2023/03/08 15:44:09 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.0 (default, Oct 9 2018, 10:31:47) [GCC 7.3.0] CUDA available: True numpy_random_seed: 650923498 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/cache/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMEngine: 0.5.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None diff_rank_seed: False deterministic: False Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/03/08 15:44:10 - mmengine - INFO - Config: model = dict( type='Recognizer2D', backbone=dict( type='ResNet', pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth', depth=50, norm_eval=False), cls_head=dict( type='TSNHead', num_classes=174, in_channels=2048, spatial_type='avg', consensus=dict(type='AvgConsensus', dim=1), dropout_ratio=0.5, 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=50, val_begin=1, val_interval=5) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='MultiStepLR', begin=0, end=50, by_epoch=True, milestones=[20, 40], 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'), dict(type='TensorboardVisBackend') ] visualizer = dict( type='ActionVisualizer', vis_backends=[ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend') ]) log_level = 'INFO' load_from = None resume = False dataset_type = 'VideoDataset' data_root = 'data/sthv2/videos' ann_file_train = 'data/sthv2/sthv2_train_list_videos.txt' ann_file_val = 'data/sthv2/sthv2_val_list_videos.txt' file_client_args = dict( io_backend='petrel', path_mapping=dict( {'data/sthv2': 's254:s3://openmmlab/datasets/action/sthv2'})) sthv2_flip_label_map = dict({ 86: 87, 87: 86, 93: 94, 94: 93, 166: 167, 167: 166 }) train_pipeline = [ dict( type='DecordInit', io_backend='petrel', path_mapping=dict( {'data/sthv2': 's254:s3://openmmlab/datasets/action/sthv2'})), 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, flip_label_map=dict({ 86: 87, 87: 86, 93: 94, 94: 93, 166: 167, 167: 166 })), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] val_pipeline = [ dict( type='DecordInit', io_backend='petrel', path_mapping=dict( {'data/sthv2': 's254:s3://openmmlab/datasets/action/sthv2'})), 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='petrel', path_mapping=dict( {'data/sthv2': 's254:s3://openmmlab/datasets/action/sthv2'})), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=25, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='TenCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] train_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='VideoDataset', ann_file='data/sthv2/sthv2_train_list_videos.txt', data_prefix=dict(video='data/sthv2/videos'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/sthv2': 's254:s3://openmmlab/datasets/action/sthv2' })), 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, flip_label_map=dict({ 86: 87, 87: 86, 93: 94, 94: 93, 166: 167, 167: 166 })), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ])) val_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='VideoDataset', ann_file='data/sthv2/sthv2_val_list_videos.txt', data_prefix=dict(video='data/sthv2/videos'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/sthv2': 's254:s3://openmmlab/datasets/action/sthv2' })), 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/sthv2/sthv2_val_list_videos.txt', data_prefix=dict(video='data/sthv2/videos'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/sthv2': 's254:s3://openmmlab/datasets/action/sthv2' })), 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) launcher = 'pytorch' work_dir = 'work_dirs/fix_flip/tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_exp1' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2023/03/08 15:44:13 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (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 -------------------- 2023/03/08 15:44:15 - mmengine - INFO - These parameters in pretrained checkpoint are not loaded: {'fc.weight', 'fc.bias'} Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([64, 3, 7, 7]): Initialized by user-defined `init_weights` in ResNet backbone.conv1.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.conv1.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv1.conv.weight - torch.Size([64, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv1.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv1.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv2.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv2.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv3.conv.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv3.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.conv3.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.downsample.conv.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.downsample.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.downsample.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv1.conv.weight - torch.Size([64, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv1.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv1.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv2.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv2.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv3.conv.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv3.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.conv3.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv1.conv.weight - torch.Size([64, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv1.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv1.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv2.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv2.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv3.conv.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv3.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.conv3.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv1.conv.weight - torch.Size([128, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv1.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv1.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv2.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv2.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv3.conv.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv3.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.conv3.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.downsample.conv.weight - torch.Size([512, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.downsample.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.downsample.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv1.conv.weight - torch.Size([128, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv1.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv1.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv2.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv2.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv3.conv.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv3.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.conv3.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv1.conv.weight - torch.Size([128, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv1.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv1.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv2.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv2.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv3.conv.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv3.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.conv3.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv1.conv.weight - torch.Size([128, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv1.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv1.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv2.bn.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv2.bn.bias - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv3.conv.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv3.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.conv3.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv1.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv1.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv1.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv2.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv2.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv3.bn.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.conv3.bn.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.downsample.conv.weight - torch.Size([1024, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.downsample.bn.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.downsample.bn.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv1.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv1.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv2.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv2.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv3.bn.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.conv3.bn.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv1.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv1.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv2.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv2.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv3.bn.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.conv3.bn.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv1.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv1.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv2.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv2.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv3.bn.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.conv3.bn.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv1.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv1.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv2.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv2.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv3.bn.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.conv3.bn.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv1.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv1.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv2.bn.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv2.bn.bias - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv3.bn.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.conv3.bn.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv1.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv1.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv1.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv2.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv2.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv2.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv3.conv.weight - torch.Size([2048, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv3.bn.weight - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.conv3.bn.bias - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.downsample.conv.weight - torch.Size([2048, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.downsample.bn.weight - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.downsample.bn.bias - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv1.conv.weight - torch.Size([512, 2048, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv1.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv1.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv2.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv2.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv2.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv3.conv.weight - torch.Size([2048, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv3.bn.weight - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.conv3.bn.bias - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv1.conv.weight - torch.Size([512, 2048, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv1.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv1.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv2.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv2.bn.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv2.bn.bias - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv3.conv.weight - torch.Size([2048, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv3.bn.weight - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.conv3.bn.bias - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet cls_head.fc_cls.weight - torch.Size([174, 2048]): Initialized by user-defined `init_weights` in TSNHead cls_head.fc_cls.bias - torch.Size([174]): Initialized by user-defined `init_weights` in TSNHead 2023/03/08 15:44:15 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_exp1. 2023/03/08 15:44:48 - mmengine - INFO - Epoch(train) [1][ 20/660] lr: 1.0000e-02 eta: 15:19:22 time: 1.6726 data_time: 1.3096 memory: 21539 grad_norm: 0.9534 loss: 5.1361 top1_acc: 0.0000 top5_acc: 0.0938 loss_cls: 5.1361 2023/03/08 15:44:55 - mmengine - INFO - Epoch(train) [1][ 40/660] lr: 1.0000e-02 eta: 9:11:15 time: 0.3344 data_time: 0.0177 memory: 21539 grad_norm: 1.1401 loss: 5.0343 top1_acc: 0.0000 top5_acc: 0.0938 loss_cls: 5.0343 2023/03/08 15:45:02 - mmengine - INFO - Epoch(train) [1][ 60/660] lr: 1.0000e-02 eta: 7:09:16 time: 0.3387 data_time: 0.0221 memory: 21539 grad_norm: 1.2509 loss: 5.0034 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 5.0034 2023/03/08 15:45:08 - mmengine - INFO - Epoch(train) [1][ 80/660] lr: 1.0000e-02 eta: 6:07:38 time: 0.3345 data_time: 0.0184 memory: 21539 grad_norm: 1.2929 loss: 4.9455 top1_acc: 0.0312 top5_acc: 0.0625 loss_cls: 4.9455 2023/03/08 15:45:15 - mmengine - INFO - Epoch(train) [1][100/660] lr: 1.0000e-02 eta: 5:31:13 time: 0.3401 data_time: 0.0229 memory: 21539 grad_norm: 1.3342 loss: 4.9088 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.9088 2023/03/08 15:45:22 - mmengine - INFO - Epoch(train) [1][120/660] lr: 1.0000e-02 eta: 5:07:00 time: 0.3411 data_time: 0.0196 memory: 21539 grad_norm: 1.4088 loss: 4.8151 top1_acc: 0.0312 top5_acc: 0.0938 loss_cls: 4.8151 2023/03/08 15:45:29 - mmengine - INFO - Epoch(train) [1][140/660] lr: 1.0000e-02 eta: 4:50:27 time: 0.3512 data_time: 0.0227 memory: 21539 grad_norm: 1.4541 loss: 4.8636 top1_acc: 0.0938 top5_acc: 0.1250 loss_cls: 4.8636 2023/03/08 15:45:36 - mmengine - INFO - Epoch(train) [1][160/660] lr: 1.0000e-02 eta: 4:37:25 time: 0.3425 data_time: 0.0256 memory: 21539 grad_norm: 1.5384 loss: 4.6977 top1_acc: 0.0000 top5_acc: 0.2188 loss_cls: 4.6977 2023/03/08 15:45:43 - mmengine - INFO - Epoch(train) [1][180/660] lr: 1.0000e-02 eta: 4:27:16 time: 0.3426 data_time: 0.0221 memory: 21539 grad_norm: 1.6245 loss: 4.7828 top1_acc: 0.0625 top5_acc: 0.1875 loss_cls: 4.7828 2023/03/08 15:45:50 - mmengine - INFO - Epoch(train) [1][200/660] lr: 1.0000e-02 eta: 4:18:49 time: 0.3370 data_time: 0.0198 memory: 21539 grad_norm: 1.6883 loss: 4.6661 top1_acc: 0.0312 top5_acc: 0.2500 loss_cls: 4.6661 2023/03/08 15:45:56 - mmengine - INFO - Epoch(train) [1][220/660] lr: 1.0000e-02 eta: 4:12:06 time: 0.3415 data_time: 0.0230 memory: 21539 grad_norm: 1.7862 loss: 4.6213 top1_acc: 0.0312 top5_acc: 0.1562 loss_cls: 4.6213 2023/03/08 15:46:03 - mmengine - INFO - Epoch(train) [1][240/660] lr: 1.0000e-02 eta: 4:06:19 time: 0.3376 data_time: 0.0204 memory: 21539 grad_norm: 1.8769 loss: 4.4870 top1_acc: 0.1562 top5_acc: 0.3438 loss_cls: 4.4870 2023/03/08 15:46:10 - mmengine - INFO - Epoch(train) [1][260/660] lr: 1.0000e-02 eta: 4:01:33 time: 0.3413 data_time: 0.0212 memory: 21539 grad_norm: 1.9191 loss: 4.5085 top1_acc: 0.0625 top5_acc: 0.1875 loss_cls: 4.5085 2023/03/08 15:46:17 - mmengine - INFO - Epoch(train) [1][280/660] lr: 1.0000e-02 eta: 3:57:18 time: 0.3373 data_time: 0.0199 memory: 21539 grad_norm: 1.9988 loss: 4.4616 top1_acc: 0.1562 top5_acc: 0.3125 loss_cls: 4.4616 2023/03/08 15:46:24 - mmengine - INFO - Epoch(train) [1][300/660] lr: 1.0000e-02 eta: 3:53:48 time: 0.3425 data_time: 0.0220 memory: 21539 grad_norm: 2.0314 loss: 4.4419 top1_acc: 0.1250 top5_acc: 0.2188 loss_cls: 4.4419 2023/03/08 15:46:30 - mmengine - INFO - Epoch(train) [1][320/660] lr: 1.0000e-02 eta: 3:50:30 time: 0.3366 data_time: 0.0208 memory: 21539 grad_norm: 2.0970 loss: 4.3228 top1_acc: 0.0625 top5_acc: 0.2188 loss_cls: 4.3228 2023/03/08 15:46:37 - mmengine - INFO - Epoch(train) [1][340/660] lr: 1.0000e-02 eta: 3:47:42 time: 0.3402 data_time: 0.0210 memory: 21539 grad_norm: 2.1792 loss: 4.3764 top1_acc: 0.0000 top5_acc: 0.0938 loss_cls: 4.3764 2023/03/08 15:46:44 - mmengine - INFO - Epoch(train) [1][360/660] lr: 1.0000e-02 eta: 3:45:06 time: 0.3365 data_time: 0.0201 memory: 21539 grad_norm: 2.2030 loss: 4.2775 top1_acc: 0.0938 top5_acc: 0.2500 loss_cls: 4.2775 2023/03/08 15:46:51 - mmengine - INFO - Epoch(train) [1][380/660] lr: 1.0000e-02 eta: 3:42:58 time: 0.3442 data_time: 0.0220 memory: 21539 grad_norm: 2.2521 loss: 4.2371 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 4.2371 2023/03/08 15:46:58 - mmengine - INFO - Epoch(train) [1][400/660] lr: 1.0000e-02 eta: 3:40:58 time: 0.3415 data_time: 0.0202 memory: 21539 grad_norm: 2.3107 loss: 4.1347 top1_acc: 0.0312 top5_acc: 0.2188 loss_cls: 4.1347 2023/03/08 15:47:04 - mmengine - INFO - Epoch(train) [1][420/660] lr: 1.0000e-02 eta: 3:39:08 time: 0.3413 data_time: 0.0235 memory: 21539 grad_norm: 2.3355 loss: 4.2674 top1_acc: 0.0312 top5_acc: 0.1562 loss_cls: 4.2674 2023/03/08 15:47:11 - mmengine - INFO - Epoch(train) [1][440/660] lr: 1.0000e-02 eta: 3:37:32 time: 0.3440 data_time: 0.0254 memory: 21539 grad_norm: 2.3866 loss: 4.1319 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 4.1319 2023/03/08 15:47:18 - mmengine - INFO - Epoch(train) [1][460/660] lr: 1.0000e-02 eta: 3:36:03 time: 0.3439 data_time: 0.0224 memory: 21539 grad_norm: 2.4078 loss: 4.1157 top1_acc: 0.1875 top5_acc: 0.3750 loss_cls: 4.1157 2023/03/08 15:47:25 - mmengine - INFO - Epoch(train) [1][480/660] lr: 1.0000e-02 eta: 3:34:38 time: 0.3412 data_time: 0.0217 memory: 21539 grad_norm: 2.4234 loss: 4.1379 top1_acc: 0.2500 top5_acc: 0.4062 loss_cls: 4.1379 2023/03/08 15:47:32 - mmengine - INFO - Epoch(train) [1][500/660] lr: 1.0000e-02 eta: 3:33:27 time: 0.3474 data_time: 0.0224 memory: 21539 grad_norm: 2.4675 loss: 4.1048 top1_acc: 0.1250 top5_acc: 0.4688 loss_cls: 4.1048 2023/03/08 15:47:39 - mmengine - INFO - Epoch(train) [1][520/660] lr: 1.0000e-02 eta: 3:32:09 time: 0.3378 data_time: 0.0199 memory: 21539 grad_norm: 2.5203 loss: 4.0267 top1_acc: 0.1250 top5_acc: 0.4062 loss_cls: 4.0267 2023/03/08 15:47:47 - mmengine - INFO - Epoch(train) [1][540/660] lr: 1.0000e-02 eta: 3:32:35 time: 0.4205 data_time: 0.0219 memory: 21539 grad_norm: 2.5316 loss: 4.0194 top1_acc: 0.1250 top5_acc: 0.3438 loss_cls: 4.0194 2023/03/08 15:47:54 - mmengine - INFO - Epoch(train) [1][560/660] lr: 1.0000e-02 eta: 3:31:22 time: 0.3366 data_time: 0.0198 memory: 21539 grad_norm: 2.5441 loss: 4.0279 top1_acc: 0.1562 top5_acc: 0.3750 loss_cls: 4.0279 2023/03/08 15:48:01 - mmengine - INFO - Epoch(train) [1][580/660] lr: 1.0000e-02 eta: 3:30:20 time: 0.3425 data_time: 0.0227 memory: 21539 grad_norm: 2.5888 loss: 3.8871 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.8871 2023/03/08 15:48:07 - mmengine - INFO - Epoch(train) [1][600/660] lr: 1.0000e-02 eta: 3:29:16 time: 0.3369 data_time: 0.0207 memory: 21539 grad_norm: 2.6166 loss: 3.9953 top1_acc: 0.2188 top5_acc: 0.4062 loss_cls: 3.9953 2023/03/08 15:48:14 - mmengine - INFO - Epoch(train) [1][620/660] lr: 1.0000e-02 eta: 3:28:21 time: 0.3425 data_time: 0.0231 memory: 21539 grad_norm: 2.6414 loss: 3.9756 top1_acc: 0.0312 top5_acc: 0.3125 loss_cls: 3.9756 2023/03/08 15:48:21 - mmengine - INFO - Epoch(train) [1][640/660] lr: 1.0000e-02 eta: 3:27:24 time: 0.3379 data_time: 0.0210 memory: 21539 grad_norm: 2.6753 loss: 3.8750 top1_acc: 0.1250 top5_acc: 0.3438 loss_cls: 3.8750 2023/03/08 15:48:28 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 15:48:28 - mmengine - INFO - Epoch(train) [1][660/660] lr: 1.0000e-02 eta: 3:26:25 time: 0.3321 data_time: 0.0193 memory: 21539 grad_norm: 2.6710 loss: 3.9903 top1_acc: 0.1481 top5_acc: 0.3704 loss_cls: 3.9903 2023/03/08 15:48:36 - mmengine - INFO - Epoch(train) [2][ 20/660] lr: 1.0000e-02 eta: 3:26:51 time: 0.4181 data_time: 0.0841 memory: 21539 grad_norm: 2.6805 loss: 3.8528 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.8528 2023/03/08 15:48:43 - mmengine - INFO - Epoch(train) [2][ 40/660] lr: 1.0000e-02 eta: 3:25:56 time: 0.3325 data_time: 0.0212 memory: 21539 grad_norm: 2.7440 loss: 3.8565 top1_acc: 0.2188 top5_acc: 0.5000 loss_cls: 3.8565 2023/03/08 15:48:50 - mmengine - INFO - Epoch(train) [2][ 60/660] lr: 1.0000e-02 eta: 3:25:13 time: 0.3431 data_time: 0.0208 memory: 21539 grad_norm: 2.7533 loss: 3.8963 top1_acc: 0.0625 top5_acc: 0.4375 loss_cls: 3.8963 2023/03/08 15:48:56 - mmengine - INFO - Epoch(train) [2][ 80/660] lr: 1.0000e-02 eta: 3:24:27 time: 0.3371 data_time: 0.0221 memory: 21539 grad_norm: 2.7597 loss: 3.8313 top1_acc: 0.2188 top5_acc: 0.3438 loss_cls: 3.8313 2023/03/08 15:49:03 - mmengine - INFO - Epoch(train) [2][100/660] lr: 1.0000e-02 eta: 3:23:43 time: 0.3382 data_time: 0.0213 memory: 21539 grad_norm: 2.8225 loss: 3.8289 top1_acc: 0.0938 top5_acc: 0.2812 loss_cls: 3.8289 2023/03/08 15:49:10 - mmengine - INFO - Epoch(train) [2][120/660] lr: 1.0000e-02 eta: 3:22:58 time: 0.3331 data_time: 0.0223 memory: 21539 grad_norm: 2.7695 loss: 3.7823 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.7823 2023/03/08 15:49:17 - mmengine - INFO - Epoch(train) [2][140/660] lr: 1.0000e-02 eta: 3:22:22 time: 0.3428 data_time: 0.0270 memory: 21539 grad_norm: 2.8063 loss: 3.7711 top1_acc: 0.1250 top5_acc: 0.4062 loss_cls: 3.7711 2023/03/08 15:49:23 - mmengine - INFO - Epoch(train) [2][160/660] lr: 1.0000e-02 eta: 3:21:43 time: 0.3374 data_time: 0.0216 memory: 21539 grad_norm: 2.8564 loss: 3.7699 top1_acc: 0.1250 top5_acc: 0.2812 loss_cls: 3.7699 2023/03/08 15:49:30 - mmengine - INFO - Epoch(train) [2][180/660] lr: 1.0000e-02 eta: 3:21:07 time: 0.3388 data_time: 0.0213 memory: 21539 grad_norm: 2.8465 loss: 3.7751 top1_acc: 0.2188 top5_acc: 0.3438 loss_cls: 3.7751 2023/03/08 15:49:37 - mmengine - INFO - Epoch(train) [2][200/660] lr: 1.0000e-02 eta: 3:20:28 time: 0.3337 data_time: 0.0221 memory: 21539 grad_norm: 2.8569 loss: 3.6071 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 3.6071 2023/03/08 15:49:44 - mmengine - INFO - Epoch(train) [2][220/660] lr: 1.0000e-02 eta: 3:19:58 time: 0.3425 data_time: 0.0215 memory: 21539 grad_norm: 2.8723 loss: 3.7520 top1_acc: 0.2188 top5_acc: 0.4375 loss_cls: 3.7520 2023/03/08 15:49:50 - mmengine - INFO - Epoch(train) [2][240/660] lr: 1.0000e-02 eta: 3:19:21 time: 0.3325 data_time: 0.0226 memory: 21539 grad_norm: 2.9443 loss: 3.6622 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.6622 2023/03/08 15:49:57 - mmengine - INFO - Epoch(train) [2][260/660] lr: 1.0000e-02 eta: 3:18:48 time: 0.3360 data_time: 0.0226 memory: 21539 grad_norm: 2.9076 loss: 3.7232 top1_acc: 0.1250 top5_acc: 0.3438 loss_cls: 3.7232 2023/03/08 15:50:04 - mmengine - INFO - Epoch(train) [2][280/660] lr: 1.0000e-02 eta: 3:18:16 time: 0.3358 data_time: 0.0226 memory: 21539 grad_norm: 2.8875 loss: 3.6165 top1_acc: 0.0625 top5_acc: 0.3438 loss_cls: 3.6165 2023/03/08 15:50:10 - mmengine - INFO - Epoch(train) [2][300/660] lr: 1.0000e-02 eta: 3:17:45 time: 0.3357 data_time: 0.0218 memory: 21539 grad_norm: 2.9332 loss: 3.7023 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.7023 2023/03/08 15:50:17 - mmengine - INFO - Epoch(train) [2][320/660] lr: 1.0000e-02 eta: 3:17:11 time: 0.3305 data_time: 0.0219 memory: 21539 grad_norm: 2.9594 loss: 3.6255 top1_acc: 0.2188 top5_acc: 0.6250 loss_cls: 3.6255 2023/03/08 15:50:24 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 15:50:24 - mmengine - INFO - Epoch(train) [2][340/660] lr: 1.0000e-02 eta: 3:16:43 time: 0.3361 data_time: 0.0211 memory: 21539 grad_norm: 2.9523 loss: 3.6597 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 3.6597 2023/03/08 15:50:30 - mmengine - INFO - Epoch(train) [2][360/660] lr: 1.0000e-02 eta: 3:16:11 time: 0.3306 data_time: 0.0223 memory: 21539 grad_norm: 3.0116 loss: 3.6779 top1_acc: 0.0938 top5_acc: 0.5625 loss_cls: 3.6779 2023/03/08 15:50:37 - mmengine - INFO - Epoch(train) [2][380/660] lr: 1.0000e-02 eta: 3:15:44 time: 0.3358 data_time: 0.0212 memory: 21539 grad_norm: 2.9701 loss: 3.4799 top1_acc: 0.1562 top5_acc: 0.3750 loss_cls: 3.4799 2023/03/08 15:50:44 - mmengine - INFO - Epoch(train) [2][400/660] lr: 1.0000e-02 eta: 3:15:17 time: 0.3349 data_time: 0.0221 memory: 21539 grad_norm: 3.0079 loss: 3.7873 top1_acc: 0.0312 top5_acc: 0.3438 loss_cls: 3.7873 2023/03/08 15:50:51 - mmengine - INFO - Epoch(train) [2][420/660] lr: 1.0000e-02 eta: 3:14:51 time: 0.3357 data_time: 0.0213 memory: 21539 grad_norm: 3.0384 loss: 3.5697 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.5697 2023/03/08 15:50:57 - mmengine - INFO - Epoch(train) [2][440/660] lr: 1.0000e-02 eta: 3:14:24 time: 0.3318 data_time: 0.0221 memory: 21539 grad_norm: 2.9932 loss: 3.5591 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.5591 2023/03/08 15:51:04 - mmengine - INFO - Epoch(train) [2][460/660] lr: 1.0000e-02 eta: 3:13:59 time: 0.3347 data_time: 0.0213 memory: 21539 grad_norm: 3.0253 loss: 3.5547 top1_acc: 0.2188 top5_acc: 0.4688 loss_cls: 3.5547 2023/03/08 15:51:10 - mmengine - INFO - Epoch(train) [2][480/660] lr: 1.0000e-02 eta: 3:13:33 time: 0.3309 data_time: 0.0217 memory: 21539 grad_norm: 2.9896 loss: 3.4894 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 3.4894 2023/03/08 15:51:17 - mmengine - INFO - Epoch(train) [2][500/660] lr: 1.0000e-02 eta: 3:13:11 time: 0.3380 data_time: 0.0216 memory: 21539 grad_norm: 3.0517 loss: 3.5263 top1_acc: 0.2812 top5_acc: 0.5000 loss_cls: 3.5263 2023/03/08 15:51:24 - mmengine - INFO - Epoch(train) [2][520/660] lr: 1.0000e-02 eta: 3:12:47 time: 0.3338 data_time: 0.0256 memory: 21539 grad_norm: 3.0439 loss: 3.5445 top1_acc: 0.3438 top5_acc: 0.5312 loss_cls: 3.5445 2023/03/08 15:51:31 - mmengine - INFO - Epoch(train) [2][540/660] lr: 1.0000e-02 eta: 3:12:25 time: 0.3350 data_time: 0.0222 memory: 21539 grad_norm: 3.0690 loss: 3.5277 top1_acc: 0.3438 top5_acc: 0.5000 loss_cls: 3.5277 2023/03/08 15:51:37 - mmengine - INFO - Epoch(train) [2][560/660] lr: 1.0000e-02 eta: 3:12:00 time: 0.3296 data_time: 0.0213 memory: 21539 grad_norm: 3.0434 loss: 3.5099 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.5099 2023/03/08 15:51:44 - mmengine - INFO - Epoch(train) [2][580/660] lr: 1.0000e-02 eta: 3:11:39 time: 0.3361 data_time: 0.0226 memory: 21539 grad_norm: 3.0649 loss: 3.5757 top1_acc: 0.0938 top5_acc: 0.2812 loss_cls: 3.5757 2023/03/08 15:51:51 - mmengine - INFO - Epoch(train) [2][600/660] lr: 1.0000e-02 eta: 3:11:17 time: 0.3328 data_time: 0.0214 memory: 21539 grad_norm: 3.0880 loss: 3.3774 top1_acc: 0.2188 top5_acc: 0.4062 loss_cls: 3.3774 2023/03/08 15:51:57 - mmengine - INFO - Epoch(train) [2][620/660] lr: 1.0000e-02 eta: 3:10:56 time: 0.3336 data_time: 0.0245 memory: 21539 grad_norm: 3.0446 loss: 3.5234 top1_acc: 0.0938 top5_acc: 0.4375 loss_cls: 3.5234 2023/03/08 15:52:04 - mmengine - INFO - Epoch(train) [2][640/660] lr: 1.0000e-02 eta: 3:10:34 time: 0.3305 data_time: 0.0226 memory: 21539 grad_norm: 3.0777 loss: 3.4161 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.4161 2023/03/08 15:52:10 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 15:52:10 - mmengine - INFO - Epoch(train) [2][660/660] lr: 1.0000e-02 eta: 3:10:09 time: 0.3242 data_time: 0.0203 memory: 21539 grad_norm: 3.1261 loss: 3.5250 top1_acc: 0.1481 top5_acc: 0.5926 loss_cls: 3.5250 2023/03/08 15:52:19 - mmengine - INFO - Epoch(train) [3][ 20/660] lr: 1.0000e-02 eta: 3:10:25 time: 0.4088 data_time: 0.0855 memory: 21539 grad_norm: 3.0745 loss: 3.5204 top1_acc: 0.1562 top5_acc: 0.4375 loss_cls: 3.5204 2023/03/08 15:52:25 - mmengine - INFO - Epoch(train) [3][ 40/660] lr: 1.0000e-02 eta: 3:10:03 time: 0.3292 data_time: 0.0189 memory: 21539 grad_norm: 3.1133 loss: 3.5664 top1_acc: 0.1562 top5_acc: 0.4375 loss_cls: 3.5664 2023/03/08 15:52:32 - mmengine - INFO - Epoch(train) [3][ 60/660] lr: 1.0000e-02 eta: 3:09:43 time: 0.3328 data_time: 0.0213 memory: 21539 grad_norm: 3.0994 loss: 3.2966 top1_acc: 0.2812 top5_acc: 0.5000 loss_cls: 3.2966 2023/03/08 15:52:38 - mmengine - INFO - Epoch(train) [3][ 80/660] lr: 1.0000e-02 eta: 3:09:23 time: 0.3304 data_time: 0.0189 memory: 21539 grad_norm: 3.1970 loss: 3.3725 top1_acc: 0.2812 top5_acc: 0.4375 loss_cls: 3.3725 2023/03/08 15:52:45 - mmengine - INFO - Epoch(train) [3][100/660] lr: 1.0000e-02 eta: 3:09:05 time: 0.3351 data_time: 0.0226 memory: 21539 grad_norm: 3.1720 loss: 3.5192 top1_acc: 0.1250 top5_acc: 0.4062 loss_cls: 3.5192 2023/03/08 15:52:52 - mmengine - INFO - Epoch(train) [3][120/660] lr: 1.0000e-02 eta: 3:08:44 time: 0.3295 data_time: 0.0189 memory: 21539 grad_norm: 3.1650 loss: 3.3731 top1_acc: 0.1562 top5_acc: 0.3750 loss_cls: 3.3731 2023/03/08 15:52:58 - mmengine - INFO - Epoch(train) [3][140/660] lr: 1.0000e-02 eta: 3:08:27 time: 0.3356 data_time: 0.0221 memory: 21539 grad_norm: 3.1801 loss: 3.4615 top1_acc: 0.2188 top5_acc: 0.4375 loss_cls: 3.4615 2023/03/08 15:53:05 - mmengine - INFO - Epoch(train) [3][160/660] lr: 1.0000e-02 eta: 3:08:09 time: 0.3320 data_time: 0.0189 memory: 21539 grad_norm: 3.2118 loss: 3.3429 top1_acc: 0.3438 top5_acc: 0.6250 loss_cls: 3.3429 2023/03/08 15:53:12 - mmengine - INFO - Epoch(train) [3][180/660] lr: 1.0000e-02 eta: 3:07:52 time: 0.3356 data_time: 0.0212 memory: 21539 grad_norm: 3.1715 loss: 3.3886 top1_acc: 0.1562 top5_acc: 0.4062 loss_cls: 3.3886 2023/03/08 15:53:18 - mmengine - INFO - Epoch(train) [3][200/660] lr: 1.0000e-02 eta: 3:07:35 time: 0.3345 data_time: 0.0227 memory: 21539 grad_norm: 3.1623 loss: 3.4487 top1_acc: 0.1562 top5_acc: 0.5312 loss_cls: 3.4487 2023/03/08 15:53:25 - mmengine - INFO - Epoch(train) [3][220/660] lr: 1.0000e-02 eta: 3:07:21 time: 0.3404 data_time: 0.0211 memory: 21539 grad_norm: 3.1568 loss: 3.3961 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 3.3961 2023/03/08 15:53:32 - mmengine - INFO - Epoch(train) [3][240/660] lr: 1.0000e-02 eta: 3:07:04 time: 0.3316 data_time: 0.0200 memory: 21539 grad_norm: 3.1871 loss: 3.2645 top1_acc: 0.1562 top5_acc: 0.4688 loss_cls: 3.2645 2023/03/08 15:53:39 - mmengine - INFO - Epoch(train) [3][260/660] lr: 1.0000e-02 eta: 3:06:48 time: 0.3357 data_time: 0.0217 memory: 21539 grad_norm: 3.2196 loss: 3.2992 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.2992 2023/03/08 15:53:45 - mmengine - INFO - Epoch(train) [3][280/660] lr: 1.0000e-02 eta: 3:06:31 time: 0.3328 data_time: 0.0193 memory: 21539 grad_norm: 3.2205 loss: 3.4178 top1_acc: 0.1562 top5_acc: 0.4062 loss_cls: 3.4178 2023/03/08 15:53:52 - mmengine - INFO - Epoch(train) [3][300/660] lr: 1.0000e-02 eta: 3:06:17 time: 0.3382 data_time: 0.0214 memory: 21539 grad_norm: 3.2239 loss: 3.2961 top1_acc: 0.2188 top5_acc: 0.4375 loss_cls: 3.2961 2023/03/08 15:53:59 - mmengine - INFO - Epoch(train) [3][320/660] lr: 1.0000e-02 eta: 3:06:01 time: 0.3326 data_time: 0.0194 memory: 21539 grad_norm: 3.2126 loss: 3.3694 top1_acc: 0.1875 top5_acc: 0.2812 loss_cls: 3.3694 2023/03/08 15:54:05 - mmengine - INFO - Epoch(train) [3][340/660] lr: 1.0000e-02 eta: 3:05:46 time: 0.3346 data_time: 0.0212 memory: 21539 grad_norm: 3.2260 loss: 3.4515 top1_acc: 0.2188 top5_acc: 0.3750 loss_cls: 3.4515 2023/03/08 15:54:12 - mmengine - INFO - Epoch(train) [3][360/660] lr: 1.0000e-02 eta: 3:05:31 time: 0.3340 data_time: 0.0193 memory: 21539 grad_norm: 3.1808 loss: 3.3950 top1_acc: 0.2188 top5_acc: 0.5000 loss_cls: 3.3950 2023/03/08 15:54:19 - mmengine - INFO - Epoch(train) [3][380/660] lr: 1.0000e-02 eta: 3:05:16 time: 0.3336 data_time: 0.0211 memory: 21539 grad_norm: 3.2221 loss: 3.2600 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.2600 2023/03/08 15:54:25 - mmengine - INFO - Epoch(train) [3][400/660] lr: 1.0000e-02 eta: 3:05:01 time: 0.3354 data_time: 0.0195 memory: 21539 grad_norm: 3.2742 loss: 3.2723 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 3.2723 2023/03/08 15:54:32 - mmengine - INFO - Epoch(train) [3][420/660] lr: 1.0000e-02 eta: 3:04:47 time: 0.3349 data_time: 0.0210 memory: 21539 grad_norm: 3.2440 loss: 3.1554 top1_acc: 0.2500 top5_acc: 0.4062 loss_cls: 3.1554 2023/03/08 15:54:39 - mmengine - INFO - Epoch(train) [3][440/660] lr: 1.0000e-02 eta: 3:04:33 time: 0.3361 data_time: 0.0184 memory: 21539 grad_norm: 3.3026 loss: 3.3068 top1_acc: 0.1875 top5_acc: 0.4062 loss_cls: 3.3068 2023/03/08 15:54:45 - mmengine - INFO - Epoch(train) [3][460/660] lr: 1.0000e-02 eta: 3:04:19 time: 0.3336 data_time: 0.0213 memory: 21539 grad_norm: 3.3314 loss: 3.1925 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 3.1925 2023/03/08 15:54:52 - mmengine - INFO - Epoch(train) [3][480/660] lr: 1.0000e-02 eta: 3:04:06 time: 0.3381 data_time: 0.0238 memory: 21539 grad_norm: 3.3372 loss: 3.1941 top1_acc: 0.0938 top5_acc: 0.3750 loss_cls: 3.1941 2023/03/08 15:54:59 - mmengine - INFO - Epoch(train) [3][500/660] lr: 1.0000e-02 eta: 3:03:53 time: 0.3351 data_time: 0.0216 memory: 21539 grad_norm: 3.3070 loss: 3.3536 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 3.3536 2023/03/08 15:55:06 - mmengine - INFO - Epoch(train) [3][520/660] lr: 1.0000e-02 eta: 3:03:38 time: 0.3301 data_time: 0.0191 memory: 21539 grad_norm: 3.3205 loss: 3.2725 top1_acc: 0.1875 top5_acc: 0.5312 loss_cls: 3.2725 2023/03/08 15:55:12 - mmengine - INFO - Epoch(train) [3][540/660] lr: 1.0000e-02 eta: 3:03:24 time: 0.3343 data_time: 0.0208 memory: 21539 grad_norm: 3.2776 loss: 3.2193 top1_acc: 0.2188 top5_acc: 0.5625 loss_cls: 3.2193 2023/03/08 15:55:19 - mmengine - INFO - Epoch(train) [3][560/660] lr: 1.0000e-02 eta: 3:03:10 time: 0.3318 data_time: 0.0188 memory: 21539 grad_norm: 3.3091 loss: 3.3188 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 3.3188 2023/03/08 15:55:26 - mmengine - INFO - Epoch(train) [3][580/660] lr: 1.0000e-02 eta: 3:02:58 time: 0.3379 data_time: 0.0215 memory: 21539 grad_norm: 3.3131 loss: 3.3589 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 3.3589 2023/03/08 15:55:32 - mmengine - INFO - Epoch(train) [3][600/660] lr: 1.0000e-02 eta: 3:02:44 time: 0.3339 data_time: 0.0191 memory: 21539 grad_norm: 3.2393 loss: 3.2389 top1_acc: 0.0312 top5_acc: 0.5938 loss_cls: 3.2389 2023/03/08 15:55:39 - mmengine - INFO - Epoch(train) [3][620/660] lr: 1.0000e-02 eta: 3:02:32 time: 0.3349 data_time: 0.0223 memory: 21539 grad_norm: 3.2962 loss: 3.2262 top1_acc: 0.1875 top5_acc: 0.3750 loss_cls: 3.2262 2023/03/08 15:55:46 - mmengine - INFO - Epoch(train) [3][640/660] lr: 1.0000e-02 eta: 3:02:19 time: 0.3359 data_time: 0.0188 memory: 21539 grad_norm: 3.3063 loss: 3.2957 top1_acc: 0.2812 top5_acc: 0.5312 loss_cls: 3.2957 2023/03/08 15:55:52 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 15:55:52 - mmengine - INFO - Epoch(train) [3][660/660] lr: 1.0000e-02 eta: 3:02:04 time: 0.3265 data_time: 0.0189 memory: 21539 grad_norm: 3.2720 loss: 3.2559 top1_acc: 0.3704 top5_acc: 0.5926 loss_cls: 3.2559 2023/03/08 15:55:52 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/03/08 15:56:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 15:56:02 - mmengine - INFO - Epoch(train) [4][ 20/660] lr: 1.0000e-02 eta: 3:02:14 time: 0.4050 data_time: 0.0876 memory: 21539 grad_norm: 3.2818 loss: 3.1575 top1_acc: 0.2188 top5_acc: 0.6250 loss_cls: 3.1575 2023/03/08 15:56:08 - mmengine - INFO - Epoch(train) [4][ 40/660] lr: 1.0000e-02 eta: 3:02:01 time: 0.3361 data_time: 0.0197 memory: 21539 grad_norm: 3.3119 loss: 3.3569 top1_acc: 0.2812 top5_acc: 0.5000 loss_cls: 3.3569 2023/03/08 15:56:15 - mmengine - INFO - Epoch(train) [4][ 60/660] lr: 1.0000e-02 eta: 3:01:52 time: 0.3438 data_time: 0.0219 memory: 21539 grad_norm: 3.2842 loss: 3.0775 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 3.0775 2023/03/08 15:56:22 - mmengine - INFO - Epoch(train) [4][ 80/660] lr: 1.0000e-02 eta: 3:01:39 time: 0.3333 data_time: 0.0195 memory: 21539 grad_norm: 3.3752 loss: 3.2164 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 3.2164 2023/03/08 15:56:29 - mmengine - INFO - Epoch(train) [4][100/660] lr: 1.0000e-02 eta: 3:01:29 time: 0.3419 data_time: 0.0217 memory: 21539 grad_norm: 3.3538 loss: 3.1227 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 3.1227 2023/03/08 15:56:35 - mmengine - INFO - Epoch(train) [4][120/660] lr: 1.0000e-02 eta: 3:01:16 time: 0.3327 data_time: 0.0189 memory: 21539 grad_norm: 3.2999 loss: 3.0423 top1_acc: 0.2812 top5_acc: 0.5938 loss_cls: 3.0423 2023/03/08 15:56:42 - mmengine - INFO - Epoch(train) [4][140/660] lr: 1.0000e-02 eta: 3:01:06 time: 0.3405 data_time: 0.0213 memory: 21539 grad_norm: 3.2971 loss: 3.1578 top1_acc: 0.2500 top5_acc: 0.4062 loss_cls: 3.1578 2023/03/08 15:56:49 - mmengine - INFO - Epoch(train) [4][160/660] lr: 1.0000e-02 eta: 3:00:53 time: 0.3328 data_time: 0.0206 memory: 21539 grad_norm: 3.3302 loss: 3.0604 top1_acc: 0.2188 top5_acc: 0.6562 loss_cls: 3.0604 2023/03/08 15:56:56 - mmengine - INFO - Epoch(train) [4][180/660] lr: 1.0000e-02 eta: 3:00:42 time: 0.3388 data_time: 0.0222 memory: 21539 grad_norm: 3.3655 loss: 3.0769 top1_acc: 0.3438 top5_acc: 0.4375 loss_cls: 3.0769 2023/03/08 15:57:02 - mmengine - INFO - Epoch(train) [4][200/660] lr: 1.0000e-02 eta: 3:00:30 time: 0.3321 data_time: 0.0196 memory: 21539 grad_norm: 3.3937 loss: 3.0933 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 3.0933 2023/03/08 15:57:09 - mmengine - INFO - Epoch(train) [4][220/660] lr: 1.0000e-02 eta: 3:00:22 time: 0.3483 data_time: 0.0214 memory: 21539 grad_norm: 3.3946 loss: 3.2663 top1_acc: 0.2188 top5_acc: 0.4062 loss_cls: 3.2663 2023/03/08 15:57:16 - mmengine - INFO - Epoch(train) [4][240/660] lr: 1.0000e-02 eta: 3:00:09 time: 0.3311 data_time: 0.0192 memory: 21539 grad_norm: 3.3664 loss: 3.1421 top1_acc: 0.1875 top5_acc: 0.5938 loss_cls: 3.1421 2023/03/08 15:57:23 - mmengine - INFO - Epoch(train) [4][260/660] lr: 1.0000e-02 eta: 2:59:58 time: 0.3373 data_time: 0.0220 memory: 21539 grad_norm: 3.3581 loss: 3.0885 top1_acc: 0.1562 top5_acc: 0.5000 loss_cls: 3.0885 2023/03/08 15:57:29 - mmengine - INFO - Epoch(train) [4][280/660] lr: 1.0000e-02 eta: 2:59:47 time: 0.3356 data_time: 0.0240 memory: 21539 grad_norm: 3.3755 loss: 3.1896 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 3.1896 2023/03/08 15:57:36 - mmengine - INFO - Epoch(train) [4][300/660] lr: 1.0000e-02 eta: 2:59:37 time: 0.3396 data_time: 0.0213 memory: 21539 grad_norm: 3.3407 loss: 3.2344 top1_acc: 0.2812 top5_acc: 0.5312 loss_cls: 3.2344 2023/03/08 15:57:43 - mmengine - INFO - Epoch(train) [4][320/660] lr: 1.0000e-02 eta: 2:59:25 time: 0.3319 data_time: 0.0204 memory: 21539 grad_norm: 3.3626 loss: 3.1360 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 3.1360 2023/03/08 15:57:49 - mmengine - INFO - Epoch(train) [4][340/660] lr: 1.0000e-02 eta: 2:59:15 time: 0.3404 data_time: 0.0216 memory: 21539 grad_norm: 3.3900 loss: 3.0516 top1_acc: 0.2188 top5_acc: 0.5312 loss_cls: 3.0516 2023/03/08 15:57:56 - mmengine - INFO - Epoch(train) [4][360/660] lr: 1.0000e-02 eta: 2:59:03 time: 0.3312 data_time: 0.0197 memory: 21539 grad_norm: 3.4068 loss: 3.1819 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.1819 2023/03/08 15:58:03 - mmengine - INFO - Epoch(train) [4][380/660] lr: 1.0000e-02 eta: 2:58:54 time: 0.3406 data_time: 0.0226 memory: 21539 grad_norm: 3.4154 loss: 3.1229 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1229 2023/03/08 15:58:10 - mmengine - INFO - Epoch(train) [4][400/660] lr: 1.0000e-02 eta: 2:58:43 time: 0.3352 data_time: 0.0191 memory: 21539 grad_norm: 3.3358 loss: 3.1187 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 3.1187 2023/03/08 15:58:16 - mmengine - INFO - Epoch(train) [4][420/660] lr: 1.0000e-02 eta: 2:58:33 time: 0.3398 data_time: 0.0216 memory: 21539 grad_norm: 3.3626 loss: 3.1458 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 3.1458 2023/03/08 15:58:23 - mmengine - INFO - Epoch(train) [4][440/660] lr: 1.0000e-02 eta: 2:58:22 time: 0.3347 data_time: 0.0190 memory: 21539 grad_norm: 3.4039 loss: 3.1097 top1_acc: 0.2188 top5_acc: 0.5000 loss_cls: 3.1097 2023/03/08 15:58:30 - mmengine - INFO - Epoch(train) [4][460/660] lr: 1.0000e-02 eta: 2:58:13 time: 0.3424 data_time: 0.0217 memory: 21539 grad_norm: 3.4253 loss: 2.9385 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 2.9385 2023/03/08 15:58:37 - mmengine - INFO - Epoch(train) [4][480/660] lr: 1.0000e-02 eta: 2:58:03 time: 0.3363 data_time: 0.0193 memory: 21539 grad_norm: 3.4551 loss: 3.0624 top1_acc: 0.3438 top5_acc: 0.5312 loss_cls: 3.0624 2023/03/08 15:58:43 - mmengine - INFO - Epoch(train) [4][500/660] lr: 1.0000e-02 eta: 2:57:53 time: 0.3387 data_time: 0.0216 memory: 21539 grad_norm: 3.4478 loss: 3.0748 top1_acc: 0.1562 top5_acc: 0.5625 loss_cls: 3.0748 2023/03/08 15:58:50 - mmengine - INFO - Epoch(train) [4][520/660] lr: 1.0000e-02 eta: 2:57:43 time: 0.3349 data_time: 0.0193 memory: 21539 grad_norm: 3.4285 loss: 2.9966 top1_acc: 0.2812 top5_acc: 0.7188 loss_cls: 2.9966 2023/03/08 15:58:57 - mmengine - INFO - Epoch(train) [4][540/660] lr: 1.0000e-02 eta: 2:57:34 time: 0.3431 data_time: 0.0220 memory: 21539 grad_norm: 3.4021 loss: 3.0655 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 3.0655 2023/03/08 15:59:04 - mmengine - INFO - Epoch(train) [4][560/660] lr: 1.0000e-02 eta: 2:57:23 time: 0.3334 data_time: 0.0193 memory: 21539 grad_norm: 3.4138 loss: 2.9981 top1_acc: 0.2812 top5_acc: 0.6562 loss_cls: 2.9981 2023/03/08 15:59:10 - mmengine - INFO - Epoch(train) [4][580/660] lr: 1.0000e-02 eta: 2:57:14 time: 0.3406 data_time: 0.0256 memory: 21539 grad_norm: 3.4514 loss: 3.0412 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 3.0412 2023/03/08 15:59:17 - mmengine - INFO - Epoch(train) [4][600/660] lr: 1.0000e-02 eta: 2:57:03 time: 0.3328 data_time: 0.0192 memory: 21539 grad_norm: 3.3854 loss: 3.1007 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 3.1007 2023/03/08 15:59:24 - mmengine - INFO - Epoch(train) [4][620/660] lr: 1.0000e-02 eta: 2:56:54 time: 0.3401 data_time: 0.0214 memory: 21539 grad_norm: 3.3634 loss: 3.0454 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 3.0454 2023/03/08 15:59:31 - mmengine - INFO - Epoch(train) [4][640/660] lr: 1.0000e-02 eta: 2:56:43 time: 0.3331 data_time: 0.0184 memory: 21539 grad_norm: 3.4277 loss: 2.9807 top1_acc: 0.1875 top5_acc: 0.5312 loss_cls: 2.9807 2023/03/08 15:59:37 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 15:59:37 - mmengine - INFO - Epoch(train) [4][660/660] lr: 1.0000e-02 eta: 2:56:32 time: 0.3296 data_time: 0.0198 memory: 21539 grad_norm: 3.4933 loss: 3.1429 top1_acc: 0.3704 top5_acc: 0.6296 loss_cls: 3.1429 2023/03/08 15:59:45 - mmengine - INFO - Epoch(train) [5][ 20/660] lr: 1.0000e-02 eta: 2:56:39 time: 0.4087 data_time: 0.0860 memory: 21539 grad_norm: 3.3904 loss: 3.0040 top1_acc: 0.1562 top5_acc: 0.5938 loss_cls: 3.0040 2023/03/08 15:59:52 - mmengine - INFO - Epoch(train) [5][ 40/660] lr: 1.0000e-02 eta: 2:56:27 time: 0.3303 data_time: 0.0212 memory: 21539 grad_norm: 3.3896 loss: 2.8690 top1_acc: 0.3125 top5_acc: 0.7812 loss_cls: 2.8690 2023/03/08 15:59:59 - mmengine - INFO - Epoch(train) [5][ 60/660] lr: 1.0000e-02 eta: 2:56:17 time: 0.3353 data_time: 0.0220 memory: 21539 grad_norm: 3.4467 loss: 2.8848 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.8848 2023/03/08 16:00:05 - mmengine - INFO - Epoch(train) [5][ 80/660] lr: 1.0000e-02 eta: 2:56:06 time: 0.3308 data_time: 0.0203 memory: 21539 grad_norm: 3.4464 loss: 2.9639 top1_acc: 0.2188 top5_acc: 0.5625 loss_cls: 2.9639 2023/03/08 16:00:12 - mmengine - INFO - Epoch(train) [5][100/660] lr: 1.0000e-02 eta: 2:55:56 time: 0.3371 data_time: 0.0219 memory: 21539 grad_norm: 3.4393 loss: 3.1691 top1_acc: 0.2188 top5_acc: 0.5312 loss_cls: 3.1691 2023/03/08 16:00:19 - mmengine - INFO - Epoch(train) [5][120/660] lr: 1.0000e-02 eta: 2:55:47 time: 0.3375 data_time: 0.0200 memory: 21539 grad_norm: 3.4665 loss: 3.0156 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 3.0156 2023/03/08 16:00:26 - mmengine - INFO - Epoch(train) [5][140/660] lr: 1.0000e-02 eta: 2:55:37 time: 0.3352 data_time: 0.0227 memory: 21539 grad_norm: 3.4150 loss: 2.9821 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 2.9821 2023/03/08 16:00:32 - mmengine - INFO - Epoch(train) [5][160/660] lr: 1.0000e-02 eta: 2:55:27 time: 0.3326 data_time: 0.0206 memory: 21539 grad_norm: 3.4619 loss: 3.1085 top1_acc: 0.2188 top5_acc: 0.4375 loss_cls: 3.1085 2023/03/08 16:00:39 - mmengine - INFO - Epoch(train) [5][180/660] lr: 1.0000e-02 eta: 2:55:18 time: 0.3409 data_time: 0.0255 memory: 21539 grad_norm: 3.4250 loss: 3.0883 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0883 2023/03/08 16:00:46 - mmengine - INFO - Epoch(train) [5][200/660] lr: 1.0000e-02 eta: 2:55:07 time: 0.3316 data_time: 0.0200 memory: 21539 grad_norm: 3.4747 loss: 3.0377 top1_acc: 0.2500 top5_acc: 0.6562 loss_cls: 3.0377 2023/03/08 16:00:52 - mmengine - INFO - Epoch(train) [5][220/660] lr: 1.0000e-02 eta: 2:54:58 time: 0.3347 data_time: 0.0220 memory: 21539 grad_norm: 3.4804 loss: 2.9205 top1_acc: 0.2188 top5_acc: 0.6562 loss_cls: 2.9205 2023/03/08 16:00:59 - mmengine - INFO - Epoch(train) [5][240/660] lr: 1.0000e-02 eta: 2:54:47 time: 0.3309 data_time: 0.0216 memory: 21539 grad_norm: 3.4786 loss: 2.9895 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.9895 2023/03/08 16:01:06 - mmengine - INFO - Epoch(train) [5][260/660] lr: 1.0000e-02 eta: 2:54:38 time: 0.3378 data_time: 0.0217 memory: 21539 grad_norm: 3.4608 loss: 2.8060 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.8060 2023/03/08 16:01:12 - mmengine - INFO - Epoch(train) [5][280/660] lr: 1.0000e-02 eta: 2:54:28 time: 0.3318 data_time: 0.0210 memory: 21539 grad_norm: 3.5064 loss: 3.0549 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 3.0549 2023/03/08 16:01:19 - mmengine - INFO - Epoch(train) [5][300/660] lr: 1.0000e-02 eta: 2:54:18 time: 0.3355 data_time: 0.0226 memory: 21539 grad_norm: 3.5129 loss: 2.9498 top1_acc: 0.1875 top5_acc: 0.4062 loss_cls: 2.9498 2023/03/08 16:01:26 - mmengine - INFO - Epoch(train) [5][320/660] lr: 1.0000e-02 eta: 2:54:08 time: 0.3306 data_time: 0.0197 memory: 21539 grad_norm: 3.4568 loss: 2.9406 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.9406 2023/03/08 16:01:32 - mmengine - INFO - Epoch(train) [5][340/660] lr: 1.0000e-02 eta: 2:53:58 time: 0.3337 data_time: 0.0223 memory: 21539 grad_norm: 3.4981 loss: 2.9646 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.9646 2023/03/08 16:01:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:01:39 - mmengine - INFO - Epoch(train) [5][360/660] lr: 1.0000e-02 eta: 2:53:48 time: 0.3327 data_time: 0.0203 memory: 21539 grad_norm: 3.5052 loss: 2.9885 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 2.9885 2023/03/08 16:01:46 - mmengine - INFO - Epoch(train) [5][380/660] lr: 1.0000e-02 eta: 2:53:39 time: 0.3368 data_time: 0.0215 memory: 21539 grad_norm: 3.5070 loss: 3.0181 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.0181 2023/03/08 16:01:52 - mmengine - INFO - Epoch(train) [5][400/660] lr: 1.0000e-02 eta: 2:53:29 time: 0.3314 data_time: 0.0204 memory: 21539 grad_norm: 3.4876 loss: 2.9982 top1_acc: 0.2188 top5_acc: 0.5625 loss_cls: 2.9982 2023/03/08 16:01:59 - mmengine - INFO - Epoch(train) [5][420/660] lr: 1.0000e-02 eta: 2:53:19 time: 0.3345 data_time: 0.0217 memory: 21539 grad_norm: 3.5089 loss: 2.9674 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.9674 2023/03/08 16:02:06 - mmengine - INFO - Epoch(train) [5][440/660] lr: 1.0000e-02 eta: 2:53:09 time: 0.3335 data_time: 0.0206 memory: 21539 grad_norm: 3.5082 loss: 3.0676 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 3.0676 2023/03/08 16:02:12 - mmengine - INFO - Epoch(train) [5][460/660] lr: 1.0000e-02 eta: 2:53:00 time: 0.3341 data_time: 0.0211 memory: 21539 grad_norm: 3.5088 loss: 2.8602 top1_acc: 0.2812 top5_acc: 0.6250 loss_cls: 2.8602 2023/03/08 16:02:19 - mmengine - INFO - Epoch(train) [5][480/660] lr: 1.0000e-02 eta: 2:52:50 time: 0.3288 data_time: 0.0206 memory: 21539 grad_norm: 3.5212 loss: 2.8770 top1_acc: 0.2188 top5_acc: 0.5938 loss_cls: 2.8770 2023/03/08 16:02:26 - mmengine - INFO - Epoch(train) [5][500/660] lr: 1.0000e-02 eta: 2:52:42 time: 0.3427 data_time: 0.0259 memory: 21539 grad_norm: 3.5011 loss: 2.7488 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7488 2023/03/08 16:02:32 - mmengine - INFO - Epoch(train) [5][520/660] lr: 1.0000e-02 eta: 2:52:32 time: 0.3319 data_time: 0.0208 memory: 21539 grad_norm: 3.5119 loss: 2.9813 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.9813 2023/03/08 16:02:39 - mmengine - INFO - Epoch(train) [5][540/660] lr: 1.0000e-02 eta: 2:52:22 time: 0.3332 data_time: 0.0213 memory: 21539 grad_norm: 3.5090 loss: 2.9504 top1_acc: 0.2812 top5_acc: 0.6875 loss_cls: 2.9504 2023/03/08 16:02:46 - mmengine - INFO - Epoch(train) [5][560/660] lr: 1.0000e-02 eta: 2:52:12 time: 0.3302 data_time: 0.0205 memory: 21539 grad_norm: 3.4728 loss: 2.9650 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.9650 2023/03/08 16:02:52 - mmengine - INFO - Epoch(train) [5][580/660] lr: 1.0000e-02 eta: 2:52:03 time: 0.3344 data_time: 0.0215 memory: 21539 grad_norm: 3.5578 loss: 2.9098 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.9098 2023/03/08 16:02:59 - mmengine - INFO - Epoch(train) [5][600/660] lr: 1.0000e-02 eta: 2:51:54 time: 0.3328 data_time: 0.0204 memory: 21539 grad_norm: 3.5188 loss: 2.9345 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 2.9345 2023/03/08 16:03:06 - mmengine - INFO - Epoch(train) [5][620/660] lr: 1.0000e-02 eta: 2:51:44 time: 0.3327 data_time: 0.0219 memory: 21539 grad_norm: 3.5592 loss: 2.8683 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.8683 2023/03/08 16:03:12 - mmengine - INFO - Epoch(train) [5][640/660] lr: 1.0000e-02 eta: 2:51:34 time: 0.3307 data_time: 0.0206 memory: 21539 grad_norm: 3.5563 loss: 2.9359 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.9359 2023/03/08 16:03:19 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:03:19 - mmengine - INFO - Epoch(train) [5][660/660] lr: 1.0000e-02 eta: 2:51:24 time: 0.3289 data_time: 0.0198 memory: 21539 grad_norm: 3.5570 loss: 2.8517 top1_acc: 0.3333 top5_acc: 0.7037 loss_cls: 2.8517 2023/03/08 16:04:13 - mmengine - INFO - Epoch(val) [5][20/97] eta: 0:03:27 time: 2.6957 data_time: 2.5881 memory: 3261 2023/03/08 16:04:16 - mmengine - INFO - Epoch(val) [5][40/97] eta: 0:01:21 time: 0.1594 data_time: 0.0525 memory: 3261 2023/03/08 16:04:20 - mmengine - INFO - Epoch(val) [5][60/97] eta: 0:00:37 time: 0.1807 data_time: 0.0673 memory: 3261 2023/03/08 16:04:23 - mmengine - INFO - Epoch(val) [5][80/97] eta: 0:00:13 time: 0.1540 data_time: 0.0468 memory: 3261 2023/03/08 16:04:27 - mmengine - INFO - Epoch(val) [5][97/97] acc/top1: 0.2734 acc/top5: 0.5798 acc/mean1: 0.2040 2023/03/08 16:04:28 - mmengine - INFO - The best checkpoint with 0.2734 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2023/03/08 16:04:36 - mmengine - INFO - Epoch(train) [6][ 20/660] lr: 1.0000e-02 eta: 2:51:28 time: 0.4069 data_time: 0.0821 memory: 21539 grad_norm: 3.5233 loss: 2.7619 top1_acc: 0.2812 top5_acc: 0.6875 loss_cls: 2.7619 2023/03/08 16:04:43 - mmengine - INFO - Epoch(train) [6][ 40/660] lr: 1.0000e-02 eta: 2:51:19 time: 0.3323 data_time: 0.0215 memory: 21539 grad_norm: 3.5444 loss: 2.8271 top1_acc: 0.2812 top5_acc: 0.7188 loss_cls: 2.8271 2023/03/08 16:04:49 - mmengine - INFO - Epoch(train) [6][ 60/660] lr: 1.0000e-02 eta: 2:51:11 time: 0.3392 data_time: 0.0224 memory: 21539 grad_norm: 3.5347 loss: 2.9587 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.9587 2023/03/08 16:04:56 - mmengine - INFO - Epoch(train) [6][ 80/660] lr: 1.0000e-02 eta: 2:51:01 time: 0.3317 data_time: 0.0204 memory: 21539 grad_norm: 3.5546 loss: 2.8230 top1_acc: 0.2188 top5_acc: 0.6562 loss_cls: 2.8230 2023/03/08 16:05:03 - mmengine - INFO - Epoch(train) [6][100/660] lr: 1.0000e-02 eta: 2:50:53 time: 0.3389 data_time: 0.0220 memory: 21539 grad_norm: 3.5191 loss: 2.9478 top1_acc: 0.1562 top5_acc: 0.3750 loss_cls: 2.9478 2023/03/08 16:05:09 - mmengine - INFO - Epoch(train) [6][120/660] lr: 1.0000e-02 eta: 2:50:43 time: 0.3325 data_time: 0.0197 memory: 21539 grad_norm: 3.5291 loss: 2.8495 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.8495 2023/03/08 16:05:16 - mmengine - INFO - Epoch(train) [6][140/660] lr: 1.0000e-02 eta: 2:50:36 time: 0.3454 data_time: 0.0261 memory: 21539 grad_norm: 3.4930 loss: 2.8523 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.8523 2023/03/08 16:05:23 - mmengine - INFO - Epoch(train) [6][160/660] lr: 1.0000e-02 eta: 2:50:27 time: 0.3317 data_time: 0.0205 memory: 21539 grad_norm: 3.5200 loss: 2.8893 top1_acc: 0.1562 top5_acc: 0.4688 loss_cls: 2.8893 2023/03/08 16:05:30 - mmengine - INFO - Epoch(train) [6][180/660] lr: 1.0000e-02 eta: 2:50:19 time: 0.3417 data_time: 0.0216 memory: 21539 grad_norm: 3.5584 loss: 2.8892 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 2.8892 2023/03/08 16:05:36 - mmengine - INFO - Epoch(train) [6][200/660] lr: 1.0000e-02 eta: 2:50:10 time: 0.3304 data_time: 0.0204 memory: 21539 grad_norm: 3.5987 loss: 2.7776 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.7776 2023/03/08 16:05:43 - mmengine - INFO - Epoch(train) [6][220/660] lr: 1.0000e-02 eta: 2:50:02 time: 0.3409 data_time: 0.0203 memory: 21539 grad_norm: 3.5874 loss: 2.8737 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.8737 2023/03/08 16:05:50 - mmengine - INFO - Epoch(train) [6][240/660] lr: 1.0000e-02 eta: 2:49:53 time: 0.3319 data_time: 0.0199 memory: 21539 grad_norm: 3.5670 loss: 2.9644 top1_acc: 0.2188 top5_acc: 0.6875 loss_cls: 2.9644 2023/03/08 16:05:57 - mmengine - INFO - Epoch(train) [6][260/660] lr: 1.0000e-02 eta: 2:49:45 time: 0.3397 data_time: 0.0209 memory: 21539 grad_norm: 3.5715 loss: 2.8646 top1_acc: 0.2812 top5_acc: 0.3750 loss_cls: 2.8646 2023/03/08 16:06:03 - mmengine - INFO - Epoch(train) [6][280/660] lr: 1.0000e-02 eta: 2:49:35 time: 0.3318 data_time: 0.0207 memory: 21539 grad_norm: 3.6104 loss: 2.9208 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.9208 2023/03/08 16:06:10 - mmengine - INFO - Epoch(train) [6][300/660] lr: 1.0000e-02 eta: 2:49:27 time: 0.3390 data_time: 0.0210 memory: 21539 grad_norm: 3.5456 loss: 2.8253 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.8253 2023/03/08 16:06:17 - mmengine - INFO - Epoch(train) [6][320/660] lr: 1.0000e-02 eta: 2:49:19 time: 0.3384 data_time: 0.0220 memory: 21539 grad_norm: 3.5682 loss: 2.8925 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.8925 2023/03/08 16:06:24 - mmengine - INFO - Epoch(train) [6][340/660] lr: 1.0000e-02 eta: 2:49:12 time: 0.3426 data_time: 0.0208 memory: 21539 grad_norm: 3.5556 loss: 2.8193 top1_acc: 0.2812 top5_acc: 0.6875 loss_cls: 2.8193 2023/03/08 16:06:30 - mmengine - INFO - Epoch(train) [6][360/660] lr: 1.0000e-02 eta: 2:49:03 time: 0.3347 data_time: 0.0208 memory: 21539 grad_norm: 3.6455 loss: 2.8901 top1_acc: 0.2812 top5_acc: 0.5000 loss_cls: 2.8901 2023/03/08 16:06:37 - mmengine - INFO - Epoch(train) [6][380/660] lr: 1.0000e-02 eta: 2:48:55 time: 0.3384 data_time: 0.0209 memory: 21539 grad_norm: 3.5752 loss: 2.8315 top1_acc: 0.2500 top5_acc: 0.5938 loss_cls: 2.8315 2023/03/08 16:06:44 - mmengine - INFO - Epoch(train) [6][400/660] lr: 1.0000e-02 eta: 2:48:46 time: 0.3328 data_time: 0.0215 memory: 21539 grad_norm: 3.6162 loss: 2.9382 top1_acc: 0.2188 top5_acc: 0.5312 loss_cls: 2.9382 2023/03/08 16:06:51 - mmengine - INFO - Epoch(train) [6][420/660] lr: 1.0000e-02 eta: 2:48:38 time: 0.3410 data_time: 0.0203 memory: 21539 grad_norm: 3.6162 loss: 2.9050 top1_acc: 0.3125 top5_acc: 0.5938 loss_cls: 2.9050 2023/03/08 16:06:57 - mmengine - INFO - Epoch(train) [6][440/660] lr: 1.0000e-02 eta: 2:48:30 time: 0.3381 data_time: 0.0203 memory: 21539 grad_norm: 3.5532 loss: 2.8588 top1_acc: 0.2188 top5_acc: 0.3750 loss_cls: 2.8588 2023/03/08 16:07:04 - mmengine - INFO - Epoch(train) [6][460/660] lr: 1.0000e-02 eta: 2:48:22 time: 0.3371 data_time: 0.0213 memory: 21539 grad_norm: 3.6282 loss: 2.7901 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.7901 2023/03/08 16:07:11 - mmengine - INFO - Epoch(train) [6][480/660] lr: 1.0000e-02 eta: 2:48:13 time: 0.3335 data_time: 0.0207 memory: 21539 grad_norm: 3.6129 loss: 2.8144 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 2.8144 2023/03/08 16:07:18 - mmengine - INFO - Epoch(train) [6][500/660] lr: 1.0000e-02 eta: 2:48:06 time: 0.3397 data_time: 0.0247 memory: 21539 grad_norm: 3.6050 loss: 3.0421 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 3.0421 2023/03/08 16:07:24 - mmengine - INFO - Epoch(train) [6][520/660] lr: 1.0000e-02 eta: 2:47:57 time: 0.3336 data_time: 0.0204 memory: 21539 grad_norm: 3.6095 loss: 2.9257 top1_acc: 0.2812 top5_acc: 0.4688 loss_cls: 2.9257 2023/03/08 16:07:31 - mmengine - INFO - Epoch(train) [6][540/660] lr: 1.0000e-02 eta: 2:47:48 time: 0.3345 data_time: 0.0208 memory: 21539 grad_norm: 3.5815 loss: 2.7968 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.7968 2023/03/08 16:07:38 - mmengine - INFO - Epoch(train) [6][560/660] lr: 1.0000e-02 eta: 2:47:39 time: 0.3317 data_time: 0.0216 memory: 21539 grad_norm: 3.6466 loss: 2.9203 top1_acc: 0.1250 top5_acc: 0.5938 loss_cls: 2.9203 2023/03/08 16:07:44 - mmengine - INFO - Epoch(train) [6][580/660] lr: 1.0000e-02 eta: 2:47:31 time: 0.3356 data_time: 0.0218 memory: 21539 grad_norm: 3.6353 loss: 2.7998 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.7998 2023/03/08 16:07:51 - mmengine - INFO - Epoch(train) [6][600/660] lr: 1.0000e-02 eta: 2:47:22 time: 0.3307 data_time: 0.0208 memory: 21539 grad_norm: 3.6430 loss: 2.9198 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9198 2023/03/08 16:07:58 - mmengine - INFO - Epoch(train) [6][620/660] lr: 1.0000e-02 eta: 2:47:14 time: 0.3370 data_time: 0.0208 memory: 21539 grad_norm: 3.6231 loss: 2.8990 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.8990 2023/03/08 16:08:04 - mmengine - INFO - Epoch(train) [6][640/660] lr: 1.0000e-02 eta: 2:47:05 time: 0.3305 data_time: 0.0209 memory: 21539 grad_norm: 3.5253 loss: 2.8129 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.8129 2023/03/08 16:08:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:08:11 - mmengine - INFO - Epoch(train) [6][660/660] lr: 1.0000e-02 eta: 2:46:55 time: 0.3250 data_time: 0.0190 memory: 21539 grad_norm: 3.6121 loss: 2.7791 top1_acc: 0.2222 top5_acc: 0.7778 loss_cls: 2.7791 2023/03/08 16:08:11 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/03/08 16:08:20 - mmengine - INFO - Epoch(train) [7][ 20/660] lr: 1.0000e-02 eta: 2:46:57 time: 0.4087 data_time: 0.0878 memory: 21539 grad_norm: 3.5950 loss: 2.8383 top1_acc: 0.2812 top5_acc: 0.6875 loss_cls: 2.8383 2023/03/08 16:08:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:08:27 - mmengine - INFO - Epoch(train) [7][ 40/660] lr: 1.0000e-02 eta: 2:46:49 time: 0.3353 data_time: 0.0204 memory: 21539 grad_norm: 3.6527 loss: 2.7054 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.7054 2023/03/08 16:08:33 - mmengine - INFO - Epoch(train) [7][ 60/660] lr: 1.0000e-02 eta: 2:46:41 time: 0.3358 data_time: 0.0205 memory: 21539 grad_norm: 3.6569 loss: 2.7290 top1_acc: 0.3125 top5_acc: 0.5938 loss_cls: 2.7290 2023/03/08 16:08:40 - mmengine - INFO - Epoch(train) [7][ 80/660] lr: 1.0000e-02 eta: 2:46:33 time: 0.3383 data_time: 0.0204 memory: 21539 grad_norm: 3.6478 loss: 2.9948 top1_acc: 0.4688 top5_acc: 0.6250 loss_cls: 2.9948 2023/03/08 16:08:47 - mmengine - INFO - Epoch(train) [7][100/660] lr: 1.0000e-02 eta: 2:46:25 time: 0.3389 data_time: 0.0251 memory: 21539 grad_norm: 3.6535 loss: 2.7722 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7722 2023/03/08 16:08:54 - mmengine - INFO - Epoch(train) [7][120/660] lr: 1.0000e-02 eta: 2:46:16 time: 0.3314 data_time: 0.0212 memory: 21539 grad_norm: 3.6169 loss: 2.6600 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.6600 2023/03/08 16:09:00 - mmengine - INFO - Epoch(train) [7][140/660] lr: 1.0000e-02 eta: 2:46:09 time: 0.3408 data_time: 0.0208 memory: 21539 grad_norm: 3.6488 loss: 2.6005 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6005 2023/03/08 16:09:07 - mmengine - INFO - Epoch(train) [7][160/660] lr: 1.0000e-02 eta: 2:46:00 time: 0.3320 data_time: 0.0205 memory: 21539 grad_norm: 3.7098 loss: 2.7592 top1_acc: 0.3750 top5_acc: 0.5938 loss_cls: 2.7592 2023/03/08 16:09:14 - mmengine - INFO - Epoch(train) [7][180/660] lr: 1.0000e-02 eta: 2:45:52 time: 0.3382 data_time: 0.0202 memory: 21539 grad_norm: 3.6662 loss: 2.8062 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.8062 2023/03/08 16:09:20 - mmengine - INFO - Epoch(train) [7][200/660] lr: 1.0000e-02 eta: 2:45:43 time: 0.3304 data_time: 0.0206 memory: 21539 grad_norm: 3.6676 loss: 2.8790 top1_acc: 0.2188 top5_acc: 0.5625 loss_cls: 2.8790 2023/03/08 16:09:27 - mmengine - INFO - Epoch(train) [7][220/660] lr: 1.0000e-02 eta: 2:45:35 time: 0.3375 data_time: 0.0209 memory: 21539 grad_norm: 3.5853 loss: 2.7796 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.7796 2023/03/08 16:09:34 - mmengine - INFO - Epoch(train) [7][240/660] lr: 1.0000e-02 eta: 2:45:27 time: 0.3319 data_time: 0.0203 memory: 21539 grad_norm: 3.7142 loss: 2.8360 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 2.8360 2023/03/08 16:09:40 - mmengine - INFO - Epoch(train) [7][260/660] lr: 1.0000e-02 eta: 2:45:19 time: 0.3364 data_time: 0.0206 memory: 21539 grad_norm: 3.6341 loss: 2.7554 top1_acc: 0.3125 top5_acc: 0.7188 loss_cls: 2.7554 2023/03/08 16:09:47 - mmengine - INFO - Epoch(train) [7][280/660] lr: 1.0000e-02 eta: 2:45:10 time: 0.3310 data_time: 0.0209 memory: 21539 grad_norm: 3.6541 loss: 2.6428 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.6428 2023/03/08 16:09:54 - mmengine - INFO - Epoch(train) [7][300/660] lr: 1.0000e-02 eta: 2:45:02 time: 0.3394 data_time: 0.0203 memory: 21539 grad_norm: 3.7501 loss: 2.7935 top1_acc: 0.2500 top5_acc: 0.5938 loss_cls: 2.7935 2023/03/08 16:10:01 - mmengine - INFO - Epoch(train) [7][320/660] lr: 1.0000e-02 eta: 2:44:54 time: 0.3319 data_time: 0.0217 memory: 21539 grad_norm: 3.6851 loss: 2.8065 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8065 2023/03/08 16:10:11 - mmengine - INFO - Epoch(train) [7][340/660] lr: 1.0000e-02 eta: 2:45:11 time: 0.5272 data_time: 0.0212 memory: 21539 grad_norm: 3.6806 loss: 2.7292 top1_acc: 0.3438 top5_acc: 0.5312 loss_cls: 2.7292 2023/03/08 16:10:18 - mmengine - INFO - Epoch(train) [7][360/660] lr: 1.0000e-02 eta: 2:45:03 time: 0.3331 data_time: 0.0205 memory: 21539 grad_norm: 3.7051 loss: 2.8839 top1_acc: 0.2188 top5_acc: 0.5938 loss_cls: 2.8839 2023/03/08 16:10:25 - mmengine - INFO - Epoch(train) [7][380/660] lr: 1.0000e-02 eta: 2:44:55 time: 0.3422 data_time: 0.0206 memory: 21539 grad_norm: 3.6946 loss: 2.7710 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.7710 2023/03/08 16:10:31 - mmengine - INFO - Epoch(train) [7][400/660] lr: 1.0000e-02 eta: 2:44:47 time: 0.3333 data_time: 0.0221 memory: 21539 grad_norm: 3.6509 loss: 2.8002 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8002 2023/03/08 16:10:38 - mmengine - INFO - Epoch(train) [7][420/660] lr: 1.0000e-02 eta: 2:44:39 time: 0.3400 data_time: 0.0211 memory: 21539 grad_norm: 3.6595 loss: 2.6951 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.6951 2023/03/08 16:10:45 - mmengine - INFO - Epoch(train) [7][440/660] lr: 1.0000e-02 eta: 2:44:32 time: 0.3383 data_time: 0.0211 memory: 21539 grad_norm: 3.6020 loss: 2.7598 top1_acc: 0.4062 top5_acc: 0.5625 loss_cls: 2.7598 2023/03/08 16:10:52 - mmengine - INFO - Epoch(train) [7][460/660] lr: 1.0000e-02 eta: 2:44:24 time: 0.3391 data_time: 0.0242 memory: 21539 grad_norm: 3.6638 loss: 2.7899 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.7899 2023/03/08 16:10:58 - mmengine - INFO - Epoch(train) [7][480/660] lr: 1.0000e-02 eta: 2:44:15 time: 0.3321 data_time: 0.0207 memory: 21539 grad_norm: 3.6888 loss: 2.7400 top1_acc: 0.3438 top5_acc: 0.5938 loss_cls: 2.7400 2023/03/08 16:11:05 - mmengine - INFO - Epoch(train) [7][500/660] lr: 1.0000e-02 eta: 2:44:07 time: 0.3363 data_time: 0.0207 memory: 21539 grad_norm: 3.6901 loss: 2.6763 top1_acc: 0.3438 top5_acc: 0.6250 loss_cls: 2.6763 2023/03/08 16:11:12 - mmengine - INFO - Epoch(train) [7][520/660] lr: 1.0000e-02 eta: 2:43:59 time: 0.3323 data_time: 0.0210 memory: 21539 grad_norm: 3.6642 loss: 2.7745 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 2.7745 2023/03/08 16:11:18 - mmengine - INFO - Epoch(train) [7][540/660] lr: 1.0000e-02 eta: 2:43:51 time: 0.3369 data_time: 0.0208 memory: 21539 grad_norm: 3.6918 loss: 2.7434 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 2.7434 2023/03/08 16:11:25 - mmengine - INFO - Epoch(train) [7][560/660] lr: 1.0000e-02 eta: 2:43:42 time: 0.3325 data_time: 0.0202 memory: 21539 grad_norm: 3.6814 loss: 2.6987 top1_acc: 0.2188 top5_acc: 0.6250 loss_cls: 2.6987 2023/03/08 16:11:32 - mmengine - INFO - Epoch(train) [7][580/660] lr: 1.0000e-02 eta: 2:43:35 time: 0.3395 data_time: 0.0210 memory: 21539 grad_norm: 3.6387 loss: 2.7764 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.7764 2023/03/08 16:11:38 - mmengine - INFO - Epoch(train) [7][600/660] lr: 1.0000e-02 eta: 2:43:26 time: 0.3309 data_time: 0.0211 memory: 21539 grad_norm: 3.7266 loss: 2.8055 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8055 2023/03/08 16:11:45 - mmengine - INFO - Epoch(train) [7][620/660] lr: 1.0000e-02 eta: 2:43:19 time: 0.3423 data_time: 0.0216 memory: 21539 grad_norm: 3.6851 loss: 2.6989 top1_acc: 0.2188 top5_acc: 0.5938 loss_cls: 2.6989 2023/03/08 16:11:52 - mmengine - INFO - Epoch(train) [7][640/660] lr: 1.0000e-02 eta: 2:43:10 time: 0.3319 data_time: 0.0209 memory: 21539 grad_norm: 3.7409 loss: 2.7764 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 2.7764 2023/03/08 16:11:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:11:58 - mmengine - INFO - Epoch(train) [7][660/660] lr: 1.0000e-02 eta: 2:43:02 time: 0.3287 data_time: 0.0194 memory: 21539 grad_norm: 3.6639 loss: 2.5874 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.5874 2023/03/08 16:12:07 - mmengine - INFO - Epoch(train) [8][ 20/660] lr: 1.0000e-02 eta: 2:43:03 time: 0.4133 data_time: 0.0852 memory: 21539 grad_norm: 3.6601 loss: 2.8233 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.8233 2023/03/08 16:12:13 - mmengine - INFO - Epoch(train) [8][ 40/660] lr: 1.0000e-02 eta: 2:42:55 time: 0.3343 data_time: 0.0181 memory: 21539 grad_norm: 3.6964 loss: 2.8053 top1_acc: 0.2812 top5_acc: 0.5312 loss_cls: 2.8053 2023/03/08 16:12:20 - mmengine - INFO - Epoch(train) [8][ 60/660] lr: 1.0000e-02 eta: 2:42:48 time: 0.3465 data_time: 0.0229 memory: 21539 grad_norm: 3.7428 loss: 2.7018 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.7018 2023/03/08 16:12:27 - mmengine - INFO - Epoch(train) [8][ 80/660] lr: 1.0000e-02 eta: 2:42:40 time: 0.3359 data_time: 0.0183 memory: 21539 grad_norm: 3.6886 loss: 2.6947 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.6947 2023/03/08 16:12:34 - mmengine - INFO - Epoch(train) [8][100/660] lr: 1.0000e-02 eta: 2:42:32 time: 0.3385 data_time: 0.0217 memory: 21539 grad_norm: 3.7039 loss: 2.7103 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.7103 2023/03/08 16:12:41 - mmengine - INFO - Epoch(train) [8][120/660] lr: 1.0000e-02 eta: 2:42:24 time: 0.3323 data_time: 0.0178 memory: 21539 grad_norm: 3.7306 loss: 2.6957 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.6957 2023/03/08 16:12:47 - mmengine - INFO - Epoch(train) [8][140/660] lr: 1.0000e-02 eta: 2:42:16 time: 0.3384 data_time: 0.0262 memory: 21539 grad_norm: 3.6808 loss: 2.6463 top1_acc: 0.2188 top5_acc: 0.5938 loss_cls: 2.6463 2023/03/08 16:12:54 - mmengine - INFO - Epoch(train) [8][160/660] lr: 1.0000e-02 eta: 2:42:08 time: 0.3330 data_time: 0.0188 memory: 21539 grad_norm: 3.7444 loss: 2.7435 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.7435 2023/03/08 16:13:01 - mmengine - INFO - Epoch(train) [8][180/660] lr: 1.0000e-02 eta: 2:42:00 time: 0.3362 data_time: 0.0223 memory: 21539 grad_norm: 3.7311 loss: 2.7683 top1_acc: 0.2500 top5_acc: 0.5938 loss_cls: 2.7683 2023/03/08 16:13:07 - mmengine - INFO - Epoch(train) [8][200/660] lr: 1.0000e-02 eta: 2:41:52 time: 0.3324 data_time: 0.0185 memory: 21539 grad_norm: 3.7158 loss: 2.7350 top1_acc: 0.2188 top5_acc: 0.5625 loss_cls: 2.7350 2023/03/08 16:13:14 - mmengine - INFO - Epoch(train) [8][220/660] lr: 1.0000e-02 eta: 2:41:44 time: 0.3356 data_time: 0.0235 memory: 21539 grad_norm: 3.6830 loss: 2.6864 top1_acc: 0.2188 top5_acc: 0.6562 loss_cls: 2.6864 2023/03/08 16:13:21 - mmengine - INFO - Epoch(train) [8][240/660] lr: 1.0000e-02 eta: 2:41:36 time: 0.3347 data_time: 0.0182 memory: 21539 grad_norm: 3.7688 loss: 2.6767 top1_acc: 0.2812 top5_acc: 0.5938 loss_cls: 2.6767 2023/03/08 16:13:27 - mmengine - INFO - Epoch(train) [8][260/660] lr: 1.0000e-02 eta: 2:41:28 time: 0.3348 data_time: 0.0220 memory: 21539 grad_norm: 3.7557 loss: 2.6148 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 2.6148 2023/03/08 16:13:34 - mmengine - INFO - Epoch(train) [8][280/660] lr: 1.0000e-02 eta: 2:41:20 time: 0.3357 data_time: 0.0183 memory: 21539 grad_norm: 3.8103 loss: 2.6410 top1_acc: 0.1562 top5_acc: 0.5625 loss_cls: 2.6410 2023/03/08 16:13:41 - mmengine - INFO - Epoch(train) [8][300/660] lr: 1.0000e-02 eta: 2:41:12 time: 0.3344 data_time: 0.0223 memory: 21539 grad_norm: 3.7387 loss: 2.5956 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5956 2023/03/08 16:13:47 - mmengine - INFO - Epoch(train) [8][320/660] lr: 1.0000e-02 eta: 2:41:04 time: 0.3312 data_time: 0.0183 memory: 21539 grad_norm: 3.6947 loss: 2.6307 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.6307 2023/03/08 16:13:54 - mmengine - INFO - Epoch(train) [8][340/660] lr: 1.0000e-02 eta: 2:40:56 time: 0.3354 data_time: 0.0215 memory: 21539 grad_norm: 3.7650 loss: 2.7860 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.7860 2023/03/08 16:14:01 - mmengine - INFO - Epoch(train) [8][360/660] lr: 1.0000e-02 eta: 2:40:47 time: 0.3310 data_time: 0.0186 memory: 21539 grad_norm: 3.7001 loss: 2.6481 top1_acc: 0.2812 top5_acc: 0.5312 loss_cls: 2.6481 2023/03/08 16:14:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:14:07 - mmengine - INFO - Epoch(train) [8][380/660] lr: 1.0000e-02 eta: 2:40:39 time: 0.3338 data_time: 0.0218 memory: 21539 grad_norm: 3.7452 loss: 2.7377 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.7377 2023/03/08 16:14:14 - mmengine - INFO - Epoch(train) [8][400/660] lr: 1.0000e-02 eta: 2:40:31 time: 0.3359 data_time: 0.0182 memory: 21539 grad_norm: 3.7766 loss: 2.7446 top1_acc: 0.1875 top5_acc: 0.6562 loss_cls: 2.7446 2023/03/08 16:14:21 - mmengine - INFO - Epoch(train) [8][420/660] lr: 1.0000e-02 eta: 2:40:24 time: 0.3367 data_time: 0.0224 memory: 21539 grad_norm: 3.7366 loss: 2.7863 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 2.7863 2023/03/08 16:14:28 - mmengine - INFO - Epoch(train) [8][440/660] lr: 1.0000e-02 eta: 2:40:16 time: 0.3355 data_time: 0.0181 memory: 21539 grad_norm: 3.7169 loss: 2.6812 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.6812 2023/03/08 16:14:34 - mmengine - INFO - Epoch(train) [8][460/660] lr: 1.0000e-02 eta: 2:40:08 time: 0.3337 data_time: 0.0219 memory: 21539 grad_norm: 3.6961 loss: 2.7854 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.7854 2023/03/08 16:14:41 - mmengine - INFO - Epoch(train) [8][480/660] lr: 1.0000e-02 eta: 2:40:00 time: 0.3336 data_time: 0.0178 memory: 21539 grad_norm: 3.7996 loss: 2.6225 top1_acc: 0.2500 top5_acc: 0.4688 loss_cls: 2.6225 2023/03/08 16:14:48 - mmengine - INFO - Epoch(train) [8][500/660] lr: 1.0000e-02 eta: 2:39:52 time: 0.3373 data_time: 0.0263 memory: 21539 grad_norm: 3.7538 loss: 2.6442 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 2.6442 2023/03/08 16:14:54 - mmengine - INFO - Epoch(train) [8][520/660] lr: 1.0000e-02 eta: 2:39:44 time: 0.3347 data_time: 0.0181 memory: 21539 grad_norm: 3.7678 loss: 2.7149 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 2.7149 2023/03/08 16:15:01 - mmengine - INFO - Epoch(train) [8][540/660] lr: 1.0000e-02 eta: 2:39:36 time: 0.3330 data_time: 0.0217 memory: 21539 grad_norm: 3.7070 loss: 2.7295 top1_acc: 0.3125 top5_acc: 0.7812 loss_cls: 2.7295 2023/03/08 16:15:08 - mmengine - INFO - Epoch(train) [8][560/660] lr: 1.0000e-02 eta: 2:39:28 time: 0.3322 data_time: 0.0183 memory: 21539 grad_norm: 3.8135 loss: 2.7225 top1_acc: 0.3438 top5_acc: 0.8125 loss_cls: 2.7225 2023/03/08 16:15:14 - mmengine - INFO - Epoch(train) [8][580/660] lr: 1.0000e-02 eta: 2:39:20 time: 0.3341 data_time: 0.0219 memory: 21539 grad_norm: 3.7759 loss: 2.5530 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 2.5530 2023/03/08 16:15:21 - mmengine - INFO - Epoch(train) [8][600/660] lr: 1.0000e-02 eta: 2:39:12 time: 0.3346 data_time: 0.0184 memory: 21539 grad_norm: 3.7539 loss: 2.6094 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6094 2023/03/08 16:15:28 - mmengine - INFO - Epoch(train) [8][620/660] lr: 1.0000e-02 eta: 2:39:04 time: 0.3338 data_time: 0.0220 memory: 21539 grad_norm: 3.7442 loss: 2.6653 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.6653 2023/03/08 16:15:34 - mmengine - INFO - Epoch(train) [8][640/660] lr: 1.0000e-02 eta: 2:38:56 time: 0.3306 data_time: 0.0180 memory: 21539 grad_norm: 3.7138 loss: 2.6970 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.6970 2023/03/08 16:15:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:15:41 - mmengine - INFO - Epoch(train) [8][660/660] lr: 1.0000e-02 eta: 2:38:48 time: 0.3298 data_time: 0.0193 memory: 21539 grad_norm: 3.8433 loss: 2.7820 top1_acc: 0.3704 top5_acc: 0.8148 loss_cls: 2.7820 2023/03/08 16:15:49 - mmengine - INFO - Epoch(train) [9][ 20/660] lr: 1.0000e-02 eta: 2:38:48 time: 0.4144 data_time: 0.0873 memory: 21539 grad_norm: 3.7185 loss: 2.6049 top1_acc: 0.2812 top5_acc: 0.6250 loss_cls: 2.6049 2023/03/08 16:15:56 - mmengine - INFO - Epoch(train) [9][ 40/660] lr: 1.0000e-02 eta: 2:38:40 time: 0.3311 data_time: 0.0210 memory: 21539 grad_norm: 3.8186 loss: 2.6424 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.6424 2023/03/08 16:16:03 - mmengine - INFO - Epoch(train) [9][ 60/660] lr: 1.0000e-02 eta: 2:38:32 time: 0.3329 data_time: 0.0218 memory: 21539 grad_norm: 3.7369 loss: 2.5701 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.5701 2023/03/08 16:16:09 - mmengine - INFO - Epoch(train) [9][ 80/660] lr: 1.0000e-02 eta: 2:38:24 time: 0.3335 data_time: 0.0201 memory: 21539 grad_norm: 3.7012 loss: 2.6335 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.6335 2023/03/08 16:16:16 - mmengine - INFO - Epoch(train) [9][100/660] lr: 1.0000e-02 eta: 2:38:16 time: 0.3357 data_time: 0.0214 memory: 21539 grad_norm: 3.7437 loss: 2.5694 top1_acc: 0.2500 top5_acc: 0.6562 loss_cls: 2.5694 2023/03/08 16:16:23 - mmengine - INFO - Epoch(train) [9][120/660] lr: 1.0000e-02 eta: 2:38:08 time: 0.3299 data_time: 0.0208 memory: 21539 grad_norm: 3.7249 loss: 2.7180 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.7180 2023/03/08 16:16:29 - mmengine - INFO - Epoch(train) [9][140/660] lr: 1.0000e-02 eta: 2:38:00 time: 0.3368 data_time: 0.0228 memory: 21539 grad_norm: 3.8201 loss: 2.6414 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 2.6414 2023/03/08 16:16:36 - mmengine - INFO - Epoch(train) [9][160/660] lr: 1.0000e-02 eta: 2:37:53 time: 0.3350 data_time: 0.0207 memory: 21539 grad_norm: 3.7756 loss: 2.7015 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.7015 2023/03/08 16:16:43 - mmengine - INFO - Epoch(train) [9][180/660] lr: 1.0000e-02 eta: 2:37:45 time: 0.3379 data_time: 0.0265 memory: 21539 grad_norm: 3.8809 loss: 2.5751 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 2.5751 2023/03/08 16:16:49 - mmengine - INFO - Epoch(train) [9][200/660] lr: 1.0000e-02 eta: 2:37:37 time: 0.3322 data_time: 0.0207 memory: 21539 grad_norm: 3.8370 loss: 2.6207 top1_acc: 0.2812 top5_acc: 0.6250 loss_cls: 2.6207 2023/03/08 16:16:56 - mmengine - INFO - Epoch(train) [9][220/660] lr: 1.0000e-02 eta: 2:37:29 time: 0.3327 data_time: 0.0220 memory: 21539 grad_norm: 3.7790 loss: 2.5464 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5464 2023/03/08 16:17:03 - mmengine - INFO - Epoch(train) [9][240/660] lr: 1.0000e-02 eta: 2:37:21 time: 0.3314 data_time: 0.0215 memory: 21539 grad_norm: 3.8209 loss: 2.5815 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.5815 2023/03/08 16:17:09 - mmengine - INFO - Epoch(train) [9][260/660] lr: 1.0000e-02 eta: 2:37:13 time: 0.3363 data_time: 0.0218 memory: 21539 grad_norm: 3.8860 loss: 2.6697 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.6697 2023/03/08 16:17:16 - mmengine - INFO - Epoch(train) [9][280/660] lr: 1.0000e-02 eta: 2:37:06 time: 0.3343 data_time: 0.0211 memory: 21539 grad_norm: 3.7612 loss: 2.6426 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.6426 2023/03/08 16:17:23 - mmengine - INFO - Epoch(train) [9][300/660] lr: 1.0000e-02 eta: 2:36:59 time: 0.3407 data_time: 0.0217 memory: 21539 grad_norm: 3.8604 loss: 2.5315 top1_acc: 0.4375 top5_acc: 0.5938 loss_cls: 2.5315 2023/03/08 16:17:30 - mmengine - INFO - Epoch(train) [9][320/660] lr: 1.0000e-02 eta: 2:36:51 time: 0.3349 data_time: 0.0214 memory: 21539 grad_norm: 3.8450 loss: 2.5915 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.5915 2023/03/08 16:17:36 - mmengine - INFO - Epoch(train) [9][340/660] lr: 1.0000e-02 eta: 2:36:44 time: 0.3406 data_time: 0.0219 memory: 21539 grad_norm: 3.8983 loss: 2.6571 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.6571 2023/03/08 16:17:43 - mmengine - INFO - Epoch(train) [9][360/660] lr: 1.0000e-02 eta: 2:36:36 time: 0.3360 data_time: 0.0203 memory: 21539 grad_norm: 3.8393 loss: 2.5698 top1_acc: 0.3750 top5_acc: 0.5312 loss_cls: 2.5698 2023/03/08 16:17:50 - mmengine - INFO - Epoch(train) [9][380/660] lr: 1.0000e-02 eta: 2:36:29 time: 0.3395 data_time: 0.0223 memory: 21539 grad_norm: 3.8303 loss: 2.6146 top1_acc: 0.2188 top5_acc: 0.5312 loss_cls: 2.6146 2023/03/08 16:17:57 - mmengine - INFO - Epoch(train) [9][400/660] lr: 1.0000e-02 eta: 2:36:21 time: 0.3344 data_time: 0.0205 memory: 21539 grad_norm: 3.8170 loss: 2.4433 top1_acc: 0.2188 top5_acc: 0.5625 loss_cls: 2.4433 2023/03/08 16:18:03 - mmengine - INFO - Epoch(train) [9][420/660] lr: 1.0000e-02 eta: 2:36:14 time: 0.3407 data_time: 0.0220 memory: 21539 grad_norm: 3.8596 loss: 2.6483 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.6483 2023/03/08 16:18:10 - mmengine - INFO - Epoch(train) [9][440/660] lr: 1.0000e-02 eta: 2:36:06 time: 0.3346 data_time: 0.0210 memory: 21539 grad_norm: 3.8067 loss: 2.4893 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4893 2023/03/08 16:18:17 - mmengine - INFO - Epoch(train) [9][460/660] lr: 1.0000e-02 eta: 2:35:59 time: 0.3368 data_time: 0.0219 memory: 21539 grad_norm: 3.7650 loss: 2.5314 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5314 2023/03/08 16:18:24 - mmengine - INFO - Epoch(train) [9][480/660] lr: 1.0000e-02 eta: 2:35:51 time: 0.3333 data_time: 0.0211 memory: 21539 grad_norm: 3.8201 loss: 2.6239 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.6239 2023/03/08 16:18:30 - mmengine - INFO - Epoch(train) [9][500/660] lr: 1.0000e-02 eta: 2:35:44 time: 0.3389 data_time: 0.0216 memory: 21539 grad_norm: 3.8973 loss: 2.6420 top1_acc: 0.4062 top5_acc: 0.5000 loss_cls: 2.6420 2023/03/08 16:18:37 - mmengine - INFO - Epoch(train) [9][520/660] lr: 1.0000e-02 eta: 2:35:37 time: 0.3410 data_time: 0.0253 memory: 21539 grad_norm: 3.8134 loss: 2.5467 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 2.5467 2023/03/08 16:18:44 - mmengine - INFO - Epoch(train) [9][540/660] lr: 1.0000e-02 eta: 2:35:30 time: 0.3420 data_time: 0.0220 memory: 21539 grad_norm: 3.7820 loss: 2.5931 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 2.5931 2023/03/08 16:18:51 - mmengine - INFO - Epoch(train) [9][560/660] lr: 1.0000e-02 eta: 2:35:22 time: 0.3329 data_time: 0.0211 memory: 21539 grad_norm: 3.7744 loss: 2.6413 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.6413 2023/03/08 16:18:57 - mmengine - INFO - Epoch(train) [9][580/660] lr: 1.0000e-02 eta: 2:35:15 time: 0.3416 data_time: 0.0219 memory: 21539 grad_norm: 3.8189 loss: 2.6644 top1_acc: 0.2188 top5_acc: 0.5625 loss_cls: 2.6644 2023/03/08 16:19:04 - mmengine - INFO - Epoch(train) [9][600/660] lr: 1.0000e-02 eta: 2:35:07 time: 0.3340 data_time: 0.0214 memory: 21539 grad_norm: 3.8833 loss: 2.5792 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.5792 2023/03/08 16:19:11 - mmengine - INFO - Epoch(train) [9][620/660] lr: 1.0000e-02 eta: 2:35:00 time: 0.3413 data_time: 0.0219 memory: 21539 grad_norm: 3.8024 loss: 2.5797 top1_acc: 0.2812 top5_acc: 0.7188 loss_cls: 2.5797 2023/03/08 16:19:18 - mmengine - INFO - Epoch(train) [9][640/660] lr: 1.0000e-02 eta: 2:34:53 time: 0.3365 data_time: 0.0205 memory: 21539 grad_norm: 3.7769 loss: 2.5345 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.5345 2023/03/08 16:19:24 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:19:24 - mmengine - INFO - Epoch(train) [9][660/660] lr: 1.0000e-02 eta: 2:34:44 time: 0.3296 data_time: 0.0204 memory: 21539 grad_norm: 3.8860 loss: 2.5671 top1_acc: 0.2593 top5_acc: 0.4815 loss_cls: 2.5671 2023/03/08 16:19:24 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/03/08 16:19:34 - mmengine - INFO - Epoch(train) [10][ 20/660] lr: 1.0000e-02 eta: 2:34:44 time: 0.4094 data_time: 0.0888 memory: 21539 grad_norm: 3.7746 loss: 2.6013 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.6013 2023/03/08 16:19:40 - mmengine - INFO - Epoch(train) [10][ 40/660] lr: 1.0000e-02 eta: 2:34:36 time: 0.3347 data_time: 0.0218 memory: 21539 grad_norm: 3.8254 loss: 2.5596 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.5596 2023/03/08 16:19:47 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:19:47 - mmengine - INFO - Epoch(train) [10][ 60/660] lr: 1.0000e-02 eta: 2:34:29 time: 0.3377 data_time: 0.0209 memory: 21539 grad_norm: 3.8171 loss: 2.4587 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4587 2023/03/08 16:19:54 - mmengine - INFO - Epoch(train) [10][ 80/660] lr: 1.0000e-02 eta: 2:34:21 time: 0.3360 data_time: 0.0209 memory: 21539 grad_norm: 3.8628 loss: 2.4585 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.4585 2023/03/08 16:20:00 - mmengine - INFO - Epoch(train) [10][100/660] lr: 1.0000e-02 eta: 2:34:14 time: 0.3390 data_time: 0.0221 memory: 21539 grad_norm: 3.8222 loss: 2.5098 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5098 2023/03/08 16:20:07 - mmengine - INFO - Epoch(train) [10][120/660] lr: 1.0000e-02 eta: 2:34:06 time: 0.3345 data_time: 0.0216 memory: 21539 grad_norm: 3.8344 loss: 2.5138 top1_acc: 0.2188 top5_acc: 0.5938 loss_cls: 2.5138 2023/03/08 16:20:14 - mmengine - INFO - Epoch(train) [10][140/660] lr: 1.0000e-02 eta: 2:33:59 time: 0.3437 data_time: 0.0214 memory: 21539 grad_norm: 3.8794 loss: 2.4635 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.4635 2023/03/08 16:20:21 - mmengine - INFO - Epoch(train) [10][160/660] lr: 1.0000e-02 eta: 2:33:52 time: 0.3358 data_time: 0.0215 memory: 21539 grad_norm: 3.9176 loss: 2.4930 top1_acc: 0.2812 top5_acc: 0.6562 loss_cls: 2.4930 2023/03/08 16:20:28 - mmengine - INFO - Epoch(train) [10][180/660] lr: 1.0000e-02 eta: 2:33:45 time: 0.3434 data_time: 0.0212 memory: 21539 grad_norm: 3.9287 loss: 2.5352 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5352 2023/03/08 16:20:34 - mmengine - INFO - Epoch(train) [10][200/660] lr: 1.0000e-02 eta: 2:33:37 time: 0.3330 data_time: 0.0217 memory: 21539 grad_norm: 3.8816 loss: 2.5650 top1_acc: 0.2188 top5_acc: 0.6250 loss_cls: 2.5650 2023/03/08 16:20:41 - mmengine - INFO - Epoch(train) [10][220/660] lr: 1.0000e-02 eta: 2:33:30 time: 0.3405 data_time: 0.0217 memory: 21539 grad_norm: 3.8579 loss: 2.3691 top1_acc: 0.2812 top5_acc: 0.7500 loss_cls: 2.3691 2023/03/08 16:20:48 - mmengine - INFO - Epoch(train) [10][240/660] lr: 1.0000e-02 eta: 2:33:23 time: 0.3417 data_time: 0.0208 memory: 21539 grad_norm: 3.8440 loss: 2.5086 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.5086 2023/03/08 16:20:55 - mmengine - INFO - Epoch(train) [10][260/660] lr: 1.0000e-02 eta: 2:33:16 time: 0.3408 data_time: 0.0211 memory: 21539 grad_norm: 3.8672 loss: 2.5880 top1_acc: 0.2812 top5_acc: 0.7188 loss_cls: 2.5880 2023/03/08 16:21:02 - mmengine - INFO - Epoch(train) [10][280/660] lr: 1.0000e-02 eta: 2:33:10 time: 0.3534 data_time: 0.0411 memory: 21539 grad_norm: 3.9051 loss: 2.5170 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.5170 2023/03/08 16:21:09 - mmengine - INFO - Epoch(train) [10][300/660] lr: 1.0000e-02 eta: 2:33:03 time: 0.3401 data_time: 0.0209 memory: 21539 grad_norm: 3.8452 loss: 2.4881 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.4881 2023/03/08 16:21:15 - mmengine - INFO - Epoch(train) [10][320/660] lr: 1.0000e-02 eta: 2:32:55 time: 0.3329 data_time: 0.0211 memory: 21539 grad_norm: 3.9245 loss: 2.4944 top1_acc: 0.3125 top5_acc: 0.5938 loss_cls: 2.4944 2023/03/08 16:21:22 - mmengine - INFO - Epoch(train) [10][340/660] lr: 1.0000e-02 eta: 2:32:48 time: 0.3416 data_time: 0.0254 memory: 21539 grad_norm: 3.8696 loss: 2.6220 top1_acc: 0.2500 top5_acc: 0.6562 loss_cls: 2.6220 2023/03/08 16:21:29 - mmengine - INFO - Epoch(train) [10][360/660] lr: 1.0000e-02 eta: 2:32:41 time: 0.3346 data_time: 0.0212 memory: 21539 grad_norm: 3.8730 loss: 2.4079 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4079 2023/03/08 16:21:36 - mmengine - INFO - Epoch(train) [10][380/660] lr: 1.0000e-02 eta: 2:32:34 time: 0.3431 data_time: 0.0210 memory: 21539 grad_norm: 3.8773 loss: 2.4738 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.4738 2023/03/08 16:21:42 - mmengine - INFO - Epoch(train) [10][400/660] lr: 1.0000e-02 eta: 2:32:26 time: 0.3341 data_time: 0.0209 memory: 21539 grad_norm: 3.9509 loss: 2.5861 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5861 2023/03/08 16:21:49 - mmengine - INFO - Epoch(train) [10][420/660] lr: 1.0000e-02 eta: 2:32:19 time: 0.3417 data_time: 0.0215 memory: 21539 grad_norm: 3.8927 loss: 2.3985 top1_acc: 0.2812 top5_acc: 0.5938 loss_cls: 2.3985 2023/03/08 16:21:56 - mmengine - INFO - Epoch(train) [10][440/660] lr: 1.0000e-02 eta: 2:32:12 time: 0.3389 data_time: 0.0210 memory: 21539 grad_norm: 3.8938 loss: 2.4137 top1_acc: 0.3438 top5_acc: 0.7812 loss_cls: 2.4137 2023/03/08 16:22:03 - mmengine - INFO - Epoch(train) [10][460/660] lr: 1.0000e-02 eta: 2:32:05 time: 0.3453 data_time: 0.0211 memory: 21539 grad_norm: 3.9477 loss: 2.5753 top1_acc: 0.5312 top5_acc: 0.5938 loss_cls: 2.5753 2023/03/08 16:22:10 - mmengine - INFO - Epoch(train) [10][480/660] lr: 1.0000e-02 eta: 2:31:58 time: 0.3334 data_time: 0.0213 memory: 21539 grad_norm: 3.9230 loss: 2.4755 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.4755 2023/03/08 16:22:16 - mmengine - INFO - Epoch(train) [10][500/660] lr: 1.0000e-02 eta: 2:31:51 time: 0.3407 data_time: 0.0212 memory: 21539 grad_norm: 3.9748 loss: 2.6056 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 2.6056 2023/03/08 16:22:23 - mmengine - INFO - Epoch(train) [10][520/660] lr: 1.0000e-02 eta: 2:31:43 time: 0.3368 data_time: 0.0208 memory: 21539 grad_norm: 3.8220 loss: 2.6451 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.6451 2023/03/08 16:22:30 - mmengine - INFO - Epoch(train) [10][540/660] lr: 1.0000e-02 eta: 2:31:36 time: 0.3421 data_time: 0.0217 memory: 21539 grad_norm: 3.9383 loss: 2.4263 top1_acc: 0.3438 top5_acc: 0.7812 loss_cls: 2.4263 2023/03/08 16:22:37 - mmengine - INFO - Epoch(train) [10][560/660] lr: 1.0000e-02 eta: 2:31:29 time: 0.3358 data_time: 0.0208 memory: 21539 grad_norm: 3.8867 loss: 2.5664 top1_acc: 0.3125 top5_acc: 0.7188 loss_cls: 2.5664 2023/03/08 16:22:43 - mmengine - INFO - Epoch(train) [10][580/660] lr: 1.0000e-02 eta: 2:31:22 time: 0.3405 data_time: 0.0210 memory: 21539 grad_norm: 3.8455 loss: 2.4366 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.4366 2023/03/08 16:22:50 - mmengine - INFO - Epoch(train) [10][600/660] lr: 1.0000e-02 eta: 2:31:14 time: 0.3328 data_time: 0.0216 memory: 21539 grad_norm: 3.8856 loss: 2.5728 top1_acc: 0.2188 top5_acc: 0.7188 loss_cls: 2.5728 2023/03/08 16:22:57 - mmengine - INFO - Epoch(train) [10][620/660] lr: 1.0000e-02 eta: 2:31:07 time: 0.3392 data_time: 0.0218 memory: 21539 grad_norm: 3.9807 loss: 2.4543 top1_acc: 0.3750 top5_acc: 0.5938 loss_cls: 2.4543 2023/03/08 16:23:04 - mmengine - INFO - Epoch(train) [10][640/660] lr: 1.0000e-02 eta: 2:31:00 time: 0.3372 data_time: 0.0252 memory: 21539 grad_norm: 3.8580 loss: 2.5765 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.5765 2023/03/08 16:23:10 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:23:10 - mmengine - INFO - Epoch(train) [10][660/660] lr: 1.0000e-02 eta: 2:30:52 time: 0.3308 data_time: 0.0195 memory: 21539 grad_norm: 3.8522 loss: 2.4829 top1_acc: 0.4815 top5_acc: 0.7778 loss_cls: 2.4829 2023/03/08 16:23:15 - mmengine - INFO - Epoch(val) [10][20/97] eta: 0:00:18 time: 0.2342 data_time: 0.1271 memory: 3261 2023/03/08 16:23:18 - mmengine - INFO - Epoch(val) [10][40/97] eta: 0:00:11 time: 0.1586 data_time: 0.0524 memory: 3261 2023/03/08 16:23:22 - mmengine - INFO - Epoch(val) [10][60/97] eta: 0:00:07 time: 0.1835 data_time: 0.0770 memory: 3261 2023/03/08 16:23:25 - mmengine - INFO - Epoch(val) [10][80/97] eta: 0:00:03 time: 0.1564 data_time: 0.0492 memory: 3261 2023/03/08 16:23:28 - mmengine - INFO - Epoch(val) [10][97/97] acc/top1: 0.2876 acc/top5: 0.5981 acc/mean1: 0.2248 2023/03/08 16:23:28 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_exp1/best_acc/top1_epoch_5.pth is removed 2023/03/08 16:23:29 - mmengine - INFO - The best checkpoint with 0.2876 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2023/03/08 16:23:37 - mmengine - INFO - Epoch(train) [11][ 20/660] lr: 1.0000e-02 eta: 2:30:50 time: 0.4047 data_time: 0.0877 memory: 21539 grad_norm: 3.8720 loss: 2.4178 top1_acc: 0.4688 top5_acc: 0.6250 loss_cls: 2.4178 2023/03/08 16:23:44 - mmengine - INFO - Epoch(train) [11][ 40/660] lr: 1.0000e-02 eta: 2:30:42 time: 0.3291 data_time: 0.0228 memory: 21539 grad_norm: 3.8027 loss: 2.4629 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.4629 2023/03/08 16:23:50 - mmengine - INFO - Epoch(train) [11][ 60/660] lr: 1.0000e-02 eta: 2:30:34 time: 0.3263 data_time: 0.0229 memory: 21539 grad_norm: 3.8706 loss: 2.6059 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.6059 2023/03/08 16:23:57 - mmengine - INFO - Epoch(train) [11][ 80/660] lr: 1.0000e-02 eta: 2:30:25 time: 0.3248 data_time: 0.0226 memory: 21539 grad_norm: 3.9239 loss: 2.4617 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.4617 2023/03/08 16:24:04 - mmengine - INFO - Epoch(train) [11][100/660] lr: 1.0000e-02 eta: 2:30:17 time: 0.3262 data_time: 0.0222 memory: 21539 grad_norm: 3.9967 loss: 2.4637 top1_acc: 0.3750 top5_acc: 0.5938 loss_cls: 2.4637 2023/03/08 16:24:10 - mmengine - INFO - Epoch(train) [11][120/660] lr: 1.0000e-02 eta: 2:30:09 time: 0.3316 data_time: 0.0224 memory: 21539 grad_norm: 3.9652 loss: 2.5888 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.5888 2023/03/08 16:24:17 - mmengine - INFO - Epoch(train) [11][140/660] lr: 1.0000e-02 eta: 2:30:02 time: 0.3295 data_time: 0.0237 memory: 21539 grad_norm: 3.9435 loss: 2.4787 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4787 2023/03/08 16:24:23 - mmengine - INFO - Epoch(train) [11][160/660] lr: 1.0000e-02 eta: 2:29:53 time: 0.3248 data_time: 0.0221 memory: 21539 grad_norm: 3.8986 loss: 2.4809 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.4809 2023/03/08 16:24:30 - mmengine - INFO - Epoch(train) [11][180/660] lr: 1.0000e-02 eta: 2:29:46 time: 0.3311 data_time: 0.0270 memory: 21539 grad_norm: 3.8567 loss: 2.5751 top1_acc: 0.2188 top5_acc: 0.5312 loss_cls: 2.5751 2023/03/08 16:24:36 - mmengine - INFO - Epoch(train) [11][200/660] lr: 1.0000e-02 eta: 2:29:38 time: 0.3316 data_time: 0.0230 memory: 21539 grad_norm: 3.9017 loss: 2.4313 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4313 2023/03/08 16:24:43 - mmengine - INFO - Epoch(train) [11][220/660] lr: 1.0000e-02 eta: 2:29:30 time: 0.3282 data_time: 0.0238 memory: 21539 grad_norm: 3.9542 loss: 2.4129 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.4129 2023/03/08 16:24:50 - mmengine - INFO - Epoch(train) [11][240/660] lr: 1.0000e-02 eta: 2:29:22 time: 0.3241 data_time: 0.0208 memory: 21539 grad_norm: 3.9166 loss: 2.4663 top1_acc: 0.2188 top5_acc: 0.6250 loss_cls: 2.4663 2023/03/08 16:24:56 - mmengine - INFO - Epoch(train) [11][260/660] lr: 1.0000e-02 eta: 2:29:14 time: 0.3268 data_time: 0.0236 memory: 21539 grad_norm: 3.9235 loss: 2.4258 top1_acc: 0.4062 top5_acc: 0.8438 loss_cls: 2.4258 2023/03/08 16:25:03 - mmengine - INFO - Epoch(train) [11][280/660] lr: 1.0000e-02 eta: 2:29:05 time: 0.3247 data_time: 0.0223 memory: 21539 grad_norm: 3.9652 loss: 2.3292 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 2.3292 2023/03/08 16:25:09 - mmengine - INFO - Epoch(train) [11][300/660] lr: 1.0000e-02 eta: 2:28:57 time: 0.3270 data_time: 0.0225 memory: 21539 grad_norm: 4.0192 loss: 2.4636 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.4636 2023/03/08 16:25:16 - mmengine - INFO - Epoch(train) [11][320/660] lr: 1.0000e-02 eta: 2:28:50 time: 0.3319 data_time: 0.0229 memory: 21539 grad_norm: 3.9664 loss: 2.4661 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.4661 2023/03/08 16:25:22 - mmengine - INFO - Epoch(train) [11][340/660] lr: 1.0000e-02 eta: 2:28:42 time: 0.3269 data_time: 0.0231 memory: 21539 grad_norm: 3.9590 loss: 2.3523 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.3523 2023/03/08 16:25:29 - mmengine - INFO - Epoch(train) [11][360/660] lr: 1.0000e-02 eta: 2:28:33 time: 0.3235 data_time: 0.0211 memory: 21539 grad_norm: 3.9040 loss: 2.4118 top1_acc: 0.4062 top5_acc: 0.5938 loss_cls: 2.4118 2023/03/08 16:25:35 - mmengine - INFO - Epoch(train) [11][380/660] lr: 1.0000e-02 eta: 2:28:26 time: 0.3311 data_time: 0.0234 memory: 21539 grad_norm: 3.9434 loss: 2.4804 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4804 2023/03/08 16:25:42 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:25:42 - mmengine - INFO - Epoch(train) [11][400/660] lr: 1.0000e-02 eta: 2:28:18 time: 0.3257 data_time: 0.0221 memory: 21539 grad_norm: 4.0042 loss: 2.2907 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.2907 2023/03/08 16:25:49 - mmengine - INFO - Epoch(train) [11][420/660] lr: 1.0000e-02 eta: 2:28:10 time: 0.3324 data_time: 0.0224 memory: 21539 grad_norm: 4.0148 loss: 2.3253 top1_acc: 0.2188 top5_acc: 0.7812 loss_cls: 2.3253 2023/03/08 16:25:55 - mmengine - INFO - Epoch(train) [11][440/660] lr: 1.0000e-02 eta: 2:28:02 time: 0.3255 data_time: 0.0213 memory: 21539 grad_norm: 3.9721 loss: 2.4240 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.4240 2023/03/08 16:26:02 - mmengine - INFO - Epoch(train) [11][460/660] lr: 1.0000e-02 eta: 2:27:54 time: 0.3272 data_time: 0.0230 memory: 21539 grad_norm: 3.9822 loss: 2.4529 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.4529 2023/03/08 16:26:08 - mmengine - INFO - Epoch(train) [11][480/660] lr: 1.0000e-02 eta: 2:27:46 time: 0.3280 data_time: 0.0212 memory: 21539 grad_norm: 3.9652 loss: 2.5122 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.5122 2023/03/08 16:26:15 - mmengine - INFO - Epoch(train) [11][500/660] lr: 1.0000e-02 eta: 2:27:38 time: 0.3269 data_time: 0.0224 memory: 21539 grad_norm: 3.9256 loss: 2.4888 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.4888 2023/03/08 16:26:21 - mmengine - INFO - Epoch(train) [11][520/660] lr: 1.0000e-02 eta: 2:27:30 time: 0.3284 data_time: 0.0214 memory: 21539 grad_norm: 3.9663 loss: 2.3390 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.3390 2023/03/08 16:26:28 - mmengine - INFO - Epoch(train) [11][540/660] lr: 1.0000e-02 eta: 2:27:22 time: 0.3278 data_time: 0.0239 memory: 21539 grad_norm: 3.9791 loss: 2.4048 top1_acc: 0.2812 top5_acc: 0.4688 loss_cls: 2.4048 2023/03/08 16:26:34 - mmengine - INFO - Epoch(train) [11][560/660] lr: 1.0000e-02 eta: 2:27:15 time: 0.3320 data_time: 0.0250 memory: 21539 grad_norm: 4.0115 loss: 2.4281 top1_acc: 0.3125 top5_acc: 0.7188 loss_cls: 2.4281 2023/03/08 16:26:41 - mmengine - INFO - Epoch(train) [11][580/660] lr: 1.0000e-02 eta: 2:27:07 time: 0.3281 data_time: 0.0225 memory: 21539 grad_norm: 3.9765 loss: 2.4692 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.4692 2023/03/08 16:26:48 - mmengine - INFO - Epoch(train) [11][600/660] lr: 1.0000e-02 eta: 2:26:59 time: 0.3259 data_time: 0.0211 memory: 21539 grad_norm: 4.0343 loss: 2.4986 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.4986 2023/03/08 16:26:54 - mmengine - INFO - Epoch(train) [11][620/660] lr: 1.0000e-02 eta: 2:26:51 time: 0.3273 data_time: 0.0226 memory: 21539 grad_norm: 4.0103 loss: 2.4361 top1_acc: 0.3438 top5_acc: 0.8438 loss_cls: 2.4361 2023/03/08 16:27:01 - mmengine - INFO - Epoch(train) [11][640/660] lr: 1.0000e-02 eta: 2:26:43 time: 0.3265 data_time: 0.0219 memory: 21539 grad_norm: 3.9716 loss: 2.4611 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.4611 2023/03/08 16:27:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:27:07 - mmengine - INFO - Epoch(train) [11][660/660] lr: 1.0000e-02 eta: 2:26:35 time: 0.3205 data_time: 0.0202 memory: 21539 grad_norm: 3.9828 loss: 2.4985 top1_acc: 0.5185 top5_acc: 0.7407 loss_cls: 2.4985 2023/03/08 16:27:15 - mmengine - INFO - Epoch(train) [12][ 20/660] lr: 1.0000e-02 eta: 2:26:33 time: 0.4116 data_time: 0.0905 memory: 21539 grad_norm: 3.9127 loss: 2.4608 top1_acc: 0.2500 top5_acc: 0.6562 loss_cls: 2.4608 2023/03/08 16:27:22 - mmengine - INFO - Epoch(train) [12][ 40/660] lr: 1.0000e-02 eta: 2:26:26 time: 0.3316 data_time: 0.0222 memory: 21539 grad_norm: 3.9665 loss: 2.2579 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2579 2023/03/08 16:27:29 - mmengine - INFO - Epoch(train) [12][ 60/660] lr: 1.0000e-02 eta: 2:26:18 time: 0.3318 data_time: 0.0215 memory: 21539 grad_norm: 4.0613 loss: 2.3262 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.3262 2023/03/08 16:27:35 - mmengine - INFO - Epoch(train) [12][ 80/660] lr: 1.0000e-02 eta: 2:26:10 time: 0.3312 data_time: 0.0213 memory: 21539 grad_norm: 3.9742 loss: 2.3563 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3563 2023/03/08 16:27:42 - mmengine - INFO - Epoch(train) [12][100/660] lr: 1.0000e-02 eta: 2:26:03 time: 0.3324 data_time: 0.0216 memory: 21539 grad_norm: 3.9098 loss: 2.2855 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.2855 2023/03/08 16:27:48 - mmengine - INFO - Epoch(train) [12][120/660] lr: 1.0000e-02 eta: 2:25:55 time: 0.3268 data_time: 0.0215 memory: 21539 grad_norm: 4.0406 loss: 2.3711 top1_acc: 0.4062 top5_acc: 0.9062 loss_cls: 2.3711 2023/03/08 16:27:55 - mmengine - INFO - Epoch(train) [12][140/660] lr: 1.0000e-02 eta: 2:25:48 time: 0.3335 data_time: 0.0217 memory: 21539 grad_norm: 3.8996 loss: 2.4172 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.4172 2023/03/08 16:28:02 - mmengine - INFO - Epoch(train) [12][160/660] lr: 1.0000e-02 eta: 2:25:40 time: 0.3259 data_time: 0.0210 memory: 21539 grad_norm: 3.9466 loss: 2.3991 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.3991 2023/03/08 16:28:08 - mmengine - INFO - Epoch(train) [12][180/660] lr: 1.0000e-02 eta: 2:25:32 time: 0.3325 data_time: 0.0210 memory: 21539 grad_norm: 4.0965 loss: 2.4602 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 2.4602 2023/03/08 16:28:15 - mmengine - INFO - Epoch(train) [12][200/660] lr: 1.0000e-02 eta: 2:25:25 time: 0.3344 data_time: 0.0220 memory: 21539 grad_norm: 3.9296 loss: 2.4379 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.4379 2023/03/08 16:28:21 - mmengine - INFO - Epoch(train) [12][220/660] lr: 1.0000e-02 eta: 2:25:17 time: 0.3293 data_time: 0.0214 memory: 21539 grad_norm: 3.9774 loss: 2.4865 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.4865 2023/03/08 16:28:28 - mmengine - INFO - Epoch(train) [12][240/660] lr: 1.0000e-02 eta: 2:25:09 time: 0.3284 data_time: 0.0214 memory: 21539 grad_norm: 4.0536 loss: 2.4322 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.4322 2023/03/08 16:28:35 - mmengine - INFO - Epoch(train) [12][260/660] lr: 1.0000e-02 eta: 2:25:02 time: 0.3337 data_time: 0.0253 memory: 21539 grad_norm: 4.0476 loss: 2.3624 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.3624 2023/03/08 16:28:41 - mmengine - INFO - Epoch(train) [12][280/660] lr: 1.0000e-02 eta: 2:24:54 time: 0.3266 data_time: 0.0217 memory: 21539 grad_norm: 4.0042 loss: 2.4069 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.4069 2023/03/08 16:28:48 - mmengine - INFO - Epoch(train) [12][300/660] lr: 