2023/03/17 17:01:29 - 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: 1603614164 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 ------------------------------------------------------------ 2023/03/17 17:01:30 - mmengine - INFO - Config: preprocess_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]) model = dict( type='Recognizer2D', backbone=dict( type='ResNetTSM', pretrained='torchvision://resnet101', depth=101, norm_eval=False, shift_div=8), cls_head=dict( type='TSMHead', num_classes=174, in_channels=2048, spatial_type='avg', consensus=dict(type='AvgConsensus', dim=1), dropout_ratio=0.5, init_std=0.001, is_shift=True, average_clips='prob'), data_preprocessor=dict( type='ActionDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), train_cfg=None, test_cfg=None) default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=20, ignore_last=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=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, num_fixed_crops=13), 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=8, test_mode=True, twice_sample=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='ThreeCrop', crop_size=256), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] train_dataloader = dict( batch_size=16, 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, num_fixed_crops=13), 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=16, 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=8, test_mode=True, twice_sample=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='ThreeCrop', crop_size=256), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ], test_mode=True)) val_evaluator = dict(type='AccMetric') test_evaluator = dict(type='AccMetric') train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict(type='LinearLR', start_factor=0.1, by_epoch=True, begin=0, end=5), dict( type='MultiStepLR', begin=0, end=50, by_epoch=True, milestones=[25, 45], gamma=0.1) ] optim_wrapper = dict( constructor='TSMOptimWrapperConstructor', paramwise_cfg=dict(fc_lr5=True), optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0005), clip_grad=dict(max_norm=20, norm_type=2)) auto_scale_lr = dict(enable=True, base_batch_size=128) r101_checkpoint = 'torchvision://resnet101' launcher = 'pytorch' work_dir = 'work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2023/03/17 17:03:30 - mmengine - INFO - These parameters in pretrained checkpoint are not loaded: {'fc.bias', 'fc.weight'} 2023/03/17 17:03:30 - 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 -------------------- 2023/03/17 17:03:31 - mmengine - INFO - LR is set based on batch size of 128 and the current batch size is 128. Scaling the original LR by 1.0. Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([64, 3, 7, 7]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv1.conv.net.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv2.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv3.conv.weight - torch.Size([256, 64, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv3.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.conv3.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.downsample.conv.weight - torch.Size([256, 64, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.downsample.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.0.downsample.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv1.conv.weight - torch.Size([64, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv2.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv3.conv.weight - torch.Size([256, 64, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv3.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.1.conv3.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv1.conv.net.weight - torch.Size([64, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv2.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv3.conv.weight - torch.Size([256, 64, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv3.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer1.2.conv3.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv1.conv.net.weight - torch.Size([128, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv3.conv.weight - torch.Size([512, 128, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv3.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.conv3.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.downsample.conv.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.downsample.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.0.downsample.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv1.conv.weight - torch.Size([128, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv3.conv.weight - torch.Size([512, 128, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv3.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.1.conv3.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv1.conv.net.weight - torch.Size([128, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv3.conv.weight - torch.Size([512, 128, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv3.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.2.conv3.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv1.conv.weight - torch.Size([128, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv2.conv.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv3.conv.weight - torch.Size([512, 128, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv3.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer2.3.conv3.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv1.conv.net.weight - torch.Size([256, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.downsample.conv.weight - torch.Size([1024, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.downsample.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.0.downsample.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.1.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.2.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.3.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.4.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.5.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.6.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.7.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.8.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.9.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.10.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.11.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.12.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.13.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.14.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.15.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.16.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.17.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.18.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.19.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.20.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv1.conv.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.21.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv1.conv.net.weight - torch.Size([256, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv2.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv3.conv.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv3.bn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer3.22.conv3.bn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv1.conv.net.weight - torch.Size([512, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv2.conv.weight - torch.Size([512, 512, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv3.conv.weight - torch.Size([2048, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv3.bn.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.conv3.bn.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.downsample.conv.weight - torch.Size([2048, 1024, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.downsample.bn.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.0.downsample.bn.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv1.conv.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv2.conv.weight - torch.Size([512, 512, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv3.conv.weight - torch.Size([2048, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv3.bn.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.1.conv3.bn.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv1.conv.net.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv2.conv.weight - torch.Size([512, 512, 3, 3]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv3.conv.weight - torch.Size([2048, 512, 1, 1]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv3.bn.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D backbone.layer4.2.conv3.bn.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of Recognizer2D cls_head.fc_cls.weight - torch.Size([174, 2048]): Initialized by user-defined `init_weights` in TSMHead cls_head.fc_cls.bias - torch.Size([174]): Initialized by user-defined `init_weights` in TSMHead 2023/03/17 17:03:32 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain. 2023/03/17 17:03:44 - mmengine - INFO - Epoch(train) [1][ 20/1320] lr: 2.0000e-03 eta: 10:49:40 time: 0.5908 data_time: 0.1626 memory: 18752 grad_norm: 2.5492 loss: 5.0027 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 5.0027 2023/03/17 17:03:50 - mmengine - INFO - Epoch(train) [1][ 40/1320] lr: 2.0000e-03 eta: 8:28:18 time: 0.3340 data_time: 0.0116 memory: 18752 grad_norm: 2.5951 loss: 4.9691 top1_acc: 0.0000 top5_acc: 0.0625 loss_cls: 4.9691 2023/03/17 17:03:57 - mmengine - INFO - Epoch(train) [1][ 60/1320] lr: 2.0000e-03 eta: 7:40:51 time: 0.3333 data_time: 0.0108 memory: 18752 grad_norm: 2.6574 loss: 4.9592 top1_acc: 0.0000 top5_acc: 0.0625 loss_cls: 4.9592 2023/03/17 17:04:04 - mmengine - INFO - Epoch(train) [1][ 80/1320] lr: 2.0000e-03 eta: 7:16:53 time: 0.3326 data_time: 0.0106 memory: 18752 grad_norm: 2.7459 loss: 4.9154 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 4.9154 2023/03/17 17:04:10 - mmengine - INFO - Epoch(train) [1][ 100/1320] lr: 2.0000e-03 eta: 7:02:37 time: 0.3333 data_time: 0.0109 memory: 18752 grad_norm: 2.8237 loss: 4.7995 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.7995 2023/03/17 17:04:17 - mmengine - INFO - Epoch(train) [1][ 120/1320] lr: 2.0000e-03 eta: 6:53:09 time: 0.3338 data_time: 0.0106 memory: 18752 grad_norm: 2.9421 loss: 4.7893 top1_acc: 0.0000 top5_acc: 0.0625 loss_cls: 4.7893 2023/03/17 17:04:24 - mmengine - INFO - Epoch(train) [1][ 140/1320] lr: 2.0000e-03 eta: 6:46:30 time: 0.3347 data_time: 0.0107 memory: 18752 grad_norm: 3.0693 loss: 4.6535 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.6535 2023/03/17 17:04:30 - mmengine - INFO - Epoch(train) [1][ 160/1320] lr: 2.0000e-03 eta: 6:41:28 time: 0.3345 data_time: 0.0107 memory: 18752 grad_norm: 3.1861 loss: 4.5809 top1_acc: 0.0625 top5_acc: 0.2500 loss_cls: 4.5809 2023/03/17 17:04:37 - mmengine - INFO - Epoch(train) [1][ 180/1320] lr: 2.0000e-03 eta: 6:37:33 time: 0.3347 data_time: 0.0107 memory: 18752 grad_norm: 3.2458 loss: 4.5970 top1_acc: 0.0000 top5_acc: 0.0625 loss_cls: 4.5970 2023/03/17 17:04:44 - mmengine - INFO - Epoch(train) [1][ 200/1320] lr: 2.0000e-03 eta: 6:34:23 time: 0.3347 data_time: 0.0109 memory: 18752 grad_norm: 3.3572 loss: 4.5006 top1_acc: 0.0625 top5_acc: 0.1875 loss_cls: 4.5006 2023/03/17 17:04:50 - mmengine - INFO - Epoch(train) [1][ 220/1320] lr: 2.0000e-03 eta: 6:31:46 time: 0.3346 data_time: 0.0114 memory: 18752 grad_norm: 3.4371 loss: 4.3570 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 4.3570 2023/03/17 17:04:57 - mmengine - INFO - Epoch(train) [1][ 240/1320] lr: 2.0000e-03 eta: 6:29:32 time: 0.3343 data_time: 0.0108 memory: 18752 grad_norm: 3.6212 loss: 4.4894 top1_acc: 0.0625 top5_acc: 0.2500 loss_cls: 4.4894 2023/03/17 17:05:04 - mmengine - INFO - Epoch(train) [1][ 260/1320] lr: 2.0000e-03 eta: 6:27:40 time: 0.3346 data_time: 0.0116 memory: 18752 grad_norm: 3.6863 loss: 4.4134 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 4.4134 2023/03/17 17:05:10 - mmengine - INFO - Epoch(train) [1][ 280/1320] lr: 2.0000e-03 eta: 6:26:05 time: 0.3350 data_time: 0.0110 memory: 18752 grad_norm: 3.8175 loss: 4.3398 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 4.3398 2023/03/17 17:05:17 - mmengine - INFO - Epoch(train) [1][ 300/1320] lr: 2.0000e-03 eta: 6:24:38 time: 0.3343 data_time: 0.0110 memory: 18752 grad_norm: 3.7900 loss: 4.3094 top1_acc: 0.0625 top5_acc: 0.1875 loss_cls: 4.3094 2023/03/17 17:05:24 - mmengine - INFO - Epoch(train) [1][ 320/1320] lr: 2.0000e-03 eta: 6:23:22 time: 0.3345 data_time: 0.0112 memory: 18752 grad_norm: 3.9254 loss: 4.2364 top1_acc: 0.0625 top5_acc: 0.1875 loss_cls: 4.2364 2023/03/17 17:05:31 - mmengine - INFO - Epoch(train) [1][ 340/1320] lr: 2.0000e-03 eta: 6:22:13 time: 0.3342 data_time: 0.0123 memory: 18752 grad_norm: 3.9374 loss: 4.2884 top1_acc: 0.1250 top5_acc: 0.3125 loss_cls: 4.2884 2023/03/17 17:05:37 - mmengine - INFO - Epoch(train) [1][ 360/1320] lr: 2.0000e-03 eta: 6:21:11 time: 0.3341 data_time: 0.0118 memory: 18752 grad_norm: 4.0860 loss: 4.2037 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 4.2037 2023/03/17 17:05:44 - mmengine - INFO - Epoch(train) [1][ 380/1320] lr: 2.0000e-03 eta: 6:20:15 time: 0.3344 data_time: 0.0112 memory: 18752 grad_norm: 4.2084 loss: 4.2597 top1_acc: 0.0625 top5_acc: 0.2500 loss_cls: 4.2597 2023/03/17 17:05:51 - mmengine - INFO - Epoch(train) [1][ 400/1320] lr: 2.0000e-03 eta: 6:19:25 time: 0.3344 data_time: 0.0110 memory: 18752 grad_norm: 4.1796 loss: 4.0989 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 4.0989 2023/03/17 17:05:57 - mmengine - INFO - Epoch(train) [1][ 420/1320] lr: 2.0000e-03 eta: 6:18:40 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 4.4156 loss: 4.1017 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.1017 2023/03/17 17:06:04 - mmengine - INFO - Epoch(train) [1][ 440/1320] lr: 2.0000e-03 eta: 6:17:57 time: 0.3344 data_time: 0.0116 memory: 18752 grad_norm: 4.6068 loss: 4.1844 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 4.1844 2023/03/17 17:06:11 - mmengine - INFO - Epoch(train) [1][ 460/1320] lr: 2.0000e-03 eta: 6:17:16 time: 0.3342 data_time: 0.0108 memory: 18752 grad_norm: 4.6597 loss: 4.0856 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 4.0856 2023/03/17 17:06:17 - mmengine - INFO - Epoch(train) [1][ 480/1320] lr: 2.0000e-03 eta: 6:16:40 time: 0.3346 data_time: 0.0117 