loss: 1.0891 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0891 2023/07/25 10:10:10 - mmengine - INFO - Epoch(train) [66][760/940] lr: 1.0000e-03 eta: 9:52:10 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.5105 loss: 0.7310 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7310 2023/07/25 10:10:32 - mmengine - INFO - Epoch(train) [66][780/940] lr: 1.0000e-03 eta: 9:51:47 time: 1.0996 data_time: 0.0144 memory: 15768 grad_norm: 4.4115 loss: 1.0250 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0250 2023/07/25 10:10:54 - mmengine - INFO - Epoch(train) [66][800/940] lr: 1.0000e-03 eta: 9:51:25 time: 1.1026 data_time: 0.0144 memory: 15768 grad_norm: 4.3356 loss: 0.8845 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8845 2023/07/25 10:11:16 - mmengine - INFO - Epoch(train) [66][820/940] lr: 1.0000e-03 eta: 9:51:03 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.4339 loss: 0.7874 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7874 2023/07/25 10:11:38 - mmengine - INFO - Epoch(train) [66][840/940] lr: 1.0000e-03 eta: 9:50:41 time: 1.1006 data_time: 0.0143 memory: 15768 grad_norm: 4.4276 loss: 0.9575 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9575 2023/07/25 10:12:00 - mmengine - INFO - Epoch(train) [66][860/940] lr: 1.0000e-03 eta: 9:50:19 time: 1.0995 data_time: 0.0142 memory: 15768 grad_norm: 4.3527 loss: 0.8947 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8947 2023/07/25 10:12:22 - mmengine - INFO - Epoch(train) [66][880/940] lr: 1.0000e-03 eta: 9:49:57 time: 1.0994 data_time: 0.0145 memory: 15768 grad_norm: 4.4395 loss: 0.8223 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8223 2023/07/25 10:12:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 10:12:45 - mmengine - INFO - Epoch(train) [66][900/940] lr: 1.0000e-03 eta: 9:49:35 time: 1.1547 data_time: 0.0138 memory: 15768 grad_norm: 4.3506 loss: 0.8796 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8796 2023/07/25 10:13:09 - mmengine - INFO - Epoch(train) [66][920/940] lr: 1.0000e-03 eta: 9:49:13 time: 1.1716 data_time: 0.0141 memory: 15768 grad_norm: 4.4167 loss: 0.7388 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7388 2023/07/25 10:13:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 10:13:31 - mmengine - INFO - Epoch(train) [66][940/940] lr: 1.0000e-03 eta: 9:48:51 time: 1.1140 data_time: 0.0137 memory: 15768 grad_norm: 4.6465 loss: 0.9114 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9114 2023/07/25 10:13:31 - mmengine - INFO - Saving checkpoint at 66 epochs 2023/07/25 10:13:42 - mmengine - INFO - Epoch(val) [66][20/78] eta: 0:00:28 time: 0.4872 data_time: 0.3303 memory: 2147 2023/07/25 10:13:49 - mmengine - INFO - Epoch(val) [66][40/78] eta: 0:00:16 time: 0.3576 data_time: 0.2004 memory: 2147 2023/07/25 10:13:58 - mmengine - INFO - Epoch(val) [66][60/78] eta: 0:00:07 time: 0.4511 data_time: 0.2940 memory: 2147 2023/07/25 10:14:07 - mmengine - INFO - Epoch(val) [66][78/78] acc/top1: 0.7119 acc/top5: 0.8978 acc/mean1: 0.7118 data_time: 0.2443 time: 0.3984 2023/07/25 10:14:33 - mmengine - INFO - Epoch(train) [67][ 20/940] lr: 1.0000e-03 eta: 9:48:31 time: 1.2830 data_time: 0.1658 memory: 15768 grad_norm: 4.5469 loss: 0.8898 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8898 2023/07/25 10:14:55 - mmengine - INFO - Epoch(train) [67][ 40/940] lr: 1.0000e-03 eta: 9:48:09 time: 1.1055 data_time: 0.0132 memory: 15768 grad_norm: 4.4163 loss: 0.8542 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8542 2023/07/25 10:15:17 - mmengine - INFO - Epoch(train) [67][ 60/940] lr: 1.0000e-03 eta: 9:47:47 time: 1.1037 data_time: 0.0139 memory: 15768 grad_norm: 4.4016 loss: 0.9772 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9772 2023/07/25 10:15:39 - mmengine - INFO - Epoch(train) [67][ 80/940] lr: 1.0000e-03 eta: 9:47:25 time: 1.1003 data_time: 0.0138 memory: 15768 grad_norm: 4.5139 loss: 0.9575 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9575 2023/07/25 10:16:01 - mmengine - INFO - Epoch(train) [67][100/940] lr: 1.0000e-03 eta: 9:47:03 time: 1.1020 data_time: 0.0138 memory: 15768 grad_norm: 4.4420 loss: 0.8837 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8837 2023/07/25 10:16:23 - mmengine - INFO - Epoch(train) [67][120/940] lr: 1.0000e-03 eta: 9:46:40 time: 1.1009 data_time: 0.0136 memory: 15768 grad_norm: 4.3524 loss: 0.8253 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8253 2023/07/25 10:16:45 - mmengine - INFO - Epoch(train) [67][140/940] lr: 1.0000e-03 eta: 9:46:18 time: 1.1012 data_time: 0.0137 memory: 15768 grad_norm: 4.4034 loss: 0.8424 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8424 2023/07/25 10:17:07 - mmengine - INFO - Epoch(train) [67][160/940] lr: 1.0000e-03 eta: 9:45:56 time: 1.1008 data_time: 0.0142 memory: 15768 grad_norm: 4.4153 loss: 0.8119 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8119 2023/07/25 10:17:29 - mmengine - INFO - Epoch(train) [67][180/940] lr: 1.0000e-03 eta: 9:45:34 time: 1.1011 data_time: 0.0134 memory: 15768 grad_norm: 4.4896 loss: 0.9492 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9492 2023/07/25 10:17:51 - mmengine - INFO - Epoch(train) [67][200/940] lr: 1.0000e-03 eta: 9:45:12 time: 1.1022 data_time: 0.0138 memory: 15768 grad_norm: 4.3706 loss: 0.8333 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8333 2023/07/25 10:18:13 - mmengine - INFO - Epoch(train) [67][220/940] lr: 1.0000e-03 eta: 9:44:50 time: 1.1041 data_time: 0.0143 memory: 15768 grad_norm: 4.5105 loss: 0.9585 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9585 2023/07/25 10:18:35 - mmengine - INFO - Epoch(train) [67][240/940] lr: 1.0000e-03 eta: 9:44:28 time: 1.1024 data_time: 0.0136 memory: 15768 grad_norm: 4.4482 loss: 0.9261 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9261 2023/07/25 10:18:57 - mmengine - INFO - Epoch(train) [67][260/940] lr: 1.0000e-03 eta: 9:44:05 time: 1.1028 data_time: 0.0139 memory: 15768 grad_norm: 4.3726 loss: 0.9483 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9483 2023/07/25 10:19:19 - mmengine - INFO - Epoch(train) [67][280/940] lr: 1.0000e-03 eta: 9:43:43 time: 1.0986 data_time: 0.0139 memory: 15768 grad_norm: 4.4558 loss: 0.8602 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8602 2023/07/25 10:19:41 - mmengine - INFO - Epoch(train) [67][300/940] lr: 1.0000e-03 eta: 9:43:21 time: 1.1000 data_time: 0.0137 memory: 15768 grad_norm: 4.4316 loss: 0.8655 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8655 2023/07/25 10:20:03 - mmengine - INFO - Epoch(train) [67][320/940] lr: 1.0000e-03 eta: 9:42:59 time: 1.1029 data_time: 0.0139 memory: 15768 grad_norm: 4.3873 loss: 1.0382 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0382 2023/07/25 10:20:25 - mmengine - INFO - Epoch(train) [67][340/940] lr: 1.0000e-03 eta: 9:42:37 time: 1.0997 data_time: 0.0137 memory: 15768 grad_norm: 4.5024 loss: 0.8556 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8556 2023/07/25 10:20:47 - mmengine - INFO - Epoch(train) [67][360/940] lr: 1.0000e-03 eta: 9:42:15 time: 1.1024 data_time: 0.0139 memory: 15768 grad_norm: 4.6086 loss: 0.9434 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9434 2023/07/25 10:21:09 - mmengine - INFO - Epoch(train) [67][380/940] lr: 1.0000e-03 eta: 9:41:52 time: 1.1012 data_time: 0.0139 memory: 15768 grad_norm: 4.4968 loss: 0.7711 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.7711 2023/07/25 10:21:31 - mmengine - INFO - Epoch(train) [67][400/940] lr: 1.0000e-03 eta: 9:41:30 time: 1.0990 data_time: 0.0140 memory: 15768 grad_norm: 4.5768 loss: 0.9507 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9507 2023/07/25 10:21:54 - mmengine - INFO - Epoch(train) [67][420/940] lr: 1.0000e-03 eta: 9:41:08 time: 1.1044 data_time: 0.0138 memory: 15768 grad_norm: 4.4849 loss: 0.8251 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8251 2023/07/25 10:22:16 - mmengine - INFO - Epoch(train) [67][440/940] lr: 1.0000e-03 eta: 9:40:46 time: 1.1039 data_time: 0.0142 memory: 15768 grad_norm: 4.5493 loss: 0.9863 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9863 2023/07/25 10:22:38 - mmengine - INFO - Epoch(train) [67][460/940] lr: 1.0000e-03 eta: 9:40:24 time: 1.0995 data_time: 0.0136 memory: 15768 grad_norm: 4.4915 loss: 0.8604 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8604 2023/07/25 10:23:00 - mmengine - INFO - Epoch(train) [67][480/940] lr: 1.0000e-03 eta: 9:40:02 time: 1.1029 data_time: 0.0143 memory: 15768 grad_norm: 4.4421 loss: 0.8705 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8705 2023/07/25 10:23:22 - mmengine - INFO - Epoch(train) [67][500/940] lr: 1.0000e-03 eta: 9:39:40 time: 1.0984 data_time: 0.0136 memory: 15768 grad_norm: 4.3372 loss: 0.8654 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8654 2023/07/25 10:23:44 - mmengine - INFO - Epoch(train) [67][520/940] lr: 1.0000e-03 eta: 9:39:17 time: 1.1022 data_time: 0.0138 memory: 15768 grad_norm: 4.5079 loss: 0.8689 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8689 2023/07/25 10:24:06 - mmengine - INFO - Epoch(train) [67][540/940] lr: 1.0000e-03 eta: 9:38:55 time: 1.1019 data_time: 0.0144 memory: 15768 grad_norm: 4.5157 loss: 0.7894 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7894 2023/07/25 10:24:28 - mmengine - INFO - Epoch(train) [67][560/940] lr: 1.0000e-03 eta: 9:38:33 time: 1.1013 data_time: 0.0143 memory: 15768 grad_norm: 4.4639 loss: 0.8772 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8772 2023/07/25 10:24:50 - mmengine - INFO - Epoch(train) [67][580/940] lr: 1.0000e-03 eta: 9:38:11 time: 1.1008 data_time: 0.0146 memory: 15768 grad_norm: 4.5560 loss: 0.9611 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9611 2023/07/25 10:25:12 - mmengine - INFO - Epoch(train) [67][600/940] lr: 1.0000e-03 eta: 9:37:49 time: 1.1050 data_time: 0.0143 memory: 15768 grad_norm: 4.4267 loss: 0.9395 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9395 2023/07/25 10:25:34 - mmengine - INFO - Epoch(train) [67][620/940] lr: 1.0000e-03 eta: 9:37:27 time: 1.1016 data_time: 0.0142 memory: 15768 grad_norm: 4.6014 loss: 0.9174 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9174 2023/07/25 10:25:56 - mmengine - INFO - Epoch(train) [67][640/940] lr: 1.0000e-03 eta: 9:37:05 time: 1.0999 data_time: 0.0143 memory: 15768 grad_norm: 4.3785 loss: 0.8725 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8725 2023/07/25 10:26:18 - mmengine - INFO - Epoch(train) [67][660/940] lr: 1.0000e-03 eta: 9:36:42 time: 1.1010 data_time: 0.0143 memory: 15768 grad_norm: 4.4277 loss: 0.8906 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8906 2023/07/25 10:26:40 - mmengine - INFO - Epoch(train) [67][680/940] lr: 1.0000e-03 eta: 9:36:20 time: 1.1017 data_time: 0.0143 memory: 15768 grad_norm: 4.4082 loss: 0.9252 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9252 2023/07/25 10:27:02 - mmengine - INFO - Epoch(train) [67][700/940] lr: 1.0000e-03 eta: 9:35:58 time: 1.1021 data_time: 0.0139 memory: 15768 grad_norm: 4.4411 loss: 0.9318 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9318 2023/07/25 10:27:24 - mmengine - INFO - Epoch(train) [67][720/940] lr: 1.0000e-03 eta: 9:35:36 time: 1.1001 data_time: 0.0141 memory: 15768 grad_norm: 4.5248 loss: 0.9473 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9473 2023/07/25 10:27:46 - mmengine - INFO - Epoch(train) [67][740/940] lr: 1.0000e-03 eta: 9:35:14 time: 1.1005 data_time: 0.0139 memory: 15768 grad_norm: 4.4931 loss: 0.8927 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8927 2023/07/25 10:28:08 - mmengine - INFO - Epoch(train) [67][760/940] lr: 1.0000e-03 eta: 9:34:52 time: 1.0996 data_time: 0.0137 memory: 15768 grad_norm: 4.4557 loss: 0.7325 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7325 2023/07/25 10:28:30 - mmengine - INFO - Epoch(train) [67][780/940] lr: 1.0000e-03 eta: 9:34:29 time: 1.1020 data_time: 0.0145 memory: 15768 grad_norm: 4.5173 loss: 1.0329 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0329 2023/07/25 10:28:52 - mmengine - INFO - Epoch(train) [67][800/940] lr: 1.0000e-03 eta: 9:34:07 time: 1.0983 data_time: 0.0138 memory: 15768 grad_norm: 4.5282 loss: 0.8854 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8854 2023/07/25 10:29:14 - mmengine - INFO - Epoch(train) [67][820/940] lr: 1.0000e-03 eta: 9:33:45 time: 1.0989 data_time: 0.0141 memory: 15768 grad_norm: 4.4902 loss: 0.8989 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8989 2023/07/25 10:29:36 - mmengine - INFO - Epoch(train) [67][840/940] lr: 1.0000e-03 eta: 9:33:23 time: 1.1001 data_time: 0.0138 memory: 15768 grad_norm: 4.4861 loss: 0.7228 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7228 2023/07/25 10:29:58 - mmengine - INFO - Epoch(train) [67][860/940] lr: 1.0000e-03 eta: 9:33:01 time: 1.1001 data_time: 0.0135 memory: 15768 grad_norm: 4.5473 loss: 0.8331 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8331 2023/07/25 10:30:20 - mmengine - INFO - Epoch(train) [67][880/940] lr: 1.0000e-03 eta: 9:32:39 time: 1.1033 data_time: 0.0138 memory: 15768 grad_norm: 4.4382 loss: 0.9840 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9840 2023/07/25 10:30:42 - mmengine - INFO - Epoch(train) [67][900/940] lr: 1.0000e-03 eta: 9:32:16 time: 1.0987 data_time: 0.0140 memory: 15768 grad_norm: 4.4251 loss: 0.8048 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.8048 2023/07/25 10:31:04 - mmengine - INFO - Epoch(train) [67][920/940] lr: 1.0000e-03 eta: 9:31:54 time: 1.1018 data_time: 0.0140 memory: 15768 grad_norm: 4.5066 loss: 0.7970 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7970 2023/07/25 10:31:25 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 10:31:25 - mmengine - INFO - Epoch(train) [67][940/940] lr: 1.0000e-03 eta: 9:31:32 time: 1.0542 data_time: 0.0135 memory: 15768 grad_norm: 4.8708 loss: 0.9496 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9496 2023/07/25 10:31:35 - mmengine - INFO - Epoch(val) [67][20/78] eta: 0:00:28 time: 0.4888 data_time: 0.3308 memory: 2147 2023/07/25 10:31:42 - mmengine - INFO - Epoch(val) [67][40/78] eta: 0:00:15 time: 0.3352 data_time: 0.1783 memory: 2147 2023/07/25 10:31:50 - mmengine - INFO - Epoch(val) [67][60/78] eta: 0:00:07 time: 0.4247 data_time: 0.2677 memory: 2147 2023/07/25 10:32:01 - mmengine - INFO - Epoch(val) [67][78/78] acc/top1: 0.7116 acc/top5: 0.8982 acc/mean1: 0.7115 data_time: 0.2355 time: 0.3900 2023/07/25 10:32:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 10:32:29 - mmengine - INFO - Epoch(train) [68][ 20/940] lr: 1.0000e-03 eta: 9:31:12 time: 1.3884 data_time: 0.1612 memory: 15768 grad_norm: 4.4200 loss: 0.8646 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8646 2023/07/25 10:32:52 - mmengine - INFO - Epoch(train) [68][ 40/940] lr: 1.0000e-03 eta: 9:30:51 time: 1.1685 data_time: 0.0142 memory: 15768 grad_norm: 4.5147 loss: 0.8820 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8820 2023/07/25 10:33:15 - mmengine - INFO - Epoch(train) [68][ 60/940] lr: 1.0000e-03 eta: 9:30:29 time: 1.1518 data_time: 0.0142 memory: 15768 grad_norm: 4.5006 loss: 0.8977 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8977 2023/07/25 10:33:37 - mmengine - INFO - Epoch(train) [68][ 80/940] lr: 1.0000e-03 eta: 9:30:07 time: 1.1009 data_time: 0.0141 memory: 15768 grad_norm: 4.4463 loss: 0.9428 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9428 2023/07/25 10:33:59 - mmengine - INFO - Epoch(train) [68][100/940] lr: 1.0000e-03 eta: 9:29:45 time: 1.0981 data_time: 0.0138 memory: 15768 grad_norm: 4.4678 loss: 0.7595 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7595 2023/07/25 10:34:21 - mmengine - INFO - Epoch(train) [68][120/940] lr: 1.0000e-03 eta: 9:29:23 time: 1.1020 data_time: 0.0135 memory: 15768 grad_norm: 4.5996 loss: 0.8109 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8109 2023/07/25 10:34:43 - mmengine - INFO - Epoch(train) [68][140/940] lr: 1.0000e-03 eta: 9:29:01 time: 1.0986 data_time: 0.0141 memory: 15768 grad_norm: 4.3532 loss: 0.7588 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7588 2023/07/25 10:35:05 - mmengine - INFO - Epoch(train) [68][160/940] lr: 1.0000e-03 eta: 9:28:38 time: 1.0990 data_time: 0.0138 memory: 15768 grad_norm: 4.3517 loss: 0.8765 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8765 2023/07/25 10:35:27 - mmengine - INFO - Epoch(train) [68][180/940] lr: 1.0000e-03 eta: 9:28:16 time: 1.1008 data_time: 0.0140 memory: 15768 grad_norm: 4.4110 loss: 0.8227 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8227 2023/07/25 10:35:49 - mmengine - INFO - Epoch(train) [68][200/940] lr: 1.0000e-03 eta: 9:27:54 time: 1.0984 data_time: 0.0138 memory: 15768 grad_norm: 4.5080 loss: 0.8911 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8911 2023/07/25 10:36:11 - mmengine - INFO - Epoch(train) [68][220/940] lr: 1.0000e-03 eta: 9:27:32 time: 1.1003 data_time: 0.0144 memory: 15768 grad_norm: 4.4832 loss: 0.9435 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9435 2023/07/25 10:36:33 - mmengine - INFO - Epoch(train) [68][240/940] lr: 1.0000e-03 eta: 9:27:10 time: 1.0993 data_time: 0.0140 memory: 15768 grad_norm: 4.4432 loss: 0.8581 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8581 2023/07/25 10:36:55 - mmengine - INFO - Epoch(train) [68][260/940] lr: 1.0000e-03 eta: 9:26:48 time: 1.1008 data_time: 0.0140 memory: 15768 grad_norm: 4.5505 loss: 0.8997 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8997 2023/07/25 10:37:17 - mmengine - INFO - Epoch(train) [68][280/940] lr: 1.0000e-03 eta: 9:26:25 time: 1.0988 data_time: 0.0139 memory: 15768 grad_norm: 4.5362 loss: 0.7442 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.7442 2023/07/25 10:37:39 - mmengine - INFO - Epoch(train) [68][300/940] lr: 1.0000e-03 eta: 9:26:03 time: 1.0990 data_time: 0.0137 memory: 15768 grad_norm: 4.5315 loss: 0.8381 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8381 2023/07/25 10:38:01 - mmengine - INFO - Epoch(train) [68][320/940] lr: 1.0000e-03 eta: 9:25:41 time: 1.1013 data_time: 0.0144 memory: 15768 grad_norm: 4.5069 loss: 0.8198 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8198 2023/07/25 10:38:23 - mmengine - INFO - Epoch(train) [68][340/940] lr: 1.0000e-03 eta: 9:25:19 time: 1.1019 data_time: 0.0141 memory: 15768 grad_norm: 4.3877 loss: 0.9148 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9148 2023/07/25 10:38:45 - mmengine - INFO - Epoch(train) [68][360/940] lr: 1.0000e-03 eta: 9:24:57 time: 1.0985 data_time: 0.0142 memory: 15768 grad_norm: 4.4122 loss: 0.8162 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8162 2023/07/25 10:39:07 - mmengine - INFO - Epoch(train) [68][380/940] lr: 1.0000e-03 eta: 9:24:35 time: 1.1028 data_time: 0.0141 memory: 15768 grad_norm: 4.3857 loss: 0.8121 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8121 2023/07/25 10:39:29 - mmengine - INFO - Epoch(train) [68][400/940] lr: 1.0000e-03 eta: 9:24:12 time: 1.1023 data_time: 0.0143 memory: 15768 grad_norm: 4.4937 loss: 0.9458 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9458 2023/07/25 10:39:51 - mmengine - INFO - Epoch(train) [68][420/940] lr: 1.0000e-03 eta: 9:23:50 time: 1.0989 data_time: 0.0141 memory: 15768 grad_norm: 4.4597 loss: 0.8715 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8715 2023/07/25 10:40:13 - mmengine - INFO - Epoch(train) [68][440/940] lr: 1.0000e-03 eta: 9:23:28 time: 1.1031 data_time: 0.0141 memory: 15768 grad_norm: 4.4727 loss: 0.8124 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8124 2023/07/25 10:40:35 - mmengine - INFO - Epoch(train) [68][460/940] lr: 1.0000e-03 eta: 9:23:06 time: 1.0998 data_time: 0.0140 memory: 15768 grad_norm: 4.5701 loss: 0.9468 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9468 2023/07/25 10:40:57 - mmengine - INFO - Epoch(train) [68][480/940] lr: 1.0000e-03 eta: 9:22:44 time: 1.0996 data_time: 0.0137 memory: 15768 grad_norm: 4.3866 loss: 0.9488 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9488 2023/07/25 10:41:20 - mmengine - INFO - Epoch(train) [68][500/940] lr: 1.0000e-03 eta: 9:22:22 time: 1.1008 data_time: 0.0142 memory: 15768 grad_norm: 4.6187 loss: 0.8483 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8483 2023/07/25 10:41:42 - mmengine - INFO - Epoch(train) [68][520/940] lr: 1.0000e-03 eta: 9:21:59 time: 1.1005 data_time: 0.0142 memory: 15768 grad_norm: 4.4615 loss: 0.8884 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8884 2023/07/25 10:42:04 - mmengine - INFO - Epoch(train) [68][540/940] lr: 1.0000e-03 eta: 9:21:37 time: 1.1000 data_time: 0.0141 memory: 15768 grad_norm: 4.3845 loss: 0.8636 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8636 2023/07/25 10:42:26 - mmengine - INFO - Epoch(train) [68][560/940] lr: 1.0000e-03 eta: 9:21:15 time: 1.1011 data_time: 0.0141 memory: 15768 grad_norm: 4.4789 loss: 0.9233 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9233 2023/07/25 10:42:48 - mmengine - INFO - Epoch(train) [68][580/940] lr: 1.0000e-03 eta: 9:20:53 time: 1.1001 data_time: 0.0143 memory: 15768 grad_norm: 4.5079 loss: 0.7797 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7797 2023/07/25 10:43:10 - mmengine - INFO - Epoch(train) [68][600/940] lr: 1.0000e-03 eta: 9:20:31 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 4.4619 loss: 0.9294 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9294 2023/07/25 10:43:32 - mmengine - INFO - Epoch(train) [68][620/940] lr: 1.0000e-03 eta: 9:20:09 time: 1.1014 data_time: 0.0143 memory: 15768 grad_norm: 4.5317 loss: 0.9179 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9179 2023/07/25 10:43:54 - mmengine - INFO - Epoch(train) [68][640/940] lr: 1.0000e-03 eta: 9:19:46 time: 1.1010 data_time: 0.0140 memory: 15768 grad_norm: 4.4987 loss: 0.7950 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7950 2023/07/25 10:44:16 - mmengine - INFO - Epoch(train) [68][660/940] lr: 1.0000e-03 eta: 9:19:24 time: 1.0999 data_time: 0.0134 memory: 15768 grad_norm: 4.4537 loss: 0.9191 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9191 2023/07/25 10:44:38 - mmengine - INFO - Epoch(train) [68][680/940] lr: 1.0000e-03 eta: 9:19:02 time: 1.1035 data_time: 0.0138 memory: 15768 grad_norm: 4.4355 loss: 0.7247 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7247 2023/07/25 10:45:00 - mmengine - INFO - Epoch(train) [68][700/940] lr: 1.0000e-03 eta: 9:18:40 time: 1.1029 data_time: 0.0136 memory: 15768 grad_norm: 4.5166 loss: 0.8146 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8146 2023/07/25 10:45:22 - mmengine - INFO - Epoch(train) [68][720/940] lr: 1.0000e-03 eta: 9:18:18 time: 1.1003 data_time: 0.0137 memory: 15768 grad_norm: 4.4966 loss: 0.7661 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7661 2023/07/25 10:45:44 - mmengine - INFO - Epoch(train) [68][740/940] lr: 1.0000e-03 eta: 9:17:56 time: 1.1027 data_time: 0.0137 memory: 15768 grad_norm: 4.5311 loss: 0.9985 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9985 2023/07/25 10:46:06 - mmengine - INFO - Epoch(train) [68][760/940] lr: 1.0000e-03 eta: 9:17:34 time: 1.0999 data_time: 0.0141 memory: 15768 grad_norm: 4.6475 loss: 0.8876 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8876 2023/07/25 10:46:28 - mmengine - INFO - Epoch(train) [68][780/940] lr: 1.0000e-03 eta: 9:17:11 time: 1.0980 data_time: 0.0137 memory: 15768 grad_norm: 4.5066 loss: 0.8898 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8898 2023/07/25 10:46:50 - mmengine - INFO - Epoch(train) [68][800/940] lr: 1.0000e-03 eta: 9:16:49 time: 1.0980 data_time: 0.0138 memory: 15768 grad_norm: 4.5272 loss: 0.7857 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7857 2023/07/25 10:47:12 - mmengine - INFO - Epoch(train) [68][820/940] lr: 1.0000e-03 eta: 9:16:27 time: 1.1039 data_time: 0.0141 memory: 15768 grad_norm: 4.5448 loss: 0.9226 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9226 2023/07/25 10:47:34 - mmengine - INFO - Epoch(train) [68][840/940] lr: 1.0000e-03 eta: 9:16:05 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.5424 loss: 0.7626 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7626 2023/07/25 10:47:56 - mmengine - INFO - Epoch(train) [68][860/940] lr: 1.0000e-03 eta: 9:15:43 time: 1.0988 data_time: 0.0138 memory: 15768 grad_norm: 4.4414 loss: 0.8104 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8104 2023/07/25 10:48:18 - mmengine - INFO - Epoch(train) [68][880/940] lr: 1.0000e-03 eta: 9:15:21 time: 1.1007 data_time: 0.0141 memory: 15768 grad_norm: 4.5094 loss: 0.8154 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8154 2023/07/25 10:48:40 - mmengine - INFO - Epoch(train) [68][900/940] lr: 1.0000e-03 eta: 9:14:58 time: 1.0999 data_time: 0.0140 memory: 15768 grad_norm: 4.5679 loss: 0.8572 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8572 2023/07/25 10:49:02 - mmengine - INFO - Epoch(train) [68][920/940] lr: 1.0000e-03 eta: 9:14:36 time: 1.0985 data_time: 0.0142 memory: 15768 grad_norm: 4.4319 loss: 0.8949 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8949 2023/07/25 10:49:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 10:49:23 - mmengine - INFO - Epoch(train) [68][940/940] lr: 1.0000e-03 eta: 9:14:14 time: 1.0533 data_time: 0.0135 memory: 15768 grad_norm: 4.7000 loss: 0.8358 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.8358 2023/07/25 10:49:32 - mmengine - INFO - Epoch(val) [68][20/78] eta: 0:00:27 time: 0.4792 data_time: 0.3216 memory: 2147 2023/07/25 10:49:39 - mmengine - INFO - Epoch(val) [68][40/78] eta: 0:00:15 time: 0.3483 data_time: 0.1908 memory: 2147 2023/07/25 10:49:48 - mmengine - INFO - Epoch(val) [68][60/78] eta: 0:00:07 time: 0.4411 data_time: 0.2844 memory: 2147 2023/07/25 10:49:59 - mmengine - INFO - Epoch(val) [68][78/78] acc/top1: 0.7106 acc/top5: 0.8982 acc/mean1: 0.7105 data_time: 0.2418 time: 0.3962 2023/07/25 10:50:25 - mmengine - INFO - Epoch(train) [69][ 20/940] lr: 1.0000e-03 eta: 9:13:53 time: 1.2903 data_time: 0.1429 memory: 15768 grad_norm: 4.5776 loss: 0.9738 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9738 2023/07/25 10:50:47 - mmengine - INFO - Epoch(train) [69][ 40/940] lr: 1.0000e-03 eta: 9:13:31 time: 1.1007 data_time: 0.0140 memory: 15768 grad_norm: 4.4239 loss: 0.9600 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9600 2023/07/25 10:51:09 - mmengine - INFO - Epoch(train) [69][ 60/940] lr: 1.0000e-03 eta: 9:13:09 time: 1.1010 data_time: 0.0137 memory: 15768 grad_norm: 4.4620 loss: 0.7218 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7218 2023/07/25 10:51:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 10:51:31 - mmengine - INFO - Epoch(train) [69][ 80/940] lr: 1.0000e-03 eta: 9:12:47 time: 1.1013 data_time: 0.0138 memory: 15768 grad_norm: 4.4306 loss: 0.7565 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7565 2023/07/25 10:51:53 - mmengine - INFO - Epoch(train) [69][100/940] lr: 1.0000e-03 eta: 9:12:25 time: 1.0978 data_time: 0.0140 memory: 15768 grad_norm: 4.4138 loss: 0.8082 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8082 2023/07/25 10:52:15 - mmengine - INFO - Epoch(train) [69][120/940] lr: 1.0000e-03 eta: 9:12:03 time: 1.1016 data_time: 0.0140 memory: 15768 grad_norm: 4.5502 loss: 0.9150 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9150 2023/07/25 10:52:37 - mmengine - INFO - Epoch(train) [69][140/940] lr: 1.0000e-03 eta: 9:11:40 time: 1.1000 data_time: 0.0137 memory: 15768 grad_norm: 4.4545 loss: 1.0480 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0480 2023/07/25 10:52:59 - mmengine - INFO - Epoch(train) [69][160/940] lr: 1.0000e-03 eta: 9:11:18 time: 1.0997 data_time: 0.0136 memory: 15768 grad_norm: 4.4587 loss: 0.9886 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9886 2023/07/25 10:53:21 - mmengine - INFO - Epoch(train) [69][180/940] lr: 1.0000e-03 eta: 9:10:56 time: 1.1007 data_time: 0.0139 memory: 15768 grad_norm: 4.5537 loss: 0.9373 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9373 2023/07/25 10:53:43 - mmengine - INFO - Epoch(train) [69][200/940] lr: 1.0000e-03 eta: 9:10:34 time: 1.1011 data_time: 0.0138 memory: 15768 grad_norm: 4.5428 loss: 0.9207 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9207 2023/07/25 10:54:05 - mmengine - INFO - Epoch(train) [69][220/940] lr: 1.0000e-03 eta: 9:10:12 time: 1.1008 data_time: 0.0137 memory: 15768 grad_norm: 4.5297 loss: 0.8676 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8676 2023/07/25 10:54:27 - mmengine - INFO - Epoch(train) [69][240/940] lr: 1.0000e-03 eta: 9:09:50 time: 1.1035 data_time: 0.0134 memory: 15768 grad_norm: 4.4513 loss: 0.9962 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9962 2023/07/25 10:54:49 - mmengine - INFO - Epoch(train) [69][260/940] lr: 1.0000e-03 eta: 9:09:27 time: 1.1036 data_time: 0.0141 memory: 15768 grad_norm: 4.3869 loss: 0.7974 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7974 2023/07/25 10:55:11 - mmengine - INFO - Epoch(train) [69][280/940] lr: 1.0000e-03 eta: 9:09:05 time: 1.0988 data_time: 0.0141 memory: 15768 grad_norm: 4.4085 loss: 0.8224 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8224 2023/07/25 10:55:33 - mmengine - INFO - Epoch(train) [69][300/940] lr: 1.0000e-03 eta: 9:08:43 time: 1.1022 data_time: 0.0141 memory: 15768 grad_norm: 4.5310 loss: 0.9029 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9029 2023/07/25 10:55:55 - mmengine - INFO - Epoch(train) [69][320/940] lr: 1.0000e-03 eta: 9:08:21 time: 1.1000 data_time: 0.0138 memory: 15768 grad_norm: 4.4271 loss: 0.8500 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8500 2023/07/25 10:56:17 - mmengine - INFO - Epoch(train) [69][340/940] lr: 1.0000e-03 eta: 9:07:59 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.5297 loss: 0.7976 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7976 2023/07/25 10:56:39 - mmengine - INFO - Epoch(train) [69][360/940] lr: 1.0000e-03 eta: 9:07:37 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.4811 loss: 0.8409 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8409 2023/07/25 10:57:01 - mmengine - INFO - Epoch(train) [69][380/940] lr: 1.0000e-03 eta: 9:07:14 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.4220 loss: 0.8786 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8786 2023/07/25 10:57:23 - mmengine - INFO - Epoch(train) [69][400/940] lr: 1.0000e-03 eta: 9:06:52 time: 1.1048 data_time: 0.0136 memory: 15768 grad_norm: 4.4390 loss: 0.7873 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7873 2023/07/25 10:57:45 - mmengine - INFO - Epoch(train) [69][420/940] lr: 1.0000e-03 eta: 9:06:30 time: 1.1032 data_time: 0.0139 memory: 15768 grad_norm: 4.5550 loss: 0.8703 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8703 2023/07/25 10:58:07 - mmengine - INFO - Epoch(train) [69][440/940] lr: 1.0000e-03 eta: 9:06:08 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 4.3587 loss: 0.7487 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7487 2023/07/25 10:58:29 - mmengine - INFO - Epoch(train) [69][460/940] lr: 1.0000e-03 eta: 9:05:46 time: 1.1039 data_time: 0.0137 memory: 15768 grad_norm: 4.4601 loss: 0.9085 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9085 2023/07/25 10:58:51 - mmengine - INFO - Epoch(train) [69][480/940] lr: 1.0000e-03 eta: 9:05:24 time: 1.1008 data_time: 0.0142 memory: 15768 grad_norm: 4.5008 loss: 0.7975 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7975 2023/07/25 10:59:13 - mmengine - INFO - Epoch(train) [69][500/940] lr: 1.0000e-03 eta: 9:05:02 time: 1.1010 data_time: 0.0143 memory: 15768 grad_norm: 4.4128 loss: 0.8374 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8374 2023/07/25 10:59:35 - mmengine - INFO - Epoch(train) [69][520/940] lr: 1.0000e-03 eta: 9:04:40 time: 1.1033 data_time: 0.0142 memory: 15768 grad_norm: 4.4990 loss: 0.7472 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7472 2023/07/25 10:59:57 - mmengine - INFO - Epoch(train) [69][540/940] lr: 1.0000e-03 eta: 9:04:17 time: 1.1023 data_time: 0.0146 memory: 15768 grad_norm: 4.6596 loss: 0.8020 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8020 2023/07/25 11:00:19 - mmengine - INFO - Epoch(train) [69][560/940] lr: 1.0000e-03 eta: 9:03:55 time: 1.1002 data_time: 0.0138 memory: 15768 grad_norm: 4.5614 loss: 0.8461 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8461 2023/07/25 11:00:41 - mmengine - INFO - Epoch(train) [69][580/940] lr: 1.0000e-03 eta: 9:03:33 time: 1.1017 data_time: 0.0136 memory: 15768 grad_norm: 4.4523 loss: 0.7809 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7809 2023/07/25 11:01:03 - mmengine - INFO - Epoch(train) [69][600/940] lr: 1.0000e-03 eta: 9:03:11 time: 1.0996 data_time: 0.0143 memory: 15768 grad_norm: 4.5006 loss: 0.8604 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.8604 2023/07/25 11:01:25 - mmengine - INFO - Epoch(train) [69][620/940] lr: 1.0000e-03 eta: 9:02:49 time: 1.1023 data_time: 0.0139 memory: 15768 grad_norm: 4.5594 loss: 0.7936 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7936 2023/07/25 11:01:47 - mmengine - INFO - Epoch(train) [69][640/940] lr: 1.0000e-03 eta: 9:02:27 time: 1.1000 data_time: 0.0139 memory: 15768 grad_norm: 4.4109 loss: 0.8428 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8428 2023/07/25 11:02:09 - mmengine - INFO - Epoch(train) [69][660/940] lr: 1.0000e-03 eta: 9:02:04 time: 1.1010 data_time: 0.0141 memory: 15768 grad_norm: 4.3547 loss: 0.8616 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8616 2023/07/25 11:02:31 - mmengine - INFO - Epoch(train) [69][680/940] lr: 1.0000e-03 eta: 9:01:42 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 4.5056 loss: 0.8285 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8285 2023/07/25 11:02:53 - mmengine - INFO - Epoch(train) [69][700/940] lr: 1.0000e-03 eta: 9:01:20 time: 1.0992 data_time: 0.0139 memory: 15768 grad_norm: 4.4713 loss: 0.9592 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9592 2023/07/25 11:03:15 - mmengine - INFO - Epoch(train) [69][720/940] lr: 1.0000e-03 eta: 9:00:58 time: 1.0999 data_time: 0.0139 memory: 15768 grad_norm: 4.5213 loss: 0.8762 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8762 2023/07/25 11:03:37 - mmengine - INFO - Epoch(train) [69][740/940] lr: 1.0000e-03 eta: 9:00:36 time: 1.1040 data_time: 0.0135 memory: 15768 grad_norm: 4.3485 loss: 0.9092 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9092 2023/07/25 11:03:59 - mmengine - INFO - Epoch(train) [69][760/940] lr: 1.0000e-03 eta: 9:00:14 time: 1.0986 data_time: 0.0138 memory: 15768 grad_norm: 4.4616 loss: 0.9593 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9593 2023/07/25 11:04:21 - mmengine - INFO - Epoch(train) [69][780/940] lr: 1.0000e-03 eta: 8:59:51 time: 1.0991 data_time: 0.0139 memory: 15768 grad_norm: 4.6020 loss: 0.8462 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8462 2023/07/25 11:04:43 - mmengine - INFO - Epoch(train) [69][800/940] lr: 1.0000e-03 eta: 8:59:29 time: 1.1024 data_time: 0.0141 memory: 15768 grad_norm: 4.5035 loss: 0.7963 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7963 2023/07/25 11:05:06 - mmengine - INFO - Epoch(train) [69][820/940] lr: 1.0000e-03 eta: 8:59:07 time: 1.1017 data_time: 0.0141 memory: 15768 grad_norm: 4.4799 loss: 0.8778 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8778 2023/07/25 11:05:28 - mmengine - INFO - Epoch(train) [69][840/940] lr: 1.0000e-03 eta: 8:58:45 time: 1.1002 data_time: 0.0142 memory: 15768 grad_norm: 4.4664 loss: 0.9978 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9978 2023/07/25 11:05:50 - mmengine - INFO - Epoch(train) [69][860/940] lr: 1.0000e-03 eta: 8:58:23 time: 1.1089 data_time: 0.0138 memory: 15768 grad_norm: 4.4985 loss: 0.9108 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9108 2023/07/25 11:06:12 - mmengine - INFO - Epoch(train) [69][880/940] lr: 1.0000e-03 eta: 8:58:01 time: 1.0990 data_time: 0.0139 memory: 15768 grad_norm: 4.4620 loss: 0.6682 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6682 2023/07/25 11:06:34 - mmengine - INFO - Epoch(train) [69][900/940] lr: 1.0000e-03 eta: 8:57:39 time: 1.1017 data_time: 0.0140 memory: 15768 grad_norm: 4.4357 loss: 0.8922 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8922 2023/07/25 11:06:56 - mmengine - INFO - Epoch(train) [69][920/940] lr: 1.0000e-03 eta: 8:57:17 time: 1.1011 data_time: 0.0137 memory: 15768 grad_norm: 4.5344 loss: 0.7404 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7404 2023/07/25 11:07:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 11:07:17 - mmengine - INFO - Epoch(train) [69][940/940] lr: 1.0000e-03 eta: 8:56:54 time: 1.0551 data_time: 0.0134 memory: 15768 grad_norm: 4.8620 loss: 1.0231 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.0231 2023/07/25 11:07:17 - mmengine - INFO - Saving checkpoint at 69 epochs 2023/07/25 11:07:28 - mmengine - INFO - Epoch(val) [69][20/78] eta: 0:00:27 time: 0.4823 data_time: 0.3249 memory: 2147 2023/07/25 11:07:35 - mmengine - INFO - Epoch(val) [69][40/78] eta: 0:00:16 time: 0.3615 data_time: 0.2044 memory: 2147 2023/07/25 11:07:44 - mmengine - INFO - Epoch(val) [69][60/78] eta: 0:00:07 time: 0.4502 data_time: 0.2934 memory: 2147 2023/07/25 11:07:53 - mmengine - INFO - Epoch(val) [69][78/78] acc/top1: 0.7113 acc/top5: 0.8986 acc/mean1: 0.7112 data_time: 0.2469 time: 0.4011 2023/07/25 11:08:19 - mmengine - INFO - Epoch(train) [70][ 20/940] lr: 1.0000e-03 eta: 8:56:34 time: 1.2945 data_time: 0.1575 memory: 15768 grad_norm: 4.3765 loss: 0.7721 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7721 2023/07/25 11:08:41 - mmengine - INFO - Epoch(train) [70][ 40/940] lr: 1.0000e-03 eta: 8:56:11 time: 1.0988 data_time: 0.0140 memory: 15768 grad_norm: 4.4912 loss: 0.7145 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7145 2023/07/25 11:09:03 - mmengine - INFO - Epoch(train) [70][ 60/940] lr: 1.0000e-03 eta: 8:55:49 time: 1.0994 data_time: 0.0139 memory: 15768 grad_norm: 4.4902 loss: 0.8964 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.8964 2023/07/25 11:09:25 - mmengine - INFO - Epoch(train) [70][ 80/940] lr: 1.0000e-03 eta: 8:55:27 time: 1.1007 data_time: 0.0139 memory: 15768 grad_norm: 4.4788 loss: 0.7379 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7379 2023/07/25 11:09:47 - mmengine - INFO - Epoch(train) [70][100/940] lr: 1.0000e-03 eta: 8:55:05 time: 1.1020 data_time: 0.0137 memory: 15768 grad_norm: 4.3997 loss: 0.8135 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8135 2023/07/25 11:10:09 - mmengine - INFO - Epoch(train) [70][120/940] lr: 1.0000e-03 eta: 8:54:43 time: 1.1034 data_time: 0.0136 memory: 15768 grad_norm: 4.5005 loss: 0.6988 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6988 2023/07/25 11:10:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 11:10:31 - mmengine - INFO - Epoch(train) [70][140/940] lr: 1.0000e-03 eta: 8:54:21 time: 1.1009 data_time: 0.0136 memory: 15768 grad_norm: 4.5024 loss: 0.7728 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7728 2023/07/25 11:10:53 - mmengine - INFO - Epoch(train) [70][160/940] lr: 1.0000e-03 eta: 8:53:59 time: 1.1069 data_time: 0.0133 memory: 15768 grad_norm: 4.4200 loss: 0.8150 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8150 2023/07/25 11:11:15 - mmengine - INFO - Epoch(train) [70][180/940] lr: 1.0000e-03 eta: 8:53:36 time: 1.1029 data_time: 0.0140 memory: 15768 grad_norm: 4.5384 loss: 0.8431 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8431 2023/07/25 11:11:37 - mmengine - INFO - Epoch(train) [70][200/940] lr: 1.0000e-03 eta: 8:53:14 time: 1.0992 data_time: 0.0143 memory: 15768 grad_norm: 4.4839 loss: 0.8570 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8570 2023/07/25 11:11:59 - mmengine - INFO - Epoch(train) [70][220/940] lr: 1.0000e-03 eta: 8:52:52 time: 1.1025 data_time: 0.0142 memory: 15768 grad_norm: 4.5087 loss: 0.8104 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8104 2023/07/25 11:12:21 - mmengine - INFO - Epoch(train) [70][240/940] lr: 1.0000e-03 eta: 8:52:30 time: 1.0989 data_time: 0.0142 memory: 15768 grad_norm: 4.5148 loss: 0.7828 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7828 2023/07/25 11:12:43 - mmengine - INFO - Epoch(train) [70][260/940] lr: 1.0000e-03 eta: 8:52:08 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.5728 loss: 0.7836 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7836 2023/07/25 11:13:05 - mmengine - INFO - Epoch(train) [70][280/940] lr: 1.0000e-03 eta: 8:51:46 time: 1.1017 data_time: 0.0136 memory: 15768 grad_norm: 4.5228 loss: 0.9189 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9189 2023/07/25 11:13:27 - mmengine - INFO - Epoch(train) [70][300/940] lr: 1.0000e-03 eta: 8:51:23 time: 1.1036 data_time: 0.0140 memory: 15768 grad_norm: 4.4929 loss: 0.9374 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9374 2023/07/25 11:13:49 - mmengine - INFO - Epoch(train) [70][320/940] lr: 1.0000e-03 eta: 8:51:01 time: 1.1024 data_time: 0.0138 memory: 15768 grad_norm: 4.4615 loss: 0.8655 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8655 2023/07/25 11:14:11 - mmengine - INFO - Epoch(train) [70][340/940] lr: 1.0000e-03 eta: 8:50:39 time: 1.1055 data_time: 0.0140 memory: 15768 grad_norm: 4.5330 loss: 0.8804 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8804 2023/07/25 11:14:33 - mmengine - INFO - Epoch(train) [70][360/940] lr: 1.0000e-03 eta: 8:50:17 time: 1.1062 data_time: 0.0143 memory: 15768 grad_norm: 4.5328 loss: 0.9432 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9432 2023/07/25 11:14:55 - mmengine - INFO - Epoch(train) [70][380/940] lr: 1.0000e-03 eta: 8:49:55 time: 1.1018 data_time: 0.0143 memory: 15768 grad_norm: 4.5026 loss: 0.7471 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7471 2023/07/25 11:15:17 - mmengine - INFO - Epoch(train) [70][400/940] lr: 1.0000e-03 eta: 8:49:33 time: 1.1006 data_time: 0.0139 memory: 15768 grad_norm: 4.4789 loss: 0.8100 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8100 2023/07/25 11:15:39 - mmengine - INFO - Epoch(train) [70][420/940] lr: 1.0000e-03 eta: 8:49:11 time: 1.1034 data_time: 0.0137 memory: 15768 grad_norm: 4.5738 loss: 0.7673 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7673 2023/07/25 11:16:02 - mmengine - INFO - Epoch(train) [70][440/940] lr: 1.0000e-03 eta: 8:48:49 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.5131 loss: 0.7804 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7804 2023/07/25 11:16:24 - mmengine - INFO - Epoch(train) [70][460/940] lr: 1.0000e-03 eta: 8:48:26 time: 1.0997 data_time: 0.0140 memory: 15768 grad_norm: 4.5346 loss: 0.9114 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9114 2023/07/25 11:16:46 - mmengine - INFO - Epoch(train) [70][480/940] lr: 1.0000e-03 eta: 8:48:04 time: 1.1010 data_time: 0.0138 memory: 15768 grad_norm: 4.4663 loss: 0.7127 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.7127 2023/07/25 11:17:08 - mmengine - INFO - Epoch(train) [70][500/940] lr: 1.0000e-03 eta: 8:47:42 time: 1.1027 data_time: 0.0138 memory: 15768 grad_norm: 4.5143 loss: 0.8659 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8659 2023/07/25 11:17:30 - mmengine - INFO - Epoch(train) [70][520/940] lr: 1.0000e-03 eta: 8:47:20 time: 1.1003 data_time: 0.0138 memory: 15768 grad_norm: 4.4665 loss: 0.7987 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7987 2023/07/25 11:17:52 - mmengine - INFO - Epoch(train) [70][540/940] lr: 1.0000e-03 eta: 8:46:58 time: 1.1021 data_time: 0.0142 memory: 15768 grad_norm: 4.5177 loss: 0.8169 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8169 2023/07/25 11:18:14 - mmengine - INFO - Epoch(train) [70][560/940] lr: 1.0000e-03 eta: 8:46:36 time: 1.1002 data_time: 0.0142 memory: 15768 grad_norm: 4.5524 loss: 0.8247 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8247 2023/07/25 11:18:36 - mmengine - INFO - Epoch(train) [70][580/940] lr: 1.0000e-03 eta: 8:46:13 time: 1.1002 data_time: 0.0140 memory: 15768 grad_norm: 4.4779 loss: 0.7457 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.7457 2023/07/25 11:18:58 - mmengine - INFO - Epoch(train) [70][600/940] lr: 1.0000e-03 eta: 8:45:51 time: 1.1180 data_time: 0.0140 memory: 15768 grad_norm: 4.5527 loss: 0.9045 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9045 2023/07/25 11:19:21 - mmengine - INFO - Epoch(train) [70][620/940] lr: 1.0000e-03 eta: 8:45:30 time: 1.1732 data_time: 0.0140 memory: 15768 grad_norm: 4.5701 loss: 0.9296 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9296 2023/07/25 11:19:45 - mmengine - INFO - Epoch(train) [70][640/940] lr: 1.0000e-03 eta: 8:45:08 time: 1.1639 data_time: 0.0142 memory: 15768 grad_norm: 4.5110 loss: 0.9531 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9531 2023/07/25 11:20:07 - mmengine - INFO - Epoch(train) [70][660/940] lr: 1.0000e-03 eta: 8:44:46 time: 1.1071 data_time: 0.0141 memory: 15768 grad_norm: 4.4482 loss: 0.9191 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9191 2023/07/25 11:20:29 - mmengine - INFO - Epoch(train) [70][680/940] lr: 1.0000e-03 eta: 8:44:24 time: 1.1055 data_time: 0.0140 memory: 15768 grad_norm: 4.5565 loss: 0.7825 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7825 2023/07/25 11:20:51 - mmengine - INFO - Epoch(train) [70][700/940] lr: 1.0000e-03 eta: 8:44:02 time: 1.1026 data_time: 0.0135 memory: 15768 grad_norm: 4.4417 loss: 0.7278 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7278 2023/07/25 11:21:13 - mmengine - INFO - Epoch(train) [70][720/940] lr: 1.0000e-03 eta: 8:43:40 time: 1.1019 data_time: 0.0137 memory: 15768 grad_norm: 4.4650 loss: 0.9248 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9248 2023/07/25 11:21:35 - mmengine - INFO - Epoch(train) [70][740/940] lr: 1.0000e-03 eta: 8:43:18 time: 1.1002 data_time: 0.0138 memory: 15768 grad_norm: 4.5647 loss: 0.8523 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8523 2023/07/25 11:21:57 - mmengine - INFO - Epoch(train) [70][760/940] lr: 1.0000e-03 eta: 8:42:56 time: 1.1034 data_time: 0.0137 memory: 15768 grad_norm: 4.4765 loss: 0.8556 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8556 2023/07/25 11:22:19 - mmengine - INFO - Epoch(train) [70][780/940] lr: 1.0000e-03 eta: 8:42:33 time: 1.0996 data_time: 0.0140 memory: 15768 grad_norm: 4.5713 loss: 0.8293 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8293 2023/07/25 11:22:41 - mmengine - INFO - Epoch(train) [70][800/940] lr: 1.0000e-03 eta: 8:42:11 time: 1.1013 data_time: 0.0138 memory: 15768 grad_norm: 4.7147 loss: 0.9591 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9591 2023/07/25 11:23:03 - mmengine - INFO - Epoch(train) [70][820/940] lr: 1.0000e-03 eta: 8:41:49 time: 1.1015 data_time: 0.0139 memory: 15768 grad_norm: 4.5992 loss: 0.7714 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7714 2023/07/25 11:23:25 - mmengine - INFO - Epoch(train) [70][840/940] lr: 1.0000e-03 eta: 8:41:27 time: 1.1001 data_time: 0.0140 memory: 15768 grad_norm: 4.5935 loss: 0.8437 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8437 2023/07/25 11:23:47 - mmengine - INFO - Epoch(train) [70][860/940] lr: 1.0000e-03 eta: 8:41:05 time: 1.1018 data_time: 0.0137 memory: 15768 grad_norm: 4.5290 loss: 0.9334 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9334 2023/07/25 11:24:09 - mmengine - INFO - Epoch(train) [70][880/940] lr: 1.0000e-03 eta: 8:40:43 time: 1.1041 data_time: 0.0140 memory: 15768 grad_norm: 4.6491 loss: 0.9921 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9921 2023/07/25 11:24:31 - mmengine - INFO - Epoch(train) [70][900/940] lr: 1.0000e-03 eta: 8:40:21 time: 1.0992 data_time: 0.0137 memory: 15768 grad_norm: 4.3980 loss: 0.7761 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7761 2023/07/25 11:24:53 - mmengine - INFO - Epoch(train) [70][920/940] lr: 1.0000e-03 eta: 8:39:58 time: 1.1016 data_time: 0.0135 memory: 15768 grad_norm: 4.5633 loss: 0.8608 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8608 2023/07/25 11:25:14 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 11:25:14 - mmengine - INFO - Epoch(train) [70][940/940] lr: 1.0000e-03 eta: 8:39:36 time: 1.0548 data_time: 0.0134 memory: 15768 grad_norm: 4.9404 loss: 1.0476 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.0476 2023/07/25 11:25:25 - mmengine - INFO - Epoch(val) [70][20/78] eta: 0:00:29 time: 0.5043 data_time: 0.3465 memory: 2147 2023/07/25 11:25:31 - mmengine - INFO - Epoch(val) [70][40/78] eta: 0:00:16 time: 0.3387 data_time: 0.1813 memory: 2147 2023/07/25 11:25:40 - mmengine - INFO - Epoch(val) [70][60/78] eta: 0:00:07 time: 0.4415 data_time: 0.2847 memory: 2147 2023/07/25 11:25:51 - mmengine - INFO - Epoch(val) [70][78/78] acc/top1: 0.7095 acc/top5: 0.8996 acc/mean1: 0.7094 data_time: 0.2482 time: 0.4027 2023/07/25 11:26:16 - mmengine - INFO - Epoch(train) [71][ 20/940] lr: 1.0000e-03 eta: 8:39:15 time: 1.2877 data_time: 0.1572 memory: 15768 grad_norm: 4.5337 loss: 0.9006 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9006 2023/07/25 11:26:38 - mmengine - INFO - Epoch(train) [71][ 40/940] lr: 1.0000e-03 eta: 8:38:53 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.4908 loss: 0.7328 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7328 2023/07/25 11:27:00 - mmengine - INFO - Epoch(train) [71][ 60/940] lr: 1.0000e-03 eta: 8:38:31 time: 1.1016 data_time: 0.0139 memory: 15768 grad_norm: 4.5781 loss: 0.8012 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8012 2023/07/25 11:27:22 - mmengine - INFO - Epoch(train) [71][ 80/940] lr: 1.0000e-03 eta: 8:38:09 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.4076 loss: 0.8472 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8472 2023/07/25 11:27:44 - mmengine - INFO - Epoch(train) [71][100/940] lr: 1.0000e-03 eta: 8:37:47 time: 1.1002 data_time: 0.0137 memory: 15768 grad_norm: 4.4700 loss: 0.8175 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8175 2023/07/25 11:28:06 - mmengine - INFO - Epoch(train) [71][120/940] lr: 1.0000e-03 eta: 8:37:25 time: 1.1020 data_time: 0.0140 memory: 15768 grad_norm: 4.4215 loss: 0.7879 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7879 2023/07/25 11:28:28 - mmengine - INFO - Epoch(train) [71][140/940] lr: 1.0000e-03 eta: 8:37:02 time: 1.0990 data_time: 0.0145 memory: 15768 grad_norm: 4.4574 loss: 0.8416 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8416 2023/07/25 11:28:50 - mmengine - INFO - Epoch(train) [71][160/940] lr: 1.0000e-03 eta: 8:36:40 time: 1.1023 data_time: 0.0140 memory: 15768 grad_norm: 4.5904 loss: 0.9690 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9690 2023/07/25 11:29:12 - mmengine - INFO - Epoch(train) [71][180/940] lr: 1.0000e-03 eta: 8:36:18 time: 1.0998 data_time: 0.0139 memory: 15768 grad_norm: 4.4602 loss: 0.7947 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7947 2023/07/25 11:29:35 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 11:29:35 - mmengine - INFO - Epoch(train) [71][200/940] lr: 1.0000e-03 eta: 8:35:56 time: 1.1032 data_time: 0.0139 memory: 15768 grad_norm: 4.5482 loss: 0.8269 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8269 2023/07/25 11:29:57 - mmengine - INFO - Epoch(train) [71][220/940] lr: 1.0000e-03 eta: 8:35:34 time: 1.0996 data_time: 0.0144 memory: 15768 grad_norm: 4.6392 loss: 0.8892 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8892 2023/07/25 11:30:19 - mmengine - INFO - Epoch(train) [71][240/940] lr: 1.0000e-03 eta: 8:35:12 time: 1.1030 data_time: 0.0144 memory: 15768 grad_norm: 4.5364 loss: 0.9544 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9544 2023/07/25 11:30:41 - mmengine - INFO - Epoch(train) [71][260/940] lr: 1.0000e-03 eta: 8:34:50 time: 1.1107 data_time: 0.0154 memory: 15768 grad_norm: 4.4351 loss: 0.6985 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6985 2023/07/25 11:31:03 - mmengine - INFO - Epoch(train) [71][280/940] lr: 1.0000e-03 eta: 8:34:27 time: 1.1003 data_time: 0.0138 memory: 15768 grad_norm: 4.5229 loss: 0.8404 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8404 2023/07/25 11:31:25 - mmengine - INFO - Epoch(train) [71][300/940] lr: 1.0000e-03 eta: 8:34:05 time: 1.0994 data_time: 0.0141 memory: 15768 grad_norm: 4.4915 loss: 0.9566 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9566 2023/07/25 11:31:47 - mmengine - INFO - Epoch(train) [71][320/940] lr: 1.0000e-03 eta: 8:33:43 time: 1.1004 data_time: 0.0136 memory: 15768 grad_norm: 4.5297 loss: 0.8509 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8509 2023/07/25 11:32:09 - mmengine - INFO - Epoch(train) [71][340/940] lr: 1.0000e-03 eta: 8:33:21 time: 1.1021 data_time: 0.0138 memory: 15768 grad_norm: 4.4726 loss: 0.8233 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8233 2023/07/25 11:32:31 - mmengine - INFO - Epoch(train) [71][360/940] lr: 1.0000e-03 eta: 8:32:59 time: 1.0981 data_time: 0.0141 memory: 15768 grad_norm: 4.5246 loss: 0.7774 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7774 2023/07/25 11:32:53 - mmengine - INFO - Epoch(train) [71][380/940] lr: 1.0000e-03 eta: 8:32:37 time: 1.1034 data_time: 0.0141 memory: 15768 grad_norm: 4.5557 loss: 0.9480 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9480 2023/07/25 11:33:15 - mmengine - INFO - Epoch(train) [71][400/940] lr: 1.0000e-03 eta: 8:32:14 time: 1.0993 data_time: 0.0140 memory: 15768 grad_norm: 4.5347 loss: 0.8321 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8321 2023/07/25 11:33:37 - mmengine - INFO - Epoch(train) [71][420/940] lr: 1.0000e-03 eta: 8:31:52 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.5798 loss: 0.8839 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8839 2023/07/25 11:33:59 - mmengine - INFO - Epoch(train) [71][440/940] lr: 1.0000e-03 eta: 8:31:30 time: 1.1034 data_time: 0.0138 memory: 15768 grad_norm: 4.5432 loss: 0.7714 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7714 2023/07/25 11:34:21 - mmengine - INFO - Epoch(train) [71][460/940] lr: 1.0000e-03 eta: 8:31:08 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.4988 loss: 0.9146 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9146 2023/07/25 11:34:43 - mmengine - INFO - Epoch(train) [71][480/940] lr: 1.0000e-03 eta: 8:30:46 time: 1.1009 data_time: 0.0140 memory: 15768 grad_norm: 4.6015 loss: 0.8022 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8022 2023/07/25 11:35:05 - mmengine - INFO - Epoch(train) [71][500/940] lr: 1.0000e-03 eta: 8:30:24 time: 1.0986 data_time: 0.0142 memory: 15768 grad_norm: 4.5769 loss: 0.9460 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9460 2023/07/25 11:35:27 - mmengine - INFO - Epoch(train) [71][520/940] lr: 1.0000e-03 eta: 8:30:02 time: 1.1026 data_time: 0.0143 memory: 15768 grad_norm: 4.5460 loss: 0.8706 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8706 2023/07/25 11:35:49 - mmengine - INFO - Epoch(train) [71][540/940] lr: 1.0000e-03 eta: 8:29:39 time: 1.1044 data_time: 0.0137 memory: 15768 grad_norm: 4.5170 loss: 0.8773 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8773 2023/07/25 11:36:11 - mmengine - INFO - Epoch(train) [71][560/940] lr: 1.0000e-03 eta: 8:29:17 time: 1.1045 data_time: 0.0137 memory: 15768 grad_norm: 4.6554 loss: 0.8606 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8606 2023/07/25 11:36:33 - mmengine - INFO - Epoch(train) [71][580/940] lr: 1.0000e-03 eta: 8:28:55 time: 1.0997 data_time: 0.0141 memory: 15768 grad_norm: 4.4511 loss: 0.9211 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9211 2023/07/25 11:36:55 - mmengine - INFO - Epoch(train) [71][600/940] lr: 1.0000e-03 eta: 8:28:33 time: 1.1045 data_time: 0.0138 memory: 15768 grad_norm: 4.5268 loss: 0.9482 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9482 2023/07/25 11:37:17 - mmengine - INFO - Epoch(train) [71][620/940] lr: 1.0000e-03 eta: 8:28:11 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.5093 loss: 0.8976 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8976 2023/07/25 11:37:39 - mmengine - INFO - Epoch(train) [71][640/940] lr: 1.0000e-03 eta: 8:27:49 time: 1.1034 data_time: 0.0139 memory: 15768 grad_norm: 4.5583 loss: 1.0417 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0417 2023/07/25 11:38:01 - mmengine - INFO - Epoch(train) [71][660/940] lr: 1.0000e-03 eta: 8:27:27 time: 1.1015 data_time: 0.0138 memory: 15768 grad_norm: 4.5624 loss: 0.8414 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8414 2023/07/25 11:38:23 - mmengine - INFO - Epoch(train) [71][680/940] lr: 1.0000e-03 eta: 8:27:04 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 4.5421 loss: 0.8427 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8427 2023/07/25 11:38:45 - mmengine - INFO - Epoch(train) [71][700/940] lr: 1.0000e-03 eta: 8:26:42 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.5052 loss: 0.8153 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8153 2023/07/25 11:39:07 - mmengine - INFO - Epoch(train) [71][720/940] lr: 1.0000e-03 eta: 8:26:20 time: 1.1026 data_time: 0.0140 memory: 15768 grad_norm: 4.5711 loss: 0.8050 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8050 2023/07/25 11:39:30 - mmengine - INFO - Epoch(train) [71][740/940] lr: 1.0000e-03 eta: 8:25:58 time: 1.1026 data_time: 0.0140 memory: 15768 grad_norm: 4.5303 loss: 0.8234 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8234 2023/07/25 11:39:52 - mmengine - INFO - Epoch(train) [71][760/940] lr: 1.0000e-03 eta: 8:25:36 time: 1.1015 data_time: 0.0143 memory: 15768 grad_norm: 4.5322 loss: 0.8147 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8147 2023/07/25 11:40:14 - mmengine - INFO - Epoch(train) [71][780/940] lr: 1.0000e-03 eta: 8:25:14 time: 1.1023 data_time: 0.0138 memory: 15768 grad_norm: 4.5552 loss: 0.9189 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9189 2023/07/25 11:40:36 - mmengine - INFO - Epoch(train) [71][800/940] lr: 1.0000e-03 eta: 8:24:52 time: 1.1027 data_time: 0.0140 memory: 15768 grad_norm: 4.5277 loss: 0.7847 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7847 2023/07/25 11:40:58 - mmengine - INFO - Epoch(train) [71][820/940] lr: 1.0000e-03 eta: 8:24:29 time: 1.1007 data_time: 0.0138 memory: 15768 grad_norm: 4.5353 loss: 0.8203 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8203 2023/07/25 11:41:20 - mmengine - INFO - Epoch(train) [71][840/940] lr: 1.0000e-03 eta: 8:24:07 time: 1.1018 data_time: 0.0136 memory: 15768 grad_norm: 4.5808 loss: 0.8447 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8447 2023/07/25 11:41:42 - mmengine - INFO - Epoch(train) [71][860/940] lr: 1.0000e-03 eta: 8:23:45 time: 1.1019 data_time: 0.0136 memory: 15768 grad_norm: 4.6162 loss: 0.8063 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8063 2023/07/25 11:42:04 - mmengine - INFO - Epoch(train) [71][880/940] lr: 1.0000e-03 eta: 8:23:23 time: 1.1026 data_time: 0.0140 memory: 15768 grad_norm: 4.5622 loss: 0.7965 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7965 2023/07/25 11:42:26 - mmengine - INFO - Epoch(train) [71][900/940] lr: 1.0000e-03 eta: 8:23:01 time: 1.0998 data_time: 0.0146 memory: 15768 grad_norm: 4.5378 loss: 0.8353 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8353 2023/07/25 11:42:48 - mmengine - INFO - Epoch(train) [71][920/940] lr: 1.0000e-03 eta: 8:22:39 time: 1.1038 data_time: 0.0137 memory: 15768 grad_norm: 4.4947 loss: 0.9292 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9292 2023/07/25 11:43:09 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 11:43:09 - mmengine - INFO - Epoch(train) [71][940/940] lr: 1.0000e-03 eta: 8:22:16 time: 1.0545 data_time: 0.0135 memory: 15768 grad_norm: 4.8977 loss: 0.9029 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9029 2023/07/25 11:43:19 - mmengine - INFO - Epoch(val) [71][20/78] eta: 0:00:28 time: 0.4950 data_time: 0.3368 memory: 2147 2023/07/25 11:43:26 - mmengine - INFO - Epoch(val) [71][40/78] eta: 0:00:16 time: 0.3474 data_time: 0.1903 memory: 2147 2023/07/25 11:43:35 - mmengine - INFO - Epoch(val) [71][60/78] eta: 0:00:07 time: 0.4383 data_time: 0.2811 memory: 2147 2023/07/25 11:43:46 - mmengine - INFO - Epoch(val) [71][78/78] acc/top1: 0.7099 acc/top5: 0.8994 acc/mean1: 0.7098 data_time: 0.2461 time: 0.4007 2023/07/25 11:44:12 - mmengine - INFO - Epoch(train) [72][ 20/940] lr: 1.0000e-03 eta: 8:21:56 time: 1.3141 data_time: 0.1556 memory: 15768 grad_norm: 4.4713 loss: 0.8425 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8425 2023/07/25 11:44:34 - mmengine - INFO - Epoch(train) [72][ 40/940] lr: 1.0000e-03 eta: 8:21:34 time: 1.1026 data_time: 0.0141 memory: 15768 grad_norm: 4.5954 loss: 0.8685 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8685 2023/07/25 11:44:56 - mmengine - INFO - Epoch(train) [72][ 60/940] lr: 1.0000e-03 eta: 8:21:12 time: 1.1018 data_time: 0.0143 memory: 15768 grad_norm: 4.4989 loss: 0.8496 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8496 2023/07/25 11:45:18 - mmengine - INFO - Epoch(train) [72][ 80/940] lr: 1.0000e-03 eta: 8:20:49 time: 1.1038 data_time: 0.0139 memory: 15768 grad_norm: 4.4812 loss: 0.8842 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8842 2023/07/25 11:45:40 - mmengine - INFO - Epoch(train) [72][100/940] lr: 1.0000e-03 eta: 8:20:27 time: 1.1002 data_time: 0.0141 memory: 15768 grad_norm: 4.4991 loss: 0.8931 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8931 2023/07/25 11:46:02 - mmengine - INFO - Epoch(train) [72][120/940] lr: 1.0000e-03 eta: 8:20:05 time: 1.1007 data_time: 0.0141 memory: 15768 grad_norm: 4.4747 loss: 0.7861 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7861 2023/07/25 11:46:24 - mmengine - INFO - Epoch(train) [72][140/940] lr: 1.0000e-03 eta: 8:19:43 time: 1.1033 data_time: 0.0139 memory: 15768 grad_norm: 4.5286 loss: 0.8436 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8436 2023/07/25 11:46:46 - mmengine - INFO - Epoch(train) [72][160/940] lr: 1.0000e-03 eta: 8:19:21 time: 1.0980 data_time: 0.0139 memory: 15768 grad_norm: 4.5189 loss: 0.8163 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8163 2023/07/25 11:47:08 - mmengine - INFO - Epoch(train) [72][180/940] lr: 1.0000e-03 eta: 8:18:59 time: 1.0996 data_time: 0.0140 memory: 15768 grad_norm: 4.5322 loss: 0.7453 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7453 2023/07/25 11:47:30 - mmengine - INFO - Epoch(train) [72][200/940] lr: 1.0000e-03 eta: 8:18:36 time: 1.0973 data_time: 0.0138 memory: 15768 grad_norm: 4.5822 loss: 0.7602 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7602 2023/07/25 11:47:52 - mmengine - INFO - Epoch(train) [72][220/940] lr: 1.0000e-03 eta: 8:18:14 time: 1.0993 data_time: 0.0140 memory: 15768 grad_norm: 4.5904 loss: 0.7637 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7637 2023/07/25 11:48:14 - mmengine - INFO - Epoch(train) [72][240/940] lr: 1.0000e-03 eta: 8:17:52 time: 1.1008 data_time: 0.0137 memory: 15768 grad_norm: 4.5283 loss: 0.7984 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7984 2023/07/25 11:48:36 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 11:48:36 - mmengine - INFO - Epoch(train) [72][260/940] lr: 1.0000e-03 eta: 8:17:30 time: 1.0999 data_time: 0.0138 memory: 15768 grad_norm: 4.6598 loss: 0.9666 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9666 2023/07/25 11:48:58 - mmengine - INFO - Epoch(train) [72][280/940] lr: 1.0000e-03 eta: 8:17:08 time: 1.1026 data_time: 0.0138 memory: 15768 grad_norm: 4.6627 loss: 0.9142 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9142 2023/07/25 11:49:20 - mmengine - INFO - Epoch(train) [72][300/940] lr: 1.0000e-03 eta: 8:16:46 time: 1.1028 data_time: 0.0140 memory: 15768 grad_norm: 4.5082 loss: 0.8842 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8842 2023/07/25 11:49:42 - mmengine - INFO - Epoch(train) [72][320/940] lr: 1.0000e-03 eta: 8:16:24 time: 1.1024 data_time: 0.0140 memory: 15768 grad_norm: 4.4453 loss: 0.9072 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9072 2023/07/25 11:50:04 - mmengine - INFO - Epoch(train) [72][340/940] lr: 1.0000e-03 eta: 8:16:01 time: 1.1009 data_time: 0.0141 memory: 15768 grad_norm: 4.5576 loss: 1.1162 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1162 2023/07/25 11:50:26 - mmengine - INFO - Epoch(train) [72][360/940] lr: 1.0000e-03 eta: 8:15:39 time: 1.0999 data_time: 0.0140 memory: 15768 grad_norm: 4.6083 loss: 0.8351 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8351 2023/07/25 11:50:48 - mmengine - INFO - Epoch(train) [72][380/940] lr: 1.0000e-03 eta: 8:15:17 time: 1.0994 data_time: 0.0139 memory: 15768 grad_norm: 4.5572 loss: 0.8737 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8737 2023/07/25 11:51:10 - mmengine - INFO - Epoch(train) [72][400/940] lr: 1.0000e-03 eta: 8:14:55 time: 1.1027 data_time: 0.0139 memory: 15768 grad_norm: 4.5298 loss: 0.9578 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9578 2023/07/25 11:51:32 - mmengine - INFO - Epoch(train) [72][420/940] lr: 1.0000e-03 eta: 8:14:33 time: 1.1017 data_time: 0.0134 memory: 15768 grad_norm: 4.5655 loss: 0.8975 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8975 2023/07/25 11:51:54 - mmengine - INFO - Epoch(train) [72][440/940] lr: 1.0000e-03 eta: 8:14:11 time: 1.1018 data_time: 0.0140 memory: 15768 grad_norm: 4.5693 loss: 0.8277 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8277 2023/07/25 11:52:16 - mmengine - INFO - Epoch(train) [72][460/940] lr: 1.0000e-03 eta: 8:13:49 time: 1.1020 data_time: 0.0138 memory: 15768 grad_norm: 4.4783 loss: 0.6495 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6495 2023/07/25 11:52:38 - mmengine - INFO - Epoch(train) [72][480/940] lr: 1.0000e-03 eta: 8:13:26 time: 1.1008 data_time: 0.0138 memory: 15768 grad_norm: 4.6204 loss: 0.7050 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7050 2023/07/25 11:53:00 - mmengine - INFO - Epoch(train) [72][500/940] lr: 1.0000e-03 eta: 8:13:04 time: 1.0985 data_time: 0.0139 memory: 15768 grad_norm: 4.6090 loss: 0.9259 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9259 2023/07/25 11:53:23 - mmengine - INFO - Epoch(train) [72][520/940] lr: 1.0000e-03 eta: 8:12:42 time: 1.1037 data_time: 0.0139 memory: 15768 grad_norm: 4.5741 loss: 0.8347 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8347 2023/07/25 11:53:45 - mmengine - INFO - Epoch(train) [72][540/940] lr: 1.0000e-03 eta: 8:12:20 time: 1.1018 data_time: 0.0138 memory: 15768 grad_norm: 4.5385 loss: 0.8405 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8405 2023/07/25 11:54:07 - mmengine - INFO - Epoch(train) [72][560/940] lr: 1.0000e-03 eta: 8:11:58 time: 1.1001 data_time: 0.0140 memory: 15768 grad_norm: 4.6049 loss: 0.8390 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8390 2023/07/25 11:54:29 - mmengine - INFO - Epoch(train) [72][580/940] lr: 1.0000e-03 eta: 8:11:36 time: 1.1045 data_time: 0.0139 memory: 15768 grad_norm: 4.6531 loss: 0.8767 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8767 2023/07/25 11:54:51 - mmengine - INFO - Epoch(train) [72][600/940] lr: 1.0000e-03 eta: 8:11:14 time: 1.1019 data_time: 0.0137 memory: 15768 grad_norm: 4.6323 loss: 0.7982 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7982 2023/07/25 11:55:13 - mmengine - INFO - Epoch(train) [72][620/940] lr: 1.0000e-03 eta: 8:10:51 time: 1.0990 data_time: 0.0138 memory: 15768 grad_norm: 4.6263 loss: 0.7909 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7909 2023/07/25 11:55:35 - mmengine - INFO - Epoch(train) [72][640/940] lr: 1.0000e-03 eta: 8:10:29 time: 1.1029 data_time: 0.0138 memory: 15768 grad_norm: 4.5846 loss: 0.7903 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7903 2023/07/25 11:55:57 - mmengine - INFO - Epoch(train) [72][660/940] lr: 1.0000e-03 eta: 8:10:07 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.5485 loss: 0.9204 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9204 2023/07/25 11:56:19 - mmengine - INFO - Epoch(train) [72][680/940] lr: 1.0000e-03 eta: 8:09:45 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 4.6542 loss: 0.8158 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8158 2023/07/25 11:56:41 - mmengine - INFO - Epoch(train) [72][700/940] lr: 1.0000e-03 eta: 8:09:23 time: 1.0993 data_time: 0.0137 memory: 15768 grad_norm: 4.5775 loss: 0.8101 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8101 2023/07/25 11:57:03 - mmengine - INFO - Epoch(train) [72][720/940] lr: 1.0000e-03 eta: 8:09:01 time: 1.0991 data_time: 0.0138 memory: 15768 grad_norm: 4.5649 loss: 0.8085 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8085 2023/07/25 11:57:25 - mmengine - INFO - Epoch(train) [72][740/940] lr: 1.0000e-03 eta: 8:08:39 time: 1.1055 data_time: 0.0140 memory: 15768 grad_norm: 4.5665 loss: 0.7744 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7744 2023/07/25 11:57:47 - mmengine - INFO - Epoch(train) [72][760/940] lr: 1.0000e-03 eta: 8:08:16 time: 1.1004 data_time: 0.0139 memory: 15768 grad_norm: 4.6905 loss: 0.9818 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9818 2023/07/25 11:58:09 - mmengine - INFO - Epoch(train) [72][780/940] lr: 1.0000e-03 eta: 8:07:54 time: 1.1041 data_time: 0.0136 memory: 15768 grad_norm: 4.6322 loss: 0.8838 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8838 2023/07/25 11:58:31 - mmengine - INFO - Epoch(train) [72][800/940] lr: 1.0000e-03 eta: 8:07:32 time: 1.1021 data_time: 0.0142 memory: 15768 grad_norm: 4.5352 loss: 0.7534 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7534 2023/07/25 11:58:53 - mmengine - INFO - Epoch(train) [72][820/940] lr: 1.0000e-03 eta: 8:07:10 time: 1.1051 data_time: 0.0141 memory: 15768 grad_norm: 4.5483 loss: 0.8915 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8915 2023/07/25 11:59:15 - mmengine - INFO - Epoch(train) [72][840/940] lr: 1.0000e-03 eta: 8:06:48 time: 1.1011 data_time: 0.0140 memory: 15768 grad_norm: 4.6441 loss: 0.8826 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8826 2023/07/25 11:59:37 - mmengine - INFO - Epoch(train) [72][860/940] lr: 1.0000e-03 eta: 8:06:26 time: 1.1030 data_time: 0.0138 memory: 15768 grad_norm: 4.5721 loss: 0.8636 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8636 2023/07/25 11:59:59 - mmengine - INFO - Epoch(train) [72][880/940] lr: 1.0000e-03 eta: 8:06:04 time: 1.1035 data_time: 0.0138 memory: 15768 grad_norm: 4.6226 loss: 0.8141 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8141 2023/07/25 12:00:21 - mmengine - INFO - Epoch(train) [72][900/940] lr: 1.0000e-03 eta: 8:05:41 time: 1.0975 data_time: 0.0139 memory: 15768 grad_norm: 4.6406 loss: 0.9217 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9217 2023/07/25 12:00:43 - mmengine - INFO - Epoch(train) [72][920/940] lr: 1.0000e-03 eta: 8:05:19 time: 1.1047 data_time: 0.0138 memory: 15768 grad_norm: 4.5999 loss: 0.9345 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9345 2023/07/25 12:01:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 12:01:04 - mmengine - INFO - Epoch(train) [72][940/940] lr: 1.0000e-03 eta: 8:04:57 time: 1.0536 data_time: 0.0134 memory: 15768 grad_norm: 4.9499 loss: 0.7752 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.7752 2023/07/25 12:01:04 - mmengine - INFO - Saving checkpoint at 72 epochs 2023/07/25 12:01:15 - mmengine - INFO - Epoch(val) [72][20/78] eta: 0:00:28 time: 0.4921 data_time: 0.3339 memory: 2147 2023/07/25 12:01:22 - mmengine - INFO - Epoch(val) [72][40/78] eta: 0:00:16 time: 0.3508 data_time: 0.1936 memory: 2147 2023/07/25 12:01:31 - mmengine - INFO - Epoch(val) [72][60/78] eta: 0:00:07 time: 0.4424 data_time: 0.2860 memory: 2147 2023/07/25 12:01:41 - mmengine - INFO - Epoch(val) [72][78/78] acc/top1: 0.7113 acc/top5: 0.8978 acc/mean1: 0.7112 data_time: 0.2462 time: 0.4005 2023/07/25 12:02:07 - mmengine - INFO - Epoch(train) [73][ 20/940] lr: 1.0000e-03 eta: 8:04:36 time: 1.3008 data_time: 0.1463 memory: 15768 grad_norm: 4.4772 loss: 0.8311 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8311 2023/07/25 12:02:29 - mmengine - INFO - Epoch(train) [73][ 40/940] lr: 1.0000e-03 eta: 8:04:14 time: 1.1003 data_time: 0.0140 memory: 15768 grad_norm: 4.4241 loss: 0.8490 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8490 2023/07/25 12:02:51 - mmengine - INFO - Epoch(train) [73][ 60/940] lr: 1.0000e-03 eta: 8:03:52 time: 1.1036 data_time: 0.0135 memory: 15768 grad_norm: 4.5045 loss: 0.7969 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7969 2023/07/25 12:03:13 - mmengine - INFO - Epoch(train) [73][ 80/940] lr: 1.0000e-03 eta: 8:03:30 time: 1.1008 data_time: 0.0138 memory: 15768 grad_norm: 4.6028 loss: 0.7514 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7514 2023/07/25 12:03:35 - mmengine - INFO - Epoch(train) [73][100/940] lr: 1.0000e-03 eta: 8:03:08 time: 1.1031 data_time: 0.0137 memory: 15768 grad_norm: 4.5796 loss: 0.9451 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.9451 2023/07/25 12:03:57 - mmengine - INFO - Epoch(train) [73][120/940] lr: 1.0000e-03 eta: 8:02:46 time: 1.1009 data_time: 0.0131 memory: 15768 grad_norm: 4.4277 loss: 0.7798 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7798 2023/07/25 12:04:19 - mmengine - INFO - Epoch(train) [73][140/940] lr: 1.0000e-03 eta: 8:02:23 time: 1.0994 data_time: 0.0135 memory: 15768 grad_norm: 4.5626 loss: 0.9163 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9163 2023/07/25 12:04:41 - mmengine - INFO - Epoch(train) [73][160/940] lr: 1.0000e-03 eta: 8:02:01 time: 1.0997 data_time: 0.0138 memory: 15768 grad_norm: 4.5530 loss: 0.8339 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8339 2023/07/25 12:05:03 - mmengine - INFO - Epoch(train) [73][180/940] lr: 1.0000e-03 eta: 8:01:39 time: 1.1001 data_time: 0.0133 memory: 15768 grad_norm: 4.5442 loss: 1.0649 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0649 2023/07/25 12:05:25 - mmengine - INFO - Epoch(train) [73][200/940] lr: 1.0000e-03 eta: 8:01:17 time: 1.0989 data_time: 0.0142 memory: 15768 grad_norm: 4.5419 loss: 0.8451 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8451 2023/07/25 12:05:47 - mmengine - INFO - Epoch(train) [73][220/940] lr: 1.0000e-03 eta: 8:00:55 time: 1.1011 data_time: 0.0135 memory: 15768 grad_norm: 4.5472 loss: 0.8795 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8795 2023/07/25 12:06:09 - mmengine - INFO - Epoch(train) [73][240/940] lr: 1.0000e-03 eta: 8:00:33 time: 1.1010 data_time: 0.0139 memory: 15768 grad_norm: 4.4635 loss: 0.8166 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8166 2023/07/25 12:06:31 - mmengine - INFO - Epoch(train) [73][260/940] lr: 1.0000e-03 eta: 8:00:10 time: 1.1027 data_time: 0.0139 memory: 15768 grad_norm: 4.6079 loss: 0.7872 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7872 2023/07/25 12:06:53 - mmengine - INFO - Epoch(train) [73][280/940] lr: 1.0000e-03 eta: 7:59:48 time: 1.1024 data_time: 0.0137 memory: 15768 grad_norm: 4.4445 loss: 0.8580 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8580 2023/07/25 12:07:15 - mmengine - INFO - Epoch(train) [73][300/940] lr: 1.0000e-03 eta: 7:59:26 time: 1.1024 data_time: 0.0136 memory: 15768 grad_norm: 4.5326 loss: 0.8935 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8935 2023/07/25 12:07:37 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 12:07:37 - mmengine - INFO - Epoch(train) [73][320/940] lr: 1.0000e-03 eta: 7:59:04 time: 1.1023 data_time: 0.0135 memory: 15768 grad_norm: 4.5656 loss: 0.7009 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7009 2023/07/25 12:07:59 - mmengine - INFO - Epoch(train) [73][340/940] lr: 1.0000e-03 eta: 7:58:42 time: 1.1008 data_time: 0.0138 memory: 15768 grad_norm: 4.4586 loss: 0.7076 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7076 2023/07/25 12:08:21 - mmengine - INFO - Epoch(train) [73][360/940] lr: 1.0000e-03 eta: 7:58:20 time: 1.1007 data_time: 0.0140 memory: 15768 grad_norm: 4.5926 loss: 0.9142 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9142 2023/07/25 12:08:43 - mmengine - INFO - Epoch(train) [73][380/940] lr: 1.0000e-03 eta: 7:57:58 time: 1.1022 data_time: 0.0138 memory: 15768 grad_norm: 4.6481 loss: 0.7480 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7480 2023/07/25 12:09:05 - mmengine - INFO - Epoch(train) [73][400/940] lr: 1.0000e-03 eta: 7:57:35 time: 1.1023 data_time: 0.0136 memory: 15768 grad_norm: 4.6119 loss: 0.9303 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9303 2023/07/25 12:09:27 - mmengine - INFO - Epoch(train) [73][420/940] lr: 1.0000e-03 eta: 7:57:13 time: 1.1010 data_time: 0.0142 memory: 15768 grad_norm: 4.4899 loss: 0.8023 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8023 2023/07/25 12:09:49 - mmengine - INFO - Epoch(train) [73][440/940] lr: 1.0000e-03 eta: 7:56:51 time: 1.1017 data_time: 0.0141 memory: 15768 grad_norm: 4.5832 loss: 0.7730 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7730 2023/07/25 12:10:12 - mmengine - INFO - Epoch(train) [73][460/940] lr: 1.0000e-03 eta: 7:56:29 time: 1.1038 data_time: 0.0144 memory: 15768 grad_norm: 4.5360 loss: 0.9048 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9048 2023/07/25 12:10:34 - mmengine - INFO - Epoch(train) [73][480/940] lr: 1.0000e-03 eta: 7:56:07 time: 1.0991 data_time: 0.0142 memory: 15768 grad_norm: 4.6309 loss: 0.8676 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8676 2023/07/25 12:10:56 - mmengine - INFO - Epoch(train) [73][500/940] lr: 1.0000e-03 eta: 7:55:45 time: 1.0994 data_time: 0.0139 memory: 15768 grad_norm: 4.6595 loss: 0.8594 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8594 2023/07/25 12:11:18 - mmengine - INFO - Epoch(train) [73][520/940] lr: 1.0000e-03 eta: 7:55:23 time: 1.1045 data_time: 0.0134 memory: 15768 grad_norm: 4.5770 loss: 0.7611 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7611 2023/07/25 12:11:41 - mmengine - INFO - Epoch(train) [73][540/940] lr: 1.0000e-03 eta: 7:55:01 time: 1.1593 data_time: 0.0136 memory: 15768 grad_norm: 4.5102 loss: 0.8819 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8819 2023/07/25 12:12:03 - mmengine - INFO - Epoch(train) [73][560/940] lr: 1.0000e-03 eta: 7:54:39 time: 1.1008 data_time: 0.0138 memory: 15768 grad_norm: 4.4888 loss: 0.9144 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9144 2023/07/25 12:12:25 - mmengine - INFO - Epoch(train) [73][580/940] lr: 1.0000e-03 eta: 7:54:17 time: 1.1012 data_time: 0.0140 memory: 15768 grad_norm: 4.4794 loss: 0.8259 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8259 2023/07/25 12:12:47 - mmengine - INFO - Epoch(train) [73][600/940] lr: 1.0000e-03 eta: 7:53:54 time: 1.1007 data_time: 0.0142 memory: 15768 grad_norm: 4.5545 loss: 0.7227 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7227 2023/07/25 12:13:09 - mmengine - INFO - Epoch(train) [73][620/940] lr: 1.0000e-03 eta: 7:53:32 time: 1.0996 data_time: 0.0140 memory: 15768 grad_norm: 4.6184 loss: 0.9198 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9198 2023/07/25 12:13:31 - mmengine - INFO - Epoch(train) [73][640/940] lr: 1.0000e-03 eta: 7:53:10 time: 1.0986 data_time: 0.0138 memory: 15768 grad_norm: 4.5859 loss: 0.8127 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8127 2023/07/25 12:13:53 - mmengine - INFO - Epoch(train) [73][660/940] lr: 1.0000e-03 eta: 7:52:48 time: 1.0982 data_time: 0.0141 memory: 15768 grad_norm: 4.5405 loss: 0.7595 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7595 2023/07/25 12:14:15 - mmengine - INFO - Epoch(train) [73][680/940] lr: 1.0000e-03 eta: 7:52:26 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.7764 loss: 0.8572 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8572 2023/07/25 12:14:37 - mmengine - INFO - Epoch(train) [73][700/940] lr: 1.0000e-03 eta: 7:52:04 time: 1.1002 data_time: 0.0141 memory: 15768 grad_norm: 4.5641 loss: 0.7073 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7073 2023/07/25 12:14:59 - mmengine - INFO - Epoch(train) [73][720/940] lr: 1.0000e-03 eta: 7:51:42 time: 1.1021 data_time: 0.0137 memory: 15768 grad_norm: 4.7088 loss: 0.8915 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8915 2023/07/25 12:15:21 - mmengine - INFO - Epoch(train) [73][740/940] lr: 1.0000e-03 eta: 7:51:19 time: 1.0994 data_time: 0.0136 memory: 15768 grad_norm: 4.6064 loss: 0.8984 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8984 2023/07/25 12:15:43 - mmengine - INFO - Epoch(train) [73][760/940] lr: 1.0000e-03 eta: 7:50:57 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.6260 loss: 0.8740 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8740 2023/07/25 12:16:05 - mmengine - INFO - Epoch(train) [73][780/940] lr: 1.0000e-03 eta: 7:50:35 time: 1.1007 data_time: 0.0136 memory: 15768 grad_norm: 4.6810 loss: 1.0014 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0014 2023/07/25 12:16:27 - mmengine - INFO - Epoch(train) [73][800/940] lr: 1.0000e-03 eta: 7:50:13 time: 1.1019 data_time: 0.0136 memory: 15768 grad_norm: 4.6302 loss: 0.8525 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8525 2023/07/25 12:16:49 - mmengine - INFO - Epoch(train) [73][820/940] lr: 1.0000e-03 eta: 7:49:51 time: 1.1026 data_time: 0.0135 memory: 15768 grad_norm: 4.5188 loss: 0.7851 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7851 2023/07/25 12:17:11 - mmengine - INFO - Epoch(train) [73][840/940] lr: 1.0000e-03 eta: 7:49:29 time: 1.0992 data_time: 0.0140 memory: 15768 grad_norm: 4.5977 loss: 0.9317 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9317 2023/07/25 12:17:33 - mmengine - INFO - Epoch(train) [73][860/940] lr: 1.0000e-03 eta: 7:49:07 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.5305 loss: 0.7676 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7676 2023/07/25 12:17:55 - mmengine - INFO - Epoch(train) [73][880/940] lr: 1.0000e-03 eta: 7:48:44 time: 1.0986 data_time: 0.0139 memory: 15768 grad_norm: 4.4974 loss: 0.7715 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7715 2023/07/25 12:18:17 - mmengine - INFO - Epoch(train) [73][900/940] lr: 1.0000e-03 eta: 7:48:22 time: 1.0998 data_time: 0.0144 memory: 15768 grad_norm: 4.5745 loss: 0.8675 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8675 2023/07/25 12:18:39 - mmengine - INFO - Epoch(train) [73][920/940] lr: 1.0000e-03 eta: 7:48:00 time: 1.1025 data_time: 0.0143 memory: 15768 grad_norm: 4.8001 loss: 0.9765 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9765 2023/07/25 12:19:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 12:19:00 - mmengine - INFO - Epoch(train) [73][940/940] lr: 1.0000e-03 eta: 7:47:38 time: 1.0601 data_time: 0.0135 memory: 15768 grad_norm: 4.8658 loss: 1.0316 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0316 2023/07/25 12:19:10 - mmengine - INFO - Epoch(val) [73][20/78] eta: 0:00:29 time: 0.5010 data_time: 0.3435 memory: 2147 2023/07/25 12:19:18 - mmengine - INFO - Epoch(val) [73][40/78] eta: 0:00:16 time: 0.3672 data_time: 0.2103 memory: 2147 2023/07/25 12:19:27 - mmengine - INFO - Epoch(val) [73][60/78] eta: 0:00:08 time: 0.4694 data_time: 0.3117 memory: 2147 2023/07/25 12:19:37 - mmengine - INFO - Epoch(val) [73][78/78] acc/top1: 0.7102 acc/top5: 0.8985 acc/mean1: 0.7101 data_time: 0.2587 time: 0.4131 2023/07/25 12:20:03 - mmengine - INFO - Epoch(train) [74][ 20/940] lr: 1.0000e-03 eta: 7:47:17 time: 1.3071 data_time: 0.1476 memory: 15768 grad_norm: 4.6047 loss: 0.9130 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9130 2023/07/25 12:20:25 - mmengine - INFO - Epoch(train) [74][ 40/940] lr: 1.0000e-03 eta: 7:46:55 time: 1.1025 data_time: 0.0137 memory: 15768 grad_norm: 4.5088 loss: 0.7738 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7738 2023/07/25 12:20:47 - mmengine - INFO - Epoch(train) [74][ 60/940] lr: 1.0000e-03 eta: 7:46:33 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 4.6129 loss: 0.7633 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7633 2023/07/25 12:21:09 - mmengine - INFO - Epoch(train) [74][ 80/940] lr: 1.0000e-03 eta: 7:46:11 time: 1.1051 data_time: 0.0135 memory: 15768 grad_norm: 4.5816 loss: 0.9192 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9192 2023/07/25 12:21:31 - mmengine - INFO - Epoch(train) [74][100/940] lr: 1.0000e-03 eta: 7:45:49 time: 1.1028 data_time: 0.0139 memory: 15768 grad_norm: 4.5451 loss: 0.9309 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9309 2023/07/25 12:21:53 - mmengine - INFO - Epoch(train) [74][120/940] lr: 1.0000e-03 eta: 7:45:26 time: 1.1024 data_time: 0.0136 memory: 15768 grad_norm: 4.6628 loss: 0.8768 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8768 2023/07/25 12:22:15 - mmengine - INFO - Epoch(train) [74][140/940] lr: 1.0000e-03 eta: 7:45:04 time: 1.1066 data_time: 0.0133 memory: 15768 grad_norm: 4.6864 loss: 0.7246 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7246 2023/07/25 12:22:37 - mmengine - INFO - Epoch(train) [74][160/940] lr: 1.0000e-03 eta: 7:44:42 time: 1.1044 data_time: 0.0136 memory: 15768 grad_norm: 4.6364 loss: 0.8013 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8013 2023/07/25 12:23:00 - mmengine - INFO - Epoch(train) [74][180/940] lr: 1.0000e-03 eta: 7:44:20 time: 1.1038 data_time: 0.0137 memory: 15768 grad_norm: 4.5990 loss: 0.8652 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8652 2023/07/25 12:23:22 - mmengine - INFO - Epoch(train) [74][200/940] lr: 1.0000e-03 eta: 7:43:58 time: 1.1043 data_time: 0.0134 memory: 15768 grad_norm: 4.5674 loss: 0.7598 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7598 2023/07/25 12:23:44 - mmengine - INFO - Epoch(train) [74][220/940] lr: 1.0000e-03 eta: 7:43:36 time: 1.1025 data_time: 0.0136 memory: 15768 grad_norm: 4.4787 loss: 0.8360 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8360 2023/07/25 12:24:06 - mmengine - INFO - Epoch(train) [74][240/940] lr: 1.0000e-03 eta: 7:43:14 time: 1.0991 data_time: 0.0139 memory: 15768 grad_norm: 4.5382 loss: 0.8466 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8466 2023/07/25 12:24:28 - mmengine - INFO - Epoch(train) [74][260/940] lr: 1.0000e-03 eta: 7:42:51 time: 1.0992 data_time: 0.0136 memory: 15768 grad_norm: 4.5842 loss: 0.8998 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8998 2023/07/25 12:24:50 - mmengine - INFO - Epoch(train) [74][280/940] lr: 1.0000e-03 eta: 7:42:29 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 4.4639 loss: 0.7907 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7907 2023/07/25 12:25:12 - mmengine - INFO - Epoch(train) [74][300/940] lr: 1.0000e-03 eta: 7:42:07 time: 1.1025 data_time: 0.0139 memory: 15768 grad_norm: 4.6095 loss: 0.8795 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8795 2023/07/25 12:25:34 - mmengine - INFO - Epoch(train) [74][320/940] lr: 1.0000e-03 eta: 7:41:45 time: 1.0989 data_time: 0.0142 memory: 15768 grad_norm: 4.5800 loss: 0.8570 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8570 2023/07/25 12:25:56 - mmengine - INFO - Epoch(train) [74][340/940] lr: 1.0000e-03 eta: 7:41:23 time: 1.0999 data_time: 0.0142 memory: 15768 grad_norm: 4.5852 loss: 0.7013 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7013 2023/07/25 12:26:18 - mmengine - INFO - Epoch(train) [74][360/940] lr: 1.0000e-03 eta: 7:41:01 time: 1.1044 data_time: 0.0141 memory: 15768 grad_norm: 4.5994 loss: 0.8236 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8236 2023/07/25 12:26:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 12:26:40 - mmengine - INFO - Epoch(train) [74][380/940] lr: 1.0000e-03 eta: 7:40:39 time: 1.0997 data_time: 0.0139 memory: 15768 grad_norm: 4.6028 loss: 0.8675 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8675 2023/07/25 12:27:02 - mmengine - INFO - Epoch(train) [74][400/940] lr: 1.0000e-03 eta: 7:40:16 time: 1.0979 data_time: 0.0140 memory: 15768 grad_norm: 4.7344 loss: 0.8113 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8113 2023/07/25 12:27:24 - mmengine - INFO - Epoch(train) [74][420/940] lr: 1.0000e-03 eta: 7:39:54 time: 1.1022 data_time: 0.0138 memory: 15768 grad_norm: 4.6706 loss: 0.8308 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8308 2023/07/25 12:27:46 - mmengine - INFO - Epoch(train) [74][440/940] lr: 1.0000e-03 eta: 7:39:32 time: 1.1019 data_time: 0.0144 memory: 15768 grad_norm: 4.5576 loss: 0.7067 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7067 2023/07/25 12:28:08 - mmengine - INFO - Epoch(train) [74][460/940] lr: 1.0000e-03 eta: 7:39:10 time: 1.0995 data_time: 0.0145 memory: 15768 grad_norm: 4.6523 loss: 0.9465 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9465 2023/07/25 12:28:30 - mmengine - INFO - Epoch(train) [74][480/940] lr: 1.0000e-03 eta: 7:38:48 time: 1.1015 data_time: 0.0144 memory: 15768 grad_norm: 4.5527 loss: 1.0142 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0142 2023/07/25 12:28:52 - mmengine - INFO - Epoch(train) [74][500/940] lr: 1.0000e-03 eta: 7:38:26 time: 1.1042 data_time: 0.0138 memory: 15768 grad_norm: 4.4826 loss: 0.9113 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9113 2023/07/25 12:29:14 - mmengine - INFO - Epoch(train) [74][520/940] lr: 1.0000e-03 eta: 7:38:04 time: 1.1008 data_time: 0.0141 memory: 15768 grad_norm: 4.7041 loss: 0.8250 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8250 2023/07/25 12:29:36 - mmengine - INFO - Epoch(train) [74][540/940] lr: 1.0000e-03 eta: 7:37:41 time: 1.0996 data_time: 0.0141 memory: 15768 grad_norm: 4.6090 loss: 0.8193 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8193 2023/07/25 12:29:58 - mmengine - INFO - Epoch(train) [74][560/940] lr: 1.0000e-03 eta: 7:37:19 time: 1.1000 data_time: 0.0141 memory: 15768 grad_norm: 4.5620 loss: 0.7211 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7211 2023/07/25 12:30:20 - mmengine - INFO - Epoch(train) [74][580/940] lr: 1.0000e-03 eta: 7:36:57 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.6486 loss: 0.7948 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7948 2023/07/25 12:30:42 - mmengine - INFO - Epoch(train) [74][600/940] lr: 1.0000e-03 eta: 7:36:35 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.6036 loss: 0.8406 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8406 2023/07/25 12:31:04 - mmengine - INFO - Epoch(train) [74][620/940] lr: 1.0000e-03 eta: 7:36:13 time: 1.1002 data_time: 0.0142 memory: 15768 grad_norm: 4.5277 loss: 0.8093 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8093 2023/07/25 12:31:26 - mmengine - INFO - Epoch(train) [74][640/940] lr: 1.0000e-03 eta: 7:35:51 time: 1.1014 data_time: 0.0138 memory: 15768 grad_norm: 4.5955 loss: 0.8267 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8267 2023/07/25 12:31:48 - mmengine - INFO - Epoch(train) [74][660/940] lr: 1.0000e-03 eta: 7:35:29 time: 1.0996 data_time: 0.0140 memory: 15768 grad_norm: 4.5340 loss: 0.8909 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8909 2023/07/25 12:32:10 - mmengine - INFO - Epoch(train) [74][680/940] lr: 1.0000e-03 eta: 7:35:06 time: 1.0996 data_time: 0.0138 memory: 15768 grad_norm: 4.6527 loss: 0.9309 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 0.9309 2023/07/25 12:32:32 - mmengine - INFO - Epoch(train) [74][700/940] lr: 1.0000e-03 eta: 7:34:44 time: 1.0989 data_time: 0.0139 memory: 15768 grad_norm: 4.5777 loss: 1.0229 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0229 2023/07/25 12:32:54 - mmengine - INFO - Epoch(train) [74][720/940] lr: 1.0000e-03 eta: 7:34:22 time: 1.0991 data_time: 0.0138 memory: 15768 grad_norm: 4.5747 loss: 0.9081 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9081 2023/07/25 12:33:16 - mmengine - INFO - Epoch(train) [74][740/940] lr: 1.0000e-03 eta: 7:34:00 time: 1.1002 data_time: 0.0139 memory: 15768 grad_norm: 4.6058 loss: 0.9589 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9589 2023/07/25 12:33:38 - mmengine - INFO - Epoch(train) [74][760/940] lr: 1.0000e-03 eta: 7:33:38 time: 1.1010 data_time: 0.0137 memory: 15768 grad_norm: 4.5081 loss: 0.8758 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8758 2023/07/25 12:34:00 - mmengine - INFO - Epoch(train) [74][780/940] lr: 1.0000e-03 eta: 7:33:16 time: 1.1003 data_time: 0.0133 memory: 15768 grad_norm: 4.6720 loss: 0.9235 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9235 2023/07/25 12:34:22 - mmengine - INFO - Epoch(train) [74][800/940] lr: 1.0000e-03 eta: 7:32:53 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 4.6160 loss: 0.7945 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7945 2023/07/25 12:34:44 - mmengine - INFO - Epoch(train) [74][820/940] lr: 1.0000e-03 eta: 7:32:31 time: 1.1005 data_time: 0.0138 memory: 15768 grad_norm: 4.7523 loss: 0.8724 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8724 2023/07/25 12:35:06 - mmengine - INFO - Epoch(train) [74][840/940] lr: 1.0000e-03 eta: 7:32:09 time: 1.1014 data_time: 0.0140 memory: 15768 grad_norm: 4.6044 loss: 0.8305 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8305 2023/07/25 12:35:28 - mmengine - INFO - Epoch(train) [74][860/940] lr: 1.0000e-03 eta: 7:31:47 time: 1.0998 data_time: 0.0140 memory: 15768 grad_norm: 4.6032 loss: 0.8022 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8022 2023/07/25 12:35:50 - mmengine - INFO - Epoch(train) [74][880/940] lr: 1.0000e-03 eta: 7:31:25 time: 1.0988 data_time: 0.0140 memory: 15768 grad_norm: 4.7201 loss: 0.8463 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8463 2023/07/25 12:36:12 - mmengine - INFO - Epoch(train) [74][900/940] lr: 1.0000e-03 eta: 7:31:03 time: 1.1030 data_time: 0.0139 memory: 15768 grad_norm: 4.6959 loss: 0.7954 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7954 2023/07/25 12:36:34 - mmengine - INFO - Epoch(train) [74][920/940] lr: 1.0000e-03 eta: 7:30:41 time: 1.1026 data_time: 0.0135 memory: 15768 grad_norm: 4.7043 loss: 0.7973 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7973 2023/07/25 12:36:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 12:36:55 - mmengine - INFO - Epoch(train) [74][940/940] lr: 1.0000e-03 eta: 7:30:18 time: 1.0568 data_time: 0.0138 memory: 15768 grad_norm: 4.8701 loss: 0.8772 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8772 2023/07/25 12:37:05 - mmengine - INFO - Epoch(val) [74][20/78] eta: 0:00:28 time: 0.4969 data_time: 0.3388 memory: 2147 2023/07/25 12:37:12 - mmengine - INFO - Epoch(val) [74][40/78] eta: 0:00:16 time: 0.3459 data_time: 0.1887 memory: 2147 2023/07/25 12:37:21 - mmengine - INFO - Epoch(val) [74][60/78] eta: 0:00:07 time: 0.4417 data_time: 0.2848 memory: 2147 2023/07/25 12:37:32 - mmengine - INFO - Epoch(val) [74][78/78] acc/top1: 0.7077 acc/top5: 0.8980 acc/mean1: 0.7076 data_time: 0.2458 time: 0.4002 2023/07/25 12:37:57 - mmengine - INFO - Epoch(train) [75][ 20/940] lr: 1.0000e-03 eta: 7:29:57 time: 1.2808 data_time: 0.1417 memory: 15768 grad_norm: 4.6239 loss: 1.0817 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0817 2023/07/25 12:38:19 - mmengine - INFO - Epoch(train) [75][ 40/940] lr: 1.0000e-03 eta: 7:29:35 time: 1.0985 data_time: 0.0141 memory: 15768 grad_norm: 4.6771 loss: 0.9488 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9488 2023/07/25 12:38:41 - mmengine - INFO - Epoch(train) [75][ 60/940] lr: 1.0000e-03 eta: 7:29:13 time: 1.1018 data_time: 0.0144 memory: 15768 grad_norm: 4.5140 loss: 0.8706 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8706 2023/07/25 12:39:03 - mmengine - INFO - Epoch(train) [75][ 80/940] lr: 1.0000e-03 eta: 7:28:51 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.7056 loss: 0.9939 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9939 2023/07/25 12:39:25 - mmengine - INFO - Epoch(train) [75][100/940] lr: 1.0000e-03 eta: 7:28:29 time: 1.1013 data_time: 0.0140 memory: 15768 grad_norm: 4.6963 loss: 0.8252 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8252 2023/07/25 12:39:47 - mmengine - INFO - Epoch(train) [75][120/940] lr: 1.0000e-03 eta: 7:28:07 time: 1.0995 data_time: 0.0137 memory: 15768 grad_norm: 4.5379 loss: 0.7905 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7905 2023/07/25 12:40:09 - mmengine - INFO - Epoch(train) [75][140/940] lr: 1.0000e-03 eta: 7:27:44 time: 1.1044 data_time: 0.0144 memory: 15768 grad_norm: 4.7189 loss: 0.9676 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9676 2023/07/25 12:40:31 - mmengine - INFO - Epoch(train) [75][160/940] lr: 1.0000e-03 eta: 7:27:22 time: 1.1018 data_time: 0.0147 memory: 15768 grad_norm: 4.5497 loss: 0.7956 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7956 2023/07/25 12:40:53 - mmengine - INFO - Epoch(train) [75][180/940] lr: 1.0000e-03 eta: 7:27:00 time: 1.1040 data_time: 0.0144 memory: 15768 grad_norm: 4.5609 loss: 0.7986 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7986 2023/07/25 12:41:16 - mmengine - INFO - Epoch(train) [75][200/940] lr: 1.0000e-03 eta: 7:26:38 time: 1.1021 data_time: 0.0137 memory: 15768 grad_norm: 4.5428 loss: 0.7567 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7567 2023/07/25 12:41:38 - mmengine - INFO - Epoch(train) [75][220/940] lr: 1.0000e-03 eta: 7:26:16 time: 1.0994 data_time: 0.0140 memory: 15768 grad_norm: 4.6463 loss: 0.8500 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8500 2023/07/25 12:42:00 - mmengine - INFO - Epoch(train) [75][240/940] lr: 1.0000e-03 eta: 7:25:54 time: 1.1034 data_time: 0.0140 memory: 15768 grad_norm: 4.5510 loss: 0.8317 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8317 2023/07/25 12:42:22 - mmengine - INFO - Epoch(train) [75][260/940] lr: 1.0000e-03 eta: 7:25:32 time: 1.1000 data_time: 0.0138 memory: 15768 grad_norm: 4.5747 loss: 0.8186 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8186 2023/07/25 12:42:44 - mmengine - INFO - Epoch(train) [75][280/940] lr: 1.0000e-03 eta: 7:25:09 time: 1.1010 data_time: 0.0136 memory: 15768 grad_norm: 4.6090 loss: 0.8409 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8409 2023/07/25 12:43:06 - mmengine - INFO - Epoch(train) [75][300/940] lr: 1.0000e-03 eta: 7:24:47 time: 1.1000 data_time: 0.0137 memory: 15768 grad_norm: 4.5824 loss: 0.7666 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7666 2023/07/25 12:43:28 - mmengine - INFO - Epoch(train) [75][320/940] lr: 1.0000e-03 eta: 7:24:25 time: 