1.0000e-02 eta: 2:24:47 time: 0.3303 data_time: 0.0208 memory: 21539 grad_norm: 4.0183 loss: 2.3968 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.3968 2023/03/08 16:28:54 - mmengine - INFO - Epoch(train) [12][320/660] lr: 1.0000e-02 eta: 2:24:39 time: 0.3289 data_time: 0.0213 memory: 21539 grad_norm: 4.0109 loss: 2.4810 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.4810 2023/03/08 16:29:01 - mmengine - INFO - Epoch(train) [12][340/660] lr: 1.0000e-02 eta: 2:24:32 time: 0.3335 data_time: 0.0215 memory: 21539 grad_norm: 4.0126 loss: 2.3989 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3989 2023/03/08 16:29:08 - mmengine - INFO - Epoch(train) [12][360/660] lr: 1.0000e-02 eta: 2:24:24 time: 0.3268 data_time: 0.0212 memory: 21539 grad_norm: 3.9913 loss: 2.4174 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.4174 2023/03/08 16:29:14 - mmengine - INFO - Epoch(train) [12][380/660] lr: 1.0000e-02 eta: 2:24:16 time: 0.3316 data_time: 0.0219 memory: 21539 grad_norm: 3.9164 loss: 2.4296 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.4296 2023/03/08 16:29:21 - mmengine - INFO - Epoch(train) [12][400/660] lr: 1.0000e-02 eta: 2:24:09 time: 0.3307 data_time: 0.0204 memory: 21539 grad_norm: 4.0301 loss: 2.4727 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.4727 2023/03/08 16:29:27 - mmengine - INFO - Epoch(train) [12][420/660] lr: 1.0000e-02 eta: 2:24:01 time: 0.3311 data_time: 0.0207 memory: 21539 grad_norm: 4.0308 loss: 2.4566 top1_acc: 0.4688 top5_acc: 0.5625 loss_cls: 2.4566 2023/03/08 16:29:34 - mmengine - INFO - Epoch(train) [12][440/660] lr: 1.0000e-02 eta: 2:23:54 time: 0.3306 data_time: 0.0214 memory: 21539 grad_norm: 4.0131 loss: 2.4449 top1_acc: 0.2500 top5_acc: 0.7188 loss_cls: 2.4449 2023/03/08 16:29:41 - mmengine - INFO - Epoch(train) [12][460/660] lr: 1.0000e-02 eta: 2:23:46 time: 0.3296 data_time: 0.0212 memory: 21539 grad_norm: 4.0814 loss: 2.4627 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.4627 2023/03/08 16:29:47 - mmengine - INFO - Epoch(train) [12][480/660] lr: 1.0000e-02 eta: 2:23:38 time: 0.3259 data_time: 0.0209 memory: 21539 grad_norm: 4.0329 loss: 2.4278 top1_acc: 0.4688 top5_acc: 0.6250 loss_cls: 2.4278 2023/03/08 16:29:54 - mmengine - INFO - Epoch(train) [12][500/660] lr: 1.0000e-02 eta: 2:23:31 time: 0.3310 data_time: 0.0214 memory: 21539 grad_norm: 4.0281 loss: 2.4120 top1_acc: 0.2812 top5_acc: 0.6875 loss_cls: 2.4120 2023/03/08 16:30:00 - mmengine - INFO - Epoch(train) [12][520/660] lr: 1.0000e-02 eta: 2:23:23 time: 0.3296 data_time: 0.0221 memory: 21539 grad_norm: 4.0621 loss: 2.4351 top1_acc: 0.3438 top5_acc: 0.5312 loss_cls: 2.4351 2023/03/08 16:30:07 - mmengine - INFO - Epoch(train) [12][540/660] lr: 1.0000e-02 eta: 2:23:16 time: 0.3331 data_time: 0.0220 memory: 21539 grad_norm: 4.1034 loss: 2.4266 top1_acc: 0.3438 top5_acc: 0.5938 loss_cls: 2.4266 2023/03/08 16:30:14 - mmengine - INFO - Epoch(train) [12][560/660] lr: 1.0000e-02 eta: 2:23:08 time: 0.3263 data_time: 0.0211 memory: 21539 grad_norm: 3.9704 loss: 2.3954 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.3954 2023/03/08 16:30:20 - mmengine - INFO - Epoch(train) [12][580/660] lr: 1.0000e-02 eta: 2:23:01 time: 0.3285 data_time: 0.0220 memory: 21539 grad_norm: 4.0369 loss: 2.4652 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4652 2023/03/08 16:30:27 - mmengine - INFO - Epoch(train) [12][600/660] lr: 1.0000e-02 eta: 2:22:53 time: 0.3303 data_time: 0.0216 memory: 21539 grad_norm: 3.9838 loss: 2.4788 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 2.4788 2023/03/08 16:30:33 - mmengine - INFO - Epoch(train) [12][620/660] lr: 1.0000e-02 eta: 2:22:45 time: 0.3292 data_time: 0.0222 memory: 21539 grad_norm: 4.0039 loss: 2.4009 top1_acc: 0.4688 top5_acc: 0.6250 loss_cls: 2.4009 2023/03/08 16:30:40 - mmengine - INFO - Epoch(train) [12][640/660] lr: 1.0000e-02 eta: 2:22:38 time: 0.3266 data_time: 0.0217 memory: 21539 grad_norm: 4.0391 loss: 2.4462 top1_acc: 0.4688 top5_acc: 0.5625 loss_cls: 2.4462 2023/03/08 16:30:46 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:30:46 - mmengine - INFO - Epoch(train) [12][660/660] lr: 1.0000e-02 eta: 2:22:30 time: 0.3236 data_time: 0.0231 memory: 21539 grad_norm: 4.0661 loss: 2.4044 top1_acc: 0.4074 top5_acc: 0.7407 loss_cls: 2.4044 2023/03/08 16:30:46 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/03/08 16:30:56 - mmengine - INFO - Epoch(train) [13][ 20/660] lr: 1.0000e-02 eta: 2:22:28 time: 0.4129 data_time: 0.0911 memory: 21539 grad_norm: 3.9989 loss: 2.3750 top1_acc: 0.2812 top5_acc: 0.5000 loss_cls: 2.3750 2023/03/08 16:31:02 - mmengine - INFO - Epoch(train) [13][ 40/660] lr: 1.0000e-02 eta: 2:22:20 time: 0.3321 data_time: 0.0205 memory: 21539 grad_norm: 4.0277 loss: 2.2820 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2820 2023/03/08 16:31:09 - mmengine - INFO - Epoch(train) [13][ 60/660] lr: 1.0000e-02 eta: 2:22:13 time: 0.3374 data_time: 0.0213 memory: 21539 grad_norm: 3.9779 loss: 2.4099 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 2.4099 2023/03/08 16:31:16 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:31:16 - mmengine - INFO - Epoch(train) [13][ 80/660] lr: 1.0000e-02 eta: 2:22:06 time: 0.3369 data_time: 0.0206 memory: 21539 grad_norm: 4.0128 loss: 2.3072 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3072 2023/03/08 16:31:23 - mmengine - INFO - Epoch(train) [13][100/660] lr: 1.0000e-02 eta: 2:21:59 time: 0.3389 data_time: 0.0206 memory: 21539 grad_norm: 4.0351 loss: 2.3910 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.3910 2023/03/08 16:31:29 - mmengine - INFO - Epoch(train) [13][120/660] lr: 1.0000e-02 eta: 2:21:53 time: 0.3448 data_time: 0.0247 memory: 21539 grad_norm: 4.0248 loss: 2.3130 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3130 2023/03/08 16:31:36 - mmengine - INFO - Epoch(train) [13][140/660] lr: 1.0000e-02 eta: 2:21:46 time: 0.3387 data_time: 0.0215 memory: 21539 grad_norm: 4.0705 loss: 2.3571 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3571 2023/03/08 16:31:43 - mmengine - INFO - Epoch(train) [13][160/660] lr: 1.0000e-02 eta: 2:21:38 time: 0.3338 data_time: 0.0210 memory: 21539 grad_norm: 4.1279 loss: 2.5804 top1_acc: 0.2500 top5_acc: 0.6562 loss_cls: 2.5804 2023/03/08 16:31:50 - mmengine - INFO - Epoch(train) [13][180/660] lr: 1.0000e-02 eta: 2:21:32 time: 0.3421 data_time: 0.0209 memory: 21539 grad_norm: 4.0400 loss: 2.3744 top1_acc: 0.2188 top5_acc: 0.7812 loss_cls: 2.3744 2023/03/08 16:31:57 - mmengine - INFO - Epoch(train) [13][200/660] lr: 1.0000e-02 eta: 2:21:25 time: 0.3387 data_time: 0.0219 memory: 21539 grad_norm: 4.1095 loss: 2.3901 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.3901 2023/03/08 16:32:03 - mmengine - INFO - Epoch(train) [13][220/660] lr: 1.0000e-02 eta: 2:21:18 time: 0.3387 data_time: 0.0215 memory: 21539 grad_norm: 4.0413 loss: 2.3339 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.3339 2023/03/08 16:32:10 - mmengine - INFO - Epoch(train) [13][240/660] lr: 1.0000e-02 eta: 2:21:10 time: 0.3320 data_time: 0.0199 memory: 21539 grad_norm: 4.1193 loss: 2.3640 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3640 2023/03/08 16:32:17 - mmengine - INFO - Epoch(train) [13][260/660] lr: 1.0000e-02 eta: 2:21:03 time: 0.3427 data_time: 0.0207 memory: 21539 grad_norm: 4.1024 loss: 2.2909 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.2909 2023/03/08 16:32:23 - mmengine - INFO - Epoch(train) [13][280/660] lr: 1.0000e-02 eta: 2:20:56 time: 0.3334 data_time: 0.0209 memory: 21539 grad_norm: 4.0275 loss: 2.4272 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 2.4272 2023/03/08 16:32:30 - mmengine - INFO - Epoch(train) [13][300/660] lr: 1.0000e-02 eta: 2:20:49 time: 0.3383 data_time: 0.0215 memory: 21539 grad_norm: 4.1699 loss: 2.3105 top1_acc: 0.3125 top5_acc: 0.7812 loss_cls: 2.3105 2023/03/08 16:32:37 - mmengine - INFO - Epoch(train) [13][320/660] lr: 1.0000e-02 eta: 2:20:42 time: 0.3369 data_time: 0.0205 memory: 21539 grad_norm: 4.0694 loss: 2.4788 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.4788 2023/03/08 16:32:44 - mmengine - INFO - Epoch(train) [13][340/660] lr: 1.0000e-02 eta: 2:20:35 time: 0.3378 data_time: 0.0214 memory: 21539 grad_norm: 4.0423 loss: 2.4001 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4001 2023/03/08 16:32:50 - mmengine - INFO - Epoch(train) [13][360/660] lr: 1.0000e-02 eta: 2:20:28 time: 0.3333 data_time: 0.0201 memory: 21539 grad_norm: 4.0165 loss: 2.2731 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2731 2023/03/08 16:32:57 - mmengine - INFO - Epoch(train) [13][380/660] lr: 1.0000e-02 eta: 2:20:21 time: 0.3446 data_time: 0.0257 memory: 21539 grad_norm: 4.0687 loss: 2.2138 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 2.2138 2023/03/08 16:33:04 - mmengine - INFO - Epoch(train) [13][400/660] lr: 1.0000e-02 eta: 2:20:14 time: 0.3376 data_time: 0.0204 memory: 21539 grad_norm: 4.0698 loss: 2.4217 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.4217 2023/03/08 16:33:11 - mmengine - INFO - Epoch(train) [13][420/660] lr: 1.0000e-02 eta: 2:20:08 time: 0.3442 data_time: 0.0203 memory: 21539 grad_norm: 4.1847 loss: 2.3550 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.3550 2023/03/08 16:33:18 - mmengine - INFO - Epoch(train) [13][440/660] lr: 1.0000e-02 eta: 2:20:00 time: 0.3334 data_time: 0.0211 memory: 21539 grad_norm: 4.1175 loss: 2.3173 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.3173 2023/03/08 16:33:25 - mmengine - INFO - Epoch(train) [13][460/660] lr: 1.0000e-02 eta: 2:19:56 time: 0.3867 data_time: 0.0215 memory: 21539 grad_norm: 4.1099 loss: 2.4481 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 2.4481 2023/03/08 16:33:32 - mmengine - INFO - Epoch(train) [13][480/660] lr: 1.0000e-02 eta: 2:19:49 time: 0.3334 data_time: 0.0198 memory: 21539 grad_norm: 4.1573 loss: 2.2830 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.2830 2023/03/08 16:33:39 - mmengine - INFO - Epoch(train) [13][500/660] lr: 1.0000e-02 eta: 2:19:42 time: 0.3406 data_time: 0.0214 memory: 21539 grad_norm: 4.1235 loss: 2.2392 top1_acc: 0.2812 top5_acc: 0.6250 loss_cls: 2.2392 2023/03/08 16:33:46 - mmengine - INFO - Epoch(train) [13][520/660] lr: 1.0000e-02 eta: 2:19:35 time: 0.3351 data_time: 0.0197 memory: 21539 grad_norm: 4.0836 loss: 2.4313 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4313 2023/03/08 16:33:52 - mmengine - INFO - Epoch(train) [13][540/660] lr: 1.0000e-02 eta: 2:19:28 time: 0.3386 data_time: 0.0212 memory: 21539 grad_norm: 4.1585 loss: 2.5757 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5757 2023/03/08 16:33:59 - mmengine - INFO - Epoch(train) [13][560/660] lr: 1.0000e-02 eta: 2:19:21 time: 0.3390 data_time: 0.0204 memory: 21539 grad_norm: 4.1541 loss: 2.3448 top1_acc: 0.2812 top5_acc: 0.6562 loss_cls: 2.3448 2023/03/08 16:34:06 - mmengine - INFO - Epoch(train) [13][580/660] lr: 1.0000e-02 eta: 2:19:15 time: 0.3466 data_time: 0.0214 memory: 21539 grad_norm: 4.1033 loss: 2.2959 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 2.2959 2023/03/08 16:34:13 - mmengine - INFO - Epoch(train) [13][600/660] lr: 1.0000e-02 eta: 2:19:07 time: 0.3339 data_time: 0.0204 memory: 21539 grad_norm: 4.0239 loss: 2.3302 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.3302 2023/03/08 16:34:19 - mmengine - INFO - Epoch(train) [13][620/660] lr: 1.0000e-02 eta: 2:19:00 time: 0.3397 data_time: 0.0208 memory: 21539 grad_norm: 4.0383 loss: 2.3697 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3697 2023/03/08 16:34:26 - mmengine - INFO - Epoch(train) [13][640/660] lr: 1.0000e-02 eta: 2:18:53 time: 0.3342 data_time: 0.0203 memory: 21539 grad_norm: 4.0755 loss: 2.3194 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 2.3194 2023/03/08 16:34:33 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:34:33 - mmengine - INFO - Epoch(train) [13][660/660] lr: 1.0000e-02 eta: 2:18:46 time: 0.3288 data_time: 0.0189 memory: 21539 grad_norm: 4.1189 loss: 2.4250 top1_acc: 0.2593 top5_acc: 0.7037 loss_cls: 2.4250 2023/03/08 16:34:41 - mmengine - INFO - Epoch(train) [14][ 20/660] lr: 1.0000e-02 eta: 2:18:43 time: 0.4096 data_time: 0.0890 memory: 21539 grad_norm: 4.0048 loss: 2.4275 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.4275 2023/03/08 16:34:48 - mmengine - INFO - Epoch(train) [14][ 40/660] lr: 1.0000e-02 eta: 2:18:36 time: 0.3383 data_time: 0.0248 memory: 21539 grad_norm: 4.1163 loss: 2.3602 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.3602 2023/03/08 16:34:55 - mmengine - INFO - Epoch(train) [14][ 60/660] lr: 1.0000e-02 eta: 2:18:29 time: 0.3392 data_time: 0.0205 memory: 21539 grad_norm: 4.0344 loss: 2.2607 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 2.2607 2023/03/08 16:35:01 - mmengine - INFO - Epoch(train) [14][ 80/660] lr: 1.0000e-02 eta: 2:18:22 time: 0.3327 data_time: 0.0196 memory: 21539 grad_norm: 4.1394 loss: 2.3163 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.3163 2023/03/08 16:35:08 - mmengine - INFO - Epoch(train) [14][100/660] lr: 1.0000e-02 eta: 2:18:15 time: 0.3430 data_time: 0.0219 memory: 21539 grad_norm: 4.1222 loss: 2.3265 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.3265 2023/03/08 16:35:15 - mmengine - INFO - Epoch(train) [14][120/660] lr: 1.0000e-02 eta: 2:18:08 time: 0.3321 data_time: 0.0196 memory: 21539 grad_norm: 4.0858 loss: 2.2731 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.2731 2023/03/08 16:35:21 - mmengine - INFO - Epoch(train) [14][140/660] lr: 1.0000e-02 eta: 2:18:01 time: 0.3377 data_time: 0.0211 memory: 21539 grad_norm: 4.0803 loss: 2.1185 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1185 2023/03/08 16:35:28 - mmengine - INFO - Epoch(train) [14][160/660] lr: 1.0000e-02 eta: 2:17:53 time: 0.3370 data_time: 0.0197 memory: 21539 grad_norm: 4.1332 loss: 2.3163 top1_acc: 0.2812 top5_acc: 0.6250 loss_cls: 2.3163 2023/03/08 16:35:35 - mmengine - INFO - Epoch(train) [14][180/660] lr: 1.0000e-02 eta: 2:17:46 time: 0.3381 data_time: 0.0206 memory: 21539 grad_norm: 4.1522 loss: 2.2972 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 2.2972 2023/03/08 16:35:42 - mmengine - INFO - Epoch(train) [14][200/660] lr: 1.0000e-02 eta: 2:17:39 time: 0.3338 data_time: 0.0202 memory: 21539 grad_norm: 4.0950 loss: 2.3554 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.3554 2023/03/08 16:35:48 - mmengine - INFO - Epoch(train) [14][220/660] lr: 1.0000e-02 eta: 2:17:32 time: 0.3379 data_time: 0.0204 memory: 21539 grad_norm: 4.0862 loss: 2.1772 top1_acc: 0.2812 top5_acc: 0.7500 loss_cls: 2.1772 2023/03/08 16:35:55 - mmengine - INFO - Epoch(train) [14][240/660] lr: 1.0000e-02 eta: 2:17:25 time: 0.3413 data_time: 0.0194 memory: 21539 grad_norm: 4.1679 loss: 2.3016 top1_acc: 0.2812 top5_acc: 0.6562 loss_cls: 2.3016 2023/03/08 16:36:02 - mmengine - INFO - Epoch(train) [14][260/660] lr: 1.0000e-02 eta: 2:17:18 time: 0.3380 data_time: 0.0210 memory: 21539 grad_norm: 4.0590 loss: 2.3113 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.3113 2023/03/08 16:36:09 - mmengine - INFO - Epoch(train) [14][280/660] lr: 1.0000e-02 eta: 2:17:11 time: 0.3328 data_time: 0.0195 memory: 21539 grad_norm: 4.2041 loss: 2.3483 top1_acc: 0.4062 top5_acc: 0.8750 loss_cls: 2.3483 2023/03/08 16:36:15 - mmengine - INFO - Epoch(train) [14][300/660] lr: 1.0000e-02 eta: 2:17:04 time: 0.3373 data_time: 0.0206 memory: 21539 grad_norm: 4.2192 loss: 2.3830 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.3830 2023/03/08 16:36:22 - mmengine - INFO - Epoch(train) [14][320/660] lr: 1.0000e-02 eta: 2:16:57 time: 0.3318 data_time: 0.0204 memory: 21539 grad_norm: 4.1575 loss: 2.2399 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 2.2399 2023/03/08 16:36:29 - mmengine - INFO - Epoch(train) [14][340/660] lr: 1.0000e-02 eta: 2:16:50 time: 0.3420 data_time: 0.0247 memory: 21539 grad_norm: 4.1251 loss: 2.2797 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 2.2797 2023/03/08 16:36:35 - mmengine - INFO - Epoch(train) [14][360/660] lr: 1.0000e-02 eta: 2:16:43 time: 0.3325 data_time: 0.0192 memory: 21539 grad_norm: 4.1712 loss: 2.2383 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.2383 2023/03/08 16:36:42 - mmengine - INFO - Epoch(train) [14][380/660] lr: 1.0000e-02 eta: 2:16:36 time: 0.3411 data_time: 0.0218 memory: 21539 grad_norm: 4.1620 loss: 2.3730 top1_acc: 0.3438 top5_acc: 0.7500 loss_cls: 2.3730 2023/03/08 16:36:49 - mmengine - INFO - Epoch(train) [14][400/660] lr: 1.0000e-02 eta: 2:16:29 time: 0.3330 data_time: 0.0197 memory: 21539 grad_norm: 4.1791 loss: 2.4149 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.4149 2023/03/08 16:36:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:36:56 - mmengine - INFO - Epoch(train) [14][420/660] lr: 1.0000e-02 eta: 2:16:22 time: 0.3428 data_time: 0.0208 memory: 21539 grad_norm: 4.1060 loss: 2.3258 top1_acc: 0.3438 top5_acc: 0.7500 loss_cls: 2.3258 2023/03/08 16:37:02 - mmengine - INFO - Epoch(train) [14][440/660] lr: 1.0000e-02 eta: 2:16:15 time: 0.3332 data_time: 0.0200 memory: 21539 grad_norm: 4.1837 loss: 2.3082 top1_acc: 0.3750 top5_acc: 0.8438 loss_cls: 2.3082 2023/03/08 16:37:09 - mmengine - INFO - Epoch(train) [14][460/660] lr: 1.0000e-02 eta: 2:16:08 time: 0.3400 data_time: 0.0225 memory: 21539 grad_norm: 4.1446 loss: 2.3532 top1_acc: 0.3438 top5_acc: 0.8125 loss_cls: 2.3532 2023/03/08 16:37:16 - mmengine - INFO - Epoch(train) [14][480/660] lr: 1.0000e-02 eta: 2:16:01 time: 0.3323 data_time: 0.0188 memory: 21539 grad_norm: 4.1405 loss: 2.2378 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2378 2023/03/08 16:37:23 - mmengine - INFO - Epoch(train) [14][500/660] lr: 1.0000e-02 eta: 2:15:54 time: 0.3393 data_time: 0.0215 memory: 21539 grad_norm: 4.1660 loss: 2.3788 top1_acc: 0.2188 top5_acc: 0.5938 loss_cls: 2.3788 2023/03/08 16:37:29 - mmengine - INFO - Epoch(train) [14][520/660] lr: 1.0000e-02 eta: 2:15:47 time: 0.3368 data_time: 0.0192 memory: 21539 grad_norm: 4.1199 loss: 2.4331 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.4331 2023/03/08 16:37:36 - mmengine - INFO - Epoch(train) [14][540/660] lr: 1.0000e-02 eta: 2:15:40 time: 0.3380 data_time: 0.0203 memory: 21539 grad_norm: 4.0740 loss: 2.2587 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.2587 2023/03/08 16:37:43 - mmengine - INFO - Epoch(train) [14][560/660] lr: 1.0000e-02 eta: 2:15:33 time: 0.3330 data_time: 0.0196 memory: 21539 grad_norm: 4.2095 loss: 2.2520 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.2520 2023/03/08 16:37:50 - mmengine - INFO - Epoch(train) [14][580/660] lr: 1.0000e-02 eta: 2:15:26 time: 0.3407 data_time: 0.0204 memory: 21539 grad_norm: 4.2051 loss: 2.4028 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.4028 2023/03/08 16:37:56 - mmengine - INFO - Epoch(train) [14][600/660] lr: 1.0000e-02 eta: 2:15:18 time: 0.3326 data_time: 0.0188 memory: 21539 grad_norm: 4.1898 loss: 2.3536 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.3536 2023/03/08 16:38:03 - mmengine - INFO - Epoch(train) [14][620/660] lr: 1.0000e-02 eta: 2:15:11 time: 0.3364 data_time: 0.0205 memory: 21539 grad_norm: 4.1416 loss: 2.4256 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.4256 2023/03/08 16:38:10 - mmengine - INFO - Epoch(train) [14][640/660] lr: 1.0000e-02 eta: 2:15:04 time: 0.3372 data_time: 0.0239 memory: 21539 grad_norm: 4.1211 loss: 2.3305 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.3305 2023/03/08 16:38:16 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:38:16 - mmengine - INFO - Epoch(train) [14][660/660] lr: 1.0000e-02 eta: 2:14:57 time: 0.3275 data_time: 0.0180 memory: 21539 grad_norm: 4.2057 loss: 2.1733 top1_acc: 0.6667 top5_acc: 0.8519 loss_cls: 2.1733 2023/03/08 16:38:25 - mmengine - INFO - Epoch(train) [15][ 20/660] lr: 1.0000e-02 eta: 2:14:54 time: 0.4161 data_time: 0.0858 memory: 21539 grad_norm: 4.1072 loss: 2.3250 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.3250 2023/03/08 16:38:31 - mmengine - INFO - Epoch(train) [15][ 40/660] lr: 1.0000e-02 eta: 2:14:47 time: 0.3330 data_time: 0.0198 memory: 21539 grad_norm: 4.0737 loss: 2.0964 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.0964 2023/03/08 16:38:38 - mmengine - INFO - Epoch(train) [15][ 60/660] lr: 1.0000e-02 eta: 2:14:40 time: 0.3411 data_time: 0.0217 memory: 21539 grad_norm: 4.2123 loss: 2.3537 top1_acc: 0.3438 top5_acc: 0.6250 loss_cls: 2.3537 2023/03/08 16:38:45 - mmengine - INFO - Epoch(train) [15][ 80/660] lr: 1.0000e-02 eta: 2:14:33 time: 0.3397 data_time: 0.0193 memory: 21539 grad_norm: 4.1431 loss: 2.1966 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.1966 2023/03/08 16:38:52 - mmengine - INFO - Epoch(train) [15][100/660] lr: 1.0000e-02 eta: 2:14:26 time: 0.3375 data_time: 0.0220 memory: 21539 grad_norm: 4.2237 loss: 2.2214 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.2214 2023/03/08 16:38:58 - mmengine - INFO - Epoch(train) [15][120/660] lr: 1.0000e-02 eta: 2:14:19 time: 0.3333 data_time: 0.0190 memory: 21539 grad_norm: 4.1709 loss: 2.2211 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.2211 2023/03/08 16:39:05 - mmengine - INFO - Epoch(train) [15][140/660] lr: 1.0000e-02 eta: 2:14:12 time: 0.3365 data_time: 0.0218 memory: 21539 grad_norm: 4.1317 loss: 2.3560 top1_acc: 0.3125 top5_acc: 0.7188 loss_cls: 2.3560 2023/03/08 16:39:12 - mmengine - INFO - Epoch(train) [15][160/660] lr: 1.0000e-02 eta: 2:14:04 time: 0.3308 data_time: 0.0192 memory: 21539 grad_norm: 4.1296 loss: 2.3269 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 2.3269 2023/03/08 16:39:18 - mmengine - INFO - Epoch(train) [15][180/660] lr: 1.0000e-02 eta: 2:13:57 time: 0.3354 data_time: 0.0212 memory: 21539 grad_norm: 4.1664 loss: 2.1677 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 2.1677 2023/03/08 16:39:25 - mmengine - INFO - Epoch(train) [15][200/660] lr: 1.0000e-02 eta: 2:13:50 time: 0.3321 data_time: 0.0191 memory: 21539 grad_norm: 4.2782 loss: 2.1227 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.1227 2023/03/08 16:39:32 - mmengine - INFO - Epoch(train) [15][220/660] lr: 1.0000e-02 eta: 2:13:43 time: 0.3362 data_time: 0.0220 memory: 21539 grad_norm: 4.2290 loss: 2.2787 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.2787 2023/03/08 16:39:38 - mmengine - INFO - Epoch(train) [15][240/660] lr: 1.0000e-02 eta: 2:13:36 time: 0.3314 data_time: 0.0198 memory: 21539 grad_norm: 4.2086 loss: 2.2975 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.2975 2023/03/08 16:39:45 - mmengine - INFO - Epoch(train) [15][260/660] lr: 1.0000e-02 eta: 2:13:29 time: 0.3359 data_time: 0.0222 memory: 21539 grad_norm: 4.1597 loss: 2.1718 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.1718 2023/03/08 16:39:52 - mmengine - INFO - Epoch(train) [15][280/660] lr: 1.0000e-02 eta: 2:13:21 time: 0.3321 data_time: 0.0249 memory: 21539 grad_norm: 4.2799 loss: 2.3217 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3217 2023/03/08 16:39:58 - mmengine - INFO - Epoch(train) [15][300/660] lr: 1.0000e-02 eta: 2:13:14 time: 0.3339 data_time: 0.0219 memory: 21539 grad_norm: 4.2276 loss: 2.1783 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1783 2023/03/08 16:40:05 - mmengine - INFO - Epoch(train) [15][320/660] lr: 1.0000e-02 eta: 2:13:07 time: 0.3296 data_time: 0.0210 memory: 21539 grad_norm: 4.2436 loss: 2.1613 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 2.1613 2023/03/08 16:40:12 - mmengine - INFO - Epoch(train) [15][340/660] lr: 1.0000e-02 eta: 2:13:00 time: 0.3361 data_time: 0.0227 memory: 21539 grad_norm: 4.1858 loss: 2.3135 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 2.3135 2023/03/08 16:40:18 - mmengine - INFO - Epoch(train) [15][360/660] lr: 1.0000e-02 eta: 2:12:53 time: 0.3302 data_time: 0.0210 memory: 21539 grad_norm: 4.1762 loss: 2.2834 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.2834 2023/03/08 16:40:25 - mmengine - INFO - Epoch(train) [15][380/660] lr: 1.0000e-02 eta: 2:12:46 time: 0.3374 data_time: 0.0225 memory: 21539 grad_norm: 4.2216 loss: 2.3264 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3264 2023/03/08 16:40:32 - mmengine - INFO - Epoch(train) [15][400/660] lr: 1.0000e-02 eta: 2:12:38 time: 0.3288 data_time: 0.0210 memory: 21539 grad_norm: 4.2116 loss: 2.2415 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 2.2415 2023/03/08 16:40:38 - mmengine - INFO - Epoch(train) [15][420/660] lr: 1.0000e-02 eta: 2:12:31 time: 0.3363 data_time: 0.0215 memory: 21539 grad_norm: 4.2380 loss: 2.3146 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.3146 2023/03/08 16:40:45 - mmengine - INFO - Epoch(train) [15][440/660] lr: 1.0000e-02 eta: 2:12:24 time: 0.3303 data_time: 0.0216 memory: 21539 grad_norm: 4.1536 loss: 2.2577 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.2577 2023/03/08 16:40:52 - mmengine - INFO - Epoch(train) [15][460/660] lr: 1.0000e-02 eta: 2:12:17 time: 0.3316 data_time: 0.0216 memory: 21539 grad_norm: 4.2267 loss: 2.2650 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.2650 2023/03/08 16:40:58 - mmengine - INFO - Epoch(train) [15][480/660] lr: 1.0000e-02 eta: 2:12:09 time: 0.3289 data_time: 0.0216 memory: 21539 grad_norm: 4.1965 loss: 2.1751 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 2.1751 2023/03/08 16:41:05 - mmengine - INFO - Epoch(train) [15][500/660] lr: 1.0000e-02 eta: 2:12:02 time: 0.3397 data_time: 0.0215 memory: 21539 grad_norm: 4.1447 loss: 2.3865 top1_acc: 0.3438 top5_acc: 0.6250 loss_cls: 2.3865 2023/03/08 16:41:12 - mmengine - INFO - Epoch(train) [15][520/660] lr: 1.0000e-02 eta: 2:11:55 time: 0.3298 data_time: 0.0211 memory: 21539 grad_norm: 4.1317 loss: 2.3213 top1_acc: 0.4062 top5_acc: 0.8438 loss_cls: 2.3213 2023/03/08 16:41:18 - mmengine - INFO - Epoch(train) [15][540/660] lr: 1.0000e-02 eta: 2:11:48 time: 0.3327 data_time: 0.0224 memory: 21539 grad_norm: 4.2707 loss: 2.3547 top1_acc: 0.3438 top5_acc: 0.7500 loss_cls: 2.3547 2023/03/08 16:41:25 - mmengine - INFO - Epoch(train) [15][560/660] lr: 1.0000e-02 eta: 2:11:41 time: 0.3298 data_time: 0.0208 memory: 21539 grad_norm: 4.1364 loss: 2.3336 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.3336 2023/03/08 16:41:32 - mmengine - INFO - Epoch(train) [15][580/660] lr: 1.0000e-02 eta: 2:11:33 time: 0.3331 data_time: 0.0223 memory: 21539 grad_norm: 4.2330 loss: 2.2972 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2972 2023/03/08 16:41:38 - mmengine - INFO - Epoch(train) [15][600/660] lr: 1.0000e-02 eta: 2:11:26 time: 0.3296 data_time: 0.0216 memory: 21539 grad_norm: 4.2130 loss: 2.3109 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.3109 2023/03/08 16:41:45 - mmengine - INFO - Epoch(train) [15][620/660] lr: 1.0000e-02 eta: 2:11:19 time: 0.3358 data_time: 0.0215 memory: 21539 grad_norm: 4.2144 loss: 2.2494 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2494 2023/03/08 16:41:52 - mmengine - INFO - Epoch(train) [15][640/660] lr: 1.0000e-02 eta: 2:11:12 time: 0.3364 data_time: 0.0243 memory: 21539 grad_norm: 4.1728 loss: 2.2140 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.2140 2023/03/08 16:41:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:41:58 - mmengine - INFO - Epoch(train) [15][660/660] lr: 1.0000e-02 eta: 2:11:05 time: 0.3254 data_time: 0.0193 memory: 21539 grad_norm: 4.2203 loss: 2.2244 top1_acc: 0.4444 top5_acc: 0.8519 loss_cls: 2.2244 2023/03/08 16:41:58 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/03/08 16:42:04 - mmengine - INFO - Epoch(val) [15][20/97] eta: 0:00:18 time: 0.2342 data_time: 0.1250 memory: 3261 2023/03/08 16:42:07 - mmengine - INFO - Epoch(val) [15][40/97] eta: 0:00:11 time: 0.1611 data_time: 0.0542 memory: 3261 2023/03/08 16:42:11 - mmengine - INFO - Epoch(val) [15][60/97] eta: 0:00:07 time: 0.1806 data_time: 0.0718 memory: 3261 2023/03/08 16:42:14 - mmengine - INFO - Epoch(val) [15][80/97] eta: 0:00:03 time: 0.1619 data_time: 0.0521 memory: 3261 2023/03/08 16:42:17 - mmengine - INFO - Epoch(val) [15][97/97] acc/top1: 0.3003 acc/top5: 0.6145 acc/mean1: 0.2399 2023/03/08 16:42:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_exp1/best_acc/top1_epoch_10.pth is removed 2023/03/08 16:42:18 - mmengine - INFO - The best checkpoint with 0.3003 acc/top1 at 15 epoch is saved to best_acc/top1_epoch_15.pth. 2023/03/08 16:42:26 - mmengine - INFO - Epoch(train) [16][ 20/660] lr: 1.0000e-02 eta: 2:11:01 time: 0.4110 data_time: 0.0852 memory: 21539 grad_norm: 4.1396 loss: 2.3261 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.3261 2023/03/08 16:42:33 - mmengine - INFO - Epoch(train) [16][ 40/660] lr: 1.0000e-02 eta: 2:10:54 time: 0.3340 data_time: 0.0249 memory: 21539 grad_norm: 4.1818 loss: 2.1066 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 2.1066 2023/03/08 16:42:39 - mmengine - INFO - Epoch(train) [16][ 60/660] lr: 1.0000e-02 eta: 2:10:47 time: 0.3347 data_time: 0.0214 memory: 21539 grad_norm: 4.2250 loss: 2.2529 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2529 2023/03/08 16:42:46 - mmengine - INFO - Epoch(train) [16][ 80/660] lr: 1.0000e-02 eta: 2:10:40 time: 0.3306 data_time: 0.0195 memory: 21539 grad_norm: 4.2211 loss: 2.2046 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2046 2023/03/08 16:42:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:42:53 - mmengine - INFO - Epoch(train) [16][100/660] lr: 1.0000e-02 eta: 2:10:32 time: 0.3334 data_time: 0.0218 memory: 21539 grad_norm: 4.2191 loss: 2.2124 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 2.2124 2023/03/08 16:42:59 - mmengine - INFO - Epoch(train) [16][120/660] lr: 1.0000e-02 eta: 2:10:25 time: 0.3313 data_time: 0.0213 memory: 21539 grad_norm: 4.2394 loss: 2.1794 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.1794 2023/03/08 16:43:06 - mmengine - INFO - Epoch(train) [16][140/660] lr: 1.0000e-02 eta: 2:10:18 time: 0.3341 data_time: 0.0224 memory: 21539 grad_norm: 4.2410 loss: 2.1199 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 2.1199 2023/03/08 16:43:13 - mmengine - INFO - Epoch(train) [16][160/660] lr: 1.0000e-02 eta: 2:10:11 time: 0.3304 data_time: 0.0207 memory: 21539 grad_norm: 4.1946 loss: 2.1786 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.1786 2023/03/08 16:43:19 - mmengine - INFO - Epoch(train) [16][180/660] lr: 1.0000e-02 eta: 2:10:04 time: 0.3367 data_time: 0.0207 memory: 21539 grad_norm: 4.2513 loss: 2.1815 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1815 2023/03/08 16:43:26 - mmengine - INFO - Epoch(train) [16][200/660] lr: 1.0000e-02 eta: 2:09:57 time: 0.3366 data_time: 0.0209 memory: 21539 grad_norm: 4.2311 loss: 2.2033 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2033 2023/03/08 16:43:33 - mmengine - INFO - Epoch(train) [16][220/660] lr: 1.0000e-02 eta: 2:09:50 time: 0.3377 data_time: 0.0208 memory: 21539 grad_norm: 4.2391 loss: 2.1270 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 2.1270 2023/03/08 16:43:39 - mmengine - INFO - Epoch(train) [16][240/660] lr: 1.0000e-02 eta: 2:09:43 time: 0.3296 data_time: 0.0209 memory: 21539 grad_norm: 4.2670 loss: 2.3389 top1_acc: 0.3750 top5_acc: 0.5938 loss_cls: 2.3389 2023/03/08 16:43:46 - mmengine - INFO - Epoch(train) [16][260/660] lr: 1.0000e-02 eta: 2:09:35 time: 0.3334 data_time: 0.0206 memory: 21539 grad_norm: 4.2279 loss: 2.2322 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2322 2023/03/08 16:43:53 - mmengine - INFO - Epoch(train) [16][280/660] lr: 1.0000e-02 eta: 2:09:28 time: 0.3352 data_time: 0.0211 memory: 21539 grad_norm: 4.3072 loss: 2.2330 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2330 2023/03/08 16:44:00 - mmengine - INFO - Epoch(train) [16][300/660] lr: 1.0000e-02 eta: 2:09:22 time: 0.3384 data_time: 0.0213 memory: 21539 grad_norm: 4.2436 loss: 2.0975 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0975 2023/03/08 16:44:06 - mmengine - INFO - Epoch(train) [16][320/660] lr: 1.0000e-02 eta: 2:09:14 time: 0.3318 data_time: 0.0215 memory: 21539 grad_norm: 4.3278 loss: 2.4053 top1_acc: 0.2812 top5_acc: 0.6562 loss_cls: 2.4053 2023/03/08 16:44:13 - mmengine - INFO - Epoch(train) [16][340/660] lr: 1.0000e-02 eta: 2:09:07 time: 0.3310 data_time: 0.0203 memory: 21539 grad_norm: 4.2916 loss: 2.1556 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 2.1556 2023/03/08 16:44:19 - mmengine - INFO - Epoch(train) [16][360/660] lr: 1.0000e-02 eta: 2:09:00 time: 0.3280 data_time: 0.0218 memory: 21539 grad_norm: 4.2518 loss: 2.1213 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.1213 2023/03/08 16:44:26 - mmengine - INFO - Epoch(train) [16][380/660] lr: 1.0000e-02 eta: 2:08:53 time: 0.3346 data_time: 0.0220 memory: 21539 grad_norm: 4.1908 loss: 2.1232 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.1232 2023/03/08 16:44:33 - mmengine - INFO - Epoch(train) [16][400/660] lr: 1.0000e-02 eta: 2:08:45 time: 0.3313 data_time: 0.0258 memory: 21539 grad_norm: 4.2729 loss: 2.2339 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.2339 2023/03/08 16:44:39 - mmengine - INFO - Epoch(train) [16][420/660] lr: 1.0000e-02 eta: 2:08:38 time: 0.3298 data_time: 0.0210 memory: 21539 grad_norm: 4.2080 loss: 2.2286 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.2286 2023/03/08 16:44:46 - mmengine - INFO - Epoch(train) [16][440/660] lr: 1.0000e-02 eta: 2:08:31 time: 0.3271 data_time: 0.0212 memory: 21539 grad_norm: 4.3201 loss: 2.2143 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.2143 2023/03/08 16:44:52 - mmengine - INFO - Epoch(train) [16][460/660] lr: 1.0000e-02 eta: 2:08:24 time: 0.3305 data_time: 0.0211 memory: 21539 grad_norm: 4.3095 loss: 2.3433 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.3433 2023/03/08 16:44:59 - mmengine - INFO - Epoch(train) [16][480/660] lr: 1.0000e-02 eta: 2:08:16 time: 0.3318 data_time: 0.0214 memory: 21539 grad_norm: 4.1828 loss: 2.2072 top1_acc: 0.4062 top5_acc: 0.5938 loss_cls: 2.2072 2023/03/08 16:45:06 - mmengine - INFO - Epoch(train) [16][500/660] lr: 1.0000e-02 eta: 2:08:09 time: 0.3369 data_time: 0.0214 memory: 21539 grad_norm: 4.2587 loss: 2.1958 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1958 2023/03/08 16:45:12 - mmengine - INFO - Epoch(train) [16][520/660] lr: 1.0000e-02 eta: 2:08:02 time: 0.3273 data_time: 0.0217 memory: 21539 grad_norm: 4.3104 loss: 2.3029 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3029 2023/03/08 16:45:19 - mmengine - INFO - Epoch(train) [16][540/660] lr: 1.0000e-02 eta: 2:07:55 time: 0.3303 data_time: 0.0214 memory: 21539 grad_norm: 4.2043 loss: 2.2443 top1_acc: 0.3438 top5_acc: 0.5938 loss_cls: 2.2443 2023/03/08 16:45:26 - mmengine - INFO - Epoch(train) [16][560/660] lr: 1.0000e-02 eta: 2:07:48 time: 0.3298 data_time: 0.0210 memory: 21539 grad_norm: 4.2987 loss: 2.1363 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1363 2023/03/08 16:45:32 - mmengine - INFO - Epoch(train) [16][580/660] lr: 1.0000e-02 eta: 2:07:41 time: 0.3336 data_time: 0.0214 memory: 21539 grad_norm: 4.3078 loss: 2.3411 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3411 2023/03/08 16:45:39 - mmengine - INFO - Epoch(train) [16][600/660] lr: 1.0000e-02 eta: 2:07:33 time: 0.3283 data_time: 0.0227 memory: 21539 grad_norm: 4.3356 loss: 2.1915 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.1915 2023/03/08 16:45:45 - mmengine - INFO - Epoch(train) [16][620/660] lr: 1.0000e-02 eta: 2:07:26 time: 0.3296 data_time: 0.0208 memory: 21539 grad_norm: 4.3203 loss: 2.2696 top1_acc: 0.3438 top5_acc: 0.8125 loss_cls: 2.2696 2023/03/08 16:45:52 - mmengine - INFO - Epoch(train) [16][640/660] lr: 1.0000e-02 eta: 2:07:19 time: 0.3267 data_time: 0.0219 memory: 21539 grad_norm: 4.3165 loss: 2.2892 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2892 2023/03/08 16:45:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:45:58 - mmengine - INFO - Epoch(train) [16][660/660] lr: 1.0000e-02 eta: 2:07:11 time: 0.3241 data_time: 0.0192 memory: 21539 grad_norm: 4.2766 loss: 2.1298 top1_acc: 0.4815 top5_acc: 0.7407 loss_cls: 2.1298 2023/03/08 16:46:07 - mmengine - INFO - Epoch(train) [17][ 20/660] lr: 1.0000e-02 eta: 2:07:07 time: 0.4123 data_time: 0.0833 memory: 21539 grad_norm: 4.1300 loss: 2.0832 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.0832 2023/03/08 16:46:13 - mmengine - INFO - Epoch(train) [17][ 40/660] lr: 1.0000e-02 eta: 2:07:00 time: 0.3356 data_time: 0.0211 memory: 21539 grad_norm: 4.2074 loss: 2.1647 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1647 2023/03/08 16:46:20 - mmengine - INFO - Epoch(train) [17][ 60/660] lr: 1.0000e-02 eta: 2:06:53 time: 0.3360 data_time: 0.0201 memory: 21539 grad_norm: 4.2980 loss: 2.2228 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.2228 2023/03/08 16:46:27 - mmengine - INFO - Epoch(train) [17][ 80/660] lr: 1.0000e-02 eta: 2:06:46 time: 0.3324 data_time: 0.0211 memory: 21539 grad_norm: 4.2645 loss: 2.0204 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0204 2023/03/08 16:46:34 - mmengine - INFO - Epoch(train) [17][100/660] lr: 1.0000e-02 eta: 2:06:39 time: 0.3403 data_time: 0.0201 memory: 21539 grad_norm: 4.2856 loss: 2.0861 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 2.0861 2023/03/08 16:46:40 - mmengine - INFO - Epoch(train) [17][120/660] lr: 1.0000e-02 eta: 2:06:32 time: 0.3331 data_time: 0.0211 memory: 21539 grad_norm: 4.2854 loss: 2.2835 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.2835 2023/03/08 16:46:47 - mmengine - INFO - Epoch(train) [17][140/660] lr: 1.0000e-02 eta: 2:06:26 time: 0.3402 data_time: 0.0242 memory: 21539 grad_norm: 4.2878 loss: 2.1716 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 2.1716 2023/03/08 16:46:54 - mmengine - INFO - Epoch(train) [17][160/660] lr: 1.0000e-02 eta: 2:06:19 time: 0.3366 data_time: 0.0209 memory: 21539 grad_norm: 4.3253 loss: 2.2915 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2915 2023/03/08 16:47:01 - mmengine - INFO - Epoch(train) [17][180/660] lr: 1.0000e-02 eta: 2:06:12 time: 0.3415 data_time: 0.0208 memory: 21539 grad_norm: 4.4182 loss: 2.1479 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.1479 2023/03/08 16:47:07 - mmengine - INFO - Epoch(train) [17][200/660] lr: 1.0000e-02 eta: 2:06:05 time: 0.3315 data_time: 0.0207 memory: 21539 grad_norm: 4.3280 loss: 2.2008 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.2008 2023/03/08 16:47:14 - mmengine - INFO - Epoch(train) [17][220/660] lr: 1.0000e-02 eta: 2:05:58 time: 0.3376 data_time: 0.0210 memory: 21539 grad_norm: 4.2048 loss: 2.1770 top1_acc: 0.2812 top5_acc: 0.6562 loss_cls: 2.1770 2023/03/08 16:47:21 - mmengine - INFO - Epoch(train) [17][240/660] lr: 1.0000e-02 eta: 2:05:51 time: 0.3307 data_time: 0.0210 memory: 21539 grad_norm: 4.2841 loss: 2.1313 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.1313 2023/03/08 16:47:27 - mmengine - INFO - Epoch(train) [17][260/660] lr: 1.0000e-02 eta: 2:05:44 time: 0.3354 data_time: 0.0210 memory: 21539 grad_norm: 4.2682 loss: 2.1370 top1_acc: 0.4062 top5_acc: 0.8438 loss_cls: 2.1370 2023/03/08 16:47:34 - mmengine - INFO - Epoch(train) [17][280/660] lr: 1.0000e-02 eta: 2:05:36 time: 0.3301 data_time: 0.0216 memory: 21539 grad_norm: 4.3194 loss: 2.1298 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1298 2023/03/08 16:47:41 - mmengine - INFO - Epoch(train) [17][300/660] lr: 1.0000e-02 eta: 2:05:29 time: 0.3364 data_time: 0.0217 memory: 21539 grad_norm: 4.3277 loss: 2.2831 top1_acc: 0.2500 top5_acc: 0.6562 loss_cls: 2.2831 2023/03/08 16:47:47 - mmengine - INFO - Epoch(train) [17][320/660] lr: 1.0000e-02 eta: 2:05:22 time: 0.3315 data_time: 0.0213 memory: 21539 grad_norm: 4.2393 loss: 2.2205 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.2205 2023/03/08 16:47:54 - mmengine - INFO - Epoch(train) [17][340/660] lr: 1.0000e-02 eta: 2:05:15 time: 0.3326 data_time: 0.0203 memory: 21539 grad_norm: 4.2832 loss: 2.0721 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0721 2023/03/08 16:48:01 - mmengine - INFO - Epoch(train) [17][360/660] lr: 1.0000e-02 eta: 2:05:08 time: 0.3305 data_time: 0.0222 memory: 21539 grad_norm: 4.3152 loss: 2.3001 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.3001 2023/03/08 16:48:07 - mmengine - INFO - Epoch(train) [17][380/660] lr: 1.0000e-02 eta: 2:05:01 time: 0.3335 data_time: 0.0210 memory: 21539 grad_norm: 4.3221 loss: 2.2118 top1_acc: 0.3125 top5_acc: 0.5938 loss_cls: 2.2118 2023/03/08 16:48:14 - mmengine - INFO - Epoch(train) [17][400/660] lr: 1.0000e-02 eta: 2:04:54 time: 0.3304 data_time: 0.0221 memory: 21539 grad_norm: 4.4311 loss: 2.1508 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.1508 2023/03/08 16:48:21 - mmengine - INFO - Epoch(train) [17][420/660] lr: 1.0000e-02 eta: 2:04:47 time: 0.3326 data_time: 0.0205 memory: 21539 grad_norm: 4.3060 loss: 2.3332 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.3332 2023/03/08 16:48:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:48:27 - mmengine - INFO - Epoch(train) [17][440/660] lr: 1.0000e-02 eta: 2:04:40 time: 0.3339 data_time: 0.0219 memory: 21539 grad_norm: 4.3044 loss: 2.2984 top1_acc: 0.2812 top5_acc: 0.8438 loss_cls: 2.2984 2023/03/08 16:48:34 - mmengine - INFO - Epoch(train) [17][460/660] lr: 1.0000e-02 eta: 2:04:32 time: 0.3324 data_time: 0.0226 memory: 21539 grad_norm: 4.3199 loss: 2.0452 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.0452 2023/03/08 16:48:40 - mmengine - INFO - Epoch(train) [17][480/660] lr: 1.0000e-02 eta: 2:04:25 time: 0.3331 data_time: 0.0215 memory: 21539 grad_norm: 4.3619 loss: 2.3074 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3074 2023/03/08 16:48:47 - mmengine - INFO - Epoch(train) [17][500/660] lr: 1.0000e-02 eta: 2:04:18 time: 0.3359 data_time: 0.0219 memory: 21539 grad_norm: 4.4076 loss: 2.1434 top1_acc: 0.2812 top5_acc: 0.7188 loss_cls: 2.1434 2023/03/08 16:48:54 - mmengine - INFO - Epoch(train) [17][520/660] lr: 1.0000e-02 eta: 2:04:11 time: 0.3368 data_time: 0.0257 memory: 21539 grad_norm: 4.3935 loss: 2.3206 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3206 2023/03/08 16:49:01 - mmengine - INFO - Epoch(train) [17][540/660] lr: 1.0000e-02 eta: 2:04:04 time: 0.3323 data_time: 0.0215 memory: 21539 grad_norm: 4.2526 loss: 2.1400 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.1400 2023/03/08 16:49:07 - mmengine - INFO - Epoch(train) [17][560/660] lr: 1.0000e-02 eta: 2:03:57 time: 0.3287 data_time: 0.0218 memory: 21539 grad_norm: 4.2739 loss: 2.1151 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1151 2023/03/08 16:49:14 - mmengine - INFO - Epoch(train) [17][580/660] lr: 1.0000e-02 eta: 2:03:50 time: 0.3324 data_time: 0.0208 memory: 21539 grad_norm: 4.3976 loss: 2.2761 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2761 2023/03/08 16:49:20 - mmengine - INFO - Epoch(train) [17][600/660] lr: 1.0000e-02 eta: 2:03:43 time: 0.3308 data_time: 0.0218 memory: 21539 grad_norm: 4.3741 loss: 2.1247 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.1247 2023/03/08 16:49:27 - mmengine - INFO - Epoch(train) [17][620/660] lr: 1.0000e-02 eta: 2:03:36 time: 0.3347 data_time: 0.0209 memory: 21539 grad_norm: 4.4118 loss: 2.2117 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.2117 2023/03/08 16:49:34 - mmengine - INFO - Epoch(train) [17][640/660] lr: 1.0000e-02 eta: 2:03:29 time: 0.3281 data_time: 0.0215 memory: 21539 grad_norm: 4.4074 loss: 2.4350 top1_acc: 0.3125 top5_acc: 0.7812 loss_cls: 2.4350 2023/03/08 16:49:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:49:40 - mmengine - INFO - Epoch(train) [17][660/660] lr: 1.0000e-02 eta: 2:03:21 time: 0.3229 data_time: 0.0194 memory: 21539 grad_norm: 4.3452 loss: 2.2233 top1_acc: 0.5185 top5_acc: 0.7037 loss_cls: 2.2233 2023/03/08 16:49:49 - mmengine - INFO - Epoch(train) [18][ 20/660] lr: 1.0000e-02 eta: 2:03:17 time: 0.4184 data_time: 0.0822 memory: 21539 grad_norm: 4.2481 loss: 2.1009 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 2.1009 2023/03/08 16:49:55 - mmengine - INFO - Epoch(train) [18][ 40/660] lr: 1.0000e-02 eta: 2:03:10 time: 0.3323 data_time: 0.0191 memory: 21539 grad_norm: 4.2511 loss: 2.1312 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 2.1312 2023/03/08 16:50:02 - mmengine - INFO - Epoch(train) [18][ 60/660] lr: 1.0000e-02 eta: 2:03:03 time: 0.3383 data_time: 0.0209 memory: 21539 grad_norm: 4.2834 loss: 2.0569 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0569 2023/03/08 16:50:09 - mmengine - INFO - Epoch(train) [18][ 80/660] lr: 1.0000e-02 eta: 2:02:57 time: 0.3387 data_time: 0.0205 memory: 21539 grad_norm: 4.3707 loss: 2.0954 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.0954 2023/03/08 16:50:16 - mmengine - INFO - Epoch(train) [18][100/660] lr: 1.0000e-02 eta: 2:02:50 time: 0.3416 data_time: 0.0219 memory: 21539 grad_norm: 4.2844 loss: 2.0849 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.0849 2023/03/08 16:50:22 - mmengine - INFO - Epoch(train) [18][120/660] lr: 1.0000e-02 eta: 2:02:43 time: 0.3365 data_time: 0.0195 memory: 21539 grad_norm: 4.3322 loss: 2.0962 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.0962 2023/03/08 16:50:29 - mmengine - INFO - Epoch(train) [18][140/660] lr: 1.0000e-02 eta: 2:02:36 time: 0.3427 data_time: 0.0207 memory: 21539 grad_norm: 4.2835 loss: 2.2030 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.2030 2023/03/08 16:50:36 - mmengine - INFO - Epoch(train) [18][160/660] lr: 1.0000e-02 eta: 2:02:29 time: 0.3403 data_time: 0.0198 memory: 21539 grad_norm: 4.3565 loss: 2.2051 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.2051 2023/03/08 16:50:43 - mmengine - INFO - Epoch(train) [18][180/660] lr: 1.0000e-02 eta: 2:02:23 time: 0.3407 data_time: 0.0210 memory: 21539 grad_norm: 4.3112 loss: 2.1448 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.1448 2023/03/08 16:50:49 - mmengine - INFO - Epoch(train) [18][200/660] lr: 1.0000e-02 eta: 2:02:16 time: 0.3342 data_time: 0.0193 memory: 21539 grad_norm: 4.4672 loss: 2.0910 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.0910 2023/03/08 16:50:56 - mmengine - INFO - Epoch(train) [18][220/660] lr: 1.0000e-02 eta: 2:02:09 time: 0.3449 data_time: 0.0207 memory: 21539 grad_norm: 4.2317 loss: 2.0443 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0443 2023/03/08 16:51:03 - mmengine - INFO - Epoch(train) [18][240/660] lr: 1.0000e-02 eta: 2:02:02 time: 0.3412 data_time: 0.0234 memory: 21539 grad_norm: 4.3395 loss: 2.2029 top1_acc: 0.3125 top5_acc: 0.7812 loss_cls: 2.2029 2023/03/08 16:51:10 - mmengine - INFO - Epoch(train) [18][260/660] lr: 1.0000e-02 eta: 2:01:55 time: 0.3390 data_time: 0.0205 memory: 21539 grad_norm: 4.3825 loss: 2.0858 top1_acc: 0.3438 top5_acc: 0.6250 loss_cls: 2.0858 2023/03/08 16:51:17 - mmengine - INFO - Epoch(train) [18][280/660] lr: 1.0000e-02 eta: 2:01:49 time: 0.3353 data_time: 0.0201 memory: 21539 grad_norm: 4.3065 loss: 2.2045 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2045 2023/03/08 16:51:23 - mmengine - INFO - Epoch(train) [18][300/660] lr: 1.0000e-02 eta: 2:01:42 time: 0.3389 data_time: 0.0210 memory: 21539 grad_norm: 4.3519 loss: 2.1892 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1892 2023/03/08 16:51:30 - mmengine - INFO - Epoch(train) [18][320/660] lr: 1.0000e-02 eta: 2:01:35 time: 0.3357 data_time: 0.0200 memory: 21539 grad_norm: 4.3356 loss: 2.1774 top1_acc: 0.3750 top5_acc: 0.5312 loss_cls: 2.1774 2023/03/08 16:51:37 - mmengine - INFO - Epoch(train) [18][340/660] lr: 1.0000e-02 eta: 2:01:28 time: 0.3403 data_time: 0.0215 memory: 21539 grad_norm: 4.3980 loss: 2.2036 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.2036 2023/03/08 16:51:44 - mmengine - INFO - Epoch(train) [18][360/660] lr: 1.0000e-02 eta: 2:01:21 time: 0.3352 data_time: 0.0200 memory: 21539 grad_norm: 4.3084 loss: 2.1384 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.1384 2023/03/08 16:51:50 - mmengine - INFO - Epoch(train) [18][380/660] lr: 1.0000e-02 eta: 2:01:14 time: 0.3384 data_time: 0.0207 memory: 21539 grad_norm: 4.3780 loss: 2.1806 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.1806 2023/03/08 16:51:57 - mmengine - INFO - Epoch(train) [18][400/660] lr: 1.0000e-02 eta: 2:01:07 time: 0.3388 data_time: 0.0196 memory: 21539 grad_norm: 4.3849 loss: 2.1680 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.1680 2023/03/08 16:52:04 - mmengine - INFO - Epoch(train) [18][420/660] lr: 1.0000e-02 eta: 2:01:00 time: 0.3386 data_time: 0.0217 memory: 21539 grad_norm: 4.3592 loss: 2.1289 top1_acc: 0.3750 top5_acc: 0.8438 loss_cls: 2.1289 2023/03/08 16:52:11 - mmengine - INFO - Epoch(train) [18][440/660] lr: 1.0000e-02 eta: 2:00:53 time: 0.3366 data_time: 0.0201 memory: 21539 grad_norm: 4.2356 loss: 2.1607 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1607 2023/03/08 16:52:18 - mmengine - INFO - Epoch(train) [18][460/660] lr: 1.0000e-02 eta: 2:00:47 time: 0.3442 data_time: 0.0208 memory: 21539 grad_norm: 4.3970 loss: 2.2030 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 2.2030 2023/03/08 16:52:24 - mmengine - INFO - Epoch(train) [18][480/660] lr: 1.0000e-02 eta: 2:00:40 time: 0.3375 data_time: 0.0195 memory: 21539 grad_norm: 4.4251 loss: 2.1475 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 2.1475 2023/03/08 16:52:31 - mmengine - INFO - Epoch(train) [18][500/660] lr: 1.0000e-02 eta: 2:00:33 time: 0.3405 data_time: 0.0221 memory: 21539 grad_norm: 4.3700 loss: 2.2069 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2069 2023/03/08 16:52:38 - mmengine - INFO - Epoch(train) [18][520/660] lr: 1.0000e-02 eta: 2:00:26 time: 0.3388 data_time: 0.0193 memory: 21539 grad_norm: 4.4604 loss: 2.1610 top1_acc: 0.5312 top5_acc: 0.6562 loss_cls: 2.1610 2023/03/08 16:52:45 - mmengine - INFO - Epoch(train) [18][540/660] lr: 1.0000e-02 eta: 2:00:20 time: 0.3402 data_time: 0.0210 memory: 21539 grad_norm: 4.2700 loss: 2.1400 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.1400 2023/03/08 16:52:52 - mmengine - INFO - Epoch(train) [18][560/660] lr: 1.0000e-02 eta: 2:00:13 time: 0.3428 data_time: 0.0199 memory: 21539 grad_norm: 4.3322 loss: 2.1261 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 2.1261 2023/03/08 16:52:58 - mmengine - INFO - Epoch(train) [18][580/660] lr: 1.0000e-02 eta: 2:00:06 time: 0.3392 data_time: 0.0204 memory: 21539 grad_norm: 4.3925 loss: 2.2594 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2594 2023/03/08 16:53:05 - mmengine - INFO - Epoch(train) [18][600/660] lr: 1.0000e-02 eta: 1:59:59 time: 0.3349 data_time: 0.0195 memory: 21539 grad_norm: 4.3437 loss: 2.1226 top1_acc: 0.4375 top5_acc: 