memory: 18752 grad_norm: 4.5760 loss: 4.0987 top1_acc: 0.0625 top5_acc: 0.3125 loss_cls: 4.0987 2023/03/17 17:06:24 - mmengine - INFO - Epoch(train) [1][ 500/1320] lr: 2.0000e-03 eta: 6:16:06 time: 0.3347 data_time: 0.0115 memory: 18752 grad_norm: 4.6970 loss: 4.0286 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 4.0286 2023/03/17 17:06:31 - mmengine - INFO - Epoch(train) [1][ 520/1320] lr: 2.0000e-03 eta: 6:15:33 time: 0.3338 data_time: 0.0117 memory: 18752 grad_norm: 4.7756 loss: 4.0542 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 4.0542 2023/03/17 17:06:37 - mmengine - INFO - Epoch(train) [1][ 540/1320] lr: 2.0000e-03 eta: 6:15:01 time: 0.3340 data_time: 0.0114 memory: 18752 grad_norm: 4.8231 loss: 3.9433 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.9433 2023/03/17 17:06:44 - mmengine - INFO - Epoch(train) [1][ 560/1320] lr: 2.0000e-03 eta: 6:14:33 time: 0.3344 data_time: 0.0115 memory: 18752 grad_norm: 4.7958 loss: 3.9706 top1_acc: 0.0625 top5_acc: 0.3125 loss_cls: 3.9706 2023/03/17 17:06:51 - mmengine - INFO - Epoch(train) [1][ 580/1320] lr: 2.0000e-03 eta: 6:14:04 time: 0.3340 data_time: 0.0112 memory: 18752 grad_norm: 4.9609 loss: 3.9283 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.9283 2023/03/17 17:06:57 - mmengine - INFO - Epoch(train) [1][ 600/1320] lr: 2.0000e-03 eta: 6:13:38 time: 0.3340 data_time: 0.0111 memory: 18752 grad_norm: 5.0287 loss: 3.8566 top1_acc: 0.0625 top5_acc: 0.5000 loss_cls: 3.8566 2023/03/17 17:07:04 - mmengine - INFO - Epoch(train) [1][ 620/1320] lr: 2.0000e-03 eta: 6:13:13 time: 0.3344 data_time: 0.0111 memory: 18752 grad_norm: 5.1362 loss: 4.1201 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 4.1201 2023/03/17 17:07:11 - mmengine - INFO - Epoch(train) [1][ 640/1320] lr: 2.0000e-03 eta: 6:12:48 time: 0.3337 data_time: 0.0108 memory: 18752 grad_norm: 5.1333 loss: 4.0014 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 4.0014 2023/03/17 17:07:18 - mmengine - INFO - Epoch(train) [1][ 660/1320] lr: 2.0000e-03 eta: 6:12:26 time: 0.3343 data_time: 0.0112 memory: 18752 grad_norm: 5.1941 loss: 3.7786 top1_acc: 0.2500 top5_acc: 0.3125 loss_cls: 3.7786 2023/03/17 17:07:24 - mmengine - INFO - Epoch(train) [1][ 680/1320] lr: 2.0000e-03 eta: 6:12:04 time: 0.3344 data_time: 0.0111 memory: 18752 grad_norm: 5.1603 loss: 3.8137 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.8137 2023/03/17 17:07:31 - mmengine - INFO - Epoch(train) [1][ 700/1320] lr: 2.0000e-03 eta: 6:11:43 time: 0.3340 data_time: 0.0109 memory: 18752 grad_norm: 5.2187 loss: 3.8605 top1_acc: 0.0625 top5_acc: 0.3750 loss_cls: 3.8605 2023/03/17 17:07:38 - mmengine - INFO - Epoch(train) [1][ 720/1320] lr: 2.0000e-03 eta: 6:11:23 time: 0.3346 data_time: 0.0114 memory: 18752 grad_norm: 5.2567 loss: 3.9158 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.9158 2023/03/17 17:07:44 - mmengine - INFO - Epoch(train) [1][ 740/1320] lr: 2.0000e-03 eta: 6:11:05 time: 0.3346 data_time: 0.0113 memory: 18752 grad_norm: 5.1979 loss: 3.9168 top1_acc: 0.1250 top5_acc: 0.3125 loss_cls: 3.9168 2023/03/17 17:07:51 - mmengine - INFO - Epoch(train) [1][ 760/1320] lr: 2.0000e-03 eta: 6:10:46 time: 0.3343 data_time: 0.0110 memory: 18752 grad_norm: 5.2344 loss: 3.8839 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.8839 2023/03/17 17:07:58 - mmengine - INFO - Epoch(train) [1][ 780/1320] lr: 2.0000e-03 eta: 6:10:28 time: 0.3340 data_time: 0.0111 memory: 18752 grad_norm: 5.3876 loss: 3.8547 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.8547 2023/03/17 17:08:04 - mmengine - INFO - Epoch(train) [1][ 800/1320] lr: 2.0000e-03 eta: 6:10:09 time: 0.3339 data_time: 0.0111 memory: 18752 grad_norm: 5.3399 loss: 3.6921 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.6921 2023/03/17 17:08:11 - mmengine - INFO - Epoch(train) [1][ 820/1320] lr: 2.0000e-03 eta: 6:09:53 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 5.5698 loss: 3.5832 top1_acc: 0.0625 top5_acc: 0.2500 loss_cls: 3.5832 2023/03/17 17:08:18 - mmengine - INFO - Epoch(train) [1][ 840/1320] lr: 2.0000e-03 eta: 6:09:37 time: 0.3343 data_time: 0.0110 memory: 18752 grad_norm: 5.5189 loss: 3.6978 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.6978 2023/03/17 17:08:24 - mmengine - INFO - Epoch(train) [1][ 860/1320] lr: 2.0000e-03 eta: 6:09:21 time: 0.3343 data_time: 0.0128 memory: 18752 grad_norm: 5.5330 loss: 3.7617 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.7617 2023/03/17 17:08:31 - mmengine - INFO - Epoch(train) [1][ 880/1320] lr: 2.0000e-03 eta: 6:09:05 time: 0.3339 data_time: 0.0118 memory: 18752 grad_norm: 5.6859 loss: 3.6838 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.6838 2023/03/17 17:08:38 - mmengine - INFO - Epoch(train) [1][ 900/1320] lr: 2.0000e-03 eta: 6:08:49 time: 0.3337 data_time: 0.0109 memory: 18752 grad_norm: 5.7410 loss: 3.7306 top1_acc: 0.1875 top5_acc: 0.3125 loss_cls: 3.7306 2023/03/17 17:08:44 - mmengine - INFO - Epoch(train) [1][ 920/1320] lr: 2.0000e-03 eta: 6:08:34 time: 0.3343 data_time: 0.0113 memory: 18752 grad_norm: 5.7941 loss: 3.5804 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.5804 2023/03/17 17:08:51 - mmengine - INFO - Epoch(train) [1][ 940/1320] lr: 2.0000e-03 eta: 6:08:20 time: 0.3346 data_time: 0.0111 memory: 18752 grad_norm: 5.6995 loss: 3.5360 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.5360 2023/03/17 17:08:58 - mmengine - INFO - Epoch(train) [1][ 960/1320] lr: 2.0000e-03 eta: 6:08:06 time: 0.3344 data_time: 0.0112 memory: 18752 grad_norm: 5.9278 loss: 3.7487 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 3.7487 2023/03/17 17:09:04 - mmengine - INFO - Epoch(train) [1][ 980/1320] lr: 2.0000e-03 eta: 6:07:52 time: 0.3342 data_time: 0.0122 memory: 18752 grad_norm: 5.8095 loss: 3.5014 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.5014 2023/03/17 17:09:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:09:11 - mmengine - INFO - Epoch(train) [1][1000/1320] lr: 2.0000e-03 eta: 6:07:38 time: 0.3339 data_time: 0.0116 memory: 18752 grad_norm: 5.9217 loss: 3.7049 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.7049 2023/03/17 17:09:18 - mmengine - INFO - Epoch(train) [1][1020/1320] lr: 2.0000e-03 eta: 6:07:25 time: 0.3343 data_time: 0.0114 memory: 18752 grad_norm: 5.9261 loss: 3.6470 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 3.6470 2023/03/17 17:09:25 - mmengine - INFO - Epoch(train) [1][1040/1320] lr: 2.0000e-03 eta: 6:07:11 time: 0.3340 data_time: 0.0114 memory: 18752 grad_norm: 6.1008 loss: 3.6380 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.6380 2023/03/17 17:09:31 - mmengine - INFO - Epoch(train) [1][1060/1320] lr: 2.0000e-03 eta: 6:06:59 time: 0.3345 data_time: 0.0112 memory: 18752 grad_norm: 6.0740 loss: 3.4452 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 3.4452 2023/03/17 17:09:38 - mmengine - INFO - Epoch(train) [1][1080/1320] lr: 2.0000e-03 eta: 6:06:46 time: 0.3339 data_time: 0.0110 memory: 18752 grad_norm: 6.1808 loss: 3.6939 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6939 2023/03/17 17:09:45 - mmengine - INFO - Epoch(train) [1][1100/1320] lr: 2.0000e-03 eta: 6:06:33 time: 0.3342 data_time: 0.0118 memory: 18752 grad_norm: 6.1208 loss: 3.6343 top1_acc: 0.1250 top5_acc: 0.3125 loss_cls: 3.6343 2023/03/17 17:09:51 - mmengine - INFO - Epoch(train) [1][1120/1320] lr: 2.0000e-03 eta: 6:06:22 time: 0.3345 data_time: 0.0118 memory: 18752 grad_norm: 6.2675 loss: 3.5307 top1_acc: 0.1875 top5_acc: 0.3125 loss_cls: 3.5307 2023/03/17 17:09:58 - mmengine - INFO - Epoch(train) [1][1140/1320] lr: 2.0000e-03 eta: 6:06:09 time: 0.3340 data_time: 0.0112 memory: 18752 grad_norm: 6.3317 loss: 3.5369 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 3.5369 2023/03/17 17:10:05 - mmengine - INFO - Epoch(train) [1][1160/1320] lr: 2.0000e-03 eta: 6:05:58 time: 0.3351 data_time: 0.0127 memory: 18752 grad_norm: 6.2423 loss: 3.4416 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 3.4416 2023/03/17 17:10:11 - mmengine - INFO - Epoch(train) [1][1180/1320] lr: 2.0000e-03 eta: 6:05:47 time: 0.3341 data_time: 0.0111 memory: 18752 grad_norm: 6.1257 loss: 3.4968 top1_acc: 0.1875 top5_acc: 0.3750 loss_cls: 3.4968 2023/03/17 17:10:18 - mmengine - INFO - Epoch(train) [1][1200/1320] lr: 2.0000e-03 eta: 6:05:35 time: 0.3342 data_time: 0.0117 memory: 18752 grad_norm: 6.2768 loss: 3.3289 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 3.3289 2023/03/17 17:10:25 - mmengine - INFO - Epoch(train) [1][1220/1320] lr: 2.0000e-03 eta: 6:05:24 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 6.3625 loss: 3.5216 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.5216 2023/03/17 17:10:31 - mmengine - INFO - Epoch(train) [1][1240/1320] lr: 2.0000e-03 eta: 6:05:13 time: 0.3343 data_time: 0.0114 memory: 18752 grad_norm: 6.4188 loss: 3.4088 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.4088 2023/03/17 17:10:38 - mmengine - INFO - Epoch(train) [1][1260/1320] lr: 2.0000e-03 eta: 6:05:02 time: 0.3343 data_time: 0.0114 memory: 18752 grad_norm: 6.3383 loss: 3.5592 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.5592 2023/03/17 17:10:45 - mmengine - INFO - Epoch(train) [1][1280/1320] lr: 2.0000e-03 eta: 6:04:52 time: 0.3348 data_time: 0.0124 memory: 18752 grad_norm: 6.3379 loss: 3.5100 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.5100 2023/03/17 17:10:51 - mmengine - INFO - Epoch(train) [1][1300/1320] lr: 2.0000e-03 eta: 6:04:41 time: 0.3343 data_time: 0.0113 memory: 18752 grad_norm: 6.4733 loss: 3.3801 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.3801 2023/03/17 17:10:58 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:10:58 - mmengine - INFO - Epoch(train) [1][1320/1320] lr: 2.0000e-03 eta: 6:04:26 time: 0.3293 data_time: 0.0125 memory: 18752 grad_norm: 6.4831 loss: 3.1372 top1_acc: 0.2727 top5_acc: 0.7273 loss_cls: 3.1372 2023/03/17 17:11:04 - mmengine - INFO - Epoch(val) [1][ 20/194] eta: 0:00:49 time: 0.2856 data_time: 0.1988 memory: 2112 2023/03/17 17:11:06 - mmengine - INFO - Epoch(val) [1][ 40/194] eta: 0:00:29 time: 0.0956 data_time: 0.0097 memory: 2112 2023/03/17 17:11:08 - mmengine - INFO - Epoch(val) [1][ 60/194] eta: 0:00:21 time: 0.0956 data_time: 0.0103 memory: 2112 2023/03/17 17:11:10 - mmengine - INFO - Epoch(val) [1][ 80/194] eta: 0:00:16 time: 0.0960 data_time: 0.0107 memory: 2112 2023/03/17 17:11:11 - mmengine - INFO - Epoch(val) [1][100/194] eta: 0:00:12 time: 0.0965 data_time: 0.0106 memory: 2112 2023/03/17 17:11:13 - mmengine - INFO - Epoch(val) [1][120/194] eta: 0:00:09 time: 0.0974 data_time: 0.0117 memory: 2112 2023/03/17 17:11:15 - mmengine - INFO - Epoch(val) [1][140/194] eta: 0:00:06 time: 0.0973 data_time: 0.0112 memory: 2112 2023/03/17 17:11:17 - mmengine - INFO - Epoch(val) [1][160/194] eta: 0:00:04 time: 0.0970 data_time: 0.0113 memory: 2112 2023/03/17 17:11:19 - mmengine - INFO - Epoch(val) [1][180/194] eta: 0:00:01 time: 0.0971 data_time: 0.0113 memory: 2112 2023/03/17 17:11:22 - mmengine - INFO - Epoch(val) [1][194/194] acc/top1: 0.2315 acc/top5: 0.5096 acc/mean1: 0.1611 2023/03/17 17:11:24 - mmengine - INFO - The best checkpoint with 0.2315 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2023/03/17 17:11:31 - mmengine - INFO - Epoch(train) [2][ 20/1320] lr: 6.5000e-03 eta: 6:04:44 time: 0.3642 data_time: 0.0353 memory: 18752 grad_norm: 6.8205 loss: 3.6922 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.6922 2023/03/17 17:11:38 - mmengine - INFO - Epoch(train) [2][ 40/1320] lr: 6.5000e-03 eta: 6:04:33 time: 0.3339 data_time: 0.0114 memory: 18752 grad_norm: 6.9911 loss: 3.6911 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.6911 2023/03/17 17:11:44 - mmengine - INFO - Epoch(train) [2][ 60/1320] lr: 6.5000e-03 eta: 6:04:23 time: 0.3345 data_time: 0.0116 memory: 18752 grad_norm: 6.6465 loss: 3.4474 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.4474 2023/03/17 17:11:51 - mmengine - INFO - Epoch(train) [2][ 80/1320] lr: 6.5000e-03 eta: 6:04:12 time: 0.3346 data_time: 0.0113 memory: 18752 grad_norm: 6.5117 loss: 3.6057 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 3.6057 2023/03/17 17:11:58 - mmengine - INFO - Epoch(train) [2][ 100/1320] lr: 6.5000e-03 eta: 6:04:03 time: 0.3352 data_time: 0.0115 memory: 18752 grad_norm: 6.3032 loss: 3.5452 top1_acc: 0.0625 top5_acc: 0.2500 loss_cls: 3.5452 2023/03/17 17:12:04 - mmengine - INFO - Epoch(train) [2][ 120/1320] lr: 6.5000e-03 eta: 6:03:53 time: 0.3345 data_time: 0.0114 memory: 18752 grad_norm: 6.3974 loss: 3.4865 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.4865 2023/03/17 17:12:11 - mmengine - INFO - Epoch(train) [2][ 140/1320] lr: 6.5000e-03 eta: 6:03:43 time: 0.3344 data_time: 0.0111 memory: 18752 grad_norm: 6.3315 loss: 3.5508 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 3.5508 2023/03/17 17:12:18 - mmengine - INFO - Epoch(train) [2][ 160/1320] lr: 6.5000e-03 eta: 6:03:32 time: 0.3340 data_time: 0.0115 memory: 18752 grad_norm: 6.4517 loss: 3.5565 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.5565 2023/03/17 17:12:24 - mmengine - INFO - Epoch(train) [2][ 180/1320] lr: 6.5000e-03 eta: 6:03:23 time: 0.3352 data_time: 0.0113 memory: 18752 grad_norm: 6.2650 loss: 3.5285 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.5285 2023/03/17 17:12:31 - mmengine - INFO - Epoch(train) [2][ 200/1320] lr: 6.5000e-03 eta: 6:03:13 time: 0.3344 data_time: 0.0114 memory: 18752 grad_norm: 6.3832 loss: 3.3775 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 3.3775 2023/03/17 17:12:38 - mmengine - INFO - Epoch(train) [2][ 220/1320] lr: 6.5000e-03 eta: 6:03:03 time: 0.3343 data_time: 0.0114 memory: 18752 grad_norm: 6.4830 loss: 3.4188 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 3.4188 2023/03/17 17:12:45 - mmengine - INFO - Epoch(train) [2][ 240/1320] lr: 6.5000e-03 eta: 6:02:53 time: 0.3342 data_time: 0.0116 memory: 18752 grad_norm: 6.4540 loss: 3.3110 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.3110 2023/03/17 17:12:51 - mmengine - INFO - Epoch(train) [2][ 260/1320] lr: 6.5000e-03 eta: 6:02:45 time: 0.3358 data_time: 0.0112 memory: 18752 grad_norm: 6.4454 loss: 3.3624 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.3624 2023/03/17 17:12:58 - mmengine - INFO - Epoch(train) [2][ 280/1320] lr: 6.5000e-03 eta: 6:02:36 time: 0.3346 data_time: 0.0116 memory: 18752 grad_norm: 6.5825 loss: 3.2266 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.2266 2023/03/17 17:13:05 - mmengine - INFO - Epoch(train) [2][ 300/1320] lr: 6.5000e-03 