1.1038 data_time: 0.0141 memory: 15768 grad_norm: 4.6811 loss: 0.8291 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8291 2023/07/25 12:43:50 - mmengine - INFO - Epoch(train) [75][340/940] lr: 1.0000e-03 eta: 7:24:03 time: 1.1009 data_time: 0.0140 memory: 15768 grad_norm: 4.6292 loss: 0.8443 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8443 2023/07/25 12:44:12 - mmengine - INFO - Epoch(train) [75][360/940] lr: 1.0000e-03 eta: 7:23:41 time: 1.1000 data_time: 0.0143 memory: 15768 grad_norm: 4.6438 loss: 0.9858 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9858 2023/07/25 12:44:34 - mmengine - INFO - Epoch(train) [75][380/940] lr: 1.0000e-03 eta: 7:23:19 time: 1.1030 data_time: 0.0139 memory: 15768 grad_norm: 4.6627 loss: 0.9360 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9360 2023/07/25 12:44:56 - mmengine - INFO - Epoch(train) [75][400/940] lr: 1.0000e-03 eta: 7:22:57 time: 1.1003 data_time: 0.0137 memory: 15768 grad_norm: 4.6396 loss: 0.9116 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9116 2023/07/25 12:45:18 - mmengine - INFO - Epoch(train) [75][420/940] lr: 1.0000e-03 eta: 7:22:35 time: 1.1218 data_time: 0.0138 memory: 15768 grad_norm: 4.5730 loss: 0.8465 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8465 2023/07/25 12:45:42 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 12:45:42 - mmengine - INFO - Epoch(train) [75][440/940] lr: 1.0000e-03 eta: 7:22:13 time: 1.1669 data_time: 0.0141 memory: 15768 grad_norm: 4.4391 loss: 0.7385 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7385 2023/07/25 12:46:05 - mmengine - INFO - Epoch(train) [75][460/940] lr: 1.0000e-03 eta: 7:21:51 time: 1.1694 data_time: 0.0141 memory: 15768 grad_norm: 4.7187 loss: 0.7978 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7978 2023/07/25 12:46:28 - mmengine - INFO - Epoch(train) [75][480/940] lr: 1.0000e-03 eta: 7:21:30 time: 1.1615 data_time: 0.0143 memory: 15768 grad_norm: 4.6925 loss: 0.7932 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7932 2023/07/25 12:46:52 - mmengine - INFO - Epoch(train) [75][500/940] lr: 1.0000e-03 eta: 7:21:08 time: 1.1688 data_time: 0.0138 memory: 15768 grad_norm: 4.5724 loss: 0.8329 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8329 2023/07/25 12:47:15 - mmengine - INFO - Epoch(train) [75][520/940] lr: 1.0000e-03 eta: 7:20:46 time: 1.1653 data_time: 0.0140 memory: 15768 grad_norm: 4.6095 loss: 0.8600 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8600 2023/07/25 12:47:38 - mmengine - INFO - Epoch(train) [75][540/940] lr: 1.0000e-03 eta: 7:20:24 time: 1.1675 data_time: 0.0141 memory: 15768 grad_norm: 4.6089 loss: 0.9045 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9045 2023/07/25 12:48:02 - mmengine - INFO - Epoch(train) [75][560/940] lr: 1.0000e-03 eta: 7:20:03 time: 1.1670 data_time: 0.0141 memory: 15768 grad_norm: 4.5561 loss: 0.9458 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9458 2023/07/25 12:48:25 - mmengine - INFO - Epoch(train) [75][580/940] lr: 1.0000e-03 eta: 7:19:41 time: 1.1691 data_time: 0.0142 memory: 15768 grad_norm: 4.5064 loss: 0.7735 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7735 2023/07/25 12:48:48 - mmengine - INFO - Epoch(train) [75][600/940] lr: 1.0000e-03 eta: 7:19:19 time: 1.1627 data_time: 0.0139 memory: 15768 grad_norm: 4.6443 loss: 0.8313 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8313 2023/07/25 12:49:12 - mmengine - INFO - Epoch(train) [75][620/940] lr: 1.0000e-03 eta: 7:18:58 time: 1.1692 data_time: 0.0140 memory: 15768 grad_norm: 4.5874 loss: 0.7509 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7509 2023/07/25 12:49:35 - mmengine - INFO - Epoch(train) [75][640/940] lr: 1.0000e-03 eta: 7:18:36 time: 1.1498 data_time: 0.0137 memory: 15768 grad_norm: 4.5917 loss: 0.7125 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7125 2023/07/25 12:49:57 - mmengine - INFO - Epoch(train) [75][660/940] lr: 1.0000e-03 eta: 7:18:14 time: 1.1024 data_time: 0.0136 memory: 15768 grad_norm: 4.6678 loss: 0.9758 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9758 2023/07/25 12:50:19 - mmengine - INFO - Epoch(train) [75][680/940] lr: 1.0000e-03 eta: 7:17:52 time: 1.1029 data_time: 0.0138 memory: 15768 grad_norm: 4.5192 loss: 0.7393 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7393 2023/07/25 12:50:41 - mmengine - INFO - Epoch(train) [75][700/940] lr: 1.0000e-03 eta: 7:17:29 time: 1.1011 data_time: 0.0135 memory: 15768 grad_norm: 4.8031 loss: 0.9975 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9975 2023/07/25 12:51:03 - mmengine - INFO - Epoch(train) [75][720/940] lr: 1.0000e-03 eta: 7:17:07 time: 1.1033 data_time: 0.0138 memory: 15768 grad_norm: 4.6273 loss: 0.6652 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6652 2023/07/25 12:51:25 - mmengine - INFO - Epoch(train) [75][740/940] lr: 1.0000e-03 eta: 7:16:45 time: 1.0995 data_time: 0.0138 memory: 15768 grad_norm: 4.6592 loss: 0.7639 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7639 2023/07/25 12:51:47 - mmengine - INFO - Epoch(train) [75][760/940] lr: 1.0000e-03 eta: 7:16:23 time: 1.1047 data_time: 0.0135 memory: 15768 grad_norm: 4.5769 loss: 0.7759 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7759 2023/07/25 12:52:09 - mmengine - INFO - Epoch(train) [75][780/940] lr: 1.0000e-03 eta: 7:16:01 time: 1.0987 data_time: 0.0140 memory: 15768 grad_norm: 4.7296 loss: 0.9739 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9739 2023/07/25 12:52:31 - mmengine - INFO - Epoch(train) [75][800/940] lr: 1.0000e-03 eta: 7:15:39 time: 1.1022 data_time: 0.0146 memory: 15768 grad_norm: 4.6950 loss: 0.8099 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8099 2023/07/25 12:52:53 - mmengine - INFO - Epoch(train) [75][820/940] lr: 1.0000e-03 eta: 7:15:17 time: 1.1014 data_time: 0.0144 memory: 15768 grad_norm: 4.6557 loss: 0.9235 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9235 2023/07/25 12:53:15 - mmengine - INFO - Epoch(train) [75][840/940] lr: 1.0000e-03 eta: 7:14:54 time: 1.1023 data_time: 0.0145 memory: 15768 grad_norm: 4.7121 loss: 0.8229 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8229 2023/07/25 12:53:37 - mmengine - INFO - Epoch(train) [75][860/940] lr: 1.0000e-03 eta: 7:14:32 time: 1.0989 data_time: 0.0137 memory: 15768 grad_norm: 4.4939 loss: 0.9101 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9101 2023/07/25 12:53:59 - mmengine - INFO - Epoch(train) [75][880/940] lr: 1.0000e-03 eta: 7:14:10 time: 1.0993 data_time: 0.0136 memory: 15768 grad_norm: 4.6724 loss: 0.8488 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8488 2023/07/25 12:54:21 - mmengine - INFO - Epoch(train) [75][900/940] lr: 1.0000e-03 eta: 7:13:48 time: 1.1016 data_time: 0.0140 memory: 15768 grad_norm: 4.7288 loss: 0.7514 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7514 2023/07/25 12:54:43 - mmengine - INFO - Epoch(train) [75][920/940] lr: 1.0000e-03 eta: 7:13:26 time: 1.1018 data_time: 0.0140 memory: 15768 grad_norm: 4.6871 loss: 0.8679 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8679 2023/07/25 12:55:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 12:55:04 - mmengine - INFO - Epoch(train) [75][940/940] lr: 1.0000e-03 eta: 7:13:03 time: 1.0551 data_time: 0.0133 memory: 15768 grad_norm: 5.0072 loss: 0.9366 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.9366 2023/07/25 12:55:04 - mmengine - INFO - Saving checkpoint at 75 epochs 2023/07/25 12:55:15 - mmengine - INFO - Epoch(val) [75][20/78] eta: 0:00:29 time: 0.5026 data_time: 0.3452 memory: 2147 2023/07/25 12:55:22 - mmengine - INFO - Epoch(val) [75][40/78] eta: 0:00:16 time: 0.3449 data_time: 0.1878 memory: 2147 2023/07/25 12:55:31 - mmengine - INFO - Epoch(val) [75][60/78] eta: 0:00:07 time: 0.4420 data_time: 0.2848 memory: 2147 2023/07/25 12:55:40 - mmengine - INFO - Epoch(val) [75][78/78] acc/top1: 0.7101 acc/top5: 0.8975 acc/mean1: 0.7100 data_time: 0.2431 time: 0.3973 2023/07/25 12:56:07 - mmengine - INFO - Epoch(train) [76][ 20/940] lr: 1.0000e-03 eta: 7:12:43 time: 1.3413 data_time: 0.1736 memory: 15768 grad_norm: 4.6492 loss: 0.9017 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9017 2023/07/25 12:56:29 - mmengine - INFO - Epoch(train) [76][ 40/940] lr: 1.0000e-03 eta: 7:12:21 time: 1.1025 data_time: 0.0136 memory: 15768 grad_norm: 4.6495 loss: 0.9720 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9720 2023/07/25 12:56:51 - mmengine - INFO - Epoch(train) [76][ 60/940] lr: 1.0000e-03 eta: 7:11:59 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.6231 loss: 0.7630 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7630 2023/07/25 12:57:13 - mmengine - INFO - Epoch(train) [76][ 80/940] lr: 1.0000e-03 eta: 7:11:36 time: 1.1014 data_time: 0.0138 memory: 15768 grad_norm: 4.6923 loss: 0.8832 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8832 2023/07/25 12:57:35 - mmengine - INFO - Epoch(train) [76][100/940] lr: 1.0000e-03 eta: 7:11:14 time: 1.1034 data_time: 0.0138 memory: 15768 grad_norm: 4.5656 loss: 0.8709 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8709 2023/07/25 12:57:57 - mmengine - INFO - Epoch(train) [76][120/940] lr: 1.0000e-03 eta: 7:10:52 time: 1.0998 data_time: 0.0139 memory: 15768 grad_norm: 4.5700 loss: 0.7414 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7414 2023/07/25 12:58:20 - mmengine - INFO - Epoch(train) [76][140/940] lr: 1.0000e-03 eta: 7:10:30 time: 1.1035 data_time: 0.0137 memory: 15768 grad_norm: 4.5804 loss: 0.8242 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8242 2023/07/25 12:58:42 - mmengine - INFO - Epoch(train) [76][160/940] lr: 1.0000e-03 eta: 7:10:08 time: 1.0998 data_time: 0.0146 memory: 15768 grad_norm: 4.5429 loss: 0.7514 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7514 2023/07/25 12:59:03 - mmengine - INFO - Epoch(train) [76][180/940] lr: 1.0000e-03 eta: 7:09:46 time: 1.0986 data_time: 0.0142 memory: 15768 grad_norm: 4.5924 loss: 0.9057 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9057 2023/07/25 12:59:26 - mmengine - INFO - Epoch(train) [76][200/940] lr: 1.0000e-03 eta: 7:09:24 time: 1.1016 data_time: 0.0136 memory: 15768 grad_norm: 4.7248 loss: 0.8299 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8299 2023/07/25 12:59:48 - mmengine - INFO - Epoch(train) [76][220/940] lr: 1.0000e-03 eta: 7:09:01 time: 1.1004 data_time: 0.0138 memory: 15768 grad_norm: 4.7536 loss: 0.9610 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9610 2023/07/25 13:00:10 - mmengine - INFO - Epoch(train) [76][240/940] lr: 1.0000e-03 eta: 7:08:39 time: 1.0998 data_time: 0.0139 memory: 15768 grad_norm: 4.5808 loss: 0.8745 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8745 2023/07/25 13:00:32 - mmengine - INFO - Epoch(train) [76][260/940] lr: 1.0000e-03 eta: 7:08:17 time: 1.1019 data_time: 0.0140 memory: 15768 grad_norm: 4.5819 loss: 0.7580 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7580 2023/07/25 13:00:54 - mmengine - INFO - Epoch(train) [76][280/940] lr: 1.0000e-03 eta: 7:07:55 time: 1.0971 data_time: 0.0139 memory: 15768 grad_norm: 4.7255 loss: 0.9304 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9304 2023/07/25 13:01:16 - mmengine - INFO - Epoch(train) [76][300/940] lr: 1.0000e-03 eta: 7:07:33 time: 1.1004 data_time: 0.0142 memory: 15768 grad_norm: 4.6496 loss: 0.8824 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8824 2023/07/25 13:01:37 - mmengine - INFO - Epoch(train) [76][320/940] lr: 1.0000e-03 eta: 7:07:11 time: 1.0980 data_time: 0.0137 memory: 15768 grad_norm: 4.5407 loss: 0.7932 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7932 2023/07/25 13:01:59 - mmengine - INFO - Epoch(train) [76][340/940] lr: 1.0000e-03 eta: 7:06:48 time: 1.0982 data_time: 0.0141 memory: 15768 grad_norm: 4.5397 loss: 0.7846 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7846 2023/07/25 13:02:21 - mmengine - INFO - Epoch(train) [76][360/940] lr: 1.0000e-03 eta: 7:06:26 time: 1.0994 data_time: 0.0138 memory: 15768 grad_norm: 4.6858 loss: 0.8756 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8756 2023/07/25 13:02:43 - mmengine - INFO - Epoch(train) [76][380/940] lr: 1.0000e-03 eta: 7:06:04 time: 1.1014 data_time: 0.0148 memory: 15768 grad_norm: 4.5987 loss: 0.8861 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8861 2023/07/25 13:03:05 - mmengine - INFO - Epoch(train) [76][400/940] lr: 1.0000e-03 eta: 7:05:42 time: 1.0997 data_time: 0.0140 memory: 15768 grad_norm: 4.6175 loss: 0.7752 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7752 2023/07/25 13:03:27 - mmengine - INFO - Epoch(train) [76][420/940] lr: 1.0000e-03 eta: 7:05:20 time: 1.1004 data_time: 0.0138 memory: 15768 grad_norm: 4.6434 loss: 0.9875 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9875 2023/07/25 13:03:50 - mmengine - INFO - Epoch(train) [76][440/940] lr: 1.0000e-03 eta: 7:04:58 time: 1.1013 data_time: 0.0139 memory: 15768 grad_norm: 4.6830 loss: 0.8796 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8796 2023/07/25 13:04:11 - mmengine - INFO - Epoch(train) [76][460/940] lr: 1.0000e-03 eta: 7:04:36 time: 1.0992 data_time: 0.0143 memory: 15768 grad_norm: 4.6199 loss: 0.8815 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8815 2023/07/25 13:04:34 - mmengine - INFO - Epoch(train) [76][480/940] lr: 1.0000e-03 eta: 7:04:13 time: 1.1026 data_time: 0.0142 memory: 15768 grad_norm: 4.6190 loss: 0.7722 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7722 2023/07/25 13:04:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 13:04:56 - mmengine - INFO - Epoch(train) [76][500/940] lr: 1.0000e-03 eta: 7:03:51 time: 1.0999 data_time: 0.0143 memory: 15768 grad_norm: 4.6824 loss: 0.7353 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7353 2023/07/25 13:05:17 - mmengine - INFO - Epoch(train) [76][520/940] lr: 1.0000e-03 eta: 7:03:29 time: 1.0976 data_time: 0.0145 memory: 15768 grad_norm: 4.6251 loss: 0.7977 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7977 2023/07/25 13:05:39 - mmengine - INFO - Epoch(train) [76][540/940] lr: 1.0000e-03 eta: 7:03:07 time: 1.0992 data_time: 0.0141 memory: 15768 grad_norm: 4.5865 loss: 0.7460 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7460 2023/07/25 13:06:02 - mmengine - INFO - Epoch(train) [76][560/940] lr: 1.0000e-03 eta: 7:02:45 time: 1.1025 data_time: 0.0140 memory: 15768 grad_norm: 4.7034 loss: 0.7618 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7618 2023/07/25 13:06:24 - mmengine - INFO - Epoch(train) [76][580/940] lr: 1.0000e-03 eta: 7:02:23 time: 1.0995 data_time: 0.0138 memory: 15768 grad_norm: 4.5851 loss: 0.9143 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9143 2023/07/25 13:06:46 - mmengine - INFO - Epoch(train) [76][600/940] lr: 1.0000e-03 eta: 7:02:01 time: 1.0995 data_time: 0.0144 memory: 15768 grad_norm: 4.7223 loss: 0.8143 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8143 2023/07/25 13:07:08 - mmengine - INFO - Epoch(train) [76][620/940] lr: 1.0000e-03 eta: 7:01:38 time: 1.1018 data_time: 0.0135 memory: 15768 grad_norm: 4.6439 loss: 0.8355 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.8355 2023/07/25 13:07:30 - mmengine - INFO - Epoch(train) [76][640/940] lr: 1.0000e-03 eta: 7:01:16 time: 1.0996 data_time: 0.0136 memory: 15768 grad_norm: 4.6446 loss: 0.8589 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8589 2023/07/25 13:07:52 - mmengine - INFO - Epoch(train) [76][660/940] lr: 1.0000e-03 eta: 7:00:54 time: 1.0982 data_time: 0.0137 memory: 15768 grad_norm: 4.7269 loss: 0.7520 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7520 2023/07/25 13:08:14 - mmengine - INFO - Epoch(train) [76][680/940] lr: 1.0000e-03 eta: 7:00:32 time: 1.1017 data_time: 0.0135 memory: 15768 grad_norm: 4.7082 loss: 0.9572 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9572 2023/07/25 13:08:36 - mmengine - INFO - Epoch(train) [76][700/940] lr: 1.0000e-03 eta: 7:00:10 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 4.6629 loss: 0.7849 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7849 2023/07/25 13:08:58 - mmengine - INFO - Epoch(train) [76][720/940] lr: 1.0000e-03 eta: 6:59:48 time: 1.0994 data_time: 0.0138 memory: 15768 grad_norm: 4.6275 loss: 0.7767 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7767 2023/07/25 13:09:20 - mmengine - INFO - Epoch(train) [76][740/940] lr: 1.0000e-03 eta: 6:59:25 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.5382 loss: 0.7846 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7846 2023/07/25 13:09:42 - mmengine - INFO - Epoch(train) [76][760/940] lr: 1.0000e-03 eta: 6:59:03 time: 1.0998 data_time: 0.0142 memory: 15768 grad_norm: 4.7490 loss: 0.8478 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8478 2023/07/25 13:10:05 - mmengine - INFO - Epoch(train) [76][780/940] lr: 1.0000e-03 eta: 6:58:41 time: 1.1530 data_time: 0.0139 memory: 15768 grad_norm: 4.6191 loss: 0.9143 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9143 2023/07/25 13:10:28 - mmengine - INFO - Epoch(train) [76][800/940] lr: 1.0000e-03 eta: 6:58:20 time: 1.1673 data_time: 0.0138 memory: 15768 grad_norm: 4.6557 loss: 0.8619 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8619 2023/07/25 13:10:51 - mmengine - INFO - Epoch(train) [76][820/940] lr: 1.0000e-03 eta: 6:57:58 time: 1.1678 data_time: 0.0142 memory: 15768 grad_norm: 4.6539 loss: 0.8307 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8307 2023/07/25 13:11:14 - mmengine - INFO - Epoch(train) [76][840/940] lr: 1.0000e-03 eta: 6:57:36 time: 1.1103 data_time: 0.0139 memory: 15768 grad_norm: 4.7434 loss: 0.7226 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7226 2023/07/25 13:11:36 - mmengine - INFO - Epoch(train) [76][860/940] lr: 1.0000e-03 eta: 6:57:14 time: 1.0995 data_time: 0.0139 memory: 15768 grad_norm: 4.7700 loss: 0.8395 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8395 2023/07/25 13:11:57 - mmengine - INFO - Epoch(train) [76][880/940] lr: 1.0000e-03 eta: 6:56:52 time: 1.0980 data_time: 0.0145 memory: 15768 grad_norm: 4.6208 loss: 0.8070 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8070 2023/07/25 13:12:19 - mmengine - INFO - Epoch(train) [76][900/940] lr: 1.0000e-03 eta: 6:56:29 time: 1.0993 data_time: 0.0139 memory: 15768 grad_norm: 4.5169 loss: 0.6720 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6720 2023/07/25 13:12:42 - mmengine - INFO - Epoch(train) [76][920/940] lr: 1.0000e-03 eta: 6:56:07 time: 1.1017 data_time: 0.0138 memory: 15768 grad_norm: 4.5937 loss: 0.8537 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8537 2023/07/25 13:13:03 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 13:13:03 - mmengine - INFO - Epoch(train) [76][940/940] lr: 1.0000e-03 eta: 6:55:45 time: 1.0570 data_time: 0.0136 memory: 15768 grad_norm: 4.9964 loss: 0.8708 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8708 2023/07/25 13:13:12 - mmengine - INFO - Epoch(val) [76][20/78] eta: 0:00:27 time: 0.4693 data_time: 0.3123 memory: 2147 2023/07/25 13:13:19 - mmengine - INFO - Epoch(val) [76][40/78] eta: 0:00:15 time: 0.3505 data_time: 0.1937 memory: 2147 2023/07/25 13:13:28 - mmengine - INFO - Epoch(val) [76][60/78] eta: 0:00:07 time: 0.4464 data_time: 0.2893 memory: 2147 2023/07/25 13:13:38 - mmengine - INFO - Epoch(val) [76][78/78] acc/top1: 0.7103 acc/top5: 0.8976 acc/mean1: 0.7102 data_time: 0.2422 time: 0.3964 2023/07/25 13:14:04 - mmengine - INFO - Epoch(train) [77][ 20/940] lr: 1.0000e-03 eta: 6:55:24 time: 1.2968 data_time: 0.1554 memory: 15768 grad_norm: 4.6347 loss: 0.8567 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8567 2023/07/25 13:14:26 - mmengine - INFO - Epoch(train) [77][ 40/940] lr: 1.0000e-03 eta: 6:55:02 time: 1.1026 data_time: 0.0137 memory: 15768 grad_norm: 4.5944 loss: 0.8081 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8081 2023/07/25 13:14:48 - mmengine - INFO - Epoch(train) [77][ 60/940] lr: 1.0000e-03 eta: 6:54:40 time: 1.1033 data_time: 0.0146 memory: 15768 grad_norm: 4.6385 loss: 0.7770 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7770 2023/07/25 13:15:10 - mmengine - INFO - Epoch(train) [77][ 80/940] lr: 1.0000e-03 eta: 6:54:18 time: 1.1031 data_time: 0.0141 memory: 15768 grad_norm: 4.6009 loss: 0.6497 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6497 2023/07/25 13:15:32 - mmengine - INFO - Epoch(train) [77][100/940] lr: 1.0000e-03 eta: 6:53:55 time: 1.1002 data_time: 0.0143 memory: 15768 grad_norm: 4.5929 loss: 1.0000 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0000 2023/07/25 13:15:54 - mmengine - INFO - Epoch(train) [77][120/940] lr: 1.0000e-03 eta: 6:53:33 time: 1.1003 data_time: 0.0137 memory: 15768 grad_norm: 4.5377 loss: 0.7601 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7601 2023/07/25 13:16:16 - mmengine - INFO - Epoch(train) [77][140/940] lr: 1.0000e-03 eta: 6:53:11 time: 1.1017 data_time: 0.0135 memory: 15768 grad_norm: 4.5086 loss: 0.8052 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8052 2023/07/25 13:16:38 - mmengine - INFO - Epoch(train) [77][160/940] lr: 1.0000e-03 eta: 6:52:49 time: 1.1002 data_time: 0.0134 memory: 15768 grad_norm: 4.6442 loss: 0.8796 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8796 2023/07/25 13:17:00 - mmengine - INFO - Epoch(train) [77][180/940] lr: 1.0000e-03 eta: 6:52:27 time: 1.1010 data_time: 0.0138 memory: 15768 grad_norm: 4.6492 loss: 0.8516 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8516 2023/07/25 13:17:22 - mmengine - INFO - Epoch(train) [77][200/940] lr: 1.0000e-03 eta: 6:52:05 time: 1.0994 data_time: 0.0136 memory: 15768 grad_norm: 4.6549 loss: 0.7438 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7438 2023/07/25 13:17:45 - mmengine - INFO - Epoch(train) [77][220/940] lr: 1.0000e-03 eta: 6:51:43 time: 1.1077 data_time: 0.0139 memory: 15768 grad_norm: 4.6070 loss: 0.9662 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9662 2023/07/25 13:18:07 - mmengine - INFO - Epoch(train) [77][240/940] lr: 1.0000e-03 eta: 6:51:20 time: 1.0986 data_time: 0.0140 memory: 15768 grad_norm: 4.6132 loss: 0.6452 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6452 2023/07/25 13:18:29 - mmengine - INFO - Epoch(train) [77][260/940] lr: 1.0000e-03 eta: 6:50:58 time: 1.1032 data_time: 0.0140 memory: 15768 grad_norm: 4.6277 loss: 0.7645 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7645 2023/07/25 13:18:51 - mmengine - INFO - Epoch(train) [77][280/940] lr: 1.0000e-03 eta: 6:50:36 time: 1.1003 data_time: 0.0141 memory: 15768 grad_norm: 4.7212 loss: 0.7586 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7586 2023/07/25 13:19:13 - mmengine - INFO - Epoch(train) [77][300/940] lr: 1.0000e-03 eta: 6:50:14 time: 1.1020 data_time: 0.0135 memory: 15768 grad_norm: 4.6324 loss: 0.8372 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8372 2023/07/25 13:19:35 - mmengine - INFO - Epoch(train) [77][320/940] lr: 1.0000e-03 eta: 6:49:52 time: 1.0981 data_time: 0.0137 memory: 15768 grad_norm: 4.6416 loss: 0.8090 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8090 2023/07/25 13:19:57 - mmengine - INFO - Epoch(train) [77][340/940] lr: 1.0000e-03 eta: 6:49:30 time: 1.0999 data_time: 0.0141 memory: 15768 grad_norm: 4.6441 loss: 0.8370 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8370 2023/07/25 13:20:19 - mmengine - INFO - Epoch(train) [77][360/940] lr: 1.0000e-03 eta: 6:49:08 time: 1.1017 data_time: 0.0141 memory: 15768 grad_norm: 4.7513 loss: 0.7924 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7924 2023/07/25 13:20:41 - mmengine - INFO - Epoch(train) [77][380/940] lr: 1.0000e-03 eta: 6:48:45 time: 1.1036 data_time: 0.0139 memory: 15768 grad_norm: 4.6451 loss: 0.8197 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8197 2023/07/25 13:21:03 - mmengine - INFO - Epoch(train) [77][400/940] lr: 1.0000e-03 eta: 6:48:23 time: 1.1028 data_time: 0.0141 memory: 15768 grad_norm: 4.6363 loss: 0.7818 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7818 2023/07/25 13:21:25 - mmengine - INFO - Epoch(train) [77][420/940] lr: 1.0000e-03 eta: 6:48:01 time: 1.1106 data_time: 0.0131 memory: 15768 grad_norm: 4.6635 loss: 0.9642 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9642 2023/07/25 13:21:47 - mmengine - INFO - Epoch(train) [77][440/940] lr: 1.0000e-03 eta: 6:47:39 time: 1.1031 data_time: 0.0137 memory: 15768 grad_norm: 4.5592 loss: 0.9231 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9231 2023/07/25 13:22:09 - mmengine - INFO - Epoch(train) [77][460/940] lr: 1.0000e-03 eta: 6:47:17 time: 1.1014 data_time: 0.0140 memory: 15768 grad_norm: 4.6937 loss: 0.7235 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7235 2023/07/25 13:22:31 - mmengine - INFO - Epoch(train) [77][480/940] lr: 1.0000e-03 eta: 6:46:55 time: 1.0985 data_time: 0.0137 memory: 15768 grad_norm: 4.6410 loss: 1.0025 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0025 2023/07/25 13:22:53 - mmengine - INFO - Epoch(train) [77][500/940] lr: 1.0000e-03 eta: 6:46:33 time: 1.1011 data_time: 0.0140 memory: 15768 grad_norm: 4.6965 loss: 0.8202 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8202 2023/07/25 13:23:15 - mmengine - INFO - Epoch(train) [77][520/940] lr: 1.0000e-03 eta: 6:46:11 time: 1.1024 data_time: 0.0132 memory: 15768 grad_norm: 4.6839 loss: 0.8151 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8151 2023/07/25 13:23:37 - mmengine - INFO - Epoch(train) [77][540/940] lr: 1.0000e-03 eta: 6:45:48 time: 1.0999 data_time: 0.0138 memory: 15768 grad_norm: 4.5718 loss: 0.8332 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8332 2023/07/25 13:23:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 13:23:59 - mmengine - INFO - Epoch(train) [77][560/940] lr: 1.0000e-03 eta: 6:45:26 time: 1.0992 data_time: 0.0143 memory: 15768 grad_norm: 4.6999 loss: 0.9473 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 0.9473 2023/07/25 13:24:21 - mmengine - INFO - Epoch(train) [77][580/940] lr: 1.0000e-03 eta: 6:45:04 time: 1.0988 data_time: 0.0142 memory: 15768 grad_norm: 4.7736 loss: 0.7517 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7517 2023/07/25 13:24:43 - mmengine - INFO - Epoch(train) [77][600/940] lr: 1.0000e-03 eta: 6:44:42 time: 1.1027 data_time: 0.0141 memory: 15768 grad_norm: 4.6394 loss: 0.7626 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7626 2023/07/25 13:25:05 - mmengine - INFO - Epoch(train) [77][620/940] lr: 1.0000e-03 eta: 6:44:20 time: 1.1023 data_time: 0.0138 memory: 15768 grad_norm: 4.6824 loss: 0.8362 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8362 2023/07/25 13:25:27 - mmengine - INFO - Epoch(train) [77][640/940] lr: 1.0000e-03 eta: 6:43:58 time: 1.0990 data_time: 0.0139 memory: 15768 grad_norm: 4.6921 loss: 0.7687 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7687 2023/07/25 13:25:49 - mmengine - INFO - Epoch(train) [77][660/940] lr: 1.0000e-03 eta: 6:43:35 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 4.6133 loss: 0.7786 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7786 2023/07/25 13:26:11 - mmengine - INFO - Epoch(train) [77][680/940] lr: 1.0000e-03 eta: 6:43:13 time: 1.0991 data_time: 0.0144 memory: 15768 grad_norm: 4.6628 loss: 0.7512 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7512 2023/07/25 13:26:33 - mmengine - INFO - Epoch(train) [77][700/940] lr: 1.0000e-03 eta: 6:42:51 time: 1.1023 data_time: 0.0140 memory: 15768 grad_norm: 4.5566 loss: 0.7360 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7360 2023/07/25 13:26:55 - mmengine - INFO - Epoch(train) [77][720/940] lr: 1.0000e-03 eta: 6:42:29 time: 1.0995 data_time: 0.0143 memory: 15768 grad_norm: 4.7108 loss: 0.9145 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9145 2023/07/25 13:27:17 - mmengine - INFO - Epoch(train) [77][740/940] lr: 1.0000e-03 eta: 6:42:07 time: 1.1019 data_time: 0.0136 memory: 15768 grad_norm: 4.7635 loss: 1.0122 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0122 2023/07/25 13:27:39 - mmengine - INFO - Epoch(train) [77][760/940] lr: 1.0000e-03 eta: 6:41:45 time: 1.1002 data_time: 0.0140 memory: 15768 grad_norm: 4.7209 loss: 0.7382 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7382 2023/07/25 13:28:01 - mmengine - INFO - Epoch(train) [77][780/940] lr: 1.0000e-03 eta: 6:41:23 time: 1.1024 data_time: 0.0139 memory: 15768 grad_norm: 4.5946 loss: 0.9422 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9422 2023/07/25 13:28:23 - mmengine - INFO - Epoch(train) [77][800/940] lr: 1.0000e-03 eta: 6:41:00 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.6879 loss: 0.7159 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7159 2023/07/25 13:28:45 - mmengine - INFO - Epoch(train) [77][820/940] lr: 1.0000e-03 eta: 6:40:38 time: 1.1024 data_time: 0.0139 memory: 15768 grad_norm: 4.6642 loss: 0.8095 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8095 2023/07/25 13:29:07 - mmengine - INFO - Epoch(train) [77][840/940] lr: 1.0000e-03 eta: 6:40:16 time: 1.0999 data_time: 0.0139 memory: 15768 grad_norm: 4.6882 loss: 0.7714 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7714 2023/07/25 13:29:30 - mmengine - INFO - Epoch(train) [77][860/940] lr: 1.0000e-03 eta: 6:39:54 time: 1.1017 data_time: 0.0137 memory: 15768 grad_norm: 4.7581 loss: 1.0538 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0538 2023/07/25 13:29:52 - mmengine - INFO - Epoch(train) [77][880/940] lr: 1.0000e-03 eta: 6:39:32 time: 1.0998 data_time: 0.0140 memory: 15768 grad_norm: 4.6487 loss: 0.8888 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8888 2023/07/25 13:30:14 - mmengine - INFO - Epoch(train) [77][900/940] lr: 1.0000e-03 eta: 6:39:10 time: 1.1015 data_time: 0.0136 memory: 15768 grad_norm: 4.6416 loss: 0.7964 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7964 2023/07/25 13:30:36 - mmengine - INFO - Epoch(train) [77][920/940] lr: 1.0000e-03 eta: 6:38:48 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.5958 loss: 0.9050 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9050 2023/07/25 13:30:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 13:30:57 - mmengine - INFO - Epoch(train) [77][940/940] lr: 1.0000e-03 eta: 6:38:25 time: 1.0550 data_time: 0.0135 memory: 15768 grad_norm: 5.0835 loss: 0.6821 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6821 2023/07/25 13:31:06 - mmengine - INFO - Epoch(val) [77][20/78] eta: 0:00:28 time: 0.4892 data_time: 0.3317 memory: 2147 2023/07/25 13:31:13 - mmengine - INFO - Epoch(val) [77][40/78] eta: 0:00:15 time: 0.3512 data_time: 0.1943 memory: 2147 2023/07/25 13:31:22 - mmengine - INFO - Epoch(val) [77][60/78] eta: 0:00:07 time: 0.4483 data_time: 0.2912 memory: 2147 2023/07/25 13:31:33 - mmengine - INFO - Epoch(val) [77][78/78] acc/top1: 0.7099 acc/top5: 0.8964 acc/mean1: 0.7098 data_time: 0.2481 time: 0.4024 2023/07/25 13:31:59 - mmengine - INFO - Epoch(train) [78][ 20/940] lr: 1.0000e-03 eta: 6:38:04 time: 1.2875 data_time: 0.1626 memory: 15768 grad_norm: 4.7106 loss: 0.8064 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8064 2023/07/25 13:32:21 - mmengine - INFO - Epoch(train) [78][ 40/940] lr: 1.0000e-03 eta: 6:37:42 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.8035 loss: 0.7630 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7630 2023/07/25 13:32:43 - mmengine - INFO - Epoch(train) [78][ 60/940] lr: 1.0000e-03 eta: 6:37:20 time: 1.1027 data_time: 0.0140 memory: 15768 grad_norm: 4.6468 loss: 0.9294 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9294 2023/07/25 13:33:05 - mmengine - INFO - Epoch(train) [78][ 80/940] lr: 1.0000e-03 eta: 6:36:58 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.5387 loss: 0.7883 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7883 2023/07/25 13:33:27 - mmengine - INFO - Epoch(train) [78][100/940] lr: 1.0000e-03 eta: 6:36:36 time: 1.1019 data_time: 0.0142 memory: 15768 grad_norm: 4.6304 loss: 0.8431 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8431 2023/07/25 13:33:49 - mmengine - INFO - Epoch(train) [78][120/940] lr: 1.0000e-03 eta: 6:36:13 time: 1.1031 data_time: 0.0136 memory: 15768 grad_norm: 4.5979 loss: 0.9667 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9667 2023/07/25 13:34:11 - mmengine - INFO - Epoch(train) [78][140/940] lr: 1.0000e-03 eta: 6:35:51 time: 1.1017 data_time: 0.0140 memory: 15768 grad_norm: 4.7170 loss: 0.8170 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8170 2023/07/25 13:34:33 - mmengine - INFO - Epoch(train) [78][160/940] lr: 1.0000e-03 eta: 6:35:29 time: 1.0976 data_time: 0.0138 memory: 15768 grad_norm: 4.6559 loss: 0.9107 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9107 2023/07/25 13:34:55 - mmengine - INFO - Epoch(train) [78][180/940] lr: 1.0000e-03 eta: 6:35:07 time: 1.1012 data_time: 0.0137 memory: 15768 grad_norm: 4.6933 loss: 0.9141 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9141 2023/07/25 13:35:17 - mmengine - INFO - Epoch(train) [78][200/940] lr: 1.0000e-03 eta: 6:34:45 time: 1.1028 data_time: 0.0130 memory: 15768 grad_norm: 4.6214 loss: 0.8000 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8000 2023/07/25 13:35:39 - mmengine - INFO - Epoch(train) [78][220/940] lr: 1.0000e-03 eta: 6:34:23 time: 1.1008 data_time: 0.0139 memory: 15768 grad_norm: 4.6789 loss: 0.8337 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8337 2023/07/25 13:36:01 - mmengine - INFO - Epoch(train) [78][240/940] lr: 1.0000e-03 eta: 6:34:01 time: 1.1019 data_time: 0.0136 memory: 15768 grad_norm: 4.6936 loss: 0.8431 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8431 2023/07/25 13:36:23 - mmengine - INFO - Epoch(train) [78][260/940] lr: 1.0000e-03 eta: 6:33:38 time: 1.0999 data_time: 0.0142 memory: 15768 grad_norm: 4.6320 loss: 0.7577 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.7577 2023/07/25 13:36:45 - mmengine - INFO - Epoch(train) [78][280/940] lr: 1.0000e-03 eta: 6:33:16 time: 1.0998 data_time: 0.0144 memory: 15768 grad_norm: 4.6578 loss: 0.8272 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8272 2023/07/25 13:37:07 - mmengine - INFO - Epoch(train) [78][300/940] lr: 1.0000e-03 eta: 6:32:54 time: 1.1067 data_time: 0.0142 memory: 15768 grad_norm: 4.6863 loss: 0.8440 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8440 2023/07/25 13:37:29 - mmengine - INFO - Epoch(train) [78][320/940] lr: 1.0000e-03 eta: 6:32:32 time: 1.0997 data_time: 0.0141 memory: 15768 grad_norm: 4.7360 loss: 0.6626 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6626 2023/07/25 13:37:51 - mmengine - INFO - Epoch(train) [78][340/940] lr: 1.0000e-03 eta: 6:32:10 time: 1.0993 data_time: 0.0136 memory: 15768 grad_norm: 4.6786 loss: 0.7957 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7957 2023/07/25 13:38:13 - mmengine - INFO - Epoch(train) [78][360/940] lr: 1.0000e-03 eta: 6:31:48 time: 1.1040 data_time: 0.0136 memory: 15768 grad_norm: 4.7727 loss: 0.8777 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8777 2023/07/25 13:38:35 - mmengine - INFO - Epoch(train) [78][380/940] lr: 1.0000e-03 eta: 6:31:26 time: 1.0986 data_time: 0.0146 memory: 15768 grad_norm: 4.7397 loss: 0.8852 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8852 2023/07/25 13:38:57 - mmengine - INFO - Epoch(train) [78][400/940] lr: 1.0000e-03 eta: 6:31:03 time: 1.1059 data_time: 0.0147 memory: 15768 grad_norm: 4.6459 loss: 0.7823 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7823 2023/07/25 13:39:19 - mmengine - INFO - Epoch(train) [78][420/940] lr: 1.0000e-03 eta: 6:30:41 time: 1.1036 data_time: 0.0141 memory: 15768 grad_norm: 4.7416 loss: 0.7216 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7216 2023/07/25 13:39:41 - mmengine - INFO - Epoch(train) [78][440/940] lr: 1.0000e-03 eta: 6:30:19 time: 1.1010 data_time: 0.0140 memory: 15768 grad_norm: 4.7578 loss: 0.9529 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9529 2023/07/25 13:40:03 - mmengine - INFO - Epoch(train) [78][460/940] lr: 1.0000e-03 eta: 6:29:57 time: 1.1029 data_time: 0.0134 memory: 15768 grad_norm: 4.7767 loss: 0.7702 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7702 2023/07/25 13:40:25 - mmengine - INFO - Epoch(train) [78][480/940] lr: 1.0000e-03 eta: 6:29:35 time: 1.0992 data_time: 0.0141 memory: 15768 grad_norm: 4.7267 loss: 0.9087 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9087 2023/07/25 13:40:47 - mmengine - INFO - Epoch(train) [78][500/940] lr: 1.0000e-03 eta: 6:29:13 time: 1.0987 data_time: 0.0142 memory: 15768 grad_norm: 4.6515 loss: 1.0096 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0096 2023/07/25 13:41:09 - mmengine - INFO - Epoch(train) [78][520/940] lr: 1.0000e-03 eta: 6:28:51 time: 1.0995 data_time: 0.0142 memory: 15768 grad_norm: 4.6630 loss: 0.8494 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8494 2023/07/25 13:41:31 - mmengine - INFO - Epoch(train) [78][540/940] lr: 1.0000e-03 eta: 6:28:28 time: 1.1011 data_time: 0.0141 memory: 15768 grad_norm: 4.8655 loss: 0.8983 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.8983 2023/07/25 13:41:53 - mmengine - INFO - Epoch(train) [78][560/940] lr: 1.0000e-03 eta: 6:28:06 time: 1.0994 data_time: 0.0139 memory: 15768 grad_norm: 4.7159 loss: 0.7738 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7738 2023/07/25 13:42:15 - mmengine - INFO - Epoch(train) [78][580/940] lr: 1.0000e-03 eta: 6:27:44 time: 1.1034 data_time: 0.0142 memory: 15768 grad_norm: 4.5476 loss: 0.8062 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8062 2023/07/25 13:42:38 - mmengine - INFO - Epoch(train) [78][600/940] lr: 1.0000e-03 eta: 6:27:22 time: 1.1036 data_time: 0.0140 memory: 15768 grad_norm: 4.7629 loss: 0.9228 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9228 2023/07/25 13:43:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 13:43:00 - mmengine - INFO - Epoch(train) [78][620/940] lr: 1.0000e-03 eta: 6:27:00 time: 1.1001 data_time: 0.0139 memory: 15768 grad_norm: 4.7131 loss: 0.9302 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9302 2023/07/25 13:43:22 - mmengine - INFO - Epoch(train) [78][640/940] lr: 1.0000e-03 eta: 6:26:38 time: 1.1014 data_time: 0.0144 memory: 15768 grad_norm: 4.6084 loss: 0.8324 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8324 2023/07/25 13:43:44 - mmengine - INFO - Epoch(train) [78][660/940] lr: 1.0000e-03 eta: 6:26:16 time: 1.0992 data_time: 0.0146 memory: 15768 grad_norm: 4.6629 loss: 0.8013 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8013 2023/07/25 13:44:06 - mmengine - INFO - Epoch(train) [78][680/940] lr: 1.0000e-03 eta: 6:25:54 time: 1.1020 data_time: 0.0143 memory: 15768 grad_norm: 4.7641 loss: 0.8218 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8218 2023/07/25 13:44:28 - mmengine - INFO - Epoch(train) [78][700/940] lr: 1.0000e-03 eta: 6:25:31 time: 1.1028 data_time: 0.0141 memory: 15768 grad_norm: 4.6574 loss: 0.8538 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8538 2023/07/25 13:44:50 - mmengine - INFO - Epoch(train) [78][720/940] lr: 1.0000e-03 eta: 6:25:09 time: 1.0993 data_time: 0.0139 memory: 15768 grad_norm: 4.7321 loss: 0.8009 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8009 2023/07/25 13:45:12 - mmengine - INFO - Epoch(train) [78][740/940] lr: 1.0000e-03 eta: 6:24:47 time: 1.1021 data_time: 0.0141 memory: 15768 grad_norm: 4.8026 loss: 0.8544 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8544 2023/07/25 13:45:34 - mmengine - INFO - Epoch(train) [78][760/940] lr: 1.0000e-03 eta: 6:24:25 time: 1.1041 data_time: 0.0139 memory: 15768 grad_norm: 4.7387 loss: 0.7876 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7876 2023/07/25 13:45:56 - mmengine - INFO - Epoch(train) [78][780/940] lr: 1.0000e-03 eta: 6:24:03 time: 1.1012 data_time: 0.0132 memory: 15768 grad_norm: 4.6204 loss: 1.0056 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0056 2023/07/25 13:46:18 - mmengine - INFO - Epoch(train) [78][800/940] lr: 1.0000e-03 eta: 6:23:41 time: 1.1024 data_time: 0.0141 memory: 15768 grad_norm: 4.7594 loss: 0.7419 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7419 2023/07/25 13:46:40 - mmengine - INFO - Epoch(train) [78][820/940] lr: 1.0000e-03 eta: 6:23:19 time: 1.0994 data_time: 0.0140 memory: 15768 grad_norm: 4.7569 loss: 0.8724 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8724 2023/07/25 13:47:02 - mmengine - INFO - Epoch(train) [78][840/940] lr: 1.0000e-03 eta: 6:22:56 time: 1.1056 data_time: 0.0151 memory: 15768 grad_norm: 4.7057 loss: 0.8888 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8888 2023/07/25 13:47:24 - mmengine - INFO - Epoch(train) [78][860/940] lr: 1.0000e-03 eta: 6:22:34 time: 1.0993 data_time: 0.0138 memory: 15768 grad_norm: 4.6537 loss: 0.7776 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7776 2023/07/25 13:47:46 - mmengine - INFO - Epoch(train) [78][880/940] lr: 1.0000e-03 eta: 6:22:12 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.7657 loss: 0.8053 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8053 2023/07/25 13:48:08 - mmengine - INFO - Epoch(train) [78][900/940] lr: 1.0000e-03 eta: 6:21:50 time: 1.1044 data_time: 0.0139 memory: 15768 grad_norm: 4.5930 loss: 0.7638 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7638 2023/07/25 13:48:30 - mmengine - INFO - Epoch(train) [78][920/940] lr: 1.0000e-03 eta: 6:21:28 time: 1.1023 data_time: 0.0136 memory: 15768 grad_norm: 4.6036 loss: 0.8575 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8575 2023/07/25 13:48:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 13:48:51 - mmengine - INFO - Epoch(train) [78][940/940] lr: 1.0000e-03 eta: 6:21:05 time: 1.0556 data_time: 0.0135 memory: 15768 grad_norm: 5.0709 loss: 0.8333 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8333 2023/07/25 13:48:51 - mmengine - INFO - Saving checkpoint at 78 epochs 2023/07/25 13:49:02 - mmengine - INFO - Epoch(val) [78][20/78] eta: 0:00:28 time: 0.4870 data_time: 0.3298 memory: 2147 2023/07/25 13:49:09 - mmengine - INFO - Epoch(val) [78][40/78] eta: 0:00:16 time: 0.3587 data_time: 0.2021 memory: 2147 2023/07/25 13:49:18 - mmengine - INFO - Epoch(val) [78][60/78] eta: 0:00:07 time: 0.4281 data_time: 0.2712 memory: 2147 2023/07/25 13:49:28 - mmengine - INFO - Epoch(val) [78][78/78] acc/top1: 0.7089 acc/top5: 0.8972 acc/mean1: 0.7088 data_time: 0.2394 time: 0.3934 2023/07/25 13:49:53 - mmengine - INFO - Epoch(train) [79][ 20/940] lr: 1.0000e-03 eta: 6:20:44 time: 1.2910 data_time: 0.1491 memory: 15768 grad_norm: 4.7588 loss: 0.8887 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8887 2023/07/25 13:50:15 - mmengine - INFO - Epoch(train) [79][ 40/940] lr: 1.0000e-03 eta: 6:20:22 time: 1.0995 data_time: 0.0142 memory: 15768 grad_norm: 4.5835 loss: 0.7500 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7500 2023/07/25 13:50:37 - mmengine - INFO - Epoch(train) [79][ 60/940] lr: 1.0000e-03 eta: 6:20:00 time: 1.1041 data_time: 0.0139 memory: 15768 grad_norm: 4.7439 loss: 0.8518 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8518 2023/07/25 13:51:00 - mmengine - INFO - Epoch(train) [79][ 80/940] lr: 1.0000e-03 eta: 6:19:38 time: 1.1007 data_time: 0.0136 memory: 15768 grad_norm: 4.7199 loss: 0.9866 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9866 2023/07/25 13:51:22 - mmengine - INFO - Epoch(train) [79][100/940] lr: 1.0000e-03 eta: 6:19:16 time: 1.1013 data_time: 0.0138 memory: 15768 grad_norm: 4.7120 loss: 0.9324 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9324 2023/07/25 13:51:44 - mmengine - INFO - Epoch(train) [79][120/940] lr: 1.0000e-03 eta: 6:18:54 time: 1.0994 data_time: 0.0140 memory: 15768 grad_norm: 4.6362 loss: 0.6577 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6577 2023/07/25 13:52:06 - mmengine - INFO - Epoch(train) [79][140/940] lr: 1.0000e-03 eta: 6:18:32 time: 1.1033 data_time: 0.0144 memory: 15768 grad_norm: 4.6669 loss: 0.7122 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7122 2023/07/25 13:52:28 - mmengine - INFO - Epoch(train) [79][160/940] lr: 1.0000e-03 eta: 6:18:09 time: 1.0985 data_time: 0.0141 memory: 15768 grad_norm: 4.7024 loss: 0.8398 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8398 2023/07/25 13:52:50 - mmengine - INFO - Epoch(train) [79][180/940] lr: 1.0000e-03 eta: 6:17:47 time: 1.1025 data_time: 0.0137 memory: 15768 grad_norm: 4.6556 loss: 1.0140 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0140 2023/07/25 13:53:12 - mmengine - INFO - Epoch(train) [79][200/940] lr: 1.0000e-03 eta: 6:17:25 time: 1.1011 data_time: 0.0139 memory: 15768 grad_norm: 4.7411 loss: 0.7894 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7894 2023/07/25 13:53:34 - mmengine - INFO - Epoch(train) [79][220/940] lr: 1.0000e-03 eta: 6:17:03 time: 1.1017 data_time: 0.0138 memory: 15768 grad_norm: 4.7803 loss: 0.8603 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8603 2023/07/25 13:53:56 - mmengine - INFO - Epoch(train) [79][240/940] lr: 1.0000e-03 eta: 6:16:41 time: 1.1004 data_time: 0.0140 memory: 15768 grad_norm: 4.6270 loss: 0.8939 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8939 2023/07/25 13:54:18 - mmengine - INFO - Epoch(train) [79][260/940] lr: 1.0000e-03 eta: 6:16:19 time: 1.1020 data_time: 0.0141 memory: 15768 grad_norm: 4.7623 loss: 0.6820 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6820 2023/07/25 13:54:40 - mmengine - INFO - Epoch(train) [79][280/940] lr: 1.0000e-03 eta: 6:15:57 time: 1.0995 data_time: 0.0142 memory: 15768 grad_norm: 4.7592 loss: 0.9886 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9886 2023/07/25 13:55:02 - mmengine - INFO - Epoch(train) [79][300/940] lr: 1.0000e-03 eta: 6:15:34 time: 1.0993 data_time: 0.0141 memory: 15768 grad_norm: 4.6346 loss: 0.7798 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7798 2023/07/25 13:55:24 - mmengine - INFO - Epoch(train) [79][320/940] lr: 1.0000e-03 eta: 6:15:12 time: 1.1031 data_time: 0.0140 memory: 15768 grad_norm: 4.6847 loss: 0.8833 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8833 2023/07/25 13:55:46 - mmengine - INFO - Epoch(train) [79][340/940] lr: 1.0000e-03 eta: 6:14:50 time: 1.1015 data_time: 0.0139 memory: 15768 grad_norm: 4.6876 loss: 0.8098 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8098 2023/07/25 13:56:08 - mmengine - INFO - Epoch(train) [79][360/940] lr: 1.0000e-03 eta: 6:14:28 time: 1.1011 data_time: 0.0140 memory: 15768 grad_norm: 4.6740 loss: 0.8058 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8058 2023/07/25 13:56:30 - mmengine - INFO - Epoch(train) [79][380/940] lr: 1.0000e-03 eta: 6:14:06 time: 1.0998 data_time: 0.0144 memory: 15768 grad_norm: 4.7432 loss: 0.9994 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9994 2023/07/25 13:56:52 - mmengine - INFO - Epoch(train) [79][400/940] lr: 1.0000e-03 eta: 6:13:44 time: 1.1014 data_time: 0.0138 memory: 15768 grad_norm: 4.8140 loss: 0.8558 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8558 2023/07/25 13:57:14 - mmengine - INFO - Epoch(train) [79][420/940] lr: 1.0000e-03 eta: 6:13:22 time: 1.0996 data_time: 0.0138 memory: 15768 grad_norm: 4.5809 loss: 0.7448 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7448 2023/07/25 13:57:36 - mmengine - INFO - Epoch(train) [79][440/940] lr: 1.0000e-03 eta: 6:12:59 time: 1.1024 data_time: 0.0144 memory: 15768 grad_norm: 4.7693 loss: 0.7124 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7124 2023/07/25 13:57:58 - mmengine - INFO - Epoch(train) [79][460/940] lr: 1.0000e-03 eta: 6:12:37 time: 1.1010 data_time: 0.0141 memory: 15768 grad_norm: 4.6871 loss: 0.7917 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7917 2023/07/25 13:58:20 - mmengine - INFO - Epoch(train) [79][480/940] lr: 1.0000e-03 eta: 6:12:15 time: 1.1027 data_time: 0.0142 memory: 15768 grad_norm: 4.6889 loss: 0.6197 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6197 2023/07/25 13:58:42 - mmengine - INFO - Epoch(train) [79][500/940] lr: 1.0000e-03 eta: 6:11:53 time: 1.1037 data_time: 0.0136 memory: 15768 grad_norm: 4.7782 loss: 0.7831 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7831 2023/07/25 13:59:04 - mmengine - INFO - Epoch(train) [79][520/940] lr: 1.0000e-03 eta: 6:11:31 time: 1.1012 data_time: 0.0133 memory: 15768 grad_norm: 4.7709 loss: 0.8413 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8413 2023/07/25 13:59:26 - mmengine - INFO - Epoch(train) [79][540/940] lr: 1.0000e-03 eta: 6:11:09 time: 1.1052 data_time: 0.0139 memory: 15768 grad_norm: 4.7746 loss: 0.8467 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8467 2023/07/25 13:59:49 - mmengine - INFO - Epoch(train) [79][560/940] lr: 1.0000e-03 eta: 6:10:47 time: 1.1205 data_time: 0.0142 memory: 15768 grad_norm: 4.7792 loss: 0.8750 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8750 2023/07/25 14:00:11 - mmengine - INFO - Epoch(train) [79][580/940] lr: 1.0000e-03 eta: 6:10:25 time: 1.1026 data_time: 0.0142 memory: 15768 grad_norm: 4.7039 loss: 0.8292 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8292 2023/07/25 14:00:33 - mmengine - INFO - Epoch(train) [79][600/940] lr: 1.0000e-03 