0.5938 loss_cls: 2.1226 2023/03/08 16:53:12 - mmengine - INFO - Epoch(train) [18][620/660] lr: 1.0000e-02 eta: 1:59:52 time: 0.3424 data_time: 0.0257 memory: 21539 grad_norm: 4.3700 loss: 2.1593 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1593 2023/03/08 16:53:23 - mmengine - INFO - Epoch(train) [18][640/660] lr: 1.0000e-02 eta: 1:59:54 time: 0.5688 data_time: 0.0204 memory: 21539 grad_norm: 4.4139 loss: 2.1927 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1927 2023/03/08 16:53:30 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:53:30 - mmengine - INFO - Epoch(train) [18][660/660] lr: 1.0000e-02 eta: 1:59:47 time: 0.3307 data_time: 0.0191 memory: 21539 grad_norm: 4.4333 loss: 2.1855 top1_acc: 0.4444 top5_acc: 0.7778 loss_cls: 2.1855 2023/03/08 16:53:30 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/03/08 16:53:39 - mmengine - INFO - Epoch(train) [19][ 20/660] lr: 1.0000e-02 eta: 1:59:42 time: 0.4093 data_time: 0.0913 memory: 21539 grad_norm: 4.2844 loss: 2.0640 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.0640 2023/03/08 16:53:46 - mmengine - INFO - Epoch(train) [19][ 40/660] lr: 1.0000e-02 eta: 1:59:35 time: 0.3400 data_time: 0.0195 memory: 21539 grad_norm: 4.2426 loss: 2.0843 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 2.0843 2023/03/08 16:53:53 - mmengine - INFO - Epoch(train) [19][ 60/660] lr: 1.0000e-02 eta: 1:59:29 time: 0.3462 data_time: 0.0207 memory: 21539 grad_norm: 4.3455 loss: 2.1063 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 2.1063 2023/03/08 16:54:00 - mmengine - INFO - Epoch(train) [19][ 80/660] lr: 1.0000e-02 eta: 1:59:22 time: 0.3404 data_time: 0.0236 memory: 21539 grad_norm: 4.3360 loss: 2.0340 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 2.0340 2023/03/08 16:54:07 - mmengine - INFO - Epoch(train) [19][100/660] lr: 1.0000e-02 eta: 1:59:15 time: 0.3402 data_time: 0.0204 memory: 21539 grad_norm: 4.3850 loss: 2.0101 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0101 2023/03/08 16:54:13 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:54:13 - mmengine - INFO - Epoch(train) [19][120/660] lr: 1.0000e-02 eta: 1:59:08 time: 0.3394 data_time: 0.0195 memory: 21539 grad_norm: 4.3611 loss: 2.0771 top1_acc: 0.2812 top5_acc: 0.7500 loss_cls: 2.0771 2023/03/08 16:54:20 - mmengine - INFO - Epoch(train) [19][140/660] lr: 1.0000e-02 eta: 1:59:02 time: 0.3459 data_time: 0.0211 memory: 21539 grad_norm: 4.3687 loss: 2.1280 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.1280 2023/03/08 16:54:27 - mmengine - INFO - Epoch(train) [19][160/660] lr: 1.0000e-02 eta: 1:58:55 time: 0.3398 data_time: 0.0238 memory: 21539 grad_norm: 4.3752 loss: 2.1741 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.1741 2023/03/08 16:54:34 - mmengine - INFO - Epoch(train) [19][180/660] lr: 1.0000e-02 eta: 1:58:48 time: 0.3442 data_time: 0.0205 memory: 21539 grad_norm: 4.4591 loss: 2.1724 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.1724 2023/03/08 16:54:41 - mmengine - INFO - Epoch(train) [19][200/660] lr: 1.0000e-02 eta: 1:58:41 time: 0.3364 data_time: 0.0199 memory: 21539 grad_norm: 4.4135 loss: 2.0045 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.0045 2023/03/08 16:54:48 - mmengine - INFO - Epoch(train) [19][220/660] lr: 1.0000e-02 eta: 1:58:35 time: 0.3471 data_time: 0.0203 memory: 21539 grad_norm: 4.2811 loss: 2.0195 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0195 2023/03/08 16:54:54 - mmengine - INFO - Epoch(train) [19][240/660] lr: 1.0000e-02 eta: 1:58:28 time: 0.3376 data_time: 0.0189 memory: 21539 grad_norm: 4.4101 loss: 2.0568 top1_acc: 0.5312 top5_acc: 0.6562 loss_cls: 2.0568 2023/03/08 16:55:01 - mmengine - INFO - Epoch(train) [19][260/660] lr: 1.0000e-02 eta: 1:58:21 time: 0.3407 data_time: 0.0211 memory: 21539 grad_norm: 4.4214 loss: 2.1090 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.1090 2023/03/08 16:55:08 - mmengine - INFO - Epoch(train) [19][280/660] lr: 1.0000e-02 eta: 1:58:14 time: 0.3356 data_time: 0.0196 memory: 21539 grad_norm: 4.4338 loss: 2.1153 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1153 2023/03/08 16:55:15 - mmengine - INFO - Epoch(train) [19][300/660] lr: 1.0000e-02 eta: 1:58:07 time: 0.3403 data_time: 0.0207 memory: 21539 grad_norm: 4.4595 loss: 2.1697 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.1697 2023/03/08 16:55:21 - mmengine - INFO - Epoch(train) [19][320/660] lr: 1.0000e-02 eta: 1:58:00 time: 0.3374 data_time: 0.0200 memory: 21539 grad_norm: 4.3120 loss: 2.0363 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0363 2023/03/08 16:55:28 - mmengine - INFO - Epoch(train) [19][340/660] lr: 1.0000e-02 eta: 1:57:53 time: 0.3417 data_time: 0.0218 memory: 21539 grad_norm: 4.4249 loss: 2.1058 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.1058 2023/03/08 16:55:35 - mmengine - INFO - Epoch(train) [19][360/660] lr: 1.0000e-02 eta: 1:57:47 time: 0.3393 data_time: 0.0196 memory: 21539 grad_norm: 4.4276 loss: 2.0307 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0307 2023/03/08 16:55:42 - mmengine - INFO - Epoch(train) [19][380/660] lr: 1.0000e-02 eta: 1:57:40 time: 0.3437 data_time: 0.0201 memory: 21539 grad_norm: 4.4626 loss: 1.9977 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.9977 2023/03/08 16:55:49 - mmengine - INFO - Epoch(train) [19][400/660] lr: 1.0000e-02 eta: 1:57:33 time: 0.3402 data_time: 0.0235 memory: 21539 grad_norm: 4.4889 loss: 2.1306 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.1306 2023/03/08 16:55:56 - mmengine - INFO - Epoch(train) [19][420/660] lr: 1.0000e-02 eta: 1:57:26 time: 0.3408 data_time: 0.0210 memory: 21539 grad_norm: 4.5664 loss: 2.0551 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 2.0551 2023/03/08 16:56:02 - mmengine - INFO - Epoch(train) [19][440/660] lr: 1.0000e-02 eta: 1:57:19 time: 0.3395 data_time: 0.0203 memory: 21539 grad_norm: 4.4720 loss: 2.2829 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.2829 2023/03/08 16:56:09 - mmengine - INFO - Epoch(train) [19][460/660] lr: 1.0000e-02 eta: 1:57:13 time: 0.3419 data_time: 0.0207 memory: 21539 grad_norm: 4.3681 loss: 2.1998 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 2.1998 2023/03/08 16:56:16 - mmengine - INFO - Epoch(train) [19][480/660] lr: 1.0000e-02 eta: 1:57:06 time: 0.3359 data_time: 0.0196 memory: 21539 grad_norm: 4.4540 loss: 2.3225 top1_acc: 0.4688 top5_acc: 0.5938 loss_cls: 2.3225 2023/03/08 16:56:23 - mmengine - INFO - Epoch(train) [19][500/660] lr: 1.0000e-02 eta: 1:56:59 time: 0.3449 data_time: 0.0217 memory: 21539 grad_norm: 4.4107 loss: 2.1333 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1333 2023/03/08 16:56:30 - mmengine - INFO - Epoch(train) [19][520/660] lr: 1.0000e-02 eta: 1:56:52 time: 0.3360 data_time: 0.0200 memory: 21539 grad_norm: 4.3964 loss: 2.1882 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.1882 2023/03/08 16:56:36 - mmengine - INFO - Epoch(train) [19][540/660] lr: 1.0000e-02 eta: 1:56:45 time: 0.3405 data_time: 0.0203 memory: 21539 grad_norm: 4.4271 loss: 2.0453 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 2.0453 2023/03/08 16:56:43 - mmengine - INFO - Epoch(train) [19][560/660] lr: 1.0000e-02 eta: 1:56:38 time: 0.3371 data_time: 0.0199 memory: 21539 grad_norm: 4.4429 loss: 2.2241 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.2241 2023/03/08 16:56:50 - mmengine - INFO - Epoch(train) [19][580/660] lr: 1.0000e-02 eta: 1:56:32 time: 0.3430 data_time: 0.0205 memory: 21539 grad_norm: 4.4150 loss: 2.1041 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1041 2023/03/08 16:56:57 - mmengine - INFO - Epoch(train) [19][600/660] lr: 1.0000e-02 eta: 1:56:25 time: 0.3364 data_time: 0.0193 memory: 21539 grad_norm: 4.3741 loss: 1.9984 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.9984 2023/03/08 16:57:04 - mmengine - INFO - Epoch(train) [19][620/660] lr: 1.0000e-02 eta: 1:56:18 time: 0.3429 data_time: 0.0212 memory: 21539 grad_norm: 4.5272 loss: 2.1837 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.1837 2023/03/08 16:57:10 - mmengine - INFO - Epoch(train) [19][640/660] lr: 1.0000e-02 eta: 1:56:11 time: 0.3361 data_time: 0.0191 memory: 21539 grad_norm: 4.3953 loss: 2.2427 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.2427 2023/03/08 16:57:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:57:17 - mmengine - INFO - Epoch(train) [19][660/660] lr: 1.0000e-02 eta: 1:56:04 time: 0.3338 data_time: 0.0181 memory: 21539 grad_norm: 4.4211 loss: 2.1671 top1_acc: 0.4444 top5_acc: 0.8148 loss_cls: 2.1671 2023/03/08 16:57:25 - mmengine - INFO - Epoch(train) [20][ 20/660] lr: 1.0000e-02 eta: 1:56:00 time: 0.4167 data_time: 0.0895 memory: 21539 grad_norm: 4.2684 loss: 2.0270 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.0270 2023/03/08 16:57:32 - mmengine - INFO - Epoch(train) [20][ 40/660] lr: 1.0000e-02 eta: 1:55:53 time: 0.3368 data_time: 0.0239 memory: 21539 grad_norm: 4.3174 loss: 1.8496 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.8496 2023/03/08 16:57:39 - mmengine - INFO - Epoch(train) [20][ 60/660] lr: 1.0000e-02 eta: 1:55:46 time: 0.3391 data_time: 0.0212 memory: 21539 grad_norm: 4.4361 loss: 2.1927 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 2.1927 2023/03/08 16:57:46 - mmengine - INFO - Epoch(train) [20][ 80/660] lr: 1.0000e-02 eta: 1:55:39 time: 0.3390 data_time: 0.0203 memory: 21539 grad_norm: 4.3878 loss: 2.1115 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1115 2023/03/08 16:57:52 - mmengine - INFO - Epoch(train) [20][100/660] lr: 1.0000e-02 eta: 1:55:32 time: 0.3385 data_time: 0.0223 memory: 21539 grad_norm: 4.3664 loss: 2.0858 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.0858 2023/03/08 16:57:59 - mmengine - INFO - Epoch(train) [20][120/660] lr: 1.0000e-02 eta: 1:55:25 time: 0.3357 data_time: 0.0205 memory: 21539 grad_norm: 4.4799 loss: 2.1111 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1111 2023/03/08 16:58:06 - mmengine - INFO - Epoch(train) [20][140/660] lr: 1.0000e-02 eta: 1:55:18 time: 0.3387 data_time: 0.0212 memory: 21539 grad_norm: 4.3845 loss: 2.0333 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.0333 2023/03/08 16:58:13 - mmengine - INFO - Epoch(train) [20][160/660] lr: 1.0000e-02 eta: 1:55:11 time: 0.3337 data_time: 0.0203 memory: 21539 grad_norm: 4.4865 loss: 2.0194 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0194 2023/03/08 16:58:19 - mmengine - INFO - Epoch(train) [20][180/660] lr: 1.0000e-02 eta: 1:55:05 time: 0.3466 data_time: 0.0213 memory: 21539 grad_norm: 4.4640 loss: 2.0862 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.0862 2023/03/08 16:58:26 - mmengine - INFO - Epoch(train) [20][200/660] lr: 1.0000e-02 eta: 1:54:58 time: 0.3338 data_time: 0.0209 memory: 21539 grad_norm: 4.5095 loss: 1.9731 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9731 2023/03/08 16:58:33 - mmengine - INFO - Epoch(train) [20][220/660] lr: 1.0000e-02 eta: 1:54:51 time: 0.3407 data_time: 0.0209 memory: 21539 grad_norm: 4.4452 loss: 2.1099 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.1099 2023/03/08 16:58:40 - mmengine - INFO - Epoch(train) [20][240/660] lr: 1.0000e-02 eta: 1:54:44 time: 0.3330 data_time: 0.0210 memory: 21539 grad_norm: 4.3493 loss: 2.0094 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0094 2023/03/08 16:58:46 - mmengine - INFO - Epoch(train) [20][260/660] lr: 1.0000e-02 eta: 1:54:37 time: 0.3399 data_time: 0.0218 memory: 21539 grad_norm: 4.4894 loss: 2.1041 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 2.1041 2023/03/08 16:58:53 - mmengine - INFO - Epoch(train) [20][280/660] lr: 1.0000e-02 eta: 1:54:30 time: 0.3322 data_time: 0.0204 memory: 21539 grad_norm: 4.3940 loss: 2.0239 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.0239 2023/03/08 16:59:00 - mmengine - INFO - Epoch(train) [20][300/660] lr: 1.0000e-02 eta: 1:54:23 time: 0.3408 data_time: 0.0211 memory: 21539 grad_norm: 4.4839 loss: 2.1757 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1757 2023/03/08 16:59:07 - mmengine - INFO - Epoch(train) [20][320/660] lr: 1.0000e-02 eta: 1:54:16 time: 0.3343 data_time: 0.0201 memory: 21539 grad_norm: 4.5317 loss: 2.0858 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0858 2023/03/08 16:59:13 - mmengine - INFO - Epoch(train) [20][340/660] lr: 1.0000e-02 eta: 1:54:09 time: 0.3409 data_time: 0.0239 memory: 21539 grad_norm: 4.5108 loss: 2.0862 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.0862 2023/03/08 16:59:20 - mmengine - INFO - Epoch(train) [20][360/660] lr: 1.0000e-02 eta: 1:54:02 time: 0.3338 data_time: 0.0202 memory: 21539 grad_norm: 4.5226 loss: 2.0425 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 2.0425 2023/03/08 16:59:27 - mmengine - INFO - Epoch(train) [20][380/660] lr: 1.0000e-02 eta: 1:53:56 time: 0.3422 data_time: 0.0208 memory: 21539 grad_norm: 4.5246 loss: 2.1088 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.1088 2023/03/08 16:59:34 - mmengine - INFO - Epoch(train) [20][400/660] lr: 1.0000e-02 eta: 1:53:49 time: 0.3337 data_time: 0.0196 memory: 21539 grad_norm: 4.4779 loss: 2.0728 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.0728 2023/03/08 16:59:40 - mmengine - INFO - Epoch(train) [20][420/660] lr: 1.0000e-02 eta: 1:53:42 time: 0.3388 data_time: 0.0204 memory: 21539 grad_norm: 4.4496 loss: 1.9957 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9957 2023/03/08 16:59:47 - mmengine - INFO - Epoch(train) [20][440/660] lr: 1.0000e-02 eta: 1:53:35 time: 0.3337 data_time: 0.0196 memory: 21539 grad_norm: 4.5159 loss: 2.0921 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.0921 2023/03/08 16:59:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 16:59:54 - mmengine - INFO - Epoch(train) [20][460/660] lr: 1.0000e-02 eta: 1:53:28 time: 0.3404 data_time: 0.0214 memory: 21539 grad_norm: 4.4125 loss: 2.1366 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.1366 2023/03/08 17:00:01 - mmengine - INFO - Epoch(train) [20][480/660] lr: 1.0000e-02 eta: 1:53:21 time: 0.3383 data_time: 0.0209 memory: 21539 grad_norm: 4.4090 loss: 2.1926 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1926 2023/03/08 17:00:07 - mmengine - INFO - Epoch(train) [20][500/660] lr: 1.0000e-02 eta: 1:53:14 time: 0.3367 data_time: 0.0214 memory: 21539 grad_norm: 4.3846 loss: 1.9548 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.9548 2023/03/08 17:00:15 - mmengine - INFO - Epoch(train) [20][520/660] lr: 1.0000e-02 eta: 1:53:08 time: 0.3712 data_time: 0.0202 memory: 21539 grad_norm: 4.4541 loss: 2.0952 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.0952 2023/03/08 17:00:21 - mmengine - INFO - Epoch(train) [20][540/660] lr: 1.0000e-02 eta: 1:53:01 time: 0.3370 data_time: 0.0218 memory: 21539 grad_norm: 4.4599 loss: 2.1120 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1120 2023/03/08 17:00:28 - mmengine - INFO - Epoch(train) [20][560/660] lr: 1.0000e-02 eta: 1:52:54 time: 0.3364 data_time: 0.0203 memory: 21539 grad_norm: 4.5391 loss: 2.1718 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 2.1718 2023/03/08 17:00:35 - mmengine - INFO - Epoch(train) [20][580/660] lr: 1.0000e-02 eta: 1:52:48 time: 0.3397 data_time: 0.0205 memory: 21539 grad_norm: 4.5257 loss: 2.0358 top1_acc: 0.4062 top5_acc: 0.5312 loss_cls: 2.0358 2023/03/08 17:00:42 - mmengine - INFO - Epoch(train) [20][600/660] lr: 1.0000e-02 eta: 1:52:41 time: 0.3384 data_time: 0.0246 memory: 21539 grad_norm: 4.4806 loss: 2.1124 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.1124 2023/03/08 17:00:49 - mmengine - INFO - Epoch(train) [20][620/660] lr: 1.0000e-02 eta: 1:52:34 time: 0.3377 data_time: 0.0205 memory: 21539 grad_norm: 4.5253 loss: 2.0534 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.0534 2023/03/08 17:00:55 - mmengine - INFO - Epoch(train) [20][640/660] lr: 1.0000e-02 eta: 1:52:27 time: 0.3342 data_time: 0.0202 memory: 21539 grad_norm: 4.4445 loss: 2.0409 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 2.0409 2023/03/08 17:01:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:01:02 - mmengine - INFO - Epoch(train) [20][660/660] lr: 1.0000e-02 eta: 1:52:20 time: 0.3362 data_time: 0.0194 memory: 21539 grad_norm: 4.5236 loss: 2.1327 top1_acc: 0.5926 top5_acc: 0.7778 loss_cls: 2.1327 2023/03/08 17:01:07 - mmengine - INFO - Epoch(val) [20][20/97] eta: 0:00:17 time: 0.2328 data_time: 0.1250 memory: 3261 2023/03/08 17:01:10 - mmengine - INFO - Epoch(val) [20][40/97] eta: 0:00:11 time: 0.1627 data_time: 0.0542 memory: 3261 2023/03/08 17:01:14 - mmengine - INFO - Epoch(val) [20][60/97] eta: 0:00:07 time: 0.1841 data_time: 0.0759 memory: 3261 2023/03/08 17:01:17 - mmengine - INFO - Epoch(val) [20][80/97] eta: 0:00:03 time: 0.1536 data_time: 0.0474 memory: 3261 2023/03/08 17:01:20 - mmengine - INFO - Epoch(val) [20][97/97] acc/top1: 0.3053 acc/top5: 0.6092 acc/mean1: 0.2433 2023/03/08 17:01:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_exp1/best_acc/top1_epoch_15.pth is removed 2023/03/08 17:01:21 - mmengine - INFO - The best checkpoint with 0.3053 acc/top1 at 20 epoch is saved to best_acc/top1_epoch_20.pth. 2023/03/08 17:01:29 - mmengine - INFO - Epoch(train) [21][ 20/660] lr: 1.0000e-03 eta: 1:52:15 time: 0.3999 data_time: 0.0858 memory: 21539 grad_norm: 4.3599 loss: 2.0663 top1_acc: 0.3438 top5_acc: 0.6250 loss_cls: 2.0663 2023/03/08 17:01:36 - mmengine - INFO - Epoch(train) [21][ 40/660] lr: 1.0000e-03 eta: 1:52:08 time: 0.3338 data_time: 0.0204 memory: 21539 grad_norm: 4.1855 loss: 1.9198 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.9198 2023/03/08 17:01:43 - mmengine - INFO - Epoch(train) [21][ 60/660] lr: 1.0000e-03 eta: 1:52:01 time: 0.3466 data_time: 0.0227 memory: 21539 grad_norm: 4.3530 loss: 2.0046 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 2.0046 2023/03/08 17:01:49 - mmengine - INFO - Epoch(train) [21][ 80/660] lr: 1.0000e-03 eta: 1:51:54 time: 0.3321 data_time: 0.0205 memory: 21539 grad_norm: 4.2702 loss: 2.0221 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0221 2023/03/08 17:01:56 - mmengine - INFO - Epoch(train) [21][100/660] lr: 1.0000e-03 eta: 1:51:47 time: 0.3382 data_time: 0.0227 memory: 21539 grad_norm: 4.2298 loss: 1.8767 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 1.8767 2023/03/08 17:02:03 - mmengine - INFO - Epoch(train) [21][120/660] lr: 1.0000e-03 eta: 1:51:40 time: 0.3312 data_time: 0.0198 memory: 21539 grad_norm: 4.3050 loss: 1.9970 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.9970 2023/03/08 17:02:09 - mmengine - INFO - Epoch(train) [21][140/660] lr: 1.0000e-03 eta: 1:51:33 time: 0.3342 data_time: 0.0213 memory: 21539 grad_norm: 4.2027 loss: 1.8192 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.8192 2023/03/08 17:02:16 - mmengine - INFO - Epoch(train) [21][160/660] lr: 1.0000e-03 eta: 1:51:26 time: 0.3320 data_time: 0.0197 memory: 21539 grad_norm: 4.2295 loss: 1.8386 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 1.8386 2023/03/08 17:02:23 - mmengine - INFO - Epoch(train) [21][180/660] lr: 1.0000e-03 eta: 1:51:19 time: 0.3405 data_time: 0.0220 memory: 21539 grad_norm: 4.2648 loss: 1.8827 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.8827 2023/03/08 17:02:30 - mmengine - INFO - Epoch(train) [21][200/660] lr: 1.0000e-03 eta: 1:51:12 time: 0.3363 data_time: 0.0248 memory: 21539 grad_norm: 4.2013 loss: 2.1061 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 2.1061 2023/03/08 17:02:36 - mmengine - INFO - Epoch(train) [21][220/660] lr: 1.0000e-03 eta: 1:51:05 time: 0.3354 data_time: 0.0220 memory: 21539 grad_norm: 4.1920 loss: 1.9680 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.9680 2023/03/08 17:02:43 - mmengine - INFO - Epoch(train) [21][240/660] lr: 1.0000e-03 eta: 1:50:58 time: 0.3308 data_time: 0.0189 memory: 21539 grad_norm: 4.2575 loss: 1.7826 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.7826 2023/03/08 17:02:50 - mmengine - INFO - Epoch(train) [21][260/660] lr: 1.0000e-03 eta: 1:50:51 time: 0.3366 data_time: 0.0230 memory: 21539 grad_norm: 4.2842 loss: 1.7663 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7663 2023/03/08 17:02:56 - mmengine - INFO - Epoch(train) [21][280/660] lr: 1.0000e-03 eta: 1:50:44 time: 0.3314 data_time: 0.0205 memory: 21539 grad_norm: 4.2679 loss: 1.8501 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.8501 2023/03/08 17:03:03 - mmengine - INFO - Epoch(train) [21][300/660] lr: 1.0000e-03 eta: 1:50:38 time: 0.3403 data_time: 0.0220 memory: 21539 grad_norm: 4.2425 loss: 1.8072 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.8072 2023/03/08 17:03:10 - mmengine - INFO - Epoch(train) [21][320/660] lr: 1.0000e-03 eta: 1:50:30 time: 0.3326 data_time: 0.0197 memory: 21539 grad_norm: 4.3759 loss: 2.0151 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.0151 2023/03/08 17:03:17 - mmengine - INFO - Epoch(train) [21][340/660] lr: 1.0000e-03 eta: 1:50:24 time: 0.3390 data_time: 0.0220 memory: 21539 grad_norm: 4.2169 loss: 1.7872 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7872 2023/03/08 17:03:23 - mmengine - INFO - Epoch(train) [21][360/660] lr: 1.0000e-03 eta: 1:50:17 time: 0.3355 data_time: 0.0193 memory: 21539 grad_norm: 4.3007 loss: 1.6946 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6946 2023/03/08 17:03:30 - mmengine - INFO - Epoch(train) [21][380/660] lr: 1.0000e-03 eta: 1:50:10 time: 0.3364 data_time: 0.0224 memory: 21539 grad_norm: 4.2714 loss: 1.8960 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.8960 2023/03/08 17:03:37 - mmengine - INFO - Epoch(train) [21][400/660] lr: 1.0000e-03 eta: 1:50:03 time: 0.3336 data_time: 0.0201 memory: 21539 grad_norm: 4.2748 loss: 1.9273 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 1.9273 2023/03/08 17:03:43 - mmengine - INFO - Epoch(train) [21][420/660] lr: 1.0000e-03 eta: 1:49:56 time: 0.3358 data_time: 0.0230 memory: 21539 grad_norm: 4.3453 loss: 1.8536 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.8536 2023/03/08 17:03:50 - mmengine - INFO - Epoch(train) [21][440/660] lr: 1.0000e-03 eta: 1:49:49 time: 0.3313 data_time: 0.0207 memory: 21539 grad_norm: 4.1794 loss: 1.9666 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.9666 2023/03/08 17:03:57 - mmengine - INFO - Epoch(train) [21][460/660] lr: 1.0000e-03 eta: 1:49:42 time: 0.3397 data_time: 0.0217 memory: 21539 grad_norm: 4.2596 loss: 1.8764 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8764 2023/03/08 17:04:03 - mmengine - INFO - Epoch(train) [21][480/660] lr: 1.0000e-03 eta: 1:49:35 time: 0.3308 data_time: 0.0201 memory: 21539 grad_norm: 4.2785 loss: 1.7833 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7833 2023/03/08 17:04:11 - mmengine - INFO - Epoch(train) [21][500/660] lr: 1.0000e-03 eta: 1:49:29 time: 0.3883 data_time: 0.0713 memory: 21539 grad_norm: 4.2465 loss: 1.7815 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.7815 2023/03/08 17:04:18 - mmengine - INFO - Epoch(train) [21][520/660] lr: 1.0000e-03 eta: 1:49:22 time: 0.3316 data_time: 0.0134 memory: 21539 grad_norm: 4.2465 loss: 1.8656 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8656 2023/03/08 17:04:25 - mmengine - INFO - Epoch(train) [21][540/660] lr: 1.0000e-03 eta: 1:49:15 time: 0.3384 data_time: 0.0218 memory: 21539 grad_norm: 4.2884 loss: 1.8531 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8531 2023/03/08 17:04:31 - mmengine - INFO - Epoch(train) [21][560/660] lr: 1.0000e-03 eta: 1:49:09 time: 0.3376 data_time: 0.0213 memory: 21539 grad_norm: 4.2460 loss: 1.8425 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8425 2023/03/08 17:04:38 - mmengine - INFO - Epoch(train) [21][580/660] lr: 1.0000e-03 eta: 1:49:02 time: 0.3370 data_time: 0.0208 memory: 21539 grad_norm: 4.3041 loss: 1.9467 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9467 2023/03/08 17:04:45 - mmengine - INFO - Epoch(train) [21][600/660] lr: 1.0000e-03 eta: 1:48:55 time: 0.3326 data_time: 0.0213 memory: 21539 grad_norm: 4.2373 loss: 1.7465 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7465 2023/03/08 17:04:51 - mmengine - INFO - Epoch(train) [21][620/660] lr: 1.0000e-03 eta: 1:48:48 time: 0.3352 data_time: 0.0210 memory: 21539 grad_norm: 4.2477 loss: 1.7845 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.7845 2023/03/08 17:04:58 - mmengine - INFO - Epoch(train) [21][640/660] lr: 1.0000e-03 eta: 1:48:41 time: 0.3319 data_time: 0.0224 memory: 21539 grad_norm: 4.3378 loss: 1.9099 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.9099 2023/03/08 17:05:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:05:05 - mmengine - INFO - Epoch(train) [21][660/660] lr: 1.0000e-03 eta: 1:48:34 time: 0.3274 data_time: 0.0192 memory: 21539 grad_norm: 4.2589 loss: 1.8154 top1_acc: 0.3333 top5_acc: 0.7407 loss_cls: 1.8154 2023/03/08 17:05:05 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/03/08 17:05:14 - mmengine - INFO - Epoch(train) [22][ 20/660] lr: 1.0000e-03 eta: 1:48:29 time: 0.4155 data_time: 0.0982 memory: 21539 grad_norm: 4.3365 loss: 1.7269 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7269 2023/03/08 17:05:21 - mmengine - INFO - Epoch(train) [22][ 40/660] lr: 1.0000e-03 eta: 1:48:22 time: 0.3312 data_time: 0.0205 memory: 21539 grad_norm: 4.1912 loss: 1.7473 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.7473 2023/03/08 17:05:28 - mmengine - INFO - Epoch(train) [22][ 60/660] lr: 1.0000e-03 eta: 1:48:15 time: 0.3427 data_time: 0.0208 memory: 21539 grad_norm: 4.2656 loss: 1.9121 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9121 2023/03/08 17:05:34 - mmengine - INFO - Epoch(train) [22][ 80/660] lr: 1.0000e-03 eta: 1:48:08 time: 0.3313 data_time: 0.0205 memory: 21539 grad_norm: 4.2190 loss: 1.8631 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.8631 2023/03/08 17:05:41 - mmengine - INFO - Epoch(train) [22][100/660] lr: 1.0000e-03 eta: 1:48:01 time: 0.3359 data_time: 0.0201 memory: 21539 grad_norm: 4.2953 loss: 1.8885 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 1.8885 2023/03/08 17:05:48 - mmengine - INFO - Epoch(train) [22][120/660] lr: 1.0000e-03 eta: 1:47:54 time: 0.3349 data_time: 0.0216 memory: 21539 grad_norm: 4.2652 loss: 1.9161 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.9161 2023/03/08 17:05:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:05:54 - mmengine - INFO - Epoch(train) [22][140/660] lr: 1.0000e-03 eta: 1:47:47 time: 0.3347 data_time: 0.0204 memory: 21539 grad_norm: 4.3168 loss: 1.7927 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7927 2023/03/08 17:06:01 - mmengine - INFO - Epoch(train) [22][160/660] lr: 1.0000e-03 eta: 1:47:40 time: 0.3315 data_time: 0.0210 memory: 21539 grad_norm: 4.3580 loss: 1.8487 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8487 2023/03/08 17:06:08 - mmengine - INFO - Epoch(train) [22][180/660] lr: 1.0000e-03 eta: 1:47:33 time: 0.3350 data_time: 0.0201 memory: 21539 grad_norm: 4.2760 loss: 1.8752 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8752 2023/03/08 17:06:14 - mmengine - INFO - Epoch(train) [22][200/660] lr: 1.0000e-03 eta: 1:47:26 time: 0.3319 data_time: 0.0225 memory: 21539 grad_norm: 4.3060 loss: 1.6448 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6448 2023/03/08 17:06:21 - mmengine - INFO - Epoch(train) [22][220/660] lr: 1.0000e-03 eta: 1:47:19 time: 0.3365 data_time: 0.0213 memory: 21539 grad_norm: 4.3663 loss: 1.8199 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 1.8199 2023/03/08 17:06:28 - mmengine - INFO - Epoch(train) [22][240/660] lr: 1.0000e-03 eta: 1:47:12 time: 0.3302 data_time: 0.0214 memory: 21539 grad_norm: 4.3319 loss: 1.8097 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.8097 2023/03/08 17:06:34 - mmengine - INFO - Epoch(train) [22][260/660] lr: 1.0000e-03 eta: 1:47:05 time: 0.3369 data_time: 0.0223 memory: 21539 grad_norm: 4.3135 loss: 1.8167 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8167 2023/03/08 17:06:41 - mmengine - INFO - Epoch(train) [22][280/660] lr: 1.0000e-03 eta: 1:46:58 time: 0.3291 data_time: 0.0209 memory: 21539 grad_norm: 4.3478 loss: 1.8722 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.8722 2023/03/08 17:06:48 - mmengine - INFO - Epoch(train) [22][300/660] lr: 1.0000e-03 eta: 1:46:51 time: 0.3333 data_time: 0.0221 memory: 21539 grad_norm: 4.3033 loss: 1.8552 top1_acc: 0.2812 top5_acc: 0.8125 loss_cls: 1.8552 2023/03/08 17:06:54 - mmengine - INFO - Epoch(train) [22][320/660] lr: 1.0000e-03 eta: 1:46:44 time: 0.3310 data_time: 0.0208 memory: 21539 grad_norm: 4.3544 loss: 1.8230 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.8230 2023/03/08 17:07:01 - mmengine - INFO - Epoch(train) [22][340/660] lr: 1.0000e-03 eta: 1:46:37 time: 0.3355 data_time: 0.0212 memory: 21539 grad_norm: 4.3071 loss: 1.7261 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.7261 2023/03/08 17:07:08 - mmengine - INFO - Epoch(train) [22][360/660] lr: 1.0000e-03 eta: 1:46:30 time: 0.3316 data_time: 0.0195 memory: 21539 grad_norm: 4.3215 loss: 1.8478 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.8478 2023/03/08 17:07:14 - mmengine - INFO - Epoch(train) [22][380/660] lr: 1.0000e-03 eta: 1:46:23 time: 0.3368 data_time: 0.0260 memory: 21539 grad_norm: 4.3086 loss: 1.7660 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.7660 2023/03/08 17:07:21 - mmengine - INFO - Epoch(train) [22][400/660] lr: 1.0000e-03 eta: 1:46:16 time: 0.3330 data_time: 0.0207 memory: 21539 grad_norm: 4.3915 loss: 1.9027 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 1.9027 2023/03/08 17:07:28 - mmengine - INFO - Epoch(train) [22][420/660] lr: 1.0000e-03 eta: 1:46:09 time: 0.3324 data_time: 0.0220 memory: 21539 grad_norm: 4.3555 loss: 1.8056 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.8056 2023/03/08 17:07:34 - mmengine - INFO - Epoch(train) [22][440/660] lr: 1.0000e-03 eta: 1:46:02 time: 0.3298 data_time: 0.0216 memory: 21539 grad_norm: 4.3176 loss: 1.8896 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8896 2023/03/08 17:07:41 - mmengine - INFO - Epoch(train) [22][460/660] lr: 1.0000e-03 eta: 1:45:55 time: 0.3341 data_time: 0.0210 memory: 21539 grad_norm: 4.3877 loss: 1.8044 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8044 2023/03/08 17:07:47 - mmengine - INFO - Epoch(train) [22][480/660] lr: 1.0000e-03 eta: 1:45:48 time: 0.3312 data_time: 0.0212 memory: 21539 grad_norm: 4.3335 loss: 1.7722 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7722 2023/03/08 17:07:54 - mmengine - INFO - Epoch(train) [22][500/660] lr: 1.0000e-03 eta: 1:45:41 time: 0.3351 data_time: 0.0221 memory: 21539 grad_norm: 4.3404 loss: 1.9113 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.9113 2023/03/08 17:08:01 - mmengine - INFO - Epoch(train) [22][520/660] lr: 1.0000e-03 eta: 1:45:34 time: 0.3328 data_time: 0.0212 memory: 21539 grad_norm: 4.3644 loss: 1.8048 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8048 2023/03/08 17:08:07 - mmengine - INFO - Epoch(train) [22][540/660] lr: 1.0000e-03 eta: 1:45:27 time: 0.3287 data_time: 0.0224 memory: 21539 grad_norm: 4.3824 loss: 1.8449 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.8449 2023/03/08 17:08:14 - mmengine - INFO - Epoch(train) [22][560/660] lr: 1.0000e-03 eta: 1:45:20 time: 0.3281 data_time: 0.0229 memory: 21539 grad_norm: 4.3507 loss: 1.8517 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8517 2023/03/08 17:08:21 - mmengine - INFO - Epoch(train) [22][580/660] lr: 1.0000e-03 eta: 1:45:13 time: 0.3313 data_time: 0.0224 memory: 21539 grad_norm: 4.3605 loss: 1.7751 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.7751 2023/03/08 17:08:27 - mmengine - INFO - Epoch(train) [22][600/660] lr: 1.0000e-03 eta: 1:45:06 time: 0.3283 data_time: 0.0239 memory: 21539 grad_norm: 4.3826 loss: 1.8146 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.8146 2023/03/08 17:08:34 - mmengine - INFO - Epoch(train) [22][620/660] lr: 1.0000e-03 eta: 1:44:59 time: 0.3321 data_time: 0.0228 memory: 21539 grad_norm: 4.3298 loss: 1.9962 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9962 2023/03/08 17:08:40 - mmengine - INFO - Epoch(train) [22][640/660] lr: 1.0000e-03 eta: 1:44:52 time: 0.3281 data_time: 0.0222 memory: 21539 grad_norm: 4.4088 loss: 1.8516 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.8516 2023/03/08 17:08:47 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:08:47 - mmengine - INFO - Epoch(train) [22][660/660] lr: 1.0000e-03 eta: 1:44:44 time: 0.3266 data_time: 0.0208 memory: 21539 grad_norm: 4.4215 loss: 1.7340 top1_acc: 0.6667 top5_acc: 0.8889 loss_cls: 1.7340 2023/03/08 17:08:55 - mmengine - INFO - Epoch(train) [23][ 20/660] lr: 1.0000e-03 eta: 1:44:40 time: 0.4179 data_time: 0.0915 memory: 21539 grad_norm: 4.3743 loss: 1.8654 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.8654 2023/03/08 17:09:02 - mmengine - INFO - Epoch(train) [23][ 40/660] lr: 1.0000e-03 eta: 1:44:33 time: 0.3313 data_time: 0.0210 memory: 21539 grad_norm: 4.2868 loss: 1.8490 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.8490 2023/03/08 17:09:09 - mmengine - INFO - Epoch(train) [23][ 60/660] lr: 1.0000e-03 eta: 1:44:26 time: 0.3365 data_time: 0.0210 memory: 21539 grad_norm: 4.3457 loss: 1.8192 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.8192 2023/03/08 17:09:15 - mmengine - INFO - Epoch(train) [23][ 80/660] lr: 1.0000e-03 eta: 1:44:19 time: 0.3310 data_time: 0.0198 memory: 21539 grad_norm: 4.2997 loss: 1.7698 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7698 2023/03/08 17:09:22 - mmengine - INFO - Epoch(train) [23][100/660] lr: 1.0000e-03 eta: 1:44:12 time: 0.3360 data_time: 0.0251 memory: 21539 grad_norm: 4.3227 loss: 1.8326 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 1.8326 2023/03/08 17:09:29 - mmengine - INFO - Epoch(train) [23][120/660] lr: 1.0000e-03 eta: 1:44:05 time: 0.3334 data_time: 0.0213 memory: 21539 grad_norm: 4.3217 loss: 1.7139 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7139 2023/03/08 17:09:35 - mmengine - INFO - Epoch(train) [23][140/660] lr: 1.0000e-03 eta: 1:43:58 time: 0.3333 data_time: 0.0206 memory: 21539 grad_norm: 4.3602 loss: 1.9343 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.9343 2023/03/08 17:09:42 - mmengine - INFO - Epoch(train) [23][160/660] lr: 1.0000e-03 eta: 1:43:51 time: 0.3308 data_time: 0.0213 memory: 21539 grad_norm: 4.3426 loss: 1.7498 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7498 2023/03/08 17:09:49 - mmengine - INFO - Epoch(train) [23][180/660] lr: 1.0000e-03 eta: 1:43:44 time: 0.3329 data_time: 0.0207 memory: 21539 grad_norm: 4.3392 loss: 1.8764 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.8764 2023/03/08 17:09:55 - mmengine - INFO - Epoch(train) [23][200/660] lr: 1.0000e-03 eta: 1:43:37 time: 0.3340 data_time: 0.0219 memory: 21539 grad_norm: 4.3683 loss: 1.7995 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.7995 2023/03/08 17:10:02 - mmengine - INFO - Epoch(train) [23][220/660] lr: 1.0000e-03 eta: 1:43:30 time: 0.3323 data_time: 0.0218 memory: 21539 grad_norm: 4.4375 loss: 1.8238 top1_acc: 0.4062 top5_acc: 0.8750 loss_cls: 1.8238 2023/03/08 17:10:09 - mmengine - INFO - Epoch(train) [23][240/660] lr: 1.0000e-03 eta: 1:43:23 time: 0.3308 data_time: 0.0217 memory: 21539 grad_norm: 4.3488 loss: 1.6571 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.6571 2023/03/08 17:10:15 - mmengine - INFO - Epoch(train) [23][260/660] lr: 1.0000e-03 eta: 1:43:16 time: 0.3320 data_time: 0.0216 memory: 21539 grad_norm: 4.3441 loss: 1.7665 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7665 2023/03/08 17:10:23 - mmengine - INFO - Epoch(train) [23][280/660] lr: 1.0000e-03 eta: 1:43:10 time: 0.3662 data_time: 0.0215 memory: 21539 grad_norm: 4.4934 loss: 1.7506 top1_acc: 0.3438 top5_acc: 0.7812 loss_cls: 1.7506 2023/03/08 17:10:29 - mmengine - INFO - Epoch(train) [23][300/660] lr: 1.0000e-03 eta: 1:43:03 time: 0.3329 data_time: 0.0222 memory: 21539 grad_norm: 4.3562 loss: 1.8696 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.8696 2023/03/08 17:10:36 - mmengine - INFO - Epoch(train) [23][320/660] lr: 1.0000e-03 eta: 1:42:56 time: 0.3333 data_time: 0.0228 memory: 21539 grad_norm: 4.3454 loss: 1.7233 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7233 2023/03/08 17:10:43 - mmengine - INFO - Epoch(train) [23][340/660] lr: 1.0000e-03 eta: 1:42:49 time: 0.3370 data_time: 0.0222 memory: 21539 grad_norm: 4.3791 loss: 1.6873 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6873 2023/03/08 17:10:49 - mmengine - INFO - Epoch(train) [23][360/660] lr: 1.0000e-03 eta: 1:42:42 time: 0.3291 data_time: 0.0227 memory: 21539 grad_norm: 4.3522 loss: 1.7444 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7444 2023/03/08 17:10:56 - mmengine - INFO - Epoch(train) [23][380/660] lr: 1.0000e-03 eta: 1:42:35 time: 0.3302 data_time: 0.0228 memory: 21539 grad_norm: 4.3881 loss: 1.8815 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 1.8815 2023/03/08 17:11:02 - mmengine - INFO - Epoch(train) [23][400/660] lr: 1.0000e-03 eta: 1:42:28 time: 0.3291 data_time: 0.0225 memory: 21539 grad_norm: 4.3767 loss: 1.7201 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7201 2023/03/08 17:11:09 - mmengine - INFO - Epoch(train) [23][420/660] lr: 1.0000e-03 eta: 1:42:21 time: 0.3335 data_time: 0.0231 memory: 21539 grad_norm: 4.4874 loss: 1.8653 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8653 2023/03/08 17:11:16 - mmengine - INFO - Epoch(train) [23][440/660] lr: 1.0000e-03 eta: 1:42:14 time: 0.3287 data_time: 0.0227 memory: 21539 grad_norm: 4.4254 loss: 1.7229 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.7229 2023/03/08 17:11:22 - mmengine - INFO - Epoch(train) [23][460/660] lr: 1.0000e-03 eta: 1:42:07 time: 0.3285 data_time: 0.0217 memory: 21539 grad_norm: 4.3900 loss: 1.9066 top1_acc: 0.4062 top5_acc: 0.8750 loss_cls: 1.9066 2023/03/08 17:11:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:11:29 - mmengine - INFO - Epoch(train) [23][480/660] lr: 1.0000e-03 eta: 1:41:59 time: 0.3270 data_time: 0.0229 memory: 21539 grad_norm: 4.4137 loss: 1.7444 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7444 2023/03/08 17:11:35 - mmengine - INFO - Epoch(train) [23][500/660] lr: 1.0000e-03 eta: 1:41:52 time: 0.3296 data_time: 0.0231 memory: 21539 grad_norm: 4.3950 loss: 1.7995 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7995 2023/03/08 17:11:42 - mmengine - INFO - Epoch(train) [23][520/660] lr: 1.0000e-03 eta: 1:41:45 time: 0.3311 data_time: 0.0263 memory: 21539 grad_norm: 4.3809 loss: 1.7739 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 1.7739 2023/03/08 17:11:48 - mmengine - INFO - Epoch(train) [23][540/660] lr: 1.0000e-03 eta: 1:41:38 time: 0.3286 data_time: 0.0244 memory: 21539 grad_norm: 4.4259 loss: 1.7871 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7871 2023/03/08 17:11:55 - mmengine - INFO - Epoch(train) [23][560/660] lr: 1.0000e-03 eta: 1:41:31 time: 0.3253 data_time: 0.0228 memory: 21539 grad_norm: 4.4778 loss: 1.7723 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.7723 2023/03/08 17:12:02 - mmengine - INFO - Epoch(train) [23][580/660] lr: 1.0000e-03 eta: 1:41:24 time: 0.3306 data_time: 0.0225 memory: 21539 grad_norm: 4.3645 loss: 1.6758 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6758 2023/03/08 17:12:08 - mmengine - INFO - Epoch(train) [23][600/660] lr: 1.0000e-03 eta: 1:41:17 time: 0.3256 data_time: 0.0229 memory: 21539 grad_norm: 4.4765 loss: 1.9299 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.9299 2023/03/08 17:12:15 - mmengine - INFO - Epoch(train) [23][620/660] lr: 1.0000e-03 eta: 1:41:10 time: 0.3305 data_time: 0.0233 memory: 21539 grad_norm: 4.3742 loss: 1.8330 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8330 2023/03/08 17:12:21 - mmengine - INFO - Epoch(train) [23][640/660] lr: 1.0000e-03 eta: 1:41:03 time: 0.3281 data_time: 0.0230 memory: 21539 grad_norm: 4.4225 loss: 1.7262 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7262 2023/03/08 17:12:28 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:12:28 - mmengine - INFO - Epoch(train) [23][660/660] lr: 1.0000e-03 eta: 1:40:56 time: 0.3185 data_time: 0.0209 memory: 21539 grad_norm: 4.4695 loss: 1.8861 top1_acc: 0.4815 top5_acc: 0.7407 loss_cls: 1.8861 2023/03/08 17:12:36 - mmengine - INFO - Epoch(train) [24][ 20/660] lr: 1.0000e-03 eta: 1:40:51 time: 0.4211 data_time: 0.0960 memory: 21539 grad_norm: 4.4250 loss: 1.7057 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7057 2023/03/08 17:12:43 - mmengine - INFO - Epoch(train) [24][ 40/660] lr: 1.0000e-03 eta: 1:40:44 time: 0.3390 data_time: 0.0199 memory: 21539 grad_norm: 4.3541 loss: 1.6557 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6557 2023/03/08 17:12:50 - mmengine - INFO - Epoch(train) [24][ 60/660] lr: 1.0000e-03 eta: 1:40:37 time: 0.3433 data_time: 0.0205 memory: 21539 grad_norm: 4.4242 loss: 1.7683 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.7683 2023/03/08 17:12:56 - mmengine - INFO - Epoch(train) [24][ 80/660] lr: 1.0000e-03 eta: 1:40:30 time: 0.3355 data_time: 0.0206 memory: 21539 grad_norm: 4.4562 loss: 1.7709 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7709 2023/03/08 17:13:03 - mmengine - INFO - Epoch(train) [24][100/660] lr: 1.0000e-03 eta: 1:40:24 time: 0.3486 data_time: 0.0216 memory: 21539 grad_norm: 4.3193 loss: 1.7445 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7445 2023/03/08 17:13:10 - mmengine - INFO - Epoch(train) [24][120/660] lr: 1.0000e-03 eta: 1:40:17 time: 0.3342 data_time: 0.0207 memory: 21539 grad_norm: 4.4304 loss: 1.7722 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.7722 2023/03/08 17:13:17 - mmengine - INFO - Epoch(train) [24][140/660] lr: 1.0000e-03 eta: 1:40:10 time: 0.3427 data_time: 0.0215 memory: 21539 grad_norm: 4.4445 loss: 1.8744 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8744 2023/03/08 17:13:24 - mmengine - INFO - Epoch(train) [24][160/660] lr: 1.0000e-03 eta: 1:40:03 time: 0.3355 data_time: 0.0209 memory: 21539 grad_norm: 4.3570 loss: 1.6833 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.6833 2023/03/08 17:13:31 - mmengine - INFO - Epoch(train) [24][180/660] lr: 1.0000e-03 eta: 1:39:56 time: 0.3424 data_time: 0.0213 memory: 21539 grad_norm: 4.4843 loss: 1.8546 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.8546 2023/03/08 17:13:37 - mmengine - INFO - Epoch(train) [24][200/660] lr: 1.0000e-03 eta: 1:39:50 time: 0.3429 data_time: 0.0240 memory: 21539 grad_norm: 4.3183 loss: 1.8920 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8920 2023/03/08 17:13:44 - mmengine - INFO - Epoch(train) [24][220/660] lr: 1.0000e-03 eta: 1:39:43 time: 0.3433 data_time: 0.0228 memory: 21539 grad_norm: 4.3854 loss: 1.8435 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8435 2023/03/08 17:13:51 - mmengine - INFO - Epoch(train) [24][240/660] lr: 1.0000e-03 eta: 1:39:36 time: 0.3339 data_time: 0.0203 memory: 21539 grad_norm: 4.4855 loss: 1.9021 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.9021 2023/03/08 17:13:58 - mmengine - INFO - Epoch(train) [24][260/660] lr: 1.0000e-03 eta: 1:39:29 time: 0.3439 data_time: 0.0208 memory: 21539 grad_norm: 4.3971 loss: 1.7061 top1_acc: 0.5000 top5_acc: 0.9062 loss_cls: 1.7061 2023/03/08 17:14:05 - mmengine - INFO - Epoch(train) [24][280/660] lr: 1.0000e-03 eta: 1:39:22 time: 0.3364 data_time: 0.0204 memory: 21539 grad_norm: 4.4639 loss: 1.7592 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7592 2023/03/08 17:14:11 - mmengine - INFO - Epoch(train) [24][300/660] lr: 1.0000e-03 eta: 1:39:16 time: 0.3402 data_time: 0.0219 memory: 21539 grad_norm: 4.3565 loss: 1.8101 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.8101 2023/03/08 17:14:18 - mmengine - INFO - Epoch(train) [24][320/660] lr: 1.0000e-03 eta: 1:39:09 time: 0.3336 data_time: 0.0208 memory: 21539 grad_norm: 4.4279 loss: 1.7340 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.7340 2023/03/08 17:14:25 - mmengine - INFO - Epoch(train) [24][340/660] lr: 1.0000e-03 eta: 1:39:02 time: 0.3394 data_time: 0.0217 memory: 21539 grad_norm: 4.4999 loss: 1.7494 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7494 2023/03/08 17:14:32 - mmengine - INFO - Epoch(train) [24][360/660] lr: 1.0000e-03 eta: 1:38:55 time: 0.3374 data_time: 0.0209 memory: 21539 grad_norm: 4.4233 loss: 1.7105 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.7105 2023/03/08 17:14:38 - mmengine - INFO - Epoch(train) [24][380/660] lr: 1.0000e-03 eta: 1:38:48 time: 0.3436 data_time: 0.0215 memory: 21539 grad_norm: 4.4554 loss: 1.6737 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6737 2023/03/08 17:14:45 - mmengine - INFO - Epoch(train) [24][400/660] lr: 1.0000e-03 eta: 1:38:41 time: 0.3342 data_time: 0.0203 memory: 21539 grad_norm: 4.4274 loss: 1.8498 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 1.8498 2023/03/08 17:14:52 - mmengine - INFO - Epoch(train) [24][420/660] lr: 1.0000e-03 eta: 1:38:35 time: 0.3400 data_time: 0.0207 memory: 21539 grad_norm: 4.4011 loss: 1.7680 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.7680 2023/03/08 17:14:59 - mmengine - INFO - Epoch(train) [24][440/660] lr: 1.0000e-03 eta: 1:38:28 time: 0.3543 data_time: 0.0397 memory: 21539 grad_norm: 4.4373 loss: 1.7510 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 1.7510 2023/03/08 17:15:06 - mmengine - INFO - Epoch(train) [24][460/660] lr: 1.0000e-03 eta: 1:38:21 time: 0.3398 data_time: 0.0207 memory: 21539 grad_norm: 4.4183 loss: 1.6242 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6242 2023/03/08 17:15:13 - mmengine - INFO - Epoch(train) [24][480/660] lr: 1.0000e-03 eta: 1:38:14 time: 0.3411 data_time: 0.0244 memory: 21539 grad_norm: 4.4260 loss: 1.8079 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.8079 2023/03/08 17:15:19 - mmengine - INFO - Epoch(train) [24][500/660] lr: 1.0000e-03 eta: 1:38:08 time: 0.3397 data_time: 0.0201 memory: 21539 grad_norm: 4.4719 loss: 1.8647 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8647 2023/03/08 17:15:26 - mmengine - INFO - Epoch(train) [24][520/660] lr: 1.0000e-03 eta: 1:38:01 time: 0.3366 data_time: 0.0196 memory: 21539 grad_norm: 4.4933 loss: 1.8017 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.8017 2023/03/08 17:15:33 - mmengine - INFO - Epoch(train) [24][540/660] lr: 1.0000e-03 eta: 1:37:54 time: 0.3401 data_time: 0.0207 memory: 21539 grad_norm: 4.4005 loss: 1.8068 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.8068 2023/03/08 17:15:40 - mmengine - INFO - Epoch(train) [24][560/660] lr: 1.0000e-03 eta: 1:37:47 time: 0.3331 data_time: 0.0200 memory: 21539 grad_norm: 4.4608 loss: 1.8159 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8159 2023/03/08 17:15:47 - mmengine - INFO - Epoch(train) [24][580/660] lr: 1.0000e-03 eta: 1:37:40 time: 0.3437 data_time: 0.0214 memory: 21539 grad_norm: 4.4900 loss: 1.7422 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.7422 2023/03/08 17:15:54 - mmengine - INFO - Epoch(train) [24][600/660] lr: 1.0000e-03 eta: 1:37:34 time: 0.3767 data_time: 0.0199 memory: 21539 grad_norm: 4.6083 loss: 1.8886 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8886 2023/03/08 17:16:01 - mmengine - INFO - Epoch(train) [24][620/660] lr: 1.0000e-03 eta: 1:37:28 time: 0.3396 data_time: 0.0219 memory: 21539 grad_norm: 4.5429 loss: 1.7483 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7483 2023/03/08 17:16:08 - mmengine - INFO - Epoch(train) [24][640/660] lr: 1.0000e-03 eta: 1:37:21 time: 0.3332 data_time: 0.0197 memory: 21539 grad_norm: 4.3634 loss: 1.6373 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6373 2023/03/08 17:16:14 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:16:14 - mmengine - INFO - Epoch(train) [24][660/660] lr: 1.0000e-03 eta: 1:37:14 time: 0.3285 data_time: 0.0191 memory: 21539 grad_norm: 4.5295 loss: 1.8242 top1_acc: 0.5556 top5_acc: 0.9630 loss_cls: 1.8242 2023/03/08 17:16:14 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/03/08 17:16:23 - mmengine - INFO - Epoch(train) [25][ 20/660] lr: 1.0000e-03 eta: 1:37:08 time: 0.4028 data_time: 0.0879 memory: 21539 grad_norm: 4.4436 loss: 1.7800 top1_acc: 0.3438 top5_acc: 0.8438 loss_cls: 1.7800 2023/03/08 17:16:30 - mmengine - INFO - Epoch(train) [25][ 40/660] lr: 1.0000e-03 eta: 1:37:01 time: 0.3401 data_time: 0.0211 memory: 21539 grad_norm: 4.4753 loss: 1.7292 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7292 2023/03/08 17:16:37 - mmengine - INFO - Epoch(train) [25][ 60/660] lr: 1.0000e-03 eta: 1:36:54 time: 0.3375 data_time: 0.0211 memory: 21539 grad_norm: 4.3558 loss: 1.6959 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.6959 2023/03/08 17:16:43 - mmengine - INFO - Epoch(train) [25][ 80/660] lr: 1.0000e-03 eta: 1:36:47 time: 0.3336 data_time: 0.0217 memory: 21539 grad_norm: 4.4713 loss: 1.6892 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6892 2023/03/08 17:16:50 - mmengine - INFO - Epoch(train) [25][100/660] lr: 1.0000e-03 eta: 1:36:41 time: 0.3371 data_time: 0.0224 memory: 21539 grad_norm: 4.4804 loss: 1.6608 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6608 2023/03/08 17:16:57 - mmengine - INFO - Epoch(train) [25][120/660] lr: 1.0000e-03 eta: 1:36:34 time: 0.3358 data_time: 0.0220 memory: 21539 grad_norm: 4.4375 loss: 1.7442 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7442 2023/03/08 17:17:04 - mmengine - INFO - Epoch(train) [25][140/660] lr: 1.0000e-03 eta: 1:36:27 time: 0.3423 data_time: 0.0212 memory: 21539 grad_norm: 4.5329 loss: 1.7042 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 1.7042 2023/03/08 17:17:10 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:17:10 - mmengine - INFO - Epoch(train) [25][160/660] lr: 1.0000e-03 eta: 1:36:20 time: 0.3314 data_time: 0.0216 memory: 21539 grad_norm: 4.3707 loss: 1.6870 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.6870 2023/03/08 17:17:17 - mmengine - INFO - Epoch(train) [25][180/660] lr: 1.0000e-03 eta: 1:36:13 time: 0.3348 data_time: 0.0212 memory: 21539 grad_norm: 4.5405 loss: 1.7464 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7464 2023/03/08 17:17:24 - mmengine - INFO - Epoch(train) [25][200/660] lr: 1.0000e-03 eta: 1:36:06 time: 0.3300 data_time: 0.0213 memory: 21539 grad_norm: 4.4796 loss: 1.7689 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.7689 2023/03/08 17:17:30 - mmengine - INFO - Epoch(train) [25][220/660] lr: 1.0000e-03 eta: 1:35:59 time: 0.3377 data_time: 0.0221 memory: 21539 