eta: 6:02:26 time: 0.3347 data_time: 0.0114 memory: 18752 grad_norm: 6.2649 loss: 3.4248 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.4248 2023/03/17 17:13:11 - mmengine - INFO - Epoch(train) [2][ 320/1320] lr: 6.5000e-03 eta: 6:02:17 time: 0.3340 data_time: 0.0111 memory: 18752 grad_norm: 6.3854 loss: 3.2677 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 3.2677 2023/03/17 17:13:18 - mmengine - INFO - Epoch(train) [2][ 340/1320] lr: 6.5000e-03 eta: 6:02:07 time: 0.3343 data_time: 0.0114 memory: 18752 grad_norm: 6.4965 loss: 3.2099 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 3.2099 2023/03/17 17:13:25 - mmengine - INFO - Epoch(train) [2][ 360/1320] lr: 6.5000e-03 eta: 6:01:58 time: 0.3344 data_time: 0.0114 memory: 18752 grad_norm: 6.3389 loss: 3.3704 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.3704 2023/03/17 17:13:31 - mmengine - INFO - Epoch(train) [2][ 380/1320] lr: 6.5000e-03 eta: 6:01:49 time: 0.3345 data_time: 0.0120 memory: 18752 grad_norm: 6.2152 loss: 3.0998 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.0998 2023/03/17 17:13:38 - mmengine - INFO - Epoch(train) [2][ 400/1320] lr: 6.5000e-03 eta: 6:01:40 time: 0.3346 data_time: 0.0118 memory: 18752 grad_norm: 6.4584 loss: 3.1307 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.1307 2023/03/17 17:13:45 - mmengine - INFO - Epoch(train) [2][ 420/1320] lr: 6.5000e-03 eta: 6:01:31 time: 0.3346 data_time: 0.0115 memory: 18752 grad_norm: 6.4085 loss: 3.1279 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 3.1279 2023/03/17 17:13:51 - mmengine - INFO - Epoch(train) [2][ 440/1320] lr: 6.5000e-03 eta: 6:01:22 time: 0.3344 data_time: 0.0112 memory: 18752 grad_norm: 6.4492 loss: 3.4295 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.4295 2023/03/17 17:13:58 - mmengine - INFO - Epoch(train) [2][ 460/1320] lr: 6.5000e-03 eta: 6:01:15 time: 0.3374 data_time: 0.0114 memory: 18752 grad_norm: 6.3782 loss: 3.1092 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.1092 2023/03/17 17:14:05 - mmengine - INFO - Epoch(train) [2][ 480/1320] lr: 6.5000e-03 eta: 6:01:06 time: 0.3347 data_time: 0.0111 memory: 18752 grad_norm: 6.4726 loss: 3.1359 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.1359 2023/03/17 17:14:12 - mmengine - INFO - Epoch(train) [2][ 500/1320] lr: 6.5000e-03 eta: 6:00:57 time: 0.3348 data_time: 0.0117 memory: 18752 grad_norm: 6.4457 loss: 3.2188 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.2188 2023/03/17 17:14:18 - mmengine - INFO - Epoch(train) [2][ 520/1320] lr: 6.5000e-03 eta: 6:00:49 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 6.5461 loss: 3.1282 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1282 2023/03/17 17:14:25 - mmengine - INFO - Epoch(train) [2][ 540/1320] lr: 6.5000e-03 eta: 6:00:41 time: 0.3348 data_time: 0.0113 memory: 18752 grad_norm: 6.5601 loss: 3.1599 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.1599 2023/03/17 17:14:32 - mmengine - INFO - Epoch(train) [2][ 560/1320] lr: 6.5000e-03 eta: 6:00:32 time: 0.3348 data_time: 0.0112 memory: 18752 grad_norm: 6.4457 loss: 3.1675 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.1675 2023/03/17 17:14:38 - mmengine - INFO - Epoch(train) [2][ 580/1320] lr: 6.5000e-03 eta: 6:00:24 time: 0.3354 data_time: 0.0106 memory: 18752 grad_norm: 6.5386 loss: 3.1419 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.1419 2023/03/17 17:14:45 - mmengine - INFO - Epoch(train) [2][ 600/1320] lr: 6.5000e-03 eta: 6:00:15 time: 0.3349 data_time: 0.0114 memory: 18752 grad_norm: 6.2660 loss: 2.9601 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.9601 2023/03/17 17:14:52 - mmengine - INFO - Epoch(train) [2][ 620/1320] lr: 6.5000e-03 eta: 6:00:07 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 6.2915 loss: 3.1831 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 3.1831 2023/03/17 17:14:59 - mmengine - INFO - Epoch(train) [2][ 640/1320] lr: 6.5000e-03 eta: 5:59:59 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 6.4799 loss: 2.9904 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.9904 2023/03/17 17:15:05 - mmengine - INFO - Epoch(train) [2][ 660/1320] lr: 6.5000e-03 eta: 5:59:51 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 6.4785 loss: 3.0092 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 3.0092 2023/03/17 17:15:12 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:15:12 - mmengine - INFO - Epoch(train) [2][ 680/1320] lr: 6.5000e-03 eta: 5:59:42 time: 0.3347 data_time: 0.0119 memory: 18752 grad_norm: 6.4432 loss: 2.8597 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.8597 2023/03/17 17:15:19 - mmengine - INFO - Epoch(train) [2][ 700/1320] lr: 6.5000e-03 eta: 5:59:34 time: 0.3346 data_time: 0.0119 memory: 18752 grad_norm: 6.4852 loss: 2.8966 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.8966 2023/03/17 17:15:25 - mmengine - INFO - Epoch(train) [2][ 720/1320] lr: 6.5000e-03 eta: 5:59:27 time: 0.3371 data_time: 0.0112 memory: 18752 grad_norm: 6.5359 loss: 2.9371 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.9371 2023/03/17 17:15:32 - mmengine - INFO - Epoch(train) [2][ 740/1320] lr: 6.5000e-03 eta: 5:59:19 time: 0.3346 data_time: 0.0111 memory: 18752 grad_norm: 6.5026 loss: 3.0631 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.0631 2023/03/17 17:15:39 - mmengine - INFO - Epoch(train) [2][ 760/1320] lr: 6.5000e-03 eta: 5:59:10 time: 0.3345 data_time: 0.0123 memory: 18752 grad_norm: 6.3322 loss: 2.8359 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.8359 2023/03/17 17:15:45 - mmengine - INFO - Epoch(train) [2][ 780/1320] lr: 6.5000e-03 eta: 5:59:02 time: 0.3345 data_time: 0.0117 memory: 18752 grad_norm: 6.4352 loss: 2.8714 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8714 2023/03/17 17:15:52 - mmengine - INFO - Epoch(train) [2][ 800/1320] lr: 6.5000e-03 eta: 5:58:54 time: 0.3350 data_time: 0.0115 memory: 18752 grad_norm: 6.5517 loss: 2.8897 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.8897 2023/03/17 17:15:59 - mmengine - INFO - Epoch(train) [2][ 820/1320] lr: 6.5000e-03 eta: 5:58:47 time: 0.3358 data_time: 0.0111 memory: 18752 grad_norm: 6.5891 loss: 2.9048 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 2.9048 2023/03/17 17:16:06 - mmengine - INFO - Epoch(train) [2][ 840/1320] lr: 6.5000e-03 eta: 5:58:39 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 6.3737 loss: 2.9055 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.9055 2023/03/17 17:16:12 - mmengine - INFO - Epoch(train) [2][ 860/1320] lr: 6.5000e-03 eta: 5:58:31 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 6.5606 loss: 2.8694 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.8694 2023/03/17 17:16:19 - mmengine - INFO - Epoch(train) [2][ 880/1320] lr: 6.5000e-03 eta: 5:58:23 time: 0.3350 data_time: 0.0112 memory: 18752 grad_norm: 6.5974 loss: 2.9246 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.9246 2023/03/17 17:16:26 - mmengine - INFO - Epoch(train) [2][ 900/1320] lr: 6.5000e-03 eta: 5:58:16 time: 0.3352 data_time: 0.0115 memory: 18752 grad_norm: 6.6453 loss: 2.9862 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 2.9862 2023/03/17 17:16:32 - mmengine - INFO - Epoch(train) [2][ 920/1320] lr: 6.5000e-03 eta: 5:58:08 time: 0.3349 data_time: 0.0123 memory: 18752 grad_norm: 6.4319 loss: 2.7852 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.7852 2023/03/17 17:16:39 - mmengine - INFO - Epoch(train) [2][ 940/1320] lr: 6.5000e-03 eta: 5:58:00 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 6.5791 loss: 2.8972 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 2.8972 2023/03/17 17:16:46 - mmengine - INFO - Epoch(train) [2][ 960/1320] lr: 6.5000e-03 eta: 5:57:51 time: 0.3343 data_time: 0.0115 memory: 18752 grad_norm: 6.5029 loss: 3.0045 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.0045 2023/03/17 17:16:52 - mmengine - INFO - Epoch(train) [2][ 980/1320] lr: 6.5000e-03 eta: 5:57:44 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 6.5750 loss: 2.7981 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7981 2023/03/17 17:16:59 - mmengine - INFO - Epoch(train) [2][1000/1320] lr: 6.5000e-03 eta: 5:57:36 time: 0.3347 data_time: 0.0116 memory: 18752 grad_norm: 6.6293 loss: 2.7807 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7807 2023/03/17 17:17:06 - mmengine - INFO - Epoch(train) [2][1020/1320] lr: 6.5000e-03 eta: 5:57:28 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 6.6719 loss: 2.8592 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 2.8592 2023/03/17 17:17:13 - mmengine - INFO - Epoch(train) [2][1040/1320] lr: 6.5000e-03 eta: 5:57:21 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 6.4333 loss: 2.6776 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6776 2023/03/17 17:17:19 - mmengine - INFO - Epoch(train) [2][1060/1320] lr: 6.5000e-03 eta: 5:57:13 time: 0.3349 data_time: 0.0123 memory: 18752 grad_norm: 6.4659 loss: 2.8182 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.8182 2023/03/17 17:17:26 - mmengine - INFO - Epoch(train) [2][1080/1320] lr: 6.5000e-03 eta: 5:57:06 time: 0.3373 data_time: 0.0117 memory: 18752 grad_norm: 6.5172 loss: 2.7648 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.7648 2023/03/17 17:17:33 - mmengine - INFO - Epoch(train) [2][1100/1320] lr: 6.5000e-03 eta: 5:57:00 time: 0.3374 data_time: 0.0115 memory: 18752 grad_norm: 6.5042 loss: 2.6584 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.6584 2023/03/17 17:17:39 - mmengine - INFO - Epoch(train) [2][1120/1320] lr: 6.5000e-03 eta: 5:56:52 time: 0.3348 data_time: 0.0123 memory: 18752 grad_norm: 6.6415 loss: 2.9136 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.9136 2023/03/17 17:17:46 - mmengine - INFO - Epoch(train) [2][1140/1320] lr: 6.5000e-03 eta: 5:56:44 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 6.5048 loss: 2.8242 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.8242 2023/03/17 17:17:53 - mmengine - INFO - Epoch(train) [2][1160/1320] lr: 6.5000e-03 eta: 5:56:36 time: 0.3346 data_time: 0.0123 memory: 18752 grad_norm: 6.4618 loss: 2.8440 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.8440 2023/03/17 17:18:00 - mmengine - INFO - Epoch(train) [2][1180/1320] lr: 6.5000e-03 eta: 5:56:29 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 6.4762 loss: 2.6893 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.6893 2023/03/17 17:18:06 - mmengine - INFO - Epoch(train) [2][1200/1320] lr: 6.5000e-03 eta: 5:56:21 time: 0.3346 data_time: 0.0114 memory: 18752 grad_norm: 6.5776 loss: 2.9568 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.9568 2023/03/17 17:18:13 - mmengine - INFO - Epoch(train) [2][1220/1320] lr: 6.5000e-03 eta: 5:56:14 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 6.4986 loss: 2.8426 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.8426 2023/03/17 17:18:20 - mmengine - INFO - Epoch(train) [2][1240/1320] lr: 6.5000e-03 eta: 5:56:06 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 6.5359 loss: 2.7760 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 2.7760 2023/03/17 17:18:26 - mmengine - INFO - Epoch(train) [2][1260/1320] lr: 6.5000e-03 eta: 5:55:59 time: 0.3349 data_time: 0.0119 memory: 18752 grad_norm: 6.5167 loss: 2.6761 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.6761 2023/03/17 17:18:33 - mmengine - INFO - Epoch(train) [2][1280/1320] lr: 6.5000e-03 eta: 5:55:51 time: 0.3345 data_time: 0.0116 memory: 18752 grad_norm: 6.4930 loss: 2.7591 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.7591 2023/03/17 17:18:40 - mmengine - INFO - Epoch(train) [2][1300/1320] lr: 6.5000e-03 eta: 5:55:43 time: 0.3347 data_time: 0.0115 memory: 18752 grad_norm: 6.6283 loss: 2.7995 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7995 2023/03/17 17:18:46 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:18:46 - mmengine - INFO - Epoch(train) [2][1320/1320] lr: 6.5000e-03 eta: 5:55:33 time: 0.3297 data_time: 0.0115 memory: 18752 grad_norm: 6.6273 loss: 2.8345 top1_acc: 0.0909 top5_acc: 0.3636 loss_cls: 2.8345 2023/03/17 17:18:49 - mmengine - INFO - Epoch(val) [2][ 20/194] eta: 0:00:21 time: 0.1262 data_time: 0.0403 memory: 2112 2023/03/17 17:18:51 - mmengine - INFO - Epoch(val) [2][ 40/194] eta: 0:00:17 time: 0.0966 data_time: 0.0110 memory: 2112 2023/03/17 17:18:53 - mmengine - INFO - Epoch(val) [2][ 60/194] eta: 0:00:14 time: 0.0955 data_time: 0.0101 memory: 2112 2023/03/17 17:18:55 - mmengine - INFO - Epoch(val) [2][ 80/194] eta: 0:00:11 time: 0.0970 data_time: 0.0109 memory: 2112 2023/03/17 17:18:57 - mmengine - INFO - Epoch(val) [2][100/194] eta: 0:00:09 time: 0.0985 data_time: 0.0126 memory: 2112 2023/03/17 17:18:59 - mmengine - INFO - Epoch(val) [2][120/194] eta: 0:00:07 time: 0.0973 data_time: 0.0115 memory: 2112 2023/03/17 17:19:01 - mmengine - INFO - Epoch(val) [2][140/194] eta: 0:00:05 time: 0.0973 data_time: 0.0115 memory: 2112 2023/03/17 17:19:02 - mmengine - INFO - Epoch(val) [2][160/194] eta: 0:00:03 time: 0.0969 data_time: 0.0111 memory: 2112 2023/03/17 17:19:04 - mmengine - INFO - Epoch(val) [2][180/194] eta: 0:00:01 time: 0.0970 data_time: 0.0114 memory: 2112 2023/03/17 17:19:08 - mmengine - INFO - Epoch(val) [2][194/194] acc/top1: 0.3485 acc/top5: 0.6549 acc/mean1: 0.2740 2023/03/17 17:19:08 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_1.pth is removed 2023/03/17 17:19:09 - mmengine - INFO - The best checkpoint with 0.3485 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2023/03/17 17:19:16 - mmengine - INFO - Epoch(train) [3][ 20/1320] lr: 1.1000e-02 eta: 5:55:39 time: 0.3647 data_time: 0.0356 memory: 18752 grad_norm: 6.4284 loss: 2.7893 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.7893 2023/03/17 17:19:23 - mmengine - INFO - Epoch(train) [3][ 40/1320] lr: 1.1000e-02 eta: 5:55:32 time: 0.3347 data_time: 0.0120 memory: 18752 grad_norm: 6.6067 loss: 2.8698 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.8698 2023/03/17 17:19:30 - mmengine - INFO - Epoch(train) [3][ 60/1320] lr: 1.1000e-02 eta: 5:55:24 time: 0.3346 data_time: 0.0115 memory: 18752 grad_norm: 6.4513 loss: 2.7838 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7838 2023/03/17 17:19:36 - mmengine - INFO - Epoch(train) [3][ 80/1320] lr: 1.1000e-02 eta: 5:55:16 time: 0.3345 data_time: 0.0125 memory: 18752 grad_norm: 6.5158 loss: 2.9542 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.9542 2023/03/17 17:19:43 - mmengine - INFO - Epoch(train) [3][ 100/1320] lr: 1.1000e-02 eta: 5:55:08 time: 0.3348 