eta: 6:10:03 time: 1.1046 data_time: 0.0141 memory: 15768 grad_norm: 4.7340 loss: 0.8516 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8516 2023/07/25 14:00:55 - mmengine - INFO - Epoch(train) [79][620/940] lr: 1.0000e-03 eta: 6:09:40 time: 1.1031 data_time: 0.0139 memory: 15768 grad_norm: 4.6784 loss: 0.8709 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8709 2023/07/25 14:01:17 - mmengine - INFO - Epoch(train) [79][640/940] lr: 1.0000e-03 eta: 6:09:18 time: 1.0999 data_time: 0.0137 memory: 15768 grad_norm: 4.6653 loss: 0.8024 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8024 2023/07/25 14:01:39 - mmengine - INFO - Epoch(train) [79][660/940] lr: 1.0000e-03 eta: 6:08:56 time: 1.0996 data_time: 0.0143 memory: 15768 grad_norm: 4.6873 loss: 0.8851 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8851 2023/07/25 14:02:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 14:02:01 - mmengine - INFO - Epoch(train) [79][680/940] lr: 1.0000e-03 eta: 6:08:34 time: 1.0993 data_time: 0.0140 memory: 15768 grad_norm: 4.7576 loss: 0.8355 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8355 2023/07/25 14:02:23 - mmengine - INFO - Epoch(train) [79][700/940] lr: 1.0000e-03 eta: 6:08:12 time: 1.1010 data_time: 0.0140 memory: 15768 grad_norm: 4.8015 loss: 0.7155 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7155 2023/07/25 14:02:45 - mmengine - INFO - Epoch(train) [79][720/940] lr: 1.0000e-03 eta: 6:07:50 time: 1.1041 data_time: 0.0138 memory: 15768 grad_norm: 4.6980 loss: 0.7260 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7260 2023/07/25 14:03:07 - mmengine - INFO - Epoch(train) [79][740/940] lr: 1.0000e-03 eta: 6:07:28 time: 1.1027 data_time: 0.0137 memory: 15768 grad_norm: 4.5995 loss: 0.8158 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8158 2023/07/25 14:03:29 - mmengine - INFO - Epoch(train) [79][760/940] lr: 1.0000e-03 eta: 6:07:05 time: 1.0996 data_time: 0.0144 memory: 15768 grad_norm: 4.7704 loss: 0.9575 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9575 2023/07/25 14:03:51 - mmengine - INFO - Epoch(train) [79][780/940] lr: 1.0000e-03 eta: 6:06:43 time: 1.1002 data_time: 0.0140 memory: 15768 grad_norm: 4.6804 loss: 0.8048 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.8048 2023/07/25 14:04:13 - mmengine - INFO - Epoch(train) [79][800/940] lr: 1.0000e-03 eta: 6:06:21 time: 1.1009 data_time: 0.0140 memory: 15768 grad_norm: 4.7344 loss: 0.8107 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8107 2023/07/25 14:04:35 - mmengine - INFO - Epoch(train) [79][820/940] lr: 1.0000e-03 eta: 6:05:59 time: 1.1000 data_time: 0.0141 memory: 15768 grad_norm: 4.7160 loss: 0.9149 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9149 2023/07/25 14:04:57 - mmengine - INFO - Epoch(train) [79][840/940] lr: 1.0000e-03 eta: 6:05:37 time: 1.1035 data_time: 0.0138 memory: 15768 grad_norm: 4.8372 loss: 0.9676 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9676 2023/07/25 14:05:19 - mmengine - INFO - Epoch(train) [79][860/940] lr: 1.0000e-03 eta: 6:05:15 time: 1.1001 data_time: 0.0138 memory: 15768 grad_norm: 4.8234 loss: 0.8745 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8745 2023/07/25 14:05:41 - mmengine - INFO - Epoch(train) [79][880/940] lr: 1.0000e-03 eta: 6:04:53 time: 1.1022 data_time: 0.0140 memory: 15768 grad_norm: 4.6530 loss: 0.8416 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8416 2023/07/25 14:06:03 - mmengine - INFO - Epoch(train) [79][900/940] lr: 1.0000e-03 eta: 6:04:30 time: 1.1023 data_time: 0.0143 memory: 15768 grad_norm: 4.7712 loss: 0.8513 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8513 2023/07/25 14:06:25 - mmengine - INFO - Epoch(train) [79][920/940] lr: 1.0000e-03 eta: 6:04:08 time: 1.0983 data_time: 0.0138 memory: 15768 grad_norm: 4.6624 loss: 0.8879 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8879 2023/07/25 14:06:46 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 14:06:46 - mmengine - INFO - Epoch(train) [79][940/940] lr: 1.0000e-03 eta: 6:03:46 time: 1.0536 data_time: 0.0134 memory: 15768 grad_norm: 4.9590 loss: 0.8459 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8459 2023/07/25 14:06:56 - mmengine - INFO - Epoch(val) [79][20/78] eta: 0:00:28 time: 0.4839 data_time: 0.3265 memory: 2147 2023/07/25 14:07:03 - mmengine - INFO - Epoch(val) [79][40/78] eta: 0:00:15 time: 0.3554 data_time: 0.1989 memory: 2147 2023/07/25 14:07:12 - mmengine - INFO - Epoch(val) [79][60/78] eta: 0:00:07 time: 0.4386 data_time: 0.2819 memory: 2147 2023/07/25 14:07:22 - mmengine - INFO - Epoch(val) [79][78/78] acc/top1: 0.7082 acc/top5: 0.8963 acc/mean1: 0.7082 data_time: 0.2446 time: 0.3987 2023/07/25 14:07:48 - mmengine - INFO - Epoch(train) [80][ 20/940] lr: 1.0000e-03 eta: 6:03:25 time: 1.2865 data_time: 0.1648 memory: 15768 grad_norm: 4.6893 loss: 0.8649 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8649 2023/07/25 14:08:10 - mmengine - INFO - Epoch(train) [80][ 40/940] lr: 1.0000e-03 eta: 6:03:03 time: 1.1027 data_time: 0.0142 memory: 15768 grad_norm: 4.8162 loss: 0.7682 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7682 2023/07/25 14:08:32 - mmengine - INFO - Epoch(train) [80][ 60/940] lr: 1.0000e-03 eta: 6:02:40 time: 1.1010 data_time: 0.0144 memory: 15768 grad_norm: 4.7744 loss: 0.7822 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7822 2023/07/25 14:08:54 - mmengine - INFO - Epoch(train) [80][ 80/940] lr: 1.0000e-03 eta: 6:02:18 time: 1.1019 data_time: 0.0143 memory: 15768 grad_norm: 4.7461 loss: 0.8647 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8647 2023/07/25 14:09:16 - mmengine - INFO - Epoch(train) [80][100/940] lr: 1.0000e-03 eta: 6:01:56 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.8431 loss: 0.8524 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8524 2023/07/25 14:09:38 - mmengine - INFO - Epoch(train) [80][120/940] lr: 1.0000e-03 eta: 6:01:34 time: 1.0990 data_time: 0.0141 memory: 15768 grad_norm: 4.7702 loss: 0.9558 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9558 2023/07/25 14:10:00 - mmengine - INFO - Epoch(train) [80][140/940] lr: 1.0000e-03 eta: 6:01:12 time: 1.1000 data_time: 0.0144 memory: 15768 grad_norm: 4.5698 loss: 0.8272 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8272 2023/07/25 14:10:22 - mmengine - INFO - Epoch(train) [80][160/940] lr: 1.0000e-03 eta: 6:00:50 time: 1.0996 data_time: 0.0139 memory: 15768 grad_norm: 4.6868 loss: 0.7170 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7170 2023/07/25 14:10:44 - mmengine - INFO - Epoch(train) [80][180/940] lr: 1.0000e-03 eta: 6:00:28 time: 1.1018 data_time: 0.0140 memory: 15768 grad_norm: 4.5348 loss: 0.7173 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7173 2023/07/25 14:11:06 - mmengine - INFO - Epoch(train) [80][200/940] lr: 1.0000e-03 eta: 6:00:05 time: 1.1013 data_time: 0.0135 memory: 15768 grad_norm: 4.7201 loss: 0.8346 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8346 2023/07/25 14:11:28 - mmengine - INFO - Epoch(train) [80][220/940] lr: 1.0000e-03 eta: 5:59:43 time: 1.1029 data_time: 0.0133 memory: 15768 grad_norm: 4.8216 loss: 0.8353 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8353 2023/07/25 14:11:50 - mmengine - INFO - Epoch(train) [80][240/940] lr: 1.0000e-03 eta: 5:59:21 time: 1.1018 data_time: 0.0140 memory: 15768 grad_norm: 4.6432 loss: 0.8887 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8887 2023/07/25 14:12:12 - mmengine - INFO - Epoch(train) [80][260/940] lr: 1.0000e-03 eta: 5:58:59 time: 1.1004 data_time: 0.0138 memory: 15768 grad_norm: 4.7296 loss: 0.8710 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8710 2023/07/25 14:12:34 - mmengine - INFO - Epoch(train) [80][280/940] lr: 1.0000e-03 eta: 5:58:37 time: 1.1020 data_time: 0.0138 memory: 15768 grad_norm: 4.7239 loss: 1.0556 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.0556 2023/07/25 14:12:56 - mmengine - INFO - Epoch(train) [80][300/940] lr: 1.0000e-03 eta: 5:58:15 time: 1.1032 data_time: 0.0141 memory: 15768 grad_norm: 4.6834 loss: 0.8438 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8438 2023/07/25 14:13:18 - mmengine - INFO - Epoch(train) [80][320/940] lr: 1.0000e-03 eta: 5:57:53 time: 1.1025 data_time: 0.0140 memory: 15768 grad_norm: 4.6910 loss: 0.8465 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8465 2023/07/25 14:13:41 - mmengine - INFO - Epoch(train) [80][340/940] lr: 1.0000e-03 eta: 5:57:31 time: 1.1046 data_time: 0.0138 memory: 15768 grad_norm: 4.8330 loss: 0.6828 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.6828 2023/07/25 14:14:03 - mmengine - INFO - Epoch(train) [80][360/940] lr: 1.0000e-03 eta: 5:57:08 time: 1.1012 data_time: 0.0140 memory: 15768 grad_norm: 4.6665 loss: 0.6546 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6546 2023/07/25 14:14:25 - mmengine - INFO - Epoch(train) [80][380/940] lr: 1.0000e-03 eta: 5:56:46 time: 1.0999 data_time: 0.0142 memory: 15768 grad_norm: 4.7912 loss: 0.8645 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8645 2023/07/25 14:14:47 - mmengine - INFO - Epoch(train) [80][400/940] lr: 1.0000e-03 eta: 5:56:24 time: 1.0996 data_time: 0.0141 memory: 15768 grad_norm: 4.7620 loss: 0.9273 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9273 2023/07/25 14:15:09 - mmengine - INFO - Epoch(train) [80][420/940] lr: 1.0000e-03 eta: 5:56:02 time: 1.1007 data_time: 0.0138 memory: 15768 grad_norm: 4.7130 loss: 0.8989 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.8989 2023/07/25 14:15:31 - mmengine - INFO - Epoch(train) [80][440/940] lr: 1.0000e-03 eta: 5:55:40 time: 1.1039 data_time: 0.0141 memory: 15768 grad_norm: 4.7119 loss: 0.9578 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9578 2023/07/25 14:15:53 - mmengine - INFO - Epoch(train) [80][460/940] lr: 1.0000e-03 eta: 5:55:18 time: 1.1002 data_time: 0.0140 memory: 15768 grad_norm: 4.7434 loss: 0.9326 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9326 2023/07/25 14:16:15 - mmengine - INFO - Epoch(train) [80][480/940] lr: 1.0000e-03 eta: 5:54:56 time: 1.1020 data_time: 0.0142 memory: 15768 grad_norm: 4.7390 loss: 0.7746 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7746 2023/07/25 14:16:37 - mmengine - INFO - Epoch(train) [80][500/940] lr: 1.0000e-03 eta: 5:54:33 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 4.7312 loss: 0.7587 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7587 2023/07/25 14:16:59 - mmengine - INFO - Epoch(train) [80][520/940] lr: 1.0000e-03 eta: 5:54:11 time: 1.0991 data_time: 0.0142 memory: 15768 grad_norm: 4.7526 loss: 0.7474 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7474 2023/07/25 14:17:21 - mmengine - INFO - Epoch(train) [80][540/940] lr: 1.0000e-03 eta: 5:53:49 time: 1.0995 data_time: 0.0138 memory: 15768 grad_norm: 4.7009 loss: 0.8014 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8014 2023/07/25 14:17:43 - mmengine - INFO - Epoch(train) [80][560/940] lr: 1.0000e-03 eta: 5:53:27 time: 1.1013 data_time: 0.0139 memory: 15768 grad_norm: 4.7024 loss: 0.7832 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7832 2023/07/25 14:18:05 - mmengine - INFO - Epoch(train) [80][580/940] lr: 1.0000e-03 eta: 5:53:05 time: 1.1013 data_time: 0.0140 memory: 15768 grad_norm: 4.7949 loss: 0.6800 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6800 2023/07/25 14:18:27 - mmengine - INFO - Epoch(train) [80][600/940] lr: 1.0000e-03 eta: 5:52:43 time: 1.1029 data_time: 0.0142 memory: 15768 grad_norm: 4.8798 loss: 0.8652 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8652 2023/07/25 14:18:49 - mmengine - INFO - Epoch(train) [80][620/940] lr: 1.0000e-03 eta: 5:52:21 time: 1.1042 data_time: 0.0134 memory: 15768 grad_norm: 4.7359 loss: 0.7829 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7829 2023/07/25 14:19:11 - mmengine - INFO - Epoch(train) [80][640/940] lr: 1.0000e-03 eta: 5:51:58 time: 1.0989 data_time: 0.0138 memory: 15768 grad_norm: 4.7964 loss: 0.8084 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8084 2023/07/25 14:19:33 - mmengine - INFO - Epoch(train) [80][660/940] lr: 1.0000e-03 eta: 5:51:36 time: 1.0986 data_time: 0.0141 memory: 15768 grad_norm: 4.6878 loss: 0.8194 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8194 2023/07/25 14:19:55 - mmengine - INFO - Epoch(train) [80][680/940] lr: 1.0000e-03 eta: 5:51:14 time: 1.1009 data_time: 0.0138 memory: 15768 grad_norm: 4.6413 loss: 0.8835 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 0.8835 2023/07/25 14:20:17 - mmengine - INFO - Epoch(train) [80][700/940] lr: 1.0000e-03 eta: 5:50:52 time: 1.1019 data_time: 0.0140 memory: 15768 grad_norm: 4.7405 loss: 0.7510 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7510 2023/07/25 14:20:39 - mmengine - INFO - Epoch(train) [80][720/940] lr: 1.0000e-03 eta: 5:50:30 time: 1.1036 data_time: 0.0138 memory: 15768 grad_norm: 4.8381 loss: 0.7206 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7206 2023/07/25 14:21:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 14:21:01 - mmengine - INFO - Epoch(train) [80][740/940] lr: 1.0000e-03 eta: 5:50:08 time: 1.0999 data_time: 0.0139 memory: 15768 grad_norm: 4.7201 loss: 0.8671 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8671 2023/07/25 14:21:23 - mmengine - INFO - Epoch(train) [80][760/940] lr: 1.0000e-03 eta: 5:49:46 time: 1.1011 data_time: 0.0137 memory: 15768 grad_norm: 4.7364 loss: 0.6892 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6892 2023/07/25 14:21:45 - mmengine - INFO - Epoch(train) [80][780/940] lr: 1.0000e-03 eta: 5:49:24 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 4.6995 loss: 0.8807 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8807 2023/07/25 14:22:07 - mmengine - INFO - Epoch(train) [80][800/940] lr: 1.0000e-03 eta: 5:49:01 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 4.7275 loss: 0.7718 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7718 2023/07/25 14:22:29 - mmengine - INFO - Epoch(train) [80][820/940] lr: 1.0000e-03 eta: 5:48:39 time: 1.1001 data_time: 0.0140 memory: 15768 grad_norm: 4.7256 loss: 0.8588 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8588 2023/07/25 14:22:51 - mmengine - INFO - Epoch(train) [80][840/940] lr: 1.0000e-03 eta: 5:48:17 time: 1.1001 data_time: 0.0144 memory: 15768 grad_norm: 4.7351 loss: 0.7375 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7375 2023/07/25 14:23:13 - mmengine - INFO - Epoch(train) [80][860/940] lr: 1.0000e-03 eta: 5:47:55 time: 1.0995 data_time: 0.0142 memory: 15768 grad_norm: 4.6749 loss: 0.6757 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6757 2023/07/25 14:23:35 - mmengine - INFO - Epoch(train) [80][880/940] lr: 1.0000e-03 eta: 5:47:33 time: 1.0992 data_time: 0.0142 memory: 15768 grad_norm: 4.7207 loss: 0.6680 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.6680 2023/07/25 14:23:57 - mmengine - INFO - Epoch(train) [80][900/940] lr: 1.0000e-03 eta: 5:47:11 time: 1.1005 data_time: 0.0140 memory: 15768 grad_norm: 4.7040 loss: 0.6215 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.6215 2023/07/25 14:24:19 - mmengine - INFO - Epoch(train) [80][920/940] lr: 1.0000e-03 eta: 5:46:49 time: 1.1043 data_time: 0.0139 memory: 15768 grad_norm: 4.7932 loss: 0.7656 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7656 2023/07/25 14:24:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 14:24:40 - mmengine - INFO - Epoch(train) [80][940/940] lr: 1.0000e-03 eta: 5:46:26 time: 1.0548 data_time: 0.0133 memory: 15768 grad_norm: 4.9950 loss: 0.9355 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 0.9355 2023/07/25 14:24:50 - mmengine - INFO - Epoch(val) [80][20/78] eta: 0:00:28 time: 0.4917 data_time: 0.3338 memory: 2147 2023/07/25 14:24:57 - mmengine - INFO - Epoch(val) [80][40/78] eta: 0:00:15 time: 0.3432 data_time: 0.1860 memory: 2147 2023/07/25 14:25:06 - mmengine - INFO - Epoch(val) [80][60/78] eta: 0:00:07 time: 0.4620 data_time: 0.3052 memory: 2147 2023/07/25 14:25:16 - mmengine - INFO - Epoch(val) [80][78/78] acc/top1: 0.7099 acc/top5: 0.8978 acc/mean1: 0.7098 data_time: 0.2482 time: 0.4026 2023/07/25 14:25:42 - mmengine - INFO - Epoch(train) [81][ 20/940] lr: 1.0000e-04 eta: 5:46:05 time: 1.2862 data_time: 0.1628 memory: 15768 grad_norm: 4.6962 loss: 0.8241 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 0.8241 2023/07/25 14:26:04 - mmengine - INFO - Epoch(train) [81][ 40/940] lr: 1.0000e-04 eta: 5:45:43 time: 1.1006 data_time: 0.0139 memory: 15768 grad_norm: 4.7043 loss: 0.7880 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7880 2023/07/25 14:26:26 - mmengine - INFO - Epoch(train) [81][ 60/940] lr: 1.0000e-04 eta: 5:45:21 time: 1.0984 data_time: 0.0140 memory: 15768 grad_norm: 4.6626 loss: 0.9466 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9466 2023/07/25 14:26:48 - mmengine - INFO - Epoch(train) [81][ 80/940] lr: 1.0000e-04 eta: 5:44:59 time: 1.1037 data_time: 0.0144 memory: 15768 grad_norm: 4.5940 loss: 0.7323 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7323 2023/07/25 14:27:10 - mmengine - INFO - Epoch(train) [81][100/940] lr: 1.0000e-04 eta: 5:44:36 time: 1.1028 data_time: 0.0139 memory: 15768 grad_norm: 4.8975 loss: 0.8972 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8972 2023/07/25 14:27:32 - mmengine - INFO - Epoch(train) [81][120/940] lr: 1.0000e-04 eta: 5:44:14 time: 1.1063 data_time: 0.0128 memory: 15768 grad_norm: 4.6182 loss: 0.7764 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7764 2023/07/25 14:27:54 - mmengine - INFO - Epoch(train) [81][140/940] lr: 1.0000e-04 eta: 5:43:52 time: 1.1027 data_time: 0.0135 memory: 15768 grad_norm: 4.5917 loss: 0.7950 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7950 2023/07/25 14:28:16 - mmengine - INFO - Epoch(train) [81][160/940] lr: 1.0000e-04 eta: 5:43:30 time: 1.1014 data_time: 0.0135 memory: 15768 grad_norm: 4.6455 loss: 0.7405 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7405 2023/07/25 14:28:38 - mmengine - INFO - Epoch(train) [81][180/940] lr: 1.0000e-04 eta: 5:43:08 time: 1.1026 data_time: 0.0134 memory: 15768 grad_norm: 4.7660 loss: 0.8494 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8494 2023/07/25 14:29:00 - mmengine - INFO - Epoch(train) [81][200/940] lr: 1.0000e-04 eta: 5:42:46 time: 1.0988 data_time: 0.0137 memory: 15768 grad_norm: 4.5814 loss: 0.7303 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7303 2023/07/25 14:29:22 - mmengine - INFO - Epoch(train) [81][220/940] lr: 1.0000e-04 eta: 5:42:24 time: 1.1014 data_time: 0.0140 memory: 15768 grad_norm: 4.7161 loss: 0.8810 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8810 2023/07/25 14:29:44 - mmengine - INFO - Epoch(train) [81][240/940] lr: 1.0000e-04 eta: 5:42:02 time: 1.1047 data_time: 0.0136 memory: 15768 grad_norm: 4.7034 loss: 0.9127 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9127 2023/07/25 14:30:06 - mmengine - INFO - Epoch(train) [81][260/940] lr: 1.0000e-04 eta: 5:41:39 time: 1.1008 data_time: 0.0135 memory: 15768 grad_norm: 4.7073 loss: 0.7633 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7633 2023/07/25 14:30:28 - mmengine - INFO - Epoch(train) [81][280/940] lr: 1.0000e-04 eta: 5:41:17 time: 1.0988 data_time: 0.0139 memory: 15768 grad_norm: 4.5984 loss: 0.6401 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6401 2023/07/25 14:30:50 - mmengine - INFO - Epoch(train) [81][300/940] lr: 1.0000e-04 eta: 5:40:55 time: 1.1005 data_time: 0.0138 memory: 15768 grad_norm: 4.6448 loss: 0.8612 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8612 2023/07/25 14:31:12 - mmengine - INFO - Epoch(train) [81][320/940] lr: 1.0000e-04 eta: 5:40:33 time: 1.1012 data_time: 0.0138 memory: 15768 grad_norm: 4.7479 loss: 0.8424 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8424 2023/07/25 14:31:34 - mmengine - INFO - Epoch(train) [81][340/940] lr: 1.0000e-04 eta: 5:40:11 time: 1.0979 data_time: 0.0139 memory: 15768 grad_norm: 4.7503 loss: 0.8039 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8039 2023/07/25 14:31:56 - mmengine - INFO - Epoch(train) [81][360/940] lr: 1.0000e-04 eta: 5:39:49 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.6848 loss: 0.8137 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8137 2023/07/25 14:32:18 - mmengine - INFO - Epoch(train) [81][380/940] lr: 1.0000e-04 eta: 5:39:27 time: 1.1015 data_time: 0.0135 memory: 15768 grad_norm: 4.7019 loss: 0.6635 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6635 2023/07/25 14:32:40 - mmengine - INFO - Epoch(train) [81][400/940] lr: 1.0000e-04 eta: 5:39:04 time: 1.1020 data_time: 0.0137 memory: 15768 grad_norm: 4.5115 loss: 0.8245 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8245 2023/07/25 14:33:02 - mmengine - INFO - Epoch(train) [81][420/940] lr: 1.0000e-04 eta: 5:38:42 time: 1.1025 data_time: 0.0137 memory: 15768 grad_norm: 4.6915 loss: 0.9264 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9264 2023/07/25 14:33:24 - mmengine - INFO - Epoch(train) [81][440/940] lr: 1.0000e-04 eta: 5:38:20 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 4.6614 loss: 0.8633 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8633 2023/07/25 14:33:47 - mmengine - INFO - Epoch(train) [81][460/940] lr: 1.0000e-04 eta: 5:37:58 time: 1.1026 data_time: 0.0137 memory: 15768 grad_norm: 4.6760 loss: 0.6516 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6516 2023/07/25 14:34:09 - mmengine - INFO - Epoch(train) [81][480/940] lr: 1.0000e-04 eta: 5:37:36 time: 1.1006 data_time: 0.0142 memory: 15768 grad_norm: 4.7105 loss: 0.8721 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8721 2023/07/25 14:34:31 - mmengine - INFO - Epoch(train) [81][500/940] lr: 1.0000e-04 eta: 5:37:14 time: 1.1040 data_time: 0.0142 memory: 15768 grad_norm: 4.5459 loss: 0.8475 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8475 2023/07/25 14:34:53 - mmengine - INFO - Epoch(train) [81][520/940] lr: 1.0000e-04 eta: 5:36:52 time: 1.0998 data_time: 0.0140 memory: 15768 grad_norm: 4.6470 loss: 0.8599 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8599 2023/07/25 14:35:15 - mmengine - INFO - Epoch(train) [81][540/940] lr: 1.0000e-04 eta: 5:36:29 time: 1.1016 data_time: 0.0140 memory: 15768 grad_norm: 4.7484 loss: 0.8018 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8018 2023/07/25 14:35:37 - mmengine - INFO - Epoch(train) [81][560/940] lr: 1.0000e-04 eta: 5:36:07 time: 1.1015 data_time: 0.0139 memory: 15768 grad_norm: 4.6860 loss: 0.7151 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7151 2023/07/25 14:35:59 - mmengine - INFO - Epoch(train) [81][580/940] lr: 1.0000e-04 eta: 5:35:45 time: 1.1002 data_time: 0.0138 memory: 15768 grad_norm: 4.6372 loss: 0.9246 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9246 2023/07/25 14:36:21 - mmengine - INFO - Epoch(train) [81][600/940] lr: 1.0000e-04 eta: 5:35:23 time: 1.1006 data_time: 0.0137 memory: 15768 grad_norm: 4.7718 loss: 0.7836 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7836 2023/07/25 14:36:43 - mmengine - INFO - Epoch(train) [81][620/940] lr: 1.0000e-04 eta: 5:35:01 time: 1.1007 data_time: 0.0142 memory: 15768 grad_norm: 4.6426 loss: 0.6373 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6373 2023/07/25 14:37:05 - mmengine - INFO - Epoch(train) [81][640/940] lr: 1.0000e-04 eta: 5:34:39 time: 1.1025 data_time: 0.0140 memory: 15768 grad_norm: 4.7041 loss: 0.8172 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8172 2023/07/25 14:37:27 - mmengine - INFO - Epoch(train) [81][660/940] lr: 1.0000e-04 eta: 5:34:17 time: 1.1019 data_time: 0.0139 memory: 15768 grad_norm: 4.6837 loss: 0.7271 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7271 2023/07/25 14:37:49 - mmengine - INFO - Epoch(train) [81][680/940] lr: 1.0000e-04 eta: 5:33:54 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 4.6879 loss: 0.8209 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8209 2023/07/25 14:38:11 - mmengine - INFO - Epoch(train) [81][700/940] lr: 1.0000e-04 eta: 5:33:32 time: 1.1041 data_time: 0.0143 memory: 15768 grad_norm: 4.6995 loss: 0.7944 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7944 2023/07/25 14:38:33 - mmengine - INFO - Epoch(train) [81][720/940] lr: 1.0000e-04 eta: 5:33:10 time: 1.1038 data_time: 0.0140 memory: 15768 grad_norm: 4.6406 loss: 0.8059 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8059 2023/07/25 14:38:55 - mmengine - INFO - Epoch(train) [81][740/940] lr: 1.0000e-04 eta: 5:32:48 time: 1.1020 data_time: 0.0141 memory: 15768 grad_norm: 4.6726 loss: 0.8574 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8574 2023/07/25 14:39:17 - mmengine - INFO - Epoch(train) [81][760/940] lr: 1.0000e-04 eta: 5:32:26 time: 1.0994 data_time: 0.0138 memory: 15768 grad_norm: 4.7311 loss: 0.7115 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7115 2023/07/25 14:39:39 - mmengine - INFO - Epoch(train) [81][780/940] lr: 1.0000e-04 eta: 5:32:04 time: 1.0997 data_time: 0.0138 memory: 15768 grad_norm: 4.6639 loss: 0.9029 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9029 2023/07/25 14:40:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 14:40:01 - mmengine - INFO - Epoch(train) [81][800/940] lr: 1.0000e-04 eta: 5:31:42 time: 1.0998 data_time: 0.0138 memory: 15768 grad_norm: 4.6426 loss: 0.6033 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6033 2023/07/25 14:40:23 - mmengine - INFO - Epoch(train) [81][820/940] lr: 1.0000e-04 eta: 5:31:20 time: 1.1018 data_time: 0.0141 memory: 15768 grad_norm: 4.7858 loss: 0.9279 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9279 2023/07/25 14:40:45 - mmengine - INFO - Epoch(train) [81][840/940] lr: 1.0000e-04 eta: 5:30:57 time: 1.0991 data_time: 0.0140 memory: 15768 grad_norm: 4.6511 loss: 0.7865 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7865 2023/07/25 14:41:07 - mmengine - INFO - Epoch(train) [81][860/940] lr: 1.0000e-04 eta: 5:30:35 time: 1.1053 data_time: 0.0139 memory: 15768 grad_norm: 4.6778 loss: 0.7133 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.7133 2023/07/25 14:41:29 - mmengine - INFO - Epoch(train) [81][880/940] lr: 1.0000e-04 eta: 5:30:13 time: 1.0991 data_time: 0.0142 memory: 15768 grad_norm: 4.6917 loss: 0.7173 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7173 2023/07/25 14:41:51 - mmengine - INFO - Epoch(train) [81][900/940] lr: 1.0000e-04 eta: 5:29:51 time: 1.1074 data_time: 0.0138 memory: 15768 grad_norm: 4.6810 loss: 0.8428 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8428 2023/07/25 14:42:13 - mmengine - INFO - Epoch(train) [81][920/940] lr: 1.0000e-04 eta: 5:29:29 time: 1.1003 data_time: 0.0142 memory: 15768 grad_norm: 4.5364 loss: 0.7940 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.7940 2023/07/25 14:42:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 14:42:34 - mmengine - INFO - Epoch(train) [81][940/940] lr: 1.0000e-04 eta: 5:29:07 time: 1.0556 data_time: 0.0138 memory: 15768 grad_norm: 4.9338 loss: 0.8818 top1_acc: 0.2500 top5_acc: 1.0000 loss_cls: 0.8818 2023/07/25 14:42:34 - mmengine - INFO - Saving checkpoint at 81 epochs 2023/07/25 14:42:45 - mmengine - INFO - Epoch(val) [81][20/78] eta: 0:00:28 time: 0.4867 data_time: 0.3293 memory: 2147 2023/07/25 14:42:52 - mmengine - INFO - Epoch(val) [81][40/78] eta: 0:00:15 time: 0.3538 data_time: 0.1967 memory: 2147 2023/07/25 14:43:01 - mmengine - INFO - Epoch(val) [81][60/78] eta: 0:00:07 time: 0.4414 data_time: 0.2845 memory: 2147 2023/07/25 14:43:10 - mmengine - INFO - Epoch(val) [81][78/78] acc/top1: 0.7123 acc/top5: 0.8981 acc/mean1: 0.7122 data_time: 0.2430 time: 0.3972 2023/07/25 14:43:36 - mmengine - INFO - Epoch(train) [82][ 20/940] lr: 1.0000e-04 eta: 5:28:45 time: 1.2940 data_time: 0.1584 memory: 15768 grad_norm: 4.6731 loss: 0.8293 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8293 2023/07/25 14:43:58 - mmengine - INFO - Epoch(train) [82][ 40/940] lr: 1.0000e-04 eta: 5:28:23 time: 1.1025 data_time: 0.0139 memory: 15768 grad_norm: 4.5719 loss: 0.6614 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6614 2023/07/25 14:44:20 - mmengine - INFO - Epoch(train) [82][ 60/940] lr: 1.0000e-04 eta: 5:28:01 time: 1.1022 data_time: 0.0137 memory: 15768 grad_norm: 4.6779 loss: 0.7129 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7129 2023/07/25 14:44:42 - mmengine - INFO - Epoch(train) [82][ 80/940] lr: 1.0000e-04 eta: 5:27:39 time: 1.0999 data_time: 0.0138 memory: 15768 grad_norm: 4.6266 loss: 0.8000 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8000 2023/07/25 14:45:04 - mmengine - INFO - Epoch(train) [82][100/940] lr: 1.0000e-04 eta: 5:27:17 time: 1.0974 data_time: 0.0139 memory: 15768 grad_norm: 4.7551 loss: 0.7322 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7322 2023/07/25 14:45:26 - mmengine - INFO - Epoch(train) [82][120/940] lr: 1.0000e-04 eta: 5:26:55 time: 1.0985 data_time: 0.0138 memory: 15768 grad_norm: 4.6540 loss: 0.7776 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7776 2023/07/25 14:45:48 - mmengine - INFO - Epoch(train) [82][140/940] lr: 1.0000e-04 eta: 5:26:32 time: 1.0990 data_time: 0.0138 memory: 15768 grad_norm: 4.6134 loss: 0.7354 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7354 2023/07/25 14:46:10 - mmengine - INFO - Epoch(train) [82][160/940] lr: 1.0000e-04 eta: 5:26:10 time: 1.1012 data_time: 0.0139 memory: 15768 grad_norm: 4.5847 loss: 0.7738 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7738 2023/07/25 14:46:32 - mmengine - INFO - Epoch(train) [82][180/940] lr: 1.0000e-04 eta: 5:25:48 time: 1.1003 data_time: 0.0137 memory: 15768 grad_norm: 4.6938 loss: 0.8974 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8974 2023/07/25 14:46:54 - mmengine - INFO - Epoch(train) [82][200/940] lr: 1.0000e-04 eta: 5:25:26 time: 1.0983 data_time: 0.0139 memory: 15768 grad_norm: 4.6778 loss: 0.8528 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8528 2023/07/25 14:47:16 - mmengine - INFO - Epoch(train) [82][220/940] lr: 1.0000e-04 eta: 5:25:04 time: 1.0999 data_time: 0.0139 memory: 15768 grad_norm: 4.5823 loss: 0.6470 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6470 2023/07/25 14:47:38 - mmengine - INFO - Epoch(train) [82][240/940] lr: 1.0000e-04 eta: 5:24:42 time: 1.0992 data_time: 0.0136 memory: 15768 grad_norm: 4.7102 loss: 0.8492 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8492 2023/07/25 14:48:00 - mmengine - INFO - Epoch(train) [82][260/940] lr: 1.0000e-04 eta: 5:24:20 time: 1.1025 data_time: 0.0139 memory: 15768 grad_norm: 4.6744 loss: 0.7336 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7336 2023/07/25 14:48:22 - mmengine - INFO - Epoch(train) [82][280/940] lr: 1.0000e-04 eta: 5:23:58 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 4.6244 loss: 0.7092 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7092 2023/07/25 14:48:44 - mmengine - INFO - Epoch(train) [82][300/940] lr: 1.0000e-04 eta: 5:23:35 time: 1.0987 data_time: 0.0140 memory: 15768 grad_norm: 4.6392 loss: 0.7464 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7464 2023/07/25 14:49:06 - mmengine - INFO - Epoch(train) [82][320/940] lr: 1.0000e-04 eta: 5:23:13 time: 1.0993 data_time: 0.0141 memory: 15768 grad_norm: 4.7938 loss: 0.7469 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7469 2023/07/25 14:49:28 - mmengine - INFO - Epoch(train) [82][340/940] lr: 1.0000e-04 eta: 5:22:51 time: 1.0999 data_time: 0.0144 memory: 15768 grad_norm: 4.6659 loss: 0.8198 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8198 2023/07/25 14:49:50 - mmengine - INFO - Epoch(train) [82][360/940] lr: 1.0000e-04 eta: 5:22:29 time: 1.1011 data_time: 0.0143 memory: 15768 grad_norm: 4.6804 loss: 0.8399 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8399 2023/07/25 14:50:12 - mmengine - INFO - Epoch(train) [82][380/940] lr: 1.0000e-04 eta: 5:22:07 time: 1.1007 data_time: 0.0149 memory: 15768 grad_norm: 4.7225 loss: 0.8089 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8089 2023/07/25 14:50:34 - mmengine - INFO - Epoch(train) [82][400/940] lr: 1.0000e-04 eta: 5:21:45 time: 1.0997 data_time: 0.0143 memory: 15768 grad_norm: 4.5644 loss: 0.7839 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7839 2023/07/25 14:50:56 - mmengine - INFO - Epoch(train) [82][420/940] lr: 1.0000e-04 eta: 5:21:23 time: 1.1023 data_time: 0.0144 memory: 15768 grad_norm: 4.7372 loss: 0.8155 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8155 2023/07/25 14:51:18 - mmengine - INFO - Epoch(train) [82][440/940] lr: 1.0000e-04 eta: 5:21:00 time: 1.1026 data_time: 0.0139 memory: 15768 grad_norm: 4.6775 loss: 0.8549 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8549 2023/07/25 14:51:40 - mmengine - INFO - Epoch(train) [82][460/940] lr: 1.0000e-04 eta: 5:20:38 time: 1.1014 data_time: 0.0140 memory: 15768 grad_norm: 4.6341 loss: 0.8181 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8181 2023/07/25 14:52:02 - mmengine - INFO - Epoch(train) [82][480/940] lr: 1.0000e-04 eta: 5:20:16 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.7134 loss: 0.6716 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6716 2023/07/25 14:52:24 - mmengine - INFO - Epoch(train) [82][500/940] lr: 1.0000e-04 eta: 5:19:54 time: 1.1053 data_time: 0.0143 memory: 15768 grad_norm: 4.7816 loss: 0.8125 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8125 2023/07/25 14:52:46 - mmengine - INFO - Epoch(train) [82][520/940] lr: 1.0000e-04 eta: 5:19:32 time: 1.1006 data_time: 0.0143 memory: 15768 grad_norm: 4.7185 loss: 0.6654 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6654 2023/07/25 14:53:08 - mmengine - INFO - Epoch(train) [82][540/940] lr: 1.0000e-04 eta: 5:19:10 time: 1.1018 data_time: 0.0141 memory: 15768 grad_norm: 4.6134 loss: 0.7032 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7032 2023/07/25 14:53:31 - mmengine - INFO - Epoch(train) [82][560/940] lr: 1.0000e-04 eta: 5:18:48 time: 1.1004 data_time: 0.0140 memory: 15768 grad_norm: 4.7804 loss: 0.7901 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7901 2023/07/25 14:53:53 - mmengine - INFO - Epoch(train) [82][580/940] lr: 1.0000e-04 eta: 5:18:25 time: 1.1013 data_time: 0.0139 memory: 15768 grad_norm: 4.6718 loss: 0.7220 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7220 2023/07/25 14:54:15 - mmengine - INFO - Epoch(train) [82][600/940] lr: 1.0000e-04 eta: 5:18:03 time: 1.1015 data_time: 0.0139 memory: 15768 grad_norm: 4.7083 loss: 0.8670 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8670 2023/07/25 14:54:37 - mmengine - INFO - Epoch(train) [82][620/940] lr: 1.0000e-04 eta: 5:17:41 time: 1.0991 data_time: 0.0142 memory: 15768 grad_norm: 4.6084 loss: 0.7659 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7659 2023/07/25 14:54:59 - mmengine - INFO - Epoch(train) [82][640/940] lr: 1.0000e-04 eta: 5:17:19 time: 1.1015 data_time: 0.0141 memory: 15768 grad_norm: 4.5840 loss: 0.6893 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6893 2023/07/25 14:55:21 - mmengine - INFO - Epoch(train) [82][660/940] lr: 1.0000e-04 eta: 5:16:57 time: 1.0993 data_time: 0.0141 memory: 15768 grad_norm: 4.6058 loss: 0.7848 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7848 2023/07/25 14:55:43 - mmengine - INFO - Epoch(train) [82][680/940] lr: 1.0000e-04 eta: 5:16:35 time: 1.1011 data_time: 0.0138 memory: 15768 grad_norm: 4.7233 loss: 0.8638 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8638 2023/07/25 14:56:05 - mmengine - INFO - Epoch(train) [82][700/940] lr: 1.0000e-04 eta: 5:16:13 time: 1.1055 data_time: 0.0137 memory: 15768 grad_norm: 4.6566 loss: 0.7951 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7951 2023/07/25 14:56:27 - mmengine - INFO - Epoch(train) [82][720/940] lr: 1.0000e-04 eta: 5:15:51 time: 1.1000 data_time: 0.0143 memory: 15768 grad_norm: 4.7002 loss: 0.8775 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8775 2023/07/25 14:56:49 - mmengine - INFO - Epoch(train) [82][740/940] lr: 1.0000e-04 eta: 5:15:28 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.6523 loss: 0.7808 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7808 2023/07/25 14:57:11 - mmengine - INFO - Epoch(train) [82][760/940] lr: 1.0000e-04 eta: 5:15:06 time: 1.1018 data_time: 0.0142 memory: 15768 grad_norm: 4.6711 loss: 0.9510 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9510 2023/07/25 14:57:33 - mmengine - INFO - Epoch(train) [82][780/940] lr: 1.0000e-04 eta: 5:14:44 time: 1.1019 data_time: 0.0146 memory: 15768 grad_norm: 4.6452 loss: 0.7859 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7859 2023/07/25 14:57:55 - mmengine - INFO - Epoch(train) [82][800/940] lr: 1.0000e-04 eta: 5:14:22 time: 1.1036 data_time: 0.0142 memory: 15768 grad_norm: 4.7807 loss: 0.7584 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7584 2023/07/25 14:58:17 - mmengine - INFO - Epoch(train) [82][820/940] lr: 1.0000e-04 eta: 5:14:00 time: 1.1007 data_time: 0.0139 memory: 15768 grad_norm: 4.6826 loss: 0.9653 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9653 2023/07/25 14:58:39 - mmengine - INFO - Epoch(train) [82][840/940] lr: 1.0000e-04 eta: 5:13:38 time: 1.1001 data_time: 0.0143 memory: 15768 grad_norm: 4.6148 loss: 0.7505 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7505 2023/07/25 14:59:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 14:59:01 - mmengine - INFO - Epoch(train) [82][860/940] lr: 1.0000e-04 eta: 5:13:16 time: 1.1017 data_time: 0.0139 memory: 15768 grad_norm: 4.6727 loss: 0.8991 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8991 2023/07/25 14:59:23 - mmengine - INFO - Epoch(train) [82][880/940] lr: 1.0000e-04 eta: 5:12:54 time: 1.1025 data_time: 0.0144 memory: 15768 grad_norm: 4.7629 loss: 0.7113 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7113 2023/07/25 14:59:45 - mmengine - INFO - Epoch(train) [82][900/940] lr: 1.0000e-04 eta: 5:12:31 time: 1.0999 data_time: 0.0139 memory: 15768 grad_norm: 4.7054 loss: 0.8345 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8345 2023/07/25 15:00:07 - mmengine - INFO - Epoch(train) [82][920/940] lr: 1.0000e-04 eta: 5:12:09 time: 1.1006 data_time: 0.0141 memory: 15768 grad_norm: 4.7467 loss: 0.7717 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7717 2023/07/25 15:00:28 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 15:00:28 - mmengine - INFO - Epoch(train) [82][940/940] lr: 1.0000e-04 eta: 5:11:47 time: 1.0558 data_time: 0.0137 memory: 15768 grad_norm: 4.9424 loss: 0.7196 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7196 2023/07/25 15:00:38 - mmengine - INFO - Epoch(val) [82][20/78] eta: 0:00:28 time: 0.4890 data_time: 0.3319 memory: 2147 2023/07/25 15:00:45 - mmengine - INFO - Epoch(val) [82][40/78] eta: 0:00:15 time: 0.3521 data_time: 0.1949 memory: 2147 2023/07/25 15:00:54 - mmengine - INFO - Epoch(val) [82][60/78] eta: 0:00:07 time: 0.4469 data_time: 0.2902 memory: 2147 2023/07/25 15:01:04 - mmengine - INFO - Epoch(val) [82][78/78] acc/top1: 0.7107 acc/top5: 0.8986 acc/mean1: 0.7106 data_time: 0.2475 time: 0.4017 2023/07/25 15:01:30 - mmengine - INFO - Epoch(train) [83][ 20/940] lr: 1.0000e-04 eta: 5:11:26 time: 1.2935 data_time: 0.1430 memory: 15768 grad_norm: 4.6881 loss: 0.7771 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7771 2023/07/25 15:01:52 - mmengine - INFO - Epoch(train) [83][ 40/940] lr: 1.0000e-04 eta: 5:11:03 time: 1.1022 data_time: 0.0137 memory: 15768 grad_norm: 4.6439 loss: 0.7447 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7447 2023/07/25 15:02:14 - mmengine - INFO - Epoch(train) [83][ 60/940] lr: 1.0000e-04 eta: 5:10:41 time: 1.1010 data_time: 0.0136 memory: 15768 grad_norm: 4.6412 loss: 0.7685 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7685 2023/07/25 15:02:36 - mmengine - INFO - Epoch(train) [83][ 80/940] lr: 1.0000e-04 eta: 5:10:19 time: 1.0999 data_time: 0.0133 memory: 15768 grad_norm: 4.7150 loss: 0.9617 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9617 2023/07/25 15:02:58 - mmengine - INFO - Epoch(train) [83][100/940] lr: 1.0000e-04 eta: 5:09:57 time: 1.1010 data_time: 0.0138 memory: 15768 grad_norm: 4.6524 loss: 0.8335 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8335 2023/07/25 15:03:20 - mmengine - INFO - Epoch(train) [83][120/940] lr: 1.0000e-04 eta: 5:09:35 time: 1.0990 data_time: 0.0138 memory: 15768 grad_norm: 4.7424 loss: 0.8412 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8412 2023/07/25 15:03:42 - mmengine - INFO - Epoch(train) [83][140/940] lr: 1.0000e-04 eta: 5:09:13 time: 1.1290 data_time: 0.0137 memory: 15768 grad_norm: 4.6668 loss: 0.8369 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8369 2023/07/25 15:04:05 - mmengine - INFO - Epoch(train) [83][160/940] lr: 1.0000e-04 eta: 5:08:51 time: 1.1171 data_time: 0.0144 memory: 15768 grad_norm: 4.6609 loss: 0.7317 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7317 2023/07/25 15:04:27 - mmengine - INFO - Epoch(train) [83][180/940] lr: 1.0000e-04 eta: 5:08:29 time: 1.1014 data_time: 0.0138 memory: 15768 grad_norm: 4.6858 loss: 0.8351 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8351 2023/07/25 15:04:49 - mmengine - INFO - Epoch(train) [83][200/940] lr: 1.0000e-04 eta: 5:08:07 time: 1.1027 data_time: 0.0141 memory: 15768 grad_norm: 4.6481 loss: 0.7107 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.7107 2023/07/25 15:05:11 - mmengine - INFO - Epoch(train) [83][220/940] lr: 1.0000e-04 eta: 5:07:44 time: 1.0992 data_time: 0.0141 memory: 15768 grad_norm: 4.6966 loss: 0.6883 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6883 2023/07/25 15:05:33 - mmengine - INFO - Epoch(train) [83][240/940] lr: 1.0000e-04 eta: 5:07:22 time: 1.1026 data_time: 0.0138 memory: 15768 grad_norm: 4.6823 loss: 0.8031 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8031 2023/07/25 15:05:55 - mmengine - INFO - Epoch(train) [83][260/940] lr: 1.0000e-04 eta: 5:07:00 time: 1.1045 data_time: 0.0141 memory: 15768 grad_norm: 4.5508 loss: 0.7390 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7390 2023/07/25 15:06:17 - mmengine - INFO - Epoch(train) [83][280/940] lr: 1.0000e-04 eta: 5:06:38 time: 1.1037 data_time: 0.0145 memory: 15768 grad_norm: 4.6827 loss: 0.7573 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7573 2023/07/25 15:06:39 - mmengine - INFO - Epoch(train) [83][300/940] lr: 1.0000e-04 eta: 5:06:16 time: 1.1012 data_time: 0.0139 memory: 15768 grad_norm: 4.7407 loss: 0.8992 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8992 2023/07/25 15:07:01 - mmengine - INFO - Epoch(train) [83][320/940] lr: 1.0000e-04 eta: 5:05:54 time: 1.1025 data_time: 0.0140 memory: 15768 grad_norm: 4.5587 loss: 0.8365 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8365 2023/07/25 15:07:23 - mmengine - INFO - Epoch(train) [83][340/940] lr: 1.0000e-04 eta: 5:05:32 time: 1.1005 data_time: 0.0142 memory: 15768 grad_norm: 4.7972 loss: 0.7883 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7883 2023/07/25 15:07:45 - mmengine - INFO - Epoch(train) [83][360/940] lr: 1.0000e-04 eta: 5:05:10 time: 1.1053 data_time: 0.0140 memory: 15768 grad_norm: 4.6034 loss: 0.6976 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6976 2023/07/25 15:08:07 - mmengine - INFO - Epoch(train) [83][380/940] lr: 1.0000e-04 eta: 5:04:47 time: 1.1021 data_time: 0.0144 memory: 15768 grad_norm: 4.6203 loss: 0.7889 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7889 2023/07/25 15:08:29 - mmengine - INFO - Epoch(train) [83][400/940] lr: 1.0000e-04 eta: 5:04:25 time: 1.1003 data_time: 0.0144 memory: 15768 grad_norm: 4.6264 loss: 0.7793 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7793 2023/07/25 15:08:51 - mmengine - INFO - Epoch(train) [83][420/940] lr: 1.0000e-04 eta: 5:04:03 time: 1.0991 data_time: 0.0144 memory: 15768 grad_norm: 4.6536 loss: 0.7467 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7467 2023/07/25 15:09:13 - mmengine - INFO - Epoch(train) [83][440/940] lr: 1.0000e-04 eta: 5:03:41 time: 1.1026 data_time: 0.0141 memory: 15768 grad_norm: 4.6241 loss: 0.7429 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7429 2023/07/25 15:09:35 - mmengine - INFO - Epoch(train) [83][460/940] lr: 1.0000e-04 eta: 5:03:19 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.6705 loss: 0.7920 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7920 2023/07/25 15:09:57 - mmengine - INFO - Epoch(train) [83][480/940] lr: 1.0000e-04 eta: 5:02:57 time: 1.0995 data_time: 0.0138 memory: 15768 grad_norm: 4.7649 loss: 0.7693 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7693 2023/07/25 15:10:19 - mmengine - INFO - Epoch(train) [83][500/940] lr: 1.0000e-04 eta: 5:02:35 time: 1.0990 data_time: 0.0142 memory: 15768 grad_norm: 4.7835 loss: 0.7869 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7869 2023/07/25 15:10:41 - mmengine - INFO - Epoch(train) [83][520/940] lr: 1.0000e-04 eta: 5:02:12 time: 1.0990 data_time: 0.0140 memory: 15768 grad_norm: 4.6743 loss: 0.7322 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7322 2023/07/25 15:11:03 - mmengine - INFO - Epoch(train) [83][540/940] lr: 1.0000e-04 eta: 5:01:50 time: 1.0984 data_time: 0.0141 memory: 15768 grad_norm: 4.6814 loss: 0.8936 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8936 2023/07/25 15:11:25 - mmengine - INFO - Epoch(train) [83][560/940] lr: 1.0000e-04 eta: 5:01:28 time: 1.0987 data_time: 0.0140 memory: 15768 grad_norm: 4.7274 loss: 0.8494 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8494 2023/07/25 15:11:47 - mmengine - INFO - Epoch(train) [83][580/940] lr: 1.0000e-04 eta: 5:01:06 time: 1.0997 data_time: 0.0139 memory: 15768 grad_norm: 4.7745 loss: 0.8203 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8203 2023/07/25 15:12:09 - mmengine - INFO - Epoch(train) [83][600/940] lr: 1.0000e-04 eta: 5:00:44 time: 1.0982 data_time: 0.0140 memory: 15768 grad_norm: 4.6472 loss: 0.7405 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7405 2023/07/25 15:12:31 - mmengine - INFO - Epoch(train) [83][620/940] lr: 1.0000e-04 eta: 5:00:22 time: 1.1031 data_time: 0.0139 memory: 15768 grad_norm: 4.6895 loss: 0.7472 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7472 2023/07/25 15:12:53 - mmengine - INFO - Epoch(train) [83][640/940] lr: 1.0000e-04 eta: 5:00:00 time: 1.1023 data_time: 0.0134 memory: 15768 grad_norm: 4.6736 loss: 0.7364 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7364 2023/07/25 15:13:15 - mmengine - INFO - Epoch(train) [83][660/940] lr: 1.0000e-04 eta: 4:59:38 time: 1.1012 data_time: 0.0137 memory: 15768 grad_norm: 4.7544 loss: 0.8315 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.8315 2023/07/25 15:13:37 - mmengine - INFO - Epoch(train) [83][680/940] lr: 1.0000e-04 eta: 4:59:15 time: 1.1007 data_time: 0.0140 memory: 15768 grad_norm: 4.7145 loss: 0.8558 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8558 2023/07/25 15:13:59 - mmengine - INFO - Epoch(train) [83][700/940] lr: 1.0000e-04 eta: 4:58:53 time: 1.1028 data_time: 0.0140 memory: 15768 grad_norm: 4.7017 loss: 0.8600 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8600 2023/07/25 15:14:21 - mmengine - INFO - Epoch(train) [83][720/940] lr: 1.0000e-04 eta: 4:58:31 time: 1.0997 data_time: 0.0140 memory: 15768 grad_norm: 4.7233 loss: 0.8171 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8171 2023/07/25 15:14:43 - mmengine - INFO - Epoch(train) [83][740/940] lr: 1.0000e-04 eta: 4:58:09 time: 1.1004 data_time: 0.0143 memory: 15768 grad_norm: 4.6479 loss: 0.6983 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6983 2023/07/25 15:15:05 - mmengine - INFO - Epoch(train) [83][760/940] lr: 1.0000e-04 eta: 4:57:47 time: 1.1007 data_time: 0.0140 memory: 15768 grad_norm: 4.7516 loss: 0.8255 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8255 2023/07/25 15:15:27 - mmengine - INFO - Epoch(train) [83][780/940] lr: 1.0000e-04 eta: 4:57:25 time: 1.1026 data_time: 0.0138 memory: 15768 grad_norm: 4.7648 loss: 0.8753 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8753 2023/07/25 15:15:49 - mmengine - INFO - Epoch(train) [83][800/940] lr: 1.0000e-04 eta: 4:57:03 time: 1.0989 data_time: 0.0139 memory: 15768 grad_norm: 4.6450 loss: 0.7715 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7715 2023/07/25 15:16:11 - mmengine - INFO - Epoch(train) [83][820/940] lr: 1.0000e-04 eta: 4:56:40 time: 1.1025 data_time: 0.0137 memory: 15768 grad_norm: 4.7434 loss: 0.7367 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7367 2023/07/25 15:16:33 - mmengine - INFO - Epoch(train) [83][840/940] lr: 1.0000e-04 eta: 4:56:18 time: 1.1033 data_time: 0.0134 memory: 15768 grad_norm: 4.5795 loss: 0.7208 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7208 2023/07/25 15:16:56 - mmengine - INFO - Epoch(train) [83][860/940] lr: 1.0000e-04 eta: 4:55:56 time: 1.1035 data_time: 0.0141 memory: 15768 grad_norm: 4.6364 loss: 0.7764 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7764 2023/07/25 15:17:18 - mmengine - INFO - Epoch(train) [83][880/940] lr: 1.0000e-04 eta: 4:55:34 time: 1.1004 data_time: 0.0144 