grad_norm: 4.5187 loss: 1.8156 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.8156 2023/03/08 17:17:37 - mmengine - INFO - Epoch(train) [25][240/660] lr: 1.0000e-03 eta: 1:35:52 time: 0.3308 data_time: 0.0221 memory: 21539 grad_norm: 4.4931 loss: 1.8959 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.8959 2023/03/08 17:17:44 - mmengine - INFO - Epoch(train) [25][260/660] lr: 1.0000e-03 eta: 1:35:45 time: 0.3404 data_time: 0.0227 memory: 21539 grad_norm: 4.4623 loss: 1.6641 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6641 2023/03/08 17:17:51 - mmengine - INFO - Epoch(train) [25][280/660] lr: 1.0000e-03 eta: 1:35:38 time: 0.3311 data_time: 0.0218 memory: 21539 grad_norm: 4.5146 loss: 1.6342 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6342 2023/03/08 17:17:57 - mmengine - INFO - Epoch(train) [25][300/660] lr: 1.0000e-03 eta: 1:35:32 time: 0.3379 data_time: 0.0221 memory: 21539 grad_norm: 4.5486 loss: 1.7681 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.7681 2023/03/08 17:18:04 - mmengine - INFO - Epoch(train) [25][320/660] lr: 1.0000e-03 eta: 1:35:25 time: 0.3343 data_time: 0.0266 memory: 21539 grad_norm: 4.5493 loss: 1.7173 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7173 2023/03/08 17:18:11 - mmengine - INFO - Epoch(train) [25][340/660] lr: 1.0000e-03 eta: 1:35:18 time: 0.3378 data_time: 0.0220 memory: 21539 grad_norm: 4.4740 loss: 1.6891 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6891 2023/03/08 17:18:17 - mmengine - INFO - Epoch(train) [25][360/660] lr: 1.0000e-03 eta: 1:35:11 time: 0.3313 data_time: 0.0219 memory: 21539 grad_norm: 4.4512 loss: 1.6476 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.6476 2023/03/08 17:18:24 - mmengine - INFO - Epoch(train) [25][380/660] lr: 1.0000e-03 eta: 1:35:04 time: 0.3376 data_time: 0.0210 memory: 21539 grad_norm: 4.4841 loss: 1.7420 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7420 2023/03/08 17:18:31 - mmengine - INFO - Epoch(train) [25][400/660] lr: 1.0000e-03 eta: 1:34:57 time: 0.3351 data_time: 0.0217 memory: 21539 grad_norm: 4.4651 loss: 1.5875 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5875 2023/03/08 17:18:38 - mmengine - INFO - Epoch(train) [25][420/660] lr: 1.0000e-03 eta: 1:34:50 time: 0.3364 data_time: 0.0222 memory: 21539 grad_norm: 4.5225 loss: 1.8433 top1_acc: 0.3438 top5_acc: 0.5938 loss_cls: 1.8433 2023/03/08 17:18:44 - mmengine - INFO - Epoch(train) [25][440/660] lr: 1.0000e-03 eta: 1:34:43 time: 0.3334 data_time: 0.0229 memory: 21539 grad_norm: 4.4714 loss: 1.7895 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7895 2023/03/08 17:18:51 - mmengine - INFO - Epoch(train) [25][460/660] lr: 1.0000e-03 eta: 1:34:36 time: 0.3361 data_time: 0.0215 memory: 21539 grad_norm: 4.5500 loss: 1.8183 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8183 2023/03/08 17:18:58 - mmengine - INFO - Epoch(train) [25][480/660] lr: 1.0000e-03 eta: 1:34:30 time: 0.3346 data_time: 0.0221 memory: 21539 grad_norm: 4.4698 loss: 1.7158 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7158 2023/03/08 17:19:04 - mmengine - INFO - Epoch(train) [25][500/660] lr: 1.0000e-03 eta: 1:34:23 time: 0.3365 data_time: 0.0218 memory: 21539 grad_norm: 4.4771 loss: 1.7178 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.7178 2023/03/08 17:19:11 - mmengine - INFO - Epoch(train) [25][520/660] lr: 1.0000e-03 eta: 1:34:16 time: 0.3351 data_time: 0.0225 memory: 21539 grad_norm: 4.4782 loss: 1.6206 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.6206 2023/03/08 17:19:18 - mmengine - INFO - Epoch(train) [25][540/660] lr: 1.0000e-03 eta: 1:34:09 time: 0.3363 data_time: 0.0221 memory: 21539 grad_norm: 4.6455 loss: 1.8456 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.8456 2023/03/08 17:19:25 - mmengine - INFO - Epoch(train) [25][560/660] lr: 1.0000e-03 eta: 1:34:02 time: 0.3361 data_time: 0.0214 memory: 21539 grad_norm: 4.3885 loss: 1.6802 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6802 2023/03/08 17:19:31 - mmengine - INFO - Epoch(train) [25][580/660] lr: 1.0000e-03 eta: 1:33:55 time: 0.3369 data_time: 0.0216 memory: 21539 grad_norm: 4.4243 loss: 1.6752 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6752 2023/03/08 17:19:38 - mmengine - INFO - Epoch(train) [25][600/660] lr: 1.0000e-03 eta: 1:33:48 time: 0.3315 data_time: 0.0216 memory: 21539 grad_norm: 4.5800 loss: 1.7896 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.7896 2023/03/08 17:19:46 - mmengine - INFO - Epoch(train) [25][620/660] lr: 1.0000e-03 eta: 1:33:42 time: 0.3834 data_time: 0.0207 memory: 21539 grad_norm: 4.6133 loss: 1.8307 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.8307 2023/03/08 17:19:52 - mmengine - INFO - Epoch(train) [25][640/660] lr: 1.0000e-03 eta: 1:33:35 time: 0.3345 data_time: 0.0212 memory: 21539 grad_norm: 4.5146 loss: 1.8321 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8321 2023/03/08 17:19:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:19:59 - mmengine - INFO - Epoch(train) [25][660/660] lr: 1.0000e-03 eta: 1:33:28 time: 0.3278 data_time: 0.0200 memory: 21539 grad_norm: 4.5169 loss: 1.7352 top1_acc: 0.4074 top5_acc: 0.7407 loss_cls: 1.7352 2023/03/08 17:20:04 - mmengine - INFO - Epoch(val) [25][20/97] eta: 0:00:21 time: 0.2743 data_time: 0.1602 memory: 3261 2023/03/08 17:20:07 - mmengine - INFO - Epoch(val) [25][40/97] eta: 0:00:12 time: 0.1539 data_time: 0.0471 memory: 3261 2023/03/08 17:20:11 - mmengine - INFO - Epoch(val) [25][60/97] eta: 0:00:07 time: 0.1880 data_time: 0.0801 memory: 3261 2023/03/08 17:20:14 - mmengine - INFO - Epoch(val) [25][80/97] eta: 0:00:03 time: 0.1514 data_time: 0.0428 memory: 3261 2023/03/08 17:20:18 - mmengine - INFO - Epoch(val) [25][97/97] acc/top1: 0.3409 acc/top5: 0.6517 acc/mean1: 0.2753 2023/03/08 17:20:18 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_exp1/best_acc/top1_epoch_20.pth is removed 2023/03/08 17:20:19 - mmengine - INFO - The best checkpoint with 0.3409 acc/top1 at 25 epoch is saved to best_acc/top1_epoch_25.pth. 2023/03/08 17:20:27 - mmengine - INFO - Epoch(train) [26][ 20/660] lr: 1.0000e-03 eta: 1:33:23 time: 0.4050 data_time: 0.0839 memory: 21539 grad_norm: 4.5750 loss: 1.8466 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8466 2023/03/08 17:20:34 - mmengine - INFO - Epoch(train) [26][ 40/660] lr: 1.0000e-03 eta: 1:33:16 time: 0.3361 data_time: 0.0219 memory: 21539 grad_norm: 4.4826 loss: 1.6709 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6709 2023/03/08 17:20:41 - mmengine - INFO - Epoch(train) [26][ 60/660] lr: 1.0000e-03 eta: 1:33:09 time: 0.3376 data_time: 0.0220 memory: 21539 grad_norm: 4.4754 loss: 1.7569 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7569 2023/03/08 17:20:47 - mmengine - INFO - Epoch(train) [26][ 80/660] lr: 1.0000e-03 eta: 1:33:02 time: 0.3380 data_time: 0.0211 memory: 21539 grad_norm: 4.5565 loss: 1.8224 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.8224 2023/03/08 17:20:54 - mmengine - INFO - Epoch(train) [26][100/660] lr: 1.0000e-03 eta: 1:32:56 time: 0.3585 data_time: 0.0423 memory: 21539 grad_norm: 4.4557 loss: 1.7653 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7653 2023/03/08 17:21:01 - mmengine - INFO - Epoch(train) [26][120/660] lr: 1.0000e-03 eta: 1:32:49 time: 0.3328 data_time: 0.0212 memory: 21539 grad_norm: 4.5345 loss: 1.5893 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.5893 2023/03/08 17:21:08 - mmengine - INFO - Epoch(train) [26][140/660] lr: 1.0000e-03 eta: 1:32:42 time: 0.3357 data_time: 0.0217 memory: 21539 grad_norm: 4.5478 loss: 1.6956 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6956 2023/03/08 17:21:15 - mmengine - INFO - Epoch(train) [26][160/660] lr: 1.0000e-03 eta: 1:32:35 time: 0.3349 data_time: 0.0216 memory: 21539 grad_norm: 4.5088 loss: 1.6343 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6343 2023/03/08 17:21:21 - mmengine - INFO - Epoch(train) [26][180/660] lr: 1.0000e-03 eta: 1:32:28 time: 0.3354 data_time: 0.0204 memory: 21539 grad_norm: 4.5353 loss: 1.8291 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.8291 2023/03/08 17:21:28 - mmengine - INFO - Epoch(train) [26][200/660] lr: 1.0000e-03 eta: 1:32:21 time: 0.3312 data_time: 0.0202 memory: 21539 grad_norm: 4.5680 loss: 1.7730 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 1.7730 2023/03/08 17:21:35 - mmengine - INFO - Epoch(train) [26][220/660] lr: 1.0000e-03 eta: 1:32:14 time: 0.3419 data_time: 0.0205 memory: 21539 grad_norm: 4.4851 loss: 1.7469 top1_acc: 0.5000 top5_acc: 0.9062 loss_cls: 1.7469 2023/03/08 17:21:41 - mmengine - INFO - Epoch(train) [26][240/660] lr: 1.0000e-03 eta: 1:32:08 time: 0.3318 data_time: 0.0215 memory: 21539 grad_norm: 4.4920 loss: 1.7685 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7685 2023/03/08 17:21:48 - mmengine - INFO - Epoch(train) [26][260/660] lr: 1.0000e-03 eta: 1:32:01 time: 0.3374 data_time: 0.0217 memory: 21539 grad_norm: 4.5093 loss: 1.7299 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.7299 2023/03/08 17:21:55 - mmengine - INFO - Epoch(train) [26][280/660] lr: 1.0000e-03 eta: 1:31:54 time: 0.3382 data_time: 0.0218 memory: 21539 grad_norm: 4.5502 loss: 1.6751 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.6751 2023/03/08 17:22:02 - mmengine - INFO - Epoch(train) [26][300/660] lr: 1.0000e-03 eta: 1:31:47 time: 0.3381 data_time: 0.0258 memory: 21539 grad_norm: 4.5000 loss: 1.7993 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7993 2023/03/08 17:22:08 - mmengine - INFO - Epoch(train) [26][320/660] lr: 1.0000e-03 eta: 1:31:40 time: 0.3298 data_time: 0.0217 memory: 21539 grad_norm: 4.5218 loss: 1.8073 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8073 2023/03/08 17:22:15 - mmengine - INFO - Epoch(train) [26][340/660] lr: 1.0000e-03 eta: 1:31:33 time: 0.3342 data_time: 0.0218 memory: 21539 grad_norm: 4.6018 loss: 1.7955 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 1.7955 2023/03/08 17:22:22 - mmengine - INFO - Epoch(train) [26][360/660] lr: 1.0000e-03 eta: 1:31:26 time: 0.3307 data_time: 0.0213 memory: 21539 grad_norm: 4.5440 loss: 1.7249 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.7249 2023/03/08 17:22:28 - mmengine - INFO - Epoch(train) [26][380/660] lr: 1.0000e-03 eta: 1:31:19 time: 0.3385 data_time: 0.0208 memory: 21539 grad_norm: 4.5136 loss: 1.6160 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.6160 2023/03/08 17:22:35 - mmengine - INFO - Epoch(train) [26][400/660] lr: 1.0000e-03 eta: 1:31:12 time: 0.3296 data_time: 0.0206 memory: 21539 grad_norm: 4.4809 loss: 1.8037 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.8037 2023/03/08 17:22:42 - mmengine - INFO - Epoch(train) [26][420/660] lr: 1.0000e-03 eta: 1:31:05 time: 0.3322 data_time: 0.0211 memory: 21539 grad_norm: 4.5800 loss: 1.6119 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6119 2023/03/08 17:22:48 - mmengine - INFO - Epoch(train) [26][440/660] lr: 1.0000e-03 eta: 1:30:58 time: 0.3359 data_time: 0.0211 memory: 21539 grad_norm: 4.5244 loss: 1.8183 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.8183 2023/03/08 17:22:55 - mmengine - INFO - Epoch(train) [26][460/660] lr: 1.0000e-03 eta: 1:30:52 time: 0.3338 data_time: 0.0213 memory: 21539 grad_norm: 4.6735 loss: 1.7631 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7631 2023/03/08 17:23:02 - mmengine - INFO - Epoch(train) [26][480/660] lr: 1.0000e-03 eta: 1:30:45 time: 0.3308 data_time: 0.0233 memory: 21539 grad_norm: 4.5661 loss: 1.7475 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7475 2023/03/08 17:23:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:23:08 - mmengine - INFO - Epoch(train) [26][500/660] lr: 1.0000e-03 eta: 1:30:38 time: 0.3317 data_time: 0.0209 memory: 21539 grad_norm: 4.5480 loss: 1.7687 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7687 2023/03/08 17:23:15 - mmengine - INFO - Epoch(train) [26][520/660] lr: 1.0000e-03 eta: 1:30:31 time: 0.3294 data_time: 0.0209 memory: 21539 grad_norm: 4.4650 loss: 1.7905 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7905 2023/03/08 17:23:21 - mmengine - INFO - Epoch(train) [26][540/660] lr: 1.0000e-03 eta: 1:30:24 time: 0.3322 data_time: 0.0207 memory: 21539 grad_norm: 4.5129 loss: 1.5236 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.5236 2023/03/08 17:23:28 - mmengine - INFO - Epoch(train) [26][560/660] lr: 1.0000e-03 eta: 1:30:17 time: 0.3336 data_time: 0.0226 memory: 21539 grad_norm: 4.4289 loss: 1.5950 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.5950 2023/03/08 17:23:36 - mmengine - INFO - Epoch(train) [26][580/660] lr: 1.0000e-03 eta: 1:30:11 time: 0.3744 data_time: 0.0207 memory: 21539 grad_norm: 4.5579 loss: 1.6756 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.6756 2023/03/08 17:23:42 - mmengine - INFO - Epoch(train) [26][600/660] lr: 1.0000e-03 eta: 1:30:04 time: 0.3329 data_time: 0.0207 memory: 21539 grad_norm: 4.5993 loss: 1.7539 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.7539 2023/03/08 17:23:49 - mmengine - INFO - Epoch(train) [26][620/660] lr: 1.0000e-03 eta: 1:29:57 time: 0.3334 data_time: 0.0226 memory: 21539 grad_norm: 4.6000 loss: 1.7348 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7348 2023/03/08 17:23:56 - mmengine - INFO - Epoch(train) [26][640/660] lr: 1.0000e-03 eta: 1:29:50 time: 0.3294 data_time: 0.0211 memory: 21539 grad_norm: 4.5441 loss: 1.7608 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7608 2023/03/08 17:24:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:24:02 - mmengine - INFO - Epoch(train) [26][660/660] lr: 1.0000e-03 eta: 1:29:43 time: 0.3327 data_time: 0.0223 memory: 21539 grad_norm: 4.7003 loss: 1.6764 top1_acc: 0.5556 top5_acc: 0.8148 loss_cls: 1.6764 2023/03/08 17:24:11 - mmengine - INFO - Epoch(train) [27][ 20/660] lr: 1.0000e-03 eta: 1:29:37 time: 0.4166 data_time: 0.0870 memory: 21539 grad_norm: 4.5368 loss: 1.6567 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.6567 2023/03/08 17:24:17 - mmengine - INFO - Epoch(train) [27][ 40/660] lr: 1.0000e-03 eta: 1:29:30 time: 0.3301 data_time: 0.0209 memory: 21539 grad_norm: 4.5681 loss: 1.6970 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6970 2023/03/08 17:24:24 - mmengine - INFO - Epoch(train) [27][ 60/660] lr: 1.0000e-03 eta: 1:29:24 time: 0.3356 data_time: 0.0215 memory: 21539 grad_norm: 4.5143 loss: 1.6240 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6240 2023/03/08 17:24:31 - mmengine - INFO - Epoch(train) [27][ 80/660] lr: 1.0000e-03 eta: 1:29:17 time: 0.3370 data_time: 0.0222 memory: 21539 grad_norm: 4.5348 loss: 1.7685 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7685 2023/03/08 17:24:37 - mmengine - INFO - Epoch(train) [27][100/660] lr: 1.0000e-03 eta: 1:29:10 time: 0.3354 data_time: 0.0223 memory: 21539 grad_norm: 4.6490 loss: 1.7809 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.7809 2023/03/08 17:24:44 - mmengine - INFO - Epoch(train) [27][120/660] lr: 1.0000e-03 eta: 1:29:03 time: 0.3307 data_time: 0.0213 memory: 21539 grad_norm: 4.5107 loss: 1.6850 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.6850 2023/03/08 17:24:51 - mmengine - INFO - Epoch(train) [27][140/660] lr: 1.0000e-03 eta: 1:28:56 time: 0.3381 data_time: 0.0219 memory: 21539 grad_norm: 4.5599 loss: 1.8217 top1_acc: 0.5000 top5_acc: 0.9062 loss_cls: 1.8217 2023/03/08 17:24:57 - mmengine - INFO - Epoch(train) [27][160/660] lr: 1.0000e-03 eta: 1:28:49 time: 0.3289 data_time: 0.0228 memory: 21539 grad_norm: 4.5871 loss: 1.7587 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.7587 2023/03/08 17:25:04 - mmengine - INFO - Epoch(train) [27][180/660] lr: 1.0000e-03 eta: 1:28:42 time: 0.3310 data_time: 0.0221 memory: 21539 grad_norm: 4.5968 loss: 1.8062 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.8062 2023/03/08 17:25:10 - mmengine - INFO - Epoch(train) [27][200/660] lr: 1.0000e-03 eta: 1:28:35 time: 0.3303 data_time: 0.0224 memory: 21539 grad_norm: 4.5002 loss: 1.6616 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.6616 2023/03/08 17:25:17 - mmengine - INFO - Epoch(train) [27][220/660] lr: 1.0000e-03 eta: 1:28:28 time: 0.3319 data_time: 0.0215 memory: 21539 grad_norm: 4.6096 loss: 1.7456 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 1.7456 2023/03/08 17:25:24 - mmengine - INFO - Epoch(train) [27][240/660] lr: 1.0000e-03 eta: 1:28:21 time: 0.3320 data_time: 0.0226 memory: 21539 grad_norm: 4.5877 loss: 1.6777 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6777 2023/03/08 17:25:31 - mmengine - INFO - Epoch(train) [27][260/660] lr: 1.0000e-03 eta: 1:28:15 time: 0.3812 data_time: 0.0219 memory: 21539 grad_norm: 4.6200 loss: 1.8238 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.8238 2023/03/08 17:25:38 - mmengine - INFO - Epoch(train) [27][280/660] lr: 1.0000e-03 eta: 1:28:08 time: 0.3300 data_time: 0.0225 memory: 21539 grad_norm: 4.5310 loss: 1.7459 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7459 2023/03/08 17:25:45 - mmengine - INFO - Epoch(train) [27][300/660] lr: 1.0000e-03 eta: 1:28:01 time: 0.3352 data_time: 0.0226 memory: 21539 grad_norm: 4.5901 loss: 1.6730 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6730 2023/03/08 17:25:51 - mmengine - INFO - Epoch(train) [27][320/660] lr: 1.0000e-03 eta: 1:27:54 time: 0.3308 data_time: 0.0222 memory: 21539 grad_norm: 4.5413 loss: 1.7050 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.7050 2023/03/08 17:25:58 - mmengine - INFO - Epoch(train) [27][340/660] lr: 1.0000e-03 eta: 1:27:47 time: 0.3311 data_time: 0.0226 memory: 21539 grad_norm: 4.4455 loss: 1.6998 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.6998 2023/03/08 17:26:05 - mmengine - INFO - Epoch(train) [27][360/660] lr: 1.0000e-03 eta: 1:27:41 time: 0.3324 data_time: 0.0264 memory: 21539 grad_norm: 4.6079 loss: 1.5976 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5976 2023/03/08 17:26:11 - mmengine - INFO - Epoch(train) [27][380/660] lr: 1.0000e-03 eta: 1:27:34 time: 0.3320 data_time: 0.0217 memory: 21539 grad_norm: 4.6285 loss: 1.7507 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.7507 2023/03/08 17:26:18 - mmengine - INFO - Epoch(train) [27][400/660] lr: 1.0000e-03 eta: 1:27:27 time: 0.3282 data_time: 0.0225 memory: 21539 grad_norm: 4.6541 loss: 1.6449 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.6449 2023/03/08 17:26:24 - mmengine - INFO - Epoch(train) [27][420/660] lr: 1.0000e-03 eta: 1:27:20 time: 0.3331 data_time: 0.0224 memory: 21539 grad_norm: 4.6433 loss: 1.6132 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6132 2023/03/08 17:26:31 - mmengine - INFO - Epoch(train) [27][440/660] lr: 1.0000e-03 eta: 1:27:13 time: 0.3294 data_time: 0.0220 memory: 21539 grad_norm: 4.5552 loss: 1.7754 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7754 2023/03/08 17:26:38 - mmengine - INFO - Epoch(train) [27][460/660] lr: 1.0000e-03 eta: 1:27:06 time: 0.3335 data_time: 0.0214 memory: 21539 grad_norm: 4.5859 loss: 1.6536 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 1.6536 2023/03/08 17:26:44 - mmengine - INFO - Epoch(train) [27][480/660] lr: 1.0000e-03 eta: 1:26:59 time: 0.3337 data_time: 0.0228 memory: 21539 grad_norm: 4.5745 loss: 1.6429 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.6429 2023/03/08 17:26:51 - mmengine - INFO - Epoch(train) [27][500/660] lr: 1.0000e-03 eta: 1:26:52 time: 0.3323 data_time: 0.0223 memory: 21539 grad_norm: 4.5531 loss: 1.7229 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.7229 2023/03/08 17:26:58 - mmengine - INFO - Epoch(train) [27][520/660] lr: 1.0000e-03 eta: 1:26:45 time: 0.3291 data_time: 0.0228 memory: 21539 grad_norm: 4.6402 loss: 1.6184 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6184 2023/03/08 17:27:04 - mmengine - INFO - Epoch(train) [27][540/660] lr: 1.0000e-03 eta: 1:26:38 time: 0.3358 data_time: 0.0231 memory: 21539 grad_norm: 4.5716 loss: 1.6042 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6042 2023/03/08 17:27:11 - mmengine - INFO - Epoch(train) [27][560/660] lr: 1.0000e-03 eta: 1:26:31 time: 0.3303 data_time: 0.0229 memory: 21539 grad_norm: 4.5448 loss: 1.7865 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.7865 2023/03/08 17:27:18 - mmengine - INFO - Epoch(train) [27][580/660] lr: 1.0000e-03 eta: 1:26:24 time: 0.3337 data_time: 0.0216 memory: 21539 grad_norm: 4.6694 loss: 1.6600 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6600 2023/03/08 17:27:24 - mmengine - INFO - Epoch(train) [27][600/660] lr: 1.0000e-03 eta: 1:26:17 time: 0.3362 data_time: 0.0226 memory: 21539 grad_norm: 4.6115 loss: 1.6747 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6747 2023/03/08 17:27:31 - mmengine - INFO - Epoch(train) [27][620/660] lr: 1.0000e-03 eta: 1:26:10 time: 0.3337 data_time: 0.0219 memory: 21539 grad_norm: 4.5847 loss: 1.7335 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7335 2023/03/08 17:27:38 - mmengine - INFO - Epoch(train) [27][640/660] lr: 1.0000e-03 eta: 1:26:04 time: 0.3309 data_time: 0.0217 memory: 21539 grad_norm: 4.5781 loss: 1.7700 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7700 2023/03/08 17:27:44 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:27:44 - mmengine - INFO - Epoch(train) [27][660/660] lr: 1.0000e-03 eta: 1:25:57 time: 0.3279 data_time: 0.0199 memory: 21539 grad_norm: 4.6638 loss: 1.7940 top1_acc: 0.6667 top5_acc: 0.8519 loss_cls: 1.7940 2023/03/08 17:27:44 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/03/08 17:27:54 - mmengine - INFO - Epoch(train) [28][ 20/660] lr: 1.0000e-03 eta: 1:25:51 time: 0.4117 data_time: 0.0886 memory: 21539 grad_norm: 4.5496 loss: 1.7408 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.7408 2023/03/08 17:28:00 - mmengine - INFO - Epoch(train) [28][ 40/660] lr: 1.0000e-03 eta: 1:25:44 time: 0.3351 data_time: 0.0210 memory: 21539 grad_norm: 4.5045 loss: 1.6646 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6646 2023/03/08 17:28:07 - mmengine - INFO - Epoch(train) [28][ 60/660] lr: 1.0000e-03 eta: 1:25:37 time: 0.3448 data_time: 0.0210 memory: 21539 grad_norm: 4.5553 loss: 1.7348 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7348 2023/03/08 17:28:14 - mmengine - INFO - Epoch(train) [28][ 80/660] lr: 1.0000e-03 eta: 1:25:31 time: 0.3343 data_time: 0.0200 memory: 21539 grad_norm: 4.5909 loss: 1.7312 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7312 2023/03/08 17:28:21 - mmengine - INFO - Epoch(train) [28][100/660] lr: 1.0000e-03 eta: 1:25:24 time: 0.3396 data_time: 0.0214 memory: 21539 grad_norm: 4.5979 loss: 1.7475 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7475 2023/03/08 17:28:27 - mmengine - INFO - Epoch(train) [28][120/660] lr: 1.0000e-03 eta: 1:25:17 time: 0.3373 data_time: 0.0221 memory: 21539 grad_norm: 4.7124 loss: 1.7532 top1_acc: 0.3125 top5_acc: 0.9062 loss_cls: 1.7532 2023/03/08 17:28:34 - mmengine - INFO - Epoch(train) [28][140/660] lr: 1.0000e-03 eta: 1:25:10 time: 0.3414 data_time: 0.0214 memory: 21539 grad_norm: 4.5405 loss: 1.5868 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.5868 2023/03/08 17:28:41 - mmengine - INFO - Epoch(train) [28][160/660] lr: 1.0000e-03 eta: 1:25:03 time: 0.3336 data_time: 0.0203 memory: 21539 grad_norm: 4.5380 loss: 1.6491 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6491 2023/03/08 17:28:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:28:48 - mmengine - INFO - Epoch(train) [28][180/660] lr: 1.0000e-03 eta: 1:24:56 time: 0.3411 data_time: 0.0208 memory: 21539 grad_norm: 4.5785 loss: 1.6300 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6300 2023/03/08 17:28:54 - mmengine - INFO - Epoch(train) [28][200/660] lr: 1.0000e-03 eta: 1:24:50 time: 0.3329 data_time: 0.0205 memory: 21539 grad_norm: 4.6337 loss: 1.6903 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 1.6903 2023/03/08 17:29:01 - mmengine - INFO - Epoch(train) [28][220/660] lr: 1.0000e-03 eta: 1:24:43 time: 0.3399 data_time: 0.0222 memory: 21539 grad_norm: 4.6194 loss: 1.7705 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7705 2023/03/08 17:29:08 - mmengine - INFO - Epoch(train) [28][240/660] lr: 1.0000e-03 eta: 1:24:36 time: 0.3369 data_time: 0.0248 memory: 21539 grad_norm: 4.6197 loss: 1.7778 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 1.7778 2023/03/08 17:29:15 - mmengine - INFO - Epoch(train) [28][260/660] lr: 1.0000e-03 eta: 1:24:29 time: 0.3413 data_time: 0.0208 memory: 21539 grad_norm: 4.6672 loss: 1.6603 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.6603 2023/03/08 17:29:21 - mmengine - INFO - Epoch(train) [28][280/660] lr: 1.0000e-03 eta: 1:24:22 time: 0.3361 data_time: 0.0202 memory: 21539 grad_norm: 4.6032 loss: 1.6913 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6913 2023/03/08 17:29:28 - mmengine - INFO - Epoch(train) [28][300/660] lr: 1.0000e-03 eta: 1:24:15 time: 0.3365 data_time: 0.0213 memory: 21539 grad_norm: 4.6237 loss: 1.5852 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5852 2023/03/08 17:29:36 - mmengine - INFO - Epoch(train) [28][320/660] lr: 1.0000e-03 eta: 1:24:09 time: 0.3714 data_time: 0.0208 memory: 21539 grad_norm: 4.6396 loss: 1.7620 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.7620 2023/03/08 17:29:42 - mmengine - INFO - Epoch(train) [28][340/660] lr: 1.0000e-03 eta: 1:24:02 time: 0.3358 data_time: 0.0212 memory: 21539 grad_norm: 4.6472 loss: 1.7014 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.7014 2023/03/08 17:29:49 - mmengine - INFO - Epoch(train) [28][360/660] lr: 1.0000e-03 eta: 1:23:55 time: 0.3337 data_time: 0.0208 memory: 21539 grad_norm: 4.6725 loss: 1.6639 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6639 2023/03/08 17:29:56 - mmengine - INFO - Epoch(train) [28][380/660] lr: 1.0000e-03 eta: 1:23:49 time: 0.3355 data_time: 0.0215 memory: 21539 grad_norm: 4.6381 loss: 1.6813 top1_acc: 0.3438 top5_acc: 0.8125 loss_cls: 1.6813 2023/03/08 17:30:02 - mmengine - INFO - Epoch(train) [28][400/660] lr: 1.0000e-03 eta: 1:23:42 time: 0.3379 data_time: 0.0209 memory: 21539 grad_norm: 4.7170 loss: 1.7568 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7568 2023/03/08 17:30:09 - mmengine - INFO - Epoch(train) [28][420/660] lr: 1.0000e-03 eta: 1:23:35 time: 0.3378 data_time: 0.0212 memory: 21539 grad_norm: 4.6431 loss: 1.6869 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.6869 2023/03/08 17:30:17 - mmengine - INFO - Epoch(train) [28][440/660] lr: 1.0000e-03 eta: 1:23:29 time: 0.3734 data_time: 0.0549 memory: 21539 grad_norm: 4.5435 loss: 1.6187 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6187 2023/03/08 17:30:23 - mmengine - INFO - Epoch(train) [28][460/660] lr: 1.0000e-03 eta: 1:23:22 time: 0.3386 data_time: 0.0174 memory: 21539 grad_norm: 4.5334 loss: 1.7086 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.7086 2023/03/08 17:30:30 - mmengine - INFO - Epoch(train) [28][480/660] lr: 1.0000e-03 eta: 1:23:15 time: 0.3327 data_time: 0.0224 memory: 21539 grad_norm: 4.6779 loss: 1.7962 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7962 2023/03/08 17:30:37 - mmengine - INFO - Epoch(train) [28][500/660] lr: 1.0000e-03 eta: 1:23:08 time: 0.3374 data_time: 0.0213 memory: 21539 grad_norm: 4.5644 loss: 1.6561 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6561 2023/03/08 17:30:44 - mmengine - INFO - Epoch(train) [28][520/660] lr: 1.0000e-03 eta: 1:23:01 time: 0.3396 data_time: 0.0260 memory: 21539 grad_norm: 4.5885 loss: 1.7508 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7508 2023/03/08 17:30:51 - mmengine - INFO - Epoch(train) [28][540/660] lr: 1.0000e-03 eta: 1:22:54 time: 0.3437 data_time: 0.0214 memory: 21539 grad_norm: 4.6497 loss: 1.7073 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7073 2023/03/08 17:30:57 - mmengine - INFO - Epoch(train) [28][560/660] lr: 1.0000e-03 eta: 1:22:48 time: 0.3343 data_time: 0.0221 memory: 21539 grad_norm: 4.6076 loss: 1.7342 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7342 2023/03/08 17:31:04 - mmengine - INFO - Epoch(train) [28][580/660] lr: 1.0000e-03 eta: 1:22:41 time: 0.3402 data_time: 0.0212 memory: 21539 grad_norm: 4.6848 loss: 1.8092 top1_acc: 0.4688 top5_acc: 1.0000 loss_cls: 1.8092 2023/03/08 17:31:11 - mmengine - INFO - Epoch(train) [28][600/660] lr: 1.0000e-03 eta: 1:22:34 time: 0.3320 data_time: 0.0216 memory: 21539 grad_norm: 4.5697 loss: 1.7245 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.7245 2023/03/08 17:31:17 - mmengine - INFO - Epoch(train) [28][620/660] lr: 1.0000e-03 eta: 1:22:27 time: 0.3395 data_time: 0.0218 memory: 21539 grad_norm: 4.6336 loss: 1.7504 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7504 2023/03/08 17:31:24 - mmengine - INFO - Epoch(train) [28][640/660] lr: 1.0000e-03 eta: 1:22:20 time: 0.3341 data_time: 0.0224 memory: 21539 grad_norm: 4.7157 loss: 1.7424 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7424 2023/03/08 17:31:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:31:31 - mmengine - INFO - Epoch(train) [28][660/660] lr: 1.0000e-03 eta: 1:22:13 time: 0.3273 data_time: 0.0201 memory: 21539 grad_norm: 4.7915 loss: 1.7379 top1_acc: 0.5185 top5_acc: 0.8148 loss_cls: 1.7379 2023/03/08 17:31:39 - mmengine - INFO - Epoch(train) [29][ 20/660] lr: 1.0000e-03 eta: 1:22:08 time: 0.4198 data_time: 0.0821 memory: 21539 grad_norm: 4.6580 loss: 1.7309 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7309 2023/03/08 17:31:46 - mmengine - INFO - Epoch(train) [29][ 40/660] lr: 1.0000e-03 eta: 1:22:01 time: 0.3346 data_time: 0.0207 memory: 21539 grad_norm: 4.6622 loss: 1.6888 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.6888 2023/03/08 17:31:52 - mmengine - INFO - Epoch(train) [29][ 60/660] lr: 1.0000e-03 eta: 1:21:54 time: 0.3331 data_time: 0.0215 memory: 21539 grad_norm: 4.5101 loss: 1.6655 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.6655 2023/03/08 17:31:59 - mmengine - INFO - Epoch(train) [29][ 80/660] lr: 1.0000e-03 eta: 1:21:47 time: 0.3348 data_time: 0.0205 memory: 21539 grad_norm: 4.6792 loss: 1.7064 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7064 2023/03/08 17:32:06 - mmengine - INFO - Epoch(train) [29][100/660] lr: 1.0000e-03 eta: 1:21:40 time: 0.3361 data_time: 0.0200 memory: 21539 grad_norm: 4.6309 loss: 1.7737 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7737 2023/03/08 17:32:13 - mmengine - INFO - Epoch(train) [29][120/660] lr: 1.0000e-03 eta: 1:21:33 time: 0.3317 data_time: 0.0208 memory: 21539 grad_norm: 4.6997 loss: 1.6301 top1_acc: 0.5000 top5_acc: 0.9062 loss_cls: 1.6301 2023/03/08 17:32:19 - mmengine - INFO - Epoch(train) [29][140/660] lr: 1.0000e-03 eta: 1:21:26 time: 0.3351 data_time: 0.0210 memory: 21539 grad_norm: 4.6623 loss: 1.6758 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6758 2023/03/08 17:32:26 - mmengine - INFO - Epoch(train) [29][160/660] lr: 1.0000e-03 eta: 1:21:19 time: 0.3344 data_time: 0.0218 memory: 21539 grad_norm: 4.6922 loss: 1.6982 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6982 2023/03/08 17:32:33 - mmengine - INFO - Epoch(train) [29][180/660] lr: 1.0000e-03 eta: 1:21:13 time: 0.3382 data_time: 0.0214 memory: 21539 grad_norm: 4.7393 loss: 1.7255 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7255 2023/03/08 17:32:39 - mmengine - INFO - Epoch(train) [29][200/660] lr: 1.0000e-03 eta: 1:21:06 time: 0.3346 data_time: 0.0249 memory: 21539 grad_norm: 4.5553 loss: 1.6388 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.6388 2023/03/08 17:32:46 - mmengine - INFO - Epoch(train) [29][220/660] lr: 1.0000e-03 eta: 1:20:59 time: 0.3347 data_time: 0.0211 memory: 21539 grad_norm: 4.6168 loss: 1.6338 top1_acc: 0.3438 top5_acc: 0.8750 loss_cls: 1.6338 2023/03/08 17:32:53 - mmengine - INFO - Epoch(train) [29][240/660] lr: 1.0000e-03 eta: 1:20:52 time: 0.3338 data_time: 0.0207 memory: 21539 grad_norm: 4.6521 loss: 1.7202 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.7202 2023/03/08 17:32:59 - mmengine - INFO - Epoch(train) [29][260/660] lr: 1.0000e-03 eta: 1:20:45 time: 0.3349 data_time: 0.0206 memory: 21539 grad_norm: 4.6228 loss: 1.6904 top1_acc: 0.3438 top5_acc: 0.8438 loss_cls: 1.6904 2023/03/08 17:33:06 - mmengine - INFO - Epoch(train) [29][280/660] lr: 1.0000e-03 eta: 1:20:38 time: 0.3326 data_time: 0.0205 memory: 21539 grad_norm: 4.7004 loss: 1.7758 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 1.7758 2023/03/08 17:33:13 - mmengine - INFO - Epoch(train) [29][300/660] lr: 1.0000e-03 eta: 1:20:31 time: 0.3332 data_time: 0.0205 memory: 21539 grad_norm: 4.6468 loss: 1.7079 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7079 2023/03/08 17:33:19 - mmengine - INFO - Epoch(train) [29][320/660] lr: 1.0000e-03 eta: 1:20:24 time: 0.3306 data_time: 0.0215 memory: 21539 grad_norm: 4.6259 loss: 1.7611 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7611 2023/03/08 17:33:26 - mmengine - INFO - Epoch(train) [29][340/660] lr: 1.0000e-03 eta: 1:20:18 time: 0.3397 data_time: 0.0213 memory: 21539 grad_norm: 4.6101 loss: 1.7242 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7242 2023/03/08 17:33:33 - mmengine - INFO - Epoch(train) [29][360/660] lr: 1.0000e-03 eta: 1:20:11 time: 0.3301 data_time: 0.0216 memory: 21539 grad_norm: 4.6086 loss: 1.5898 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.5898 2023/03/08 17:33:39 - mmengine - INFO - Epoch(train) [29][380/660] lr: 1.0000e-03 eta: 1:20:04 time: 0.3327 data_time: 0.0207 memory: 21539 grad_norm: 4.6991 loss: 1.7226 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7226 2023/03/08 17:33:46 - mmengine - INFO - Epoch(train) [29][400/660] lr: 1.0000e-03 eta: 1:19:57 time: 0.3325 data_time: 0.0216 memory: 21539 grad_norm: 4.7195 loss: 1.7866 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 1.7866 2023/03/08 17:33:53 - mmengine - INFO - Epoch(train) [29][420/660] lr: 1.0000e-03 eta: 1:19:50 time: 0.3303 data_time: 0.0216 memory: 21539 grad_norm: 4.6331 loss: 1.8467 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.8467 2023/03/08 17:33:59 - mmengine - INFO - Epoch(train) [29][440/660] lr: 1.0000e-03 eta: 1:19:43 time: 0.3308 data_time: 0.0218 memory: 21539 grad_norm: 4.6212 loss: 1.6954 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6954 2023/03/08 17:34:06 - mmengine - INFO - Epoch(train) [29][460/660] lr: 1.0000e-03 eta: 1:19:36 time: 0.3327 data_time: 0.0213 memory: 21539 grad_norm: 4.6889 loss: 1.6753 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6753 2023/03/08 17:34:13 - mmengine - INFO - Epoch(train) [29][480/660] lr: 1.0000e-03 eta: 1:19:29 time: 0.3293 data_time: 0.0226 memory: 21539 grad_norm: 4.6402 loss: 1.6037 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6037 2023/03/08 17:34:19 - mmengine - INFO - Epoch(train) [29][500/660] lr: 1.0000e-03 eta: 1:19:22 time: 0.3362 data_time: 0.0243 memory: 21539 grad_norm: 4.6704 loss: 1.6776 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6776 2023/03/08 17:34:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:34:26 - mmengine - INFO - Epoch(train) [29][520/660] lr: 1.0000e-03 eta: 1:19:15 time: 0.3345 data_time: 0.0217 memory: 21539 grad_norm: 4.7772 loss: 1.7245 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7245 2023/03/08 17:34:33 - mmengine - INFO - Epoch(train) [29][540/660] lr: 1.0000e-03 eta: 1:19:08 time: 0.3290 data_time: 0.0194 memory: 21539 grad_norm: 4.6195 loss: 1.6790 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.6790 2023/03/08 17:34:39 - mmengine - INFO - Epoch(train) [29][560/660] lr: 1.0000e-03 eta: 1:19:02 time: 0.3264 data_time: 0.0209 memory: 21539 grad_norm: 4.6530 loss: 1.5809 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5809 2023/03/08 17:34:46 - mmengine - INFO - Epoch(train) [29][580/660] lr: 1.0000e-03 eta: 1:18:55 time: 0.3325 data_time: 0.0197 memory: 21539 grad_norm: 4.7157 loss: 1.7931 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7931 2023/03/08 17:34:52 - mmengine - INFO - Epoch(train) [29][600/660] lr: 1.0000e-03 eta: 1:18:48 time: 0.3278 data_time: 0.0226 memory: 21539 grad_norm: 4.6623 loss: 1.7779 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.7779 2023/03/08 17:34:59 - mmengine - INFO - Epoch(train) [29][620/660] lr: 1.0000e-03 eta: 1:18:41 time: 0.3332 data_time: 0.0200 memory: 21539 grad_norm: 4.6998 loss: 1.8028 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.8028 2023/03/08 17:35:06 - mmengine - INFO - Epoch(train) [29][640/660] lr: 1.0000e-03 eta: 1:18:34 time: 0.3306 data_time: 0.0220 memory: 21539 grad_norm: 4.6410 loss: 1.6650 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.6650 2023/03/08 17:35:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:35:12 - mmengine - INFO - Epoch(train) [29][660/660] lr: 1.0000e-03 eta: 1:18:27 time: 0.3277 data_time: 0.0185 memory: 21539 grad_norm: 4.6739 loss: 1.7475 top1_acc: 0.5556 top5_acc: 0.7778 loss_cls: 1.7475 2023/03/08 17:35:20 - mmengine - INFO - Epoch(train) [30][ 20/660] lr: 1.0000e-03 eta: 1:18:21 time: 0.4108 data_time: 0.0853 memory: 21539 grad_norm: 4.6476 loss: 1.8463 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.8463 2023/03/08 17:35:27 - mmengine - INFO - Epoch(train) [30][ 40/660] lr: 1.0000e-03 eta: 1:18:14 time: 0.3297 data_time: 0.0199 memory: 21539 grad_norm: 4.5819 loss: 1.6465 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6465 2023/03/08 17:35:34 - mmengine - INFO - Epoch(train) [30][ 60/660] lr: 1.0000e-03 eta: 1:18:07 time: 0.3335 data_time: 0.0221 memory: 21539 grad_norm: 4.6440 loss: 1.7693 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7693 2023/03/08 17:35:40 - mmengine - INFO - Epoch(train) [30][ 80/660] lr: 1.0000e-03 eta: 1:18:00 time: 0.3311 data_time: 0.0204 memory: 21539 grad_norm: 4.6051 loss: 1.7214 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.7214 2023/03/08 17:35:47 - mmengine - INFO - Epoch(train) [30][100/660] lr: 1.0000e-03 eta: 1:17:53 time: 0.3322 data_time: 0.0221 memory: 21539 grad_norm: 4.6536 loss: 1.8384 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 1.8384 2023/03/08 17:35:54 - mmengine - INFO - Epoch(train) [30][120/660] lr: 1.0000e-03 eta: 1:17:47 time: 0.3293 data_time: 0.0207 memory: 21539 grad_norm: 4.6367 loss: 1.7235 top1_acc: 0.5312 top5_acc: 0.9375 loss_cls: 1.7235 2023/03/08 17:36:00 - mmengine - INFO - Epoch(train) [30][140/660] lr: 1.0000e-03 eta: 1:17:40 time: 0.3325 data_time: 0.0220 memory: 21539 grad_norm: 4.6410 loss: 1.7114 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7114 2023/03/08 17:36:07 - mmengine - INFO - Epoch(train) [30][160/660] lr: 1.0000e-03 eta: 1:17:33 time: 0.3363 data_time: 0.0233 memory: 21539 grad_norm: 4.7068 loss: 1.8456 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8456 2023/03/08 17:36:14 - mmengine - INFO - Epoch(train) [30][180/660] lr: 1.0000e-03 eta: 1:17:26 time: 0.3351 data_time: 0.0202 memory: 21539 grad_norm: 4.7005 loss: 1.6309 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6309 2023/03/08 17:36:20 - mmengine - INFO - Epoch(train) [30][200/660] lr: 1.0000e-03 eta: 1:17:19 time: 0.3321 data_time: 0.0193 memory: 21539 grad_norm: 4.7412 loss: 1.6734 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.6734 2023/03/08 17:36:27 - mmengine - INFO - Epoch(train) [30][220/660] lr: 1.0000e-03 eta: 1:17:12 time: 0.3314 data_time: 0.0210 memory: 21539 grad_norm: 4.7166 loss: 1.7117 top1_acc: 0.5312 top5_acc: 0.9375 loss_cls: 1.7117 2023/03/08 17:36:34 - mmengine - INFO - Epoch(train) [30][240/660] lr: 1.0000e-03 eta: 1:17:05 time: 0.3342 data_time: 0.0205 memory: 21539 grad_norm: 4.7436 loss: 1.6925 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6925 2023/03/08 17:36:40 - mmengine - INFO - Epoch(train) [30][260/660] lr: 1.0000e-03 eta: 1:16:58 time: 0.3306 data_time: 0.0208 memory: 21539 grad_norm: 4.7362 loss: 1.6964 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.6964 2023/03/08 17:36:47 - mmengine - INFO - Epoch(train) [30][280/660] lr: 1.0000e-03 eta: 1:16:51 time: 0.3277 data_time: 0.0200 memory: 21539 grad_norm: 4.6706 loss: 1.6504 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6504 2023/03/08 17:36:53 - mmengine - INFO - Epoch(train) [30][300/660] lr: 1.0000e-03 eta: 1:16:45 time: 0.3344 data_time: 0.0207 memory: 21539 grad_norm: 4.5968 loss: 1.5914 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.5914 2023/03/08 17:37:00 - mmengine - INFO - Epoch(train) [30][320/660] lr: 1.0000e-03 eta: 1:16:38 time: 0.3288 data_time: 0.0198 memory: 21539 grad_norm: 4.5928 loss: 1.6281 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6281 2023/03/08 17:37:07 - mmengine - INFO - Epoch(train) [30][340/660] lr: 1.0000e-03 eta: 1:16:31 time: 0.3282 data_time: 0.0205 memory: 21539 grad_norm: 4.6692 loss: 1.6675 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6675 2023/03/08 17:37:13 - mmengine - INFO - Epoch(train) [30][360/660] lr: 1.0000e-03 eta: 1:16:24 time: 0.3286 data_time: 0.0201 memory: 21539 grad_norm: 4.6359 loss: 1.6510 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6510 2023/03/08 17:37:20 - mmengine - INFO - Epoch(train) [30][380/660] lr: 1.0000e-03 eta: 1:16:17 time: 0.3311 data_time: 0.0215 memory: 21539 grad_norm: 4.7620 loss: 1.7129 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7129 2023/03/08 17:37:26 - mmengine - INFO - Epoch(train) [30][400/660] lr: 1.0000e-03 eta: 1:16:10 time: 0.3275 data_time: 0.0199 memory: 21539 grad_norm: 4.7102 loss: 1.7310 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7310 2023/03/08 17:37:33 - mmengine - INFO - Epoch(train) [30][420/660] lr: 1.0000e-03 eta: 1:16:03 time: 0.3275 data_time: 0.0218 memory: 21539 grad_norm: 4.6787 loss: 1.6287 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6287 2023/03/08 17:37:39 - mmengine - INFO - Epoch(train) [30][440/660] lr: 1.0000e-03 eta: 1:15:56 time: 0.3255 data_time: 0.0198 memory: 21539 grad_norm: 4.6943 loss: 1.6062 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.6062 2023/03/08 17:37:46 - mmengine - INFO - Epoch(train) [30][460/660] lr: 1.0000e-03 eta: 1:15:49 time: 0.3270 data_time: 0.0224 memory: 21539 grad_norm: 4.7102 loss: 1.7007 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7007 2023/03/08 17:37:52 - mmengine - INFO - Epoch(train) [30][480/660] lr: 1.0000e-03 eta: 1:15:42 time: 0.3273 data_time: 0.0203 memory: 21539 grad_norm: 4.7592 loss: 1.7032 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.7032 2023/03/08 17:37:59 - mmengine - INFO - Epoch(train) [30][500/660] lr: 1.0000e-03 eta: 1:15:35 time: 0.3278 data_time: 0.0224 memory: 21539 grad_norm: 4.6359 loss: 1.6087 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6087 2023/03/08 17:38:06 - mmengine - INFO - Epoch(train) [30][520/660] lr: 1.0000e-03 eta: 1:15:28 time: 0.3310 data_time: 0.0244 memory: 21539 grad_norm: 4.7813 loss: 1.6854 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6854 2023/03/08 17:38:12 - mmengine - INFO - Epoch(train) [30][540/660] lr: 1.0000e-03 eta: 1:15:21 time: 0.3293 data_time: 0.0214 memory: 21539 grad_norm: 4.7702 loss: 1.6467 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6467 2023/03/08 17:38:19 - mmengine - INFO - Epoch(train) [30][560/660] lr: 1.0000e-03 eta: 1:15:14 time: 0.3292 data_time: 0.0222 memory: 21539 grad_norm: 4.7015 loss: 1.6629 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6629 2023/03/08 17:38:25 - mmengine - INFO - Epoch(train) [30][580/660] lr: 1.0000e-03 eta: 1:15:07 time: 0.3245 data_time: 0.0217 memory: 21539 grad_norm: 4.7141 loss: 1.6277 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6277 2023/03/08 17:38:32 - mmengine - INFO - Epoch(train) [30][600/660] lr: 1.0000e-03 eta: 1:15:00 time: 0.3289 data_time: 0.0223 memory: 21539 grad_norm: 4.6834 loss: 1.6796 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6796 2023/03/08 17:38:38 - mmengine - INFO - Epoch(train) [30][620/660] lr: 1.0000e-03 eta: 1:14:53 time: 0.3255 data_time: 0.0227 memory: 21539 grad_norm: 4.7615 loss: 1.7363 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7363 2023/03/08 17:38:45 - mmengine - INFO - Epoch(train) [30][640/660] lr: 1.0000e-03 eta: 1:14:46 time: 0.3318 data_time: 0.0223 memory: 21539 grad_norm: 4.7363 loss: 1.8146 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.8146 2023/03/08 17:38:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:38:51 - mmengine - INFO - Epoch(train) [30][660/660] lr: 1.0000e-03 eta: 1:14:39 time: 0.3201 data_time: 0.0211 memory: 21539 grad_norm: 4.7103 loss: 1.8206 top1_acc: 0.4074 top5_acc: 0.8519 loss_cls: 1.8206 2023/03/08 17:38:51 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/03/08 17:38:57 - mmengine - INFO - Epoch(val) [30][20/97] eta: 0:00:18 time: 0.2375 data_time: 0.1212 memory: 3261 2023/03/08 17:39:01 - mmengine - INFO - Epoch(val) [30][40/97] eta: 0:00:11 time: 0.1726 data_time: 0.0635 memory: 3261 2023/03/08 17:39:04 - mmengine - INFO - Epoch(val) [30][60/97] eta: 0:00:07 time: 0.1878 data_time: 0.0797 memory: 3261 2023/03/08 17:39:08 - mmengine - INFO - Epoch(val) [30][80/97] eta: 0:00:03 time: 0.1664 data_time: 0.0579 memory: 3261 2023/03/08 17:39:12 - mmengine - INFO - Epoch(val) [30][97/97] acc/top1: 0.3423 acc/top5: 0.6503 acc/mean1: 0.2754 2023/03/08 17:39:12 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_exp1/best_acc/top1_epoch_25.pth is removed 2023/03/08 17:39:13 - mmengine - INFO - The best checkpoint with 0.3423 acc/top1 at 30 epoch is saved to best_acc/top1_epoch_30.pth. 2023/03/08 17:39:21 - mmengine - INFO - Epoch(train) [31][ 20/660] lr: 1.0000e-03 eta: 1:14:34 time: 0.4131 data_time: 0.0871 memory: 21539 grad_norm: 4.6806 loss: 1.7221 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.7221 2023/03/08 17:39:28 - mmengine - INFO - Epoch(train) [31][ 40/660] lr: 1.0000e-03 eta: 1:14:27 time: 0.3374 data_time: 0.0213 memory: 21539 grad_norm: 4.7173 loss: 1.6233 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6233 2023/03/08 17:39:34 - mmengine - INFO - Epoch(train) [31][ 60/660] lr: 1.0000e-03 eta: 1:14:20 time: 0.3358 data_time: 0.0217 memory: 21539 grad_norm: 4.6532 loss: 1.6175 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.6175 2023/03/08 17:39:41 - mmengine - INFO - Epoch(train) [31][ 80/660] lr: 1.0000e-03 eta: 1:14:13 time: 0.3344 data_time: 0.0204 memory: 21539 grad_norm: 4.7321 loss: 1.6059 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6059 2023/03/08 17:39:48 - mmengine - INFO - Epoch(train) [31][100/660] lr: 1.0000e-03 eta: 1:14:06 time: 0.3406 data_time: 0.0222 memory: 21539 grad_norm: 4.7039 loss: 1.6860 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.6860 2023/03/08 17:39:55 - mmengine - INFO - Epoch(train) [31][120/660] lr: 1.0000e-03 eta: 1:14:00 time: 0.3622 data_time: 0.0215 memory: 21539 grad_norm: 4.7080 loss: 1.6623 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6623 2023/03/08 17:40:02 - mmengine - INFO - Epoch(train) [31][140/660] lr: 1.0000e-03 eta: 1:13:53 time: 0.3408 data_time: 0.0221 memory: 21539 grad_norm: 4.6785 loss: 1.7159 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7159 2023/03/08 17:40:09 - mmengine - INFO - Epoch(train) [31][160/660] lr: 1.0000e-03 eta: 1:13:46 time: 0.3322 data_time: 0.0211 memory: 21539 grad_norm: 4.7040 loss: 1.6364 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6364 2023/03/08 17:40:15 - mmengine - INFO - Epoch(train) [31][180/660] lr: 1.0000e-03 eta: 1:13:39 time: 0.3315 data_time: 0.0223 memory: 21539 grad_norm: 4.7092 loss: 1.6357 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.6357 2023/03/08 17:40:22 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:40:22 - mmengine - INFO - Epoch(train) [31][200/660] lr: 1.0000e-03 eta: 1:13:32 time: 0.3294 data_time: 0.0214 memory: 21539 grad_norm: 4.7987 loss: 1.7680 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.7680 2023/03/08 17:40:28 - mmengine - INFO - Epoch(train) [31][220/660] lr: 1.0000e-03 eta: 1:13:25 time: 0.3302 data_time: 0.0228 memory: 21539 grad_norm: 4.7398 loss: 1.6234 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6234 2023/03/08 17:40:35 - mmengine - INFO - Epoch(train) [31][240/660] lr: 1.0000e-03 eta: 1:13:19 time: 0.3298 data_time: 0.0220 memory: 21539 grad_norm: 4.8036 loss: 1.8485 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.8485 2023/03/08 17:40:42 - mmengine - INFO - Epoch(train) [31][260/660] lr: 1.0000e-03 eta: 1:13:12 time: 0.3378 data_time: 0.0212 memory: 21539 grad_norm: 4.7176 loss: 1.6655 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.6655 2023/03/08 17:40:48 - mmengine - INFO - Epoch(train) [31][280/660] lr: 1.0000e-03 eta: 1:13:05 time: 0.3300 data_time: 0.0216 memory: 21539 grad_norm: 4.8171 loss: 1.6593 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6593 2023/03/08 17:40:55 - mmengine - INFO - Epoch(train) [31][300/660] lr: 1.0000e-03 eta: 1:12:58 time: 0.3289 data_time: 0.0223 memory: 21539 grad_norm: 4.7982 loss: 1.6524 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6524 2023/03/08 17:41:02 - mmengine - INFO - Epoch(train) [31][320/660] lr: 1.0000e-03 eta: 1:12:51 time: 0.3279 data_time: 0.0217 memory: 21539 grad_norm: 4.7080 loss: 1.7274 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 1.7274 2023/03/08 17:41:08 - mmengine - INFO - Epoch(train) [31][340/660] lr: 1.0000e-03 eta: 1:12:44 time: 0.3347 data_time: 0.0241 memory: 21539 grad_norm: 4.7617 loss: 1.8099 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8099 2023/03/08 17:41:15 - mmengine - INFO - Epoch(train) [31][360/660] lr: 1.0000e-03 eta: 1:12:37 time: 0.3343 data_time: 0.0263 memory: 21539 grad_norm: 4.6955 loss: 1.5989 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5989 2023/03/08 17:41:22 - mmengine - INFO - Epoch(train) [31][380/660] lr: 1.0000e-03 eta: 1:12:30 time: 0.3317 data_time: 0.0217 memory: 21539 grad_norm: 4.7571 loss: 1.7316 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7316 2023/03/08 17:41:28 - mmengine - INFO - Epoch(train) [31][400/660] lr: 1.0000e-03 eta: 1:12:24 time: 0.3387 data_time: 0.0238 memory: 21539 grad_norm: 4.7682 loss: 1.7755 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7755 2023/03/08 17:41:35 - mmengine - INFO - Epoch(train) [31][420/660] lr: 1.0000e-03 eta: 1:12:17 time: 0.3307 data_time: 0.0228 memory: 21539 grad_norm: 4.7700 loss: 1.7903 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7903 2023/03/08 17:41:42 - mmengine - INFO - Epoch(train) [31][440/660] lr: 1.0000e-03 eta: 1:12:10 time: 0.3303 data_time: 0.0225 memory: 21539 grad_norm: 4.6990 loss: 1.6067 