data_time: 0.0116 memory: 18752 grad_norm: 6.2793 loss: 2.8272 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8272 2023/03/17 17:19:50 - mmengine - INFO - Epoch(train) [3][ 120/1320] lr: 1.1000e-02 eta: 5:55:00 time: 0.3341 data_time: 0.0122 memory: 18752 grad_norm: 6.2301 loss: 2.9934 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.9934 2023/03/17 17:19:57 - mmengine - INFO - Epoch(train) [3][ 140/1320] lr: 1.1000e-02 eta: 5:54:52 time: 0.3342 data_time: 0.0116 memory: 18752 grad_norm: 6.1954 loss: 2.9916 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 2.9916 2023/03/17 17:20:03 - mmengine - INFO - Epoch(train) [3][ 160/1320] lr: 1.1000e-02 eta: 5:54:44 time: 0.3339 data_time: 0.0121 memory: 18752 grad_norm: 6.2429 loss: 2.8475 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.8475 2023/03/17 17:20:10 - mmengine - INFO - Epoch(train) [3][ 180/1320] lr: 1.1000e-02 eta: 5:54:37 time: 0.3349 data_time: 0.0115 memory: 18752 grad_norm: 6.1479 loss: 2.7312 top1_acc: 0.1875 top5_acc: 0.7500 loss_cls: 2.7312 2023/03/17 17:20:17 - mmengine - INFO - Epoch(train) [3][ 200/1320] lr: 1.1000e-02 eta: 5:54:29 time: 0.3350 data_time: 0.0116 memory: 18752 grad_norm: 6.3031 loss: 2.9347 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9347 2023/03/17 17:20:23 - mmengine - INFO - Epoch(train) [3][ 220/1320] lr: 1.1000e-02 eta: 5:54:22 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 6.0429 loss: 3.1274 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.1274 2023/03/17 17:20:30 - mmengine - INFO - Epoch(train) [3][ 240/1320] lr: 1.1000e-02 eta: 5:54:14 time: 0.3351 data_time: 0.0114 memory: 18752 grad_norm: 6.0742 loss: 2.8772 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.8772 2023/03/17 17:20:37 - mmengine - INFO - Epoch(train) [3][ 260/1320] lr: 1.1000e-02 eta: 5:54:07 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 6.0281 loss: 2.9069 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9069 2023/03/17 17:20:43 - mmengine - INFO - Epoch(train) [3][ 280/1320] lr: 1.1000e-02 eta: 5:53:59 time: 0.3346 data_time: 0.0111 memory: 18752 grad_norm: 5.8701 loss: 2.8808 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8808 2023/03/17 17:20:50 - mmengine - INFO - Epoch(train) [3][ 300/1320] lr: 1.1000e-02 eta: 5:53:52 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 6.0134 loss: 2.8548 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.8548 2023/03/17 17:20:57 - mmengine - INFO - Epoch(train) [3][ 320/1320] lr: 1.1000e-02 eta: 5:53:44 time: 0.3347 data_time: 0.0128 memory: 18752 grad_norm: 6.1041 loss: 3.1428 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 3.1428 2023/03/17 17:21:04 - mmengine - INFO - Epoch(train) [3][ 340/1320] lr: 1.1000e-02 eta: 5:53:37 time: 0.3344 data_time: 0.0116 memory: 18752 grad_norm: 5.8908 loss: 2.9027 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.9027 2023/03/17 17:21:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:21:10 - mmengine - INFO - Epoch(train) [3][ 360/1320] lr: 1.1000e-02 eta: 5:53:29 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 6.0563 loss: 2.8576 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.8576 2023/03/17 17:21:17 - mmengine - INFO - Epoch(train) [3][ 380/1320] lr: 1.1000e-02 eta: 5:53:22 time: 0.3345 data_time: 0.0119 memory: 18752 grad_norm: 6.0248 loss: 2.9189 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.9189 2023/03/17 17:21:24 - mmengine - INFO - Epoch(train) [3][ 400/1320] lr: 1.1000e-02 eta: 5:53:14 time: 0.3345 data_time: 0.0119 memory: 18752 grad_norm: 5.8905 loss: 2.9758 top1_acc: 0.1875 top5_acc: 0.3125 loss_cls: 2.9758 2023/03/17 17:21:30 - mmengine - INFO - Epoch(train) [3][ 420/1320] lr: 1.1000e-02 eta: 5:53:06 time: 0.3346 data_time: 0.0118 memory: 18752 grad_norm: 5.9986 loss: 2.7989 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.7989 2023/03/17 17:21:37 - mmengine - INFO - Epoch(train) [3][ 440/1320] lr: 1.1000e-02 eta: 5:52:59 time: 0.3350 data_time: 0.0114 memory: 18752 grad_norm: 5.8140 loss: 2.7170 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7170 2023/03/17 17:21:44 - mmengine - INFO - Epoch(train) [3][ 460/1320] lr: 1.1000e-02 eta: 5:52:51 time: 0.3347 data_time: 0.0120 memory: 18752 grad_norm: 5.9533 loss: 2.8169 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.8169 2023/03/17 17:21:50 - mmengine - INFO - Epoch(train) [3][ 480/1320] lr: 1.1000e-02 eta: 5:52:44 time: 0.3347 data_time: 0.0119 memory: 18752 grad_norm: 5.9517 loss: 2.6266 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6266 2023/03/17 17:21:57 - mmengine - INFO - Epoch(train) [3][ 500/1320] lr: 1.1000e-02 eta: 5:52:36 time: 0.3345 data_time: 0.0120 memory: 18752 grad_norm: 5.9607 loss: 2.7321 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.7321 2023/03/17 17:22:04 - mmengine - INFO - Epoch(train) [3][ 520/1320] lr: 1.1000e-02 eta: 5:52:29 time: 0.3346 data_time: 0.0130 memory: 18752 grad_norm: 5.8403 loss: 2.6376 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.6376 2023/03/17 17:22:10 - mmengine - INFO - Epoch(train) [3][ 540/1320] lr: 1.1000e-02 eta: 5:52:22 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 5.7962 loss: 2.7072 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.7072 2023/03/17 17:22:17 - mmengine - INFO - Epoch(train) [3][ 560/1320] lr: 1.1000e-02 eta: 5:52:14 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 5.7953 loss: 2.6880 top1_acc: 0.1250 top5_acc: 0.6875 loss_cls: 2.6880 2023/03/17 17:22:24 - mmengine - INFO - Epoch(train) [3][ 580/1320] lr: 1.1000e-02 eta: 5:52:07 time: 0.3346 data_time: 0.0116 memory: 18752 grad_norm: 6.0108 loss: 2.8846 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.8846 2023/03/17 17:22:31 - mmengine - INFO - Epoch(train) [3][ 600/1320] lr: 1.1000e-02 eta: 5:51:59 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 5.6263 loss: 2.7695 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.7695 2023/03/17 17:22:37 - mmengine - INFO - Epoch(train) [3][ 620/1320] lr: 1.1000e-02 eta: 5:51:52 time: 0.3348 data_time: 0.0116 memory: 18752 grad_norm: 5.7361 loss: 2.3998 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3998 2023/03/17 17:22:44 - mmengine - INFO - Epoch(train) [3][ 640/1320] lr: 1.1000e-02 eta: 5:51:45 time: 0.3351 data_time: 0.0115 memory: 18752 grad_norm: 5.7471 loss: 2.8699 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.8699 2023/03/17 17:22:51 - mmengine - INFO - Epoch(train) [3][ 660/1320] lr: 1.1000e-02 eta: 5:51:38 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 5.7936 loss: 2.9076 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.9076 2023/03/17 17:22:57 - mmengine - INFO - Epoch(train) [3][ 680/1320] lr: 1.1000e-02 eta: 5:51:30 time: 0.3350 data_time: 0.0125 memory: 18752 grad_norm: 5.8478 loss: 2.7662 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.7662 2023/03/17 17:23:04 - mmengine - INFO - Epoch(train) [3][ 700/1320] lr: 1.1000e-02 eta: 5:51:23 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 5.8296 loss: 2.7504 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.7504 2023/03/17 17:23:11 - mmengine - INFO - Epoch(train) [3][ 720/1320] lr: 1.1000e-02 eta: 5:51:16 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 5.7255 loss: 2.7274 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.7274 2023/03/17 17:23:17 - mmengine - INFO - Epoch(train) [3][ 740/1320] lr: 1.1000e-02 eta: 5:51:09 time: 0.3349 data_time: 0.0121 memory: 18752 grad_norm: 5.8510 loss: 2.9509 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.9509 2023/03/17 17:23:24 - mmengine - INFO - Epoch(train) [3][ 760/1320] lr: 1.1000e-02 eta: 5:51:01 time: 0.3345 data_time: 0.0118 memory: 18752 grad_norm: 5.7638 loss: 2.3547 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3547 2023/03/17 17:23:31 - mmengine - INFO - Epoch(train) [3][ 780/1320] lr: 1.1000e-02 eta: 5:50:54 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 5.8319 loss: 2.7162 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7162 2023/03/17 17:23:38 - mmengine - INFO - Epoch(train) [3][ 800/1320] lr: 1.1000e-02 eta: 5:50:47 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 5.6211 loss: 2.6289 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.6289 2023/03/17 17:23:45 - mmengine - INFO - Epoch(train) [3][ 820/1320] lr: 1.1000e-02 eta: 5:50:44 time: 0.3466 data_time: 0.0118 memory: 18752 grad_norm: 5.5837 loss: 2.4254 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.4254 2023/03/17 17:23:51 - mmengine - INFO - Epoch(train) [3][ 840/1320] lr: 1.1000e-02 eta: 5:50:37 time: 0.3346 data_time: 0.0120 memory: 18752 grad_norm: 5.7072 loss: 2.6376 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.6376 2023/03/17 17:23:58 - mmengine - INFO - Epoch(train) [3][ 860/1320] lr: 1.1000e-02 eta: 5:50:29 time: 0.3349 data_time: 0.0124 memory: 18752 grad_norm: 5.6811 loss: 2.7817 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.7817 2023/03/17 17:24:05 - mmengine - INFO - Epoch(train) [3][ 880/1320] lr: 1.1000e-02 eta: 5:50:22 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 5.6513 loss: 2.6754 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.6754 2023/03/17 17:24:11 - mmengine - INFO - Epoch(train) [3][ 900/1320] lr: 1.1000e-02 eta: 5:50:15 time: 0.3347 data_time: 0.0124 memory: 18752 grad_norm: 5.5143 loss: 2.5109 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.5109 2023/03/17 17:24:18 - mmengine - INFO - Epoch(train) [3][ 920/1320] lr: 1.1000e-02 eta: 5:50:07 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 5.7263 loss: 2.5575 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.5575 2023/03/17 17:24:25 - mmengine - INFO - Epoch(train) [3][ 940/1320] lr: 1.1000e-02 eta: 5:50:00 time: 0.3348 data_time: 0.0122 memory: 18752 grad_norm: 5.6653 loss: 2.7457 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7457 2023/03/17 17:24:31 - mmengine - INFO - Epoch(train) [3][ 960/1320] lr: 1.1000e-02 eta: 5:49:53 time: 0.3345 data_time: 0.0115 memory: 18752 grad_norm: 5.6595 loss: 2.6522 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.6522 2023/03/17 17:24:38 - mmengine - INFO - Epoch(train) [3][ 980/1320] lr: 1.1000e-02 eta: 5:49:46 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 5.6539 loss: 2.6351 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.6351 2023/03/17 17:24:45 - mmengine - INFO - Epoch(train) [3][1000/1320] lr: 1.1000e-02 eta: 5:49:38 time: 0.3346 data_time: 0.0118 memory: 18752 grad_norm: 5.6317 loss: 2.6030 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.6030 2023/03/17 17:24:51 - mmengine - INFO - Epoch(train) [3][1020/1320] lr: 1.1000e-02 eta: 5:49:31 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 5.5311 loss: 2.7148 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.7148 2023/03/17 17:24:58 - mmengine - INFO - Epoch(train) [3][1040/1320] lr: 1.1000e-02 eta: 5:49:24 time: 0.3347 data_time: 0.0120 memory: 18752 grad_norm: 5.7772 loss: 2.7156 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.7156 2023/03/17 17:25:05 - mmengine - INFO - Epoch(train) [3][1060/1320] lr: 1.1000e-02 eta: 5:49:17 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 5.4747 loss: 2.6727 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.6727 2023/03/17 17:25:12 - mmengine - INFO - Epoch(train) [3][1080/1320] lr: 1.1000e-02 eta: 5:49:10 time: 0.3350 data_time: 0.0122 memory: 18752 grad_norm: 5.6351 loss: 2.7759 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7759 2023/03/17 17:25:18 - mmengine - INFO - Epoch(train) [3][1100/1320] lr: 1.1000e-02 eta: 5:49:03 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 5.5096 loss: 2.5881 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5881 2023/03/17 17:25:25 - mmengine - INFO - Epoch(train) [3][1120/1320] lr: 1.1000e-02 eta: 5:48:56 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 5.5680 loss: 2.5367 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5367 2023/03/17 17:25:32 - mmengine - INFO - Epoch(train) [3][1140/1320] lr: 1.1000e-02 eta: 5:48:48 time: 0.3347 data_time: 0.0119 memory: 18752 grad_norm: 5.6820 loss: 2.4170 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.4170 2023/03/17 17:25:38 - mmengine - INFO - Epoch(train) [3][1160/1320] lr: 1.1000e-02 eta: 5:48:41 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 5.7105 loss: 2.4167 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4167 2023/03/17 17:25:45 - mmengine - INFO - Epoch(train) [3][1180/1320] lr: 1.1000e-02 eta: 5:48:34 time: 0.3350 data_time: 0.0114 memory: 18752 grad_norm: 5.6342 loss: 2.6525 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.6525 2023/03/17 17:25:52 - mmengine - INFO - Epoch(train) [3][1200/1320] lr: 1.1000e-02 eta: 5:48:27 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 5.5332 loss: 2.5285 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.5285 2023/03/17 17:25:59 - mmengine - INFO - Epoch(train) [3][1220/1320] lr: 1.1000e-02 eta: 5:48:20 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 5.5772 loss: 2.6317 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.6317 2023/03/17 17:26:05 - mmengine - INFO - Epoch(train) [3][1240/1320] lr: 1.1000e-02 eta: 5:48:13 time: 0.3352 data_time: 0.0125 memory: 18752 grad_norm: 5.4644 loss: 2.3530 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3530 2023/03/17 17:26:12 - mmengine - INFO - Epoch(train) [3][1260/1320] lr: 1.1000e-02 eta: 5:48:06 time: 0.3355 data_time: 0.0129 memory: 18752 grad_norm: 5.5845 loss: 2.4793 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.4793 2023/03/17 17:26:19 - mmengine - INFO - Epoch(train) [3][1280/1320] lr: 1.1000e-02 eta: 5:47:59 time: 0.3355 data_time: 0.0130 memory: 18752 grad_norm: 5.4773 loss: 2.5890 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5890 2023/03/17 17:26:25 - mmengine - INFO - Epoch(train) [3][1300/1320] lr: 1.1000e-02 eta: 5:47:51 time: 0.3352 data_time: 0.0128 memory: 18752 grad_norm: 5.6114 loss: 2.6140 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.6140 2023/03/17 17:26:32 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:26:32 - mmengine - INFO - Epoch(train) [3][1320/1320] lr: 1.1000e-02 eta: 5:47:43 time: 0.3308 data_time: 0.0131 memory: 18752 grad_norm: 5.7286 loss: 2.4570 top1_acc: 0.0909 top5_acc: 0.3636 loss_cls: 2.4570 2023/03/17 17:26:32 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/03/17 17:26:37 - mmengine - INFO - Epoch(val) [3][ 20/194] eta: 0:00:22 time: 0.1294 data_time: 0.0423 memory: 2112 2023/03/17 17:26:39 - mmengine - INFO - Epoch(val) [3][ 