memory: 15768 grad_norm: 4.6927 loss: 0.7955 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7955 2023/07/25 15:17:40 - mmengine - INFO - Epoch(train) [83][900/940] lr: 1.0000e-04 eta: 4:55:12 time: 1.1030 data_time: 0.0136 memory: 15768 grad_norm: 4.7813 loss: 0.8489 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8489 2023/07/25 15:18:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 15:18:02 - mmengine - INFO - Epoch(train) [83][920/940] lr: 1.0000e-04 eta: 4:54:50 time: 1.1029 data_time: 0.0140 memory: 15768 grad_norm: 4.5991 loss: 0.7025 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7025 2023/07/25 15:18:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 15:18:23 - mmengine - INFO - Epoch(train) [83][940/940] lr: 1.0000e-04 eta: 4:54:28 time: 1.0537 data_time: 0.0132 memory: 15768 grad_norm: 4.9146 loss: 0.8432 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8432 2023/07/25 15:18:32 - mmengine - INFO - Epoch(val) [83][20/78] eta: 0:00:27 time: 0.4800 data_time: 0.3226 memory: 2147 2023/07/25 15:18:40 - mmengine - INFO - Epoch(val) [83][40/78] eta: 0:00:16 time: 0.3654 data_time: 0.2084 memory: 2147 2023/07/25 15:18:48 - mmengine - INFO - Epoch(val) [83][60/78] eta: 0:00:07 time: 0.4363 data_time: 0.2796 memory: 2147 2023/07/25 15:18:58 - mmengine - INFO - Epoch(val) [83][78/78] acc/top1: 0.7114 acc/top5: 0.8986 acc/mean1: 0.7113 data_time: 0.2476 time: 0.4018 2023/07/25 15:19:24 - mmengine - INFO - Epoch(train) [84][ 20/940] lr: 1.0000e-04 eta: 4:54:06 time: 1.2987 data_time: 0.1720 memory: 15768 grad_norm: 4.7856 loss: 0.9573 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9573 2023/07/25 15:19:47 - mmengine - INFO - Epoch(train) [84][ 40/940] lr: 1.0000e-04 eta: 4:53:44 time: 1.1038 data_time: 0.0140 memory: 15768 grad_norm: 4.6978 loss: 0.8024 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8024 2023/07/25 15:20:09 - mmengine - INFO - Epoch(train) [84][ 60/940] lr: 1.0000e-04 eta: 4:53:22 time: 1.1007 data_time: 0.0139 memory: 15768 grad_norm: 4.7192 loss: 0.7997 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7997 2023/07/25 15:20:31 - mmengine - INFO - Epoch(train) [84][ 80/940] lr: 1.0000e-04 eta: 4:53:00 time: 1.1000 data_time: 0.0139 memory: 15768 grad_norm: 4.6496 loss: 0.7607 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7607 2023/07/25 15:20:53 - mmengine - INFO - Epoch(train) [84][100/940] lr: 1.0000e-04 eta: 4:52:38 time: 1.1000 data_time: 0.0142 memory: 15768 grad_norm: 4.7515 loss: 0.8691 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8691 2023/07/25 15:21:15 - mmengine - INFO - Epoch(train) [84][120/940] lr: 1.0000e-04 eta: 4:52:16 time: 1.1023 data_time: 0.0141 memory: 15768 grad_norm: 4.6737 loss: 0.7545 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7545 2023/07/25 15:21:37 - mmengine - INFO - Epoch(train) [84][140/940] lr: 1.0000e-04 eta: 4:51:53 time: 1.1063 data_time: 0.0137 memory: 15768 grad_norm: 4.7444 loss: 0.8684 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8684 2023/07/25 15:21:59 - mmengine - INFO - Epoch(train) [84][160/940] lr: 1.0000e-04 eta: 4:51:31 time: 1.1004 data_time: 0.0137 memory: 15768 grad_norm: 4.7432 loss: 0.8190 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8190 2023/07/25 15:22:21 - mmengine - INFO - Epoch(train) [84][180/940] lr: 1.0000e-04 eta: 4:51:09 time: 1.1042 data_time: 0.0138 memory: 15768 grad_norm: 4.6037 loss: 0.8130 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8130 2023/07/25 15:22:43 - mmengine - INFO - Epoch(train) [84][200/940] lr: 1.0000e-04 eta: 4:50:47 time: 1.0990 data_time: 0.0141 memory: 15768 grad_norm: 4.6615 loss: 0.7211 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7211 2023/07/25 15:23:05 - mmengine - INFO - Epoch(train) [84][220/940] lr: 1.0000e-04 eta: 4:50:25 time: 1.1029 data_time: 0.0141 memory: 15768 grad_norm: 4.5676 loss: 0.8186 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8186 2023/07/25 15:23:27 - mmengine - INFO - Epoch(train) [84][240/940] lr: 1.0000e-04 eta: 4:50:03 time: 1.1009 data_time: 0.0141 memory: 15768 grad_norm: 4.6631 loss: 0.8994 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8994 2023/07/25 15:23:49 - mmengine - INFO - Epoch(train) [84][260/940] lr: 1.0000e-04 eta: 4:49:41 time: 1.1037 data_time: 0.0141 memory: 15768 grad_norm: 4.6065 loss: 0.8739 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8739 2023/07/25 15:24:11 - mmengine - INFO - Epoch(train) [84][280/940] lr: 1.0000e-04 eta: 4:49:19 time: 1.1022 data_time: 0.0144 memory: 15768 grad_norm: 4.7718 loss: 0.7633 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7633 2023/07/25 15:24:33 - mmengine - INFO - Epoch(train) [84][300/940] lr: 1.0000e-04 eta: 4:48:56 time: 1.1018 data_time: 0.0142 memory: 15768 grad_norm: 4.5657 loss: 0.7457 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7457 2023/07/25 15:24:55 - mmengine - INFO - Epoch(train) [84][320/940] lr: 1.0000e-04 eta: 4:48:34 time: 1.1007 data_time: 0.0146 memory: 15768 grad_norm: 4.7369 loss: 0.7255 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.7255 2023/07/25 15:25:17 - mmengine - INFO - Epoch(train) [84][340/940] lr: 1.0000e-04 eta: 4:48:12 time: 1.1025 data_time: 0.0141 memory: 15768 grad_norm: 4.6776 loss: 0.7127 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7127 2023/07/25 15:25:39 - mmengine - INFO - Epoch(train) [84][360/940] lr: 1.0000e-04 eta: 4:47:50 time: 1.0993 data_time: 0.0136 memory: 15768 grad_norm: 4.6042 loss: 0.9055 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9055 2023/07/25 15:26:01 - mmengine - INFO - Epoch(train) [84][380/940] lr: 1.0000e-04 eta: 4:47:28 time: 1.1002 data_time: 0.0141 memory: 15768 grad_norm: 4.5829 loss: 0.6673 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6673 2023/07/25 15:26:23 - mmengine - INFO - Epoch(train) [84][400/940] lr: 1.0000e-04 eta: 4:47:06 time: 1.0984 data_time: 0.0141 memory: 15768 grad_norm: 4.7815 loss: 0.8279 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8279 2023/07/25 15:26:45 - mmengine - INFO - Epoch(train) [84][420/940] lr: 1.0000e-04 eta: 4:46:44 time: 1.1006 data_time: 0.0139 memory: 15768 grad_norm: 4.7590 loss: 0.7873 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7873 2023/07/25 15:27:07 - mmengine - INFO - Epoch(train) [84][440/940] lr: 1.0000e-04 eta: 4:46:21 time: 1.1002 data_time: 0.0142 memory: 15768 grad_norm: 4.6982 loss: 0.7767 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7767 2023/07/25 15:27:29 - mmengine - INFO - Epoch(train) [84][460/940] lr: 1.0000e-04 eta: 4:45:59 time: 1.0994 data_time: 0.0144 memory: 15768 grad_norm: 4.6201 loss: 0.9522 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9522 2023/07/25 15:27:51 - mmengine - INFO - Epoch(train) [84][480/940] lr: 1.0000e-04 eta: 4:45:37 time: 1.1018 data_time: 0.0139 memory: 15768 grad_norm: 4.5998 loss: 0.9494 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9494 2023/07/25 15:28:13 - mmengine - INFO - Epoch(train) [84][500/940] lr: 1.0000e-04 eta: 4:45:15 time: 1.0986 data_time: 0.0139 memory: 15768 grad_norm: 4.4964 loss: 0.7198 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 0.7198 2023/07/25 15:28:35 - mmengine - INFO - Epoch(train) [84][520/940] lr: 1.0000e-04 eta: 4:44:53 time: 1.1011 data_time: 0.0142 memory: 15768 grad_norm: 4.6744 loss: 0.7835 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7835 2023/07/25 15:28:57 - mmengine - INFO - Epoch(train) [84][540/940] lr: 1.0000e-04 eta: 4:44:31 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.7730 loss: 0.9380 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9380 2023/07/25 15:29:19 - mmengine - INFO - Epoch(train) [84][560/940] lr: 1.0000e-04 eta: 4:44:09 time: 1.0997 data_time: 0.0142 memory: 15768 grad_norm: 4.5969 loss: 0.9123 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9123 2023/07/25 15:29:41 - mmengine - INFO - Epoch(train) [84][580/940] lr: 1.0000e-04 eta: 4:43:47 time: 1.1008 data_time: 0.0139 memory: 15768 grad_norm: 4.6760 loss: 0.8334 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8334 2023/07/25 15:30:03 - mmengine - INFO - Epoch(train) [84][600/940] lr: 1.0000e-04 eta: 4:43:24 time: 1.1001 data_time: 0.0140 memory: 15768 grad_norm: 4.6905 loss: 0.7327 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7327 2023/07/25 15:30:25 - mmengine - INFO - Epoch(train) [84][620/940] lr: 1.0000e-04 eta: 4:43:02 time: 1.1021 data_time: 0.0137 memory: 15768 grad_norm: 4.6755 loss: 0.8469 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8469 2023/07/25 15:30:47 - mmengine - INFO - Epoch(train) [84][640/940] lr: 1.0000e-04 eta: 4:42:40 time: 1.0990 data_time: 0.0140 memory: 15768 grad_norm: 4.6098 loss: 0.7300 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7300 2023/07/25 15:31:09 - mmengine - INFO - Epoch(train) [84][660/940] lr: 1.0000e-04 eta: 4:42:18 time: 1.1018 data_time: 0.0135 memory: 15768 grad_norm: 4.7006 loss: 0.7730 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7730 2023/07/25 15:31:31 - mmengine - INFO - Epoch(train) [84][680/940] lr: 1.0000e-04 eta: 4:41:56 time: 1.1005 data_time: 0.0141 memory: 15768 grad_norm: 4.6784 loss: 0.7357 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7357 2023/07/25 15:31:53 - mmengine - INFO - Epoch(train) [84][700/940] lr: 1.0000e-04 eta: 4:41:34 time: 1.1016 data_time: 0.0140 memory: 15768 grad_norm: 4.6474 loss: 0.7586 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7586 2023/07/25 15:32:15 - mmengine - INFO - Epoch(train) [84][720/940] lr: 1.0000e-04 eta: 4:41:12 time: 1.1012 data_time: 0.0139 memory: 15768 grad_norm: 4.6884 loss: 0.6898 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.6898 2023/07/25 15:32:37 - mmengine - INFO - Epoch(train) [84][740/940] lr: 1.0000e-04 eta: 4:40:49 time: 1.1002 data_time: 0.0138 memory: 15768 grad_norm: 4.7071 loss: 0.9582 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9582 2023/07/25 15:32:59 - mmengine - INFO - Epoch(train) [84][760/940] lr: 1.0000e-04 eta: 4:40:27 time: 1.1000 data_time: 0.0138 memory: 15768 grad_norm: 4.6703 loss: 0.9393 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9393 2023/07/25 15:33:21 - mmengine - INFO - Epoch(train) [84][780/940] lr: 1.0000e-04 eta: 4:40:05 time: 1.1008 data_time: 0.0137 memory: 15768 grad_norm: 4.8436 loss: 0.9758 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9758 2023/07/25 15:33:43 - mmengine - INFO - Epoch(train) [84][800/940] lr: 1.0000e-04 eta: 4:39:43 time: 1.1022 data_time: 0.0142 memory: 15768 grad_norm: 4.6781 loss: 0.8105 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 0.8105 2023/07/25 15:34:06 - mmengine - INFO - Epoch(train) [84][820/940] lr: 1.0000e-04 eta: 4:39:21 time: 1.1066 data_time: 0.0143 memory: 15768 grad_norm: 4.6939 loss: 0.9545 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9545 2023/07/25 15:34:28 - mmengine - INFO - Epoch(train) [84][840/940] lr: 1.0000e-04 eta: 4:38:59 time: 1.1003 data_time: 0.0136 memory: 15768 grad_norm: 4.5187 loss: 0.6539 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6539 2023/07/25 15:34:50 - mmengine - INFO - Epoch(train) [84][860/940] lr: 1.0000e-04 eta: 4:38:37 time: 1.1030 data_time: 0.0141 memory: 15768 grad_norm: 4.6787 loss: 0.8747 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8747 2023/07/25 15:35:12 - mmengine - INFO - Epoch(train) [84][880/940] lr: 1.0000e-04 eta: 4:38:15 time: 1.1020 data_time: 0.0134 memory: 15768 grad_norm: 4.7864 loss: 0.8718 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8718 2023/07/25 15:35:34 - mmengine - INFO - Epoch(train) [84][900/940] lr: 1.0000e-04 eta: 4:37:52 time: 1.1010 data_time: 0.0132 memory: 15768 grad_norm: 4.6941 loss: 0.7345 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7345 2023/07/25 15:35:56 - mmengine - INFO - Epoch(train) [84][920/940] lr: 1.0000e-04 eta: 4:37:30 time: 1.1020 data_time: 0.0139 memory: 15768 grad_norm: 4.6510 loss: 0.6835 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6835 2023/07/25 15:36:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 15:36:17 - mmengine - INFO - Epoch(train) [84][940/940] lr: 1.0000e-04 eta: 4:37:08 time: 1.0568 data_time: 0.0134 memory: 15768 grad_norm: 4.9362 loss: 0.8337 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8337 2023/07/25 15:36:17 - mmengine - INFO - Saving checkpoint at 84 epochs 2023/07/25 15:36:28 - mmengine - INFO - Epoch(val) [84][20/78] eta: 0:00:28 time: 0.4963 data_time: 0.3390 memory: 2147 2023/07/25 15:36:35 - mmengine - INFO - Epoch(val) [84][40/78] eta: 0:00:16 time: 0.3558 data_time: 0.1985 memory: 2147 2023/07/25 15:36:44 - mmengine - INFO - Epoch(val) [84][60/78] eta: 0:00:07 time: 0.4421 data_time: 0.2856 memory: 2147 2023/07/25 15:36:53 - mmengine - INFO - Epoch(val) [84][78/78] acc/top1: 0.7099 acc/top5: 0.8986 acc/mean1: 0.7098 data_time: 0.2446 time: 0.3987 2023/07/25 15:37:19 - mmengine - INFO - Epoch(train) [85][ 20/940] lr: 1.0000e-04 eta: 4:36:47 time: 1.2958 data_time: 0.1381 memory: 15768 grad_norm: 4.8047 loss: 0.7345 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7345 2023/07/25 15:37:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 15:37:41 - mmengine - INFO - Epoch(train) [85][ 40/940] lr: 1.0000e-04 eta: 4:36:24 time: 1.1001 data_time: 0.0141 memory: 15768 grad_norm: 4.6396 loss: 0.7088 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7088 2023/07/25 15:38:03 - mmengine - INFO - Epoch(train) [85][ 60/940] lr: 1.0000e-04 eta: 4:36:02 time: 1.1017 data_time: 0.0138 memory: 15768 grad_norm: 4.7948 loss: 0.8041 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8041 2023/07/25 15:38:25 - mmengine - INFO - Epoch(train) [85][ 80/940] lr: 1.0000e-04 eta: 4:35:40 time: 1.0998 data_time: 0.0139 memory: 15768 grad_norm: 4.6855 loss: 0.8724 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8724 2023/07/25 15:38:47 - mmengine - INFO - Epoch(train) [85][100/940] lr: 1.0000e-04 eta: 4:35:18 time: 1.1024 data_time: 0.0140 memory: 15768 grad_norm: 4.8146 loss: 0.8697 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.8697 2023/07/25 15:39:09 - mmengine - INFO - Epoch(train) [85][120/940] lr: 1.0000e-04 eta: 4:34:56 time: 1.1026 data_time: 0.0137 memory: 15768 grad_norm: 4.6643 loss: 0.8919 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8919 2023/07/25 15:39:31 - mmengine - INFO - Epoch(train) [85][140/940] lr: 1.0000e-04 eta: 4:34:34 time: 1.1035 data_time: 0.0139 memory: 15768 grad_norm: 4.6702 loss: 0.7182 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7182 2023/07/25 15:39:53 - mmengine - INFO - Epoch(train) [85][160/940] lr: 1.0000e-04 eta: 4:34:12 time: 1.1020 data_time: 0.0138 memory: 15768 grad_norm: 4.7304 loss: 0.7904 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7904 2023/07/25 15:40:15 - mmengine - INFO - Epoch(train) [85][180/940] lr: 1.0000e-04 eta: 4:33:50 time: 1.1026 data_time: 0.0139 memory: 15768 grad_norm: 4.5387 loss: 0.7130 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7130 2023/07/25 15:40:37 - mmengine - INFO - Epoch(train) [85][200/940] lr: 1.0000e-04 eta: 4:33:27 time: 1.0988 data_time: 0.0140 memory: 15768 grad_norm: 4.7292 loss: 0.8898 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8898 2023/07/25 15:40:59 - mmengine - INFO - Epoch(train) [85][220/940] lr: 1.0000e-04 eta: 4:33:05 time: 1.0986 data_time: 0.0140 memory: 15768 grad_norm: 4.6483 loss: 0.6534 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6534 2023/07/25 15:41:21 - mmengine - INFO - Epoch(train) [85][240/940] lr: 1.0000e-04 eta: 4:32:43 time: 1.1010 data_time: 0.0142 memory: 15768 grad_norm: 4.6956 loss: 0.7083 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7083 2023/07/25 15:41:43 - mmengine - INFO - Epoch(train) [85][260/940] lr: 1.0000e-04 eta: 4:32:21 time: 1.1021 data_time: 0.0141 memory: 15768 grad_norm: 4.5626 loss: 0.6818 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6818 2023/07/25 15:42:05 - mmengine - INFO - Epoch(train) [85][280/940] lr: 1.0000e-04 eta: 4:31:59 time: 1.1010 data_time: 0.0142 memory: 15768 grad_norm: 4.6271 loss: 0.7959 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7959 2023/07/25 15:42:27 - mmengine - INFO - Epoch(train) [85][300/940] lr: 1.0000e-04 eta: 4:31:37 time: 1.1000 data_time: 0.0140 memory: 15768 grad_norm: 4.6600 loss: 0.7483 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7483 2023/07/25 15:42:49 - mmengine - INFO - Epoch(train) [85][320/940] lr: 1.0000e-04 eta: 4:31:15 time: 1.1024 data_time: 0.0141 memory: 15768 grad_norm: 4.6597 loss: 0.7852 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7852 2023/07/25 15:43:11 - mmengine - INFO - Epoch(train) [85][340/940] lr: 1.0000e-04 eta: 4:30:53 time: 1.1034 data_time: 0.0139 memory: 15768 grad_norm: 4.6544 loss: 0.8176 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8176 2023/07/25 15:43:33 - mmengine - INFO - Epoch(train) [85][360/940] lr: 1.0000e-04 eta: 4:30:30 time: 1.1021 data_time: 0.0140 memory: 15768 grad_norm: 4.6794 loss: 0.6840 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6840 2023/07/25 15:43:55 - mmengine - INFO - Epoch(train) [85][380/940] lr: 1.0000e-04 eta: 4:30:08 time: 1.1039 data_time: 0.0133 memory: 15768 grad_norm: 4.6424 loss: 0.7194 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7194 2023/07/25 15:44:17 - mmengine - INFO - Epoch(train) [85][400/940] lr: 1.0000e-04 eta: 4:29:46 time: 1.0990 data_time: 0.0140 memory: 15768 grad_norm: 4.6542 loss: 0.7526 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7526 2023/07/25 15:44:39 - mmengine - INFO - Epoch(train) [85][420/940] lr: 1.0000e-04 eta: 4:29:24 time: 1.1026 data_time: 0.0137 memory: 15768 grad_norm: 4.6044 loss: 0.8331 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8331 2023/07/25 15:45:01 - mmengine - INFO - Epoch(train) [85][440/940] lr: 1.0000e-04 eta: 4:29:02 time: 1.1045 data_time: 0.0142 memory: 15768 grad_norm: 4.7595 loss: 0.6724 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.6724 2023/07/25 15:45:23 - mmengine - INFO - Epoch(train) [85][460/940] lr: 1.0000e-04 eta: 4:28:40 time: 1.1010 data_time: 0.0142 memory: 15768 grad_norm: 4.6110 loss: 0.7273 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7273 2023/07/25 15:45:45 - mmengine - INFO - Epoch(train) [85][480/940] lr: 1.0000e-04 eta: 4:28:18 time: 1.1012 data_time: 0.0144 memory: 15768 grad_norm: 4.6435 loss: 0.7387 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7387 2023/07/25 15:46:07 - mmengine - INFO - Epoch(train) [85][500/940] lr: 1.0000e-04 eta: 4:27:56 time: 1.0996 data_time: 0.0141 memory: 15768 grad_norm: 4.5705 loss: 0.7367 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7367 2023/07/25 15:46:30 - mmengine - INFO - Epoch(train) [85][520/940] lr: 1.0000e-04 eta: 4:27:33 time: 1.1039 data_time: 0.0138 memory: 15768 grad_norm: 4.6705 loss: 0.8191 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8191 2023/07/25 15:46:52 - mmengine - INFO - Epoch(train) [85][540/940] lr: 1.0000e-04 eta: 4:27:11 time: 1.1030 data_time: 0.0140 memory: 15768 grad_norm: 4.6915 loss: 0.7758 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7758 2023/07/25 15:47:14 - mmengine - INFO - Epoch(train) [85][560/940] lr: 1.0000e-04 eta: 4:26:49 time: 1.1043 data_time: 0.0138 memory: 15768 grad_norm: 4.7170 loss: 0.8936 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8936 2023/07/25 15:47:36 - mmengine - INFO - Epoch(train) [85][580/940] lr: 1.0000e-04 eta: 4:26:27 time: 1.1018 data_time: 0.0144 memory: 15768 grad_norm: 4.6770 loss: 0.7593 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7593 2023/07/25 15:47:58 - mmengine - INFO - Epoch(train) [85][600/940] lr: 1.0000e-04 eta: 4:26:05 time: 1.1031 data_time: 0.0138 memory: 15768 grad_norm: 4.7027 loss: 0.8779 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8779 2023/07/25 15:48:20 - mmengine - INFO - Epoch(train) [85][620/940] lr: 1.0000e-04 eta: 4:25:43 time: 1.0978 data_time: 0.0142 memory: 15768 grad_norm: 4.5626 loss: 0.8027 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8027 2023/07/25 15:48:42 - mmengine - INFO - Epoch(train) [85][640/940] lr: 1.0000e-04 eta: 4:25:21 time: 1.1018 data_time: 0.0138 memory: 15768 grad_norm: 4.7524 loss: 0.7601 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7601 2023/07/25 15:49:04 - mmengine - INFO - Epoch(train) [85][660/940] lr: 1.0000e-04 eta: 4:24:59 time: 1.1006 data_time: 0.0138 memory: 15768 grad_norm: 4.6630 loss: 0.6859 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6859 2023/07/25 15:49:26 - mmengine - INFO - Epoch(train) [85][680/940] lr: 1.0000e-04 eta: 4:24:36 time: 1.0987 data_time: 0.0140 memory: 15768 grad_norm: 4.7710 loss: 0.9313 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9313 2023/07/25 15:49:48 - mmengine - INFO - Epoch(train) [85][700/940] lr: 1.0000e-04 eta: 4:24:14 time: 1.1011 data_time: 0.0149 memory: 15768 grad_norm: 4.5635 loss: 0.8220 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8220 2023/07/25 15:50:10 - mmengine - INFO - Epoch(train) [85][720/940] lr: 1.0000e-04 eta: 4:23:52 time: 1.1021 data_time: 0.0150 memory: 15768 grad_norm: 4.8315 loss: 0.8540 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8540 2023/07/25 15:50:32 - mmengine - INFO - Epoch(train) [85][740/940] lr: 1.0000e-04 eta: 4:23:30 time: 1.0992 data_time: 0.0136 memory: 15768 grad_norm: 4.6943 loss: 0.8658 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8658 2023/07/25 15:50:54 - mmengine - INFO - Epoch(train) [85][760/940] lr: 1.0000e-04 eta: 4:23:08 time: 1.1001 data_time: 0.0136 memory: 15768 grad_norm: 4.7543 loss: 0.8294 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8294 2023/07/25 15:51:16 - mmengine - INFO - Epoch(train) [85][780/940] lr: 1.0000e-04 eta: 4:22:46 time: 1.1003 data_time: 0.0136 memory: 15768 grad_norm: 4.6720 loss: 0.7058 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7058 2023/07/25 15:51:38 - mmengine - INFO - Epoch(train) [85][800/940] lr: 1.0000e-04 eta: 4:22:24 time: 1.1015 data_time: 0.0143 memory: 15768 grad_norm: 4.7557 loss: 0.8633 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8633 2023/07/25 15:52:00 - mmengine - INFO - Epoch(train) [85][820/940] lr: 1.0000e-04 eta: 4:22:01 time: 1.1001 data_time: 0.0141 memory: 15768 grad_norm: 4.6917 loss: 0.8976 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8976 2023/07/25 15:52:22 - mmengine - INFO - Epoch(train) [85][840/940] lr: 1.0000e-04 eta: 4:21:39 time: 1.1016 data_time: 0.0144 memory: 15768 grad_norm: 4.7264 loss: 0.9750 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9750 2023/07/25 15:52:44 - mmengine - INFO - Epoch(train) [85][860/940] lr: 1.0000e-04 eta: 4:21:17 time: 1.1007 data_time: 0.0143 memory: 15768 grad_norm: 4.6079 loss: 0.8032 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8032 2023/07/25 15:53:06 - mmengine - INFO - Epoch(train) [85][880/940] lr: 1.0000e-04 eta: 4:20:55 time: 1.1061 data_time: 0.0137 memory: 15768 grad_norm: 4.7356 loss: 0.7847 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7847 2023/07/25 15:53:28 - mmengine - INFO - Epoch(train) [85][900/940] lr: 1.0000e-04 eta: 4:20:33 time: 1.1016 data_time: 0.0135 memory: 15768 grad_norm: 4.6282 loss: 0.7513 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7513 2023/07/25 15:53:50 - mmengine - INFO - Epoch(train) [85][920/940] lr: 1.0000e-04 eta: 4:20:11 time: 1.1058 data_time: 0.0139 memory: 15768 grad_norm: 4.7293 loss: 0.8667 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8667 2023/07/25 15:54:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 15:54:11 - mmengine - INFO - Epoch(train) [85][940/940] lr: 1.0000e-04 eta: 4:19:49 time: 1.0585 data_time: 0.0140 memory: 15768 grad_norm: 4.9866 loss: 0.8982 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.8982 2023/07/25 15:54:21 - mmengine - INFO - Epoch(val) [85][20/78] eta: 0:00:28 time: 0.4951 data_time: 0.3377 memory: 2147 2023/07/25 15:54:28 - mmengine - INFO - Epoch(val) [85][40/78] eta: 0:00:15 time: 0.3434 data_time: 0.1863 memory: 2147 2023/07/25 15:54:37 - mmengine - INFO - Epoch(val) [85][60/78] eta: 0:00:07 time: 0.4417 data_time: 0.2847 memory: 2147 2023/07/25 15:54:47 - mmengine - INFO - Epoch(val) [85][78/78] acc/top1: 0.7105 acc/top5: 0.8991 acc/mean1: 0.7104 data_time: 0.2461 time: 0.4005 2023/07/25 15:55:13 - mmengine - INFO - Epoch(train) [86][ 20/940] lr: 1.0000e-04 eta: 4:19:27 time: 1.2682 data_time: 0.1426 memory: 15768 grad_norm: 4.6522 loss: 0.8584 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8584 2023/07/25 15:55:35 - mmengine - INFO - Epoch(train) [86][ 40/940] lr: 1.0000e-04 eta: 4:19:05 time: 1.1145 data_time: 0.0141 memory: 15768 grad_norm: 4.7857 loss: 0.8533 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8533 2023/07/25 15:55:57 - mmengine - INFO - Epoch(train) [86][ 60/940] lr: 1.0000e-04 eta: 4:18:43 time: 1.1042 data_time: 0.0138 memory: 15768 grad_norm: 4.7964 loss: 0.8511 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8511 2023/07/25 15:56:19 - mmengine - INFO - Epoch(train) [86][ 80/940] lr: 1.0000e-04 eta: 4:18:21 time: 1.1047 data_time: 0.0133 memory: 15768 grad_norm: 4.7307 loss: 0.8996 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8996 2023/07/25 15:56:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 15:56:41 - mmengine - INFO - Epoch(train) [86][100/940] lr: 1.0000e-04 eta: 4:17:59 time: 1.1033 data_time: 0.0138 memory: 15768 grad_norm: 4.6660 loss: 0.8643 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8643 2023/07/25 15:57:03 - mmengine - INFO - Epoch(train) [86][120/940] lr: 1.0000e-04 eta: 4:17:37 time: 1.1041 data_time: 0.0137 memory: 15768 grad_norm: 4.7199 loss: 0.6365 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.6365 2023/07/25 15:57:25 - mmengine - INFO - Epoch(train) [86][140/940] lr: 1.0000e-04 eta: 4:17:14 time: 1.1077 data_time: 0.0136 memory: 15768 grad_norm: 4.7739 loss: 0.7823 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7823 2023/07/25 15:57:47 - mmengine - INFO - Epoch(train) [86][160/940] lr: 1.0000e-04 eta: 4:16:52 time: 1.1016 data_time: 0.0137 memory: 15768 grad_norm: 4.7590 loss: 0.7103 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7103 2023/07/25 15:58:10 - mmengine - INFO - Epoch(train) [86][180/940] lr: 1.0000e-04 eta: 4:16:30 time: 1.1052 data_time: 0.0132 memory: 15768 grad_norm: 4.7240 loss: 0.6766 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6766 2023/07/25 15:58:32 - mmengine - INFO - Epoch(train) [86][200/940] lr: 1.0000e-04 eta: 4:16:08 time: 1.1048 data_time: 0.0136 memory: 15768 grad_norm: 4.7457 loss: 0.8785 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8785 2023/07/25 15:58:54 - mmengine - INFO - Epoch(train) [86][220/940] lr: 1.0000e-04 eta: 4:15:46 time: 1.1151 data_time: 0.0138 memory: 15768 grad_norm: 4.5204 loss: 0.7802 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7802 2023/07/25 15:59:17 - mmengine - INFO - Epoch(train) [86][240/940] lr: 1.0000e-04 eta: 4:15:24 time: 1.1296 data_time: 0.0139 memory: 15768 grad_norm: 4.7365 loss: 0.7789 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7789 2023/07/25 15:59:39 - mmengine - INFO - Epoch(train) [86][260/940] lr: 1.0000e-04 eta: 4:15:02 time: 1.1033 data_time: 0.0138 memory: 15768 grad_norm: 4.6284 loss: 0.7935 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7935 2023/07/25 16:00:01 - mmengine - INFO - Epoch(train) [86][280/940] lr: 1.0000e-04 eta: 4:14:40 time: 1.1013 data_time: 0.0140 memory: 15768 grad_norm: 4.7584 loss: 0.7239 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7239 2023/07/25 16:00:23 - mmengine - INFO - Epoch(train) [86][300/940] lr: 1.0000e-04 eta: 4:14:18 time: 1.1013 data_time: 0.0135 memory: 15768 grad_norm: 4.5791 loss: 0.6850 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6850 2023/07/25 16:00:45 - mmengine - INFO - Epoch(train) [86][320/940] lr: 1.0000e-04 eta: 4:13:55 time: 1.1015 data_time: 0.0139 memory: 15768 grad_norm: 4.6174 loss: 0.7696 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7696 2023/07/25 16:01:07 - mmengine - INFO - Epoch(train) [86][340/940] lr: 1.0000e-04 eta: 4:13:33 time: 1.0987 data_time: 0.0144 memory: 15768 grad_norm: 4.6700 loss: 0.6795 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6795 2023/07/25 16:01:29 - mmengine - INFO - Epoch(train) [86][360/940] lr: 1.0000e-04 eta: 4:13:11 time: 1.1019 data_time: 0.0133 memory: 15768 grad_norm: 4.7460 loss: 0.7586 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7586 2023/07/25 16:01:51 - mmengine - INFO - Epoch(train) [86][380/940] lr: 1.0000e-04 eta: 4:12:49 time: 1.1009 data_time: 0.0136 memory: 15768 grad_norm: 4.6718 loss: 0.6324 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6324 2023/07/25 16:02:13 - mmengine - INFO - Epoch(train) [86][400/940] lr: 1.0000e-04 eta: 4:12:27 time: 1.1050 data_time: 0.0130 memory: 15768 grad_norm: 4.5786 loss: 0.7796 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7796 2023/07/25 16:02:35 - mmengine - INFO - Epoch(train) [86][420/940] lr: 1.0000e-04 eta: 4:12:05 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.6895 loss: 0.7231 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7231 2023/07/25 16:02:57 - mmengine - INFO - Epoch(train) [86][440/940] lr: 1.0000e-04 eta: 4:11:43 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 4.6746 loss: 0.8747 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8747 2023/07/25 16:03:19 - mmengine - INFO - Epoch(train) [86][460/940] lr: 1.0000e-04 eta: 4:11:21 time: 1.1030 data_time: 0.0140 memory: 15768 grad_norm: 4.7129 loss: 0.8304 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8304 2023/07/25 16:03:41 - mmengine - INFO - Epoch(train) [86][480/940] lr: 1.0000e-04 eta: 4:10:58 time: 1.0997 data_time: 0.0136 memory: 15768 grad_norm: 4.6519 loss: 0.8370 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8370 2023/07/25 16:04:03 - mmengine - INFO - Epoch(train) [86][500/940] lr: 1.0000e-04 eta: 4:10:36 time: 1.1012 data_time: 0.0136 memory: 15768 grad_norm: 4.6113 loss: 0.7758 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7758 2023/07/25 16:04:25 - mmengine - INFO - Epoch(train) [86][520/940] lr: 1.0000e-04 eta: 4:10:14 time: 1.1015 data_time: 0.0140 memory: 15768 grad_norm: 4.7906 loss: 0.6737 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6737 2023/07/25 16:04:47 - mmengine - INFO - Epoch(train) [86][540/940] lr: 1.0000e-04 eta: 4:09:52 time: 1.0996 data_time: 0.0140 memory: 15768 grad_norm: 4.6792 loss: 0.6932 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6932 2023/07/25 16:05:09 - mmengine - INFO - Epoch(train) [86][560/940] lr: 1.0000e-04 eta: 4:09:30 time: 1.0998 data_time: 0.0144 memory: 15768 grad_norm: 4.7811 loss: 0.8083 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8083 2023/07/25 16:05:31 - mmengine - INFO - Epoch(train) [86][580/940] lr: 1.0000e-04 eta: 4:09:08 time: 1.1010 data_time: 0.0138 memory: 15768 grad_norm: 4.7978 loss: 0.7533 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7533 2023/07/25 16:05:53 - mmengine - INFO - Epoch(train) [86][600/940] lr: 1.0000e-04 eta: 4:08:46 time: 1.1007 data_time: 0.0141 memory: 15768 grad_norm: 4.5423 loss: 0.7820 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7820 2023/07/25 16:06:15 - mmengine - INFO - Epoch(train) [86][620/940] lr: 1.0000e-04 eta: 4:08:24 time: 1.1021 data_time: 0.0138 memory: 15768 grad_norm: 4.6522 loss: 0.8331 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8331 2023/07/25 16:06:37 - mmengine - INFO - Epoch(train) [86][640/940] lr: 1.0000e-04 eta: 4:08:01 time: 1.1009 data_time: 0.0134 memory: 15768 grad_norm: 4.6285 loss: 0.7839 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7839 2023/07/25 16:06:59 - mmengine - INFO - Epoch(train) [86][660/940] lr: 1.0000e-04 eta: 4:07:39 time: 1.0990 data_time: 0.0136 memory: 15768 grad_norm: 4.5758 loss: 0.7895 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7895 2023/07/25 16:07:21 - mmengine - INFO - Epoch(train) [86][680/940] lr: 1.0000e-04 eta: 4:07:17 time: 1.1021 data_time: 0.0139 memory: 15768 grad_norm: 4.6140 loss: 0.7981 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7981 2023/07/25 16:07:43 - mmengine - INFO - Epoch(train) [86][700/940] lr: 1.0000e-04 eta: 4:06:55 time: 1.1008 data_time: 0.0142 memory: 15768 grad_norm: 4.7713 loss: 0.8099 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8099 2023/07/25 16:08:05 - mmengine - INFO - Epoch(train) [86][720/940] lr: 1.0000e-04 eta: 4:06:33 time: 1.1017 data_time: 0.0138 memory: 15768 grad_norm: 4.6778 loss: 0.7727 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7727 2023/07/25 16:08:27 - mmengine - INFO - Epoch(train) [86][740/940] lr: 1.0000e-04 eta: 4:06:11 time: 1.0987 data_time: 0.0141 memory: 15768 grad_norm: 4.6552 loss: 0.7869 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7869 2023/07/25 16:08:49 - mmengine - INFO - Epoch(train) [86][760/940] lr: 1.0000e-04 eta: 4:05:49 time: 1.1016 data_time: 0.0141 memory: 15768 grad_norm: 4.6378 loss: 0.7456 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7456 2023/07/25 16:09:11 - mmengine - INFO - Epoch(train) [86][780/940] lr: 1.0000e-04 eta: 4:05:26 time: 1.1050 data_time: 0.0149 memory: 15768 grad_norm: 4.5684 loss: 0.7460 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7460 2023/07/25 16:09:33 - mmengine - INFO - Epoch(train) [86][800/940] lr: 1.0000e-04 eta: 4:05:04 time: 1.1002 data_time: 0.0139 memory: 15768 grad_norm: 4.6737 loss: 0.6743 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6743 2023/07/25 16:09:55 - mmengine - INFO - Epoch(train) [86][820/940] lr: 1.0000e-04 eta: 4:04:42 time: 1.1005 data_time: 0.0137 memory: 15768 grad_norm: 4.5708 loss: 0.6695 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6695 2023/07/25 16:10:17 - mmengine - INFO - Epoch(train) [86][840/940] lr: 1.0000e-04 eta: 4:04:20 time: 1.0999 data_time: 0.0141 memory: 15768 grad_norm: 4.7049 loss: 0.6459 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6459 2023/07/25 16:10:39 - mmengine - INFO - Epoch(train) [86][860/940] lr: 1.0000e-04 eta: 4:03:58 time: 1.0994 data_time: 0.0145 memory: 15768 grad_norm: 4.6323 loss: 0.7997 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7997 2023/07/25 16:11:01 - mmengine - INFO - Epoch(train) [86][880/940] lr: 1.0000e-04 eta: 4:03:36 time: 1.1027 data_time: 0.0148 memory: 15768 grad_norm: 4.7689 loss: 0.8854 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8854 2023/07/25 16:11:23 - mmengine - INFO - Epoch(train) [86][900/940] lr: 1.0000e-04 eta: 4:03:14 time: 1.1027 data_time: 0.0145 memory: 15768 grad_norm: 4.7430 loss: 0.8555 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8555 2023/07/25 16:11:45 - mmengine - INFO - Epoch(train) [86][920/940] lr: 1.0000e-04 eta: 4:02:52 time: 1.0984 data_time: 0.0138 memory: 15768 grad_norm: 4.6438 loss: 0.6819 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6819 2023/07/25 16:12:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 16:12:07 - mmengine - INFO - Epoch(train) [86][940/940] lr: 1.0000e-04 eta: 4:02:29 time: 1.0554 data_time: 0.0138 memory: 15768 grad_norm: 5.0855 loss: 0.8645 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8645 2023/07/25 16:12:17 - mmengine - INFO - Epoch(val) [86][20/78] eta: 0:00:29 time: 0.5031 data_time: 0.3454 memory: 2147 2023/07/25 16:12:23 - mmengine - INFO - Epoch(val) [86][40/78] eta: 0:00:16 time: 0.3437 data_time: 0.1868 memory: 2147 2023/07/25 16:12:32 - mmengine - INFO - Epoch(val) [86][60/78] eta: 0:00:07 time: 0.4506 data_time: 0.2936 memory: 2147 2023/07/25 16:12:43 - mmengine - INFO - Epoch(val) [86][78/78] acc/top1: 0.7113 acc/top5: 0.8991 acc/mean1: 0.7112 data_time: 0.2497 time: 0.4040 2023/07/25 16:13:09 - mmengine - INFO - Epoch(train) [87][ 20/940] lr: 1.0000e-04 eta: 4:02:08 time: 1.3140 data_time: 0.1493 memory: 15768 grad_norm: 4.7420 loss: 0.7775 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7775 2023/07/25 16:13:31 - mmengine - INFO - Epoch(train) [87][ 40/940] lr: 1.0000e-04 eta: 4:01:46 time: 1.1022 data_time: 0.0139 memory: 15768 grad_norm: 4.6727 loss: 0.9176 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9176 2023/07/25 16:13:53 - mmengine - INFO - Epoch(train) [87][ 60/940] lr: 1.0000e-04 eta: 4:01:24 time: 1.1011 data_time: 0.0135 memory: 15768 grad_norm: 4.6490 loss: 0.8122 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8122 2023/07/25 16:14:15 - mmengine - INFO - Epoch(train) [87][ 80/940] lr: 1.0000e-04 eta: 4:01:01 time: 1.1036 data_time: 0.0140 memory: 15768 grad_norm: 4.7391 loss: 0.7782 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7782 2023/07/25 16:14:37 - mmengine - INFO - Epoch(train) [87][100/940] lr: 1.0000e-04 eta: 4:00:39 time: 1.1005 data_time: 0.0139 memory: 15768 grad_norm: 4.7310 loss: 0.8272 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8272 2023/07/25 16:14:59 - mmengine - INFO - Epoch(train) [87][120/940] lr: 1.0000e-04 eta: 4:00:17 time: 1.1024 data_time: 0.0138 memory: 15768 grad_norm: 4.6851 loss: 0.7451 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7451 2023/07/25 16:15:21 - mmengine - INFO - Epoch(train) [87][140/940] lr: 1.0000e-04 eta: 3:59:55 time: 1.1051 data_time: 0.0140 memory: 15768 grad_norm: 4.6358 loss: 0.7545 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7545 2023/07/25 16:15:43 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 16:15:43 - mmengine - INFO - Epoch(train) [87][160/940] lr: 1.0000e-04 eta: 3:59:33 time: 1.1039 data_time: 0.0143 memory: 15768 grad_norm: 4.6106 loss: 0.7979 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7979 2023/07/25 16:16:05 - mmengine - INFO - Epoch(train) [87][180/940] lr: 1.0000e-04 eta: 3:59:11 time: 1.0988 data_time: 0.0137 memory: 15768 grad_norm: 4.7068 loss: 0.8107 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8107 2023/07/25 16:16:27 - mmengine - INFO - Epoch(train) [87][200/940] lr: 1.0000e-04 eta: 3:58:49 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.7155 loss: 0.8311 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8311 2023/07/25 16:16:50 - mmengine - INFO - Epoch(train) [87][220/940] lr: 1.0000e-04 eta: 3:58:27 time: 1.1057 data_time: 0.0134 memory: 15768 grad_norm: 4.7488 loss: 0.7947 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7947 2023/07/25 16:17:12 - mmengine - INFO - Epoch(train) [87][240/940] lr: 1.0000e-04 eta: 3:58:05 time: 1.1013 data_time: 0.0138 memory: 15768 grad_norm: 4.6704 loss: 0.7045 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7045 2023/07/25 16:17:34 - mmengine - INFO - Epoch(train) [87][260/940] lr: 1.0000e-04 eta: 3:57:42 time: 1.1018 data_time: 0.0137 memory: 15768 grad_norm: 4.8043 loss: 0.9191 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9191 2023/07/25 16:17:56 - mmengine - INFO - Epoch(train) [87][280/940] lr: 1.0000e-04 eta: 3:57:20 time: 1.1034 data_time: 0.0137 memory: 15768 grad_norm: 4.7546 loss: 0.8777 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8777 2023/07/25 16:18:18 - mmengine - INFO - Epoch(train) [87][300/940] lr: 1.0000e-04 eta: 3:56:58 time: 1.0987 data_time: 0.0141 memory: 15768 grad_norm: 4.6623 loss: 0.9626 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9626 2023/07/25 16:18:40 - mmengine - INFO - Epoch(train) [87][320/940] lr: 1.0000e-04 eta: 3:56:36 time: 1.1005 data_time: 0.0140 memory: 15768 grad_norm: 4.6273 loss: 0.7847 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7847 2023/07/25 16:19:02 - mmengine - INFO - Epoch(train) [87][340/940] lr: 1.0000e-04 eta: 3:56:14 time: 1.1020 data_time: 0.0141 memory: 15768 grad_norm: 4.6546 loss: 0.8326 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8326 2023/07/25 16:19:24 - mmengine - INFO - Epoch(train) [87][360/940] lr: 1.0000e-04 eta: 3:55:52 time: 1.1017 data_time: 0.0142 memory: 15768 grad_norm: 4.6926 loss: 0.6226 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6226 2023/07/25 16:19:46 - mmengine - INFO - Epoch(train) [87][380/940] lr: 1.0000e-04 eta: 3:55:30 time: 1.1013 data_time: 0.0141 memory: 15768 grad_norm: 4.7665 loss: 0.7339 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7339 2023/07/25 16:20:08 - mmengine - INFO - Epoch(train) [87][400/940] lr: 1.0000e-04 eta: 3:55:07 time: 1.1015 data_time: 0.0138 memory: 15768 grad_norm: 4.7532 loss: 0.7996 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7996 2023/07/25 16:20:30 - mmengine - INFO - Epoch(train) [87][420/940] lr: 1.0000e-04 eta: 3:54:45 time: 1.1000 data_time: 0.0139 memory: 15768 grad_norm: 4.6857 loss: 0.8450 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8450 2023/07/25 16:20:52 - mmengine - INFO - Epoch(train) [87][440/940] lr: 1.0000e-04 eta: 3:54:23 time: 1.1007 data_time: 0.0134 memory: 15768 grad_norm: 4.6457 loss: 0.7659 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7659 2023/07/25 16:21:14 - mmengine - INFO - Epoch(train) [87][460/940] lr: 1.0000e-04 eta: 3:54:01 time: 1.1006 data_time: 0.0139 memory: 15768 grad_norm: 4.7062 loss: 0.7614 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7614 2023/07/25 16:21:36 - mmengine - INFO - Epoch(train) [87][480/940] lr: 1.0000e-04 eta: 3:53:39 time: 1.0981 data_time: 0.0139 memory: 15768 grad_norm: 4.7349 loss: 0.7516 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7516 2023/07/25 16:21:58 - mmengine - INFO - Epoch(train) [87][500/940] lr: 1.0000e-04 eta: 3:53:17 time: 1.1010 data_time: 0.0138 memory: 15768 grad_norm: 4.6813 loss: 0.7769 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7769 2023/07/25 16:22:20 - mmengine - INFO - Epoch(train) [87][520/940] lr: 1.0000e-04 eta: 3:52:55 time: 1.1028 data_time: 0.0139 memory: 15768 grad_norm: 4.6226 loss: 0.8591 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8591 2023/07/25 16:22:42 - mmengine - INFO - Epoch(train) [87][540/940] lr: 1.0000e-04 eta: 3:52:33 time: 1.1014 data_time: 0.0143 memory: 15768 grad_norm: 4.6644 loss: 0.8385 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8385 2023/07/25 16:23:04 - mmengine - INFO - Epoch(train) [87][560/940] lr: 1.0000e-04 eta: 3:52:10 time: 1.1004 data_time: 0.0140 memory: 15768 grad_norm: 4.6263 loss: 0.9293 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9293 2023/07/25 16:23:26 - mmengine - INFO - Epoch(train) [87][580/940] lr: 1.0000e-04 eta: 3:51:48 time: 1.0995 data_time: 0.0141 memory: 15768 grad_norm: 4.6994 loss: 0.8259 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8259 2023/07/25 16:23:48 - mmengine - INFO - Epoch(train) [87][600/940] lr: 1.0000e-04 eta: 3:51:26 time: 1.1027 data_time: 0.0140 memory: 15768 grad_norm: 4.7231 loss: 0.7582 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7582 2023/07/25 16:24:10 - mmengine - INFO - Epoch(train) [87][620/940] lr: 1.0000e-04 eta: 3:51:04 time: 1.0983 data_time: 0.0143 memory: 15768 grad_norm: 4.6055 loss: 0.7080 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7080 2023/07/25 16:24:32 - mmengine - INFO - Epoch(train) [87][640/940] lr: 1.0000e-04 eta: 3:50:42 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 4.6191 loss: 0.7909 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7909 2023/07/25 16:24:54 - mmengine - INFO - Epoch(train) [87][660/940] lr: 1.0000e-04 eta: 3:50:20 time: 1.0986 data_time: 0.0140 memory: 15768 grad_norm: 4.6619 loss: 0.9519 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9519 2023/07/25 16:25:16 - mmengine - INFO - Epoch(train) [87][680/940] lr: 1.0000e-04 eta: 3:49:58 time: 1.0994 data_time: 0.0138 memory: 15768 grad_norm: 4.6057 loss: 0.7513 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7513 2023/07/25 16:25:38 - mmengine - INFO - Epoch(train) [87][700/940] lr: 1.0000e-04 eta: 3:49:36 time: 1.1011 data_time: 0.0141 memory: 15768 grad_norm: 4.6707 loss: 0.6972 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6972 2023/07/25 16:26:00 - mmengine - INFO - Epoch(train) [87][720/940] lr: 1.0000e-04 eta: 3:49:13 time: 1.1008 data_time: 0.0143 memory: 15768 grad_norm: 4.7153 loss: 0.8049 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8049 2023/07/25 16:26:22 - mmengine - INFO - Epoch(train) [87][740/940] lr: 1.0000e-04 eta: 3:48:51 time: 1.1020 data_time: 0.0138 memory: 15768 grad_norm: 4.6603 loss: 0.8004 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8004 2023/07/25 16:26:44 - mmengine - INFO - Epoch(train) [87][760/940] lr: 1.0000e-04 eta: 3:48:29 time: 1.1019 data_time: 0.0138 memory: 15768 grad_norm: 4.5214 loss: 0.7048 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7048 2023/07/25 16:27:06 - mmengine - INFO - Epoch(train) [87][780/940] lr: 1.0000e-04 eta: 3:48:07 time: 1.1003 data_time: 0.0141 memory: 15768 grad_norm: 4.5871 loss: 0.7875 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7875 2023/07/25 16:27:28 - mmengine - INFO - Epoch(train) [87][800/940] lr: 1.0000e-04 eta: 3:47:45 time: 1.1022 data_time: 0.0140 memory: 15768 grad_norm: 4.7283 loss: 0.8115 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8115 2023/07/25 16:27:50 - mmengine - INFO - Epoch(train) [87][820/940] lr: 1.0000e-04 eta: 3:47:23 time: 1.1017 data_time: 0.0138 memory: 15768 grad_norm: 4.6883 loss: 0.8631 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8631 2023/07/25 16:28:12 - mmengine - INFO - Epoch(train) [87][840/940] lr: 1.0000e-04 eta: 3:47:01 time: 1.0990 data_time: 0.0140 memory: 15768 grad_norm: 4.7255 loss: 0.8124 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8124 2023/07/25 16:28:34 - mmengine - INFO - Epoch(train) [87][860/940] lr: 1.0000e-04 eta: 3:46:39 time: 1.1006 data_time: 0.0136 memory: 15768 grad_norm: 4.8065 loss: 0.8066 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.8066 2023/07/25 16:28:56 - mmengine - INFO - Epoch(train) [87][880/940] lr: 1.0000e-04 eta: 3:46:16 time: 1.1011 data_time: 0.0140 memory: 15768 grad_norm: 4.8047 loss: 0.8749 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.8749 2023/07/25 16:29:18 - mmengine - INFO - Epoch(train) [87][900/940] lr: 1.0000e-04 eta: 3:45:54 time: 1.1009 data_time: 0.0143 memory: 15768 grad_norm: 4.5529 loss: 0.7171 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7171 2023/07/25 16:29:40 - mmengine - INFO - Epoch(train) [87][920/940] lr: 1.0000e-04 eta: 3:45:32 time: 1.1022 data_time: 0.0139 memory: 15768 grad_norm: 4.7577 loss: 0.7567 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7567 2023/07/25 16:30:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 16:30:01 - mmengine - INFO - Epoch(train) [87][940/940] lr: 1.0000e-04 eta: 3:45:10 time: 1.0560 data_time: 0.0137 memory: 15768 grad_norm: 5.0715 loss: 0.9910 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 0.9910 2023/07/25 16:30:01 - mmengine - INFO - Saving checkpoint at 87 epochs 2023/07/25 16:30:13 - mmengine - INFO - Epoch(val) [87][20/78] eta: 0:00:29 time: 0.5008 data_time: 0.3433 memory: 2147 2023/07/25 16:30:20 - mmengine - INFO - Epoch(val) [87][40/78] eta: 0:00:16 time: 0.3593 data_time: 0.2023 memory: 2147 2023/07/25 16:30:29 - mmengine - INFO - Epoch(val) [87][60/78] eta: 0:00:07 time: 0.4425 data_time: 0.2853 memory: 2147 2023/07/25 16:30:38 - mmengine - INFO - Epoch(val) [87][78/78] acc/top1: 0.7102 acc/top5: 0.8986 acc/mean1: 0.7102 data_time: 0.2473 time: 0.4016 2023/07/25 16:31:04 - mmengine - INFO - Epoch(train) [88][ 20/940] lr: 1.0000e-04 eta: 3:44:48 time: 1.2910 data_time: 0.1452 memory: 15768 grad_norm: 4.6698 loss: 0.7297 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7297 2023/07/25 16:31:26 - mmengine - INFO - Epoch(train) [88][ 40/940] lr: 1.0000e-04 eta: 3:44:26 time: 1.1020 data_time: 0.0137 memory: 15768 grad_norm: 4.6607 loss: 0.7800 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7800 2023/07/25 16:31:48 - mmengine - INFO - Epoch(train) [88][ 60/940] lr: 1.0000e-04 eta: 3:44:04 time: 1.1002 data_time: 0.0138 memory: 15768 grad_norm: 4.6227 loss: 0.6787 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6787 2023/07/25 16:32:10 - mmengine - INFO - Epoch(train) [88][ 80/940] lr: 1.0000e-04 eta: 3:43:42 time: 1.1032 data_time: 0.0140 memory: 15768 grad_norm: 4.6468 loss: 0.7924 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7924 2023/07/25 16:32:32 - mmengine - INFO - Epoch(train) [88][100/940] lr: 1.0000e-04 eta: 3:43:20 time: 1.1031 data_time: 0.0138 memory: 15768 grad_norm: 4.6434 loss: 0.9204 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9204 2023/07/25 16:32:54 - mmengine - INFO - Epoch(train) [88][120/940] lr: 1.0000e-04 eta: 3:42:58 time: 1.1039 data_time: 0.0135 memory: 15768 grad_norm: 4.7619 loss: 0.7191 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7191 2023/07/25 16:33:16 - mmengine - INFO - Epoch(train) [88][140/940] lr: 1.0000e-04 eta: 3:42:36 time: 1.1026 data_time: 0.0140 memory: 15768 grad_norm: 4.5906 loss: 0.7339 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7339 2023/07/25 16:33:38 - mmengine - INFO - Epoch(train) [88][160/940] lr: 1.0000e-04 eta: 3:42:13 time: 1.0985 data_time: 0.0140 memory: 15768 grad_norm: 4.6256 loss: 0.6809 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6809 