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6067 2023/03/08 17:41:48 - mmengine - INFO - Epoch(train) [31][460/660] lr: 1.0000e-03 eta: 1:12:03 time: 0.3298 data_time: 0.0246 memory: 21539 grad_norm: 4.7655 loss: 1.7599 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.7599 2023/03/08 17:41:55 - mmengine - INFO - Epoch(train) [31][480/660] lr: 1.0000e-03 eta: 1:11:56 time: 0.3277 data_time: 0.0223 memory: 21539 grad_norm: 4.6704 loss: 1.6538 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6538 2023/03/08 17:42:01 - mmengine - INFO - Epoch(train) [31][500/660] lr: 1.0000e-03 eta: 1:11:49 time: 0.3303 data_time: 0.0217 memory: 21539 grad_norm: 4.7724 loss: 1.5374 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5374 2023/03/08 17:42:08 - mmengine - INFO - Epoch(train) [31][520/660] lr: 1.0000e-03 eta: 1:11:42 time: 0.3264 data_time: 0.0219 memory: 21539 grad_norm: 4.7426 loss: 1.7215 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7215 2023/03/08 17:42:15 - mmengine - INFO - Epoch(train) [31][540/660] lr: 1.0000e-03 eta: 1:11:35 time: 0.3327 data_time: 0.0229 memory: 21539 grad_norm: 4.7489 loss: 1.7617 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7617 2023/03/08 17:42:21 - mmengine - INFO - Epoch(train) [31][560/660] lr: 1.0000e-03 eta: 1:11:28 time: 0.3276 data_time: 0.0221 memory: 21539 grad_norm: 4.7796 loss: 1.7329 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7329 2023/03/08 17:42:28 - mmengine - INFO - Epoch(train) [31][580/660] lr: 1.0000e-03 eta: 1:11:21 time: 0.3311 data_time: 0.0220 memory: 21539 grad_norm: 4.7027 loss: 1.6417 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6417 2023/03/08 17:42:34 - mmengine - INFO - Epoch(train) [31][600/660] lr: 1.0000e-03 eta: 1:11:14 time: 0.3281 data_time: 0.0216 memory: 21539 grad_norm: 4.8374 loss: 1.5945 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5945 2023/03/08 17:42:41 - mmengine - INFO - Epoch(train) [31][620/660] lr: 1.0000e-03 eta: 1:11:08 time: 0.3339 data_time: 0.0216 memory: 21539 grad_norm: 4.7755 loss: 1.5725 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5725 2023/03/08 17:42:48 - mmengine - INFO - Epoch(train) [31][640/660] lr: 1.0000e-03 eta: 1:11:01 time: 0.3293 data_time: 0.0217 memory: 21539 grad_norm: 4.6734 loss: 1.7313 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.7313 2023/03/08 17:42:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:42:54 - mmengine - INFO - Epoch(train) [31][660/660] lr: 1.0000e-03 eta: 1:10:54 time: 0.3235 data_time: 0.0193 memory: 21539 grad_norm: 4.7248 loss: 1.6028 top1_acc: 0.5926 top5_acc: 0.7037 loss_cls: 1.6028 2023/03/08 17:43:03 - mmengine - INFO - Epoch(train) [32][ 20/660] lr: 1.0000e-03 eta: 1:10:48 time: 0.4256 data_time: 0.0904 memory: 21539 grad_norm: 4.7715 loss: 1.6434 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.6434 2023/03/08 17:43:09 - mmengine - INFO - Epoch(train) [32][ 40/660] lr: 1.0000e-03 eta: 1:10:41 time: 0.3433 data_time: 0.0205 memory: 21539 grad_norm: 4.7091 loss: 1.6330 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6330 2023/03/08 17:43:16 - mmengine - INFO - Epoch(train) [32][ 60/660] lr: 1.0000e-03 eta: 1:10:34 time: 0.3398 data_time: 0.0229 memory: 21539 grad_norm: 4.6710 loss: 1.6760 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6760 2023/03/08 17:43:23 - mmengine - INFO - Epoch(train) [32][ 80/660] lr: 1.0000e-03 eta: 1:10:28 time: 0.3320 data_time: 0.0208 memory: 21539 grad_norm: 4.8675 loss: 1.6893 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 1.6893 2023/03/08 17:43:30 - mmengine - INFO - Epoch(train) [32][100/660] lr: 1.0000e-03 eta: 1:10:21 time: 0.3769 data_time: 0.0216 memory: 21539 grad_norm: 4.7730 loss: 1.6317 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6317 2023/03/08 17:43:37 - mmengine - INFO - Epoch(train) [32][120/660] lr: 1.0000e-03 eta: 1:10:14 time: 0.3328 data_time: 0.0199 memory: 21539 grad_norm: 4.7537 loss: 1.7086 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.7086 2023/03/08 17:43:44 - mmengine - INFO - Epoch(train) [32][140/660] lr: 1.0000e-03 eta: 1:10:08 time: 0.3406 data_time: 0.0219 memory: 21539 grad_norm: 4.7632 loss: 1.6489 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.6489 2023/03/08 17:43:51 - mmengine - INFO - Epoch(train) [32][160/660] lr: 1.0000e-03 eta: 1:10:01 time: 0.3321 data_time: 0.0207 memory: 21539 grad_norm: 4.8303 loss: 1.7356 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.7356 2023/03/08 17:43:57 - mmengine - INFO - Epoch(train) [32][180/660] lr: 1.0000e-03 eta: 1:09:54 time: 0.3412 data_time: 0.0218 memory: 21539 grad_norm: 4.7870 loss: 1.7628 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 1.7628 2023/03/08 17:44:04 - mmengine - INFO - Epoch(train) [32][200/660] lr: 1.0000e-03 eta: 1:09:47 time: 0.3339 data_time: 0.0203 memory: 21539 grad_norm: 4.8556 loss: 1.7216 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7216 2023/03/08 17:44:11 - mmengine - INFO - Epoch(train) [32][220/660] lr: 1.0000e-03 eta: 1:09:40 time: 0.3390 data_time: 0.0202 memory: 21539 grad_norm: 4.6548 loss: 1.7081 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.7081 2023/03/08 17:44:17 - mmengine - INFO - Epoch(train) [32][240/660] lr: 1.0000e-03 eta: 1:09:34 time: 0.3340 data_time: 0.0203 memory: 21539 grad_norm: 4.8182 loss: 1.7170 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.7170 2023/03/08 17:44:24 - mmengine - INFO - Epoch(train) [32][260/660] lr: 1.0000e-03 eta: 1:09:27 time: 0.3367 data_time: 0.0209 memory: 21539 grad_norm: 4.8262 loss: 1.6237 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.6237 2023/03/08 17:44:31 - mmengine - INFO - Epoch(train) [32][280/660] lr: 1.0000e-03 eta: 1:09:20 time: 0.3344 data_time: 0.0201 memory: 21539 grad_norm: 4.7385 loss: 1.6946 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6946 2023/03/08 17:44:38 - mmengine - INFO - Epoch(train) [32][300/660] lr: 1.0000e-03 eta: 1:09:13 time: 0.3400 data_time: 0.0215 memory: 21539 grad_norm: 4.8185 loss: 1.7009 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7009 2023/03/08 17:44:45 - mmengine - INFO - Epoch(train) [32][320/660] lr: 1.0000e-03 eta: 1:09:06 time: 0.3398 data_time: 0.0207 memory: 21539 grad_norm: 4.8158 loss: 1.6672 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6672 2023/03/08 17:44:51 - mmengine - INFO - Epoch(train) [32][340/660] lr: 1.0000e-03 eta: 1:08:59 time: 0.3387 data_time: 0.0211 memory: 21539 grad_norm: 4.8837 loss: 1.6632 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.6632 2023/03/08 17:44:58 - mmengine - INFO - Epoch(train) [32][360/660] lr: 1.0000e-03 eta: 1:08:53 time: 0.3365 data_time: 0.0209 memory: 21539 grad_norm: 4.8190 loss: 1.6759 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6759 2023/03/08 17:45:05 - mmengine - INFO - Epoch(train) [32][380/660] lr: 1.0000e-03 eta: 1:08:46 time: 0.3425 data_time: 0.0259 memory: 21539 grad_norm: 4.7506 loss: 1.6043 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6043 2023/03/08 17:45:12 - mmengine - INFO - Epoch(train) [32][400/660] lr: 1.0000e-03 eta: 1:08:39 time: 0.3371 data_time: 0.0202 memory: 21539 grad_norm: 4.7799 loss: 1.5639 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5639 2023/03/08 17:45:18 - mmengine - INFO - Epoch(train) [32][420/660] lr: 1.0000e-03 eta: 1:08:32 time: 0.3369 data_time: 0.0227 memory: 21539 grad_norm: 4.8390 loss: 1.6615 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6615 2023/03/08 17:45:25 - mmengine - INFO - Epoch(train) [32][440/660] lr: 1.0000e-03 eta: 1:08:25 time: 0.3362 data_time: 0.0213 memory: 21539 grad_norm: 4.7571 loss: 1.7160 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.7160 2023/03/08 17:45:32 - mmengine - INFO - Epoch(train) [32][460/660] lr: 1.0000e-03 eta: 1:08:19 time: 0.3359 data_time: 0.0211 memory: 21539 grad_norm: 4.8495 loss: 1.6710 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6710 2023/03/08 17:45:38 - mmengine - INFO - Epoch(train) [32][480/660] lr: 1.0000e-03 eta: 1:08:12 time: 0.3312 data_time: 0.0202 memory: 21539 grad_norm: 4.8114 loss: 1.6544 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6544 2023/03/08 17:45:45 - mmengine - INFO - Epoch(train) [32][500/660] lr: 1.0000e-03 eta: 1:08:05 time: 0.3349 data_time: 0.0214 memory: 21539 grad_norm: 4.7558 loss: 1.7781 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.7781 2023/03/08 17:45:52 - mmengine - INFO - Epoch(train) [32][520/660] lr: 1.0000e-03 eta: 1:07:58 time: 0.3328 data_time: 0.0199 memory: 21539 grad_norm: 4.7068 loss: 1.5837 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5837 2023/03/08 17:45:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:45:59 - mmengine - INFO - Epoch(train) [32][540/660] lr: 1.0000e-03 eta: 1:07:51 time: 0.3369 data_time: 0.0212 memory: 21539 grad_norm: 4.8230 loss: 1.6364 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6364 2023/03/08 17:46:05 - mmengine - INFO - Epoch(train) [32][560/660] lr: 1.0000e-03 eta: 1:07:44 time: 0.3322 data_time: 0.0232 memory: 21539 grad_norm: 4.8042 loss: 1.6799 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 1.6799 2023/03/08 17:46:12 - mmengine - INFO - Epoch(train) [32][580/660] lr: 1.0000e-03 eta: 1:07:38 time: 0.3333 data_time: 0.0224 memory: 21539 grad_norm: 4.8054 loss: 1.6604 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.6604 2023/03/08 17:46:18 - mmengine - INFO - Epoch(train) [32][600/660] lr: 1.0000e-03 eta: 1:07:31 time: 0.3317 data_time: 0.0219 memory: 21539 grad_norm: 4.6947 loss: 1.6181 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6181 2023/03/08 17:46:25 - mmengine - INFO - Epoch(train) [32][620/660] lr: 1.0000e-03 eta: 1:07:24 time: 0.3341 data_time: 0.0211 memory: 21539 grad_norm: 4.8752 loss: 1.7733 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.7733 2023/03/08 17:46:32 - mmengine - INFO - Epoch(train) [32][640/660] lr: 1.0000e-03 eta: 1:07:17 time: 0.3296 data_time: 0.0233 memory: 21539 grad_norm: 4.8054 loss: 1.7272 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7272 2023/03/08 17:46:38 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:46:38 - mmengine - INFO - Epoch(train) [32][660/660] lr: 1.0000e-03 eta: 1:07:10 time: 0.3257 data_time: 0.0203 memory: 21539 grad_norm: 4.7343 loss: 1.6967 top1_acc: 0.6667 top5_acc: 0.7778 loss_cls: 1.6967 2023/03/08 17:46:47 - mmengine - INFO - Epoch(train) [33][ 20/660] lr: 1.0000e-03 eta: 1:07:04 time: 0.4224 data_time: 0.0882 memory: 21539 grad_norm: 4.7420 loss: 1.6899 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6899 2023/03/08 17:46:53 - mmengine - INFO - Epoch(train) [33][ 40/660] lr: 1.0000e-03 eta: 1:06:57 time: 0.3382 data_time: 0.0211 memory: 21539 grad_norm: 4.7110 loss: 1.6713 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6713 2023/03/08 17:47:00 - mmengine - INFO - Epoch(train) [33][ 60/660] lr: 1.0000e-03 eta: 1:06:51 time: 0.3479 data_time: 0.0253 memory: 21539 grad_norm: 4.7791 loss: 1.6319 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6319 2023/03/08 17:47:07 - mmengine - INFO - Epoch(train) [33][ 80/660] lr: 1.0000e-03 eta: 1:06:44 time: 0.3339 data_time: 0.0222 memory: 21539 grad_norm: 4.7538 loss: 1.7000 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7000 2023/03/08 17:47:14 - mmengine - INFO - Epoch(train) [33][100/660] lr: 1.0000e-03 eta: 1:06:37 time: 0.3370 data_time: 0.0218 memory: 21539 grad_norm: 4.7800 loss: 1.5983 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5983 2023/03/08 17:47:21 - mmengine - INFO - Epoch(train) [33][120/660] lr: 1.0000e-03 eta: 1:06:30 time: 0.3325 data_time: 0.0212 memory: 21539 grad_norm: 4.6989 loss: 1.6188 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.6188 2023/03/08 17:47:27 - mmengine - INFO - Epoch(train) [33][140/660] lr: 1.0000e-03 eta: 1:06:23 time: 0.3373 data_time: 0.0212 memory: 21539 grad_norm: 4.8023 loss: 1.6864 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6864 2023/03/08 17:47:34 - mmengine - INFO - Epoch(train) [33][160/660] lr: 1.0000e-03 eta: 1:06:16 time: 0.3319 data_time: 0.0216 memory: 21539 grad_norm: 4.6832 loss: 1.5439 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5439 2023/03/08 17:47:41 - mmengine - INFO - Epoch(train) [33][180/660] lr: 1.0000e-03 eta: 1:06:10 time: 0.3376 data_time: 0.0207 memory: 21539 grad_norm: 4.8681 loss: 1.6397 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6397 2023/03/08 17:47:47 - mmengine - INFO - Epoch(train) [33][200/660] lr: 1.0000e-03 eta: 1:06:03 time: 0.3328 data_time: 0.0222 memory: 21539 grad_norm: 4.8426 loss: 1.6115 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.6115 2023/03/08 17:47:54 - mmengine - INFO - Epoch(train) [33][220/660] lr: 1.0000e-03 eta: 1:05:56 time: 0.3368 data_time: 0.0217 memory: 21539 grad_norm: 4.8336 loss: 1.6364 top1_acc: 0.4062 top5_acc: 0.8438 loss_cls: 1.6364 2023/03/08 17:48:01 - mmengine - INFO - Epoch(train) [33][240/660] lr: 1.0000e-03 eta: 1:05:49 time: 0.3320 data_time: 0.0215 memory: 21539 grad_norm: 4.7760 loss: 1.7639 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7639 2023/03/08 17:48:07 - mmengine - INFO - Epoch(train) [33][260/660] lr: 1.0000e-03 eta: 1:05:42 time: 0.3368 data_time: 0.0214 memory: 21539 grad_norm: 4.7221 loss: 1.6154 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6154 2023/03/08 17:48:14 - mmengine - INFO - Epoch(train) [33][280/660] lr: 1.0000e-03 eta: 1:05:35 time: 0.3343 data_time: 0.0216 memory: 21539 grad_norm: 4.7094 loss: 1.5987 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5987 2023/03/08 17:48:21 - mmengine - INFO - Epoch(train) [33][300/660] lr: 1.0000e-03 eta: 1:05:29 time: 0.3394 data_time: 0.0218 memory: 21539 grad_norm: 4.7737 loss: 1.7235 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.7235 2023/03/08 17:48:28 - mmengine - INFO - Epoch(train) [33][320/660] lr: 1.0000e-03 eta: 1:05:22 time: 0.3364 data_time: 0.0216 memory: 21539 grad_norm: 4.8191 loss: 1.7113 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 1.7113 2023/03/08 17:48:34 - mmengine - INFO - Epoch(train) [33][340/660] lr: 1.0000e-03 eta: 1:05:15 time: 0.3414 data_time: 0.0225 memory: 21539 grad_norm: 4.8642 loss: 1.7285 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.7285 2023/03/08 17:48:44 - mmengine - INFO - Epoch(train) [33][360/660] lr: 1.0000e-03 eta: 1:05:10 time: 0.4741 data_time: 0.0256 memory: 21539 grad_norm: 4.7205 loss: 1.5704 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5704 2023/03/08 17:48:51 - mmengine - INFO - Epoch(train) [33][380/660] lr: 1.0000e-03 eta: 1:05:03 time: 0.3358 data_time: 0.0218 memory: 21539 grad_norm: 4.7938 loss: 1.6939 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6939 2023/03/08 17:48:57 - mmengine - INFO - Epoch(train) [33][400/660] lr: 1.0000e-03 eta: 1:04:56 time: 0.3359 data_time: 0.0217 memory: 21539 grad_norm: 4.7222 loss: 1.7274 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7274 2023/03/08 17:49:04 - mmengine - INFO - Epoch(train) [33][420/660] lr: 1.0000e-03 eta: 1:04:49 time: 0.3371 data_time: 0.0213 memory: 21539 grad_norm: 4.7587 loss: 1.7582 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 1.7582 2023/03/08 17:49:11 - mmengine - INFO - Epoch(train) [33][440/660] lr: 1.0000e-03 eta: 1:04:42 time: 0.3318 data_time: 0.0211 memory: 21539 grad_norm: 4.8373 loss: 1.6564 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.6564 2023/03/08 17:49:17 - mmengine - INFO - Epoch(train) [33][460/660] lr: 1.0000e-03 eta: 1:04:36 time: 0.3340 data_time: 0.0222 memory: 21539 grad_norm: 4.7736 loss: 1.5447 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.5447 2023/03/08 17:49:24 - mmengine - INFO - Epoch(train) [33][480/660] lr: 1.0000e-03 eta: 1:04:29 time: 0.3317 data_time: 0.0222 memory: 21539 grad_norm: 4.8744 loss: 1.7635 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.7635 2023/03/08 17:49:32 - mmengine - INFO - Epoch(train) [33][500/660] lr: 1.0000e-03 eta: 1:04:22 time: 0.3838 data_time: 0.0233 memory: 21539 grad_norm: 4.8123 loss: 1.5967 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.5967 2023/03/08 17:49:38 - mmengine - INFO - Epoch(train) [33][520/660] lr: 1.0000e-03 eta: 1:04:15 time: 0.3272 data_time: 0.0232 memory: 21539 grad_norm: 4.8447 loss: 1.7104 top1_acc: 0.3750 top5_acc: 0.8438 loss_cls: 1.7104 2023/03/08 17:49:45 - mmengine - INFO - Epoch(train) [33][540/660] lr: 1.0000e-03 eta: 1:04:09 time: 0.3332 data_time: 0.0228 memory: 21539 grad_norm: 4.8413 loss: 1.6606 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6606 2023/03/08 17:49:52 - mmengine - INFO - Epoch(train) [33][560/660] lr: 1.0000e-03 eta: 1:04:02 time: 0.3314 data_time: 0.0226 memory: 21539 grad_norm: 4.8647 loss: 1.6923 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.6923 2023/03/08 17:49:58 - mmengine - INFO - Epoch(train) [33][580/660] lr: 1.0000e-03 eta: 1:03:55 time: 0.3319 data_time: 0.0209 memory: 21539 grad_norm: 4.7950 loss: 1.6397 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.6397 2023/03/08 17:50:05 - mmengine - INFO - Epoch(train) [33][600/660] lr: 1.0000e-03 eta: 1:03:48 time: 0.3328 data_time: 0.0216 memory: 21539 grad_norm: 4.7946 loss: 1.7426 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7426 2023/03/08 17:50:11 - mmengine - INFO - Epoch(train) [33][620/660] lr: 1.0000e-03 eta: 1:03:41 time: 0.3288 data_time: 0.0214 memory: 21539 grad_norm: 4.8568 loss: 1.6018 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.6018 2023/03/08 17:50:18 - mmengine - INFO - Epoch(train) [33][640/660] lr: 1.0000e-03 eta: 1:03:34 time: 0.3287 data_time: 0.0217 memory: 21539 grad_norm: 4.7853 loss: 1.6609 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.6609 2023/03/08 17:50:25 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:50:25 - mmengine - INFO - Epoch(train) [33][660/660] lr: 1.0000e-03 eta: 1:03:27 time: 0.3223 data_time: 0.0193 memory: 21539 grad_norm: 4.9648 loss: 1.7178 top1_acc: 0.5185 top5_acc: 0.7407 loss_cls: 1.7178 2023/03/08 17:50:25 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/03/08 17:50:34 - mmengine - INFO - Epoch(train) [34][ 20/660] lr: 1.0000e-03 eta: 1:03:21 time: 0.4151 data_time: 0.0924 memory: 21539 grad_norm: 4.6652 loss: 1.6972 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6972 2023/03/08 17:50:41 - mmengine - INFO - Epoch(train) [34][ 40/660] lr: 1.0000e-03 eta: 1:03:14 time: 0.3368 data_time: 0.0205 memory: 21539 grad_norm: 4.7940 loss: 1.5690 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.5690 2023/03/08 17:50:47 - mmengine - INFO - Epoch(train) [34][ 60/660] lr: 1.0000e-03 eta: 1:03:08 time: 0.3382 data_time: 0.0221 memory: 21539 grad_norm: 4.8261 loss: 1.7258 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7258 2023/03/08 17:50:54 - mmengine - INFO - Epoch(train) [34][ 80/660] lr: 1.0000e-03 eta: 1:03:01 time: 0.3358 data_time: 0.0197 memory: 21539 grad_norm: 4.8310 loss: 1.6171 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6171 2023/03/08 17:51:01 - mmengine - INFO - Epoch(train) [34][100/660] lr: 1.0000e-03 eta: 1:02:54 time: 0.3456 data_time: 0.0211 memory: 21539 grad_norm: 4.7871 loss: 1.6045 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.6045 2023/03/08 17:51:08 - mmengine - INFO - Epoch(train) [34][120/660] lr: 1.0000e-03 eta: 1:02:47 time: 0.3330 data_time: 0.0194 memory: 21539 grad_norm: 4.8124 loss: 1.6159 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6159 2023/03/08 17:51:14 - mmengine - INFO - Epoch(train) [34][140/660] lr: 1.0000e-03 eta: 1:02:40 time: 0.3382 data_time: 0.0214 memory: 21539 grad_norm: 4.8254 loss: 1.6356 top1_acc: 0.4062 top5_acc: 0.8438 loss_cls: 1.6356 2023/03/08 17:51:21 - mmengine - INFO - Epoch(train) [34][160/660] lr: 1.0000e-03 eta: 1:02:34 time: 0.3328 data_time: 0.0205 memory: 21539 grad_norm: 4.7888 loss: 1.5560 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5560 2023/03/08 17:51:28 - mmengine - INFO - Epoch(train) [34][180/660] lr: 1.0000e-03 eta: 1:02:27 time: 0.3370 data_time: 0.0209 memory: 21539 grad_norm: 4.8396 loss: 1.6644 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 1.6644 2023/03/08 17:51:35 - mmengine - INFO - Epoch(train) [34][200/660] lr: 1.0000e-03 eta: 1:02:20 time: 0.3364 data_time: 0.0203 memory: 21539 grad_norm: 4.8540 loss: 1.7044 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7044 2023/03/08 17:51:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:51:41 - mmengine - INFO - Epoch(train) [34][220/660] lr: 1.0000e-03 eta: 1:02:13 time: 0.3379 data_time: 0.0203 memory: 21539 grad_norm: 4.9461 loss: 1.6808 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.6808 2023/03/08 17:51:48 - mmengine - INFO - Epoch(train) [34][240/660] lr: 1.0000e-03 eta: 1:02:06 time: 0.3356 data_time: 0.0246 memory: 21539 grad_norm: 4.8379 loss: 1.5966 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.5966 2023/03/08 17:51:55 - mmengine - INFO - Epoch(train) [34][260/660] lr: 1.0000e-03 eta: 1:01:59 time: 0.3368 data_time: 0.0209 memory: 21539 grad_norm: 4.8037 loss: 1.7022 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.7022 2023/03/08 17:52:01 - mmengine - INFO - Epoch(train) [34][280/660] lr: 1.0000e-03 eta: 1:01:53 time: 0.3327 data_time: 0.0202 memory: 21539 grad_norm: 4.7996 loss: 1.6708 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6708 2023/03/08 17:52:08 - mmengine - INFO - Epoch(train) [34][300/660] lr: 1.0000e-03 eta: 1:01:46 time: 0.3382 data_time: 0.0213 memory: 21539 grad_norm: 4.8700 loss: 1.6464 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6464 2023/03/08 17:52:15 - mmengine - INFO - Epoch(train) [34][320/660] lr: 1.0000e-03 eta: 1:01:39 time: 0.3331 data_time: 0.0199 memory: 21539 grad_norm: 4.9158 loss: 1.6414 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6414 2023/03/08 17:52:22 - mmengine - INFO - Epoch(train) [34][340/660] lr: 1.0000e-03 eta: 1:01:32 time: 0.3338 data_time: 0.0207 memory: 21539 grad_norm: 4.9192 loss: 1.7674 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7674 2023/03/08 17:52:28 - mmengine - INFO - Epoch(train) [34][360/660] lr: 1.0000e-03 eta: 1:01:25 time: 0.3317 data_time: 0.0209 memory: 21539 grad_norm: 4.7830 loss: 1.6375 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.6375 2023/03/08 17:52:35 - mmengine - INFO - Epoch(train) [34][380/660] lr: 1.0000e-03 eta: 1:01:18 time: 0.3346 data_time: 0.0230 memory: 21539 grad_norm: 4.8773 loss: 1.7277 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.7277 2023/03/08 17:52:42 - mmengine - INFO - Epoch(train) [34][400/660] lr: 1.0000e-03 eta: 1:01:12 time: 0.3333 data_time: 0.0203 memory: 21539 grad_norm: 4.8265 loss: 1.6235 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.6235 2023/03/08 17:52:48 - mmengine - INFO - Epoch(train) [34][420/660] lr: 1.0000e-03 eta: 1:01:05 time: 0.3312 data_time: 0.0214 memory: 21539 grad_norm: 4.8114 loss: 1.6929 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6929 2023/03/08 17:52:55 - mmengine - INFO - Epoch(train) [34][440/660] lr: 1.0000e-03 eta: 1:00:58 time: 0.3349 data_time: 0.0205 memory: 21539 grad_norm: 4.9013 loss: 1.6602 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6602 2023/03/08 17:53:02 - mmengine - INFO - Epoch(train) [34][460/660] lr: 1.0000e-03 eta: 1:00:51 time: 0.3315 data_time: 0.0216 memory: 21539 grad_norm: 4.8434 loss: 1.6430 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.6430 2023/03/08 17:53:08 - mmengine - INFO - Epoch(train) [34][480/660] lr: 1.0000e-03 eta: 1:00:44 time: 0.3278 data_time: 0.0207 memory: 21539 grad_norm: 4.9356 loss: 1.5436 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5436 2023/03/08 17:53:15 - mmengine - INFO - Epoch(train) [34][500/660] lr: 1.0000e-03 eta: 1:00:37 time: 0.3281 data_time: 0.0221 memory: 21539 grad_norm: 4.8842 loss: 1.6757 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6757 2023/03/08 17:53:21 - mmengine - INFO - Epoch(train) [34][520/660] lr: 1.0000e-03 eta: 1:00:30 time: 0.3294 data_time: 0.0204 memory: 21539 grad_norm: 4.8791 loss: 1.6904 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6904 2023/03/08 17:53:28 - mmengine - INFO - Epoch(train) [34][540/660] lr: 1.0000e-03 eta: 1:00:23 time: 0.3272 data_time: 0.0215 memory: 21539 grad_norm: 4.8369 loss: 1.5547 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.5547 2023/03/08 17:53:34 - mmengine - INFO - Epoch(train) [34][560/660] lr: 1.0000e-03 eta: 1:00:17 time: 0.3284 data_time: 0.0210 memory: 21539 grad_norm: 4.8930 loss: 1.7115 top1_acc: 0.6562 top5_acc: 1.0000 loss_cls: 1.7115 2023/03/08 17:53:41 - mmengine - INFO - Epoch(train) [34][580/660] lr: 1.0000e-03 eta: 1:00:10 time: 0.3305 data_time: 0.0251 memory: 21539 grad_norm: 4.8299 loss: 1.6417 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6417 2023/03/08 17:53:48 - mmengine - INFO - Epoch(train) [34][600/660] lr: 1.0000e-03 eta: 1:00:03 time: 0.3315 data_time: 0.0211 memory: 21539 grad_norm: 4.8960 loss: 1.6837 top1_acc: 0.6250 top5_acc: 0.9688 loss_cls: 1.6837 2023/03/08 17:53:54 - mmengine - INFO - Epoch(train) [34][620/660] lr: 1.0000e-03 eta: 0:59:56 time: 0.3283 data_time: 0.0231 memory: 21539 grad_norm: 4.9299 loss: 1.6577 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6577 2023/03/08 17:54:01 - mmengine - INFO - Epoch(train) [34][640/660] lr: 1.0000e-03 eta: 0:59:49 time: 0.3310 data_time: 0.0216 memory: 21539 grad_norm: 4.8542 loss: 1.6117 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6117 2023/03/08 17:54:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:54:07 - mmengine - INFO - Epoch(train) [34][660/660] lr: 1.0000e-03 eta: 0:59:42 time: 0.3205 data_time: 0.0197 memory: 21539 grad_norm: 4.8710 loss: 1.7186 top1_acc: 0.6296 top5_acc: 0.8519 loss_cls: 1.7186 2023/03/08 17:54:15 - mmengine - INFO - Epoch(train) [35][ 20/660] lr: 1.0000e-03 eta: 0:59:36 time: 0.4136 data_time: 0.0866 memory: 21539 grad_norm: 4.8633 loss: 1.5273 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.5273 2023/03/08 17:54:22 - mmengine - INFO - Epoch(train) [35][ 40/660] lr: 1.0000e-03 eta: 0:59:29 time: 0.3348 data_time: 0.0191 memory: 21539 grad_norm: 4.7689 loss: 1.6433 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6433 2023/03/08 17:54:29 - mmengine - INFO - Epoch(train) [35][ 60/660] lr: 1.0000e-03 eta: 0:59:22 time: 0.3417 data_time: 0.0222 memory: 21539 grad_norm: 4.8101 loss: 1.6023 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6023 2023/03/08 17:54:36 - mmengine - INFO - Epoch(train) [35][ 80/660] lr: 1.0000e-03 eta: 0:59:16 time: 0.3345 data_time: 0.0180 memory: 21539 grad_norm: 4.7784 loss: 1.5806 top1_acc: 0.6250 top5_acc: 0.9688 loss_cls: 1.5806 2023/03/08 17:54:43 - mmengine - INFO - Epoch(train) [35][100/660] lr: 1.0000e-03 eta: 0:59:09 time: 0.3514 data_time: 0.0214 memory: 21539 grad_norm: 4.7678 loss: 1.6238 top1_acc: 0.3438 top5_acc: 0.8438 loss_cls: 1.6238 2023/03/08 17:54:49 - mmengine - INFO - Epoch(train) [35][120/660] lr: 1.0000e-03 eta: 0:59:02 time: 0.3346 data_time: 0.0180 memory: 21539 grad_norm: 4.8114 loss: 1.6123 top1_acc: 0.5312 top5_acc: 0.9375 loss_cls: 1.6123 2023/03/08 17:54:56 - mmengine - INFO - Epoch(train) [35][140/660] lr: 1.0000e-03 eta: 0:58:55 time: 0.3455 data_time: 0.0221 memory: 21539 grad_norm: 4.8775 loss: 1.7617 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7617 2023/03/08 17:55:03 - mmengine - INFO - Epoch(train) [35][160/660] lr: 1.0000e-03 eta: 0:58:49 time: 0.3369 data_time: 0.0177 memory: 21539 grad_norm: 4.8844 loss: 1.6500 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6500 2023/03/08 17:55:10 - mmengine - INFO - Epoch(train) [35][180/660] lr: 1.0000e-03 eta: 0:58:42 time: 0.3425 data_time: 0.0218 memory: 21539 grad_norm: 4.8928 loss: 1.5854 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5854 2023/03/08 17:55:17 - mmengine - INFO - Epoch(train) [35][200/660] lr: 1.0000e-03 eta: 0:58:35 time: 0.3339 data_time: 0.0186 memory: 21539 grad_norm: 4.8663 loss: 1.7062 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.7062 2023/03/08 17:55:23 - mmengine - INFO - Epoch(train) [35][220/660] lr: 1.0000e-03 eta: 0:58:28 time: 0.3409 data_time: 0.0222 memory: 21539 grad_norm: 4.8441 loss: 1.6105 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.6105 2023/03/08 17:55:30 - mmengine - INFO - Epoch(train) [35][240/660] lr: 1.0000e-03 eta: 0:58:21 time: 0.3388 data_time: 0.0220 memory: 21539 grad_norm: 4.8214 loss: 1.6319 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6319 2023/03/08 17:55:37 - mmengine - INFO - Epoch(train) [35][260/660] lr: 1.0000e-03 eta: 0:58:15 time: 0.3457 data_time: 0.0221 memory: 21539 grad_norm: 4.9470 loss: 1.6357 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6357 2023/03/08 17:55:44 - mmengine - INFO - Epoch(train) [35][280/660] lr: 1.0000e-03 eta: 0:58:08 time: 0.3417 data_time: 0.0184 memory: 21539 grad_norm: 4.8964 loss: 1.7011 top1_acc: 0.4375 top5_acc: 0.9062 loss_cls: 1.7011 2023/03/08 17:55:51 - mmengine - INFO - Epoch(train) [35][300/660] lr: 1.0000e-03 eta: 0:58:01 time: 0.3407 data_time: 0.0227 memory: 21539 grad_norm: 4.9313 loss: 1.7185 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7185 2023/03/08 17:55:57 - mmengine - INFO - Epoch(train) [35][320/660] lr: 1.0000e-03 eta: 0:57:54 time: 0.3344 data_time: 0.0180 memory: 21539 grad_norm: 4.8921 loss: 1.6123 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6123 2023/03/08 17:56:04 - mmengine - INFO - Epoch(train) [35][340/660] lr: 1.0000e-03 eta: 0:57:48 time: 0.3418 data_time: 0.0234 memory: 21539 grad_norm: 4.9202 loss: 1.7764 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7764 2023/03/08 17:56:11 - mmengine - INFO - Epoch(train) [35][360/660] lr: 1.0000e-03 eta: 0:57:41 time: 0.3348 data_time: 0.0179 memory: 21539 grad_norm: 4.9245 loss: 1.7139 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7139 2023/03/08 17:56:18 - mmengine - INFO - Epoch(train) [35][380/660] lr: 1.0000e-03 eta: 0:57:34 time: 0.3419 data_time: 0.0224 memory: 21539 grad_norm: 4.8639 loss: 1.6705 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.6705 2023/03/08 17:56:25 - mmengine - INFO - Epoch(train) [35][400/660] lr: 1.0000e-03 eta: 0:57:27 time: 0.3356 data_time: 0.0180 memory: 21539 grad_norm: 4.9695 loss: 1.6530 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6530 2023/03/08 17:56:31 - mmengine - INFO - Epoch(train) [35][420/660] lr: 1.0000e-03 eta: 0:57:20 time: 0.3407 data_time: 0.0218 memory: 21539 grad_norm: 4.9393 loss: 1.7081 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7081 2023/03/08 17:56:38 - mmengine - INFO - Epoch(train) [35][440/660] lr: 1.0000e-03 eta: 0:57:14 time: 0.3376 data_time: 0.0182 memory: 21539 grad_norm: 4.9668 loss: 1.7169 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.7169 2023/03/08 17:56:45 - mmengine - INFO - Epoch(train) [35][460/660] lr: 1.0000e-03 eta: 0:57:07 time: 0.3441 data_time: 0.0225 memory: 21539 grad_norm: 4.8698 loss: 1.7721 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7721 2023/03/08 17:56:52 - mmengine - INFO - Epoch(train) [35][480/660] lr: 1.0000e-03 eta: 0:57:00 time: 0.3369 data_time: 0.0182 memory: 21539 grad_norm: 4.9766 loss: 1.6084 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6084 2023/03/08 17:56:59 - mmengine - INFO - Epoch(train) [35][500/660] lr: 1.0000e-03 eta: 0:56:53 time: 0.3421 data_time: 0.0225 memory: 21539 grad_norm: 4.8944 loss: 1.8181 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8181 2023/03/08 17:57:05 - mmengine - INFO - Epoch(train) [35][520/660] lr: 1.0000e-03 eta: 0:56:46 time: 0.3361 data_time: 0.0185 memory: 21539 grad_norm: 4.9314 loss: 1.5466 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5466 2023/03/08 17:57:12 - mmengine - INFO - Epoch(train) [35][540/660] lr: 1.0000e-03 eta: 0:56:40 time: 0.3399 data_time: 0.0259 memory: 21539 grad_norm: 4.9138 loss: 1.7858 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7858 2023/03/08 17:57:19 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:57:19 - mmengine - INFO - Epoch(train) [35][560/660] lr: 1.0000e-03 eta: 0:56:33 time: 0.3346 data_time: 0.0193 memory: 21539 grad_norm: 4.8543 loss: 1.5907 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5907 2023/03/08 17:57:26 - mmengine - INFO - Epoch(train) [35][580/660] lr: 1.0000e-03 eta: 0:56:26 time: 0.3344 data_time: 0.0198 memory: 21539 grad_norm: 4.8406 loss: 1.6195 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6195 2023/03/08 17:57:32 - mmengine - INFO - Epoch(train) [35][600/660] lr: 1.0000e-03 eta: 0:56:19 time: 0.3332 data_time: 0.0208 memory: 21539 grad_norm: 4.9160 loss: 1.6995 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.6995 2023/03/08 17:57:39 - mmengine - INFO - Epoch(train) [35][620/660] lr: 1.0000e-03 eta: 0:56:12 time: 0.3336 data_time: 0.0218 memory: 21539 grad_norm: 4.9590 loss: 1.6804 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6804 2023/03/08 17:57:45 - mmengine - INFO - Epoch(train) [35][640/660] lr: 1.0000e-03 eta: 0:56:05 time: 0.3299 data_time: 0.0214 memory: 21539 grad_norm: 4.9087 loss: 1.5795 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5795 2023/03/08 17:57:52 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 17:57:52 - mmengine - INFO - Epoch(train) [35][660/660] lr: 1.0000e-03 eta: 0:55:58 time: 0.3248 data_time: 0.0198 memory: 21539 grad_norm: 4.8905 loss: 1.7134 top1_acc: 0.3704 top5_acc: 0.6667 loss_cls: 1.7134 2023/03/08 17:57:57 - mmengine - INFO - Epoch(val) [35][20/97] eta: 0:00:19 time: 0.2485 data_time: 0.1398 memory: 3261 2023/03/08 17:58:00 - mmengine - INFO - Epoch(val) [35][40/97] eta: 0:00:11 time: 0.1635 data_time: 0.0555 memory: 3261 2023/03/08 17:58:04 - mmengine - INFO - Epoch(val) [35][60/97] eta: 0:00:07 time: 0.1791 data_time: 0.0723 memory: 3261 2023/03/08 17:58:07 - mmengine - INFO - Epoch(val) [35][80/97] eta: 0:00:03 time: 0.1670 data_time: 0.0607 memory: 3261 2023/03/08 17:58:12 - mmengine - INFO - Epoch(val) [35][97/97] acc/top1: 0.3404 acc/top5: 0.6470 acc/mean1: 0.2754 2023/03/08 17:58:21 - mmengine - INFO - Epoch(train) [36][ 20/660] lr: 1.0000e-03 eta: 0:55:52 time: 0.4277 data_time: 0.1012 memory: 21539 grad_norm: 4.8215 loss: 1.6205 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6205 2023/03/08 17:58:27 - mmengine - INFO - Epoch(train) [36][ 40/660] lr: 1.0000e-03 eta: 0:55:46 time: 0.3335 data_time: 0.0217 memory: 21539 grad_norm: 4.8665 loss: 1.5486 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5486 2023/03/08 17:58:34 - mmengine - INFO - Epoch(train) [36][ 60/660] lr: 1.0000e-03 eta: 0:55:39 time: 0.3366 data_time: 0.0219 memory: 21539 grad_norm: 4.9081 loss: 1.6061 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6061 2023/03/08 17:58:41 - mmengine - INFO - Epoch(train) [36][ 80/660] lr: 1.0000e-03 eta: 0:55:32 time: 0.3325 data_time: 0.0206 memory: 21539 grad_norm: 4.8867 loss: 1.6360 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6360 2023/03/08 17:58:48 - mmengine - INFO - Epoch(train) [36][100/660] lr: 1.0000e-03 eta: 0:55:25 time: 0.3381 data_time: 0.0225 memory: 21539 grad_norm: 5.0389 loss: 1.5557 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5557 2023/03/08 17:58:54 - mmengine - INFO - Epoch(train) [36][120/660] lr: 1.0000e-03 eta: 0:55:18 time: 0.3313 data_time: 0.0208 memory: 21539 grad_norm: 4.8320 loss: 1.6175 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6175 2023/03/08 17:59:01 - mmengine - INFO - Epoch(train) [36][140/660] lr: 1.0000e-03 eta: 0:55:12 time: 0.3354 data_time: 0.0213 memory: 21539 grad_norm: 4.9035 loss: 1.6326 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6326 2023/03/08 17:59:08 - mmengine - INFO - Epoch(train) [36][160/660] lr: 1.0000e-03 eta: 0:55:05 time: 0.3395 data_time: 0.0214 memory: 21539 grad_norm: 4.8177 loss: 1.5332 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5332 2023/03/08 17:59:14 - mmengine - INFO - Epoch(train) [36][180/660] lr: 1.0000e-03 eta: 0:54:58 time: 0.3355 data_time: 0.0204 memory: 21539 grad_norm: 4.8802 loss: 1.6133 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6133 2023/03/08 17:59:22 - mmengine - INFO - Epoch(train) [36][200/660] lr: 1.0000e-03 eta: 0:54:51 time: 0.3691 data_time: 0.0218 memory: 21539 grad_norm: 4.8010 loss: 1.7015 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7015 2023/03/08 17:59:29 - mmengine - INFO - Epoch(train) [36][220/660] lr: 1.0000e-03 eta: 0:54:45 time: 0.3473 data_time: 0.0210 memory: 21539 grad_norm: 4.8923 loss: 1.6339 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6339 2023/03/08 17:59:35 - mmengine - INFO - Epoch(train) [36][240/660] lr: 1.0000e-03 eta: 0:54:38 time: 0.3374 data_time: 0.0214 memory: 21539 grad_norm: 4.8522 loss: 1.6771 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6771 2023/03/08 17:59:42 - mmengine - INFO - Epoch(train) [36][260/660] lr: 1.0000e-03 eta: 0:54:31 time: 0.3382 data_time: 0.0210 memory: 21539 grad_norm: 4.8759 loss: 1.5919 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5919 2023/03/08 17:59:49 - mmengine - INFO - Epoch(train) [36][280/660] lr: 1.0000e-03 eta: 0:54:24 time: 0.3373 data_time: 0.0257 memory: 21539 grad_norm: 4.9315 loss: 1.4575 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.4575 2023/03/08 17:59:56 - mmengine - INFO - Epoch(train) [36][300/660] lr: 1.0000e-03 eta: 0:54:18 time: 0.3716 data_time: 0.0540 memory: 21539 grad_norm: 4.9409 loss: 1.6929 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6929 2023/03/08 18:00:03 - mmengine - INFO - Epoch(train) [36][320/660] lr: 1.0000e-03 eta: 0:54:11 time: 0.3344 data_time: 0.0161 memory: 21539 grad_norm: 4.9145 loss: 1.6858 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6858 2023/03/08 18:00:10 - mmengine - INFO - Epoch(train) [36][340/660] lr: 1.0000e-03 eta: 0:54:04 time: 0.3370 data_time: 0.0231 memory: 21539 grad_norm: 4.9985 loss: 1.5404 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5404 2023/03/08 18:00:17 - mmengine - INFO - Epoch(train) [36][360/660] lr: 1.0000e-03 eta: 0:53:57 time: 0.3339 data_time: 0.0207 memory: 21539 grad_norm: 4.9247 loss: 1.6586 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.6586 2023/03/08 18:00:23 - mmengine - INFO - Epoch(train) [36][380/660] lr: 1.0000e-03 eta: 0:53:50 time: 0.3356 data_time: 0.0216 memory: 21539 grad_norm: 4.9450 loss: 1.5641 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5641 2023/03/08 18:00:30 - mmengine - INFO - Epoch(train) [36][400/660] lr: 1.0000e-03 eta: 0:53:44 time: 0.3314 data_time: 0.0218 memory: 21539 grad_norm: 4.9753 loss: 1.7241 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.7241 2023/03/08 18:00:37 - mmengine - INFO - Epoch(train) [36][420/660] lr: 1.0000e-03 eta: 0:53:37 time: 0.3332 data_time: 0.0214 memory: 21539 grad_norm: 4.9692 loss: 1.6251 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6251 2023/03/08 18:00:43 - mmengine - INFO - Epoch(train) [36][440/660] lr: 1.0000e-03 eta: 0:53:30 time: 0.3364 data_time: 0.0219 memory: 21539 grad_norm: 4.8759 loss: 1.6784 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6784 2023/03/08 18:00:50 - mmengine - INFO - Epoch(train) [36][460/660] lr: 1.0000e-03 eta: 0:53:23 time: 0.3334 data_time: 0.0208 memory: 21539 grad_norm: 4.8920 loss: 1.6829 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6829 2023/03/08 18:00:57 - mmengine - INFO - Epoch(train) [36][480/660] lr: 1.0000e-03 eta: 0:53:16 time: 0.3360 data_time: 0.0217 memory: 21539 grad_norm: 4.9573 loss: 1.5924 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5924 2023/03/08 18:01:03 - mmengine - INFO - Epoch(train) [36][500/660] lr: 1.0000e-03 eta: 0:53:10 time: 0.3427 data_time: 0.0211 memory: 21539 grad_norm: 4.9545 loss: 1.6243 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6243 2023/03/08 18:01:10 - mmengine - INFO - Epoch(train) [36][520/660] lr: 1.0000e-03 eta: 0:53:03 time: 0.3320 data_time: 0.0212 memory: 21539 grad_norm: 4.9097 loss: 1.6581 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.6581 2023/03/08 18:01:17 - mmengine - INFO - Epoch(train) [36][540/660] lr: 1.0000e-03 eta: 0:52:56 time: 0.3414 data_time: 0.0215 memory: 21539 grad_norm: 4.8694 loss: 1.6830 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.6830 2023/03/08 18:01:24 - mmengine - INFO - Epoch(train) [36][560/660] lr: 1.0000e-03 eta: 0:52:49 time: 0.3326 data_time: 0.0206 memory: 21539 grad_norm: 4.8798 loss: 1.7266 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7266 2023/03/08 18:01:30 - mmengine - INFO - Epoch(train) [36][580/660] lr: 1.0000e-03 eta: 0:52:42 time: 0.3360 data_time: 0.0214 memory: 21539 grad_norm: 5.0104 loss: 1.6496 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6496 2023/03/08 18:01:37 - mmengine - INFO - Epoch(train) [36][600/660] lr: 1.0000e-03 eta: 0:52:35 time: 0.3313 data_time: 0.0212 memory: 21539 grad_norm: 4.9706 loss: 1.5618 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5618 2023/03/08 18:01:44 - mmengine - INFO - Epoch(train) [36][620/660] lr: 1.0000e-03 eta: 0:52:29 time: 0.3368 data_time: 0.0251 memory: 21539 grad_norm: 4.9108 loss: 1.5905 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5905 2023/03/08 18:01:50 - mmengine - INFO - Epoch(train) [36][640/660] lr: 1.0000e-03 eta: 0:52:22 time: 0.3321 data_time: 0.0218 memory: 21539 grad_norm: 4.8705 loss: 1.5744 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5744 2023/03/08 18:01:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:01:57 - mmengine - INFO - Epoch(train) [36][660/660] lr: 1.0000e-03 eta: 0:52:15 time: 0.3230 data_time: 0.0196 memory: 21539 grad_norm: 4.9374 loss: 1.6769 top1_acc: 0.4074 top5_acc: 0.8889 loss_cls: 1.6769 2023/03/08 18:01:57 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/03/08 18:02:06 - mmengine - INFO - Epoch(train) [37][ 20/660] lr: 1.0000e-03 eta: 0:52:09 time: 0.4028 data_time: 0.0867 memory: 21539 grad_norm: 4.9152 loss: 1.6662 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6662 2023/03/08 18:02:13 - mmengine - INFO - Epoch(train) [37][ 40/660] lr: 1.0000e-03 eta: 0:52:02 time: 0.3324 data_time: 0.0214 memory: 21539 grad_norm: 4.8949 loss: 1.7755 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7755 2023/03/08 18:02:19 - mmengine - INFO - Epoch(train) [37][ 60/660] lr: 1.0000e-03 eta: 0:51:55 time: 0.3368 data_time: 0.0204 memory: 21539 grad_norm: 4.8817 loss: 1.6294 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.6294 2023/03/08 18:02:26 - mmengine - INFO - Epoch(train) [37][ 80/660] lr: 1.0000e-03 eta: 0:51:48 time: 0.3320 data_time: 0.0208 memory: 21539 grad_norm: 4.9385 loss: 1.6849 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6849 2023/03/08 18:02:33 - mmengine - INFO - Epoch(train) [37][100/660] lr: 1.0000e-03 eta: 0:51:41 time: 0.3390 data_time: 0.0216 memory: 21539 grad_norm: 4.8483 loss: 1.7455 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7455 2023/03/08 18:02:39 - mmengine - INFO - Epoch(train) [37][120/660] lr: 1.0000e-03 eta: 0:51:34 time: 0.3350 data_time: 0.0215 memory: 21539 grad_norm: 4.9842 loss: 1.6129 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6129 2023/03/08 18:02:46 - mmengine - INFO - Epoch(train) [37][140/660] lr: 1.0000e-03 eta: 0:51:28 time: 0.3423 data_time: 0.0259 memory: 21539 grad_norm: 4.9256 loss: 1.5606 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5606 2023/03/08 18:02:53 - mmengine - INFO - Epoch(train) [37][160/660] lr: 1.0000e-03 eta: 0:51:21 time: 0.3350 data_time: 0.0218 memory: 21539 grad_norm: 4.8300 loss: 1.7169 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7169 2023/03/08 18:03:00 - mmengine - INFO - Epoch(train) [37][180/660] lr: 1.0000e-03 eta: 0:51:14 time: 0.3389 data_time: 0.0215 memory: 21539 grad_norm: 5.0053 loss: 1.5206 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5206 2023/03/08 18:03:06 - mmengine - INFO - Epoch(train) [37][200/660] lr: 1.0000e-03 eta: 0:51:07 time: 0.3342 data_time: 0.0220 memory: 21539 grad_norm: 4.9671 loss: 1.5937 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.5937 2023/03/08 18:03:13 - mmengine - INFO - Epoch(train) [37][220/660] lr: 1.0000e-03 eta: 0:51:00 time: 0.3346 data_time: 0.0213 memory: 21539 grad_norm: 5.0106 loss: 1.5935 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5935 2023/03/08 18:03:20 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:03:20 - mmengine - INFO - Epoch(train) [37][240/660] lr: 1.0000e-03 eta: 0:50:54 time: 0.3352 data_time: 0.0216 memory: 21539 grad_norm: 4.9100 loss: 1.5942 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5942 2023/03/08 18:03:27 - mmengine - INFO - Epoch(train) [37][260/660] lr: 1.0000e-03 eta: 0:50:47 time: 0.3375 data_time: 0.0214 memory: 21539 grad_norm: 4.9562 loss: 1.6906 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6906 2023/03/08 18:03:33 - mmengine - INFO - Epoch(train) [37][280/660] lr: 1.0000e-03 eta: 0:50:40 time: 0.3312 data_time: 0.0218 memory: 21539 grad_norm: 4.9488 loss: 1.6878 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6878 2023/03/08 18:03:40 - mmengine - INFO - Epoch(train) [37][300/660] lr: 1.0000e-03 eta: 0:50:33 time: 0.3345 data_time: 0.0214 memory: 21539 grad_norm: 4.9501 loss: 1.5161 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5161 2023/03/08 18:03:46 - mmengine - INFO - Epoch(train) [37][320/660] lr: 1.0000e-03 eta: 0:50:26 time: 0.3309 data_time: 0.0211 memory: 21539 grad_norm: 5.0054 loss: 1.6606 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.6606 2023/03/08 18:03:53 - mmengine - INFO - Epoch(train) [37][340/660] lr: 1.0000e-03 eta: 0:50:19 time: 0.3326 data_time: 0.0214 memory: 21539 grad_norm: 4.8651 loss: 1.5886 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5886 2023/03/08 18:04:00 - mmengine - INFO - Epoch(train) [37][360/660] lr: 1.0000e-03 eta: 0:50:13 time: 0.3295 data_time: 0.0222 memory: 21539 grad_norm: 4.9785 loss: 1.6995 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.6995 2023/03/08 18:04:06 - mmengine - INFO - Epoch(train) [37][380/660] lr: 1.0000e-03 eta: 0:50:06 time: 0.3331 data_time: 0.0212 memory: 21539 grad_norm: 5.0154 loss: 1.7037 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7037 2023/03/08 18:04:13 - mmengine - INFO - Epoch(train) [37][400/660] lr: 1.0000e-03 eta: 0:49:59 time: 0.3291 data_time: 0.0219 memory: 21539 grad_norm: 4.9452 loss: 1.6327 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6327 2023/03/08 18:04:20 - mmengine - INFO - Epoch(train) [37][420/660] lr: 1.0000e-03 eta: 0:49:52 time: 0.3346 data_time: 0.0223 memory: 21539 grad_norm: 4.9877 loss: 1.7090 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7090 2023/03/08 18:04:26 - mmengine - INFO - Epoch(train) [37][440/660] lr: 1.0000e-03 eta: 0:49:45 time: 0.3363 data_time: 0.0219 memory: 21539 grad_norm: 4.9725 loss: 1.7053 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7053 2023/03/08 18:04:33 - mmengine - INFO - Epoch(train) [37][460/660] lr: 1.0000e-03 eta: 0:49:38 time: 0.3370 data_time: 0.0219 memory: 21539 grad_norm: 4.9389 loss: 1.6444 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.6444 2023/03/08 18:04:40 - mmengine - INFO - Epoch(train) [37][480/660] lr: 1.0000e-03 eta: 0:49:32 time: 0.3356 data_time: 0.0227 memory: 21539 grad_norm: 4.8742 loss: 1.5880 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5880 2023/03/08 18:04:47 - mmengine - INFO - Epoch(train) [37][500/660] lr: 1.0000e-03 eta: 0:49:25 time: 0.3355 data_time: 0.0255 memory: 21539 grad_norm: 4.9833 loss: 1.6520 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6520 2023/03/08 18:04:53 - mmengine - INFO - Epoch(train) [37][520/660] lr: 1.0000e-03 eta: 0:49:18 time: 0.3329 data_time: 0.0221 memory: 21539 grad_norm: 4.9538 loss: 1.7315 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.7315 2023/03/08 18:05:00 - mmengine - INFO - Epoch(train) [37][540/660] lr: 1.0000e-03 eta: 0:49:11 time: 0.3346 data_time: 0.0212 memory: 21539 grad_norm: 4.9553 loss: 1.4537 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4537 2023/03/08 18:05:07 - mmengine - INFO - Epoch(train) [37][560/660] lr: 1.0000e-03 eta: 0:49:04 time: 0.3301 data_time: 0.0213 memory: 21539 grad_norm: 4.8851 loss: 1.6013 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.6013 2023/03/08 18:05:14 - mmengine - INFO - Epoch(train) [37][580/660] lr: 1.0000e-03 eta: 0:48:58 time: 0.3723 data_time: 0.0207 memory: 21539 grad_norm: 4.9653 loss: 1.6435 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6435 2023/03/08 18:05:21 - mmengine - INFO - Epoch(train) [37][600/660] lr: 1.0000e-03 eta: 0:48:51 time: 0.3303 data_time: 0.0218 memory: 21539 grad_norm: 4.9248 loss: 1.6408 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6408 2023/03/08 18:05:27 - mmengine - INFO - Epoch(train) [37][620/660] lr: 1.0000e-03 eta: 0:48:44 time: 0.3303 data_time: 0.0226 memory: 21539 grad_norm: 4.8879 loss: 1.5089 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5089 2023/03/08 18:05:34 - mmengine - INFO - Epoch(train) [37][640/660] lr: 1.0000e-03 eta: 0:48:37 time: 0.3284 data_time: 0.0225 memory: 21539 grad_norm: 4.9724 loss: 1.6076 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6076 2023/03/08 18:05:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:05:40 - mmengine - INFO - Epoch(train) [37][660/660] lr: 1.0000e-03 eta: 0:48:30 time: 0.3230 data_time: 0.0202 memory: 21539 grad_norm: 5.0864 loss: 1.6462 top1_acc: 0.4074 top5_acc: 0.7407 loss_cls: 1.6462 2023/03/08 18:05:48 - mmengine - INFO - Epoch(train) [38][ 20/660] lr: 1.0000e-03 eta: 0:48:24 time: 0.4116 data_time: 0.0853 memory: 21539 grad_norm: 4.8466 loss: 1.6982 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6982 2023/03/08 18:05:55 - mmengine - INFO - Epoch(train) [38][ 40/660] lr: 1.0000e-03 eta: 0:48:17 time: 0.3335 data_time: 0.0213 memory: 21539 grad_norm: 4.8999 loss: 1.6542 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6542 2023/03/08 18:06:02 - mmengine - INFO - Epoch(train) [38][ 60/660] lr: 1.0000e-03 eta: 0:48:10 time: 0.3398 data_time: 0.0214 memory: 21539 grad_norm: 4.7942 loss: 1.5600 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.5600 2023/03/08 18:06:09 - mmengine - INFO - Epoch(train) [38][ 80/660] lr: 1.0000e-03 eta: 0:48:04 time: 0.3334 data_time: 0.0202 memory: 21539 grad_norm: 5.0308 loss: 1.5684 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5684 2023/03/08 18:06:15 - mmengine - INFO - Epoch(train) [38][100/660] lr: 1.0000e-03 eta: 0:47:57 time: 0.3396 data_time: 