40/194] eta: 0:00:17 time: 0.0954 data_time: 0.0096 memory: 2112 2023/03/17 17:26:41 - mmengine - INFO - Epoch(val) [3][ 60/194] eta: 0:00:14 time: 0.1015 data_time: 0.0157 memory: 2112 2023/03/17 17:26:43 - mmengine - INFO - Epoch(val) [3][ 80/194] eta: 0:00:12 time: 0.0960 data_time: 0.0103 memory: 2112 2023/03/17 17:26:45 - mmengine - INFO - Epoch(val) [3][100/194] eta: 0:00:09 time: 0.0978 data_time: 0.0113 memory: 2112 2023/03/17 17:26:47 - mmengine - INFO - Epoch(val) [3][120/194] eta: 0:00:07 time: 0.0966 data_time: 0.0105 memory: 2112 2023/03/17 17:26:49 - mmengine - INFO - Epoch(val) [3][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 17:26:51 - mmengine - INFO - Epoch(val) [3][160/194] eta: 0:00:03 time: 0.0964 data_time: 0.0106 memory: 2112 2023/03/17 17:26:53 - mmengine - INFO - Epoch(val) [3][180/194] eta: 0:00:01 time: 0.0958 data_time: 0.0100 memory: 2112 2023/03/17 17:26:55 - mmengine - INFO - Epoch(val) [3][194/194] acc/top1: 0.3736 acc/top5: 0.6719 acc/mean1: 0.2979 2023/03/17 17:26:55 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_2.pth is removed 2023/03/17 17:26:57 - mmengine - INFO - The best checkpoint with 0.3736 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2023/03/17 17:27:04 - mmengine - INFO - Epoch(train) [4][ 20/1320] lr: 1.5500e-02 eta: 5:47:46 time: 0.3669 data_time: 0.0380 memory: 18752 grad_norm: 5.8020 loss: 2.7460 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.7460 2023/03/17 17:27:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:27:11 - mmengine - INFO - Epoch(train) [4][ 40/1320] lr: 1.5500e-02 eta: 5:47:39 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 5.5126 loss: 2.6669 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.6669 2023/03/17 17:27:18 - mmengine - INFO - Epoch(train) [4][ 60/1320] lr: 1.5500e-02 eta: 5:47:31 time: 0.3351 data_time: 0.0114 memory: 18752 grad_norm: 5.6012 loss: 2.9303 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9303 2023/03/17 17:27:24 - mmengine - INFO - Epoch(train) [4][ 80/1320] lr: 1.5500e-02 eta: 5:47:24 time: 0.3347 data_time: 0.0116 memory: 18752 grad_norm: 5.5142 loss: 2.3798 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3798 2023/03/17 17:27:31 - mmengine - INFO - Epoch(train) [4][ 100/1320] lr: 1.5500e-02 eta: 5:47:17 time: 0.3350 data_time: 0.0113 memory: 18752 grad_norm: 5.5114 loss: 2.7916 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.7916 2023/03/17 17:27:38 - mmengine - INFO - Epoch(train) [4][ 120/1320] lr: 1.5500e-02 eta: 5:47:10 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 5.3412 loss: 2.7605 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.7605 2023/03/17 17:27:45 - mmengine - INFO - Epoch(train) [4][ 140/1320] lr: 1.5500e-02 eta: 5:47:03 time: 0.3351 data_time: 0.0116 memory: 18752 grad_norm: 5.4326 loss: 2.6315 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6315 2023/03/17 17:27:51 - mmengine - INFO - Epoch(train) [4][ 160/1320] lr: 1.5500e-02 eta: 5:46:56 time: 0.3349 data_time: 0.0114 memory: 18752 grad_norm: 5.4482 loss: 2.7112 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.7112 2023/03/17 17:27:58 - mmengine - INFO - Epoch(train) [4][ 180/1320] lr: 1.5500e-02 eta: 5:46:49 time: 0.3358 data_time: 0.0114 memory: 18752 grad_norm: 5.3632 loss: 2.7468 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.7468 2023/03/17 17:28:05 - mmengine - INFO - Epoch(train) [4][ 200/1320] lr: 1.5500e-02 eta: 5:46:42 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 5.3786 loss: 2.6988 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.6988 2023/03/17 17:28:11 - mmengine - INFO - Epoch(train) [4][ 220/1320] lr: 1.5500e-02 eta: 5:46:34 time: 0.3345 data_time: 0.0116 memory: 18752 grad_norm: 5.4265 loss: 2.8061 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8061 2023/03/17 17:28:18 - mmengine - INFO - Epoch(train) [4][ 240/1320] lr: 1.5500e-02 eta: 5:46:27 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 5.3942 loss: 2.7303 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.7303 2023/03/17 17:28:25 - mmengine - INFO - Epoch(train) [4][ 260/1320] lr: 1.5500e-02 eta: 5:46:20 time: 0.3357 data_time: 0.0111 memory: 18752 grad_norm: 5.1188 loss: 2.6552 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.6552 2023/03/17 17:28:32 - mmengine - INFO - Epoch(train) [4][ 280/1320] lr: 1.5500e-02 eta: 5:46:13 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 5.1939 loss: 2.6558 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6558 2023/03/17 17:28:38 - mmengine - INFO - Epoch(train) [4][ 300/1320] lr: 1.5500e-02 eta: 5:46:06 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 5.3225 loss: 2.5010 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5010 2023/03/17 17:28:45 - mmengine - INFO - Epoch(train) [4][ 320/1320] lr: 1.5500e-02 eta: 5:45:59 time: 0.3345 data_time: 0.0120 memory: 18752 grad_norm: 5.3098 loss: 2.8779 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8779 2023/03/17 17:28:52 - mmengine - INFO - Epoch(train) [4][ 340/1320] lr: 1.5500e-02 eta: 5:45:52 time: 0.3354 data_time: 0.0114 memory: 18752 grad_norm: 5.0832 loss: 2.4556 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4556 2023/03/17 17:28:58 - mmengine - INFO - Epoch(train) [4][ 360/1320] lr: 1.5500e-02 eta: 5:45:45 time: 0.3349 data_time: 0.0120 memory: 18752 grad_norm: 5.1904 loss: 2.5585 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5585 2023/03/17 17:29:05 - mmengine - INFO - Epoch(train) [4][ 380/1320] lr: 1.5500e-02 eta: 5:45:38 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 5.1813 loss: 2.7049 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.7049 2023/03/17 17:29:12 - mmengine - INFO - Epoch(train) [4][ 400/1320] lr: 1.5500e-02 eta: 5:45:31 time: 0.3344 data_time: 0.0116 memory: 18752 grad_norm: 5.1759 loss: 2.6722 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.6722 2023/03/17 17:29:18 - mmengine - INFO - Epoch(train) [4][ 420/1320] lr: 1.5500e-02 eta: 5:45:23 time: 0.3347 data_time: 0.0116 memory: 18752 grad_norm: 5.2878 loss: 2.7010 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.7010 2023/03/17 17:29:25 - mmengine - INFO - Epoch(train) [4][ 440/1320] lr: 1.5500e-02 eta: 5:45:16 time: 0.3349 data_time: 0.0120 memory: 18752 grad_norm: 5.3351 loss: 2.6019 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.6019 2023/03/17 17:29:32 - mmengine - INFO - Epoch(train) [4][ 460/1320] lr: 1.5500e-02 eta: 5:45:09 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 5.1592 loss: 2.7307 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7307 2023/03/17 17:29:39 - mmengine - INFO - Epoch(train) [4][ 480/1320] lr: 1.5500e-02 eta: 5:45:02 time: 0.3347 data_time: 0.0117 memory: 18752 grad_norm: 5.1111 loss: 2.4811 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.4811 2023/03/17 17:29:45 - mmengine - INFO - Epoch(train) [4][ 500/1320] lr: 1.5500e-02 eta: 5:44:55 time: 0.3351 data_time: 0.0123 memory: 18752 grad_norm: 5.3536 loss: 2.6391 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.6391 2023/03/17 17:29:52 - mmengine - INFO - Epoch(train) [4][ 520/1320] lr: 1.5500e-02 eta: 5:44:48 time: 0.3352 data_time: 0.0125 memory: 18752 grad_norm: 5.1341 loss: 2.8181 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.8181 2023/03/17 17:29:59 - mmengine - INFO - Epoch(train) [4][ 540/1320] lr: 1.5500e-02 eta: 5:44:41 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 5.1713 loss: 2.6167 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.6167 2023/03/17 17:30:05 - mmengine - INFO - Epoch(train) [4][ 560/1320] lr: 1.5500e-02 eta: 5:44:34 time: 0.3348 data_time: 0.0120 memory: 18752 grad_norm: 5.0805 loss: 2.5340 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5340 2023/03/17 17:30:12 - mmengine - INFO - Epoch(train) [4][ 580/1320] lr: 1.5500e-02 eta: 5:44:27 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 5.3057 loss: 2.7182 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.7182 2023/03/17 17:30:19 - mmengine - INFO - Epoch(train) [4][ 600/1320] lr: 1.5500e-02 eta: 5:44:20 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 5.0259 loss: 2.6603 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.6603 2023/03/17 17:30:25 - mmengine - INFO - Epoch(train) [4][ 620/1320] lr: 1.5500e-02 eta: 5:44:13 time: 0.3356 data_time: 0.0114 memory: 18752 grad_norm: 5.2140 loss: 2.8318 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.8318 2023/03/17 17:30:32 - mmengine - INFO - Epoch(train) [4][ 640/1320] lr: 1.5500e-02 eta: 5:44:06 time: 0.3353 data_time: 0.0112 memory: 18752 grad_norm: 5.0784 loss: 2.5343 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.5343 2023/03/17 17:30:39 - mmengine - INFO - Epoch(train) [4][ 660/1320] lr: 1.5500e-02 eta: 5:43:59 time: 0.3352 data_time: 0.0114 memory: 18752 grad_norm: 5.1722 loss: 2.6193 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.6193 2023/03/17 17:30:46 - mmengine - INFO - Epoch(train) [4][ 680/1320] lr: 1.5500e-02 eta: 5:43:52 time: 0.3351 data_time: 0.0122 memory: 18752 grad_norm: 5.0747 loss: 2.4378 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4378 2023/03/17 17:30:52 - mmengine - INFO - Epoch(train) [4][ 700/1320] lr: 1.5500e-02 eta: 5:43:45 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 5.2750 loss: 2.6211 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.6211 2023/03/17 17:30:59 - mmengine - INFO - Epoch(train) [4][ 720/1320] lr: 1.5500e-02 eta: 5:43:38 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 5.1212 loss: 2.5127 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.5127 2023/03/17 17:31:06 - mmengine - INFO - Epoch(train) [4][ 740/1320] lr: 1.5500e-02 eta: 5:43:31 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 5.1034 loss: 2.5433 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5433 2023/03/17 17:31:12 - mmengine - INFO - Epoch(train) [4][ 760/1320] lr: 1.5500e-02 eta: 5:43:24 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 5.1452 loss: 2.4359 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4359 2023/03/17 17:31:19 - mmengine - INFO - Epoch(train) [4][ 780/1320] lr: 1.5500e-02 eta: 5:43:17 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 5.2290 loss: 2.6038 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.6038 2023/03/17 17:31:26 - mmengine - INFO - Epoch(train) [4][ 800/1320] lr: 1.5500e-02 eta: 5:43:10 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.9533 loss: 2.7844 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7844 2023/03/17 17:31:33 - mmengine - INFO - Epoch(train) [4][ 820/1320] lr: 1.5500e-02 eta: 5:43:03 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 5.2002 loss: 2.5804 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.5804 2023/03/17 17:31:39 - mmengine - INFO - Epoch(train) [4][ 840/1320] lr: 1.5500e-02 eta: 5:42:57 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 5.0911 loss: 2.3583 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3583 2023/03/17 17:31:46 - mmengine - INFO - Epoch(train) [4][ 860/1320] lr: 1.5500e-02 eta: 5:42:50 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.9895 loss: 2.7202 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7202 2023/03/17 17:31:53 - mmengine - INFO - Epoch(train) [4][ 880/1320] lr: 1.5500e-02 eta: 5:42:43 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.8556 loss: 2.3780 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3780 2023/03/17 17:31:59 - mmengine - INFO - Epoch(train) [4][ 900/1320] lr: 1.5500e-02 eta: 5:42:36 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 5.0941 loss: 2.7079 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.7079 2023/03/17 17:32:06 - mmengine - INFO - Epoch(train) [4][ 920/1320] lr: 1.5500e-02 eta: 5:42:29 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 5.1670 loss: 2.3681 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.3681 2023/03/17 17:32:13 - mmengine - INFO - Epoch(train) [4][ 940/1320] lr: 1.5500e-02 eta: 5:42:22 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 5.1137 loss: 2.4452 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4452 2023/03/17 17:32:19 - mmengine - INFO - Epoch(train) [4][ 960/1320] lr: 1.5500e-02 eta: 5:42:15 time: 0.3348 data_time: 0.0124 memory: 18752 grad_norm: 5.0078 loss: 2.5816 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.5816 2023/03/17 17:32:26 - mmengine - INFO - Epoch(train) [4][ 980/1320] lr: 1.5500e-02 eta: 5:42:08 time: 0.3353 data_time: 0.0114 memory: 18752 grad_norm: 5.0662 loss: 2.5454 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5454 2023/03/17 17:32:33 - mmengine - INFO - Epoch(train) [4][1000/1320] lr: 1.5500e-02 eta: 5:42:01 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 5.0794 loss: 2.4731 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4731 2023/03/17 17:32:40 - mmengine - INFO - Epoch(train) [4][1020/1320] lr: 1.5500e-02 eta: 5:41:54 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.8900 loss: 2.2926 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2926 2023/03/17 17:32:46 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:32:46 - mmengine - INFO - Epoch(train) [4][1040/1320] lr: 1.5500e-02 eta: 5:41:47 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 4.9632 loss: 2.4784 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4784 2023/03/17 17:32:53 - mmengine - INFO - Epoch(train) [4][1060/1320] lr: 1.5500e-02 eta: 5:41:41 time: 0.3360 data_time: 0.0116 memory: 18752 grad_norm: 5.1272 loss: 2.4152 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.4152 2023/03/17 17:33:00 - mmengine - INFO - Epoch(train) [4][1080/1320] lr: 1.5500e-02 eta: 5:41:34 time: 0.3367 data_time: 0.0123 memory: 18752 grad_norm: 5.0512 loss: 2.2902 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.2902 2023/03/17 17:33:07 - mmengine - INFO - Epoch(train) [4][1100/1320] lr: 1.5500e-02 eta: 5:41:27 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 5.0655 loss: 2.6336 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.6336 2023/03/17 17:33:13 - mmengine - INFO - Epoch(train) [4][1120/1320] lr: 1.5500e-02 eta: 5:41:20 time: 0.3354 data_time: 0.0126 memory: 18752 grad_norm: 4.8411 loss: 2.6394 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6394 2023/03/17 17:33:20 - mmengine - INFO - Epoch(train) [4][1140/1320] lr: 1.5500e-02 eta: 5:41:13 time: 0.3351 data_time: 0.0116 memory: 18752 grad_norm: 5.1249 loss: 2.5166 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5166 2023/03/17 17:33:27 - mmengine - INFO - Epoch(train) [4][1160/1320] lr: 