2023/07/25 16:34:00 - mmengine - INFO - Epoch(train) [88][180/940] lr: 1.0000e-04 eta: 3:41:51 time: 1.1034 data_time: 0.0140 memory: 15768 grad_norm: 4.7613 loss: 0.8065 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8065 2023/07/25 16:34:22 - mmengine - INFO - Epoch(train) [88][200/940] lr: 1.0000e-04 eta: 3:41:29 time: 1.1005 data_time: 0.0141 memory: 15768 grad_norm: 4.6735 loss: 0.7225 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7225 2023/07/25 16:34:44 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 16:34:44 - mmengine - INFO - Epoch(train) [88][220/940] lr: 1.0000e-04 eta: 3:41:07 time: 1.1015 data_time: 0.0142 memory: 15768 grad_norm: 4.6310 loss: 0.7954 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7954 2023/07/25 16:35:06 - mmengine - INFO - Epoch(train) [88][240/940] lr: 1.0000e-04 eta: 3:40:45 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.5560 loss: 0.5979 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5979 2023/07/25 16:35:28 - mmengine - INFO - Epoch(train) [88][260/940] lr: 1.0000e-04 eta: 3:40:23 time: 1.1003 data_time: 0.0138 memory: 15768 grad_norm: 4.5999 loss: 0.6372 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6372 2023/07/25 16:35:50 - mmengine - INFO - Epoch(train) [88][280/940] lr: 1.0000e-04 eta: 3:40:01 time: 1.1019 data_time: 0.0138 memory: 15768 grad_norm: 4.7190 loss: 0.6177 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6177 2023/07/25 16:36:12 - mmengine - INFO - Epoch(train) [88][300/940] lr: 1.0000e-04 eta: 3:39:39 time: 1.1027 data_time: 0.0139 memory: 15768 grad_norm: 4.5766 loss: 0.6747 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6747 2023/07/25 16:36:34 - mmengine - INFO - Epoch(train) [88][320/940] lr: 1.0000e-04 eta: 3:39:16 time: 1.0996 data_time: 0.0141 memory: 15768 grad_norm: 4.6921 loss: 0.6734 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6734 2023/07/25 16:36:56 - mmengine - INFO - Epoch(train) [88][340/940] lr: 1.0000e-04 eta: 3:38:54 time: 1.0990 data_time: 0.0140 memory: 15768 grad_norm: 4.7762 loss: 0.7263 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7263 2023/07/25 16:37:18 - mmengine - INFO - Epoch(train) [88][360/940] lr: 1.0000e-04 eta: 3:38:32 time: 1.1045 data_time: 0.0143 memory: 15768 grad_norm: 4.6770 loss: 0.6612 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6612 2023/07/25 16:37:41 - mmengine - INFO - Epoch(train) [88][380/940] lr: 1.0000e-04 eta: 3:38:10 time: 1.1038 data_time: 0.0139 memory: 15768 grad_norm: 4.7185 loss: 0.8831 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8831 2023/07/25 16:38:03 - mmengine - INFO - Epoch(train) [88][400/940] lr: 1.0000e-04 eta: 3:37:48 time: 1.1003 data_time: 0.0141 memory: 15768 grad_norm: 4.6459 loss: 0.7196 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7196 2023/07/25 16:38:25 - mmengine - INFO - Epoch(train) [88][420/940] lr: 1.0000e-04 eta: 3:37:26 time: 1.0999 data_time: 0.0140 memory: 15768 grad_norm: 4.7340 loss: 0.7195 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7195 2023/07/25 16:38:47 - mmengine - INFO - Epoch(train) [88][440/940] lr: 1.0000e-04 eta: 3:37:04 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.8065 loss: 0.8746 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8746 2023/07/25 16:39:09 - mmengine - INFO - Epoch(train) [88][460/940] lr: 1.0000e-04 eta: 3:36:42 time: 1.1037 data_time: 0.0143 memory: 15768 grad_norm: 4.6688 loss: 0.7782 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7782 2023/07/25 16:39:31 - mmengine - INFO - Epoch(train) [88][480/940] lr: 1.0000e-04 eta: 3:36:19 time: 1.1001 data_time: 0.0139 memory: 15768 grad_norm: 4.6969 loss: 0.7891 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7891 2023/07/25 16:39:53 - mmengine - INFO - Epoch(train) [88][500/940] lr: 1.0000e-04 eta: 3:35:57 time: 1.1006 data_time: 0.0138 memory: 15768 grad_norm: 4.6916 loss: 0.8481 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8481 2023/07/25 16:40:15 - mmengine - INFO - Epoch(train) [88][520/940] lr: 1.0000e-04 eta: 3:35:35 time: 1.1036 data_time: 0.0137 memory: 15768 grad_norm: 4.6857 loss: 0.6595 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6595 2023/07/25 16:40:37 - mmengine - INFO - Epoch(train) [88][540/940] lr: 1.0000e-04 eta: 3:35:13 time: 1.0999 data_time: 0.0144 memory: 15768 grad_norm: 4.7102 loss: 0.7796 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7796 2023/07/25 16:40:59 - mmengine - INFO - Epoch(train) [88][560/940] lr: 1.0000e-04 eta: 3:34:51 time: 1.1063 data_time: 0.0141 memory: 15768 grad_norm: 4.7107 loss: 0.6832 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6832 2023/07/25 16:41:21 - mmengine - INFO - Epoch(train) [88][580/940] lr: 1.0000e-04 eta: 3:34:29 time: 1.1029 data_time: 0.0143 memory: 15768 grad_norm: 4.6945 loss: 0.7742 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7742 2023/07/25 16:41:43 - mmengine - INFO - Epoch(train) [88][600/940] lr: 1.0000e-04 eta: 3:34:07 time: 1.0992 data_time: 0.0144 memory: 15768 grad_norm: 4.6797 loss: 0.8976 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8976 2023/07/25 16:42:05 - mmengine - INFO - Epoch(train) [88][620/940] lr: 1.0000e-04 eta: 3:33:45 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.7603 loss: 0.8585 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8585 2023/07/25 16:42:27 - mmengine - INFO - Epoch(train) [88][640/940] lr: 1.0000e-04 eta: 3:33:23 time: 1.1004 data_time: 0.0142 memory: 15768 grad_norm: 4.7490 loss: 0.8764 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8764 2023/07/25 16:42:49 - mmengine - INFO - Epoch(train) [88][660/940] lr: 1.0000e-04 eta: 3:33:00 time: 1.0999 data_time: 0.0137 memory: 15768 grad_norm: 4.6067 loss: 0.7565 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7565 2023/07/25 16:43:11 - mmengine - INFO - Epoch(train) [88][680/940] lr: 1.0000e-04 eta: 3:32:38 time: 1.1003 data_time: 0.0139 memory: 15768 grad_norm: 4.6953 loss: 0.7547 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7547 2023/07/25 16:43:33 - mmengine - INFO - Epoch(train) [88][700/940] lr: 1.0000e-04 eta: 3:32:16 time: 1.1019 data_time: 0.0137 memory: 15768 grad_norm: 4.6925 loss: 0.8410 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8410 2023/07/25 16:43:55 - mmengine - INFO - Epoch(train) [88][720/940] lr: 1.0000e-04 eta: 3:31:54 time: 1.1023 data_time: 0.0141 memory: 15768 grad_norm: 4.7200 loss: 0.7820 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7820 2023/07/25 16:44:17 - mmengine - INFO - Epoch(train) [88][740/940] lr: 1.0000e-04 eta: 3:31:32 time: 1.0995 data_time: 0.0147 memory: 15768 grad_norm: 4.7320 loss: 0.8038 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8038 2023/07/25 16:44:39 - mmengine - INFO - Epoch(train) [88][760/940] lr: 1.0000e-04 eta: 3:31:10 time: 1.1005 data_time: 0.0139 memory: 15768 grad_norm: 4.6640 loss: 0.8147 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8147 2023/07/25 16:45:01 - mmengine - INFO - Epoch(train) [88][780/940] lr: 1.0000e-04 eta: 3:30:48 time: 1.1037 data_time: 0.0142 memory: 15768 grad_norm: 4.7150 loss: 0.8744 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8744 2023/07/25 16:45:23 - mmengine - INFO - Epoch(train) [88][800/940] lr: 1.0000e-04 eta: 3:30:26 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.7738 loss: 0.8216 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8216 2023/07/25 16:45:45 - mmengine - INFO - Epoch(train) [88][820/940] lr: 1.0000e-04 eta: 3:30:03 time: 1.0993 data_time: 0.0140 memory: 15768 grad_norm: 4.7685 loss: 0.7835 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7835 2023/07/25 16:46:07 - mmengine - INFO - Epoch(train) [88][840/940] lr: 1.0000e-04 eta: 3:29:41 time: 1.0991 data_time: 0.0141 memory: 15768 grad_norm: 4.7290 loss: 0.8141 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8141 2023/07/25 16:46:29 - mmengine - INFO - Epoch(train) [88][860/940] lr: 1.0000e-04 eta: 3:29:19 time: 1.0991 data_time: 0.0143 memory: 15768 grad_norm: 4.7123 loss: 0.9248 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9248 2023/07/25 16:46:51 - mmengine - INFO - Epoch(train) [88][880/940] lr: 1.0000e-04 eta: 3:28:57 time: 1.0996 data_time: 0.0141 memory: 15768 grad_norm: 4.6272 loss: 0.8117 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8117 2023/07/25 16:47:13 - mmengine - INFO - Epoch(train) [88][900/940] lr: 1.0000e-04 eta: 3:28:35 time: 1.1035 data_time: 0.0141 memory: 15768 grad_norm: 4.7707 loss: 0.8237 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8237 2023/07/25 16:47:35 - mmengine - INFO - Epoch(train) [88][920/940] lr: 1.0000e-04 eta: 3:28:13 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.7126 loss: 0.8395 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8395 2023/07/25 16:47:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 16:47:56 - mmengine - INFO - Epoch(train) [88][940/940] lr: 1.0000e-04 eta: 3:27:50 time: 1.0550 data_time: 0.0126 memory: 15768 grad_norm: 5.1274 loss: 0.8419 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8419 2023/07/25 16:48:06 - mmengine - INFO - Epoch(val) [88][20/78] eta: 0:00:28 time: 0.4850 data_time: 0.3272 memory: 2147 2023/07/25 16:48:13 - mmengine - INFO - Epoch(val) [88][40/78] eta: 0:00:15 time: 0.3515 data_time: 0.1946 memory: 2147 2023/07/25 16:48:22 - mmengine - INFO - Epoch(val) [88][60/78] eta: 0:00:07 time: 0.4299 data_time: 0.2729 memory: 2147 2023/07/25 16:48:32 - mmengine - INFO - Epoch(val) [88][78/78] acc/top1: 0.7120 acc/top5: 0.8989 acc/mean1: 0.7119 data_time: 0.2420 time: 0.3963 2023/07/25 16:48:59 - mmengine - INFO - Epoch(train) [89][ 20/940] lr: 1.0000e-04 eta: 3:27:29 time: 1.3061 data_time: 0.1627 memory: 15768 grad_norm: 4.6597 loss: 0.8078 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8078 2023/07/25 16:49:21 - mmengine - INFO - Epoch(train) [89][ 40/940] lr: 1.0000e-04 eta: 3:27:07 time: 1.1023 data_time: 0.0140 memory: 15768 grad_norm: 4.7055 loss: 0.7980 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.7980 2023/07/25 16:49:43 - mmengine - INFO - Epoch(train) [89][ 60/940] lr: 1.0000e-04 eta: 3:26:45 time: 1.0985 data_time: 0.0137 memory: 15768 grad_norm: 4.6713 loss: 0.8563 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8563 2023/07/25 16:50:05 - mmengine - INFO - Epoch(train) [89][ 80/940] lr: 1.0000e-04 eta: 3:26:23 time: 1.1008 data_time: 0.0138 memory: 15768 grad_norm: 4.6528 loss: 0.6758 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6758 2023/07/25 16:50:27 - mmengine - INFO - Epoch(train) [89][100/940] lr: 1.0000e-04 eta: 3:26:00 time: 1.0998 data_time: 0.0138 memory: 15768 grad_norm: 4.6229 loss: 0.7417 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7417 2023/07/25 16:50:49 - mmengine - INFO - Epoch(train) [89][120/940] lr: 1.0000e-04 eta: 3:25:38 time: 1.1004 data_time: 0.0138 memory: 15768 grad_norm: 4.6879 loss: 0.8051 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8051 2023/07/25 16:51:11 - mmengine - INFO - Epoch(train) [89][140/940] lr: 1.0000e-04 eta: 3:25:16 time: 1.1037 data_time: 0.0137 memory: 15768 grad_norm: 4.7609 loss: 0.8115 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8115 2023/07/25 16:51:33 - mmengine - INFO - Epoch(train) [89][160/940] lr: 1.0000e-04 eta: 3:24:54 time: 1.1023 data_time: 0.0141 memory: 15768 grad_norm: 4.6654 loss: 0.7051 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7051 2023/07/25 16:51:55 - mmengine - INFO - Epoch(train) [89][180/940] lr: 1.0000e-04 eta: 3:24:32 time: 1.1025 data_time: 0.0140 memory: 15768 grad_norm: 4.5202 loss: 0.6910 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6910 2023/07/25 16:52:17 - mmengine - INFO - Epoch(train) [89][200/940] lr: 1.0000e-04 eta: 3:24:10 time: 1.1044 data_time: 0.0145 memory: 15768 grad_norm: 4.7932 loss: 0.7955 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7955 2023/07/25 16:52:39 - mmengine - INFO - Epoch(train) [89][220/940] lr: 1.0000e-04 eta: 3:23:48 time: 1.1022 data_time: 0.0140 memory: 15768 grad_norm: 4.7872 loss: 0.8110 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8110 2023/07/25 16:53:01 - mmengine - INFO - Epoch(train) [89][240/940] lr: 1.0000e-04 eta: 3:23:26 time: 1.1007 data_time: 0.0142 memory: 15768 grad_norm: 4.7418 loss: 0.7814 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7814 2023/07/25 16:53:23 - mmengine - INFO - Epoch(train) [89][260/940] lr: 1.0000e-04 eta: 3:23:03 time: 1.0990 data_time: 0.0141 memory: 15768 grad_norm: 4.7500 loss: 0.8678 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8678 2023/07/25 16:53:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 16:53:45 - mmengine - INFO - Epoch(train) [89][280/940] lr: 1.0000e-04 eta: 3:22:41 time: 1.1007 data_time: 0.0139 memory: 15768 grad_norm: 4.7130 loss: 0.6600 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6600 2023/07/25 16:54:07 - mmengine - INFO - Epoch(train) [89][300/940] lr: 1.0000e-04 eta: 3:22:19 time: 1.1005 data_time: 0.0134 memory: 15768 grad_norm: 4.7231 loss: 0.7468 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7468 2023/07/25 16:54:29 - mmengine - INFO - Epoch(train) [89][320/940] lr: 1.0000e-04 eta: 3:21:57 time: 1.0989 data_time: 0.0140 memory: 15768 grad_norm: 4.6611 loss: 0.7960 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7960 2023/07/25 16:54:51 - mmengine - INFO - Epoch(train) [89][340/940] lr: 1.0000e-04 eta: 3:21:35 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 4.7491 loss: 0.7667 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7667 2023/07/25 16:55:13 - mmengine - INFO - Epoch(train) [89][360/940] lr: 1.0000e-04 eta: 3:21:13 time: 1.1187 data_time: 0.0141 memory: 15768 grad_norm: 4.7355 loss: 0.7159 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7159 2023/07/25 16:55:37 - mmengine - INFO - Epoch(train) [89][380/940] lr: 1.0000e-04 eta: 3:20:51 time: 1.1690 data_time: 0.0134 memory: 15768 grad_norm: 4.6730 loss: 0.9295 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9295 2023/07/25 16:56:00 - mmengine - INFO - Epoch(train) [89][400/940] lr: 1.0000e-04 eta: 3:20:29 time: 1.1713 data_time: 0.0136 memory: 15768 grad_norm: 4.6503 loss: 0.8479 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8479 2023/07/25 16:56:23 - mmengine - INFO - Epoch(train) [89][420/940] lr: 1.0000e-04 eta: 3:20:07 time: 1.1649 data_time: 0.0138 memory: 15768 grad_norm: 4.8361 loss: 0.8992 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8992 2023/07/25 16:56:46 - mmengine - INFO - Epoch(train) [89][440/940] lr: 1.0000e-04 eta: 3:19:45 time: 1.1084 data_time: 0.0143 memory: 15768 grad_norm: 4.8108 loss: 0.8360 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8360 2023/07/25 16:57:08 - mmengine - INFO - Epoch(train) [89][460/940] lr: 1.0000e-04 eta: 3:19:23 time: 1.0999 data_time: 0.0138 memory: 15768 grad_norm: 4.8071 loss: 0.7914 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7914 2023/07/25 16:57:30 - mmengine - INFO - Epoch(train) [89][480/940] lr: 1.0000e-04 eta: 3:19:01 time: 1.0991 data_time: 0.0141 memory: 15768 grad_norm: 4.6032 loss: 0.7581 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7581 2023/07/25 16:57:52 - mmengine - INFO - Epoch(train) [89][500/940] lr: 1.0000e-04 eta: 3:18:39 time: 1.1012 data_time: 0.0140 memory: 15768 grad_norm: 4.7476 loss: 0.8207 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8207 2023/07/25 16:58:14 - mmengine - INFO - Epoch(train) [89][520/940] lr: 1.0000e-04 eta: 3:18:16 time: 1.1021 data_time: 0.0141 memory: 15768 grad_norm: 4.7061 loss: 0.7639 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7639 2023/07/25 16:58:36 - mmengine - INFO - Epoch(train) [89][540/940] lr: 1.0000e-04 eta: 3:17:54 time: 1.1000 data_time: 0.0145 memory: 15768 grad_norm: 4.7831 loss: 0.7378 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7378 2023/07/25 16:58:58 - mmengine - INFO - Epoch(train) [89][560/940] lr: 1.0000e-04 eta: 3:17:32 time: 1.1004 data_time: 0.0140 memory: 15768 grad_norm: 4.6732 loss: 0.6941 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6941 2023/07/25 16:59:20 - mmengine - INFO - Epoch(train) [89][580/940] lr: 1.0000e-04 eta: 3:17:10 time: 1.1034 data_time: 0.0136 memory: 15768 grad_norm: 4.6857 loss: 0.8351 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8351 2023/07/25 16:59:42 - mmengine - INFO - Epoch(train) [89][600/940] lr: 1.0000e-04 eta: 3:16:48 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.6991 loss: 0.8987 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8987 2023/07/25 17:00:04 - mmengine - INFO - Epoch(train) [89][620/940] lr: 1.0000e-04 eta: 3:16:26 time: 1.1003 data_time: 0.0141 memory: 15768 grad_norm: 4.6954 loss: 0.8082 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8082 2023/07/25 17:00:26 - mmengine - INFO - Epoch(train) [89][640/940] lr: 1.0000e-04 eta: 3:16:04 time: 1.1023 data_time: 0.0143 memory: 15768 grad_norm: 4.7479 loss: 0.7289 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7289 2023/07/25 17:00:48 - mmengine - INFO - Epoch(train) [89][660/940] lr: 1.0000e-04 eta: 3:15:42 time: 1.1000 data_time: 0.0144 memory: 15768 grad_norm: 4.7287 loss: 0.7874 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.7874 2023/07/25 17:01:10 - mmengine - INFO - Epoch(train) [89][680/940] lr: 1.0000e-04 eta: 3:15:19 time: 1.0983 data_time: 0.0138 memory: 15768 grad_norm: 4.5899 loss: 0.8292 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8292 2023/07/25 17:01:32 - mmengine - INFO - Epoch(train) [89][700/940] lr: 1.0000e-04 eta: 3:14:57 time: 1.1009 data_time: 0.0141 memory: 15768 grad_norm: 4.7208 loss: 0.8181 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8181 2023/07/25 17:01:54 - mmengine - INFO - Epoch(train) [89][720/940] lr: 1.0000e-04 eta: 3:14:35 time: 1.1011 data_time: 0.0143 memory: 15768 grad_norm: 4.6421 loss: 0.9129 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9129 2023/07/25 17:02:16 - mmengine - INFO - Epoch(train) [89][740/940] lr: 1.0000e-04 eta: 3:14:13 time: 1.1010 data_time: 0.0141 memory: 15768 grad_norm: 4.7799 loss: 0.7720 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7720 2023/07/25 17:02:38 - mmengine - INFO - Epoch(train) [89][760/940] lr: 1.0000e-04 eta: 3:13:51 time: 1.1004 data_time: 0.0140 memory: 15768 grad_norm: 4.6170 loss: 0.6815 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6815 2023/07/25 17:03:00 - mmengine - INFO - Epoch(train) [89][780/940] lr: 1.0000e-04 eta: 3:13:29 time: 1.1026 data_time: 0.0139 memory: 15768 grad_norm: 4.6595 loss: 0.7163 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.7163 2023/07/25 17:03:22 - mmengine - INFO - Epoch(train) [89][800/940] lr: 1.0000e-04 eta: 3:13:07 time: 1.1035 data_time: 0.0136 memory: 15768 grad_norm: 4.8438 loss: 0.6687 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6687 2023/07/25 17:03:44 - mmengine - INFO - Epoch(train) [89][820/940] lr: 1.0000e-04 eta: 3:12:45 time: 1.1031 data_time: 0.0141 memory: 15768 grad_norm: 4.7579 loss: 0.9317 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9317 2023/07/25 17:04:06 - mmengine - INFO - Epoch(train) [89][840/940] lr: 1.0000e-04 eta: 3:12:22 time: 1.1027 data_time: 0.0138 memory: 15768 grad_norm: 4.6659 loss: 0.8739 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8739 2023/07/25 17:04:28 - mmengine - INFO - Epoch(train) [89][860/940] lr: 1.0000e-04 eta: 3:12:00 time: 1.1025 data_time: 0.0142 memory: 15768 grad_norm: 4.8160 loss: 0.7514 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7514 2023/07/25 17:04:50 - mmengine - INFO - Epoch(train) [89][880/940] lr: 1.0000e-04 eta: 3:11:38 time: 1.1009 data_time: 0.0139 memory: 15768 grad_norm: 4.7374 loss: 0.9129 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9129 2023/07/25 17:05:12 - mmengine - INFO - Epoch(train) [89][900/940] lr: 1.0000e-04 eta: 3:11:16 time: 1.1013 data_time: 0.0135 memory: 15768 grad_norm: 4.7107 loss: 0.7158 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7158 2023/07/25 17:05:34 - mmengine - INFO - Epoch(train) [89][920/940] lr: 1.0000e-04 eta: 3:10:54 time: 1.0985 data_time: 0.0139 memory: 15768 grad_norm: 4.6601 loss: 0.7530 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7530 2023/07/25 17:05:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 17:05:55 - mmengine - INFO - Epoch(train) [89][940/940] lr: 1.0000e-04 eta: 3:10:32 time: 1.0530 data_time: 0.0135 memory: 15768 grad_norm: 5.0214 loss: 0.9366 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9366 2023/07/25 17:06:05 - mmengine - INFO - Epoch(val) [89][20/78] eta: 0:00:28 time: 0.4928 data_time: 0.3352 memory: 2147 2023/07/25 17:06:12 - mmengine - INFO - Epoch(val) [89][40/78] eta: 0:00:16 time: 0.3584 data_time: 0.2013 memory: 2147 2023/07/25 17:06:22 - mmengine - INFO - Epoch(val) [89][60/78] eta: 0:00:07 time: 0.4573 data_time: 0.3000 memory: 2147 2023/07/25 17:06:31 - mmengine - INFO - Epoch(val) [89][78/78] acc/top1: 0.7117 acc/top5: 0.8993 acc/mean1: 0.7116 data_time: 0.2508 time: 0.4052 2023/07/25 17:06:57 - mmengine - INFO - Epoch(train) [90][ 20/940] lr: 1.0000e-04 eta: 3:10:10 time: 1.3132 data_time: 0.1461 memory: 15768 grad_norm: 4.5571 loss: 0.7013 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7013 2023/07/25 17:07:20 - mmengine - INFO - Epoch(train) [90][ 40/940] lr: 1.0000e-04 eta: 3:09:48 time: 1.1023 data_time: 0.0135 memory: 15768 grad_norm: 4.6055 loss: 0.8283 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8283 2023/07/25 17:07:42 - mmengine - INFO - Epoch(train) [90][ 60/940] lr: 1.0000e-04 eta: 3:09:26 time: 1.0993 data_time: 0.0132 memory: 15768 grad_norm: 4.6020 loss: 0.7651 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7651 2023/07/25 17:08:04 - mmengine - INFO - Epoch(train) [90][ 80/940] lr: 1.0000e-04 eta: 3:09:04 time: 1.1020 data_time: 0.0136 memory: 15768 grad_norm: 4.7218 loss: 0.9005 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9005 2023/07/25 17:08:26 - mmengine - INFO - Epoch(train) [90][100/940] lr: 1.0000e-04 eta: 3:08:42 time: 1.1005 data_time: 0.0135 memory: 15768 grad_norm: 4.7931 loss: 0.8463 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8463 2023/07/25 17:08:48 - mmengine - INFO - Epoch(train) [90][120/940] lr: 1.0000e-04 eta: 3:08:19 time: 1.1053 data_time: 0.0137 memory: 15768 grad_norm: 4.6018 loss: 0.7576 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7576 2023/07/25 17:09:10 - mmengine - INFO - Epoch(train) [90][140/940] lr: 1.0000e-04 eta: 3:07:57 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 4.6346 loss: 0.7850 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7850 2023/07/25 17:09:32 - mmengine - INFO - Epoch(train) [90][160/940] lr: 1.0000e-04 eta: 3:07:35 time: 1.1008 data_time: 0.0132 memory: 15768 grad_norm: 4.6485 loss: 0.9656 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9656 2023/07/25 17:09:54 - mmengine - INFO - Epoch(train) [90][180/940] lr: 1.0000e-04 eta: 3:07:13 time: 1.1020 data_time: 0.0141 memory: 15768 grad_norm: 4.6141 loss: 0.7817 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7817 2023/07/25 17:10:16 - mmengine - INFO - Epoch(train) [90][200/940] lr: 1.0000e-04 eta: 3:06:51 time: 1.1041 data_time: 0.0131 memory: 15768 grad_norm: 4.6855 loss: 0.7533 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7533 2023/07/25 17:10:38 - mmengine - INFO - Epoch(train) [90][220/940] lr: 1.0000e-04 eta: 3:06:29 time: 1.1007 data_time: 0.0137 memory: 15768 grad_norm: 4.6834 loss: 0.7875 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7875 2023/07/25 17:11:00 - mmengine - INFO - Epoch(train) [90][240/940] lr: 1.0000e-04 eta: 3:06:07 time: 1.1027 data_time: 0.0140 memory: 15768 grad_norm: 4.8136 loss: 0.7390 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7390 2023/07/25 17:11:22 - mmengine - INFO - Epoch(train) [90][260/940] lr: 1.0000e-04 eta: 3:05:45 time: 1.1027 data_time: 0.0142 memory: 15768 grad_norm: 4.6654 loss: 0.7008 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7008 2023/07/25 17:11:44 - mmengine - INFO - Epoch(train) [90][280/940] lr: 1.0000e-04 eta: 3:05:22 time: 1.1026 data_time: 0.0136 memory: 15768 grad_norm: 4.7026 loss: 0.8447 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8447 2023/07/25 17:12:06 - mmengine - INFO - Epoch(train) [90][300/940] lr: 1.0000e-04 eta: 3:05:00 time: 1.1009 data_time: 0.0142 memory: 15768 grad_norm: 4.6636 loss: 0.6614 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6614 2023/07/25 17:12:28 - mmengine - INFO - Epoch(train) [90][320/940] lr: 1.0000e-04 eta: 3:04:38 time: 1.0999 data_time: 0.0140 memory: 15768 grad_norm: 4.7361 loss: 0.6881 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6881 2023/07/25 17:12:50 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 17:12:50 - mmengine - INFO - Epoch(train) [90][340/940] lr: 1.0000e-04 eta: 3:04:16 time: 1.1019 data_time: 0.0137 memory: 15768 grad_norm: 4.6998 loss: 0.7719 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7719 2023/07/25 17:13:12 - mmengine - INFO - Epoch(train) [90][360/940] lr: 1.0000e-04 eta: 3:03:54 time: 1.1059 data_time: 0.0134 memory: 15768 grad_norm: 4.7428 loss: 0.7072 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7072 2023/07/25 17:13:34 - mmengine - INFO - Epoch(train) [90][380/940] lr: 1.0000e-04 eta: 3:03:32 time: 1.1013 data_time: 0.0139 memory: 15768 grad_norm: 4.7080 loss: 0.7130 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7130 2023/07/25 17:13:56 - mmengine - INFO - Epoch(train) [90][400/940] lr: 1.0000e-04 eta: 3:03:10 time: 1.1014 data_time: 0.0138 memory: 15768 grad_norm: 4.7399 loss: 0.8486 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8486 2023/07/25 17:14:18 - mmengine - INFO - Epoch(train) [90][420/940] lr: 1.0000e-04 eta: 3:02:48 time: 1.1009 data_time: 0.0140 memory: 15768 grad_norm: 4.6765 loss: 0.8131 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8131 2023/07/25 17:14:40 - mmengine - INFO - Epoch(train) [90][440/940] lr: 1.0000e-04 eta: 3:02:26 time: 1.1048 data_time: 0.0140 memory: 15768 grad_norm: 4.8407 loss: 0.8747 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8747 2023/07/25 17:15:02 - mmengine - INFO - Epoch(train) [90][460/940] lr: 1.0000e-04 eta: 3:02:03 time: 1.1021 data_time: 0.0143 memory: 15768 grad_norm: 4.7862 loss: 0.8283 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8283 2023/07/25 17:15:24 - mmengine - INFO - Epoch(train) [90][480/940] lr: 1.0000e-04 eta: 3:01:41 time: 1.1006 data_time: 0.0144 memory: 15768 grad_norm: 4.6526 loss: 0.7055 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7055 2023/07/25 17:15:47 - mmengine - INFO - Epoch(train) [90][500/940] lr: 1.0000e-04 eta: 3:01:19 time: 1.1034 data_time: 0.0140 memory: 15768 grad_norm: 4.6650 loss: 0.8495 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8495 2023/07/25 17:16:09 - mmengine - INFO - Epoch(train) [90][520/940] lr: 1.0000e-04 eta: 3:00:57 time: 1.1028 data_time: 0.0141 memory: 15768 grad_norm: 4.6809 loss: 0.7798 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7798 2023/07/25 17:16:31 - mmengine - INFO - Epoch(train) [90][540/940] lr: 1.0000e-04 eta: 3:00:35 time: 1.1016 data_time: 0.0138 memory: 15768 grad_norm: 4.5716 loss: 0.8001 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8001 2023/07/25 17:16:53 - mmengine - INFO - Epoch(train) [90][560/940] lr: 1.0000e-04 eta: 3:00:13 time: 1.1015 data_time: 0.0139 memory: 15768 grad_norm: 4.6697 loss: 0.6597 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6597 2023/07/25 17:17:15 - mmengine - INFO - Epoch(train) [90][580/940] lr: 1.0000e-04 eta: 2:59:51 time: 1.1013 data_time: 0.0139 memory: 15768 grad_norm: 4.7488 loss: 0.7925 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7925 2023/07/25 17:17:37 - mmengine - INFO - Epoch(train) [90][600/940] lr: 1.0000e-04 eta: 2:59:29 time: 1.1000 data_time: 0.0142 memory: 15768 grad_norm: 4.8320 loss: 0.7889 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7889 2023/07/25 17:17:59 - mmengine - INFO - Epoch(train) [90][620/940] lr: 1.0000e-04 eta: 2:59:06 time: 1.1032 data_time: 0.0141 memory: 15768 grad_norm: 4.6516 loss: 0.7394 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7394 2023/07/25 17:18:21 - mmengine - INFO - Epoch(train) [90][640/940] lr: 1.0000e-04 eta: 2:58:44 time: 1.1006 data_time: 0.0142 memory: 15768 grad_norm: 4.6231 loss: 0.7247 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7247 2023/07/25 17:18:43 - mmengine - INFO - Epoch(train) [90][660/940] lr: 1.0000e-04 eta: 2:58:22 time: 1.1023 data_time: 0.0141 memory: 15768 grad_norm: 4.7580 loss: 0.8414 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8414 2023/07/25 17:19:05 - mmengine - INFO - Epoch(train) [90][680/940] lr: 1.0000e-04 eta: 2:58:00 time: 1.1027 data_time: 0.0135 memory: 15768 grad_norm: 4.7075 loss: 0.7876 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7876 2023/07/25 17:19:27 - mmengine - INFO - Epoch(train) [90][700/940] lr: 1.0000e-04 eta: 2:57:38 time: 1.0983 data_time: 0.0141 memory: 15768 grad_norm: 4.6783 loss: 0.8254 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8254 2023/07/25 17:19:49 - mmengine - INFO - Epoch(train) [90][720/940] lr: 1.0000e-04 eta: 2:57:16 time: 1.1055 data_time: 0.0140 memory: 15768 grad_norm: 4.6787 loss: 0.7782 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7782 2023/07/25 17:20:11 - mmengine - INFO - Epoch(train) [90][740/940] lr: 1.0000e-04 eta: 2:56:54 time: 1.1010 data_time: 0.0140 memory: 15768 grad_norm: 4.8247 loss: 0.8458 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8458 2023/07/25 17:20:33 - mmengine - INFO - Epoch(train) [90][760/940] lr: 1.0000e-04 eta: 2:56:32 time: 1.1022 data_time: 0.0141 memory: 15768 grad_norm: 4.7414 loss: 0.7432 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7432 2023/07/25 17:20:55 - mmengine - INFO - Epoch(train) [90][780/940] lr: 1.0000e-04 eta: 2:56:09 time: 1.1013 data_time: 0.0139 memory: 15768 grad_norm: 4.6050 loss: 0.7266 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7266 2023/07/25 17:21:17 - mmengine - INFO - Epoch(train) [90][800/940] lr: 1.0000e-04 eta: 2:55:47 time: 1.0996 data_time: 0.0137 memory: 15768 grad_norm: 4.6966 loss: 0.7141 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7141 2023/07/25 17:21:39 - mmengine - INFO - Epoch(train) [90][820/940] lr: 1.0000e-04 eta: 2:55:25 time: 1.1032 data_time: 0.0141 memory: 15768 grad_norm: 4.7455 loss: 0.7455 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7455 2023/07/25 17:22:01 - mmengine - INFO - Epoch(train) [90][840/940] lr: 1.0000e-04 eta: 2:55:03 time: 1.0992 data_time: 0.0138 memory: 15768 grad_norm: 4.7647 loss: 0.8275 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8275 2023/07/25 17:22:23 - mmengine - INFO - Epoch(train) [90][860/940] lr: 1.0000e-04 eta: 2:54:41 time: 1.0986 data_time: 0.0140 memory: 15768 grad_norm: 4.6730 loss: 0.7255 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7255 2023/07/25 17:22:45 - mmengine - INFO - Epoch(train) [90][880/940] lr: 1.0000e-04 eta: 2:54:19 time: 1.1007 data_time: 0.0143 memory: 15768 grad_norm: 4.7121 loss: 0.7038 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7038 2023/07/25 17:23:07 - mmengine - INFO - Epoch(train) [90][900/940] lr: 1.0000e-04 eta: 2:53:57 time: 1.1035 data_time: 0.0135 memory: 15768 grad_norm: 4.8199 loss: 0.8655 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8655 2023/07/25 17:23:29 - mmengine - INFO - Epoch(train) [90][920/940] lr: 1.0000e-04 eta: 2:53:35 time: 1.0988 data_time: 0.0139 memory: 15768 grad_norm: 4.6942 loss: 0.7052 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7052 2023/07/25 17:23:50 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 17:23:50 - mmengine - INFO - Epoch(train) [90][940/940] lr: 1.0000e-04 eta: 2:53:12 time: 1.0558 data_time: 0.0127 memory: 15768 grad_norm: 4.9722 loss: 0.8202 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8202 2023/07/25 17:23:50 - mmengine - INFO - Saving checkpoint at 90 epochs 2023/07/25 17:24:01 - mmengine - INFO - Epoch(val) [90][20/78] eta: 0:00:27 time: 0.4804 data_time: 0.3234 memory: 2147 2023/07/25 17:24:08 - mmengine - INFO - Epoch(val) [90][40/78] eta: 0:00:15 time: 0.3606 data_time: 0.2032 memory: 2147 2023/07/25 17:24:17 - mmengine - INFO - Epoch(val) [90][60/78] eta: 0:00:07 time: 0.4452 data_time: 0.2884 memory: 2147 2023/07/25 17:24:27 - mmengine - INFO - Epoch(val) [90][78/78] acc/top1: 0.7106 acc/top5: 0.8991 acc/mean1: 0.7105 data_time: 0.2424 time: 0.3966 2023/07/25 17:24:53 - mmengine - INFO - Epoch(train) [91][ 20/940] lr: 1.0000e-04 eta: 2:52:51 time: 1.2995 data_time: 0.1532 memory: 15768 grad_norm: 4.7163 loss: 0.8071 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8071 2023/07/25 17:25:15 - mmengine - INFO - Epoch(train) [91][ 40/940] lr: 1.0000e-04 eta: 2:52:29 time: 1.1003 data_time: 0.0128 memory: 15768 grad_norm: 4.5209 loss: 0.7528 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7528 2023/07/25 17:25:37 - mmengine - INFO - Epoch(train) [91][ 60/940] lr: 1.0000e-04 eta: 2:52:06 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 4.5608 loss: 0.7444 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7444 2023/07/25 17:25:59 - mmengine - INFO - Epoch(train) [91][ 80/940] lr: 1.0000e-04 eta: 2:51:44 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 4.5447 loss: 0.6141 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6141 2023/07/25 17:26:21 - mmengine - INFO - Epoch(train) [91][100/940] lr: 1.0000e-04 eta: 2:51:22 time: 1.0997 data_time: 0.0132 memory: 15768 grad_norm: 4.7508 loss: 0.9382 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9382 2023/07/25 17:26:43 - mmengine - INFO - Epoch(train) [91][120/940] lr: 1.0000e-04 eta: 2:51:00 time: 1.1024 data_time: 0.0131 memory: 15768 grad_norm: 4.7505 loss: 0.8876 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.8876 2023/07/25 17:27:05 - mmengine - INFO - Epoch(train) [91][140/940] lr: 1.0000e-04 eta: 2:50:38 time: 1.1008 data_time: 0.0129 memory: 15768 grad_norm: 4.8154 loss: 0.7736 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7736 2023/07/25 17:27:27 - mmengine - INFO - Epoch(train) [91][160/940] lr: 1.0000e-04 eta: 2:50:16 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 4.7085 loss: 0.6862 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6862 2023/07/25 17:27:49 - mmengine - INFO - Epoch(train) [91][180/940] lr: 1.0000e-04 eta: 2:49:54 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 4.6222 loss: 0.7298 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7298 2023/07/25 17:28:11 - mmengine - INFO - Epoch(train) [91][200/940] lr: 1.0000e-04 eta: 2:49:32 time: 1.1006 data_time: 0.0130 memory: 15768 grad_norm: 4.6319 loss: 0.7180 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7180 2023/07/25 17:28:33 - mmengine - INFO - Epoch(train) [91][220/940] lr: 1.0000e-04 eta: 2:49:09 time: 1.0980 data_time: 0.0130 memory: 15768 grad_norm: 4.7000 loss: 0.7459 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7459 2023/07/25 17:28:55 - mmengine - INFO - Epoch(train) [91][240/940] lr: 1.0000e-04 eta: 2:48:47 time: 1.1030 data_time: 0.0132 memory: 15768 grad_norm: 4.6287 loss: 0.8250 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8250 2023/07/25 17:29:17 - mmengine - INFO - Epoch(train) [91][260/940] lr: 1.0000e-04 eta: 2:48:25 time: 1.1072 data_time: 0.0132 memory: 15768 grad_norm: 4.6087 loss: 0.8440 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8440 2023/07/25 17:29:39 - mmengine - INFO - Epoch(train) [91][280/940] lr: 1.0000e-04 eta: 2:48:03 time: 1.1003 data_time: 0.0131 memory: 15768 grad_norm: 4.7573 loss: 0.8725 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8725 2023/07/25 17:30:01 - mmengine - INFO - Epoch(train) [91][300/940] lr: 1.0000e-04 eta: 2:47:41 time: 1.1020 data_time: 0.0129 memory: 15768 grad_norm: 4.8380 loss: 0.7637 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7637 2023/07/25 17:30:23 - mmengine - INFO - Epoch(train) [91][320/940] lr: 1.0000e-04 eta: 2:47:19 time: 1.1001 data_time: 0.0129 memory: 15768 grad_norm: 4.7155 loss: 0.6772 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6772 2023/07/25 17:30:45 - mmengine - INFO - Epoch(train) [91][340/940] lr: 1.0000e-04 eta: 2:46:57 time: 1.1002 data_time: 0.0130 memory: 15768 grad_norm: 4.6342 loss: 0.7232 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7232 2023/07/25 17:31:07 - mmengine - INFO - Epoch(train) [91][360/940] lr: 1.0000e-04 eta: 2:46:35 time: 1.1004 data_time: 0.0132 memory: 15768 grad_norm: 4.7449 loss: 0.7619 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7619 2023/07/25 17:31:29 - mmengine - INFO - Epoch(train) [91][380/940] lr: 1.0000e-04 eta: 2:46:12 time: 1.1025 data_time: 0.0135 memory: 15768 grad_norm: 4.7254 loss: 0.8026 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8026 2023/07/25 17:31:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 17:31:51 - mmengine - INFO - Epoch(train) [91][400/940] lr: 1.0000e-04 eta: 2:45:50 time: 1.1018 data_time: 0.0134 memory: 15768 grad_norm: 4.7585 loss: 0.7531 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7531 2023/07/25 17:32:13 - mmengine - INFO - Epoch(train) [91][420/940] lr: 1.0000e-04 eta: 2:45:28 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 4.7588 loss: 0.7381 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7381 2023/07/25 17:32:35 - mmengine - INFO - Epoch(train) [91][440/940] lr: 1.0000e-04 eta: 2:45:06 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 4.7613 loss: 0.8442 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8442 2023/07/25 17:32:57 - mmengine - INFO - Epoch(train) [91][460/940] lr: 1.0000e-04 eta: 2:44:44 time: 1.1055 data_time: 0.0130 memory: 15768 grad_norm: 4.7143 loss: 0.7980 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7980 2023/07/25 17:33:19 - mmengine - INFO - Epoch(train) [91][480/940] lr: 1.0000e-04 eta: 2:44:22 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.7920 loss: 0.9269 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9269 2023/07/25 17:33:41 - mmengine - INFO - Epoch(train) [91][500/940] lr: 1.0000e-04 eta: 2:44:00 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 4.6970 loss: 0.8813 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8813 2023/07/25 17:34:03 - mmengine - INFO - Epoch(train) [91][520/940] lr: 1.0000e-04 eta: 2:43:38 time: 1.1031 data_time: 0.0132 memory: 15768 grad_norm: 4.5962 loss: 0.7606 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7606 2023/07/25 17:34:25 - mmengine - INFO - Epoch(train) [91][540/940] lr: 1.0000e-04 eta: 2:43:15 time: 1.1048 data_time: 0.0135 memory: 15768 grad_norm: 4.8403 loss: 0.6857 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6857 2023/07/25 17:34:47 - mmengine - INFO - Epoch(train) [91][560/940] lr: 1.0000e-04 eta: 2:42:53 time: 1.1038 data_time: 0.0128 memory: 15768 grad_norm: 4.6354 loss: 0.8003 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8003 2023/07/25 17:35:09 - mmengine - INFO - Epoch(train) [91][580/940] lr: 1.0000e-04 eta: 2:42:31 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 4.7595 loss: 0.7904 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7904 2023/07/25 17:35:31 - mmengine - INFO - Epoch(train) [91][600/940] lr: 1.0000e-04 eta: 2:42:09 time: 1.1008 data_time: 0.0128 memory: 15768 grad_norm: 4.8352 loss: 0.7284 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7284 2023/07/25 17:35:53 - mmengine - INFO - Epoch(train) [91][620/940] lr: 1.0000e-04 eta: 2:41:47 time: 1.0994 data_time: 0.0131 memory: 15768 grad_norm: 4.7833 loss: 0.8037 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8037 2023/07/25 17:36:16 - mmengine - INFO - Epoch(train) [91][640/940] lr: 1.0000e-04 eta: 2:41:25 time: 1.1010 data_time: 0.0130 memory: 15768 grad_norm: 4.7598 loss: 0.8240 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8240 2023/07/25 17:36:38 - mmengine - INFO - Epoch(train) [91][660/940] lr: 1.0000e-04 eta: 2:41:03 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 4.6777 loss: 0.8108 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8108 2023/07/25 17:37:00 - mmengine - INFO - Epoch(train) [91][680/940] lr: 1.0000e-04 eta: 2:40:41 time: 1.0991 data_time: 0.0131 memory: 15768 grad_norm: 4.6989 loss: 0.7588 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7588 2023/07/25 17:37:22 - mmengine - INFO - Epoch(train) [91][700/940] lr: 1.0000e-04 eta: 2:40:19 time: 1.1044 data_time: 0.0130 memory: 15768 grad_norm: 4.7036 loss: 0.8074 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8074 2023/07/25 17:37:45 - mmengine - INFO - Epoch(train) [91][720/940] lr: 1.0000e-04 eta: 2:39:56 time: 1.1532 data_time: 0.0130 memory: 15768 grad_norm: 4.6753 loss: 0.8889 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8889 2023/07/25 17:38:07 - mmengine - INFO - Epoch(train) [91][740/940] lr: 1.0000e-04 eta: 2:39:34 time: 1.1305 data_time: 0.0130 memory: 15768 grad_norm: 4.7061 loss: 0.8828 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8828 2023/07/25 17:38:29 - mmengine - INFO - Epoch(train) [91][760/940] lr: 1.0000e-04 eta: 2:39:12 time: 1.1002 data_time: 0.0132 memory: 15768 grad_norm: 4.6779 loss: 0.9062 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9062 2023/07/25 17:38:51 - mmengine - INFO - Epoch(train) [91][780/940] lr: 1.0000e-04 eta: 2:38:50 time: 1.0990 data_time: 0.0130 memory: 15768 grad_norm: 4.7681 loss: 0.8337 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8337 2023/07/25 17:39:13 - mmengine - INFO - Epoch(train) [91][800/940] lr: 1.0000e-04 eta: 2:38:28 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.6973 loss: 0.7380 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7380 2023/07/25 17:39:35 - mmengine - INFO - Epoch(train) [91][820/940] lr: 1.0000e-04 eta: 2:38:06 time: 1.1052 data_time: 0.0127 memory: 15768 grad_norm: 4.6126 loss: 0.7922 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7922 2023/07/25 17:39:57 - mmengine - INFO - Epoch(train) [91][840/940] lr: 1.0000e-04 eta: 2:37:44 time: 1.0992 data_time: 0.0131 memory: 15768 grad_norm: 4.7639 loss: 0.8201 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8201 2023/07/25 17:40:19 - mmengine - INFO - Epoch(train) [91][860/940] lr: 1.0000e-04 eta: 2:37:22 time: 1.1053 data_time: 0.0135 memory: 15768 grad_norm: 4.7911 loss: 0.7649 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7649 2023/07/25 17:40:41 - mmengine - INFO - Epoch(train) [91][880/940] lr: 1.0000e-04 eta: 2:37:00 time: 1.0981 data_time: 0.0134 memory: 15768 grad_norm: 4.8675 loss: 0.8510 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8510 2023/07/25 17:41:03 - mmengine - INFO - Epoch(train) [91][900/940] lr: 1.0000e-04 eta: 2:36:37 time: 1.0994 data_time: 0.0134 memory: 15768 grad_norm: 4.6538 loss: 0.7653 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7653 2023/07/25 17:41:25 - mmengine - INFO - Epoch(train) [91][920/940] lr: 1.0000e-04 eta: 2:36:15 time: 1.0992 data_time: 0.0131 memory: 15768 grad_norm: 4.6638 loss: 0.8562 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8562 2023/07/25 17:41:46 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 17:41:46 - mmengine - INFO - Epoch(train) [91][940/940] lr: 1.0000e-04 eta: 2:35:53 time: 1.0540 data_time: 0.0125 memory: 15768 grad_norm: 5.0985 loss: 0.8565 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.8565 2023/07/25 17:41:56 - mmengine - INFO - Epoch(val) [91][20/78] eta: 0:00:28 time: 0.4941 data_time: 0.3370 memory: 2147 2023/07/25 17:42:03 - mmengine - INFO - Epoch(val) [91][40/78] eta: 0:00:16 time: 0.3496 data_time: 0.1925 memory: 2147 2023/07/25 17:42:12 - mmengine - INFO - Epoch(val) [91][60/78] eta: 0:00:07 time: 0.4364 data_time: 0.2801 memory: 2147 2023/07/25 17:42:23 - mmengine - INFO - Epoch(val) [91][78/78] acc/top1: 0.7120 acc/top5: 0.8989 acc/mean1: 0.7119 data_time: 0.2444 time: 0.3984 2023/07/25 17:42:48 - mmengine - INFO - Epoch(train) [92][ 20/940] lr: 1.0000e-04 eta: 2:35:31 time: 1.2844 data_time: 0.1638 memory: 15768 grad_norm: 4.7152 loss: 0.7566 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7566 2023/07/25 17:43:10 - mmengine - INFO - Epoch(train) [92][ 40/940] lr: 1.0000e-04 eta: 2:35:09 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 4.7364 loss: 0.7807 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7807 2023/07/25 17:43:32 - mmengine - INFO - Epoch(train) [92][ 60/940] lr: 1.0000e-04 eta: 2:34:47 time: 1.0997 data_time: 0.0129 memory: 15768 grad_norm: 4.7496 loss: 0.8231 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8231 2023/07/25 17:43:54 - mmengine - INFO - Epoch(train) [92][ 80/940] lr: 1.0000e-04 eta: 2:34:25 time: 1.0995 data_time: 0.0134 memory: 15768 grad_norm: 4.5025 loss: 0.7880 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7880 2023/07/25 17:44:16 - mmengine - INFO - Epoch(train) [92][100/940] lr: 1.0000e-04 eta: 2:34:03 time: 1.0993 data_time: 0.0129 memory: 15768 grad_norm: 4.8265 loss: 0.9313 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9313 2023/07/25 17:44:38 - mmengine - INFO - Epoch(train) [92][120/940] lr: 1.0000e-04 eta: 2:33:41 time: 1.0983 data_time: 0.0130 memory: 15768 grad_norm: 4.6650 loss: 0.8669 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8669 2023/07/25 17:45:00 - mmengine - INFO - Epoch(train) [92][140/940] lr: 1.0000e-04 eta: 2:33:19 time: 1.0999 data_time: 0.0131 memory: 15768 grad_norm: 4.8622 loss: 0.8419 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8419 2023/07/25 17:45:22 - mmengine - INFO - Epoch(train) [92][160/940] lr: 1.0000e-04 eta: 2:32:56 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 4.6657 loss: 0.7375 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7375 2023/07/25 17:45:44 - mmengine - INFO - Epoch(train) [92][180/940] lr: 1.0000e-04 eta: 2:32:34 time: 1.0988 data_time: 0.0131 memory: 15768 grad_norm: 4.7704 loss: 0.9191 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9191 2023/07/25 17:46:06 - mmengine - INFO - Epoch(train) [92][200/940] lr: 1.0000e-04 eta: 2:32:12 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 4.6921 loss: 0.7615 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7615 2023/07/25 17:46:28 - mmengine - INFO - Epoch(train) [92][220/940] lr: 1.0000e-04 eta: 2:31:50 time: 1.0979 data_time: 0.0130 memory: 15768 grad_norm: 4.7569 loss: 0.7455 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7455 2023/07/25 17:46:50 - mmengine - INFO - Epoch(train) [92][240/940] lr: 1.0000e-04 eta: 2:31:28 time: 1.1004 data_time: 0.0130 memory: 15768 grad_norm: 4.6647 loss: 0.8427 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8427 2023/07/25 17:47:12 - mmengine - INFO - Epoch(train) [92][260/940] lr: 1.0000e-04 eta: 2:31:06 time: 1.1016 data_time: 0.0132 memory: 15768 grad_norm: 4.6522 loss: 0.6896 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6896 2023/07/25 17:47:34 - mmengine - INFO - Epoch(train) [92][280/940] lr: 1.0000e-04 eta: 2:30:44 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 4.7987 loss: 0.7540 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7540 2023/07/25 17:47:56 - mmengine - INFO - Epoch(train) [92][300/940] lr: 1.0000e-04 eta: 2:30:22 time: 1.1011 data_time: 0.0131 memory: 15768 grad_norm: 4.7472 loss: 0.7067 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7067 2023/07/25 17:48:18 - mmengine - INFO - Epoch(train) [92][320/940] lr: 1.0000e-04 eta: 2:29:59 time: 1.0987 data_time: 0.0128 memory: 15768 grad_norm: 4.6859 loss: 0.8985 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8985 2023/07/25 17:48:40 - mmengine - INFO - Epoch(train) [92][340/940] lr: 1.0000e-04 eta: 2:29:37 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 4.6203 loss: 0.7559 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7559 2023/07/25 17:49:02 - mmengine - INFO - Epoch(train) [92][360/940] lr: 1.0000e-04 eta: 2:29:15 time: 1.1008 data_time: 0.0131 memory: 15768 grad_norm: 4.7217 loss: 0.8458 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8458 2023/07/25 17:49:24 - mmengine - INFO - Epoch(train) [92][380/940] lr: 1.0000e-04 eta: 2:28:53 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 4.6759 loss: 0.6472 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6472 2023/07/25 17:49:46 - mmengine - INFO - Epoch(train) [92][400/940] lr: 1.0000e-04 eta: 2:28:31 time: 1.0981 data_time: 0.0131 memory: 15768 grad_norm: 4.6963 loss: 0.7759 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7759 2023/07/25 17:50:08 - mmengine - INFO - Epoch(train) [92][420/940] lr: 1.0000e-04 eta: 2:28:09 time: 1.1027 data_time: 0.0132 memory: 15768 grad_norm: 4.6005 loss: 0.7601 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7601 2023/07/25 17:50:30 - mmengine - INFO - Epoch(train) [92][440/940] lr: 1.0000e-04 eta: 2:27:47 time: 1.1007 data_time: 0.0132 memory: 15768 grad_norm: 4.6349 loss: 0.7426 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7426 2023/07/25 17:50:52 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 