0.0223 memory: 21539 grad_norm: 5.0133 loss: 1.6196 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6196 2023/03/08 18:06:22 - mmengine - INFO - Epoch(train) [38][120/660] lr: 1.0000e-03 eta: 0:47:50 time: 0.3343 data_time: 0.0206 memory: 21539 grad_norm: 4.9526 loss: 1.6219 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6219 2023/03/08 18:06:29 - mmengine - INFO - Epoch(train) [38][140/660] lr: 1.0000e-03 eta: 0:47:43 time: 0.3386 data_time: 0.0225 memory: 21539 grad_norm: 5.0027 loss: 1.5816 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5816 2023/03/08 18:06:36 - mmengine - INFO - Epoch(train) [38][160/660] lr: 1.0000e-03 eta: 0:47:36 time: 0.3385 data_time: 0.0192 memory: 21539 grad_norm: 4.9630 loss: 1.6171 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6171 2023/03/08 18:06:42 - mmengine - INFO - Epoch(train) [38][180/660] lr: 1.0000e-03 eta: 0:47:30 time: 0.3408 data_time: 0.0257 memory: 21539 grad_norm: 5.0412 loss: 1.5241 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5241 2023/03/08 18:06:49 - mmengine - INFO - Epoch(train) [38][200/660] lr: 1.0000e-03 eta: 0:47:23 time: 0.3359 data_time: 0.0194 memory: 21539 grad_norm: 4.8792 loss: 1.5837 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5837 2023/03/08 18:06:56 - mmengine - INFO - Epoch(train) [38][220/660] lr: 1.0000e-03 eta: 0:47:16 time: 0.3355 data_time: 0.0205 memory: 21539 grad_norm: 4.9808 loss: 1.7261 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7261 2023/03/08 18:07:03 - mmengine - INFO - Epoch(train) [38][240/660] lr: 1.0000e-03 eta: 0:47:09 time: 0.3362 data_time: 0.0197 memory: 21539 grad_norm: 5.0219 loss: 1.6491 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6491 2023/03/08 18:07:09 - mmengine - INFO - Epoch(train) [38][260/660] lr: 1.0000e-03 eta: 0:47:02 time: 0.3364 data_time: 0.0212 memory: 21539 grad_norm: 4.9927 loss: 1.5999 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5999 2023/03/08 18:07:16 - mmengine - INFO - Epoch(train) [38][280/660] lr: 1.0000e-03 eta: 0:46:56 time: 0.3330 data_time: 0.0201 memory: 21539 grad_norm: 4.9134 loss: 1.6786 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6786 2023/03/08 18:07:23 - mmengine - INFO - Epoch(train) [38][300/660] lr: 1.0000e-03 eta: 0:46:49 time: 0.3357 data_time: 0.0216 memory: 21539 grad_norm: 4.9071 loss: 1.6083 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6083 2023/03/08 18:07:29 - mmengine - INFO - Epoch(train) [38][320/660] lr: 1.0000e-03 eta: 0:46:42 time: 0.3347 data_time: 0.0191 memory: 21539 grad_norm: 5.1114 loss: 1.6242 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6242 2023/03/08 18:07:36 - mmengine - INFO - Epoch(train) [38][340/660] lr: 1.0000e-03 eta: 0:46:35 time: 0.3382 data_time: 0.0204 memory: 21539 grad_norm: 4.9993 loss: 1.5481 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5481 2023/03/08 18:07:43 - mmengine - INFO - Epoch(train) [38][360/660] lr: 1.0000e-03 eta: 0:46:28 time: 0.3331 data_time: 0.0189 memory: 21539 grad_norm: 5.0303 loss: 1.6618 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6618 2023/03/08 18:07:50 - mmengine - INFO - Epoch(train) [38][380/660] lr: 1.0000e-03 eta: 0:46:22 time: 0.3378 data_time: 0.0202 memory: 21539 grad_norm: 4.9846 loss: 1.6546 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6546 2023/03/08 18:07:56 - mmengine - INFO - Epoch(train) [38][400/660] lr: 1.0000e-03 eta: 0:46:15 time: 0.3340 data_time: 0.0203 memory: 21539 grad_norm: 4.9305 loss: 1.6645 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6645 2023/03/08 18:08:03 - mmengine - INFO - Epoch(train) [38][420/660] lr: 1.0000e-03 eta: 0:46:08 time: 0.3439 data_time: 0.0202 memory: 21539 grad_norm: 4.8700 loss: 1.5895 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5895 2023/03/08 18:08:10 - mmengine - INFO - Epoch(train) [38][440/660] lr: 1.0000e-03 eta: 0:46:01 time: 0.3387 data_time: 0.0193 memory: 21539 grad_norm: 4.9850 loss: 1.6592 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6592 2023/03/08 18:08:17 - mmengine - INFO - Epoch(train) [38][460/660] lr: 1.0000e-03 eta: 0:45:54 time: 0.3403 data_time: 0.0213 memory: 21539 grad_norm: 4.9648 loss: 1.6659 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.6659 2023/03/08 18:08:24 - mmengine - INFO - Epoch(train) [38][480/660] lr: 1.0000e-03 eta: 0:45:48 time: 0.3396 data_time: 0.0187 memory: 21539 grad_norm: 5.0972 loss: 1.6676 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6676 2023/03/08 18:08:30 - mmengine - INFO - Epoch(train) [38][500/660] lr: 1.0000e-03 eta: 0:45:41 time: 0.3371 data_time: 0.0202 memory: 21539 grad_norm: 4.9078 loss: 1.6655 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6655 2023/03/08 18:08:37 - mmengine - INFO - Epoch(train) [38][520/660] lr: 1.0000e-03 eta: 0:45:34 time: 0.3350 data_time: 0.0189 memory: 21539 grad_norm: 4.9680 loss: 1.4868 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4868 2023/03/08 18:08:44 - mmengine - INFO - Epoch(train) [38][540/660] lr: 1.0000e-03 eta: 0:45:27 time: 0.3423 data_time: 0.0244 memory: 21539 grad_norm: 4.9869 loss: 1.5025 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5025 2023/03/08 18:08:51 - mmengine - INFO - Epoch(train) [38][560/660] lr: 1.0000e-03 eta: 0:45:20 time: 0.3349 data_time: 0.0197 memory: 21539 grad_norm: 4.9234 loss: 1.5974 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5974 2023/03/08 18:08:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:08:57 - mmengine - INFO - Epoch(train) [38][580/660] lr: 1.0000e-03 eta: 0:45:14 time: 0.3378 data_time: 0.0212 memory: 21539 grad_norm: 5.1058 loss: 1.6319 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6319 2023/03/08 18:09:04 - mmengine - INFO - Epoch(train) [38][600/660] lr: 1.0000e-03 eta: 0:45:07 time: 0.3336 data_time: 0.0197 memory: 21539 grad_norm: 4.9298 loss: 1.5729 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.5729 2023/03/08 18:09:11 - mmengine - INFO - Epoch(train) [38][620/660] lr: 1.0000e-03 eta: 0:45:00 time: 0.3364 data_time: 0.0212 memory: 21539 grad_norm: 5.0052 loss: 1.5971 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5971 2023/03/08 18:09:17 - mmengine - INFO - Epoch(train) [38][640/660] lr: 1.0000e-03 eta: 0:44:53 time: 0.3337 data_time: 0.0188 memory: 21539 grad_norm: 5.0385 loss: 1.5558 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.5558 2023/03/08 18:09:24 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:09:24 - mmengine - INFO - Epoch(train) [38][660/660] lr: 1.0000e-03 eta: 0:44:46 time: 0.3275 data_time: 0.0181 memory: 21539 grad_norm: 5.1224 loss: 1.7552 top1_acc: 0.5556 top5_acc: 0.8519 loss_cls: 1.7552 2023/03/08 18:09:33 - mmengine - INFO - Epoch(train) [39][ 20/660] lr: 1.0000e-03 eta: 0:44:40 time: 0.4288 data_time: 0.0905 memory: 21539 grad_norm: 5.0259 loss: 1.7207 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7207 2023/03/08 18:09:39 - mmengine - INFO - Epoch(train) [39][ 40/660] lr: 1.0000e-03 eta: 0:44:33 time: 0.3378 data_time: 0.0181 memory: 21539 grad_norm: 4.9567 loss: 1.5337 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5337 2023/03/08 18:09:46 - mmengine - INFO - Epoch(train) [39][ 60/660] lr: 1.0000e-03 eta: 0:44:27 time: 0.3459 data_time: 0.0216 memory: 21539 grad_norm: 4.9611 loss: 1.6402 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 1.6402 2023/03/08 18:09:53 - mmengine - INFO - Epoch(train) [39][ 80/660] lr: 1.0000e-03 eta: 0:44:20 time: 0.3375 data_time: 0.0183 memory: 21539 grad_norm: 5.0315 loss: 1.6591 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6591 2023/03/08 18:10:00 - mmengine - INFO - Epoch(train) [39][100/660] lr: 1.0000e-03 eta: 0:44:13 time: 0.3464 data_time: 0.0218 memory: 21539 grad_norm: 4.9711 loss: 1.5649 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5649 2023/03/08 18:10:07 - mmengine - INFO - Epoch(train) [39][120/660] lr: 1.0000e-03 eta: 0:44:06 time: 0.3768 data_time: 0.0188 memory: 21539 grad_norm: 4.9768 loss: 1.6224 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.6224 2023/03/08 18:10:14 - mmengine - INFO - Epoch(train) [39][140/660] lr: 1.0000e-03 eta: 0:44:00 time: 0.3495 data_time: 0.0214 memory: 21539 grad_norm: 4.8810 loss: 1.5442 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5442 2023/03/08 18:10:21 - mmengine - INFO - Epoch(train) [39][160/660] lr: 1.0000e-03 eta: 0:43:53 time: 0.3404 data_time: 0.0185 memory: 21539 grad_norm: 5.0183 loss: 1.6251 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6251 2023/03/08 18:10:28 - mmengine - INFO - Epoch(train) [39][180/660] lr: 1.0000e-03 eta: 0:43:46 time: 0.3471 data_time: 0.0222 memory: 21539 grad_norm: 5.0419 loss: 1.5869 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5869 2023/03/08 18:10:35 - mmengine - INFO - Epoch(train) [39][200/660] lr: 1.0000e-03 eta: 0:43:39 time: 0.3385 data_time: 0.0184 memory: 21539 grad_norm: 4.9549 loss: 1.7372 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.7372 2023/03/08 18:10:42 - mmengine - INFO - Epoch(train) [39][220/660] lr: 1.0000e-03 eta: 0:43:33 time: 0.3437 data_time: 0.0267 memory: 21539 grad_norm: 5.0497 loss: 1.6218 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6218 2023/03/08 18:10:49 - mmengine - INFO - Epoch(train) [39][240/660] lr: 1.0000e-03 eta: 0:43:26 time: 0.3407 data_time: 0.0184 memory: 21539 grad_norm: 5.1216 loss: 1.5005 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5005 2023/03/08 18:10:56 - mmengine - INFO - Epoch(train) [39][260/660] lr: 1.0000e-03 eta: 0:43:19 time: 0.3443 data_time: 0.0228 memory: 21539 grad_norm: 4.9473 loss: 1.5392 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5392 2023/03/08 18:11:02 - mmengine - INFO - Epoch(train) [39][280/660] lr: 1.0000e-03 eta: 0:43:12 time: 0.3392 data_time: 0.0188 memory: 21539 grad_norm: 5.0685 loss: 1.5783 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5783 2023/03/08 18:11:09 - mmengine - INFO - Epoch(train) [39][300/660] lr: 1.0000e-03 eta: 0:43:06 time: 0.3431 data_time: 0.0206 memory: 21539 grad_norm: 5.0897 loss: 1.6744 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6744 2023/03/08 18:11:32 - mmengine - INFO - Epoch(train) [39][320/660] lr: 1.0000e-03 eta: 0:43:03 time: 1.1190 data_time: 0.0192 memory: 21539 grad_norm: 4.9981 loss: 1.6410 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6410 2023/03/08 18:11:38 - mmengine - INFO - Epoch(train) [39][340/660] lr: 1.0000e-03 eta: 0:42:57 time: 0.3416 data_time: 0.0227 memory: 21539 grad_norm: 5.0277 loss: 1.6257 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6257 2023/03/08 18:11:45 - mmengine - INFO - Epoch(train) [39][360/660] lr: 1.0000e-03 eta: 0:42:50 time: 0.3379 data_time: 0.0183 memory: 21539 grad_norm: 5.0347 loss: 1.6139 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.6139 2023/03/08 18:11:52 - mmengine - INFO - Epoch(train) [39][380/660] lr: 1.0000e-03 eta: 0:42:43 time: 0.3413 data_time: 0.0221 memory: 21539 grad_norm: 5.0961 loss: 1.6503 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6503 2023/03/08 18:11:59 - mmengine - INFO - Epoch(train) [39][400/660] lr: 1.0000e-03 eta: 0:42:36 time: 0.3349 data_time: 0.0199 memory: 21539 grad_norm: 4.9688 loss: 1.7695 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.7695 2023/03/08 18:12:06 - mmengine - INFO - Epoch(train) [39][420/660] lr: 1.0000e-03 eta: 0:42:29 time: 0.3427 data_time: 0.0221 memory: 21539 grad_norm: 5.0416 loss: 1.6399 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6399 2023/03/08 18:12:12 - mmengine - INFO - Epoch(train) [39][440/660] lr: 1.0000e-03 eta: 0:42:23 time: 0.3324 data_time: 0.0197 memory: 21539 grad_norm: 4.9236 loss: 1.5079 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5079 2023/03/08 18:12:19 - mmengine - INFO - Epoch(train) [39][460/660] lr: 1.0000e-03 eta: 0:42:16 time: 0.3395 data_time: 0.0225 memory: 21539 grad_norm: 5.0292 loss: 1.6751 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6751 2023/03/08 18:12:26 - mmengine - INFO - Epoch(train) [39][480/660] lr: 1.0000e-03 eta: 0:42:09 time: 0.3314 data_time: 0.0189 memory: 21539 grad_norm: 5.0679 loss: 1.7106 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7106 2023/03/08 18:12:32 - mmengine - INFO - Epoch(train) [39][500/660] lr: 1.0000e-03 eta: 0:42:02 time: 0.3350 data_time: 0.0219 memory: 21539 grad_norm: 5.0564 loss: 1.7442 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7442 2023/03/08 18:12:39 - mmengine - INFO - Epoch(train) [39][520/660] lr: 1.0000e-03 eta: 0:41:55 time: 0.3360 data_time: 0.0192 memory: 21539 grad_norm: 4.9775 loss: 1.5990 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 1.5990 2023/03/08 18:12:46 - mmengine - INFO - Epoch(train) [39][540/660] lr: 1.0000e-03 eta: 0:41:48 time: 0.3387 data_time: 0.0258 memory: 21539 grad_norm: 5.1502 loss: 1.7309 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.7309 2023/03/08 18:12:52 - mmengine - INFO - Epoch(train) [39][560/660] lr: 1.0000e-03 eta: 0:41:42 time: 0.3325 data_time: 0.0205 memory: 21539 grad_norm: 5.0471 loss: 1.6399 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6399 2023/03/08 18:12:59 - mmengine - INFO - Epoch(train) [39][580/660] lr: 1.0000e-03 eta: 0:41:35 time: 0.3353 data_time: 0.0220 memory: 21539 grad_norm: 5.0760 loss: 1.4971 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.4971 2023/03/08 18:13:06 - mmengine - INFO - Epoch(train) [39][600/660] lr: 1.0000e-03 eta: 0:41:28 time: 0.3344 data_time: 0.0193 memory: 21539 grad_norm: 4.9945 loss: 1.4406 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.4406 2023/03/08 18:13:13 - mmengine - INFO - Epoch(train) [39][620/660] lr: 1.0000e-03 eta: 0:41:21 time: 0.3356 data_time: 0.0222 memory: 21539 grad_norm: 5.0512 loss: 1.5043 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5043 2023/03/08 18:13:19 - mmengine - INFO - Epoch(train) [39][640/660] lr: 1.0000e-03 eta: 0:41:14 time: 0.3349 data_time: 0.0195 memory: 21539 grad_norm: 5.0569 loss: 1.5604 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5604 2023/03/08 18:13:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:13:26 - mmengine - INFO - Epoch(train) [39][660/660] lr: 1.0000e-03 eta: 0:41:07 time: 0.3296 data_time: 0.0191 memory: 21539 grad_norm: 5.0757 loss: 1.7602 top1_acc: 0.5185 top5_acc: 0.7037 loss_cls: 1.7602 2023/03/08 18:13:26 - mmengine - INFO - Saving checkpoint at 39 epochs 2023/03/08 18:13:35 - mmengine - INFO - Epoch(train) [40][ 20/660] lr: 1.0000e-03 eta: 0:41:01 time: 0.4103 data_time: 0.0890 memory: 21539 grad_norm: 5.0193 loss: 1.4925 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4925 2023/03/08 18:13:42 - mmengine - INFO - Epoch(train) [40][ 40/660] lr: 1.0000e-03 eta: 0:40:54 time: 0.3340 data_time: 0.0203 memory: 21539 grad_norm: 4.9629 loss: 1.5757 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5757 2023/03/08 18:13:49 - mmengine - INFO - Epoch(train) [40][ 60/660] lr: 1.0000e-03 eta: 0:40:47 time: 0.3444 data_time: 0.0204 memory: 21539 grad_norm: 5.0135 loss: 1.4910 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 1.4910 2023/03/08 18:13:56 - mmengine - INFO - Epoch(train) [40][ 80/660] lr: 1.0000e-03 eta: 0:40:41 time: 0.3689 data_time: 0.0188 memory: 21539 grad_norm: 5.0414 loss: 1.4597 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4597 2023/03/08 18:14:03 - mmengine - INFO - Epoch(train) [40][100/660] lr: 1.0000e-03 eta: 0:40:34 time: 0.3385 data_time: 0.0214 memory: 21539 grad_norm: 5.0788 loss: 1.5600 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5600 2023/03/08 18:14:09 - mmengine - INFO - Epoch(train) [40][120/660] lr: 1.0000e-03 eta: 0:40:27 time: 0.3326 data_time: 0.0196 memory: 21539 grad_norm: 5.0297 loss: 1.5835 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5835 2023/03/08 18:14:16 - mmengine - INFO - Epoch(train) [40][140/660] lr: 1.0000e-03 eta: 0:40:20 time: 0.3366 data_time: 0.0203 memory: 21539 grad_norm: 4.9922 loss: 1.5483 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.5483 2023/03/08 18:14:23 - mmengine - INFO - Epoch(train) [40][160/660] lr: 1.0000e-03 eta: 0:40:14 time: 0.3325 data_time: 0.0194 memory: 21539 grad_norm: 5.0081 loss: 1.5925 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5925 2023/03/08 18:14:30 - mmengine - INFO - Epoch(train) [40][180/660] lr: 1.0000e-03 eta: 0:40:07 time: 0.3441 data_time: 0.0214 memory: 21539 grad_norm: 5.0014 loss: 1.6084 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6084 2023/03/08 18:14:36 - mmengine - INFO - Epoch(train) [40][200/660] lr: 1.0000e-03 eta: 0:40:00 time: 0.3338 data_time: 0.0194 memory: 21539 grad_norm: 5.1951 loss: 1.6179 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6179 2023/03/08 18:14:43 - mmengine - INFO - Epoch(train) [40][220/660] lr: 1.0000e-03 eta: 0:39:53 time: 0.3386 data_time: 0.0212 memory: 21539 grad_norm: 5.0684 loss: 1.5416 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5416 2023/03/08 18:14:50 - mmengine - INFO - Epoch(train) [40][240/660] lr: 1.0000e-03 eta: 0:39:46 time: 0.3334 data_time: 0.0195 memory: 21539 grad_norm: 5.0314 loss: 1.7118 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.7118 2023/03/08 18:14:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:14:57 - mmengine - INFO - Epoch(train) [40][260/660] lr: 1.0000e-03 eta: 0:39:39 time: 0.3372 data_time: 0.0208 memory: 21539 grad_norm: 5.0343 loss: 1.4957 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4957 2023/03/08 18:15:03 - mmengine - INFO - Epoch(train) [40][280/660] lr: 1.0000e-03 eta: 0:39:33 time: 0.3332 data_time: 0.0196 memory: 21539 grad_norm: 5.0664 loss: 1.5125 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5125 2023/03/08 18:15:10 - mmengine - INFO - Epoch(train) [40][300/660] lr: 1.0000e-03 eta: 0:39:26 time: 0.3370 data_time: 0.0213 memory: 21539 grad_norm: 5.0262 loss: 1.5694 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5694 2023/03/08 18:15:17 - mmengine - INFO - Epoch(train) [40][320/660] lr: 1.0000e-03 eta: 0:39:19 time: 0.3372 data_time: 0.0199 memory: 21539 grad_norm: 5.1539 loss: 1.6726 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6726 2023/03/08 18:15:24 - mmengine - INFO - Epoch(train) [40][340/660] lr: 1.0000e-03 eta: 0:39:12 time: 0.3398 data_time: 0.0208 memory: 21539 grad_norm: 5.0212 loss: 1.6153 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6153 2023/03/08 18:15:30 - mmengine - INFO - Epoch(train) [40][360/660] lr: 1.0000e-03 eta: 0:39:05 time: 0.3379 data_time: 0.0198 memory: 21539 grad_norm: 4.9316 loss: 1.5245 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5245 2023/03/08 18:15:37 - mmengine - INFO - Epoch(train) [40][380/660] lr: 1.0000e-03 eta: 0:38:59 time: 0.3434 data_time: 0.0249 memory: 21539 grad_norm: 5.0450 loss: 1.6124 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.6124 2023/03/08 18:15:44 - mmengine - INFO - Epoch(train) [40][400/660] lr: 1.0000e-03 eta: 0:38:52 time: 0.3354 data_time: 0.0199 memory: 21539 grad_norm: 5.1882 loss: 1.6323 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6323 2023/03/08 18:15:51 - mmengine - INFO - Epoch(train) [40][420/660] lr: 1.0000e-03 eta: 0:38:45 time: 0.3371 data_time: 0.0208 memory: 21539 grad_norm: 5.0904 loss: 1.6405 top1_acc: 0.2812 top5_acc: 0.6875 loss_cls: 1.6405 2023/03/08 18:15:57 - mmengine - INFO - Epoch(train) [40][440/660] lr: 1.0000e-03 eta: 0:38:38 time: 0.3330 data_time: 0.0195 memory: 21539 grad_norm: 5.0966 loss: 1.5957 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5957 2023/03/08 18:16:04 - mmengine - INFO - Epoch(train) [40][460/660] lr: 1.0000e-03 eta: 0:38:31 time: 0.3403 data_time: 0.0212 memory: 21539 grad_norm: 5.1012 loss: 1.5560 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5560 2023/03/08 18:16:11 - mmengine - INFO - Epoch(train) [40][480/660] lr: 1.0000e-03 eta: 0:38:25 time: 0.3669 data_time: 0.0196 memory: 21539 grad_norm: 5.0676 loss: 1.6660 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6660 2023/03/08 18:16:18 - mmengine - INFO - Epoch(train) [40][500/660] lr: 1.0000e-03 eta: 0:38:18 time: 0.3379 data_time: 0.0216 memory: 21539 grad_norm: 5.0316 loss: 1.5614 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5614 2023/03/08 18:16:25 - mmengine - INFO - Epoch(train) [40][520/660] lr: 1.0000e-03 eta: 0:38:11 time: 0.3358 data_time: 0.0197 memory: 21539 grad_norm: 4.9991 loss: 1.6102 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6102 2023/03/08 18:16:32 - mmengine - INFO - Epoch(train) [40][540/660] lr: 1.0000e-03 eta: 0:38:04 time: 0.3358 data_time: 0.0215 memory: 21539 grad_norm: 5.0331 loss: 1.6510 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6510 2023/03/08 18:16:38 - mmengine - INFO - Epoch(train) [40][560/660] lr: 1.0000e-03 eta: 0:37:57 time: 0.3322 data_time: 0.0194 memory: 21539 grad_norm: 5.1826 loss: 1.6379 top1_acc: 0.6562 top5_acc: 0.9688 loss_cls: 1.6379 2023/03/08 18:16:45 - mmengine - INFO - Epoch(train) [40][580/660] lr: 1.0000e-03 eta: 0:37:51 time: 0.3371 data_time: 0.0215 memory: 21539 grad_norm: 5.0234 loss: 1.6074 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.6074 2023/03/08 18:16:52 - mmengine - INFO - Epoch(train) [40][600/660] lr: 1.0000e-03 eta: 0:37:44 time: 0.3315 data_time: 0.0203 memory: 21539 grad_norm: 5.0376 loss: 1.6910 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6910 2023/03/08 18:16:58 - mmengine - INFO - Epoch(train) [40][620/660] lr: 1.0000e-03 eta: 0:37:37 time: 0.3382 data_time: 0.0221 memory: 21539 grad_norm: 5.0604 loss: 1.6538 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.6538 2023/03/08 18:17:05 - mmengine - INFO - Epoch(train) [40][640/660] lr: 1.0000e-03 eta: 0:37:30 time: 0.3365 data_time: 0.0199 memory: 21539 grad_norm: 5.0896 loss: 1.5222 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5222 2023/03/08 18:17:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:17:12 - mmengine - INFO - Epoch(train) [40][660/660] lr: 1.0000e-03 eta: 0:37:23 time: 0.3285 data_time: 0.0203 memory: 21539 grad_norm: 5.1025 loss: 1.6483 top1_acc: 0.5185 top5_acc: 0.8148 loss_cls: 1.6483 2023/03/08 18:17:17 - mmengine - INFO - Epoch(val) [40][20/97] eta: 0:00:18 time: 0.2386 data_time: 0.1229 memory: 3261 2023/03/08 18:17:20 - mmengine - INFO - Epoch(val) [40][40/97] eta: 0:00:11 time: 0.1511 data_time: 0.0432 memory: 3261 2023/03/08 18:17:23 - mmengine - INFO - Epoch(val) [40][60/97] eta: 0:00:07 time: 0.1861 data_time: 0.0778 memory: 3261 2023/03/08 18:17:27 - mmengine - INFO - Epoch(val) [40][80/97] eta: 0:00:03 time: 0.1753 data_time: 0.0680 memory: 3261 2023/03/08 18:17:32 - mmengine - INFO - Epoch(val) [40][97/97] acc/top1: 0.3351 acc/top5: 0.6439 acc/mean1: 0.2720 2023/03/08 18:17:40 - mmengine - INFO - Epoch(train) [41][ 20/660] lr: 1.0000e-04 eta: 0:37:17 time: 0.4120 data_time: 0.0874 memory: 21539 grad_norm: 5.0438 loss: 1.5003 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5003 2023/03/08 18:17:47 - mmengine - INFO - Epoch(train) [41][ 40/660] lr: 1.0000e-04 eta: 0:37:10 time: 0.3410 data_time: 0.0216 memory: 21539 grad_norm: 5.0340 loss: 1.5710 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5710 2023/03/08 18:17:53 - mmengine - INFO - Epoch(train) [41][ 60/660] lr: 1.0000e-04 eta: 0:37:03 time: 0.3374 data_time: 0.0207 memory: 21539 grad_norm: 4.9692 loss: 1.5745 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5745 2023/03/08 18:18:00 - mmengine - INFO - Epoch(train) [41][ 80/660] lr: 1.0000e-04 eta: 0:36:56 time: 0.3371 data_time: 0.0214 memory: 21539 grad_norm: 5.0376 loss: 1.6845 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.6845 2023/03/08 18:18:07 - mmengine - INFO - Epoch(train) [41][100/660] lr: 1.0000e-04 eta: 0:36:50 time: 0.3393 data_time: 0.0214 memory: 21539 grad_norm: 4.9856 loss: 1.5859 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5859 2023/03/08 18:18:14 - mmengine - INFO - Epoch(train) [41][120/660] lr: 1.0000e-04 eta: 0:36:43 time: 0.3327 data_time: 0.0220 memory: 21539 grad_norm: 5.0058 loss: 1.6157 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6157 2023/03/08 18:18:20 - mmengine - INFO - Epoch(train) [41][140/660] lr: 1.0000e-04 eta: 0:36:36 time: 0.3345 data_time: 0.0211 memory: 21539 grad_norm: 5.0119 loss: 1.6295 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6295 2023/03/08 18:18:27 - mmengine - INFO - Epoch(train) [41][160/660] lr: 1.0000e-04 eta: 0:36:29 time: 0.3350 data_time: 0.0259 memory: 21539 grad_norm: 4.9557 loss: 1.6195 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6195 2023/03/08 18:18:34 - mmengine - INFO - Epoch(train) [41][180/660] lr: 1.0000e-04 eta: 0:36:22 time: 0.3342 data_time: 0.0209 memory: 21539 grad_norm: 5.0112 loss: 1.5270 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5270 2023/03/08 18:18:40 - mmengine - INFO - Epoch(train) [41][200/660] lr: 1.0000e-04 eta: 0:36:15 time: 0.3341 data_time: 0.0209 memory: 21539 grad_norm: 4.9413 loss: 1.6136 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6136 2023/03/08 18:18:47 - mmengine - INFO - Epoch(train) [41][220/660] lr: 1.0000e-04 eta: 0:36:09 time: 0.3342 data_time: 0.0212 memory: 21539 grad_norm: 5.0909 loss: 1.6582 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6582 2023/03/08 18:18:54 - mmengine - INFO - Epoch(train) [41][240/660] lr: 1.0000e-04 eta: 0:36:02 time: 0.3312 data_time: 0.0212 memory: 21539 grad_norm: 5.0748 loss: 1.5405 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.5405 2023/03/08 18:19:00 - mmengine - INFO - Epoch(train) [41][260/660] lr: 1.0000e-04 eta: 0:35:55 time: 0.3349 data_time: 0.0208 memory: 21539 grad_norm: 5.0395 loss: 1.5516 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5516 2023/03/08 18:19:07 - mmengine - INFO - Epoch(train) [41][280/660] lr: 1.0000e-04 eta: 0:35:48 time: 0.3393 data_time: 0.0213 memory: 21539 grad_norm: 4.9156 loss: 1.5055 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.5055 2023/03/08 18:19:14 - mmengine - INFO - Epoch(train) [41][300/660] lr: 1.0000e-04 eta: 0:35:41 time: 0.3343 data_time: 0.0212 memory: 21539 grad_norm: 4.9759 loss: 1.5871 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5871 2023/03/08 18:19:20 - mmengine - INFO - Epoch(train) [41][320/660] lr: 1.0000e-04 eta: 0:35:34 time: 0.3293 data_time: 0.0221 memory: 21539 grad_norm: 4.8738 loss: 1.7008 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7008 2023/03/08 18:19:27 - mmengine - INFO - Epoch(train) [41][340/660] lr: 1.0000e-04 eta: 0:35:28 time: 0.3339 data_time: 0.0226 memory: 21539 grad_norm: 4.9990 loss: 1.7207 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7207 2023/03/08 18:19:34 - mmengine - INFO - Epoch(train) [41][360/660] lr: 1.0000e-04 eta: 0:35:21 time: 0.3308 data_time: 0.0227 memory: 21539 grad_norm: 4.9438 loss: 1.6127 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6127 2023/03/08 18:19:40 - mmengine - INFO - Epoch(train) [41][380/660] lr: 1.0000e-04 eta: 0:35:14 time: 0.3370 data_time: 0.0218 memory: 21539 grad_norm: 4.9828 loss: 1.5562 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5562 2023/03/08 18:19:47 - mmengine - INFO - Epoch(train) [41][400/660] lr: 1.0000e-04 eta: 0:35:07 time: 0.3358 data_time: 0.0226 memory: 21539 grad_norm: 4.9505 loss: 1.6492 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6492 2023/03/08 18:19:54 - mmengine - INFO - Epoch(train) [41][420/660] lr: 1.0000e-04 eta: 0:35:00 time: 0.3366 data_time: 0.0230 memory: 21539 grad_norm: 4.9852 loss: 1.6025 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6025 2023/03/08 18:20:01 - mmengine - INFO - Epoch(train) [41][440/660] lr: 1.0000e-04 eta: 0:34:53 time: 0.3323 data_time: 0.0232 memory: 21539 grad_norm: 4.9599 loss: 1.5547 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5547 2023/03/08 18:20:07 - mmengine - INFO - Epoch(train) [41][460/660] lr: 1.0000e-04 eta: 0:34:47 time: 0.3341 data_time: 0.0228 memory: 21539 grad_norm: 4.9684 loss: 1.6635 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6635 2023/03/08 18:20:14 - mmengine - INFO - Epoch(train) [41][480/660] lr: 1.0000e-04 eta: 0:34:40 time: 0.3320 data_time: 0.0236 memory: 21539 grad_norm: 5.0506 loss: 1.6190 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6190 2023/03/08 18:20:21 - mmengine - INFO - Epoch(train) [41][500/660] lr: 1.0000e-04 eta: 0:34:33 time: 0.3332 data_time: 0.0220 memory: 21539 grad_norm: 5.0526 loss: 1.5531 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5531 2023/03/08 18:20:27 - mmengine - INFO - Epoch(train) [41][520/660] lr: 1.0000e-04 eta: 0:34:26 time: 0.3310 data_time: 0.0218 memory: 21539 grad_norm: 5.0016 loss: 1.6019 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6019 2023/03/08 18:20:34 - mmengine - INFO - Epoch(train) [41][540/660] lr: 1.0000e-04 eta: 0:34:19 time: 0.3365 data_time: 0.0259 memory: 21539 grad_norm: 5.1423 loss: 1.5625 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5625 2023/03/08 18:20:41 - mmengine - INFO - Epoch(train) [41][560/660] lr: 1.0000e-04 eta: 0:34:13 time: 0.3342 data_time: 0.0213 memory: 21539 grad_norm: 5.0777 loss: 1.5726 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5726 2023/03/08 18:20:47 - mmengine - INFO - Epoch(train) [41][580/660] lr: 1.0000e-04 eta: 0:34:06 time: 0.3407 data_time: 0.0216 memory: 21539 grad_norm: 4.9469 loss: 1.6694 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6694 2023/03/08 18:20:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:20:54 - mmengine - INFO - Epoch(train) [41][600/660] lr: 1.0000e-04 eta: 0:33:59 time: 0.3315 data_time: 0.0223 memory: 21539 grad_norm: 4.9316 loss: 1.5708 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5708 2023/03/08 18:21:01 - mmengine - INFO - Epoch(train) [41][620/660] lr: 1.0000e-04 eta: 0:33:52 time: 0.3345 data_time: 0.0215 memory: 21539 grad_norm: 5.0431 loss: 1.6602 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6602 2023/03/08 18:21:07 - mmengine - INFO - Epoch(train) [41][640/660] lr: 1.0000e-04 eta: 0:33:45 time: 0.3304 data_time: 0.0211 memory: 21539 grad_norm: 4.9420 loss: 1.4738 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4738 2023/03/08 18:21:14 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:21:14 - mmengine - INFO - Epoch(train) [41][660/660] lr: 1.0000e-04 eta: 0:33:38 time: 0.3268 data_time: 0.0205 memory: 21539 grad_norm: 5.1230 loss: 1.6620 top1_acc: 0.6667 top5_acc: 0.7407 loss_cls: 1.6620 2023/03/08 18:21:22 - mmengine - INFO - Epoch(train) [42][ 20/660] lr: 1.0000e-04 eta: 0:33:32 time: 0.4207 data_time: 0.0837 memory: 21539 grad_norm: 5.1043 loss: 1.5294 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.5294 2023/03/08 18:21:29 - mmengine - INFO - Epoch(train) [42][ 40/660] lr: 1.0000e-04 eta: 0:33:25 time: 0.3328 data_time: 0.0213 memory: 21539 grad_norm: 5.0129 loss: 1.5900 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5900 2023/03/08 18:21:36 - mmengine - INFO - Epoch(train) [42][ 60/660] lr: 1.0000e-04 eta: 0:33:18 time: 0.3425 data_time: 0.0206 memory: 21539 grad_norm: 4.9814 loss: 1.5576 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5576 2023/03/08 18:21:43 - mmengine - INFO - Epoch(train) [42][ 80/660] lr: 1.0000e-04 eta: 0:33:12 time: 0.3351 data_time: 0.0210 memory: 21539 grad_norm: 5.0424 loss: 1.6433 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6433 2023/03/08 18:21:49 - mmengine - INFO - Epoch(train) [42][100/660] lr: 1.0000e-04 eta: 0:33:05 time: 0.3414 data_time: 0.0206 memory: 21539 grad_norm: 4.9339 loss: 1.5626 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5626 2023/03/08 18:21:56 - mmengine - INFO - Epoch(train) [42][120/660] lr: 1.0000e-04 eta: 0:32:58 time: 0.3337 data_time: 0.0209 memory: 21539 grad_norm: 5.0471 loss: 1.6226 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6226 2023/03/08 18:22:03 - mmengine - INFO - Epoch(train) [42][140/660] lr: 1.0000e-04 eta: 0:32:51 time: 0.3379 data_time: 0.0212 memory: 21539 grad_norm: 4.9364 loss: 1.5331 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5331 2023/03/08 18:22:09 - mmengine - INFO - Epoch(train) [42][160/660] lr: 1.0000e-04 eta: 0:32:44 time: 0.3351 data_time: 0.0211 memory: 21539 grad_norm: 4.9956 loss: 1.5771 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5771 2023/03/08 18:22:16 - mmengine - INFO - Epoch(train) [42][180/660] lr: 1.0000e-04 eta: 0:32:37 time: 0.3393 data_time: 0.0216 memory: 21539 grad_norm: 5.0611 loss: 1.5402 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5402 2023/03/08 18:22:23 - mmengine - INFO - Epoch(train) [42][200/660] lr: 1.0000e-04 eta: 0:32:31 time: 0.3347 data_time: 0.0205 memory: 21539 grad_norm: 5.0544 loss: 1.6338 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6338 2023/03/08 18:22:30 - mmengine - INFO - Epoch(train) [42][220/660] lr: 1.0000e-04 eta: 0:32:24 time: 0.3404 data_time: 0.0219 memory: 21539 grad_norm: 5.0170 loss: 1.6002 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6002 2023/03/08 18:22:37 - mmengine - INFO - Epoch(train) [42][240/660] lr: 1.0000e-04 eta: 0:32:17 time: 0.3384 data_time: 0.0250 memory: 21539 grad_norm: 5.0531 loss: 1.6115 top1_acc: 0.6562 top5_acc: 1.0000 loss_cls: 1.6115 2023/03/08 18:22:43 - mmengine - INFO - Epoch(train) [42][260/660] lr: 1.0000e-04 eta: 0:32:10 time: 0.3396 data_time: 0.0215 memory: 21539 grad_norm: 5.1611 loss: 1.6071 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6071 2023/03/08 18:22:50 - mmengine - INFO - Epoch(train) [42][280/660] lr: 1.0000e-04 eta: 0:32:03 time: 0.3337 data_time: 0.0211 memory: 21539 grad_norm: 5.1303 loss: 1.7218 top1_acc: 0.3438 top5_acc: 0.7500 loss_cls: 1.7218 2023/03/08 18:22:57 - mmengine - INFO - Epoch(train) [42][300/660] lr: 1.0000e-04 eta: 0:31:57 time: 0.3392 data_time: 0.0218 memory: 21539 grad_norm: 4.9560 loss: 1.5334 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5334 2023/03/08 18:23:03 - mmengine - INFO - Epoch(train) [42][320/660] lr: 1.0000e-04 eta: 0:31:50 time: 0.3330 data_time: 0.0207 memory: 21539 grad_norm: 5.0481 loss: 1.5976 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5976 2023/03/08 18:23:10 - mmengine - INFO - Epoch(train) [42][340/660] lr: 1.0000e-04 eta: 0:31:43 time: 0.3403 data_time: 0.0218 memory: 21539 grad_norm: 4.9184 loss: 1.6144 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6144 2023/03/08 18:23:17 - mmengine - INFO - Epoch(train) [42][360/660] lr: 1.0000e-04 eta: 0:31:36 time: 0.3333 data_time: 0.0210 memory: 21539 grad_norm: 4.8788 loss: 1.5131 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.5131 2023/03/08 18:23:24 - mmengine - INFO - Epoch(train) [42][380/660] lr: 1.0000e-04 eta: 0:31:29 time: 0.3400 data_time: 0.0221 memory: 21539 grad_norm: 5.0266 loss: 1.5685 top1_acc: 0.6562 top5_acc: 0.9688 loss_cls: 1.5685 2023/03/08 18:23:30 - mmengine - INFO - Epoch(train) [42][400/660] lr: 1.0000e-04 eta: 0:31:23 time: 0.3366 data_time: 0.0208 memory: 21539 grad_norm: 4.9310 loss: 1.6258 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6258 2023/03/08 18:23:37 - mmengine - INFO - Epoch(train) [42][420/660] lr: 1.0000e-04 eta: 0:31:16 time: 0.3404 data_time: 0.0220 memory: 21539 grad_norm: 4.9326 loss: 1.7076 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.7076 2023/03/08 18:23:44 - mmengine - INFO - Epoch(train) [42][440/660] lr: 1.0000e-04 eta: 0:31:09 time: 0.3329 data_time: 0.0212 memory: 21539 grad_norm: 4.9627 loss: 1.6581 top1_acc: 0.4062 top5_acc: 0.8750 loss_cls: 1.6581 2023/03/08 18:23:51 - mmengine - INFO - Epoch(train) [42][460/660] lr: 1.0000e-04 eta: 0:31:02 time: 0.3379 data_time: 0.0217 memory: 21539 grad_norm: 5.0729 loss: 1.5558 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5558 2023/03/08 18:23:57 - mmengine - INFO - Epoch(train) [42][480/660] lr: 1.0000e-04 eta: 0:30:55 time: 0.3343 data_time: 0.0204 memory: 21539 grad_norm: 5.0084 loss: 1.6379 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6379 2023/03/08 18:24:04 - mmengine - INFO - Epoch(train) [42][500/660] lr: 1.0000e-04 eta: 0:30:48 time: 0.3379 data_time: 0.0204 memory: 21539 grad_norm: 4.9513 loss: 1.5302 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5302 2023/03/08 18:24:11 - mmengine - INFO - Epoch(train) [42][520/660] lr: 1.0000e-04 eta: 0:30:42 time: 0.3346 data_time: 0.0202 memory: 21539 grad_norm: 5.0728 loss: 1.6098 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6098 2023/03/08 18:24:18 - mmengine - INFO - Epoch(train) [42][540/660] lr: 1.0000e-04 eta: 0:30:35 time: 0.3414 data_time: 0.0211 memory: 21539 grad_norm: 4.9929 loss: 1.4818 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4818 2023/03/08 18:24:24 - mmengine - INFO - Epoch(train) [42][560/660] lr: 1.0000e-04 eta: 0:30:28 time: 0.3343 data_time: 0.0209 memory: 21539 grad_norm: 4.9949 loss: 1.5861 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.5861 2023/03/08 18:24:31 - mmengine - INFO - Epoch(train) [42][580/660] lr: 1.0000e-04 eta: 0:30:21 time: 0.3388 data_time: 0.0218 memory: 21539 grad_norm: 5.0197 loss: 1.5255 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5255 2023/03/08 18:24:38 - mmengine - INFO - Epoch(train) [42][600/660] lr: 1.0000e-04 eta: 0:30:14 time: 0.3377 data_time: 0.0209 memory: 21539 grad_norm: 4.9729 loss: 1.5938 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5938 2023/03/08 18:24:45 - mmengine - INFO - Epoch(train) [42][620/660] lr: 1.0000e-04 eta: 0:30:08 time: 0.3380 data_time: 0.0217 memory: 21539 grad_norm: 4.9814 loss: 1.5367 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5367 2023/03/08 18:24:51 - mmengine - INFO - Epoch(train) [42][640/660] lr: 1.0000e-04 eta: 0:30:01 time: 0.3401 data_time: 0.0240 memory: 21539 grad_norm: 4.9957 loss: 1.5768 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5768 2023/03/08 18:24:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:24:58 - mmengine - INFO - Epoch(train) [42][660/660] lr: 1.0000e-04 eta: 0:29:54 time: 0.3299 data_time: 0.0190 memory: 21539 grad_norm: 5.0409 loss: 1.6326 top1_acc: 0.5556 top5_acc: 0.8148 loss_cls: 1.6326 2023/03/08 18:24:58 - mmengine - INFO - Saving checkpoint at 42 epochs 2023/03/08 18:25:07 - mmengine - INFO - Epoch(train) [43][ 20/660] lr: 1.0000e-04 eta: 0:29:48 time: 0.4186 data_time: 0.0888 memory: 21539 grad_norm: 5.0600 loss: 1.6181 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6181 2023/03/08 18:25:14 - mmengine - INFO - Epoch(train) [43][ 40/660] lr: 1.0000e-04 eta: 0:29:41 time: 0.3338 data_time: 0.0202 memory: 21539 grad_norm: 4.9311 loss: 1.5448 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.5448 2023/03/08 18:25:21 - mmengine - INFO - Epoch(train) [43][ 60/660] lr: 1.0000e-04 eta: 0:29:34 time: 0.3363 data_time: 0.0195 memory: 21539 grad_norm: 5.0161 loss: 1.4867 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.4867 2023/03/08 18:25:28 - mmengine - INFO - Epoch(train) [43][ 80/660] lr: 1.0000e-04 eta: 0:29:27 time: 0.3326 data_time: 0.0197 memory: 21539 grad_norm: 5.0066 loss: 1.5276 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5276 2023/03/08 18:25:35 - mmengine - INFO - Epoch(train) [43][100/660] lr: 1.0000e-04 eta: 0:29:20 time: 0.3694 data_time: 0.0197 memory: 21539 grad_norm: 4.9253 loss: 1.5755 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.5755 2023/03/08 18:25:42 - mmengine - INFO - Epoch(train) [43][120/660] lr: 1.0000e-04 eta: 0:29:14 time: 0.3338 data_time: 0.0196 memory: 21539 grad_norm: 4.8907 loss: 1.4413 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4413 2023/03/08 18:25:48 - mmengine - INFO - Epoch(train) [43][140/660] lr: 1.0000e-04 eta: 0:29:07 time: 0.3406 data_time: 0.0262 memory: 21539 grad_norm: 5.0999 loss: 1.6944 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6944 2023/03/08 18:25:55 - mmengine - INFO - Epoch(train) [43][160/660] lr: 1.0000e-04 eta: 0:29:00 time: 0.3341 data_time: 0.0210 memory: 21539 grad_norm: 5.0033 loss: 1.4414 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4414 2023/03/08 18:26:02 - mmengine - INFO - Epoch(train) [43][180/660] lr: 1.0000e-04 eta: 0:28:53 time: 0.3387 data_time: 0.0202 memory: 21539 grad_norm: 5.0340 loss: 1.4676 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4676 2023/03/08 18:26:09 - mmengine - INFO - Epoch(train) [43][200/660] lr: 1.0000e-04 eta: 0:28:46 time: 0.3326 data_time: 0.0208 memory: 21539 grad_norm: 4.9719 loss: 1.5375 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5375 2023/03/08 18:26:15 - mmengine - INFO - Epoch(train) [43][220/660] lr: 1.0000e-04 eta: 0:28:39 time: 0.3360 data_time: 0.0208 memory: 21539 grad_norm: 4.9660 loss: 1.5331 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5331 2023/03/08 18:26:22 - mmengine - INFO - Epoch(train) [43][240/660] lr: 1.0000e-04 eta: 0:28:33 time: 0.3370 data_time: 0.0206 memory: 21539 grad_norm: 4.9269 loss: 1.5448 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5448 2023/03/08 18:26:29 - mmengine - INFO - Epoch(train) [43][260/660] lr: 1.0000e-04 eta: 0:28:26 time: 0.3355 data_time: 0.0200 memory: 21539 grad_norm: 5.0839 loss: 1.5134 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.5134 2023/03/08 18:26:35 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:26:35 - mmengine - INFO - Epoch(train) [43][280/660] lr: 1.0000e-04 eta: 0:28:19 time: 0.3313 data_time: 0.0213 memory: 21539 grad_norm: 4.9672 loss: 1.5378 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 1.5378 2023/03/08 18:26:42 - mmengine - INFO - Epoch(train) [43][300/660] lr: 1.0000e-04 eta: 0:28:12 time: 0.3339 data_time: 0.0204 memory: 21539 grad_norm: 5.0272 loss: 1.6432 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6432 2023/03/08 18:26:49 - mmengine - INFO - Epoch(train) [43][320/660] lr: 1.0000e-04 eta: 0:28:05 time: 0.3342 data_time: 0.0203 memory: 21539 grad_norm: 4.9358 loss: 1.5790 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.5790 2023/03/08 18:26:55 - mmengine - INFO - Epoch(train) [43][340/660] lr: 1.0000e-04 eta: 0:27:59 time: 0.3351 data_time: 0.0200 memory: 21539 grad_norm: 5.0357 loss: 1.6180 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 1.6180 2023/03/08 18:27:02 - mmengine - INFO - Epoch(train) [43][360/660] lr: 1.0000e-04 eta: 0:27:52 time: 0.3386 data_time: 0.0222 memory: 21539 grad_norm: 4.9602 loss: 1.5550 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.5550 2023/03/08 18:27:09 - mmengine - INFO - Epoch(train) [43][380/660] lr: 1.0000e-04 eta: 0:27:45 time: 0.3323 data_time: 0.0203 memory: 21539 grad_norm: 4.9540 loss: 1.6148 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6148 2023/03/08 18:27:15 - mmengine - INFO - Epoch(train) [43][400/660] lr: 1.0000e-04 eta: 0:27:38 time: 0.3321 data_time: 0.0215 memory: 21539 grad_norm: 4.8807 loss: 1.5393 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5393 2023/03/08 18:27:22 - mmengine - INFO - Epoch(train) [43][420/660] lr: 1.0000e-04 eta: 0:27:31 time: 0.3332 data_time: 0.0214 memory: 21539 grad_norm: 5.0267 loss: 1.5482 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.5482 2023/03/08 18:27:29 - mmengine - INFO - Epoch(train) [43][440/660] lr: 1.0000e-04 eta: 0:27:24 time: 0.3332 data_time: 0.0217 memory: 21539 grad_norm: 4.9483 loss: 1.6862 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.6862 2023/03/08 18:27:36 - mmengine - INFO - Epoch(train) [43][460/660] lr: 1.0000e-04 eta: 0:27:18 time: 0.3378 data_time: 0.0250 memory: 21539 grad_norm: 5.0066 loss: 1.6147 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6147 2023/03/08 18:27:42 - mmengine - INFO - Epoch(train) [43][480/660] lr: 1.0000e-04 eta: 0:27:11 time: 0.3346 data_time: 0.0225 memory: 21539 grad_norm: 5.0685 loss: 1.5046 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5046 2023/03/08 18:27:49 - mmengine - INFO - Epoch(train) [43][500/660] lr: 1.0000e-04 eta: 0:27:04 time: 0.3367 data_time: 0.0207 memory: 21539 grad_norm: 4.9590 loss: 1.4997 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4997 2023/03/08 18:27:56 - mmengine - INFO - Epoch(train) [43][520/660] lr: 1.0000e-04 eta: 0:26:57 time: 0.3339 data_time: 0.0217 memory: 21539 grad_norm: 5.0330 loss: 1.5054 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5054 2023/03/08 18:28:02 - mmengine - INFO - Epoch(train) [43][540/660] lr: 1.0000e-04 eta: 0:26:50 time: 0.3323 data_time: 0.0207 memory: 21539 grad_norm: 5.0311 loss: 1.5675 top1_acc: 0.4688 top5_acc: 0.9062 loss_cls: 1.5675 2023/03/08 18:28:09 - mmengine - INFO - Epoch(train) [43][560/660] lr: 1.0000e-04 eta: 0:26:44 time: 0.3304 data_time: 0.0215 memory: 21539 grad_norm: 5.0484 loss: 1.6382 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.6382 2023/03/08 18:28:16 - mmengine - INFO - Epoch(train) [43][580/660] lr: 1.0000e-04 eta: 0:26:37 time: 0.3387 data_time: 0.0209 memory: 21539 grad_norm: 5.1065 loss: 1.5035 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5035 2023/03/08 18:28:22 - mmengine - INFO - Epoch(train) [43][600/660] lr: 1.0000e-04 eta: 0:26:30 time: 0.3306 data_time: 0.0219 memory: 21539 grad_norm: 5.0037 loss: 1.5921 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5921 2023/03/08 18:28:29 - mmengine - INFO - Epoch(train) [43][620/660] lr: 1.0000e-04 eta: 0:26:23 time: 0.3338 data_time: 0.0204 memory: 21539 grad_norm: 5.1339 loss: 1.6794 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.6794 2023/03/08 18:28:36 - mmengine - INFO - Epoch(train) [43][640/660] lr: 1.0000e-04 eta: 0:26:16 time: 0.3302 data_time: 0.0204 memory: 21539 grad_norm: 5.0197 loss: 1.4335 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.4335 2023/03/08 18:28:43 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:28:43 - mmengine - INFO - Epoch(train) [43][660/660] lr: 1.0000e-04 eta: 0:26:10 time: 0.3650 data_time: 0.0185 memory: 21539 grad_norm: 5.0879 loss: 1.4739 top1_acc: 0.6296 top5_acc: 0.8889 loss_cls: 1.4739 2023/03/08 18:28:51 - mmengine - INFO - Epoch(train) [44][ 20/660] lr: 1.0000e-04 eta: 0:26:03 time: 0.4243 data_time: 0.0850 memory: 21539 grad_norm: 5.0581 loss: 1.6241 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6241 2023/03/08 18:28:58 - mmengine - INFO - Epoch(train) [44][ 40/660] lr: 1.0000e-04 eta: 0:25:56 time: 0.3431 data_time: 0.0192 memory: 21539 grad_norm: 5.0303 loss: 1.6101 top1_acc: 0.4062 top5_acc: 0.8750 loss_cls: 1.6101 2023/03/08 18:29:05 - mmengine - INFO - Epoch(train) [44][ 60/660] lr: 1.0000e-04 eta: 0:25:50 time: 0.3485 data_time: 0.0193 memory: 21539 grad_norm: 4.9324 loss: 1.4419 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4419 2023/03/08 18:29:12 - mmengine - INFO - Epoch(train) [44][ 80/660] lr: 1.0000e-04 eta: 0:25:43 time: 0.3380 data_time: 0.0202 memory: 21539 grad_norm: 4.9727 loss: 1.4831 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4831 2023/03/08 18:29:19 - mmengine - INFO - Epoch(train) [44][100/660] lr: 1.0000e-04 eta: 0:25:36 time: 0.3447 data_time: 0.0251 memory: 21539 grad_norm: 5.0388 loss: 1.5458 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5458 2023/03/08 18:29:26 - mmengine - INFO - Epoch(train) [44][120/660] lr: 1.0000e-04 eta: 0:25:29 time: 0.3357 data_time: 0.0192 memory: 21539 grad_norm: 4.9062 loss: 1.5712 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5712 2023/03/08 18:29:33 - mmengine - INFO - Epoch(train) [44][140/660] lr: 1.0000e-04 eta: 0:25:22 time: 0.3449 data_time: 0.0204 memory: 21539 grad_norm: 4.9012 loss: 1.6781 top1_acc: 0.4375 top5_acc: 0.9062 loss_cls: 1.6781 2023/03/08 18:29:39 - mmengine - INFO - Epoch(train) [44][160/660] lr: 1.0000e-04 eta: 0:25:16 time: 0.3400 data_time: 0.0201 memory: 21539 grad_norm: 5.0286 loss: 1.6402 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.6402 2023/03/08 18:29:46 - mmengine - INFO - Epoch(train) [44][180/660] lr: 1.0000e-04 eta: 0:25:09 time: 0.3400 data_time: 0.0206 memory: 21539 grad_norm: 5.0613 loss: 1.5918 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5918 2023/03/08 18:29:53 - mmengine - INFO - Epoch(train) [44][200/660] lr: 1.0000e-04 eta: 0:25:02 time: 0.3406 data_time: 0.0209 memory: 21539 grad_norm: 5.0037 loss: 1.5383 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5383 2023/03/08 18:30:00 - mmengine - INFO - Epoch(train) [44][220/660] lr: 1.0000e-04 eta: 0:24:55 time: 0.3413 data_time: 0.0207 memory: 21539 grad_norm: 5.0349 loss: 1.6017 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6017 2023/03/08 18:30:07 - mmengine - INFO - Epoch(train) [44][240/660] lr: 1.0000e-04 eta: 0:24:48 time: 0.3762 data_time: 0.0200 memory: 21539 grad_norm: 5.0394 loss: 1.5840 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.5840 2023/03/08 18:30:14 - mmengine - INFO - Epoch(train) [44][260/660] lr: 1.0000e-04 eta: 0:24:42 time: 0.3400 data_time: 0.0197 memory: 21539 grad_norm: 4.9954 loss: 1.5911 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5911 2023/03/08 18:30:21 - mmengine - INFO - Epoch(train) [44][280/660] lr: 1.0000e-04 eta: 0:24:35 time: 0.3375 data_time: 0.0211 memory: 21539 grad_norm: 5.0227 loss: 1.5312 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.5312 2023/03/08 18:30:28 - mmengine - INFO - Epoch(train) [44][300/660] lr: 1.0000e-04 eta: 0:24:28 time: 0.3385 data_time: 0.0196 memory: 21539 grad_norm: 5.0362 loss: 1.7524 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7524 2023/03/08 18:30:34 - mmengine - INFO - Epoch(train) [44][320/660] lr: 1.0000e-04 eta: 0:24:21 time: 0.3344 data_time: 0.0196 memory: 21539 grad_norm: 5.0959 loss: 1.6224 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6224 2023/03/08 18:30:41 - mmengine - INFO - Epoch(train) [44][340/660] lr: 1.0000e-04 eta: 0:24:14 time: 0.3425 data_time: 0.0204 memory: 21539 grad_norm: 4.9406 loss: 1.4609 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.4609 2023/03/08 18:30:48 - mmengine - INFO - Epoch(train) [44][360/660] lr: 1.0000e-04 eta: 0:24:08 time: 0.3380 data_time: 0.0202 memory: 21539 grad_norm: 5.1262 loss: 1.5515 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.5515 2023/03/08 18:30:55 - mmengine - INFO - Epoch(train) [44][380/660] lr: 1.0000e-04 eta: 0:24:01 time: 0.3374 data_time: 0.0206 memory: 21539 grad_norm: 5.0040 loss: 1.5828 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5828 2023/03/08 18:31:02 - mmengine - INFO - Epoch(train) [44][400/660] lr: 1.0000e-04 eta: 0:23:54 time: 0.3404 data_time: 0.0242 memory: 21539 grad_norm: 5.0450 loss: 1.4428 top1_acc: 0.5000 top5_acc: 0.9062 loss_cls: 1.4428 2023/03/08 18:31:08 - mmengine - INFO - Epoch(train) [44][420/660] lr: 1.0000e-04 eta: 0:23:47 time: 0.3378 data_time: 0.0200 memory: 21539 