1.5500e-02 eta: 5:41:06 time: 0.3353 data_time: 0.0126 memory: 18752 grad_norm: 5.0288 loss: 2.6348 top1_acc: 0.3125 top5_acc: 0.3750 loss_cls: 2.6348 2023/03/17 17:33:33 - mmengine - INFO - Epoch(train) [4][1180/1320] lr: 1.5500e-02 eta: 5:41:00 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.7588 loss: 2.4038 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.4038 2023/03/17 17:33:40 - mmengine - INFO - Epoch(train) [4][1200/1320] lr: 1.5500e-02 eta: 5:40:53 time: 0.3353 data_time: 0.0126 memory: 18752 grad_norm: 4.9711 loss: 2.3600 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3600 2023/03/17 17:33:47 - mmengine - INFO - Epoch(train) [4][1220/1320] lr: 1.5500e-02 eta: 5:40:46 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 5.0076 loss: 2.3948 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.3948 2023/03/17 17:33:53 - mmengine - INFO - Epoch(train) [4][1240/1320] lr: 1.5500e-02 eta: 5:40:39 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 5.1198 loss: 2.2960 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.2960 2023/03/17 17:34:00 - mmengine - INFO - Epoch(train) [4][1260/1320] lr: 1.5500e-02 eta: 5:40:32 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 5.0265 loss: 2.4750 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4750 2023/03/17 17:34:07 - mmengine - INFO - Epoch(train) [4][1280/1320] lr: 1.5500e-02 eta: 5:40:25 time: 0.3350 data_time: 0.0121 memory: 18752 grad_norm: 4.8502 loss: 2.4674 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4674 2023/03/17 17:34:14 - mmengine - INFO - Epoch(train) [4][1300/1320] lr: 1.5500e-02 eta: 5:40:18 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.8448 loss: 2.6177 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.6177 2023/03/17 17:34:20 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:34:20 - mmengine - INFO - Epoch(train) [4][1320/1320] lr: 1.5500e-02 eta: 5:40:10 time: 0.3300 data_time: 0.0122 memory: 18752 grad_norm: 4.9704 loss: 2.2915 top1_acc: 0.5455 top5_acc: 0.7273 loss_cls: 2.2915 2023/03/17 17:34:23 - mmengine - INFO - Epoch(val) [4][ 20/194] eta: 0:00:22 time: 0.1277 data_time: 0.0412 memory: 2112 2023/03/17 17:34:25 - mmengine - INFO - Epoch(val) [4][ 40/194] eta: 0:00:17 time: 0.0965 data_time: 0.0107 memory: 2112 2023/03/17 17:34:27 - mmengine - INFO - Epoch(val) [4][ 60/194] eta: 0:00:14 time: 0.0964 data_time: 0.0109 memory: 2112 2023/03/17 17:34:29 - mmengine - INFO - Epoch(val) [4][ 80/194] eta: 0:00:11 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 17:34:30 - mmengine - INFO - Epoch(val) [4][100/194] eta: 0:00:09 time: 0.0974 data_time: 0.0115 memory: 2112 2023/03/17 17:34:32 - mmengine - INFO - Epoch(val) [4][120/194] eta: 0:00:07 time: 0.0977 data_time: 0.0113 memory: 2112 2023/03/17 17:34:34 - mmengine - INFO - Epoch(val) [4][140/194] eta: 0:00:05 time: 0.0972 data_time: 0.0115 memory: 2112 2023/03/17 17:34:36 - mmengine - INFO - Epoch(val) [4][160/194] eta: 0:00:03 time: 0.0981 data_time: 0.0120 memory: 2112 2023/03/17 17:34:38 - mmengine - INFO - Epoch(val) [4][180/194] eta: 0:00:01 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 17:34:42 - mmengine - INFO - Epoch(val) [4][194/194] acc/top1: 0.3549 acc/top5: 0.6521 acc/mean1: 0.2927 2023/03/17 17:34:49 - mmengine - INFO - Epoch(train) [5][ 20/1320] lr: 2.0000e-02 eta: 5:40:13 time: 0.3785 data_time: 0.0455 memory: 18752 grad_norm: 5.1347 loss: 2.5397 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.5397 2023/03/17 17:34:56 - mmengine - INFO - Epoch(train) [5][ 40/1320] lr: 2.0000e-02 eta: 5:40:06 time: 0.3350 data_time: 0.0116 memory: 18752 grad_norm: 5.1123 loss: 2.5929 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5929 2023/03/17 17:35:03 - mmengine - INFO - Epoch(train) [5][ 60/1320] lr: 2.0000e-02 eta: 5:39:59 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.9354 loss: 2.6721 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.6721 2023/03/17 17:35:09 - mmengine - INFO - Epoch(train) [5][ 80/1320] lr: 2.0000e-02 eta: 5:39:52 time: 0.3346 data_time: 0.0117 memory: 18752 grad_norm: 4.8338 loss: 2.5515 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5515 2023/03/17 17:35:16 - mmengine - INFO - Epoch(train) [5][ 100/1320] lr: 2.0000e-02 eta: 5:39:45 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 4.8760 loss: 2.4644 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.4644 2023/03/17 17:35:23 - mmengine - INFO - Epoch(train) [5][ 120/1320] lr: 2.0000e-02 eta: 5:39:38 time: 0.3345 data_time: 0.0122 memory: 18752 grad_norm: 4.9824 loss: 2.6685 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.6685 2023/03/17 17:35:29 - mmengine - INFO - Epoch(train) [5][ 140/1320] lr: 2.0000e-02 eta: 5:39:31 time: 0.3350 data_time: 0.0114 memory: 18752 grad_norm: 4.9273 loss: 2.6099 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6099 2023/03/17 17:35:36 - mmengine - INFO - Epoch(train) [5][ 160/1320] lr: 2.0000e-02 eta: 5:39:24 time: 0.3343 data_time: 0.0113 memory: 18752 grad_norm: 4.9033 loss: 2.3988 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3988 2023/03/17 17:35:43 - mmengine - INFO - Epoch(train) [5][ 180/1320] lr: 2.0000e-02 eta: 5:39:16 time: 0.3346 data_time: 0.0114 memory: 18752 grad_norm: 4.9264 loss: 2.6145 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6145 2023/03/17 17:35:50 - mmengine - INFO - Epoch(train) [5][ 200/1320] lr: 2.0000e-02 eta: 5:39:09 time: 0.3345 data_time: 0.0116 memory: 18752 grad_norm: 4.9110 loss: 2.6874 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.6874 2023/03/17 17:35:56 - mmengine - INFO - Epoch(train) [5][ 220/1320] lr: 2.0000e-02 eta: 5:39:02 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.8090 loss: 2.5813 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.5813 2023/03/17 17:36:03 - mmengine - INFO - Epoch(train) [5][ 240/1320] lr: 2.0000e-02 eta: 5:38:55 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 4.8073 loss: 2.5498 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.5498 2023/03/17 17:36:10 - mmengine - INFO - Epoch(train) [5][ 260/1320] lr: 2.0000e-02 eta: 5:38:48 time: 0.3345 data_time: 0.0114 memory: 18752 grad_norm: 4.8595 loss: 2.4839 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4839 2023/03/17 17:36:16 - mmengine - INFO - Epoch(train) [5][ 280/1320] lr: 2.0000e-02 eta: 5:38:41 time: 0.3345 data_time: 0.0117 memory: 18752 grad_norm: 4.6455 loss: 2.5420 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5420 2023/03/17 17:36:23 - mmengine - INFO - Epoch(train) [5][ 300/1320] lr: 2.0000e-02 eta: 5:38:34 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.8979 loss: 2.5667 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.5667 2023/03/17 17:36:30 - mmengine - INFO - Epoch(train) [5][ 320/1320] lr: 2.0000e-02 eta: 5:38:27 time: 0.3348 data_time: 0.0117 memory: 18752 grad_norm: 4.8077 loss: 2.6279 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6279 2023/03/17 17:36:36 - mmengine - INFO - Epoch(train) [5][ 340/1320] lr: 2.0000e-02 eta: 5:38:20 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.7808 loss: 2.5804 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.5804 2023/03/17 17:36:43 - mmengine - INFO - Epoch(train) [5][ 360/1320] lr: 2.0000e-02 eta: 5:38:13 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.7992 loss: 2.4869 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4869 2023/03/17 17:36:50 - mmengine - INFO - Epoch(train) [5][ 380/1320] lr: 2.0000e-02 eta: 5:38:06 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 4.6990 loss: 2.7897 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.7897 2023/03/17 17:36:57 - mmengine - INFO - Epoch(train) [5][ 400/1320] lr: 2.0000e-02 eta: 5:37:59 time: 0.3348 data_time: 0.0122 memory: 18752 grad_norm: 4.7369 loss: 2.7364 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.7364 2023/03/17 17:37:03 - mmengine - INFO - Epoch(train) [5][ 420/1320] lr: 2.0000e-02 eta: 5:37:52 time: 0.3349 data_time: 0.0117 memory: 18752 grad_norm: 4.7324 loss: 2.3339 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3339 2023/03/17 17:37:10 - mmengine - INFO - Epoch(train) [5][ 440/1320] lr: 2.0000e-02 eta: 5:37:45 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.7555 loss: 2.5226 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5226 2023/03/17 17:37:17 - mmengine - INFO - Epoch(train) [5][ 460/1320] lr: 2.0000e-02 eta: 5:37:38 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 4.7640 loss: 2.6569 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 2.6569 2023/03/17 17:37:23 - mmengine - INFO - Epoch(train) [5][ 480/1320] lr: 2.0000e-02 eta: 5:37:31 time: 0.3349 data_time: 0.0122 memory: 18752 grad_norm: 4.7157 loss: 2.6007 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.6007 2023/03/17 17:37:30 - mmengine - INFO - Epoch(train) [5][ 500/1320] lr: 2.0000e-02 eta: 5:37:24 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.6594 loss: 2.4604 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.4604 2023/03/17 17:37:37 - mmengine - INFO - Epoch(train) [5][ 520/1320] lr: 2.0000e-02 eta: 5:37:17 time: 0.3343 data_time: 0.0114 memory: 18752 grad_norm: 4.5671 loss: 2.8010 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.8010 2023/03/17 17:37:43 - mmengine - INFO - Epoch(train) [5][ 540/1320] lr: 2.0000e-02 eta: 5:37:10 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.7795 loss: 2.4508 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.4508 2023/03/17 17:37:50 - mmengine - INFO - Epoch(train) [5][ 560/1320] lr: 2.0000e-02 eta: 5:37:03 time: 0.3349 data_time: 0.0123 memory: 18752 grad_norm: 4.6484 loss: 2.4786 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.4786 2023/03/17 17:37:57 - mmengine - INFO - Epoch(train) [5][ 580/1320] lr: 2.0000e-02 eta: 5:36:57 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.6679 loss: 2.4404 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.4404 2023/03/17 17:38:04 - mmengine - INFO - Epoch(train) [5][ 600/1320] lr: 2.0000e-02 eta: 5:36:50 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.6743 loss: 2.4578 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4578 2023/03/17 17:38:10 - mmengine - INFO - Epoch(train) [5][ 620/1320] lr: 2.0000e-02 eta: 5:36:43 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.6399 loss: 2.5484 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5484 2023/03/17 17:38:17 - mmengine - INFO - Epoch(train) [5][ 640/1320] lr: 2.0000e-02 eta: 5:36:36 time: 0.3344 data_time: 0.0118 memory: 18752 grad_norm: 4.5656 loss: 2.4765 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.4765 2023/03/17 17:38:24 - mmengine - INFO - Epoch(train) [5][ 660/1320] lr: 2.0000e-02 eta: 5:36:29 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.6341 loss: 2.4900 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.4900 2023/03/17 17:38:30 - mmengine - INFO - Epoch(train) [5][ 680/1320] lr: 2.0000e-02 eta: 5:36:22 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.5388 loss: 2.5884 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.5884 2023/03/17 17:38:37 - mmengine - INFO - Epoch(train) [5][ 700/1320] lr: 2.0000e-02 eta: 5:36:15 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.7817 loss: 2.6622 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.6622 2023/03/17 17:38:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:38:44 - mmengine - INFO - Epoch(train) [5][ 720/1320] lr: 2.0000e-02 eta: 5:36:08 time: 0.3347 data_time: 0.0123 memory: 18752 grad_norm: 4.6798 loss: 2.4740 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4740 2023/03/17 17:38:50 - mmengine - INFO - Epoch(train) [5][ 740/1320] lr: 2.0000e-02 eta: 5:36:01 time: 0.3345 data_time: 0.0120 memory: 18752 grad_norm: 4.6842 loss: 2.5118 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5118 2023/03/17 17:38:57 - mmengine - INFO - Epoch(train) [5][ 760/1320] lr: 2.0000e-02 eta: 5:35:54 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.5328 loss: 2.4797 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.4797 2023/03/17 17:39:04 - mmengine - INFO - Epoch(train) [5][ 780/1320] lr: 2.0000e-02 eta: 5:35:47 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.5477 loss: 2.3353 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3353 2023/03/17 17:39:11 - mmengine - INFO - Epoch(train) [5][ 800/1320] lr: 2.0000e-02 eta: 5:35:40 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.6215 loss: 2.5512 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5512 2023/03/17 17:39:17 - mmengine - INFO - Epoch(train) [5][ 820/1320] lr: 2.0000e-02 eta: 5:35:34 time: 0.3375 data_time: 0.0124 memory: 18752 grad_norm: 4.5446 loss: 2.5437 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.5437 2023/03/17 17:39:24 - mmengine - INFO - Epoch(train) [5][ 840/1320] lr: 2.0000e-02 eta: 5:35:27 time: 0.3349 data_time: 0.0120 memory: 18752 grad_norm: 4.7202 loss: 2.4900 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4900 2023/03/17 17:39:31 - mmengine - INFO - Epoch(train) [5][ 860/1320] lr: 2.0000e-02 eta: 5:35:20 time: 0.3351 data_time: 0.0116 memory: 18752 grad_norm: 4.5940 loss: 2.5704 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.5704 2023/03/17 17:39:37 - mmengine - INFO - Epoch(train) [5][ 880/1320] lr: 2.0000e-02 eta: 5:35:13 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 4.5160 loss: 2.4838 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4838 2023/03/17 17:39:44 - mmengine - INFO - Epoch(train) [5][ 900/1320] lr: 2.0000e-02 eta: 5:35:06 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.6105 loss: 2.6154 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6154 2023/03/17 17:39:51 - mmengine - INFO - Epoch(train) [5][ 920/1320] lr: 2.0000e-02 eta: 5:34:59 time: 0.3350 data_time: 0.0120 memory: 18752 grad_norm: 4.5921 loss: 2.4432 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4432 2023/03/17 17:39:58 - mmengine - INFO - Epoch(train) [5][ 940/1320] lr: 2.0000e-02 eta: 5:34:52 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.5941 loss: 2.4039 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4039 2023/03/17 17:40:04 - mmengine - INFO - Epoch(train) [5][ 960/1320] lr: 2.0000e-02 eta: 5:34:46 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.7506 loss: 2.5556 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5556 2023/03/17 17:40:11 - mmengine - INFO - Epoch(train) [5][ 980/1320] lr: 2.0000e-02 eta: 5:34:39 time: 0.3350 data_time: 0.0115 memory: 18752 grad_norm: 4.5084 loss: 2.5319 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5319 