17:50:52 - mmengine - INFO - Epoch(train) [92][460/940] lr: 1.0000e-04 eta: 2:27:25 time: 1.1020 data_time: 0.0131 memory: 15768 grad_norm: 4.7713 loss: 0.7188 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7188 2023/07/25 17:51:16 - mmengine - INFO - Epoch(train) [92][480/940] lr: 1.0000e-04 eta: 2:27:03 time: 1.1562 data_time: 0.0132 memory: 15768 grad_norm: 4.6717 loss: 0.7655 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7655 2023/07/25 17:51:39 - mmengine - INFO - Epoch(train) [92][500/940] lr: 1.0000e-04 eta: 2:26:41 time: 1.1607 data_time: 0.0129 memory: 15768 grad_norm: 4.7357 loss: 0.8091 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8091 2023/07/25 17:52:02 - mmengine - INFO - Epoch(train) [92][520/940] lr: 1.0000e-04 eta: 2:26:19 time: 1.1622 data_time: 0.0130 memory: 15768 grad_norm: 4.6482 loss: 0.8320 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8320 2023/07/25 17:52:25 - mmengine - INFO - Epoch(train) [92][540/940] lr: 1.0000e-04 eta: 2:25:57 time: 1.1564 data_time: 0.0131 memory: 15768 grad_norm: 4.7206 loss: 0.7607 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7607 2023/07/25 17:52:48 - mmengine - INFO - Epoch(train) [92][560/940] lr: 1.0000e-04 eta: 2:25:35 time: 1.1600 data_time: 0.0131 memory: 15768 grad_norm: 4.6680 loss: 0.7471 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7471 2023/07/25 17:53:12 - mmengine - INFO - Epoch(train) [92][580/940] lr: 1.0000e-04 eta: 2:25:13 time: 1.1618 data_time: 0.0131 memory: 15768 grad_norm: 4.7212 loss: 0.8829 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8829 2023/07/25 17:53:34 - mmengine - INFO - Epoch(train) [92][600/940] lr: 1.0000e-04 eta: 2:24:50 time: 1.1373 data_time: 0.0131 memory: 15768 grad_norm: 4.5961 loss: 0.7322 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7322 2023/07/25 17:53:56 - mmengine - INFO - Epoch(train) [92][620/940] lr: 1.0000e-04 eta: 2:24:28 time: 1.1005 data_time: 0.0134 memory: 15768 grad_norm: 4.7427 loss: 0.9458 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9458 2023/07/25 17:54:18 - mmengine - INFO - Epoch(train) [92][640/940] lr: 1.0000e-04 eta: 2:24:06 time: 1.1022 data_time: 0.0136 memory: 15768 grad_norm: 4.6597 loss: 0.8286 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8286 2023/07/25 17:54:40 - mmengine - INFO - Epoch(train) [92][660/940] lr: 1.0000e-04 eta: 2:23:44 time: 1.1000 data_time: 0.0132 memory: 15768 grad_norm: 4.6907 loss: 0.6657 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6657 2023/07/25 17:55:02 - mmengine - INFO - Epoch(train) [92][680/940] lr: 1.0000e-04 eta: 2:23:22 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 4.7782 loss: 0.7543 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7543 2023/07/25 17:55:24 - mmengine - INFO - Epoch(train) [92][700/940] lr: 1.0000e-04 eta: 2:23:00 time: 1.0997 data_time: 0.0134 memory: 15768 grad_norm: 4.8119 loss: 0.7597 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7597 2023/07/25 17:55:46 - mmengine - INFO - Epoch(train) [92][720/940] lr: 1.0000e-04 eta: 2:22:38 time: 1.1006 data_time: 0.0131 memory: 15768 grad_norm: 4.7078 loss: 0.8530 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8530 2023/07/25 17:56:08 - mmengine - INFO - Epoch(train) [92][740/940] lr: 1.0000e-04 eta: 2:22:16 time: 1.0982 data_time: 0.0131 memory: 15768 grad_norm: 4.7219 loss: 0.7705 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7705 2023/07/25 17:56:31 - mmengine - INFO - Epoch(train) [92][760/940] lr: 1.0000e-04 eta: 2:21:54 time: 1.1059 data_time: 0.0133 memory: 15768 grad_norm: 4.6637 loss: 0.7532 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7532 2023/07/25 17:56:53 - mmengine - INFO - Epoch(train) [92][780/940] lr: 1.0000e-04 eta: 2:21:31 time: 1.1016 data_time: 0.0126 memory: 15768 grad_norm: 4.6463 loss: 0.7342 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7342 2023/07/25 17:57:15 - mmengine - INFO - Epoch(train) [92][800/940] lr: 1.0000e-04 eta: 2:21:09 time: 1.1026 data_time: 0.0132 memory: 15768 grad_norm: 4.7317 loss: 0.7923 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7923 2023/07/25 17:57:37 - mmengine - INFO - Epoch(train) [92][820/940] lr: 1.0000e-04 eta: 2:20:47 time: 1.1026 data_time: 0.0130 memory: 15768 grad_norm: 4.7045 loss: 0.7155 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7155 2023/07/25 17:57:59 - mmengine - INFO - Epoch(train) [92][840/940] lr: 1.0000e-04 eta: 2:20:25 time: 1.1031 data_time: 0.0127 memory: 15768 grad_norm: 4.7811 loss: 0.9483 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9483 2023/07/25 17:58:21 - mmengine - INFO - Epoch(train) [92][860/940] lr: 1.0000e-04 eta: 2:20:03 time: 1.0993 data_time: 0.0129 memory: 15768 grad_norm: 4.6090 loss: 0.8815 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8815 2023/07/25 17:58:43 - mmengine - INFO - Epoch(train) [92][880/940] lr: 1.0000e-04 eta: 2:19:41 time: 1.0978 data_time: 0.0131 memory: 15768 grad_norm: 4.6393 loss: 0.7609 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7609 2023/07/25 17:59:05 - mmengine - INFO - Epoch(train) [92][900/940] lr: 1.0000e-04 eta: 2:19:19 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 4.6755 loss: 0.6659 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6659 2023/07/25 17:59:27 - mmengine - INFO - Epoch(train) [92][920/940] lr: 1.0000e-04 eta: 2:18:57 time: 1.0997 data_time: 0.0128 memory: 15768 grad_norm: 4.6658 loss: 0.7505 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7505 2023/07/25 17:59:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 17:59:48 - mmengine - INFO - Epoch(train) [92][940/940] lr: 1.0000e-04 eta: 2:18:34 time: 1.0542 data_time: 0.0126 memory: 15768 grad_norm: 5.1174 loss: 1.0252 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 1.0252 2023/07/25 17:59:58 - mmengine - INFO - Epoch(val) [92][20/78] eta: 0:00:28 time: 0.4855 data_time: 0.3282 memory: 2147 2023/07/25 18:00:04 - mmengine - INFO - Epoch(val) [92][40/78] eta: 0:00:15 time: 0.3395 data_time: 0.1828 memory: 2147 2023/07/25 18:00:13 - mmengine - INFO - Epoch(val) [92][60/78] eta: 0:00:07 time: 0.4393 data_time: 0.2826 memory: 2147 2023/07/25 18:00:24 - mmengine - INFO - Epoch(val) [92][78/78] acc/top1: 0.7109 acc/top5: 0.8987 acc/mean1: 0.7108 data_time: 0.2427 time: 0.3968 2023/07/25 18:00:50 - mmengine - INFO - Epoch(train) [93][ 20/940] lr: 1.0000e-04 eta: 2:18:13 time: 1.2947 data_time: 0.1500 memory: 15768 grad_norm: 4.5942 loss: 0.8390 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8390 2023/07/25 18:01:12 - mmengine - INFO - Epoch(train) [93][ 40/940] lr: 1.0000e-04 eta: 2:17:50 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 4.7770 loss: 0.7569 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7569 2023/07/25 18:01:34 - mmengine - INFO - Epoch(train) [93][ 60/940] lr: 1.0000e-04 eta: 2:17:28 time: 1.0996 data_time: 0.0126 memory: 15768 grad_norm: 4.6398 loss: 0.8728 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8728 2023/07/25 18:01:56 - mmengine - INFO - Epoch(train) [93][ 80/940] lr: 1.0000e-04 eta: 2:17:06 time: 1.1053 data_time: 0.0128 memory: 15768 grad_norm: 4.6919 loss: 0.8039 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8039 2023/07/25 18:02:18 - mmengine - INFO - Epoch(train) [93][100/940] lr: 1.0000e-04 eta: 2:16:44 time: 1.1028 data_time: 0.0145 memory: 15768 grad_norm: 4.6745 loss: 0.7126 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7126 2023/07/25 18:02:40 - mmengine - INFO - Epoch(train) [93][120/940] lr: 1.0000e-04 eta: 2:16:22 time: 1.1021 data_time: 0.0129 memory: 15768 grad_norm: 4.7303 loss: 0.8233 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8233 2023/07/25 18:03:02 - mmengine - INFO - Epoch(train) [93][140/940] lr: 1.0000e-04 eta: 2:16:00 time: 1.1008 data_time: 0.0132 memory: 15768 grad_norm: 4.6687 loss: 0.7318 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7318 2023/07/25 18:03:24 - mmengine - INFO - Epoch(train) [93][160/940] lr: 1.0000e-04 eta: 2:15:38 time: 1.0999 data_time: 0.0129 memory: 15768 grad_norm: 4.7240 loss: 0.7411 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7411 2023/07/25 18:03:46 - mmengine - INFO - Epoch(train) [93][180/940] lr: 1.0000e-04 eta: 2:15:16 time: 1.1019 data_time: 0.0129 memory: 15768 grad_norm: 4.7463 loss: 0.7906 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7906 2023/07/25 18:04:08 - mmengine - INFO - Epoch(train) [93][200/940] lr: 1.0000e-04 eta: 2:14:53 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 4.8312 loss: 0.6784 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6784 2023/07/25 18:04:30 - mmengine - INFO - Epoch(train) [93][220/940] lr: 1.0000e-04 eta: 2:14:31 time: 1.1001 data_time: 0.0130 memory: 15768 grad_norm: 4.6792 loss: 0.8110 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8110 2023/07/25 18:04:52 - mmengine - INFO - Epoch(train) [93][240/940] lr: 1.0000e-04 eta: 2:14:09 time: 1.0989 data_time: 0.0128 memory: 15768 grad_norm: 4.7792 loss: 0.8808 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8808 2023/07/25 18:05:14 - mmengine - INFO - Epoch(train) [93][260/940] lr: 1.0000e-04 eta: 2:13:47 time: 1.1045 data_time: 0.0129 memory: 15768 grad_norm: 4.7868 loss: 0.8428 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8428 2023/07/25 18:05:36 - mmengine - INFO - Epoch(train) [93][280/940] lr: 1.0000e-04 eta: 2:13:25 time: 1.1000 data_time: 0.0131 memory: 15768 grad_norm: 4.6433 loss: 0.9050 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9050 2023/07/25 18:05:58 - mmengine - INFO - Epoch(train) [93][300/940] lr: 1.0000e-04 eta: 2:13:03 time: 1.0998 data_time: 0.0129 memory: 15768 grad_norm: 4.6203 loss: 0.9099 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9099 2023/07/25 18:06:20 - mmengine - INFO - Epoch(train) [93][320/940] lr: 1.0000e-04 eta: 2:12:41 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 4.7575 loss: 0.7831 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7831 2023/07/25 18:06:42 - mmengine - INFO - Epoch(train) [93][340/940] lr: 1.0000e-04 eta: 2:12:19 time: 1.1037 data_time: 0.0129 memory: 15768 grad_norm: 4.6040 loss: 0.7747 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7747 2023/07/25 18:07:04 - mmengine - INFO - Epoch(train) [93][360/940] lr: 1.0000e-04 eta: 2:11:56 time: 1.0989 data_time: 0.0131 memory: 15768 grad_norm: 4.6747 loss: 0.6620 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6620 2023/07/25 18:07:26 - mmengine - INFO - Epoch(train) [93][380/940] lr: 1.0000e-04 eta: 2:11:34 time: 1.1022 data_time: 0.0129 memory: 15768 grad_norm: 4.6612 loss: 0.7460 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7460 2023/07/25 18:07:48 - mmengine - INFO - Epoch(train) [93][400/940] lr: 1.0000e-04 eta: 2:11:12 time: 1.1011 data_time: 0.0129 memory: 15768 grad_norm: 4.7855 loss: 0.8448 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8448 2023/07/25 18:08:10 - mmengine - INFO - Epoch(train) [93][420/940] lr: 1.0000e-04 eta: 2:10:50 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 4.7565 loss: 0.7647 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7647 2023/07/25 18:08:32 - mmengine - INFO - Epoch(train) [93][440/940] lr: 1.0000e-04 eta: 2:10:28 time: 1.1009 data_time: 0.0130 memory: 15768 grad_norm: 4.8474 loss: 0.6386 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6386 2023/07/25 18:08:54 - mmengine - INFO - Epoch(train) [93][460/940] lr: 1.0000e-04 eta: 2:10:06 time: 1.0991 data_time: 0.0129 memory: 15768 grad_norm: 4.6392 loss: 0.7016 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7016 2023/07/25 18:09:16 - mmengine - INFO - Epoch(train) [93][480/940] lr: 1.0000e-04 eta: 2:09:44 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 4.6795 loss: 0.7021 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7021 2023/07/25 18:09:38 - mmengine - INFO - Epoch(train) [93][500/940] lr: 1.0000e-04 eta: 2:09:22 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 4.6874 loss: 0.8145 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.8145 2023/07/25 18:10:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 18:10:00 - mmengine - INFO - Epoch(train) [93][520/940] lr: 1.0000e-04 eta: 2:08:59 time: 1.1033 data_time: 0.0129 memory: 15768 grad_norm: 4.7807 loss: 0.8381 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8381 2023/07/25 18:10:22 - mmengine - INFO - Epoch(train) [93][540/940] lr: 1.0000e-04 eta: 2:08:37 time: 1.0987 data_time: 0.0129 memory: 15768 grad_norm: 4.7239 loss: 0.8171 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8171 2023/07/25 18:10:44 - mmengine - INFO - Epoch(train) [93][560/940] lr: 1.0000e-04 eta: 2:08:15 time: 1.0999 data_time: 0.0130 memory: 15768 grad_norm: 4.6694 loss: 0.8106 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8106 2023/07/25 18:11:06 - mmengine - INFO - Epoch(train) [93][580/940] lr: 1.0000e-04 eta: 2:07:53 time: 1.1013 data_time: 0.0129 memory: 15768 grad_norm: 4.5883 loss: 0.7052 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7052 2023/07/25 18:11:28 - mmengine - INFO - Epoch(train) [93][600/940] lr: 1.0000e-04 eta: 2:07:31 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 4.6907 loss: 0.9022 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9022 2023/07/25 18:11:50 - mmengine - INFO - Epoch(train) [93][620/940] lr: 1.0000e-04 eta: 2:07:09 time: 1.1032 data_time: 0.0131 memory: 15768 grad_norm: 4.7650 loss: 0.8494 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8494 2023/07/25 18:12:12 - mmengine - INFO - Epoch(train) [93][640/940] lr: 1.0000e-04 eta: 2:06:47 time: 1.1006 data_time: 0.0129 memory: 15768 grad_norm: 4.6792 loss: 0.7724 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7724 2023/07/25 18:12:34 - mmengine - INFO - Epoch(train) [93][660/940] lr: 1.0000e-04 eta: 2:06:25 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 4.7409 loss: 0.7135 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7135 2023/07/25 18:12:56 - mmengine - INFO - Epoch(train) [93][680/940] lr: 1.0000e-04 eta: 2:06:02 time: 1.0995 data_time: 0.0131 memory: 15768 grad_norm: 4.6407 loss: 0.7334 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7334 2023/07/25 18:13:18 - mmengine - INFO - Epoch(train) [93][700/940] lr: 1.0000e-04 eta: 2:05:40 time: 1.0992 data_time: 0.0132 memory: 15768 grad_norm: 4.6417 loss: 0.9402 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9402 2023/07/25 18:13:40 - mmengine - INFO - Epoch(train) [93][720/940] lr: 1.0000e-04 eta: 2:05:18 time: 1.1000 data_time: 0.0130 memory: 15768 grad_norm: 4.7552 loss: 0.8063 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8063 2023/07/25 18:14:02 - mmengine - INFO - Epoch(train) [93][740/940] lr: 1.0000e-04 eta: 2:04:56 time: 1.0996 data_time: 0.0133 memory: 15768 grad_norm: 4.7850 loss: 0.7975 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7975 2023/07/25 18:14:24 - mmengine - INFO - Epoch(train) [93][760/940] lr: 1.0000e-04 eta: 2:04:34 time: 1.1012 data_time: 0.0132 memory: 15768 grad_norm: 4.7402 loss: 0.9311 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9311 2023/07/25 18:14:46 - mmengine - INFO - Epoch(train) [93][780/940] lr: 1.0000e-04 eta: 2:04:12 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 4.7013 loss: 0.7437 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7437 2023/07/25 18:15:08 - mmengine - INFO - Epoch(train) [93][800/940] lr: 1.0000e-04 eta: 2:03:50 time: 1.0988 data_time: 0.0131 memory: 15768 grad_norm: 4.6783 loss: 0.6863 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6863 2023/07/25 18:15:30 - mmengine - INFO - Epoch(train) [93][820/940] lr: 1.0000e-04 eta: 2:03:28 time: 1.1003 data_time: 0.0131 memory: 15768 grad_norm: 4.7032 loss: 0.8454 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8454 2023/07/25 18:15:52 - mmengine - INFO - Epoch(train) [93][840/940] lr: 1.0000e-04 eta: 2:03:06 time: 1.0993 data_time: 0.0129 memory: 15768 grad_norm: 4.6861 loss: 0.7316 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7316 2023/07/25 18:16:14 - mmengine - INFO - Epoch(train) [93][860/940] lr: 1.0000e-04 eta: 2:02:43 time: 1.1009 data_time: 0.0128 memory: 15768 grad_norm: 4.6881 loss: 0.6778 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6778 2023/07/25 18:16:36 - mmengine - INFO - Epoch(train) [93][880/940] lr: 1.0000e-04 eta: 2:02:21 time: 1.1002 data_time: 0.0134 memory: 15768 grad_norm: 4.7564 loss: 0.8471 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8471 2023/07/25 18:16:58 - mmengine - INFO - Epoch(train) [93][900/940] lr: 1.0000e-04 eta: 2:01:59 time: 1.1006 data_time: 0.0135 memory: 15768 grad_norm: 4.6751 loss: 0.7729 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7729 2023/07/25 18:17:20 - mmengine - INFO - Epoch(train) [93][920/940] lr: 1.0000e-04 eta: 2:01:37 time: 1.1041 data_time: 0.0130 memory: 15768 grad_norm: 4.7292 loss: 0.7092 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7092 2023/07/25 18:17:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 18:17:41 - mmengine - INFO - Epoch(train) [93][940/940] lr: 1.0000e-04 eta: 2:01:15 time: 1.0527 data_time: 0.0126 memory: 15768 grad_norm: 5.0638 loss: 0.8243 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8243 2023/07/25 18:17:41 - mmengine - INFO - Saving checkpoint at 93 epochs 2023/07/25 18:17:53 - mmengine - INFO - Epoch(val) [93][20/78] eta: 0:00:28 time: 0.4952 data_time: 0.3375 memory: 2147 2023/07/25 18:18:00 - mmengine - INFO - Epoch(val) [93][40/78] eta: 0:00:16 time: 0.3540 data_time: 0.1967 memory: 2147 2023/07/25 18:18:09 - mmengine - INFO - Epoch(val) [93][60/78] eta: 0:00:07 time: 0.4556 data_time: 0.2989 memory: 2147 2023/07/25 18:18:18 - mmengine - INFO - Epoch(val) [93][78/78] acc/top1: 0.7119 acc/top5: 0.8990 acc/mean1: 0.7118 data_time: 0.2491 time: 0.4033 2023/07/25 18:18:45 - mmengine - INFO - Epoch(train) [94][ 20/940] lr: 1.0000e-04 eta: 2:00:53 time: 1.3525 data_time: 0.1619 memory: 15768 grad_norm: 4.6646 loss: 0.7841 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7841 2023/07/25 18:19:08 - mmengine - INFO - Epoch(train) [94][ 40/940] lr: 1.0000e-04 eta: 2:00:31 time: 1.1655 data_time: 0.0134 memory: 15768 grad_norm: 4.7717 loss: 0.8261 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8261 2023/07/25 18:19:30 - mmengine - INFO - Epoch(train) [94][ 60/940] lr: 1.0000e-04 eta: 2:00:09 time: 1.1178 data_time: 0.0133 memory: 15768 grad_norm: 4.7638 loss: 0.8234 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8234 2023/07/25 18:19:53 - mmengine - INFO - Epoch(train) [94][ 80/940] lr: 1.0000e-04 eta: 1:59:47 time: 1.1163 data_time: 0.0132 memory: 15768 grad_norm: 4.8032 loss: 0.7507 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7507 2023/07/25 18:20:15 - mmengine - INFO - Epoch(train) [94][100/940] lr: 1.0000e-04 eta: 1:59:25 time: 1.1017 data_time: 0.0135 memory: 15768 grad_norm: 4.6638 loss: 0.7688 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7688 2023/07/25 18:20:37 - mmengine - INFO - Epoch(train) [94][120/940] lr: 1.0000e-04 eta: 1:59:03 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 4.6097 loss: 0.7528 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7528 2023/07/25 18:20:59 - mmengine - INFO - Epoch(train) [94][140/940] lr: 1.0000e-04 eta: 1:58:41 time: 1.1030 data_time: 0.0129 memory: 15768 grad_norm: 4.6827 loss: 0.7614 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7614 2023/07/25 18:21:21 - mmengine - INFO - Epoch(train) [94][160/940] lr: 1.0000e-04 eta: 1:58:18 time: 1.1003 data_time: 0.0131 memory: 15768 grad_norm: 4.8177 loss: 0.7370 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7370 2023/07/25 18:21:43 - mmengine - INFO - Epoch(train) [94][180/940] lr: 1.0000e-04 eta: 1:57:56 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 4.7277 loss: 0.7371 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7371 2023/07/25 18:22:05 - mmengine - INFO - Epoch(train) [94][200/940] lr: 1.0000e-04 eta: 1:57:34 time: 1.1004 data_time: 0.0128 memory: 15768 grad_norm: 4.6561 loss: 0.7904 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7904 2023/07/25 18:22:27 - mmengine - INFO - Epoch(train) [94][220/940] lr: 1.0000e-04 eta: 1:57:12 time: 1.1001 data_time: 0.0133 memory: 15768 grad_norm: 4.6573 loss: 0.8110 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8110 2023/07/25 18:22:49 - mmengine - INFO - Epoch(train) [94][240/940] lr: 1.0000e-04 eta: 1:56:50 time: 1.1005 data_time: 0.0130 memory: 15768 grad_norm: 4.7823 loss: 0.8012 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8012 2023/07/25 18:23:11 - mmengine - INFO - Epoch(train) [94][260/940] lr: 1.0000e-04 eta: 1:56:28 time: 1.0995 data_time: 0.0128 memory: 15768 grad_norm: 4.8017 loss: 0.8076 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8076 2023/07/25 18:23:33 - mmengine - INFO - Epoch(train) [94][280/940] lr: 1.0000e-04 eta: 1:56:06 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 4.6419 loss: 0.8313 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8313 2023/07/25 18:23:55 - mmengine - INFO - Epoch(train) [94][300/940] lr: 1.0000e-04 eta: 1:55:44 time: 1.1005 data_time: 0.0135 memory: 15768 grad_norm: 4.6802 loss: 0.7988 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7988 2023/07/25 18:24:17 - mmengine - INFO - Epoch(train) [94][320/940] lr: 1.0000e-04 eta: 1:55:21 time: 1.1017 data_time: 0.0136 memory: 15768 grad_norm: 4.6602 loss: 0.7247 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7247 2023/07/25 18:24:39 - mmengine - INFO - Epoch(train) [94][340/940] lr: 1.0000e-04 eta: 1:54:59 time: 1.0995 data_time: 0.0133 memory: 15768 grad_norm: 4.7652 loss: 0.8146 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8146 2023/07/25 18:25:01 - mmengine - INFO - Epoch(train) [94][360/940] lr: 1.0000e-04 eta: 1:54:37 time: 1.0988 data_time: 0.0130 memory: 15768 grad_norm: 4.7280 loss: 0.7645 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7645 2023/07/25 18:25:23 - mmengine - INFO - Epoch(train) [94][380/940] lr: 1.0000e-04 eta: 1:54:15 time: 1.1015 data_time: 0.0131 memory: 15768 grad_norm: 4.7592 loss: 0.8889 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8889 2023/07/25 18:25:45 - mmengine - INFO - Epoch(train) [94][400/940] lr: 1.0000e-04 eta: 1:53:53 time: 1.1027 data_time: 0.0131 memory: 15768 grad_norm: 4.5697 loss: 0.7169 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7169 2023/07/25 18:26:07 - mmengine - INFO - Epoch(train) [94][420/940] lr: 1.0000e-04 eta: 1:53:31 time: 1.1062 data_time: 0.0129 memory: 15768 grad_norm: 4.7093 loss: 0.8175 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8175 2023/07/25 18:26:29 - mmengine - INFO - Epoch(train) [94][440/940] lr: 1.0000e-04 eta: 1:53:09 time: 1.1038 data_time: 0.0134 memory: 15768 grad_norm: 4.7073 loss: 0.6745 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6745 2023/07/25 18:26:51 - mmengine - INFO - Epoch(train) [94][460/940] lr: 1.0000e-04 eta: 1:52:47 time: 1.1041 data_time: 0.0132 memory: 15768 grad_norm: 4.7939 loss: 0.8477 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8477 2023/07/25 18:27:13 - mmengine - INFO - Epoch(train) [94][480/940] lr: 1.0000e-04 eta: 1:52:24 time: 1.1030 data_time: 0.0132 memory: 15768 grad_norm: 4.8127 loss: 0.9278 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9278 2023/07/25 18:27:35 - mmengine - INFO - Epoch(train) [94][500/940] lr: 1.0000e-04 eta: 1:52:02 time: 1.1032 data_time: 0.0130 memory: 15768 grad_norm: 4.7633 loss: 0.8440 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8440 2023/07/25 18:27:57 - mmengine - INFO - Epoch(train) [94][520/940] lr: 1.0000e-04 eta: 1:51:40 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 4.7964 loss: 0.8030 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8030 2023/07/25 18:28:19 - mmengine - INFO - Epoch(train) [94][540/940] lr: 1.0000e-04 eta: 1:51:18 time: 1.1011 data_time: 0.0133 memory: 15768 grad_norm: 4.6907 loss: 0.7160 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7160 2023/07/25 18:28:41 - mmengine - INFO - Epoch(train) [94][560/940] lr: 1.0000e-04 eta: 1:50:56 time: 1.1001 data_time: 0.0131 memory: 15768 grad_norm: 4.6407 loss: 0.7553 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7553 2023/07/25 18:29:03 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 18:29:03 - mmengine - INFO - Epoch(train) [94][580/940] lr: 1.0000e-04 eta: 1:50:34 time: 1.0996 data_time: 0.0131 memory: 15768 grad_norm: 4.6272 loss: 0.7076 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7076 2023/07/25 18:29:25 - mmengine - INFO - Epoch(train) [94][600/940] lr: 1.0000e-04 eta: 1:50:12 time: 1.1002 data_time: 0.0129 memory: 15768 grad_norm: 4.7869 loss: 0.8399 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8399 2023/07/25 18:29:47 - mmengine - INFO - Epoch(train) [94][620/940] lr: 1.0000e-04 eta: 1:49:50 time: 1.1029 data_time: 0.0132 memory: 15768 grad_norm: 4.6954 loss: 0.8579 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8579 2023/07/25 18:30:09 - mmengine - INFO - Epoch(train) [94][640/940] lr: 1.0000e-04 eta: 1:49:27 time: 1.1002 data_time: 0.0132 memory: 15768 grad_norm: 4.7205 loss: 0.6552 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6552 2023/07/25 18:30:31 - mmengine - INFO - Epoch(train) [94][660/940] lr: 1.0000e-04 eta: 1:49:05 time: 1.1023 data_time: 0.0127 memory: 15768 grad_norm: 4.6647 loss: 0.8808 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8808 2023/07/25 18:30:53 - mmengine - INFO - Epoch(train) [94][680/940] lr: 1.0000e-04 eta: 1:48:43 time: 1.0996 data_time: 0.0132 memory: 15768 grad_norm: 4.6889 loss: 0.7635 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7635 2023/07/25 18:31:15 - mmengine - INFO - Epoch(train) [94][700/940] lr: 1.0000e-04 eta: 1:48:21 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 4.5557 loss: 0.7754 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7754 2023/07/25 18:31:37 - mmengine - INFO - Epoch(train) [94][720/940] lr: 1.0000e-04 eta: 1:47:59 time: 1.1010 data_time: 0.0137 memory: 15768 grad_norm: 4.7947 loss: 0.8573 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8573 2023/07/25 18:31:59 - mmengine - INFO - Epoch(train) [94][740/940] lr: 1.0000e-04 eta: 1:47:37 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 4.7530 loss: 0.7872 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7872 2023/07/25 18:32:22 - mmengine - INFO - Epoch(train) [94][760/940] lr: 1.0000e-04 eta: 1:47:15 time: 1.1036 data_time: 0.0134 memory: 15768 grad_norm: 4.7955 loss: 0.7175 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7175 2023/07/25 18:32:44 - mmengine - INFO - Epoch(train) [94][780/940] lr: 1.0000e-04 eta: 1:46:53 time: 1.0974 data_time: 0.0131 memory: 15768 grad_norm: 4.6795 loss: 0.7921 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7921 2023/07/25 18:33:06 - mmengine - INFO - Epoch(train) [94][800/940] lr: 1.0000e-04 eta: 1:46:31 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 4.6568 loss: 0.8285 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8285 2023/07/25 18:33:28 - mmengine - INFO - Epoch(train) [94][820/940] lr: 1.0000e-04 eta: 1:46:08 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 4.6740 loss: 0.7377 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7377 2023/07/25 18:33:50 - mmengine - INFO - Epoch(train) [94][840/940] lr: 1.0000e-04 eta: 1:45:46 time: 1.1003 data_time: 0.0127 memory: 15768 grad_norm: 4.7554 loss: 0.6458 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6458 2023/07/25 18:34:12 - mmengine - INFO - Epoch(train) [94][860/940] lr: 1.0000e-04 eta: 1:45:24 time: 1.1005 data_time: 0.0128 memory: 15768 grad_norm: 4.7516 loss: 0.8254 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8254 2023/07/25 18:34:34 - mmengine - INFO - Epoch(train) [94][880/940] lr: 1.0000e-04 eta: 1:45:02 time: 1.1000 data_time: 0.0129 memory: 15768 grad_norm: 4.7724 loss: 0.9098 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9098 2023/07/25 18:34:56 - mmengine - INFO - Epoch(train) [94][900/940] lr: 1.0000e-04 eta: 1:44:40 time: 1.0993 data_time: 0.0131 memory: 15768 grad_norm: 4.7426 loss: 0.8993 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8993 2023/07/25 18:35:18 - mmengine - INFO - Epoch(train) [94][920/940] lr: 1.0000e-04 eta: 1:44:18 time: 1.0997 data_time: 0.0132 memory: 15768 grad_norm: 4.7791 loss: 0.6972 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6972 2023/07/25 18:35:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 18:35:39 - mmengine - INFO - Epoch(train) [94][940/940] lr: 1.0000e-04 eta: 1:43:56 time: 1.0539 data_time: 0.0131 memory: 15768 grad_norm: 5.0091 loss: 0.8769 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8769 2023/07/25 18:35:49 - mmengine - INFO - Epoch(val) [94][20/78] eta: 0:00:29 time: 0.5009 data_time: 0.3436 memory: 2147 2023/07/25 18:35:55 - mmengine - INFO - Epoch(val) [94][40/78] eta: 0:00:15 time: 0.3341 data_time: 0.1768 memory: 2147 2023/07/25 18:36:04 - mmengine - INFO - Epoch(val) [94][60/78] eta: 0:00:07 time: 0.4445 data_time: 0.2877 memory: 2147 2023/07/25 18:36:15 - mmengine - INFO - Epoch(val) [94][78/78] acc/top1: 0.7103 acc/top5: 0.8977 acc/mean1: 0.7102 data_time: 0.2446 time: 0.3989 2023/07/25 18:36:40 - mmengine - INFO - Epoch(train) [95][ 20/940] lr: 1.0000e-04 eta: 1:43:34 time: 1.2758 data_time: 0.1569 memory: 15768 grad_norm: 4.6961 loss: 0.7055 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7055 2023/07/25 18:37:02 - mmengine - INFO - Epoch(train) [95][ 40/940] lr: 1.0000e-04 eta: 1:43:12 time: 1.1004 data_time: 0.0134 memory: 15768 grad_norm: 4.7620 loss: 0.8312 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8312 2023/07/25 18:37:24 - mmengine - INFO - Epoch(train) [95][ 60/940] lr: 1.0000e-04 eta: 1:42:49 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 4.7164 loss: 0.6889 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6889 2023/07/25 18:37:46 - mmengine - INFO - Epoch(train) [95][ 80/940] lr: 1.0000e-04 eta: 1:42:27 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 4.7591 loss: 0.8183 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8183 2023/07/25 18:38:08 - mmengine - INFO - Epoch(train) [95][100/940] lr: 1.0000e-04 eta: 1:42:05 time: 1.1024 data_time: 0.0129 memory: 15768 grad_norm: 4.7451 loss: 0.8024 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8024 2023/07/25 18:38:30 - mmengine - INFO - Epoch(train) [95][120/940] lr: 1.0000e-04 eta: 1:41:43 time: 1.1029 data_time: 0.0136 memory: 15768 grad_norm: 4.6043 loss: 0.8668 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8668 2023/07/25 18:38:52 - mmengine - INFO - Epoch(train) [95][140/940] lr: 1.0000e-04 eta: 1:41:21 time: 1.0997 data_time: 0.0133 memory: 15768 grad_norm: 4.7536 loss: 0.7219 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7219 2023/07/25 18:39:15 - mmengine - INFO - Epoch(train) [95][160/940] lr: 1.0000e-04 eta: 1:40:59 time: 1.1003 data_time: 0.0134 memory: 15768 grad_norm: 4.7483 loss: 0.7502 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7502 2023/07/25 18:39:37 - mmengine - INFO - Epoch(train) [95][180/940] lr: 1.0000e-04 eta: 1:40:37 time: 1.1008 data_time: 0.0134 memory: 15768 grad_norm: 4.8530 loss: 0.6248 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6248 2023/07/25 18:40:00 - mmengine - INFO - Epoch(train) [95][200/940] lr: 1.0000e-04 eta: 1:40:15 time: 1.1562 data_time: 0.0132 memory: 15768 grad_norm: 4.6173 loss: 0.9148 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9148 2023/07/25 18:40:23 - mmengine - INFO - Epoch(train) [95][220/940] lr: 1.0000e-04 eta: 1:39:53 time: 1.1596 data_time: 0.0131 memory: 15768 grad_norm: 4.7671 loss: 0.8785 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8785 2023/07/25 18:40:46 - mmengine - INFO - Epoch(train) [95][240/940] lr: 1.0000e-04 eta: 1:39:31 time: 1.1579 data_time: 0.0128 memory: 15768 grad_norm: 4.7448 loss: 0.8249 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8249 2023/07/25 18:41:09 - mmengine - INFO - Epoch(train) [95][260/940] lr: 1.0000e-04 eta: 1:39:09 time: 1.1576 data_time: 0.0130 memory: 15768 grad_norm: 4.6161 loss: 0.6281 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6281 2023/07/25 18:41:31 - mmengine - INFO - Epoch(train) [95][280/940] lr: 1.0000e-04 eta: 1:38:46 time: 1.1133 data_time: 0.0133 memory: 15768 grad_norm: 4.7643 loss: 0.6575 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6575 2023/07/25 18:41:53 - mmengine - INFO - Epoch(train) [95][300/940] lr: 1.0000e-04 eta: 1:38:24 time: 1.0993 data_time: 0.0134 memory: 15768 grad_norm: 4.7027 loss: 0.7298 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7298 2023/07/25 18:42:15 - mmengine - INFO - Epoch(train) [95][320/940] lr: 1.0000e-04 eta: 1:38:02 time: 1.0983 data_time: 0.0131 memory: 15768 grad_norm: 4.6177 loss: 0.7173 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7173 2023/07/25 18:42:37 - mmengine - INFO - Epoch(train) [95][340/940] lr: 1.0000e-04 eta: 1:37:40 time: 1.0980 data_time: 0.0133 memory: 15768 grad_norm: 4.7910 loss: 0.7460 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7460 2023/07/25 18:42:59 - mmengine - INFO - Epoch(train) [95][360/940] lr: 1.0000e-04 eta: 1:37:18 time: 1.1026 data_time: 0.0131 memory: 15768 grad_norm: 4.6812 loss: 0.7201 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7201 2023/07/25 18:43:21 - mmengine - INFO - Epoch(train) [95][380/940] lr: 1.0000e-04 eta: 1:36:56 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 4.7168 loss: 0.7335 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7335 2023/07/25 18:43:44 - mmengine - INFO - Epoch(train) [95][400/940] lr: 1.0000e-04 eta: 1:36:34 time: 1.1032 data_time: 0.0133 memory: 15768 grad_norm: 4.7305 loss: 0.6721 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6721 2023/07/25 18:44:06 - mmengine - INFO - Epoch(train) [95][420/940] lr: 1.0000e-04 eta: 1:36:12 time: 1.1014 data_time: 0.0136 memory: 15768 grad_norm: 4.7249 loss: 0.8407 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8407 2023/07/25 18:44:28 - mmengine - INFO - Epoch(train) [95][440/940] lr: 1.0000e-04 eta: 1:35:49 time: 1.1011 data_time: 0.0132 memory: 15768 grad_norm: 4.8699 loss: 0.8445 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8445 2023/07/25 18:44:50 - mmengine - INFO - Epoch(train) [95][460/940] lr: 1.0000e-04 eta: 1:35:27 time: 1.1015 data_time: 0.0134 memory: 15768 grad_norm: 4.6584 loss: 0.7392 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7392 2023/07/25 18:45:12 - mmengine - INFO - Epoch(train) [95][480/940] lr: 1.0000e-04 eta: 1:35:05 time: 1.1028 data_time: 0.0134 memory: 15768 grad_norm: 4.8271 loss: 0.8811 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8811 2023/07/25 18:45:34 - mmengine - INFO - Epoch(train) [95][500/940] lr: 1.0000e-04 eta: 1:34:43 time: 1.1063 data_time: 0.0132 memory: 15768 grad_norm: 4.7173 loss: 0.6091 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6091 2023/07/25 18:45:56 - mmengine - INFO - Epoch(train) [95][520/940] lr: 1.0000e-04 eta: 1:34:21 time: 1.1029 data_time: 0.0135 memory: 15768 grad_norm: 4.8021 loss: 0.7712 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7712 2023/07/25 18:46:18 - mmengine - INFO - Epoch(train) [95][540/940] lr: 1.0000e-04 eta: 1:33:59 time: 1.1032 data_time: 0.0127 memory: 15768 grad_norm: 4.5530 loss: 0.7574 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7574 2023/07/25 18:46:40 - mmengine - INFO - Epoch(train) [95][560/940] lr: 1.0000e-04 eta: 1:33:37 time: 1.1004 data_time: 0.0130 memory: 15768 grad_norm: 4.8183 loss: 0.8916 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8916 2023/07/25 18:47:02 - mmengine - INFO - Epoch(train) [95][580/940] lr: 1.0000e-04 eta: 1:33:15 time: 1.1027 data_time: 0.0139 memory: 15768 grad_norm: 4.7687 loss: 0.7711 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7711 2023/07/25 18:47:24 - mmengine - INFO - Epoch(train) [95][600/940] lr: 1.0000e-04 eta: 1:32:53 time: 1.1019 data_time: 0.0131 memory: 15768 grad_norm: 4.7116 loss: 0.8105 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8105 2023/07/25 18:47:46 - mmengine - INFO - Epoch(train) [95][620/940] lr: 1.0000e-04 eta: 1:32:30 time: 1.1039 data_time: 0.0132 memory: 15768 grad_norm: 4.7394 loss: 0.6638 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6638 2023/07/25 18:48:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 18:48:08 - mmengine - INFO - Epoch(train) [95][640/940] lr: 1.0000e-04 eta: 1:32:08 time: 1.1017 data_time: 0.0131 memory: 15768 grad_norm: 4.6294 loss: 0.8610 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8610 2023/07/25 18:48:30 - mmengine - INFO - Epoch(train) [95][660/940] lr: 1.0000e-04 eta: 1:31:46 time: 1.0998 data_time: 0.0134 memory: 15768 grad_norm: 4.6865 loss: 0.7206 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7206 2023/07/25 18:48:52 - mmengine - INFO - Epoch(train) [95][680/940] lr: 1.0000e-04 eta: 1:31:24 time: 1.0996 data_time: 0.0134 memory: 15768 grad_norm: 4.6880 loss: 0.7629 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7629 2023/07/25 18:49:14 - mmengine - INFO - Epoch(train) [95][700/940] lr: 1.0000e-04 eta: 1:31:02 time: 1.1002 data_time: 0.0131 memory: 15768 grad_norm: 4.7231 loss: 0.8146 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8146 2023/07/25 18:49:36 - mmengine - INFO - Epoch(train) [95][720/940] lr: 1.0000e-04 eta: 1:30:40 time: 1.1019 data_time: 0.0130 memory: 15768 grad_norm: 4.7885 loss: 0.7577 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7577 2023/07/25 18:49:58 - mmengine - INFO - Epoch(train) [95][740/940] lr: 1.0000e-04 eta: 1:30:18 time: 1.1008 data_time: 0.0134 memory: 15768 grad_norm: 4.7405 loss: 0.7070 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7070 2023/07/25 18:50:20 - mmengine - INFO - Epoch(train) [95][760/940] lr: 1.0000e-04 eta: 1:29:56 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 4.6012 loss: 0.7539 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7539 2023/07/25 18:50:42 - mmengine - INFO - Epoch(train) [95][780/940] lr: 1.0000e-04 eta: 1:29:33 time: 1.1019 data_time: 0.0135 memory: 15768 grad_norm: 4.7196 loss: 0.7231 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7231 2023/07/25 18:51:04 - mmengine - INFO - Epoch(train) [95][800/940] lr: 1.0000e-04 eta: 1:29:11 time: 1.1001 data_time: 0.0132 memory: 15768 grad_norm: 4.6934 loss: 0.7697 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7697 2023/07/25 18:51:26 - mmengine - INFO - Epoch(train) [95][820/940] lr: 1.0000e-04 eta: 1:28:49 time: 1.0977 data_time: 0.0130 memory: 15768 grad_norm: 4.8061 loss: 0.7817 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7817 2023/07/25 18:51:48 - mmengine - INFO - Epoch(train) [95][840/940] lr: 1.0000e-04 eta: 1:28:27 time: 1.0986 data_time: 0.0133 memory: 15768 grad_norm: 4.7513 loss: 0.7343 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7343 2023/07/25 18:52:10 - mmengine - INFO - Epoch(train) [95][860/940] lr: 1.0000e-04 eta: 1:28:05 time: 1.1032 data_time: 0.0134 memory: 15768 grad_norm: 4.6460 loss: 0.7453 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7453 2023/07/25 18:52:32 - mmengine - INFO - Epoch(train) [95][880/940] lr: 1.0000e-04 eta: 1:27:43 time: 1.0994 data_time: 0.0133 memory: 15768 grad_norm: 4.6626 loss: 0.7475 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7475 2023/07/25 18:52:54 - mmengine - INFO - Epoch(train) [95][900/940] lr: 1.0000e-04 eta: 1:27:21 time: 1.1042 data_time: 0.0135 memory: 15768 grad_norm: 4.7969 loss: 0.7446 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7446 2023/07/25 18:53:16 - mmengine - INFO - Epoch(train) [95][920/940] lr: 1.0000e-04 eta: 1:26:59 time: 1.0981 data_time: 0.0133 memory: 15768 grad_norm: 4.6007 loss: 0.7769 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7769 2023/07/25 18:53:37 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 18:53:37 - mmengine - INFO - Epoch(train) [95][940/940] lr: 1.0000e-04 eta: 1:26:36 time: 1.0539 data_time: 0.0130 memory: 15768 grad_norm: 5.1671 loss: 0.7805 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7805 2023/07/25 18:53:47 - mmengine - INFO - Epoch(val) [95][20/78] eta: 0:00:28 time: 0.4848 data_time: 0.3272 memory: 2147 2023/07/25 18:53:54 - mmengine - INFO - Epoch(val) [95][40/78] eta: 0:00:15 time: 0.3549 data_time: 0.1982 memory: 2147 2023/07/25 18:54:03 - mmengine - INFO - Epoch(val) [95][60/78] eta: 0:00:07 time: 0.4372 data_time: 0.2805 memory: 2147 2023/07/25 18:54:13 - mmengine - INFO - Epoch(val) [95][78/78] acc/top1: 0.7113 acc/top5: 0.8982 acc/mean1: 0.7112 data_time: 0.2460 time: 0.4002 2023/07/25 18:54:39 - mmengine - INFO - Epoch(train) [96][ 20/940] lr: 1.0000e-04 eta: 1:26:15 time: 1.2923 data_time: 0.1409 memory: 15768 grad_norm: 4.7217 loss: 0.8137 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8137 2023/07/25 18:55:01 - mmengine - INFO - Epoch(train) [96][ 40/940] lr: 1.0000e-04 eta: 1:25:52 time: 1.1043 data_time: 0.0135 memory: 15768 grad_norm: 4.7499 loss: 0.7301 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7301 2023/07/25 18:55:23 - mmengine - INFO - Epoch(train) [96][ 60/940] lr: 1.0000e-04 eta: 1:25:30 time: 1.1049 data_time: 0.0135 memory: 15768 grad_norm: 4.6537 loss: 0.7600 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7600 2023/07/25 18:55:45 - mmengine - INFO - Epoch(train) [96][ 80/940] lr: 1.0000e-04 eta: 1:25:08 time: 1.0995 data_time: 0.0130 memory: 15768 grad_norm: 4.7908 loss: 0.8222 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8222 2023/07/25 18:56:07 - mmengine - INFO - Epoch(train) [96][100/940] lr: 1.0000e-04 eta: 1:24:46 time: 1.0984 data_time: 0.0133 memory: 15768 grad_norm: 4.7127 loss: 0.8451 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8451 2023/07/25 18:56:29 - mmengine - INFO - Epoch(train) [96][120/940] lr: 1.0000e-04 eta: 1:24:24 time: 1.1031 data_time: 0.0134 memory: 15768 grad_norm: 4.7384 loss: 0.6897 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6897 2023/07/25 18:56:51 - mmengine - INFO - Epoch(train) [96][140/940] lr: 1.0000e-04 eta: 1:24:02 time: 1.1021 data_time: 0.0135 memory: 15768 grad_norm: 4.8035 loss: 0.7624 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7624 2023/07/25 18:57:13 - mmengine - INFO - Epoch(train) [96][160/940] lr: 1.0000e-04 eta: 1:23:40 time: 1.1032 data_time: 0.0136 memory: 15768 grad_norm: 4.7768 loss: 0.8614 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8614 2023/07/25 18:57:35 - mmengine - INFO - Epoch(train) [96][180/940] lr: 1.0000e-04 eta: 1:23:18 time: 1.1030 data_time: 0.0139 memory: 15768 grad_norm: 4.7163 loss: 0.8297 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8297 2023/07/25 18:57:57 - mmengine - INFO - Epoch(train) [96][200/940] lr: 1.0000e-04 eta: 1:22:55 time: 1.1031 data_time: 0.0133 memory: 15768 grad_norm: 4.7199 loss: 0.8422 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8422 2023/07/25 18:58:19 - mmengine - INFO - Epoch(train) [96][220/940] lr: 1.0000e-04 eta: 1:22:33 time: 1.1030 data_time: 0.0134 memory: 15768 grad_norm: 4.6043 loss: 0.7466 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7466 2023/07/25 18:58:41 - mmengine - INFO - Epoch(train) [96][240/940] lr: 1.0000e-04 eta: 1:22:11 time: 1.1002 data_time: 0.0134 memory: 15768 grad_norm: 4.6692 loss: 0.8189 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8189 2023/07/25 18:59:03 - mmengine - INFO - Epoch(train) [96][260/940] lr: 1.0000e-04 eta: 1:21:49 time: 1.1004 data_time: 0.0133 memory: 15768 grad_norm: 4.7411 loss: 0.6456 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6456 2023/07/25 18:59:25 - mmengine - INFO - Epoch(train) [96][280/940] lr: 1.0000e-04 eta: 1:21:27 time: 1.1001 data_time: 0.0134 memory: 15768 grad_norm: 4.7667 loss: 0.7541 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7541 2023/07/25 18:59:47 - mmengine - INFO - Epoch(train) [96][300/940] lr: 1.0000e-04 eta: 1:21:05 time: 1.0991 data_time: 0.0132 memory: 15768 grad_norm: 4.7177 loss: 0.9033 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9033 2023/07/25 19:00:09 - mmengine - INFO - Epoch(train) [96][320/940] lr: 1.0000e-04 eta: 1:20:43 time: 1.0989 data_time: 0.0132 memory: 15768 grad_norm: 4.7710 loss: 0.8201 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8201 2023/07/25 19:00:31 - mmengine - INFO - Epoch(train) [96][340/940] lr: 1.0000e-04 eta: 1:20:21 time: 1.1043 data_time: 0.0133 memory: 15768 grad_norm: 4.6296 loss: 0.7581 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7581 2023/07/25 19:00:53 - mmengine - INFO - Epoch(train) [96][360/940] lr: 1.0000e-04 eta: 1:19:58 time: 1.1005 data_time: 0.0134 memory: 15768 grad_norm: 4.6343 loss: 0.8388 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8388 2023/07/25 19:01:15 - mmengine - INFO - Epoch(train) [96][380/940] lr: 1.0000e-04 eta: 1:19:36 time: 1.1016 data_time: 0.0134 memory: 15768 grad_norm: 4.7971 loss: 0.8077 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8077 2023/07/25 19:01:37 - mmengine - INFO - Epoch(train) [96][400/940] lr: 1.0000e-04 eta: 1:19:14 time: 1.0980 data_time: 0.0133 memory: 15768 grad_norm: 4.7302 loss: 0.8983 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8983 2023/07/25 19:01:59 - mmengine - INFO - Epoch(train) [96][420/940] lr: 1.0000e-04 eta: 1:18:52 time: 1.1013 data_time: 0.0133 memory: 15768 grad_norm: 4.7256 loss: 0.8298 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8298 2023/07/25 19:02:22 - mmengine - INFO - Epoch(train) [96][440/940] lr: 1.0000e-04 eta: 1:18:30 time: 1.1079 data_time: 0.0128 memory: 15768 grad_norm: 4.8236 loss: 0.7649 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7649 2023/07/25 19:02:44 - mmengine - INFO - Epoch(train) [96][460/940] lr: 1.0000e-04 eta: 1:18:08 time: 1.0994 data_time: 0.0132 memory: 15768 grad_norm: 4.7376 loss: 0.8216 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8216 2023/07/25 19:03:06 - mmengine - INFO - Epoch(train) [96][480/940] lr: 1.0000e-04 eta: 1:17:46 time: 1.1008 data_time: 0.0132 memory: 15768 grad_norm: 4.7363 loss: 0.6589 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6589 2023/07/25 19:03:28 - mmengine - INFO - Epoch(train) [96][500/940] lr: 1.0000e-04 eta: 1:17:24 time: 1.0983 data_time: 0.0134 memory: 15768 grad_norm: 4.8145 loss: 0.8182 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8182 2023/07/25 19:03:50 - mmengine - INFO - Epoch(train) [96][520/940] lr: 1.0000e-04 eta: 1:17:02 time: 1.0976 data_time: 0.0131 memory: 15768 grad_norm: 4.7495 loss: 0.6517 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6517 2023/07/25 19:04:13 - mmengine - INFO - Epoch(train) [96][540/940] lr: 1.0000e-04 eta: 1:16:39 time: 1.1530 data_time: 0.0136 memory: 15768 grad_norm: 4.6799 loss: 0.7690 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7690 2023/07/25 19:04:36 - mmengine - INFO - Epoch(train) [96][560/940] lr: 1.0000e-04 eta: 1:16:17 time: 1.1631 data_time: 0.0133 memory: 15768 grad_norm: 4.6840 loss: 0.7485 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7485 2023/07/25 19:04:58 - mmengine - INFO - Epoch(train) [96][580/940] lr: 1.0000e-04 eta: 1:15:55 time: 1.1089 data_time: 0.0132 memory: 15768 grad_norm: 4.7793 loss: 0.8927 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8927 2023/07/25 19:05:20 - mmengine - INFO - Epoch(train) [96][600/940] lr: 1.0000e-04 eta: 1:15:33 time: 1.1006 data_time: 0.0140 memory: 15768 grad_norm: 4.7727 loss: 0.8516 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8516 2023/07/25 19:05:42 - mmengine - INFO - Epoch(train) [96][620/940] lr: 1.0000e-04 eta: 1:15:11 time: 1.1082 data_time: 0.0131 memory: 15768 grad_norm: 4.6754 loss: 0.8242 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8242 2023/07/25 19:06:04 - mmengine - INFO - Epoch(train) [96][640/940] lr: 1.0000e-04 eta: 1:14:49 time: 1.1010 data_time: 0.0133 memory: 15768 grad_norm: 4.6773 loss: 0.6657 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6657 2023/07/25 19:06:26 - mmengine - INFO - Epoch(train) [96][660/940] lr: 1.0000e-04 eta: 1:14:27 time: 1.0995 data_time: 0.0136 memory: 15768 grad_norm: 4.7546 loss: 0.8088 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8088 2023/07/25 19:06:48 - mmengine - INFO - Epoch(train) [96][680/940] lr: 1.0000e-04 eta: 1:14:05 time: 1.0998 data_time: 0.0134 memory: 15768 grad_norm: 4.8466 loss: 0.7460 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7460 2023/07/25 19:07:10 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 19:07:10 - mmengine - INFO - Epoch(train) [96][700/940] lr: 1.0000e-04 eta: 1:13:43 time: 1.1066 data_time: 0.0135 memory: 15768 grad_norm: 4.7015 loss: 0.8416 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8416 2023/07/25 19:07:32 - mmengine - INFO - Epoch(train) [96][720/940] lr: 1.0000e-04 eta: 1:13:20 time: 1.0986 data_time: 0.0136 memory: 15768 grad_norm: 4.8180 loss: 0.6840 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6840 2023/07/25 19:07:54 - mmengine - INFO - Epoch(train) [96][740/940] lr: 1.0000e-04 eta: 1:12:58 time: 1.1010 data_time: 0.0136 memory: 15768 grad_norm: 4.7060 loss: 0.7436 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7436 2023/07/25 19:08:16 - mmengine - INFO - Epoch(train) [96][760/940] lr: 1.0000e-04 eta: 1:12:36 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 4.6572 loss: 0.6365 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6365 2023/07/25 19:08:38 - mmengine - INFO - Epoch(train) [96][780/940] lr: 1.0000e-04 eta: 1:12:14 time: 1.1004 data_time: 0.0133 memory: 15768 grad_norm: 4.6426 loss: 0.8996 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8996 2023/07/25 19:09:00 - mmengine - INFO - Epoch(train) [96][800/940] lr: 1.0000e-04 eta: 1:11:52 time: 1.0979 data_time: 0.0132 memory: 15768 grad_norm: 4.6453 loss: 0.7982 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7982 2023/07/25 19:09:22 - mmengine - INFO - Epoch(train) [96][820/940] lr: 1.0000e-04 eta: 1:11:30 time: 1.1023 data_time: 0.0134 memory: 15768 grad_norm: 4.7572 loss: 0.8758 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8758 2023/07/25 19:09:44 - mmengine - INFO - Epoch(train) [96][840/940] lr: 1.0000e-04 eta: 1:11:08 time: 1.1024 data_time: 0.0135 memory: 15768 grad_norm: 4.8527 loss: 0.8535 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8535 2023/07/25 19:10:06 - mmengine - INFO - Epoch(train) [96][860/940] lr: 1.0000e-04 eta: 1:10:46 time: 1.1023 data_time: 0.0134 memory: 15768 grad_norm: 4.8028 loss: 0.8549 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8549 2023/07/25 19:10:29 - mmengine - INFO - Epoch(train) [96][880/940] lr: 1.0000e-04 eta: 1:10:23 time: 1.1024 data_time: 0.0133 memory: 15768 grad_norm: 4.7259 loss: 0.8140 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8140 2023/07/25 19:10:50 - mmengine - INFO - Epoch(train) [96][900/940] lr: 1.0000e-04 eta: 1:10:01 time: 1.0985 data_time: 0.0136 memory: 15768 grad_norm: 4.6439 loss: 0.8032 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8032 2023/07/25 19:11:12 - mmengine - INFO - Epoch(train) [96][920/940] lr: 1.0000e-04 eta: 1:09:39 time: 1.0984 data_time: 0.0135 memory: 15768 grad_norm: 4.8347 loss: 0.8939 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.8939 2023/07/25 19:11:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 19:11:34 - mmengine - INFO - Epoch(train) [96][940/940] lr: 1.0000e-04 eta: 1:09:17 time: 1.0559 data_time: 0.0131 memory: 15768 grad_norm: 4.9888 loss: 0.9297 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 0.9297 2023/07/25 19:11:34 - mmengine - INFO - Saving checkpoint at 96 epochs 2023/07/25 19:11:45 - mmengine - INFO - Epoch(val) [96][20/78] eta: 0:00:29 time: 0.5048 data_time: 0.3472 memory: 2147 2023/07/25 19:11:52 - mmengine - INFO - Epoch(val) [96][40/78] eta: 0:00:15 time: 0.3367 data_time: 0.1800 memory: 2147 2023/07/25 19:12:01 - mmengine - INFO - Epoch(val) [96][60/78] eta: 0:00:07 time: 0.4510 data_time: 0.2938 memory: 2147 2023/07/25 19:12:10 - mmengine - INFO - Epoch(val) [96][78/78] acc/top1: 0.7115 acc/top5: 0.8992 acc/mean1: 0.7114 data_time: 0.2448 time: 0.3990 2023/07/25 19:12:36 - mmengine - INFO - Epoch(train) [97][ 20/940] lr: 1.0000e-04 eta: 1:08:55 time: 1.2902 data_time: 0.1530 memory: 15768 grad_norm: 4.8704 loss: 0.6565 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6565 2023/07/25 19:12:58 - mmengine - INFO - Epoch(train) [97][ 40/940] lr: 1.0000e-04 eta: 1:08:33 time: 1.1021 data_time: 0.0135 memory: 15768 grad_norm: 4.7926 loss: 0.7211 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7211 2023/07/25 19:13:20 - mmengine - INFO - Epoch(train) [97][ 60/940] lr: 1.0000e-04 eta: 1:08:11 time: 1.1011 data_time: 0.0134 memory: 15768 grad_norm: 4.7742 loss: 0.8495 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8495 2023/07/25 19:13:42 - mmengine - INFO - Epoch(train) [97][ 80/940] lr: 1.0000e-04 eta: 1:07:49 time: 1.1015 data_time: 0.0132 memory: 15768 grad_norm: 4.7511 loss: 0.6853 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6853 2023/07/25 19:14:04 - mmengine - INFO - Epoch(train) [97][100/940] lr: 1.0000e-04 eta: 1:07:27 time: 1.1026 data_time: 0.0132 memory: 15768 grad_norm: 4.7303 loss: 0.6421 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6421 2023/07/25 19:14:26 - mmengine - INFO - Epoch(train) [97][120/940] lr: 1.0000e-04 eta: 1:07:05 time: 1.0984 data_time: 0.0134 memory: 15768 grad_norm: 4.6616 loss: 0.7771 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7771 2023/07/25 19:14:48 - mmengine - INFO - Epoch(train) [97][140/940] lr: 1.0000e-04 eta: 1:06:42 time: 1.1029 data_time: 0.0135 memory: 15768 grad_norm: 4.8235 loss: 0.7714 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7714 2023/07/25 19:15:10 - mmengine - INFO - Epoch(train) [97][160/940] lr: 1.0000e-04 eta: 1:06:20 time: 1.1019 data_time: 0.0135 memory: 15768 grad_norm: 4.7791 loss: 0.8239 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8239 2023/07/25 19:15:32 - mmengine - INFO - Epoch(train) [97][180/940] lr: 1.0000e-04 eta: 1:05:58 time: 1.0993 data_time: 0.0134 memory: 15768 grad_norm: 4.7243 loss: 0.6715 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6715 2023/07/25 19:15:54 - mmengine - INFO - Epoch(train) [97][200/940] lr: 1.0000e-04 eta: 1:05:36 time: 1.1003 data_time: 0.0133 memory: 15768 grad_norm: 4.7985 loss: 0.8279 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8279 2023/07/25 19:16:16 - mmengine - INFO - Epoch(train) [97][220/940] lr: 1.0000e-04 eta: 1:05:14 time: 1.0989 data_time: 0.0133 memory: 15768 grad_norm: 4.6950 loss: 0.7903 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7903 2023/07/25 19:16:38 - mmengine - INFO - Epoch(train) [97][240/940] lr: 1.0000e-04 eta: 1:04:52 time: 1.1010 data_time: 0.0131 memory: 15768 grad_norm: 4.6826 loss: 0.7340 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7340 2023/07/25 19:17:00 - mmengine - INFO - Epoch(train) [97][260/940] lr: 1.0000e-04 eta: 1:04:30 time: 1.1079 data_time: 0.0132 memory: 15768 grad_norm: 4.6948 loss: 0.8832 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8832 2023/07/25 19:17:22 - mmengine - INFO - Epoch(train) [97][280/940] lr: 1.0000e-04 eta: 1:04:08 time: 1.0987 data_time: 0.0136 memory: 15768 grad_norm: 4.7722 loss: 0.9906 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9906 2023/07/25 19:17:44 - mmengine - INFO - Epoch(train) [97][300/940] lr: 1.0000e-04 eta: 1:03:45 time: 1.0992 data_time: 0.0137 memory: 15768 grad_norm: 4.5586 loss: 0.6886 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6886 2023/07/25 19:18:06 - mmengine - INFO - Epoch(train) [97][320/940] lr: 1.0000e-04 eta: 1:03:23 time: 1.1001 data_time: 0.0134 memory: 15768 grad_norm: 4.8517 loss: 0.6357 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6357 2023/07/25 19:18:28 - mmengine - INFO - Epoch(train) [97][340/940] lr: 1.0000e-04 eta: 1:03:01 time: 1.1000 data_time: 0.0141 memory: 15768 grad_norm: 4.8618 loss: 0.7962 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7962 2023/07/25 19:18:50 - mmengine - INFO - Epoch(train) [97][360/940] lr: 1.0000e-04 eta: 1:02:39 time: 1.1023 data_time: 0.0134 memory: 15768 grad_norm: 4.7161 loss: 0.7853 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7853 2023/07/25 19:19:12 - mmengine - INFO - Epoch(train) [97][380/940] lr: 1.0000e-04 eta: 1:02:17 time: 1.1040 data_time: 0.0134 memory: 15768 grad_norm: 4.7462 loss: 0.7572 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7572 2023/07/25 19:19:34 - mmengine - INFO - Epoch(train) [97][400/940] lr: 1.0000e-04 eta: 1:01:55 time: 1.1027 data_time: 0.0133 memory: 15768 grad_norm: 4.7661 loss: 0.7649 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7649 2023/07/25 19:19:56 - mmengine - INFO - Epoch(train) [97][420/940] lr: 1.0000e-04 eta: 1:01:33 time: 1.1040 data_time: 0.0135 memory: 15768 grad_norm: 4.6232 loss: 0.6609 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6609 2023/07/25 19:20:18 - mmengine - INFO - Epoch(train) [97][440/940] lr: 1.0000e-04 eta: 1:01:11 time: 1.1008 data_time: 0.0137 memory: 15768 grad_norm: 4.7723 loss: 0.6944 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6944 2023/07/25 19:20:40 - mmengine - INFO - Epoch(train) [97][460/940] lr: 1.0000e-04 eta: 1:00:49 time: 1.1010 data_time: 0.0135 memory: 15768 grad_norm: 4.7558 loss: 0.7047 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7047 2023/07/25 19:21:02 - mmengine - INFO - Epoch(train) [97][480/940] lr: 1.0000e-04 eta: 1:00:26 time: 1.1012 data_time: 0.0139 memory: 15768 grad_norm: 4.7217 loss: 0.7170 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7170 2023/07/25 19:21:24 - mmengine - INFO - Epoch(train) [97][500/940] lr: 1.0000e-04 eta: 1:00:04 time: 1.1033 data_time: 0.0135 memory: 15768 grad_norm: 4.8067 loss: 0.7867 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7867 2023/07/25 19:21:47 - mmengine - INFO - Epoch(train) [97][520/940] lr: 1.0000e-04 eta: 0:59:42 time: 1.1056 data_time: 0.0142 memory: 15768 grad_norm: 4.8973 loss: 0.8569 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8569 2023/07/25 19:22:09 - mmengine - INFO - Epoch(train) [97][540/940] lr: 1.0000e-04 eta: 0:59:20 time: 1.1026 data_time: 0.0134 memory: 15768 grad_norm: 4.6715 loss: 0.8798 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8798 2023/07/25 19:22:31 - mmengine - INFO - Epoch(train) [97][560/940] lr: 1.0000e-04 eta: 0:58:58 time: 1.0997 data_time: 0.0135 memory: 15768 grad_norm: 4.8288 loss: 0.8744 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8744 2023/07/25 19:22:53 - mmengine - INFO - Epoch(train) [97][580/940] lr: 1.0000e-04 eta: 0:58:36 time: 1.1022 data_time: 0.0136 memory: 15768 grad_norm: 4.6871 loss: 0.5939 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.5939 2023/07/25 19:23:15 - mmengine - INFO - Epoch(train) [97][600/940] lr: 1.0000e-04 eta: 0:58:14 time: 1.1010 data_time: 0.0132 memory: 15768 grad_norm: 4.7249 loss: 0.7218 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7218 2023/07/25 19:23:37 - mmengine - INFO - Epoch(train) [97][620/940] lr: 1.0000e-04 eta: 0:57:52 time: 1.1015 data_time: 0.0136 memory: 15768 grad_norm: 4.6150 loss: 0.7205 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7205 2023/07/25 19:23:59 - mmengine - INFO - Epoch(train) [97][640/940] lr: 1.0000e-04 eta: 0:57:29 time: 1.1006 data_time: 0.0132 memory: 15768 grad_norm: 4.7114 loss: 0.8304 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8304 2023/07/25 19:24:21 - mmengine - INFO - Epoch(train) [97][660/940] lr: 1.0000e-04 eta: 0:57:07 time: 1.1018 data_time: 0.0134 memory: 15768 grad_norm: 4.6982 loss: 0.7346 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7346 2023/07/25 19:24:43 - mmengine - INFO - Epoch(train) [97][680/940] lr: 1.0000e-04 eta: 0:56:45 time: 1.0999 data_time: 0.0133 memory: 15768 grad_norm: 4.6967 loss: 0.8993 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8993 2023/07/25 19:25:05 - mmengine - INFO - Epoch(train) [97][700/940] lr: 1.0000e-04 eta: 0:56:23 time: 1.1000 data_time: 0.0135 memory: 15768 grad_norm: 4.5940 loss: 0.6234 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6234 2023/07/25 19:25:27 - mmengine - INFO - Epoch(train) [97][720/940] lr: 1.0000e-04 eta: 0:56:01 time: 1.1030 data_time: 0.0136 memory: 15768 grad_norm: 4.9152 loss: 0.9135 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9135 2023/07/25 19:25:49 - mmengine - INFO - Epoch(train) [97][740/940] lr: 1.0000e-04 eta: 0:55:39 time: 1.1036 data_time: 0.0149 memory: 15768 grad_norm: 4.7797 loss: 0.7105 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7105 2023/07/25 19:26:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 19:26:11 - mmengine - INFO - Epoch(train) [97][760/940] lr: 1.0000e-04 eta: 0:55:17 time: 1.1047 data_time: 0.0128 memory: 15768 grad_norm: 4.7003 loss: 0.8049 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8049 2023/07/25 19:26:33 - mmengine - INFO - Epoch(train) [97][780/940] lr: 1.0000e-04 eta: 0:54:55 time: 1.1014 data_time: 0.0139 memory: 15768 grad_norm: 4.7242 loss: 0.6893 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6893 2023/07/25 19:26:55 - mmengine - INFO - Epoch(train) [97][800/940] lr: 1.0000e-04 eta: 0:54:33 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 4.8191 loss: 0.8419 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.8419 2023/07/25 19:27:17 - mmengine - INFO - Epoch(train) [97][820/940] lr: 1.0000e-04 eta: 0:54:10 time: 1.0999 data_time: 0.0139 memory: 15768 grad_norm: 4.7966 loss: 0.8092 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8092 2023/07/25 19:27:39 - mmengine - INFO - Epoch(train) [97][840/940] lr: 1.0000e-04 eta: 0:53:48 time: 1.1044 data_time: 0.0131 memory: 15768 grad_norm: 4.6673 loss: 0.6564 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6564 2023/07/25 19:28:01 - mmengine - INFO - Epoch(train) [97][860/940] lr: 1.0000e-04 eta: 0:53:26 time: 1.0988 data_time: 0.0134 memory: 15768 grad_norm: 4.7627 loss: 0.8077 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8077 2023/07/25 19:28:23 - mmengine - INFO - Epoch(train) [97][880/940] lr: 1.0000e-04 eta: 0:53:04 time: 1.1042 data_time: 0.0129 memory: 15768 grad_norm: 4.7363 loss: 0.7669 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7669 2023/07/25 19:28:45 - mmengine - INFO - Epoch(train) [97][900/940] lr: 1.0000e-04 eta: 0:52:42 time: 1.1015 data_time: 0.0134 memory: 15768 grad_norm: 4.7585 loss: 0.7496 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7496 2023/07/25 19:29:07 - mmengine - INFO - Epoch(train) [97][920/940] lr: 1.0000e-04 eta: 0:52:20 time: 1.0996 data_time: 0.0134 memory: 15768 grad_norm: 4.7236 loss: 0.7765 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7765 2023/07/25 19:29:28 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 19:29:28 - mmengine - INFO - Epoch(train) [97][940/940] lr: 1.0000e-04 eta: 0:51:58 time: 1.0559 data_time: 0.0131 memory: 15768 grad_norm: 5.0378 loss: 0.8594 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8594 2023/07/25 19:29:38 - mmengine - INFO - Epoch(val) [97][20/78] eta: 0:00:28 time: 0.4868 data_time: 0.3298 memory: 2147 2023/07/25 19:29:45 - mmengine - INFO - Epoch(val) [97][40/78] eta: 0:00:16 time: 0.3656 data_time: 0.2086 memory: 2147 2023/07/25 19:29:54 - mmengine - INFO - Epoch(val) [97][60/78] eta: 0:00:07 time: 0.4467 data_time: 0.2899 memory: 2147 2023/07/25 19:30:04 - mmengine - INFO - Epoch(val) [97][78/78] acc/top1: 0.7119 acc/top5: 0.8990 acc/mean1: 0.7118 data_time: 0.2514 time: 0.4054 2023/07/25 19:30:30 - mmengine - INFO - Epoch(train) [98][ 20/940] lr: 1.0000e-04 eta: 0:51:36 time: 1.3097 data_time: 0.1594 memory: 15768 grad_norm: 4.6302 loss: 0.8001 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8001 2023/07/25 19:30:52 - mmengine - INFO - Epoch(train) [98][ 40/940] lr: 1.0000e-04 eta: 0:51:14 time: 1.1033 data_time: 0.0134 memory: 15768 grad_norm: 4.6983 loss: 0.6782 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6782 2023/07/25 19:31:14 - mmengine - INFO - Epoch(train) [98][ 60/940] lr: 1.0000e-04 eta: 0:50:51 time: 1.1015 data_time: 0.0137 memory: 15768 grad_norm: 4.7423 loss: 0.6934 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6934 2023/07/25 19:31:36 - mmengine - INFO - Epoch(train) [98][ 80/940] lr: 1.0000e-04 eta: 0:50:29 time: 1.1058 data_time: 0.0135 memory: 15768 grad_norm: 4.6558 loss: 0.7411 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7411 2023/07/25 19:31:58 - mmengine - INFO - Epoch(train) [98][100/940] lr: 1.0000e-04 eta: 0:50:07 time: 1.1023 data_time: 0.0130 memory: 15768 grad_norm: 4.7456 loss: 0.7107 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7107 2023/07/25 19:32:20 - mmengine - INFO - Epoch(train) [98][120/940] lr: 1.0000e-04 eta: 0:49:45 time: 1.0992 data_time: 0.0132 memory: 15768 grad_norm: 4.6838 loss: 0.6730 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6730 2023/07/25 19:32:42 - mmengine - INFO - Epoch(train) [98][140/940] lr: 1.0000e-04 eta: 0:49:23 time: 1.1034 data_time: 0.0134 memory: 15768 grad_norm: 4.8046 loss: 0.8983 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8983 2023/07/25 19:33:05 - mmengine - INFO - Epoch(train) [98][160/940] lr: 1.0000e-04 eta: 0:49:01 time: 1.1080 data_time: 0.0134 memory: 15768 grad_norm: 4.7450 loss: 0.6994 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.6994 2023/07/25 19:33:27 - mmengine - INFO - Epoch(train) [98][180/940] lr: 1.0000e-04 eta: 0:48:39 time: 1.1001 data_time: 0.0136 memory: 15768 grad_norm: 4.6229 loss: 0.8593 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8593 2023/07/25 19:33:49 - mmengine - INFO - Epoch(train) [98][200/940] lr: 1.0000e-04 eta: 0:48:17 time: 1.1026 data_time: 0.0130 memory: 15768 grad_norm: 4.8021 loss: 0.8655 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8655 2023/07/25 19:34:11 - mmengine - INFO - Epoch(train) [98][220/940] lr: 1.0000e-04 eta: 0:47:54 time: 1.1022 data_time: 0.0134 memory: 15768 grad_norm: 4.7448 loss: 0.7544 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7544 2023/07/25 19:34:33 - mmengine - INFO - Epoch(train) [98][240/940] lr: 1.0000e-04 eta: 0:47:32 time: 1.1002 data_time: 0.0136 memory: 15768 grad_norm: 4.7255 loss: 0.7045 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7045 2023/07/25 19:34:55 - mmengine - INFO - Epoch(train) [98][260/940] lr: 1.0000e-04 eta: 0:47:10 time: 1.1041 data_time: 0.0134 memory: 15768 grad_norm: 4.6511 loss: 0.9395 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9395 2023/07/25 19:35:17 - mmengine - INFO - Epoch(train) [98][280/940] lr: 1.0000e-04 eta: 0:46:48 time: 1.1007 data_time: 0.0134 memory: 15768 grad_norm: 4.7270 loss: 0.6830 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6830 2023/07/25 19:35:39 - mmengine - INFO - Epoch(train) [98][300/940] lr: 1.0000e-04 eta: 0:46:26 time: 1.1002 data_time: 0.0133 memory: 15768 grad_norm: 4.7593 loss: 0.8159 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8159 2023/07/25 19:36:01 - mmengine - INFO - Epoch(train) [98][320/940] lr: 1.0000e-04 eta: 0:46:04 time: 1.1003 data_time: 0.0133 memory: 15768 grad_norm: 4.7522 loss: 0.7716 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7716 2023/07/25 19:36:23 - mmengine - INFO - Epoch(train) [98][340/940] lr: 1.0000e-04 eta: 0:45:42 time: 1.0999 data_time: 0.0135 memory: 15768 grad_norm: 4.7857 loss: 0.7549 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7549 2023/07/25 19:36:45 - mmengine - INFO - Epoch(train) [98][360/940] lr: 1.0000e-04 eta: 0:45:20 time: 1.1007 data_time: 0.0131 memory: 15768 grad_norm: 4.6908 loss: 0.7331 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7331 2023/07/25 19:37:07 - mmengine - INFO - Epoch(train) [98][380/940] lr: 1.0000e-04 eta: 0:44:58 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 4.8144 loss: 0.7747 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7747 2023/07/25 19:37:29 - mmengine - INFO - Epoch(train) [98][400/940] lr: 1.0000e-04 eta: 0:44:35 time: 1.1019 data_time: 0.0133 memory: 15768 grad_norm: 4.6776 loss: 0.7264 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7264 2023/07/25 19:37:51 - mmengine - INFO - Epoch(train) [98][420/940] lr: 1.0000e-04 eta: 0:44:13 time: 1.0985 data_time: 0.0134 memory: 15768 grad_norm: 4.6917 loss: 0.7891 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7891 2023/07/25 19:38:13 - mmengine - INFO - Epoch(train) [98][440/940] lr: 1.0000e-04 eta: 0:43:51 time: 1.1026 data_time: 0.0141 memory: 15768 grad_norm: 4.7811 loss: 0.7921 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7921 2023/07/25 19:38:35 - mmengine - INFO - Epoch(train) [98][460/940] lr: 1.0000e-04 eta: 0:43:29 time: 1.1026 data_time: 0.0134 memory: 15768 grad_norm: 4.7380 loss: 0.6501 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6501 2023/07/25 19:38:57 - mmengine - INFO - Epoch(train) [98][480/940] lr: 1.0000e-04 eta: 0:43:07 time: 1.1021 data_time: 0.0134 memory: 15768 grad_norm: 4.7914 loss: 0.6975 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6975 2023/07/25 19:39:19 - mmengine - INFO - Epoch(train) [98][500/940] lr: 1.0000e-04 eta: 0:42:45 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 4.7455 loss: 0.8863 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8863 2023/07/25 19:39:41 - mmengine - INFO - Epoch(train) [98][520/940] lr: 1.0000e-04 eta: 0:42:23 time: 1.1026 data_time: 0.0132 memory: 15768 grad_norm: 4.9005 loss: 0.8105 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8105 2023/07/25 19:40:03 - mmengine - INFO - Epoch(train) [98][540/940] lr: 1.0000e-04 eta: 0:42:01 time: 1.1032 data_time: 0.0134 memory: 15768 grad_norm: 4.6767 loss: 0.8727 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8727 2023/07/25 19:40:25 - mmengine - INFO - Epoch(train) [98][560/940] lr: 1.0000e-04 eta: 0:41:38 time: 1.1003 data_time: 0.0135 memory: 15768 grad_norm: 4.7934 loss: 0.9313 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9313 2023/07/25 19:40:47 - mmengine - INFO - Epoch(train) [98][580/940] lr: 1.0000e-04 eta: 0:41:16 time: 1.1032 data_time: 0.0133 memory: 15768 grad_norm: 4.6675 loss: 0.7416 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7416 2023/07/25 19:41:09 - mmengine - INFO - Epoch(train) [98][600/940] lr: 1.0000e-04 eta: 0:40:54 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 4.7238 loss: 0.8059 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8059 2023/07/25 19:41:31 - mmengine - INFO - Epoch(train) [98][620/940] lr: 1.0000e-04 eta: 0:40:32 time: 1.1012 data_time: 0.0138 memory: 15768 grad_norm: 4.7937 loss: 0.7662 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7662 2023/07/25 19:41:53 - mmengine - INFO - Epoch(train) [98][640/940] lr: 1.0000e-04 eta: 0:40:10 time: 1.1035 data_time: 0.0133 memory: 15768 grad_norm: 4.7148 loss: 0.7363 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7363 2023/07/25 19:42:15 - mmengine - INFO - Epoch(train) [98][660/940] lr: 1.0000e-04 eta: 0:39:48 time: 1.1019 data_time: 0.0133 memory: 15768 grad_norm: 4.7330 loss: 0.7982 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7982 2023/07/25 19:42:37 - mmengine - INFO - Epoch(train) [98][680/940] lr: 1.0000e-04 eta: 0:39:26 time: 1.1032 data_time: 0.0134 memory: 15768 grad_norm: 4.7343 loss: 0.8237 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8237 2023/07/25 19:42:59 - mmengine - INFO - Epoch(train) [98][700/940] lr: 1.0000e-04 eta: 0:39:04 time: 1.1039 data_time: 0.0134 memory: 15768 grad_norm: 4.6747 loss: 0.6749 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6749 2023/07/25 19:43:22 - mmengine - INFO - Epoch(train) [98][720/940] lr: 1.0000e-04 eta: 0:38:42 time: 1.1023 data_time: 0.0131 memory: 15768 grad_norm: 4.7310 loss: 0.7406 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7406 2023/07/25 19:43:44 - mmengine - INFO - Epoch(train) [98][740/940] lr: 1.0000e-04 eta: 0:38:19 time: 1.1025 data_time: 0.0134 memory: 15768 grad_norm: 4.7845 loss: 0.8752 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8752 2023/07/25 19:44:06 - mmengine - INFO - Epoch(train) [98][760/940] lr: 1.0000e-04 eta: 0:37:57 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 4.7645 loss: 0.8125 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8125 2023/07/25 19:44:28 - mmengine - INFO - Epoch(train) [98][780/940] lr: 1.0000e-04 eta: 0:37:35 time: 1.0997 data_time: 0.0133 memory: 15768 grad_norm: 4.8242 loss: 0.8158 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8158 2023/07/25 19:44:50 - mmengine - INFO - Epoch(train) [98][800/940] lr: 1.0000e-04 eta: 0:37:13 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 4.9326 loss: 0.8167 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8167 2023/07/25 19:45:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 19:45:12 - mmengine - INFO - Epoch(train) [98][820/940] lr: 1.0000e-04 eta: 0:36:51 time: 1.1015 data_time: 0.0133 memory: 15768 grad_norm: 4.8301 loss: 0.7449 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7449 2023/07/25 19:45:34 - mmengine - INFO - Epoch(train) [98][840/940] lr: 1.0000e-04 eta: 0:36:29 time: 1.1031 data_time: 0.0134 memory: 15768 grad_norm: 4.7181 loss: 0.6802 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6802 2023/07/25 19:45:56 - mmengine - INFO - Epoch(train) [98][860/940] lr: 1.0000e-04 eta: 0:36:07 time: 1.1011 data_time: 0.0135 memory: 15768 grad_norm: 4.7668 loss: 0.9630 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9630 2023/07/25 19:46:18 - mmengine - INFO - Epoch(train) [98][880/940] lr: 1.0000e-04 eta: 0:35:45 time: 1.0983 data_time: 0.0133 memory: 15768 grad_norm: 4.7024 loss: 0.6699 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6699 2023/07/25 19:46:40 - mmengine - INFO - Epoch(train) [98][900/940] lr: 1.0000e-04 eta: 0:35:23 time: 1.1035 data_time: 0.0132 memory: 15768 grad_norm: 4.7053 loss: 0.7129 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7129 2023/07/25 19:47:02 - mmengine - INFO - Epoch(train) [98][920/940] lr: 1.0000e-04 eta: 0:35:00 time: 1.0992 data_time: 0.0133 memory: 15768 grad_norm: 4.5988 loss: 0.6336 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6336 2023/07/25 19:47:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 19:47:23 - mmengine - INFO - Epoch(train) [98][940/940] lr: 1.0000e-04 eta: 0:34:38 time: 1.0531 data_time: 0.0132 memory: 15768 grad_norm: 5.0181 loss: 0.8234 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8234 2023/07/25 19:47:33 - mmengine - INFO - Epoch(val) [98][20/78] eta: 0:00:29 time: 0.5048 data_time: 0.3467 memory: 2147 2023/07/25 19:47:40 - mmengine - INFO - Epoch(val) [98][40/78] eta: 0:00:16 time: 0.3526 data_time: 0.1956 memory: 2147 2023/07/25 19:47:49 - mmengine - INFO - Epoch(val) [98][60/78] eta: 0:00:07 time: 0.4473 data_time: 0.2908 memory: 2147 2023/07/25 19:47:58 - mmengine - INFO - Epoch(val) [98][78/78] acc/top1: 0.7114 acc/top5: 0.8990 acc/mean1: 0.7113 data_time: 0.2507 time: 0.4050 2023/07/25 19:48:24 - mmengine - INFO - Epoch(train) [99][ 20/940] lr: 1.0000e-04 eta: 0:34:16 time: 1.2825 data_time: 0.1487 memory: 15768 grad_norm: 4.7295 loss: 0.6986 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6986 2023/07/25 19:48:46 - mmengine - INFO - Epoch(train) [99][ 40/940] lr: 1.0000e-04 eta: 0:33:54 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 4.6417 loss: 0.7411 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7411 2023/07/25 19:49:08 - mmengine - INFO - Epoch(train) [99][ 60/940] lr: 1.0000e-04 eta: 0:33:32 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 4.5528 loss: 0.7089 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7089 2023/07/25 19:49:30 - mmengine - INFO - Epoch(train) [99][ 80/940] lr: 1.0000e-04 eta: 0:33:10 time: 1.1027 data_time: 0.0130 memory: 15768 grad_norm: 4.8577 loss: 0.7951 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7951 2023/07/25 19:49:52 - mmengine - INFO - Epoch(train) [99][100/940] lr: 1.0000e-04 eta: 0:32:48 time: 1.0983 data_time: 0.0132 memory: 15768 grad_norm: 4.8039 loss: 0.8316 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8316 2023/07/25 19:50:14 - mmengine - INFO - Epoch(train) [99][120/940] lr: 1.0000e-04 eta: 0:32:26 time: 1.0994 data_time: 0.0134 memory: 15768 grad_norm: 4.6642 loss: 0.8305 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8305 2023/07/25 19:50:36 - mmengine - INFO - Epoch(train) [99][140/940] lr: 1.0000e-04 eta: 0:32:04 time: 1.0995 data_time: 0.0133 memory: 15768 grad_norm: 4.8314 loss: 0.7439 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7439 2023/07/25 19:50:58 - mmengine - INFO - Epoch(train) [99][160/940] lr: 1.0000e-04 eta: 0:31:41 time: 1.1020 data_time: 0.0133 memory: 15768 grad_norm: 4.8048 loss: 0.8291 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8291 2023/07/25 19:51:20 - mmengine - INFO - Epoch(train) [99][180/940] lr: 1.0000e-04 eta: 0:31:19 time: 1.0992 data_time: 0.0135 memory: 15768 grad_norm: 4.6761 loss: 0.8366 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8366 2023/07/25 19:51:42 - mmengine - INFO - Epoch(train) [99][200/940] lr: 1.0000e-04 eta: 0:30:57 time: 1.1008 data_time: 0.0130 memory: 15768 grad_norm: 4.7775 loss: 0.7563 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7563 2023/07/25 19:52:04 - mmengine - INFO - Epoch(train) [99][220/940] lr: 1.0000e-04 eta: 0:30:35 time: 1.1016 data_time: 0.0133 memory: 15768 grad_norm: 4.6927 loss: 0.8942 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8942 2023/07/25 19:52:26 - mmengine - INFO - Epoch(train) [99][240/940] lr: 1.0000e-04 eta: 0:30:13 time: 1.1033 data_time: 0.0131 memory: 15768 grad_norm: 4.7211 loss: 0.8447 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8447 2023/07/25 19:52:48 - mmengine - INFO - Epoch(train) [99][260/940] lr: 1.0000e-04 eta: 0:29:51 time: 1.1019 data_time: 0.0134 memory: 15768 grad_norm: 4.6225 loss: 0.7897 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7897 2023/07/25 19:53:10 - mmengine - INFO - Epoch(train) [99][280/940] lr: 1.0000e-04 eta: 0:29:29 time: 1.0998 data_time: 0.0133 memory: 15768 grad_norm: 4.8034 loss: 0.6754 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6754 2023/07/25 19:53:32 - mmengine - INFO - Epoch(train) [99][300/940] lr: 1.0000e-04 eta: 0:29:07 time: 1.1010 data_time: 0.0134 memory: 15768 grad_norm: 4.8106 loss: 0.7137 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7137 2023/07/25 19:53:54 - mmengine - INFO - Epoch(train) [99][320/940] lr: 1.0000e-04 eta: 0:28:44 time: 1.1005 data_time: 0.0132 memory: 15768 grad_norm: 4.8080 loss: 0.7578 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7578 2023/07/25 19:54:16 - mmengine - INFO - Epoch(train) [99][340/940] lr: 1.0000e-04 eta: 0:28:22 time: 1.0974 data_time: 0.0133 memory: 15768 grad_norm: 4.5812 loss: 0.7687 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7687 2023/07/25 19:54:38 - mmengine - INFO - Epoch(train) [99][360/940] lr: 1.0000e-04 eta: 0:28:00 time: 1.1036 data_time: 0.0134 memory: 15768 grad_norm: 4.7149 loss: 0.6311 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6311 2023/07/25 19:55:00 - mmengine - INFO - Epoch(train) [99][380/940] lr: 1.0000e-04 eta: 0:27:38 time: 1.1002 data_time: 0.0135 memory: 15768 grad_norm: 4.6211 loss: 0.7694 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7694 2023/07/25 19:55:22 - mmengine - INFO - Epoch(train) [99][400/940] lr: 1.0000e-04 eta: 0:27:16 time: 1.1053 data_time: 0.0131 memory: 15768 grad_norm: 4.7776 loss: 0.7974 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7974 2023/07/25 19:55:44 - mmengine - INFO - Epoch(train) [99][420/940] lr: 1.0000e-04 eta: 0:26:54 time: 1.0989 data_time: 0.0132 memory: 15768 grad_norm: 4.8431 loss: 0.6443 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6443 2023/07/25 19:56:06 - mmengine - INFO - Epoch(train) [99][440/940] lr: 1.0000e-04 eta: 0:26:32 time: 1.1041 data_time: 0.0133 memory: 15768 grad_norm: 4.7315 loss: 0.7181 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7181 2023/07/25 19:56:28 - mmengine - INFO - Epoch(train) [99][460/940] lr: 1.0000e-04 eta: 0:26:10 time: 1.0999 data_time: 0.0133 memory: 15768 grad_norm: 4.7542 loss: 0.7928 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7928 2023/07/25 19:56:51 - mmengine - INFO - Epoch(train) [99][480/940] lr: 1.0000e-04 eta: 0:25:48 time: 1.1043 data_time: 0.0136 memory: 15768 grad_norm: 4.6893 loss: 0.8118 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8118 2023/07/25 19:57:13 - mmengine - INFO - Epoch(train) [99][500/940] lr: 1.0000e-04 eta: 0:25:25 time: 1.0990 data_time: 0.0134 memory: 15768 grad_norm: 4.8366 loss: 0.6928 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6928 2023/07/25 19:57:35 - mmengine - INFO - Epoch(train) [99][520/940] lr: 1.0000e-04 eta: 0:25:03 time: 1.1001 data_time: 0.0132 memory: 15768 grad_norm: 4.8469 loss: 0.6766 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6766 2023/07/25 19:57:57 - mmengine - INFO - Epoch(train) [99][540/940] lr: 1.0000e-04 eta: 0:24:41 time: 1.1012 data_time: 0.0133 memory: 15768 grad_norm: 4.6687 loss: 0.5982 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5982 2023/07/25 19:58:19 - mmengine - INFO - Epoch(train) [99][560/940] lr: 1.0000e-04 eta: 0:24:19 time: 1.1029 data_time: 0.0130 memory: 15768 grad_norm: 4.8207 loss: 0.8446 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8446 2023/07/25 19:58:41 - mmengine - INFO - Epoch(train) [99][580/940] lr: 1.0000e-04 eta: 0:23:57 time: 1.1015 data_time: 0.0133 memory: 15768 grad_norm: 4.7056 loss: 0.7838 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7838 2023/07/25 19:59:03 - mmengine - INFO - Epoch(train) [99][600/940] lr: 1.0000e-04 eta: 0:23:35 time: 1.1018 data_time: 0.0132 memory: 15768 grad_norm: 4.8033 loss: 0.8020 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8020 2023/07/25 19:59:25 - mmengine - INFO - Epoch(train) [99][620/940] lr: 1.0000e-04 eta: 0:23:13 time: 1.0988 data_time: 0.0133 memory: 15768 grad_norm: 4.7605 loss: 0.9281 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9281 2023/07/25 19:59:47 - mmengine - INFO - Epoch(train) [99][640/940] lr: 1.0000e-04 eta: 0:22:51 time: 1.0998 data_time: 0.0130 memory: 15768 grad_norm: 4.8099 loss: 0.7449 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7449 2023/07/25 20:00:09 - mmengine - INFO - Epoch(train) [99][660/940] lr: 1.0000e-04 eta: 0:22:28 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 4.8519 loss: 0.7562 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7562 2023/07/25 20:00:31 - mmengine - INFO - Epoch(train) [99][680/940] lr: 1.0000e-04 eta: 0:22:06 time: 1.0995 data_time: 0.0132 memory: 15768 grad_norm: 4.5981 loss: 0.8206 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8206 2023/07/25 20:00:53 - mmengine - INFO - Epoch(train) [99][700/940] lr: 1.0000e-04 eta: 0:21:44 time: 1.1009 data_time: 0.0132 memory: 15768 grad_norm: 4.7391 loss: 0.6871 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6871 2023/07/25 20:01:15 - mmengine - INFO - Epoch(train) [99][720/940] lr: 1.0000e-04 eta: 0:21:22 time: 1.1006 data_time: 0.0134 memory: 15768 grad_norm: 4.7362 loss: 0.7755 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7755 2023/07/25 20:01:37 - mmengine - INFO - Epoch(train) [99][740/940] lr: 1.0000e-04 eta: 0:21:00 time: 1.0999 data_time: 0.0135 memory: 15768 grad_norm: 4.7886 loss: 0.7612 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7612 2023/07/25 20:01:59 - mmengine - INFO - Epoch(train) [99][760/940] lr: 1.0000e-04 eta: 0:20:38 time: 1.1021 data_time: 0.0131 memory: 15768 grad_norm: 4.6710 loss: 0.7208 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7208 2023/07/25 20:02:21 - mmengine - INFO - Epoch(train) [99][780/940] lr: 1.0000e-04 eta: 0:20:16 time: 1.1016 data_time: 0.0131 memory: 15768 grad_norm: 4.7912 loss: 0.8185 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8185 2023/07/25 20:02:43 - mmengine - INFO - Epoch(train) [99][800/940] lr: 1.0000e-04 eta: 0:19:54 time: 1.0997 data_time: 0.0134 memory: 15768 grad_norm: 4.8781 loss: 0.6536 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6536 2023/07/25 20:03:05 - mmengine - INFO - Epoch(train) [99][820/940] lr: 1.0000e-04 eta: 0:19:32 time: 1.0996 data_time: 0.0133 memory: 15768 grad_norm: 4.7850 loss: 0.8897 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8897 2023/07/25 20:03:27 - mmengine - INFO - Epoch(train) [99][840/940] lr: 1.0000e-04 eta: 0:19:09 time: 1.0991 data_time: 0.0130 memory: 15768 grad_norm: 4.7292 loss: 0.7005 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7005 2023/07/25 20:03:49 - mmengine - INFO - Epoch(train) [99][860/940] lr: 1.0000e-04 eta: 0:18:47 time: 1.1032 data_time: 0.0135 memory: 15768 grad_norm: 4.6920 loss: 0.6799 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6799 2023/07/25 20:04:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 20:04:11 - mmengine - INFO - Epoch(train) [99][880/940] lr: 1.0000e-04 eta: 0:18:25 time: 1.1016 data_time: 0.0136 memory: 15768 grad_norm: 4.6565 loss: 0.7472 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7472 2023/07/25 20:04:33 - mmengine - INFO - Epoch(train) [99][900/940] lr: 1.0000e-04 eta: 0:18:03 time: 1.0987 data_time: 0.0132 memory: 15768 grad_norm: 4.7724 loss: 0.9462 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9462 2023/07/25 20:04:55 - mmengine - INFO - Epoch(train) [99][920/940] lr: 1.0000e-04 eta: 0:17:41 time: 1.1044 data_time: 0.0128 memory: 15768 grad_norm: 4.7430 loss: 0.8842 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8842 2023/07/25 20:05:16 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 20:05:16 - mmengine - INFO - Epoch(train) [99][940/940] lr: 1.0000e-04 eta: 0:17:19 time: 1.0623 data_time: 0.0118 memory: 15768 grad_norm: 4.9980 loss: 0.9245 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 0.9245 2023/07/25 20:05:16 - mmengine - INFO - Saving checkpoint at 99 epochs 2023/07/25 20:05:27 - mmengine - INFO - Epoch(val) [99][20/78] eta: 0:00:28 time: 0.4972 data_time: 0.3394 memory: 2147 2023/07/25 20:05:34 - mmengine - INFO - Epoch(val) [99][40/78] eta: 0:00:16 time: 0.3498 data_time: 0.1923 memory: 2147 2023/07/25 20:05:43 - mmengine - INFO - Epoch(val) [99][60/78] eta: 0:00:07 time: 0.4509 data_time: 0.2943 memory: 2147 2023/07/25 20:05:52 - mmengine - INFO - Epoch(val) [99][78/78] acc/top1: 0.7097 acc/top5: 0.8996 acc/mean1: 0.7096 data_time: 0.2461 time: 0.4005 2023/07/25 20:06:18 - mmengine - INFO - Epoch(train) [100][ 20/940] lr: 1.0000e-04 eta: 0:16:57 time: 1.2843 data_time: 0.1633 memory: 15768 grad_norm: 4.7530 loss: 0.7019 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7019 2023/07/25 20:06:40 - mmengine - INFO - Epoch(train) [100][ 40/940] lr: 1.0000e-04 eta: 0:16:35 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 4.7895 loss: 0.8079 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8079 2023/07/25 20:07:02 - mmengine - INFO - Epoch(train) [100][ 60/940] lr: 1.0000e-04 eta: 0:16:13 time: 1.1011 data_time: 0.0137 memory: 15768 grad_norm: 4.7425 loss: 0.8776 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8776 2023/07/25 20:07:24 - mmengine - INFO - Epoch(train) [100][ 80/940] lr: 1.0000e-04 eta: 0:15:50 time: 1.1010 data_time: 0.0137 memory: 15768 grad_norm: 4.7751 loss: 0.9380 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9380 2023/07/25 20:07:46 - mmengine - INFO - Epoch(train) [100][100/940] lr: 1.0000e-04 eta: 0:15:28 time: 1.1061 data_time: 0.0134 memory: 15768 grad_norm: 4.6981 loss: 0.8428 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8428 2023/07/25 20:08:08 - mmengine - INFO - Epoch(train) [100][120/940] lr: 1.0000e-04 eta: 0:15:06 time: 1.0990 data_time: 0.0133 memory: 15768 grad_norm: 4.8177 loss: 0.7403 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7403 2023/07/25 20:08:30 - mmengine - INFO - Epoch(train) [100][140/940] lr: 1.0000e-04 eta: 0:14:44 time: 1.0998 data_time: 0.0135 memory: 15768 grad_norm: 4.7931 loss: 0.8461 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8461 2023/07/25 20:08:52 - mmengine - INFO - Epoch(train) [100][160/940] lr: 1.0000e-04 eta: 0:14:22 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 4.7862 loss: 0.7779 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7779 2023/07/25 20:09:14 - mmengine - INFO - Epoch(train) [100][180/940] lr: 1.0000e-04 eta: 0:14:00 time: 1.0993 data_time: 0.0135 memory: 15768 grad_norm: 4.6876 loss: 0.8483 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8483 2023/07/25 20:09:36 - mmengine - INFO - Epoch(train) [100][200/940] lr: 1.0000e-04 eta: 0:13:38 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 4.7798 loss: 0.6683 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6683 2023/07/25 20:09:59 - mmengine - INFO - Epoch(train) [100][220/940] lr: 1.0000e-04 eta: 0:13:16 time: 1.1263 data_time: 0.0133 memory: 15768 grad_norm: 4.7682 loss: 0.8201 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8201 2023/07/25 20:10:22 - mmengine - INFO - Epoch(train) [100][240/940] lr: 1.0000e-04 eta: 0:12:54 time: 1.1610 data_time: 0.0132 memory: 15768 grad_norm: 4.8011 loss: 0.7686 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7686 2023/07/25 20:10:45 - mmengine - INFO - Epoch(train) [100][260/940] lr: 1.0000e-04 eta: 0:12:31 time: 1.1574 data_time: 0.0135 memory: 15768 grad_norm: 4.6750 loss: 0.7094 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7094 2023/07/25 20:11:07 - mmengine - INFO - Epoch(train) [100][280/940] lr: 1.0000e-04 eta: 0:12:09 time: 1.0999 data_time: 0.0132 memory: 15768 grad_norm: 4.8441 loss: 0.7664 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7664 2023/07/25 20:11:29 - mmengine - INFO - Epoch(train) [100][300/940] lr: 1.0000e-04 eta: 0:11:47 time: 1.1001 data_time: 0.0130 memory: 15768 grad_norm: 4.6729 loss: 0.7553 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7553 2023/07/25 20:11:51 - mmengine - INFO - Epoch(train) [100][320/940] lr: 1.0000e-04 eta: 0:11:25 time: 1.1018 data_time: 0.0133 memory: 15768 grad_norm: 4.8295 loss: 0.7666 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7666 2023/07/25 20:12:13 - mmengine - INFO - Epoch(train) [100][340/940] lr: 1.0000e-04 eta: 0:11:03 time: 1.1033 data_time: 0.0138 memory: 15768 grad_norm: 4.7575 loss: 0.8190 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8190 2023/07/25 20:12:35 - mmengine - INFO - Epoch(train) [100][360/940] lr: 1.0000e-04 eta: 0:10:41 time: 1.1005 data_time: 0.0133 memory: 15768 grad_norm: 4.7681 loss: 0.7315 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7315 2023/07/25 20:12:57 - mmengine - INFO - Epoch(train) [100][380/940] lr: 1.0000e-04 eta: 0:10:19 time: 1.0995 data_time: 0.0133 memory: 15768 grad_norm: 4.6632 loss: 0.7784 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7784 2023/07/25 20:13:20 - mmengine - INFO - Epoch(train) [100][400/940] lr: 1.0000e-04 eta: 0:09:57 time: 1.1018 data_time: 0.0137 memory: 15768 grad_norm: 4.7081 loss: 0.7853 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7853 2023/07/25 20:13:42 - mmengine - INFO - Epoch(train) [100][420/940] lr: 1.0000e-04 eta: 0:09:34 time: 1.1026 data_time: 0.0129 memory: 15768 grad_norm: 4.7136 loss: 0.8318 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8318 2023/07/25 20:14:04 - mmengine - INFO - Epoch(train) [100][440/940] lr: 1.0000e-04 eta: 0:09:12 time: 1.1033 data_time: 0.0132 memory: 15768 grad_norm: 4.7977 loss: 0.7486 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7486 2023/07/25 20:14:26 - mmengine - INFO - Epoch(train) [100][460/940] lr: 1.0000e-04 eta: 0:08:50 time: 1.1012 data_time: 0.0131 memory: 15768 grad_norm: 4.7541 loss: 0.8335 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8335 2023/07/25 20:14:48 - mmengine - INFO - Epoch(train) [100][480/940] lr: 1.0000e-04 eta: 0:08:28 time: 1.1012 data_time: 0.0133 memory: 15768 grad_norm: 4.7846 loss: 0.8532 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8532 2023/07/25 20:15:10 - mmengine - INFO - Epoch(train) [100][500/940] lr: 1.0000e-04 eta: 0:08:06 time: 1.1013 data_time: 0.0132 memory: 15768 grad_norm: 4.7138 loss: 0.7818 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7818 2023/07/25 20:15:32 - mmengine - INFO - Epoch(train) [100][520/940] lr: 1.0000e-04 eta: 0:07:44 time: 1.1012 data_time: 0.0136 memory: 15768 grad_norm: 4.6067 loss: 0.7865 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7865 2023/07/25 20:15:54 - mmengine - INFO - Epoch(train) [100][540/940] lr: 1.0000e-04 eta: 0:07:22 time: 1.0998 data_time: 0.0137 memory: 15768 grad_norm: 4.7251 loss: 0.7626 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7626 2023/07/25 20:16:16 - mmengine - INFO - Epoch(train) [100][560/940] lr: 1.0000e-04 eta: 0:07:00 time: 1.1017 data_time: 0.0133 memory: 15768 grad_norm: 4.7251 loss: 0.7646 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7646 2023/07/25 20:16:38 - mmengine - INFO - Epoch(train) [100][580/940] lr: 1.0000e-04 eta: 0:06:38 time: 1.0980 data_time: 0.0133 memory: 15768 grad_norm: 4.6774 loss: 0.6370 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6370 2023/07/25 20:17:00 - mmengine - INFO - Epoch(train) [100][600/940] lr: 1.0000e-04 eta: 0:06:15 time: 1.0996 data_time: 0.0136 memory: 15768 grad_norm: 4.6428 loss: 0.7997 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7997 2023/07/25 20:17:22 - mmengine - INFO - Epoch(train) [100][620/940] lr: 1.0000e-04 eta: 0:05:53 time: 1.0975 data_time: 0.0135 memory: 15768 grad_norm: 4.7588 loss: 0.7189 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7189 2023/07/25 20:17:44 - mmengine - INFO - Epoch(train) [100][640/940] lr: 1.0000e-04 eta: 0:05:31 time: 1.1023 data_time: 0.0137 memory: 15768 grad_norm: 4.7078 loss: 0.8828 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8828 2023/07/25 20:18:07 - mmengine - INFO - Epoch(train) [100][660/940] lr: 1.0000e-04 eta: 0:05:09 time: 1.1465 data_time: 0.0138 memory: 15768 grad_norm: 4.8911 loss: 0.6674 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6674 2023/07/25 20:18:30 - mmengine - INFO - Epoch(train) [100][680/940] lr: 1.0000e-04 eta: 0:04:47 time: 1.1582 data_time: 0.0134 memory: 15768 grad_norm: 4.8038 loss: 0.8500 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8500 2023/07/25 20:18:53 - mmengine - INFO - Epoch(train) [100][700/940] lr: 1.0000e-04 eta: 0:04:25 time: 1.1370 data_time: 0.0134 memory: 15768 grad_norm: 4.8290 loss: 0.9210 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9210 2023/07/25 20:19:15 - mmengine - INFO - Epoch(train) [100][720/940] lr: 1.0000e-04 eta: 0:04:03 time: 1.1021 data_time: 0.0129 memory: 15768 grad_norm: 4.8507 loss: 0.8580 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8580 2023/07/25 20:19:37 - mmengine - INFO - Epoch(train) [100][740/940] lr: 1.0000e-04 eta: 0:03:41 time: 1.1012 data_time: 0.0134 memory: 15768 grad_norm: 4.7967 loss: 0.8417 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8417 2023/07/25 20:19:59 - mmengine - INFO - Epoch(train) [100][760/940] lr: 1.0000e-04 eta: 0:03:19 time: 1.1014 data_time: 0.0133 memory: 15768 grad_norm: 4.6594 loss: 0.7191 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7191 2023/07/25 20:20:21 - mmengine - INFO - Epoch(train) [100][780/940] lr: 1.0000e-04 eta: 0:02:56 time: 1.1013 data_time: 0.0134 memory: 15768 grad_norm: 4.8188 loss: 0.8049 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8049 2023/07/25 20:20:43 - mmengine - INFO - Epoch(train) [100][800/940] lr: 1.0000e-04 eta: 0:02:34 time: 1.1021 data_time: 0.0133 memory: 15768 grad_norm: 4.7034 loss: 0.8312 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8312 2023/07/25 20:21:05 - mmengine - INFO - Epoch(train) [100][820/940] lr: 1.0000e-04 eta: 0:02:12 time: 1.1050 data_time: 0.0132 memory: 15768 grad_norm: 4.7983 loss: 0.8539 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8539 2023/07/25 20:21:27 - mmengine - INFO - Epoch(train) [100][840/940] lr: 1.0000e-04 eta: 0:01:50 time: 1.1037 data_time: 0.0130 memory: 15768 grad_norm: 4.6996 loss: 0.6806 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6806 2023/07/25 20:21:49 - mmengine - INFO - Epoch(train) [100][860/940] lr: 1.0000e-04 eta: 0:01:28 time: 1.1012 data_time: 0.0130 memory: 15768 grad_norm: 4.8358 loss: 0.7302 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7302 2023/07/25 20:22:11 - mmengine - INFO - Epoch(train) [100][880/940] lr: 1.0000e-04 eta: 0:01:06 time: 1.1005 data_time: 0.0131 memory: 15768 grad_norm: 4.8570 loss: 0.8124 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8124 2023/07/25 20:22:33 - mmengine - INFO - Epoch(train) [100][900/940] lr: 1.0000e-04 eta: 0:00:44 time: 1.1022 data_time: 0.0132 memory: 15768 grad_norm: 4.7574 loss: 0.8290 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8290 2023/07/25 20:22:55 - mmengine - INFO - Epoch(train) [100][920/940] lr: 1.0000e-04 eta: 0:00:22 time: 1.1018 data_time: 0.0127 memory: 15768 grad_norm: 4.7367 loss: 0.6219 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6219 2023/07/25 20:23:16 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-mobileone_s4_8xb32-1x1x8-100e_kinetics400-rgb_20230724_143035 2023/07/25 20:23:16 - mmengine - INFO - Epoch(train) [100][940/940] lr: 1.0000e-04 eta: 0:00:00 time: 1.0541 data_time: 0.0126 memory: 15768 grad_norm: 5.0416 loss: 0.6290 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6290 2023/07/25 20:23:16 - mmengine - INFO - Saving checkpoint at 100 epochs 2023/07/25 20:23:27 - mmengine - INFO - Epoch(val) [100][20/78] eta: 0:00:28 time: 0.4909 data_time: 0.3338 memory: 2147 2023/07/25 20:23:34 - mmengine - INFO - Epoch(val) [100][40/78] eta: 0:00:15 time: 0.3494 data_time: 0.1924 memory: 2147 2023/07/25 20:23:43 - mmengine - INFO - Epoch(val) [100][60/78] eta: 0:00:07 time: 0.4480 data_time: 0.2912 memory: 2147 2023/07/25 20:23:52 - mmengine - INFO - Epoch(val) [100][78/78] acc/top1: 0.7102 acc/top5: 0.8989 acc/mean1: 0.7101 data_time: 0.2437 time: 0.3977