grad_norm: 5.0256 loss: 1.5777 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.5777 2023/03/08 18:31:15 - mmengine - INFO - Epoch(train) [44][440/660] lr: 1.0000e-04 eta: 0:23:40 time: 0.3353 data_time: 0.0202 memory: 21539 grad_norm: 4.9781 loss: 1.5201 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5201 2023/03/08 18:31:22 - mmengine - INFO - Epoch(train) [44][460/660] lr: 1.0000e-04 eta: 0:23:34 time: 0.3352 data_time: 0.0202 memory: 21539 grad_norm: 5.1254 loss: 1.6102 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6102 2023/03/08 18:31:28 - mmengine - INFO - Epoch(train) [44][480/660] lr: 1.0000e-04 eta: 0:23:27 time: 0.3364 data_time: 0.0205 memory: 21539 grad_norm: 5.1113 loss: 1.5034 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5034 2023/03/08 18:31:35 - mmengine - INFO - Epoch(train) [44][500/660] lr: 1.0000e-04 eta: 0:23:20 time: 0.3367 data_time: 0.0207 memory: 21539 grad_norm: 4.9085 loss: 1.5348 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5348 2023/03/08 18:31:42 - mmengine - INFO - Epoch(train) [44][520/660] lr: 1.0000e-04 eta: 0:23:13 time: 0.3350 data_time: 0.0209 memory: 21539 grad_norm: 5.0166 loss: 1.5988 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.5988 2023/03/08 18:31:49 - mmengine - INFO - Epoch(train) [44][540/660] lr: 1.0000e-04 eta: 0:23:06 time: 0.3358 data_time: 0.0207 memory: 21539 grad_norm: 5.0508 loss: 1.5290 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5290 2023/03/08 18:31:55 - mmengine - INFO - Epoch(train) [44][560/660] lr: 1.0000e-04 eta: 0:23:00 time: 0.3360 data_time: 0.0207 memory: 21539 grad_norm: 5.0768 loss: 1.5141 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5141 2023/03/08 18:32:02 - mmengine - INFO - Epoch(train) [44][580/660] lr: 1.0000e-04 eta: 0:22:53 time: 0.3352 data_time: 0.0203 memory: 21539 grad_norm: 5.1049 loss: 1.5745 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5745 2023/03/08 18:32:09 - mmengine - INFO - Epoch(train) [44][600/660] lr: 1.0000e-04 eta: 0:22:46 time: 0.3340 data_time: 0.0203 memory: 21539 grad_norm: 5.0090 loss: 1.5672 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5672 2023/03/08 18:32:15 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:32:15 - mmengine - INFO - Epoch(train) [44][620/660] lr: 1.0000e-04 eta: 0:22:39 time: 0.3388 data_time: 0.0212 memory: 21539 grad_norm: 5.0178 loss: 1.6058 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.6058 2023/03/08 18:32:22 - mmengine - INFO - Epoch(train) [44][640/660] lr: 1.0000e-04 eta: 0:22:32 time: 0.3307 data_time: 0.0211 memory: 21539 grad_norm: 4.9866 loss: 1.4719 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.4719 2023/03/08 18:32:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:32:29 - mmengine - INFO - Epoch(train) [44][660/660] lr: 1.0000e-04 eta: 0:22:25 time: 0.3265 data_time: 0.0187 memory: 21539 grad_norm: 4.9964 loss: 1.6089 top1_acc: 0.5556 top5_acc: 0.8148 loss_cls: 1.6089 2023/03/08 18:32:37 - mmengine - INFO - Epoch(train) [45][ 20/660] lr: 1.0000e-04 eta: 0:22:19 time: 0.4157 data_time: 0.0924 memory: 21539 grad_norm: 4.9665 loss: 1.4798 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.4798 2023/03/08 18:32:44 - mmengine - INFO - Epoch(train) [45][ 40/660] lr: 1.0000e-04 eta: 0:22:12 time: 0.3393 data_time: 0.0226 memory: 21539 grad_norm: 5.0661 loss: 1.5235 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5235 2023/03/08 18:32:51 - mmengine - INFO - Epoch(train) [45][ 60/660] lr: 1.0000e-04 eta: 0:22:05 time: 0.3385 data_time: 0.0214 memory: 21539 grad_norm: 5.0206 loss: 1.6497 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6497 2023/03/08 18:32:57 - mmengine - INFO - Epoch(train) [45][ 80/660] lr: 1.0000e-04 eta: 0:21:58 time: 0.3371 data_time: 0.0193 memory: 21539 grad_norm: 5.0247 loss: 1.6730 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.6730 2023/03/08 18:33:04 - mmengine - INFO - Epoch(train) [45][100/660] lr: 1.0000e-04 eta: 0:21:52 time: 0.3413 data_time: 0.0211 memory: 21539 grad_norm: 5.0206 loss: 1.4684 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4684 2023/03/08 18:33:11 - mmengine - INFO - Epoch(train) [45][120/660] lr: 1.0000e-04 eta: 0:21:45 time: 0.3353 data_time: 0.0196 memory: 21539 grad_norm: 4.9560 loss: 1.5558 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.5558 2023/03/08 18:33:22 - mmengine - INFO - Epoch(train) [45][140/660] lr: 1.0000e-04 eta: 0:21:39 time: 0.5570 data_time: 0.0214 memory: 21539 grad_norm: 4.9308 loss: 1.6357 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6357 2023/03/08 18:33:29 - mmengine - INFO - Epoch(train) [45][160/660] lr: 1.0000e-04 eta: 0:21:32 time: 0.3337 data_time: 0.0190 memory: 21539 grad_norm: 5.0167 loss: 1.5365 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 1.5365 2023/03/08 18:33:35 - mmengine - INFO - Epoch(train) [45][180/660] lr: 1.0000e-04 eta: 0:21:25 time: 0.3426 data_time: 0.0232 memory: 21539 grad_norm: 5.0021 loss: 1.4851 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.4851 2023/03/08 18:33:42 - mmengine - INFO - Epoch(train) [45][200/660] lr: 1.0000e-04 eta: 0:21:18 time: 0.3334 data_time: 0.0197 memory: 21539 grad_norm: 5.0950 loss: 1.5370 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5370 2023/03/08 18:33:49 - mmengine - INFO - Epoch(train) [45][220/660] lr: 1.0000e-04 eta: 0:21:11 time: 0.3459 data_time: 0.0218 memory: 21539 grad_norm: 5.1026 loss: 1.5164 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.5164 2023/03/08 18:33:56 - mmengine - INFO - Epoch(train) [45][240/660] lr: 1.0000e-04 eta: 0:21:05 time: 0.3363 data_time: 0.0196 memory: 21539 grad_norm: 5.0496 loss: 1.5497 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5497 2023/03/08 18:34:03 - mmengine - INFO - Epoch(train) [45][260/660] lr: 1.0000e-04 eta: 0:20:58 time: 0.3377 data_time: 0.0215 memory: 21539 grad_norm: 5.0021 loss: 1.6329 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6329 2023/03/08 18:34:09 - mmengine - INFO - Epoch(train) [45][280/660] lr: 1.0000e-04 eta: 0:20:51 time: 0.3339 data_time: 0.0198 memory: 21539 grad_norm: 4.8411 loss: 1.5436 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5436 2023/03/08 18:34:16 - mmengine - INFO - Epoch(train) [45][300/660] lr: 1.0000e-04 eta: 0:20:44 time: 0.3419 data_time: 0.0250 memory: 21539 grad_norm: 5.0288 loss: 1.4361 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.4361 2023/03/08 18:34:23 - mmengine - INFO - Epoch(train) [45][320/660] lr: 1.0000e-04 eta: 0:20:37 time: 0.3345 data_time: 0.0204 memory: 21539 grad_norm: 5.1099 loss: 1.6075 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6075 2023/03/08 18:34:30 - mmengine - INFO - Epoch(train) [45][340/660] lr: 1.0000e-04 eta: 0:20:31 time: 0.3405 data_time: 0.0208 memory: 21539 grad_norm: 5.0114 loss: 1.5717 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5717 2023/03/08 18:34:36 - mmengine - INFO - Epoch(train) [45][360/660] lr: 1.0000e-04 eta: 0:20:24 time: 0.3329 data_time: 0.0197 memory: 21539 grad_norm: 4.9317 loss: 1.4390 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4390 2023/03/08 18:34:43 - mmengine - INFO - Epoch(train) [45][380/660] lr: 1.0000e-04 eta: 0:20:17 time: 0.3388 data_time: 0.0226 memory: 21539 grad_norm: 5.0588 loss: 1.5262 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.5262 2023/03/08 18:34:50 - mmengine - INFO - Epoch(train) [45][400/660] lr: 1.0000e-04 eta: 0:20:10 time: 0.3348 data_time: 0.0197 memory: 21539 grad_norm: 4.9770 loss: 1.6018 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.6018 2023/03/08 18:34:56 - mmengine - INFO - Epoch(train) [45][420/660] lr: 1.0000e-04 eta: 0:20:03 time: 0.3377 data_time: 0.0211 memory: 21539 grad_norm: 4.9397 loss: 1.5553 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5553 2023/03/08 18:35:03 - mmengine - INFO - Epoch(train) [45][440/660] lr: 1.0000e-04 eta: 0:19:57 time: 0.3328 data_time: 0.0199 memory: 21539 grad_norm: 5.0732 loss: 1.6476 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6476 2023/03/08 18:35:10 - mmengine - INFO - Epoch(train) [45][460/660] lr: 1.0000e-04 eta: 0:19:50 time: 0.3393 data_time: 0.0210 memory: 21539 grad_norm: 4.9177 loss: 1.5874 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5874 2023/03/08 18:35:17 - mmengine - INFO - Epoch(train) [45][480/660] lr: 1.0000e-04 eta: 0:19:43 time: 0.3339 data_time: 0.0219 memory: 21539 grad_norm: 5.0137 loss: 1.4603 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4603 2023/03/08 18:35:23 - mmengine - INFO - Epoch(train) [45][500/660] lr: 1.0000e-04 eta: 0:19:36 time: 0.3375 data_time: 0.0218 memory: 21539 grad_norm: 4.9320 loss: 1.5661 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5661 2023/03/08 18:35:30 - mmengine - INFO - Epoch(train) [45][520/660] lr: 1.0000e-04 eta: 0:19:29 time: 0.3376 data_time: 0.0225 memory: 21539 grad_norm: 4.9688 loss: 1.5965 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5965 2023/03/08 18:35:37 - mmengine - INFO - Epoch(train) [45][540/660] lr: 1.0000e-04 eta: 0:19:22 time: 0.3346 data_time: 0.0224 memory: 21539 grad_norm: 5.0174 loss: 1.6632 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6632 2023/03/08 18:35:43 - mmengine - INFO - Epoch(train) [45][560/660] lr: 1.0000e-04 eta: 0:19:16 time: 0.3333 data_time: 0.0233 memory: 21539 grad_norm: 5.0398 loss: 1.5481 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.5481 2023/03/08 18:35:50 - mmengine - INFO - Epoch(train) [45][580/660] lr: 1.0000e-04 eta: 0:19:09 time: 0.3355 data_time: 0.0221 memory: 21539 grad_norm: 5.0909 loss: 1.5946 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5946 2023/03/08 18:35:57 - mmengine - INFO - Epoch(train) [45][600/660] lr: 1.0000e-04 eta: 0:19:02 time: 0.3361 data_time: 0.0228 memory: 21539 grad_norm: 4.9619 loss: 1.5082 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5082 2023/03/08 18:36:04 - mmengine - INFO - Epoch(train) [45][620/660] lr: 1.0000e-04 eta: 0:18:55 time: 0.3431 data_time: 0.0252 memory: 21539 grad_norm: 4.9965 loss: 1.5738 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5738 2023/03/08 18:36:10 - mmengine - INFO - Epoch(train) [45][640/660] lr: 1.0000e-04 eta: 0:18:48 time: 0.3363 data_time: 0.0214 memory: 21539 grad_norm: 5.0541 loss: 1.5894 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5894 2023/03/08 18:36:17 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:36:17 - mmengine - INFO - Epoch(train) [45][660/660] lr: 1.0000e-04 eta: 0:18:42 time: 0.3296 data_time: 0.0190 memory: 21539 grad_norm: 5.1180 loss: 1.5280 top1_acc: 0.5185 top5_acc: 0.8148 loss_cls: 1.5280 2023/03/08 18:36:17 - mmengine - INFO - Saving checkpoint at 45 epochs 2023/03/08 18:36:23 - mmengine - INFO - Epoch(val) [45][20/97] eta: 0:00:19 time: 0.2544 data_time: 0.1444 memory: 3261 2023/03/08 18:36:27 - mmengine - INFO - Epoch(val) [45][40/97] eta: 0:00:12 time: 0.1776 data_time: 0.0697 memory: 3261 2023/03/08 18:36:31 - mmengine - INFO - Epoch(val) [45][60/97] eta: 0:00:07 time: 0.1972 data_time: 0.0878 memory: 3261 2023/03/08 18:36:35 - mmengine - INFO - Epoch(val) [45][80/97] eta: 0:00:03 time: 0.1993 data_time: 0.0924 memory: 3261 2023/03/08 18:36:39 - mmengine - INFO - Epoch(val) [45][97/97] acc/top1: 0.3388 acc/top5: 0.6457 acc/mean1: 0.2743 2023/03/08 18:36:47 - mmengine - INFO - Epoch(train) [46][ 20/660] lr: 1.0000e-04 eta: 0:18:35 time: 0.4124 data_time: 0.0969 memory: 21539 grad_norm: 4.9889 loss: 1.6333 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6333 2023/03/08 18:36:53 - mmengine - INFO - Epoch(train) [46][ 40/660] lr: 1.0000e-04 eta: 0:18:28 time: 0.3318 data_time: 0.0210 memory: 21539 grad_norm: 5.0190 loss: 1.5531 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5531 2023/03/08 18:37:00 - mmengine - INFO - Epoch(train) [46][ 60/660] lr: 1.0000e-04 eta: 0:18:21 time: 0.3345 data_time: 0.0214 memory: 21539 grad_norm: 5.0950 loss: 1.6203 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6203 2023/03/08 18:37:07 - mmengine - INFO - Epoch(train) [46][ 80/660] lr: 1.0000e-04 eta: 0:18:15 time: 0.3341 data_time: 0.0206 memory: 21539 grad_norm: 4.9784 loss: 1.4791 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4791 2023/03/08 18:37:14 - mmengine - INFO - Epoch(train) [46][100/660] lr: 1.0000e-04 eta: 0:18:08 time: 0.3370 data_time: 0.0225 memory: 21539 grad_norm: 5.0551 loss: 1.6693 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.6693 2023/03/08 18:37:20 - mmengine - INFO - Epoch(train) [46][120/660] lr: 1.0000e-04 eta: 0:18:01 time: 0.3307 data_time: 0.0209 memory: 21539 grad_norm: 5.0425 loss: 1.5654 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5654 2023/03/08 18:37:27 - mmengine - INFO - Epoch(train) [46][140/660] lr: 1.0000e-04 eta: 0:17:54 time: 0.3365 data_time: 0.0230 memory: 21539 grad_norm: 5.0741 loss: 1.6502 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6502 2023/03/08 18:37:34 - mmengine - INFO - Epoch(train) [46][160/660] lr: 1.0000e-04 eta: 0:17:47 time: 0.3340 data_time: 0.0206 memory: 21539 grad_norm: 4.9793 loss: 1.6124 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6124 2023/03/08 18:37:40 - mmengine - INFO - Epoch(train) [46][180/660] lr: 1.0000e-04 eta: 0:17:40 time: 0.3356 data_time: 0.0221 memory: 21539 grad_norm: 4.9214 loss: 1.6731 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.6731 2023/03/08 18:37:47 - mmengine - INFO - Epoch(train) [46][200/660] lr: 1.0000e-04 eta: 0:17:34 time: 0.3355 data_time: 0.0250 memory: 21539 grad_norm: 4.9796 loss: 1.5439 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5439 2023/03/08 18:37:54 - mmengine - INFO - Epoch(train) [46][220/660] lr: 1.0000e-04 eta: 0:17:27 time: 0.3385 data_time: 0.0212 memory: 21539 grad_norm: 5.0231 loss: 1.5123 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5123 2023/03/08 18:38:00 - mmengine - INFO - Epoch(train) [46][240/660] lr: 1.0000e-04 eta: 0:17:20 time: 0.3356 data_time: 0.0210 memory: 21539 grad_norm: 4.9856 loss: 1.4982 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4982 2023/03/08 18:38:07 - mmengine - INFO - Epoch(train) [46][260/660] lr: 1.0000e-04 eta: 0:17:13 time: 0.3402 data_time: 0.0229 memory: 21539 grad_norm: 5.0394 loss: 1.5871 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5871 2023/03/08 18:38:14 - mmengine - INFO - Epoch(train) [46][280/660] lr: 1.0000e-04 eta: 0:17:06 time: 0.3369 data_time: 0.0206 memory: 21539 grad_norm: 5.0079 loss: 1.5375 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5375 2023/03/08 18:38:21 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:38:21 - mmengine - INFO - Epoch(train) [46][300/660] lr: 1.0000e-04 eta: 0:17:00 time: 0.3382 data_time: 0.0215 memory: 21539 grad_norm: 5.0132 loss: 1.5707 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.5707 2023/03/08 18:38:27 - mmengine - INFO - Epoch(train) [46][320/660] lr: 1.0000e-04 eta: 0:16:53 time: 0.3321 data_time: 0.0210 memory: 21539 grad_norm: 5.1130 loss: 1.5836 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5836 2023/03/08 18:38:34 - mmengine - INFO - Epoch(train) [46][340/660] lr: 1.0000e-04 eta: 0:16:46 time: 0.3346 data_time: 0.0221 memory: 21539 grad_norm: 4.9637 loss: 1.5462 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5462 2023/03/08 18:38:41 - mmengine - INFO - Epoch(train) [46][360/660] lr: 1.0000e-04 eta: 0:16:39 time: 0.3308 data_time: 0.0210 memory: 21539 grad_norm: 4.9728 loss: 1.6271 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.6271 2023/03/08 18:38:47 - mmengine - INFO - Epoch(train) [46][380/660] lr: 1.0000e-04 eta: 0:16:32 time: 0.3345 data_time: 0.0230 memory: 21539 grad_norm: 4.9328 loss: 1.5072 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5072 2023/03/08 18:38:54 - mmengine - INFO - Epoch(train) [46][400/660] lr: 1.0000e-04 eta: 0:16:26 time: 0.3346 data_time: 0.0216 memory: 21539 grad_norm: 5.0644 loss: 1.5258 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5258 2023/03/08 18:39:01 - mmengine - INFO - Epoch(train) [46][420/660] lr: 1.0000e-04 eta: 0:16:19 time: 0.3341 data_time: 0.0215 memory: 21539 grad_norm: 4.9131 loss: 1.6619 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 1.6619 2023/03/08 18:39:07 - mmengine - INFO - Epoch(train) [46][440/660] lr: 1.0000e-04 eta: 0:16:12 time: 0.3310 data_time: 0.0207 memory: 21539 grad_norm: 5.0712 loss: 1.6267 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.6267 2023/03/08 18:39:14 - mmengine - INFO - Epoch(train) [46][460/660] lr: 1.0000e-04 eta: 0:16:05 time: 0.3382 data_time: 0.0223 memory: 21539 grad_norm: 5.0250 loss: 1.5699 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5699 2023/03/08 18:39:21 - mmengine - INFO - Epoch(train) [46][480/660] lr: 1.0000e-04 eta: 0:15:58 time: 0.3326 data_time: 0.0220 memory: 21539 grad_norm: 5.0537 loss: 1.6289 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6289 2023/03/08 18:39:28 - mmengine - INFO - Epoch(train) [46][500/660] lr: 1.0000e-04 eta: 0:15:51 time: 0.3353 data_time: 0.0216 memory: 21539 grad_norm: 4.9232 loss: 1.4661 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.4661 2023/03/08 18:39:34 - mmengine - INFO - Epoch(train) [46][520/660] lr: 1.0000e-04 eta: 0:15:45 time: 0.3297 data_time: 0.0211 memory: 21539 grad_norm: 5.1829 loss: 1.5816 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.5816 2023/03/08 18:39:41 - mmengine - INFO - Epoch(train) [46][540/660] lr: 1.0000e-04 eta: 0:15:38 time: 0.3373 data_time: 0.0263 memory: 21539 grad_norm: 4.9330 loss: 1.5853 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5853 2023/03/08 18:39:48 - mmengine - INFO - Epoch(train) [46][560/660] lr: 1.0000e-04 eta: 0:15:31 time: 0.3342 data_time: 0.0204 memory: 21539 grad_norm: 4.9915 loss: 1.5745 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5745 2023/03/08 18:39:54 - mmengine - INFO - Epoch(train) [46][580/660] lr: 1.0000e-04 eta: 0:15:24 time: 0.3364 data_time: 0.0225 memory: 21539 grad_norm: 5.0311 loss: 1.7784 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 1.7784 2023/03/08 18:40:01 - mmengine - INFO - Epoch(train) [46][600/660] lr: 1.0000e-04 eta: 0:15:17 time: 0.3304 data_time: 0.0211 memory: 21539 grad_norm: 4.9976 loss: 1.4748 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4748 2023/03/08 18:40:08 - mmengine - INFO - Epoch(train) [46][620/660] lr: 1.0000e-04 eta: 0:15:11 time: 0.3747 data_time: 0.0217 memory: 21539 grad_norm: 5.0345 loss: 1.6835 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.6835 2023/03/08 18:40:15 - mmengine - INFO - Epoch(train) [46][640/660] lr: 1.0000e-04 eta: 0:15:04 time: 0.3309 data_time: 0.0223 memory: 21539 grad_norm: 4.9534 loss: 1.6120 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6120 2023/03/08 18:40:22 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:40:22 - mmengine - INFO - Epoch(train) [46][660/660] lr: 1.0000e-04 eta: 0:14:57 time: 0.3305 data_time: 0.0216 memory: 21539 grad_norm: 5.0562 loss: 1.5595 top1_acc: 0.4444 top5_acc: 0.8889 loss_cls: 1.5595 2023/03/08 18:40:30 - mmengine - INFO - Epoch(train) [47][ 20/660] lr: 1.0000e-04 eta: 0:14:50 time: 0.4187 data_time: 0.0867 memory: 21539 grad_norm: 5.1590 loss: 1.4444 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.4444 2023/03/08 18:40:37 - mmengine - INFO - Epoch(train) [47][ 40/660] lr: 1.0000e-04 eta: 0:14:44 time: 0.3356 data_time: 0.0219 memory: 21539 grad_norm: 5.1206 loss: 1.4757 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.4757 2023/03/08 18:40:43 - mmengine - INFO - Epoch(train) [47][ 60/660] lr: 1.0000e-04 eta: 0:14:37 time: 0.3341 data_time: 0.0226 memory: 21539 grad_norm: 4.9932 loss: 1.5864 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 1.5864 2023/03/08 18:40:50 - mmengine - INFO - Epoch(train) [47][ 80/660] lr: 1.0000e-04 eta: 0:14:30 time: 0.3352 data_time: 0.0217 memory: 21539 grad_norm: 4.9876 loss: 1.5228 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5228 2023/03/08 18:40:57 - mmengine - INFO - Epoch(train) [47][100/660] lr: 1.0000e-04 eta: 0:14:23 time: 0.3348 data_time: 0.0224 memory: 21539 grad_norm: 4.9313 loss: 1.5557 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5557 2023/03/08 18:41:04 - mmengine - INFO - Epoch(train) [47][120/660] lr: 1.0000e-04 eta: 0:14:16 time: 0.3323 data_time: 0.0214 memory: 21539 grad_norm: 4.9151 loss: 1.4892 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.4892 2023/03/08 18:41:10 - mmengine - INFO - Epoch(train) [47][140/660] lr: 1.0000e-04 eta: 0:14:10 time: 0.3341 data_time: 0.0223 memory: 21539 grad_norm: 4.9853 loss: 1.4626 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4626 2023/03/08 18:41:17 - mmengine - INFO - Epoch(train) [47][160/660] lr: 1.0000e-04 eta: 0:14:03 time: 0.3311 data_time: 0.0231 memory: 21539 grad_norm: 5.0815 loss: 1.5757 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5757 2023/03/08 18:41:24 - mmengine - INFO - Epoch(train) [47][180/660] lr: 1.0000e-04 eta: 0:13:56 time: 0.3336 data_time: 0.0225 memory: 21539 grad_norm: 4.9641 loss: 1.6220 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6220 2023/03/08 18:41:30 - mmengine - INFO - Epoch(train) [47][200/660] lr: 1.0000e-04 eta: 0:13:49 time: 0.3316 data_time: 0.0234 memory: 21539 grad_norm: 5.1851 loss: 1.7434 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7434 2023/03/08 18:41:37 - mmengine - INFO - Epoch(train) [47][220/660] lr: 1.0000e-04 eta: 0:13:42 time: 0.3331 data_time: 0.0226 memory: 21539 grad_norm: 5.0148 loss: 1.5430 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5430 2023/03/08 18:41:44 - mmengine - INFO - Epoch(train) [47][240/660] lr: 1.0000e-04 eta: 0:13:35 time: 0.3356 data_time: 0.0276 memory: 21539 grad_norm: 5.0766 loss: 1.6539 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.6539 2023/03/08 18:41:50 - mmengine - INFO - Epoch(train) [47][260/660] lr: 1.0000e-04 eta: 0:13:29 time: 0.3408 data_time: 0.0219 memory: 21539 grad_norm: 5.0376 loss: 1.6052 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6052 2023/03/08 18:41:57 - mmengine - INFO - Epoch(train) [47][280/660] lr: 1.0000e-04 eta: 0:13:22 time: 0.3361 data_time: 0.0243 memory: 21539 grad_norm: 5.0945 loss: 1.5750 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5750 2023/03/08 18:42:04 - mmengine - INFO - Epoch(train) [47][300/660] lr: 1.0000e-04 eta: 0:13:15 time: 0.3344 data_time: 0.0229 memory: 21539 grad_norm: 4.9440 loss: 1.6491 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6491 2023/03/08 18:42:10 - mmengine - INFO - Epoch(train) [47][320/660] lr: 1.0000e-04 eta: 0:13:08 time: 0.3342 data_time: 0.0230 memory: 21539 grad_norm: 4.9343 loss: 1.5265 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5265 2023/03/08 18:42:17 - mmengine - INFO - Epoch(train) [47][340/660] lr: 1.0000e-04 eta: 0:13:01 time: 0.3350 data_time: 0.0236 memory: 21539 grad_norm: 5.1070 loss: 1.6235 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.6235 2023/03/08 18:42:24 - mmengine - INFO - Epoch(train) [47][360/660] lr: 1.0000e-04 eta: 0:12:55 time: 0.3305 data_time: 0.0229 memory: 21539 grad_norm: 5.0605 loss: 1.5493 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.5493 2023/03/08 18:42:30 - mmengine - INFO - Epoch(train) [47][380/660] lr: 1.0000e-04 eta: 0:12:48 time: 0.3334 data_time: 0.0210 memory: 21539 grad_norm: 5.0189 loss: 1.4896 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4896 2023/03/08 18:42:37 - mmengine - INFO - Epoch(train) [47][400/660] lr: 1.0000e-04 eta: 0:12:41 time: 0.3344 data_time: 0.0226 memory: 21539 grad_norm: 5.0467 loss: 1.5808 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5808 2023/03/08 18:42:44 - mmengine - INFO - Epoch(train) [47][420/660] lr: 1.0000e-04 eta: 0:12:34 time: 0.3329 data_time: 0.0211 memory: 21539 grad_norm: 4.9994 loss: 1.5976 top1_acc: 0.4375 top5_acc: 0.9062 loss_cls: 1.5976 2023/03/08 18:42:50 - mmengine - INFO - Epoch(train) [47][440/660] lr: 1.0000e-04 eta: 0:12:27 time: 0.3324 data_time: 0.0226 memory: 21539 grad_norm: 5.0980 loss: 1.5833 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5833 2023/03/08 18:42:57 - mmengine - INFO - Epoch(train) [47][460/660] lr: 1.0000e-04 eta: 0:12:21 time: 0.3329 data_time: 0.0210 memory: 21539 grad_norm: 4.9850 loss: 1.6467 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6467 2023/03/08 18:43:04 - mmengine - INFO - Epoch(train) [47][480/660] lr: 1.0000e-04 eta: 0:12:14 time: 0.3312 data_time: 0.0229 memory: 21539 grad_norm: 5.0910 loss: 1.5175 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.5175 2023/03/08 18:43:10 - mmengine - INFO - Epoch(train) [47][500/660] lr: 1.0000e-04 eta: 0:12:07 time: 0.3341 data_time: 0.0221 memory: 21539 grad_norm: 5.0256 loss: 1.6990 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.6990 2023/03/08 18:43:17 - mmengine - INFO - Epoch(train) [47][520/660] lr: 1.0000e-04 eta: 0:12:00 time: 0.3317 data_time: 0.0226 memory: 21539 grad_norm: 5.0975 loss: 1.6909 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6909 2023/03/08 18:43:24 - mmengine - INFO - Epoch(train) [47][540/660] lr: 1.0000e-04 eta: 0:11:53 time: 0.3365 data_time: 0.0206 memory: 21539 grad_norm: 5.0680 loss: 1.5081 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5081 2023/03/08 18:43:30 - mmengine - INFO - Epoch(train) [47][560/660] lr: 1.0000e-04 eta: 0:11:47 time: 0.3322 data_time: 0.0223 memory: 21539 grad_norm: 4.9882 loss: 1.6506 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.6506 2023/03/08 18:43:37 - mmengine - INFO - Epoch(train) [47][580/660] lr: 1.0000e-04 eta: 0:11:40 time: 0.3367 data_time: 0.0210 memory: 21539 grad_norm: 4.9410 loss: 1.5120 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5120 2023/03/08 18:43:44 - mmengine - INFO - Epoch(train) [47][600/660] lr: 1.0000e-04 eta: 0:11:33 time: 0.3360 data_time: 0.0269 memory: 21539 grad_norm: 5.0406 loss: 1.4987 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4987 2023/03/08 18:43:51 - mmengine - INFO - Epoch(train) [47][620/660] lr: 1.0000e-04 eta: 0:11:26 time: 0.3394 data_time: 0.0209 memory: 21539 grad_norm: 4.9630 loss: 1.5539 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.5539 2023/03/08 18:43:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:43:57 - mmengine - INFO - Epoch(train) [47][640/660] lr: 1.0000e-04 eta: 0:11:19 time: 0.3324 data_time: 0.0220 memory: 21539 grad_norm: 5.0647 loss: 1.6511 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6511 2023/03/08 18:44:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:44:04 - mmengine - INFO - Epoch(train) [47][660/660] lr: 1.0000e-04 eta: 0:11:13 time: 0.3272 data_time: 0.0204 memory: 21539 grad_norm: 5.2009 loss: 1.6709 top1_acc: 0.4815 top5_acc: 0.8889 loss_cls: 1.6709 2023/03/08 18:44:12 - mmengine - INFO - Epoch(train) [48][ 20/660] lr: 1.0000e-04 eta: 0:11:06 time: 0.4102 data_time: 0.0854 memory: 21539 grad_norm: 5.0901 loss: 1.5323 top1_acc: 0.4375 top5_acc: 0.9062 loss_cls: 1.5323 2023/03/08 18:44:19 - mmengine - INFO - Epoch(train) [48][ 40/660] lr: 1.0000e-04 eta: 0:10:59 time: 0.3416 data_time: 0.0198 memory: 21539 grad_norm: 5.0851 loss: 1.5263 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5263 2023/03/08 18:44:26 - mmengine - INFO - Epoch(train) [48][ 60/660] lr: 1.0000e-04 eta: 0:10:52 time: 0.3383 data_time: 0.0210 memory: 21539 grad_norm: 5.0621 loss: 1.5992 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5992 2023/03/08 18:44:32 - mmengine - INFO - Epoch(train) [48][ 80/660] lr: 1.0000e-04 eta: 0:10:45 time: 0.3385 data_time: 0.0200 memory: 21539 grad_norm: 4.9853 loss: 1.4772 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4772 2023/03/08 18:44:40 - mmengine - INFO - Epoch(train) [48][100/660] lr: 1.0000e-04 eta: 0:10:39 time: 0.3820 data_time: 0.0205 memory: 21539 grad_norm: 4.9873 loss: 1.4972 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4972 2023/03/08 18:44:47 - mmengine - INFO - Epoch(train) [48][120/660] lr: 1.0000e-04 eta: 0:10:32 time: 0.3379 data_time: 0.0207 memory: 21539 grad_norm: 5.0281 loss: 1.5350 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.5350 2023/03/08 18:44:54 - mmengine - INFO - Epoch(train) [48][140/660] lr: 1.0000e-04 eta: 0:10:25 time: 0.3379 data_time: 0.0213 memory: 21539 grad_norm: 5.1116 loss: 1.6260 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6260 2023/03/08 18:45:00 - mmengine - INFO - Epoch(train) [48][160/660] lr: 1.0000e-04 eta: 0:10:18 time: 0.3378 data_time: 0.0203 memory: 21539 grad_norm: 5.1209 loss: 1.7065 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.7065 2023/03/08 18:45:07 - mmengine - INFO - Epoch(train) [48][180/660] lr: 1.0000e-04 eta: 0:10:11 time: 0.3355 data_time: 0.0216 memory: 21539 grad_norm: 5.0099 loss: 1.5220 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5220 2023/03/08 18:45:14 - mmengine - INFO - Epoch(train) [48][200/660] lr: 1.0000e-04 eta: 0:10:05 time: 0.3371 data_time: 0.0204 memory: 21539 grad_norm: 5.0176 loss: 1.5746 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.5746 2023/03/08 18:45:21 - mmengine - INFO - Epoch(train) [48][220/660] lr: 1.0000e-04 eta: 0:09:58 time: 0.3352 data_time: 0.0212 memory: 21539 grad_norm: 5.0063 loss: 1.5480 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5480 2023/03/08 18:45:27 - mmengine - INFO - Epoch(train) [48][240/660] lr: 1.0000e-04 eta: 0:09:51 time: 0.3334 data_time: 0.0211 memory: 21539 grad_norm: 4.9981 loss: 1.4437 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.4437 2023/03/08 18:45:34 - mmengine - INFO - Epoch(train) [48][260/660] lr: 1.0000e-04 eta: 0:09:44 time: 0.3415 data_time: 0.0209 memory: 21539 grad_norm: 5.0729 loss: 1.6086 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6086 2023/03/08 18:45:41 - mmengine - INFO - Epoch(train) [48][280/660] lr: 1.0000e-04 eta: 0:09:37 time: 0.3331 data_time: 0.0222 memory: 21539 grad_norm: 4.9287 loss: 1.5494 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5494 2023/03/08 18:45:47 - mmengine - INFO - Epoch(train) [48][300/660] lr: 1.0000e-04 eta: 0:09:31 time: 0.3357 data_time: 0.0214 memory: 21539 grad_norm: 5.0104 loss: 1.5422 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.5422 2023/03/08 18:45:54 - mmengine - INFO - Epoch(train) [48][320/660] lr: 1.0000e-04 eta: 0:09:24 time: 0.3332 data_time: 0.0214 memory: 21539 grad_norm: 4.9976 loss: 1.6392 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6392 2023/03/08 18:46:01 - mmengine - INFO - Epoch(train) [48][340/660] lr: 1.0000e-04 eta: 0:09:17 time: 0.3413 data_time: 0.0254 memory: 21539 grad_norm: 4.9775 loss: 1.6014 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6014 2023/03/08 18:46:08 - mmengine - INFO - Epoch(train) [48][360/660] lr: 1.0000e-04 eta: 0:09:10 time: 0.3337 data_time: 0.0223 memory: 21539 grad_norm: 5.1252 loss: 1.5442 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5442 2023/03/08 18:46:14 - mmengine - INFO - Epoch(train) [48][380/660] lr: 1.0000e-04 eta: 0:09:03 time: 0.3408 data_time: 0.0215 memory: 21539 grad_norm: 5.0877 loss: 1.4708 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.4708 2023/03/08 18:46:21 - mmengine - INFO - Epoch(train) [48][400/660] lr: 1.0000e-04 eta: 0:08:57 time: 0.3344 data_time: 0.0224 memory: 21539 grad_norm: 4.8825 loss: 1.6505 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6505 2023/03/08 18:46:28 - mmengine - INFO - Epoch(train) [48][420/660] lr: 1.0000e-04 eta: 0:08:50 time: 0.3346 data_time: 0.0216 memory: 21539 grad_norm: 4.9929 loss: 1.5937 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5937 2023/03/08 18:46:35 - mmengine - INFO - Epoch(train) [48][440/660] lr: 1.0000e-04 eta: 0:08:43 time: 0.3354 data_time: 0.0224 memory: 21539 grad_norm: 5.0061 loss: 1.5729 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5729 2023/03/08 18:46:41 - mmengine - INFO - Epoch(train) [48][460/660] lr: 1.0000e-04 eta: 0:08:36 time: 0.3350 data_time: 0.0218 memory: 21539 grad_norm: 5.0330 loss: 1.5975 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.5975 2023/03/08 18:46:48 - mmengine - INFO - Epoch(train) [48][480/660] lr: 1.0000e-04 eta: 0:08:29 time: 0.3316 data_time: 0.0230 memory: 21539 grad_norm: 5.1840 loss: 1.7532 top1_acc: 0.3125 top5_acc: 0.7188 loss_cls: 1.7532 2023/03/08 18:46:55 - mmengine - INFO - Epoch(train) [48][500/660] lr: 1.0000e-04 eta: 0:08:23 time: 0.3392 data_time: 0.0221 memory: 21539 grad_norm: 5.1706 loss: 1.5423 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5423 2023/03/08 18:47:01 - mmengine - INFO - Epoch(train) [48][520/660] lr: 1.0000e-04 eta: 0:08:16 time: 0.3352 data_time: 0.0228 memory: 21539 grad_norm: 5.0393 loss: 1.6096 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.6096 2023/03/08 18:47:08 - mmengine - INFO - Epoch(train) [48][540/660] lr: 1.0000e-04 eta: 0:08:09 time: 0.3346 data_time: 0.0220 memory: 21539 grad_norm: 4.9343 loss: 1.5968 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5968 2023/03/08 18:47:15 - mmengine - INFO - Epoch(train) [48][560/660] lr: 1.0000e-04 eta: 0:08:02 time: 0.3380 data_time: 0.0227 memory: 21539 grad_norm: 5.0384 loss: 1.6281 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6281 2023/03/08 18:47:22 - mmengine - INFO - Epoch(train) [48][580/660] lr: 1.0000e-04 eta: 0:07:55 time: 0.3349 data_time: 0.0220 memory: 21539 grad_norm: 5.1193 loss: 1.6021 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.6021 2023/03/08 18:47:28 - mmengine - INFO - Epoch(train) [48][600/660] lr: 1.0000e-04 eta: 0:07:49 time: 0.3387 data_time: 0.0226 memory: 21539 grad_norm: 4.9628 loss: 1.5619 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5619 2023/03/08 18:47:35 - mmengine - INFO - Epoch(train) [48][620/660] lr: 1.0000e-04 eta: 0:07:42 time: 0.3407 data_time: 0.0219 memory: 21539 grad_norm: 5.0558 loss: 1.5876 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5876 2023/03/08 18:47:42 - mmengine - INFO - Epoch(train) [48][640/660] lr: 1.0000e-04 eta: 0:07:35 time: 0.3488 data_time: 0.0218 memory: 21539 grad_norm: 4.9257 loss: 1.5349 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5349 2023/03/08 18:47:49 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:47:49 - mmengine - INFO - Epoch(train) [48][660/660] lr: 1.0000e-04 eta: 0:07:28 time: 0.3319 data_time: 0.0201 memory: 21539 grad_norm: 5.0920 loss: 1.5432 top1_acc: 0.7037 top5_acc: 0.8889 loss_cls: 1.5432 2023/03/08 18:47:49 - mmengine - INFO - Saving checkpoint at 48 epochs 2023/03/08 18:47:58 - mmengine - INFO - Epoch(train) [49][ 20/660] lr: 1.0000e-04 eta: 0:07:21 time: 0.4121 data_time: 0.0875 memory: 21539 grad_norm: 5.0583 loss: 1.4969 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.4969 2023/03/08 18:48:05 - mmengine - INFO - Epoch(train) [49][ 40/660] lr: 1.0000e-04 eta: 0:07:15 time: 0.3306 data_time: 0.0201 memory: 21539 grad_norm: 5.0492 loss: 1.4965 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.4965 2023/03/08 18:48:12 - mmengine - INFO - Epoch(train) [49][ 60/660] lr: 1.0000e-04 eta: 0:07:08 time: 0.3404 data_time: 0.0212 memory: 21539 grad_norm: 5.0034 loss: 1.5680 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.5680 2023/03/08 18:48:18 - mmengine - INFO - Epoch(train) [49][ 80/660] lr: 1.0000e-04 eta: 0:07:01 time: 0.3343 data_time: 0.0196 memory: 21539 grad_norm: 4.9830 loss: 1.5522 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.5522 2023/03/08 18:48:25 - mmengine - INFO - Epoch(train) [49][100/660] lr: 1.0000e-04 eta: 0:06:54 time: 0.3402 data_time: 0.0207 memory: 21539 grad_norm: 5.0344 loss: 1.5082 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.5082 2023/03/08 18:48:32 - mmengine - INFO - Epoch(train) [49][120/660] lr: 1.0000e-04 eta: 0:06:47 time: 0.3351 data_time: 0.0216 memory: 21539 grad_norm: 5.0564 loss: 1.5563 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.5563 2023/03/08 18:48:39 - mmengine - INFO - Epoch(train) [49][140/660] lr: 1.0000e-04 eta: 0:06:41 time: 0.3400 data_time: 0.0201 memory: 21539 grad_norm: 5.1081 loss: 1.5337 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5337 2023/03/08 18:48:45 - mmengine - INFO - Epoch(train) [49][160/660] lr: 1.0000e-04 eta: 0:06:34 time: 0.3372 data_time: 0.0205 memory: 21539 grad_norm: 5.0606 loss: 1.5885 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5885 2023/03/08 18:48:52 - mmengine - INFO - Epoch(train) [49][180/660] lr: 1.0000e-04 eta: 0:06:27 time: 0.3367 data_time: 0.0203 memory: 21539 grad_norm: 5.1160 loss: 1.5575 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.5575 2023/03/08 18:48:59 - mmengine - INFO - Epoch(train) [49][200/660] lr: 1.0000e-04 eta: 0:06:20 time: 0.3352 data_time: 0.0213 memory: 21539 grad_norm: 5.0224 loss: 1.5374 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5374 2023/03/08 18:49:05 - mmengine - INFO - Epoch(train) [49][220/660] lr: 1.0000e-04 eta: 0:06:13 time: 0.3379 data_time: 0.0208 memory: 21539 grad_norm: 4.9434 loss: 1.5560 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5560 2023/03/08 18:49:12 - mmengine - INFO - Epoch(train) [49][240/660] lr: 1.0000e-04 eta: 0:06:07 time: 0.3338 data_time: 0.0209 memory: 21539 grad_norm: 5.0638 loss: 1.7093 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7093 2023/03/08 18:49:19 - mmengine - INFO - Epoch(train) [49][260/660] lr: 1.0000e-04 eta: 0:06:00 time: 0.3417 data_time: 0.0251 memory: 21539 grad_norm: 5.0101 loss: 1.5004 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.5004 2023/03/08 18:49:26 - mmengine - INFO - Epoch(train) [49][280/660] lr: 1.0000e-04 eta: 0:05:53 time: 0.3389 data_time: 0.0221 memory: 21539 grad_norm: 4.9895 loss: 1.5573 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5573 2023/03/08 18:49:33 - mmengine - INFO - Epoch(train) [49][300/660] lr: 1.0000e-04 eta: 0:05:46 time: 0.3361 data_time: 0.0220 memory: 21539 grad_norm: 5.0080 loss: 1.5114 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5114 2023/03/08 18:49:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:49:39 - mmengine - INFO - Epoch(train) [49][320/660] lr: 1.0000e-04 eta: 0:05:39 time: 0.3348 data_time: 0.0217 memory: 21539 grad_norm: 5.0953 loss: 1.6471 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6471 2023/03/08 18:49:46 - mmengine - INFO - Epoch(train) [49][340/660] lr: 1.0000e-04 eta: 0:05:33 time: 0.3335 data_time: 0.0219 memory: 21539 grad_norm: 5.0463 loss: 1.5843 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5843 2023/03/08 18:49:54 - mmengine - INFO - Epoch(train) [49][360/660] lr: 1.0000e-04 eta: 0:05:26 time: 0.4066 data_time: 0.0220 memory: 21539 grad_norm: 5.1322 loss: 1.5848 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5848 2023/03/08 18:50:01 - mmengine - INFO - Epoch(train) [49][380/660] lr: 1.0000e-04 eta: 0:05:19 time: 0.3335 data_time: 0.0217 memory: 21539 grad_norm: 5.1327 loss: 1.5519 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.5519 2023/03/08 18:50:07 - mmengine - INFO - Epoch(train) [49][400/660] lr: 1.0000e-04 eta: 0:05:12 time: 0.3344 data_time: 0.0223 memory: 21539 grad_norm: 5.1536 loss: 1.6244 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6244 2023/03/08 18:50:14 - mmengine - INFO - Epoch(train) [49][420/660] lr: 1.0000e-04 eta: 0:05:05 time: 0.3334 data_time: 0.0218 memory: 21539 grad_norm: 5.1191 loss: 1.6698 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6698 2023/03/08 18:50:21 - mmengine - INFO - Epoch(train) [49][440/660] lr: 1.0000e-04 eta: 0:04:59 time: 0.3340 data_time: 0.0228 memory: 21539 grad_norm: 5.0485 loss: 1.5037 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5037 2023/03/08 18:50:27 - mmengine - INFO - Epoch(train) [49][460/660] lr: 1.0000e-04 eta: 0:04:52 time: 0.3353 data_time: 0.0228 memory: 21539 grad_norm: 5.0580 loss: 1.6132 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6132 2023/03/08 18:50:34 - mmengine - INFO - Epoch(train) [49][480/660] lr: 1.0000e-04 eta: 0:04:45 time: 0.3318 data_time: 0.0224 memory: 21539 grad_norm: 5.0245 loss: 1.5255 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5255 2023/03/08 18:50:41 - mmengine - INFO - Epoch(train) [49][500/660] lr: 1.0000e-04 eta: 0:04:38 time: 0.3364 data_time: 0.0233 memory: 21539 grad_norm: 5.0786 loss: 1.6055 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6055 2023/03/08 18:50:47 - mmengine - INFO - Epoch(train) [49][520/660] lr: 1.0000e-04 eta: 0:04:31 time: 0.3316 data_time: 0.0241 memory: 21539 grad_norm: 5.1310 loss: 1.4598 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.4598 2023/03/08 18:50:54 - mmengine - INFO - Epoch(train) [49][540/660] lr: 1.0000e-04 eta: 0:04:25 time: 0.3313 data_time: 0.0239 memory: 21539 grad_norm: 5.0025 loss: 1.5572 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5572 2023/03/08 18:51:01 - mmengine - INFO - Epoch(train) [49][560/660] lr: 1.0000e-04 eta: 0:04:18 time: 0.3308 data_time: 0.0240 memory: 21539 grad_norm: 5.1585 loss: 1.5599 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.5599 2023/03/08 18:51:07 - mmengine - INFO - Epoch(train) [49][580/660] lr: 1.0000e-04 eta: 0:04:11 time: 0.3316 data_time: 0.0240 memory: 21539 grad_norm: 5.0536 loss: 1.6161 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6161 2023/03/08 18:51:14 - mmengine - INFO - Epoch(train) [49][600/660] lr: 1.0000e-04 eta: 0:04:04 time: 0.3351 data_time: 0.0274 memory: 21539 grad_norm: 5.0216 loss: 1.5297 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.5297 2023/03/08 18:51:21 - mmengine - INFO - Epoch(train) [49][620/660] lr: 1.0000e-04 eta: 0:03:57 time: 0.3339 data_time: 0.0240 memory: 21539 grad_norm: 5.0818 loss: 1.5765 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.5765 2023/03/08 18:51:28 - mmengine - INFO - Epoch(train) [49][640/660] lr: 1.0000e-04 eta: 0:03:51 time: 0.3388 data_time: 0.0232 memory: 21539 grad_norm: 5.0608 loss: 1.6108 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6108 2023/03/08 18:51:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:51:34 - mmengine - INFO - Epoch(train) [49][660/660] lr: 1.0000e-04 eta: 0:03:44 time: 0.3280 data_time: 0.0220 memory: 21539 grad_norm: 4.9892 loss: 1.4298 top1_acc: 0.6296 top5_acc: 0.8519 loss_cls: 1.4298 2023/03/08 18:51:42 - mmengine - INFO - Epoch(train) [50][ 20/660] lr: 1.0000e-04 eta: 0:03:37 time: 0.4114 data_time: 0.0845 memory: 21539 grad_norm: 4.9997 loss: 1.6004 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6004 2023/03/08 18:51:49 - mmengine - INFO - Epoch(train) [50][ 40/660] lr: 1.0000e-04 eta: 0:03:30 time: 0.3341 data_time: 0.0209 memory: 21539 grad_norm: 5.0396 loss: 1.5277 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.5277 2023/03/08 18:51:56 - mmengine - INFO - Epoch(train) [50][ 60/660] lr: 1.0000e-04 eta: 0:03:23 time: 0.3447 data_time: 0.0214 memory: 21539 grad_norm: 5.0219 loss: 1.5464 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.5464 2023/03/08 18:52:03 - mmengine - INFO - Epoch(train) [50][ 80/660] lr: 1.0000e-04 eta: 0:03:17 time: 0.3383 data_time: 0.0215 memory: 21539 grad_norm: 5.0560 loss: 1.5507 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5507 2023/03/08 18:52:10 - mmengine - INFO - Epoch(train) [50][100/660] lr: 1.0000e-04 eta: 0:03:10 time: 0.3476 data_time: 0.0209 memory: 21539 grad_norm: 5.0593 loss: 1.5734 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5734 2023/03/08 18:52:16 - mmengine - INFO - Epoch(train) [50][120/660] lr: 1.0000e-04 eta: 0:03:03 time: 0.3428 data_time: 0.0207 memory: 21539 grad_norm: 5.0235 loss: 1.5717 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.5717 2023/03/08 18:52:23 - mmengine - INFO - Epoch(train) [50][140/660] lr: 1.0000e-04 eta: 0:02:56 time: 0.3473 data_time: 0.0203 memory: 21539 grad_norm: 5.0195 loss: 1.5348 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5348 2023/03/08 18:52:30 - mmengine - INFO - Epoch(train) [50][160/660] lr: 1.0000e-04 eta: 0:02:49 time: 0.3376 data_time: 0.0211 memory: 21539 grad_norm: 5.0427 loss: 1.5639 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5639 2023/03/08 18:52:37 - mmengine - INFO - Epoch(train) [50][180/660] lr: 1.0000e-04 eta: 0:02:43 time: 0.3435 data_time: 0.0207 memory: 21539 grad_norm: 5.0327 loss: 1.5507 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.5507 2023/03/08 18:52:44 - mmengine - INFO - Epoch(train) [50][200/660] lr: 1.0000e-04 eta: 0:02:36 time: 0.3401 data_time: 0.0230 memory: 21539 grad_norm: 5.0054 loss: 1.5425 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5425 2023/03/08 18:52:51 - mmengine - INFO - Epoch(train) [50][220/660] lr: 1.0000e-04 eta: 0:02:29 time: 0.3436 data_time: 0.0219 memory: 21539 grad_norm: 5.0456 loss: 1.6081 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6081 2023/03/08 18:52:58 - mmengine - INFO - Epoch(train) [50][240/660] lr: 1.0000e-04 eta: 0:02:22 time: 0.3405 data_time: 0.0211 memory: 21539 grad_norm: 5.0154 loss: 1.5577 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5577 2023/03/08 18:53:04 - mmengine - INFO - Epoch(train) [50][260/660] lr: 1.0000e-04 eta: 0:02:15 time: 0.3441 data_time: 0.0207 memory: 21539 grad_norm: 5.0861 loss: 1.5979 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5979 2023/03/08 18:53:11 - mmengine - INFO - Epoch(train) [50][280/660] lr: 1.0000e-04 eta: 0:02:09 time: 0.3409 data_time: 0.0210 memory: 21539 grad_norm: 5.1088 loss: 1.6108 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.6108 2023/03/08 18:53:18 - mmengine - INFO - Epoch(train) [50][300/660] lr: 1.0000e-04 eta: 0:02:02 time: 0.3418 data_time: 0.0211 memory: 21539 grad_norm: 5.1936 loss: 1.5949 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5949 2023/03/08 18:53:25 - mmengine - INFO - Epoch(train) [50][320/660] lr: 1.0000e-04 eta: 0:01:55 time: 0.3449 data_time: 0.0249 memory: 21539 grad_norm: 5.1304 loss: 1.5490 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5490 2023/03/08 18:53:32 - mmengine - INFO - Epoch(train) [50][340/660] lr: 1.0000e-04 eta: 0:01:48 time: 0.3428 data_time: 0.0217 memory: 21539 grad_norm: 5.0031 loss: 1.5156 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5156 2023/03/08 18:53:39 - mmengine - INFO - Epoch(train) [50][360/660] lr: 1.0000e-04 eta: 0:01:41 time: 0.3410 data_time: 0.0214 memory: 21539 grad_norm: 4.9699 loss: 1.5392 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5392 2023/03/08 18:53:46 - mmengine - INFO - Epoch(train) [50][380/660] lr: 1.0000e-04 eta: 0:01:35 time: 0.3450 data_time: 0.0213 memory: 21539 grad_norm: 5.0732 loss: 1.5774 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.5774 2023/03/08 18:53:52 - mmengine - INFO - Epoch(train) [50][400/660] lr: 1.0000e-04 eta: 0:01:28 time: 0.3427 data_time: 0.0210 memory: 21539 grad_norm: 5.0839 loss: 1.5451 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5451 2023/03/08 18:53:59 - mmengine - INFO - Epoch(train) [50][420/660] lr: 1.0000e-04 eta: 0:01:21 time: 0.3423 data_time: 0.0212 memory: 21539 grad_norm: 5.0378 loss: 1.4715 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4715 2023/03/08 18:54:06 - mmengine - INFO - Epoch(train) [50][440/660] lr: 1.0000e-04 eta: 0:01:14 time: 0.3404 data_time: 0.0205 memory: 21539 grad_norm: 5.0861 loss: 1.6030 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6030 2023/03/08 18:54:13 - mmengine - INFO - Epoch(train) [50][460/660] lr: 1.0000e-04 eta: 0:01:07 time: 0.3429 data_time: 0.0215 memory: 21539 grad_norm: 5.0339 loss: 1.6023 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.6023 2023/03/08 18:54:20 - mmengine - INFO - Epoch(train) [50][480/660] lr: 1.0000e-04 eta: 0:01:01 time: 0.3707 data_time: 0.0206 memory: 21539 grad_norm: 5.1458 loss: 1.5863 top1_acc: 0.3125 top5_acc: 0.8438 loss_cls: 1.5863 2023/03/08 18:54:27 - mmengine - INFO - Epoch(train) [50][500/660] lr: 1.0000e-04 eta: 0:00:54 time: 0.3388 data_time: 0.0213 memory: 21539 grad_norm: 5.1366 loss: 1.6188 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.6188 2023/03/08 18:54:34 - mmengine - INFO - Epoch(train) [50][520/660] lr: 1.0000e-04 eta: 0:00:47 time: 0.3359 data_time: 0.0220 memory: 21539 grad_norm: 5.0215 loss: 1.5271 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5271 2023/03/08 18:54:41 - mmengine - INFO - Epoch(train) [50][540/660] lr: 1.0000e-04 eta: 0:00:40 time: 0.3424 data_time: 0.0218 memory: 21539 grad_norm: 4.9997 loss: 1.4377 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.4377 2023/03/08 18:54:48 - mmengine - INFO - Epoch(train) [50][560/660] lr: 1.0000e-04 eta: 0:00:34 time: 0.3401 data_time: 0.0212 memory: 21539 grad_norm: 4.9963 loss: 1.5297 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5297 2023/03/08 18:54:54 - mmengine - INFO - Epoch(train) [50][580/660] lr: 1.0000e-04 eta: 0:00:27 time: 0.3393 data_time: 0.0215 memory: 21539 grad_norm: 5.0941 loss: 1.4788 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4788 2023/03/08 18:55:01 - mmengine - INFO - Epoch(train) [50][600/660] lr: 1.0000e-04 eta: 0:00:20 time: 0.3406 data_time: 0.0214 memory: 21539 grad_norm: 5.0009 loss: 1.5071 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5071 2023/03/08 18:55:08 - mmengine - INFO - Epoch(train) [50][620/660] lr: 1.0000e-04 eta: 0:00:13 time: 0.3383 data_time: 0.0215 memory: 21539 grad_norm: 5.0224 loss: 1.5060 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5060 2023/03/08 18:55:15 - mmengine - INFO - Epoch(train) [50][640/660] lr: 1.0000e-04 eta: 0:00:06 time: 0.3380 data_time: 0.0218 memory: 21539 grad_norm: 5.0500 loss: 1.5407 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5407 2023/03/08 18:55:21 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-r50_8xb32-1x1x8-50e_sthv2-rgb_20230308_154400 2023/03/08 18:55:21 - mmengine - INFO - Epoch(train) [50][660/660] lr: 1.0000e-04 eta: 0:00:00 time: 0.3386 data_time: 0.0208 memory: 21539 grad_norm: 5.0199 loss: 1.5780 top1_acc: 0.4444 top5_acc: 0.7778 loss_cls: 1.5780 2023/03/08 18:55:21 - mmengine - INFO - Saving checkpoint at 50 epochs 2023/03/08 18:55:28 - mmengine - INFO - Epoch(val) [50][20/97] eta: 0:00:20 time: 0.2632 data_time: 0.1531 memory: 3261 2023/03/08 18:55:31 - mmengine - INFO - Epoch(val) [50][40/97] eta: 0:00:12 time: 0.1629 data_time: 0.0544 memory: 3261 2023/03/08 18:55:35 - mmengine - INFO - Epoch(val) [50][60/97] eta: 0:00:07 time: 0.1887 data_time: 0.0812 memory: 3261 2023/03/08 18:55:38 - mmengine - INFO - Epoch(val) [50][80/97] eta: 0:00:03 time: 0.1842 data_time: 0.0772 memory: 3261 2023/03/08 18:55:42 - mmengine - INFO - Epoch(val) [50][97/97] acc/top1: 0.3373 acc/top5: 0.6455 acc/mean1: 0.2738