2023/03/17 17:40:18 - mmengine - INFO - Epoch(train) [5][1000/1320] lr: 2.0000e-02 eta: 5:34:32 time: 0.3350 data_time: 0.0121 memory: 18752 grad_norm: 4.5820 loss: 2.3700 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.3700 2023/03/17 17:40:24 - mmengine - INFO - Epoch(train) [5][1020/1320] lr: 2.0000e-02 eta: 5:34:25 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.5188 loss: 2.5507 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5507 2023/03/17 17:40:31 - mmengine - INFO - Epoch(train) [5][1040/1320] lr: 2.0000e-02 eta: 5:34:18 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.6240 loss: 2.5349 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5349 2023/03/17 17:40:38 - mmengine - INFO - Epoch(train) [5][1060/1320] lr: 2.0000e-02 eta: 5:34:11 time: 0.3352 data_time: 0.0115 memory: 18752 grad_norm: 4.4508 loss: 2.4285 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 2.4285 2023/03/17 17:40:44 - mmengine - INFO - Epoch(train) [5][1080/1320] lr: 2.0000e-02 eta: 5:34:04 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 4.6392 loss: 2.4441 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4441 2023/03/17 17:40:51 - mmengine - INFO - Epoch(train) [5][1100/1320] lr: 2.0000e-02 eta: 5:33:57 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.6857 loss: 2.4826 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4826 2023/03/17 17:40:58 - mmengine - INFO - Epoch(train) [5][1120/1320] lr: 2.0000e-02 eta: 5:33:50 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.5026 loss: 2.4063 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4063 2023/03/17 17:41:05 - mmengine - INFO - Epoch(train) [5][1140/1320] lr: 2.0000e-02 eta: 5:33:44 time: 0.3358 data_time: 0.0114 memory: 18752 grad_norm: 4.5247 loss: 2.4173 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.4173 2023/03/17 17:41:11 - mmengine - INFO - Epoch(train) [5][1160/1320] lr: 2.0000e-02 eta: 5:33:37 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.5691 loss: 2.4975 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.4975 2023/03/17 17:41:18 - mmengine - INFO - Epoch(train) [5][1180/1320] lr: 2.0000e-02 eta: 5:33:30 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.5087 loss: 2.4441 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.4441 2023/03/17 17:41:25 - mmengine - INFO - Epoch(train) [5][1200/1320] lr: 2.0000e-02 eta: 5:33:23 time: 0.3350 data_time: 0.0124 memory: 18752 grad_norm: 4.7050 loss: 2.3560 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3560 2023/03/17 17:41:31 - mmengine - INFO - Epoch(train) [5][1220/1320] lr: 2.0000e-02 eta: 5:33:16 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.5548 loss: 2.4436 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4436 2023/03/17 17:41:38 - mmengine - INFO - Epoch(train) [5][1240/1320] lr: 2.0000e-02 eta: 5:33:09 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.5435 loss: 2.4791 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4791 2023/03/17 17:41:45 - mmengine - INFO - Epoch(train) [5][1260/1320] lr: 2.0000e-02 eta: 5:33:03 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.5929 loss: 2.4464 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.4464 2023/03/17 17:41:52 - mmengine - INFO - Epoch(train) [5][1280/1320] lr: 2.0000e-02 eta: 5:32:56 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.5932 loss: 2.5534 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.5534 2023/03/17 17:41:58 - mmengine - INFO - Epoch(train) [5][1300/1320] lr: 2.0000e-02 eta: 5:32:49 time: 0.3349 data_time: 0.0116 memory: 18752 grad_norm: 4.6044 loss: 2.5139 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.5139 2023/03/17 17:42:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:42:05 - mmengine - INFO - Epoch(train) [5][1320/1320] lr: 2.0000e-02 eta: 5:32:41 time: 0.3298 data_time: 0.0118 memory: 18752 grad_norm: 4.5100 loss: 2.5073 top1_acc: 0.6364 top5_acc: 0.7273 loss_cls: 2.5073 2023/03/17 17:42:07 - mmengine - INFO - Epoch(val) [5][ 20/194] eta: 0:00:22 time: 0.1312 data_time: 0.0447 memory: 2112 2023/03/17 17:42:09 - mmengine - INFO - Epoch(val) [5][ 40/194] eta: 0:00:17 time: 0.0975 data_time: 0.0117 memory: 2112 2023/03/17 17:42:11 - mmengine - INFO - Epoch(val) [5][ 60/194] eta: 0:00:14 time: 0.0969 data_time: 0.0109 memory: 2112 2023/03/17 17:42:13 - mmengine - INFO - Epoch(val) [5][ 80/194] eta: 0:00:12 time: 0.0969 data_time: 0.0112 memory: 2112 2023/03/17 17:42:15 - mmengine - INFO - Epoch(val) [5][100/194] eta: 0:00:09 time: 0.0974 data_time: 0.0113 memory: 2112 2023/03/17 17:42:17 - mmengine - INFO - Epoch(val) [5][120/194] eta: 0:00:07 time: 0.0969 data_time: 0.0111 memory: 2112 2023/03/17 17:42:19 - mmengine - INFO - Epoch(val) [5][140/194] eta: 0:00:05 time: 0.0971 data_time: 0.0111 memory: 2112 2023/03/17 17:42:21 - mmengine - INFO - Epoch(val) [5][160/194] eta: 0:00:03 time: 0.0976 data_time: 0.0117 memory: 2112 2023/03/17 17:42:23 - mmengine - INFO - Epoch(val) [5][180/194] eta: 0:00:01 time: 0.0975 data_time: 0.0117 memory: 2112 2023/03/17 17:42:27 - mmengine - INFO - Epoch(val) [5][194/194] acc/top1: 0.3564 acc/top5: 0.6543 acc/mean1: 0.2866 2023/03/17 17:42:34 - mmengine - INFO - Epoch(train) [6][ 20/1320] lr: 2.0000e-02 eta: 5:32:40 time: 0.3696 data_time: 0.0399 memory: 18752 grad_norm: 4.4556 loss: 2.3460 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3460 2023/03/17 17:42:41 - mmengine - INFO - Epoch(train) [6][ 40/1320] lr: 2.0000e-02 eta: 5:32:33 time: 0.3348 data_time: 0.0119 memory: 18752 grad_norm: 4.5742 loss: 2.5389 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.5389 2023/03/17 17:42:47 - mmengine - INFO - Epoch(train) [6][ 60/1320] lr: 2.0000e-02 eta: 5:32:26 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.5384 loss: 2.4681 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.4681 2023/03/17 17:42:54 - mmengine - INFO - Epoch(train) [6][ 80/1320] lr: 2.0000e-02 eta: 5:32:20 time: 0.3352 data_time: 0.0115 memory: 18752 grad_norm: 4.5305 loss: 2.5610 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.5610 2023/03/17 17:43:01 - mmengine - INFO - Epoch(train) [6][ 100/1320] lr: 2.0000e-02 eta: 5:32:13 time: 0.3355 data_time: 0.0115 memory: 18752 grad_norm: 4.6667 loss: 2.4538 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4538 2023/03/17 17:43:08 - mmengine - INFO - Epoch(train) [6][ 120/1320] lr: 2.0000e-02 eta: 5:32:06 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.4715 loss: 2.2245 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2245 2023/03/17 17:43:14 - mmengine - INFO - Epoch(train) [6][ 140/1320] lr: 2.0000e-02 eta: 5:31:59 time: 0.3352 data_time: 0.0113 memory: 18752 grad_norm: 4.5067 loss: 2.1669 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1669 2023/03/17 17:43:21 - mmengine - INFO - Epoch(train) [6][ 160/1320] lr: 2.0000e-02 eta: 5:31:52 time: 0.3351 data_time: 0.0115 memory: 18752 grad_norm: 4.5483 loss: 2.3704 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3704 2023/03/17 17:43:28 - mmengine - INFO - Epoch(train) [6][ 180/1320] lr: 2.0000e-02 eta: 5:31:45 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.4508 loss: 2.3138 top1_acc: 0.3125 top5_acc: 0.9375 loss_cls: 2.3138 2023/03/17 17:43:34 - mmengine - INFO - Epoch(train) [6][ 200/1320] lr: 2.0000e-02 eta: 5:31:38 time: 0.3352 data_time: 0.0114 memory: 18752 grad_norm: 4.5031 loss: 2.5285 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.5285 2023/03/17 17:43:41 - mmengine - INFO - Epoch(train) [6][ 220/1320] lr: 2.0000e-02 eta: 5:31:32 time: 0.3356 data_time: 0.0115 memory: 18752 grad_norm: 4.4756 loss: 2.3995 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.3995 2023/03/17 17:43:48 - mmengine - INFO - Epoch(train) [6][ 240/1320] lr: 2.0000e-02 eta: 5:31:25 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 4.4640 loss: 2.4411 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.4411 2023/03/17 17:43:54 - mmengine - INFO - Epoch(train) [6][ 260/1320] lr: 2.0000e-02 eta: 5:31:18 time: 0.3356 data_time: 0.0113 memory: 18752 grad_norm: 4.6607 loss: 2.3954 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.3954 2023/03/17 17:44:01 - mmengine - INFO - Epoch(train) [6][ 280/1320] lr: 2.0000e-02 eta: 5:31:11 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.6005 loss: 2.1427 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.1427 2023/03/17 17:44:08 - mmengine - INFO - Epoch(train) [6][ 300/1320] lr: 2.0000e-02 eta: 5:31:04 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.4206 loss: 2.5439 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.5439 2023/03/17 17:44:15 - mmengine - INFO - Epoch(train) [6][ 320/1320] lr: 2.0000e-02 eta: 5:30:57 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.4809 loss: 2.2924 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2924 2023/03/17 17:44:21 - mmengine - INFO - Epoch(train) [6][ 340/1320] lr: 2.0000e-02 eta: 5:30:51 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.4954 loss: 2.5278 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5278 2023/03/17 17:44:28 - mmengine - INFO - Epoch(train) [6][ 360/1320] lr: 2.0000e-02 eta: 5:30:44 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.4655 loss: 2.1972 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1972 2023/03/17 17:44:35 - mmengine - INFO - Epoch(train) [6][ 380/1320] lr: 2.0000e-02 eta: 5:30:37 time: 0.3363 data_time: 0.0133 memory: 18752 grad_norm: 4.5319 loss: 2.2846 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2846 2023/03/17 17:44:42 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:44:42 - mmengine - INFO - Epoch(train) [6][ 400/1320] lr: 2.0000e-02 eta: 5:30:30 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.4945 loss: 2.3774 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.3774 2023/03/17 17:44:48 - mmengine - INFO - Epoch(train) [6][ 420/1320] lr: 2.0000e-02 eta: 5:30:24 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.5391 loss: 2.2989 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2989 2023/03/17 17:44:55 - mmengine - INFO - Epoch(train) [6][ 440/1320] lr: 2.0000e-02 eta: 5:30:17 time: 0.3346 data_time: 0.0118 memory: 18752 grad_norm: 4.6204 loss: 2.4403 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4403 2023/03/17 17:45:02 - mmengine - INFO - Epoch(train) [6][ 460/1320] lr: 2.0000e-02 eta: 5:30:10 time: 0.3349 data_time: 0.0117 memory: 18752 grad_norm: 4.3266 loss: 2.2426 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.2426 2023/03/17 17:45:08 - mmengine - INFO - Epoch(train) [6][ 480/1320] lr: 2.0000e-02 eta: 5:30:03 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.6411 loss: 2.4701 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4701 2023/03/17 17:45:15 - mmengine - INFO - Epoch(train) [6][ 500/1320] lr: 2.0000e-02 eta: 5:29:56 time: 0.3351 data_time: 0.0115 memory: 18752 grad_norm: 4.5803 loss: 2.5829 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5829 2023/03/17 17:45:22 - mmengine - INFO - Epoch(train) [6][ 520/1320] lr: 2.0000e-02 eta: 5:29:49 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.7010 loss: 2.3491 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3491 2023/03/17 17:45:28 - mmengine - INFO - Epoch(train) [6][ 540/1320] lr: 2.0000e-02 eta: 5:29:42 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 4.6697 loss: 2.3006 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.3006 2023/03/17 17:45:35 - mmengine - INFO - Epoch(train) [6][ 560/1320] lr: 2.0000e-02 eta: 5:29:35 time: 0.3347 data_time: 0.0120 memory: 18752 grad_norm: 4.5678 loss: 2.3765 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3765 2023/03/17 17:45:42 - mmengine - INFO - Epoch(train) [6][ 580/1320] lr: 2.0000e-02 eta: 5:29:28 time: 0.3348 data_time: 0.0119 memory: 18752 grad_norm: 4.4482 loss: 2.2754 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.2754 2023/03/17 17:45:49 - mmengine - INFO - Epoch(train) [6][ 600/1320] lr: 2.0000e-02 eta: 5:29:22 time: 0.3353 data_time: 0.0127 memory: 18752 grad_norm: 4.5632 loss: 2.3337 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3337 2023/03/17 17:45:55 - mmengine - INFO - Epoch(train) [6][ 620/1320] lr: 2.0000e-02 eta: 5:29:15 time: 0.3349 data_time: 0.0114 memory: 18752 grad_norm: 4.4901 loss: 2.3220 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3220 2023/03/17 17:46:02 - mmengine - INFO - Epoch(train) [6][ 640/1320] lr: 2.0000e-02 eta: 5:29:08 time: 0.3350 data_time: 0.0115 memory: 18752 grad_norm: 4.4867 loss: 2.4336 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.4336 2023/03/17 17:46:09 - mmengine - INFO - Epoch(train) [6][ 660/1320] lr: 2.0000e-02 eta: 5:29:01 time: 0.3348 data_time: 0.0115 memory: 18752 grad_norm: 4.5376 loss: 2.2638 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 2.2638 2023/03/17 17:46:15 - mmengine - INFO - Epoch(train) [6][ 680/1320] lr: 2.0000e-02 eta: 5:28:54 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.3872 loss: 2.2866 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.2866 2023/03/17 17:46:22 - mmengine - INFO - Epoch(train) [6][ 700/1320] lr: 2.0000e-02 eta: 5:28:47 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4515 loss: 2.3305 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3305 2023/03/17 17:46:29 - mmengine - INFO - Epoch(train) [6][ 720/1320] lr: 2.0000e-02 eta: 5:28:40 time: 0.3348 data_time: 0.0115 memory: 18752 grad_norm: 4.5304 loss: 2.3823 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.3823 2023/03/17 17:46:35 - mmengine - INFO - Epoch(train) [6][ 740/1320] lr: 2.0000e-02 eta: 5:28:33 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.5209 loss: 2.3578 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3578 2023/03/17 17:46:42 - mmengine - INFO - Epoch(train) [6][ 760/1320] lr: 2.0000e-02 eta: 5:28:27 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.3772 loss: 2.4501 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4501 2023/03/17 17:46:49 - mmengine - INFO - Epoch(train) [6][ 780/1320] lr: 2.0000e-02 eta: 5:28:20 time: 0.3349 data_time: 0.0116 memory: 18752 grad_norm: 4.4811 loss: 2.2881 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2881 2023/03/17 17:46:56 - mmengine - INFO - Epoch(train) [6][ 800/1320] lr: 2.0000e-02 eta: 5:28:13 time: 0.3348 data_time: 0.0123 memory: 18752 grad_norm: 4.4541 loss: 2.2826 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2826 2023/03/17 17:47:02 - mmengine - INFO - Epoch(train) [6][ 820/1320] lr: 2.0000e-02 eta: 5:28:06 time: 0.3349 data_time: 0.0116 memory: 18752 grad_norm: 4.4139 loss: 2.3666 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3666 2023/03/17 17:47:09 - mmengine - INFO - Epoch(train) [6][ 840/1320] lr: 2.0000e-02 eta: 5:27:59 time: 0.3349 data_time: 0.0118 memory: 18752 grad_norm: 4.4810 loss: 2.3833 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.3833 2023/03/17 17:47:16 - mmengine - INFO - Epoch(train) [6][ 860/1320] lr: 2.0000e-02 eta: 5:27:52 time: 0.3350 data_time: 0.0116 memory: 18752 grad_norm: 4.5287 loss: 2.4015 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.4015 2023/03/17 17:47:22 - mmengine - INFO - Epoch(train) [6][ 880/1320] lr: 2.0000e-02 eta: 5:27:45 time: 0.3358 data_time: 0.0127 memory: 18752 grad_norm: 4.4947 loss: 2.2606 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2606 2023/03/17 17:47:29 - mmengine - INFO - Epoch(train) [6][ 900/1320] lr: 2.0000e-02 eta: 5:27:39 time: 0.3369 data_time: 0.0119 memory: 18752 grad_norm: 4.4828 loss: 2.2982 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2982 2023/03/17 17:47:36 - mmengine - INFO - Epoch(train) [6][ 920/1320] lr: 2.0000e-02 eta: 5:27:32 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 4.6145 loss: 2.4099 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.4099 2023/03/17 17:47:43 - mmengine - INFO - Epoch(train) [6][ 940/1320] lr: 2.0000e-02 eta: 5:27:25 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.4729 loss: 2.3868 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3868 2023/03/17 17:47:49 - mmengine - INFO - Epoch(train) [6][ 960/1320] lr: 2.0000e-02 eta: 5:27:18 time: 0.3348 data_time: 0.0115 memory: 18752 grad_norm: 4.5419 loss: 2.4015 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.4015 2023/03/17 17:47:56 - mmengine - INFO - Epoch(train) [6][ 980/1320] lr: 2.0000e-02 eta: 5:27:11 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5144 loss: 2.2864 top1_acc: 0.1875 top5_acc: 0.7500 loss_cls: 2.2864 2023/03/17 17:48:03 - mmengine - INFO - Epoch(train) [6][1000/1320] lr: 2.0000e-02 eta: 5:27:05 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 4.5131 loss: 2.4437 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4437 2023/03/17 17:48:09 - mmengine - INFO - Epoch(train) [6][1020/1320] lr: 2.0000e-02 eta: 5:26:58 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.5384 loss: 2.3733 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.3733 2023/03/17 17:48:16 - mmengine - INFO - Epoch(train) [6][1040/1320] lr: 2.0000e-02 eta: 5:26:51 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.4506 loss: 2.2873 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2873 2023/03/17 17:48:23 - mmengine - INFO - Epoch(train) [6][1060/1320] lr: 2.0000e-02 eta: 5:26:44 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.6307 loss: 2.2764 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2764 2023/03/17 17:48:29 - mmengine - INFO - Epoch(train) [6][1080/1320] lr: 2.0000e-02 eta: 5:26:37 time: 0.3352 data_time: 0.0129 memory: 18752 grad_norm: 4.4588 loss: 2.3538 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3538 2023/03/17 17:48:36 - mmengine - INFO - Epoch(train) [6][1100/1320] lr: 2.0000e-02 eta: 5:26:30 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.3211 loss: 2.5699 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.5699 2023/03/17 17:48:43 - mmengine - INFO - Epoch(train) [6][1120/1320] lr: 2.0000e-02 eta: 5:26:24 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.4719 loss: 2.3389 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 2.3389 2023/03/17 17:48:50 - mmengine - INFO - Epoch(train) [6][1140/1320] lr: 2.0000e-02 eta: 5:26:17 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.3849 loss: 2.2123 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2123 2023/03/17 17:48:56 - mmengine - INFO - Epoch(train) [6][1160/1320] lr: 2.0000e-02 eta: 5:26:10 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.6208 loss: 2.2943 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2943 2023/03/17 17:49:03 - mmengine - INFO - Epoch(train) [6][1180/1320] lr: 2.0000e-02 eta: 5:26:03 time: 0.3354 data_time: 0.0113 memory: 18752 grad_norm: 4.4643 loss: 2.4950 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.4950 2023/03/17 17:49:10 - mmengine - INFO - Epoch(train) [6][1200/1320] lr: 2.0000e-02 eta: 5:25:56 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 4.4920 loss: 2.3280 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3280 2023/03/17 17:49:16 - mmengine - INFO - Epoch(train) [6][1220/1320] lr: 2.0000e-02 eta: 5:25:49 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 4.5817 loss: 2.1197 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.1197 2023/03/17 17:49:23 - mmengine - INFO - Epoch(train) [6][1240/1320] lr: 2.0000e-02 eta: 5:25:43 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.2877 loss: 2.2234 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 2.2234 2023/03/17 17:49:30 - mmengine - INFO - Epoch(train) [6][1260/1320] lr: 2.0000e-02 eta: 5:25:36 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 4.7267 loss: 2.3403 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.3403 2023/03/17 17:49:37 - mmengine - INFO - Epoch(train) [6][1280/1320] lr: 2.0000e-02 eta: 5:25:29 time: 0.3356 data_time: 0.0115 memory: 18752 grad_norm: 4.4274 loss: 2.2923 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2923 2023/03/17 17:49:43 - mmengine - INFO - Epoch(train) [6][1300/1320] lr: 2.0000e-02 eta: 5:25:22 time: 0.3355 data_time: 0.0114 memory: 18752 grad_norm: 4.6193 loss: 2.5820 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.5820 2023/03/17 17:49:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:49:50 - mmengine - INFO - Epoch(train) [6][1320/1320] lr: 2.0000e-02 eta: 5:25:15 time: 0.3301 data_time: 0.0114 memory: 18752 grad_norm: 4.5023 loss: 2.5936 top1_acc: 0.3636 top5_acc: 0.5455 loss_cls: 2.5936 2023/03/17 17:49:50 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/03/17 17:49:55 - mmengine - INFO - Epoch(val) [6][ 20/194] eta: 0:00:22 time: 0.1270 data_time: 0.0401 memory: 2112 2023/03/17 17:49:57 - mmengine - INFO - Epoch(val) [6][ 40/194] eta: 0:00:17 time: 0.0972 data_time: 0.0117 memory: 2112 2023/03/17 17:49:59 - mmengine - INFO - Epoch(val) [6][ 60/194] eta: 0:00:14 time: 0.0957 data_time: 0.0101 memory: 2112 2023/03/17 17:50:01 - mmengine - INFO - Epoch(val) [6][ 80/194] eta: 0:00:11 time: 0.0971 data_time: 0.0109 memory: 2112 2023/03/17 17:50:03 - mmengine - INFO - Epoch(val) [6][100/194] eta: 0:00:09 time: 0.0961 data_time: 0.0104 memory: 2112 2023/03/17 17:50:05 - mmengine - INFO - Epoch(val) [6][120/194] eta: 0:00:07 time: 0.0974 data_time: 0.0111 memory: 2112 2023/03/17 17:50:07 - mmengine - INFO - Epoch(val) [6][140/194] eta: 0:00:05 time: 0.0971 data_time: 0.0110 memory: 2112 2023/03/17 17:50:09 - mmengine - INFO - Epoch(val) [6][160/194] eta: 0:00:03 time: 0.0958 data_time: 0.0103 memory: 2112 2023/03/17 17:50:11 - mmengine - INFO - Epoch(val) [6][180/194] eta: 0:00:01 time: 0.0965 data_time: 0.0106 memory: 2112 2023/03/17 17:50:13 - mmengine - INFO - Epoch(val) [6][194/194] acc/top1: 0.3716 acc/top5: 0.6685 acc/mean1: 0.3038 2023/03/17 17:50:21 - mmengine - INFO - Epoch(train) [7][ 20/1320] lr: 2.0000e-02 eta: 5:25:14 time: 0.3739 data_time: 0.0395 memory: 18752 grad_norm: 4.2206 loss: 2.2357 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2357 2023/03/17 17:50:27 - mmengine - INFO - Epoch(train) [7][ 40/1320] lr: 2.0000e-02 eta: 5:25:07 time: 0.3349 data_time: 0.0111 memory: 18752 grad_norm: 4.4108 loss: 2.0504 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0504 2023/03/17 17:50:34 - mmengine - INFO - Epoch(train) [7][ 60/1320] lr: 2.0000e-02 eta: 5:25:00 time: 0.3350 data_time: 0.0116 memory: 18752 grad_norm: 4.4728 loss: 2.3128 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3128 2023/03/17 17:50:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:50:41 - mmengine - INFO - Epoch(train) [7][ 80/1320] lr: 2.0000e-02 eta: 5:24:53 time: 0.3346 data_time: 0.0115 memory: 18752 grad_norm: 4.6082 loss: 2.3243 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3243 2023/03/17 17:50:47 - mmengine - INFO - Epoch(train) [7][ 100/1320] lr: 2.0000e-02 eta: 5:24:46 time: 0.3353 data_time: 0.0124 memory: 18752 grad_norm: 4.4403 loss: 2.2192 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2192 2023/03/17 17:50:54 - mmengine - INFO - Epoch(train) [7][ 120/1320] lr: 2.0000e-02 eta: 5:24:39 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.5252 loss: 2.5357 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.5357 2023/03/17 17:51:01 - mmengine - INFO - Epoch(train) [7][ 140/1320] lr: 2.0000e-02 eta: 5:24:32 time: 0.3347 data_time: 0.0113 memory: 18752 grad_norm: 4.5210 loss: 2.0287 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0287 2023/03/17 17:51:08 - mmengine - INFO - Epoch(train) [7][ 160/1320] lr: 2.0000e-02 eta: 5:24:25 time: 0.3340 data_time: 0.0113 memory: 18752 grad_norm: 4.4084 loss: 2.2577 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2577 2023/03/17 17:51:14 - mmengine - INFO - Epoch(train) [7][ 180/1320] lr: 2.0000e-02 eta: 5:24:18 time: 0.3353 data_time: 0.0113 memory: 18752 grad_norm: 4.4611 loss: 2.2554 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2554 2023/03/17 17:51:21 - mmengine - INFO - Epoch(train) [7][ 200/1320] lr: 2.0000e-02 eta: 5:24:12 time: 0.3348 data_time: 0.0111 memory: 18752 grad_norm: 4.4818 loss: 2.2728 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2728 2023/03/17 17:51:28 - mmengine - INFO - Epoch(train) [7][ 220/1320] lr: 2.0000e-02 eta: 5:24:05 time: 0.3354 data_time: 0.0112 memory: 18752 grad_norm: 4.4879 loss: 2.3365 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.3365 2023/03/17 17:51:34 - mmengine - INFO - Epoch(train) [7][ 240/1320] lr: 2.0000e-02 eta: 5:23:58 time: 0.3345 data_time: 0.0111 memory: 18752 grad_norm: 4.5411 loss: 2.1467 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.1467 2023/03/17 17:51:41 - mmengine - INFO - Epoch(train) [7][ 260/1320] lr: 2.0000e-02 eta: 5:23:51 time: 0.3350 data_time: 0.0114 memory: 18752 grad_norm: 4.4678 loss: 2.1416 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.1416 2023/03/17 17:51:48 - mmengine - INFO - Epoch(train) [7][ 280/1320] lr: 2.0000e-02 eta: 5:23:44 time: 0.3345 data_time: 0.0115 memory: 18752 grad_norm: 4.4153 loss: 2.2542 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2542 2023/03/17 17:51:54 - mmengine - INFO - Epoch(train) [7][ 300/1320] lr: 2.0000e-02 eta: 5:23:37 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.5194 loss: 2.6115 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.6115 2023/03/17 17:52:01 - mmengine - INFO - Epoch(train) [7][ 320/1320] lr: 2.0000e-02 eta: 5:23:30 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.5311 loss: 2.6086 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.6086 2023/03/17 17:52:08 - mmengine - INFO - Epoch(train) [7][ 340/1320] lr: 2.0000e-02 eta: 5:23:24 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.5396 loss: 2.1763 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1763 2023/03/17 17:52:15 - mmengine - INFO - Epoch(train) [7][ 360/1320] lr: 2.0000e-02 eta: 5:23:17 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.5503 loss: 2.1763 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1763 2023/03/17 17:52:21 - mmengine - INFO - Epoch(train) [7][ 380/1320] lr: 2.0000e-02 eta: 5:23:10 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.4457 loss: 2.5389 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.5389 2023/03/17 17:52:28 - mmengine - INFO - Epoch(train) [7][ 400/1320] lr: 2.0000e-02 eta: 5:23:03 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.4921 loss: 2.1621 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1621 2023/03/17 17:52:35 - mmengine - INFO - Epoch(train) [7][ 420/1320] lr: 2.0000e-02 eta: 5:22:56 time: 0.3352 data_time: 0.0111 memory: 18752 grad_norm: 4.3703 loss: 2.3116 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3116 2023/03/17 17:52:41 - mmengine - INFO - Epoch(train) [7][ 440/1320] lr: 2.0000e-02 eta: 5:22:49 time: 0.3350 data_time: 0.0114 memory: 18752 grad_norm: 4.5847 loss: 2.2422 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2422 2023/03/17 17:52:48 - mmengine - INFO - Epoch(train) [7][ 460/1320] lr: 2.0000e-02 eta: 5:22:43 time: 0.3354 data_time: 0.0114 memory: 18752 grad_norm: 4.7042 loss: 2.5212 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5212 2023/03/17 17:52:55 - mmengine - INFO - Epoch(train) [7][ 480/1320] lr: 2.0000e-02 eta: 5:22:36 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.4545 loss: 2.2238 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2238 2023/03/17 17:53:02 - mmengine - INFO - Epoch(train) [7][ 500/1320] lr: 2.0000e-02 eta: 5:22:29 time: 0.3354 data_time: 0.0114 memory: 18752 grad_norm: 4.4714 loss: 2.2372 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2372 2023/03/17 17:53:08 - mmengine - INFO - Epoch(train) [7][ 520/1320] lr: 2.0000e-02 eta: 5:22:22 time: 0.3355 data_time: 0.0115 memory: 18752 grad_norm: 4.5407 loss: 2.3509 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.3509 2023/03/17 17:53:15 - mmengine - INFO - Epoch(train) [7][ 540/1320] lr: 2.0000e-02 eta: 5:22:15 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.4569 loss: 2.2509 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2509 2023/03/17 17:53:22 - mmengine - INFO - Epoch(train) [7][ 560/1320] lr: 2.0000e-02 eta: 5:22:09 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.3818 loss: 2.3553 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3553 2023/03/17 17:53:28 - mmengine - INFO - Epoch(train) [7][ 580/1320] lr: 2.0000e-02 eta: 5:22:02 time: 0.3360 data_time: 0.0116 memory: 18752 grad_norm: 4.5122 loss: 2.3357 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3357 2023/03/17 17:53:35 - mmengine - INFO - Epoch(train) [7][ 600/1320] lr: 2.0000e-02 eta: 5:21:55 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 4.5371 loss: 2.2747 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2747 2023/03/17 17:53:42 - mmengine - INFO - Epoch(train) [7][ 620/1320] lr: 2.0000e-02 eta: 5:21:48 time: 0.3361 data_time: 0.0112 memory: 18752 grad_norm: 4.5011 loss: 2.1955 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 2.1955 2023/03/17 17:53:49 - mmengine - INFO - Epoch(train) [7][ 640/1320] lr: 2.0000e-02 eta: 5:21:42 time: 0.3352 data_time: 0.0108 memory: 18752 grad_norm: 4.4684 loss: 2.2026 top1_acc: 0.1875 top5_acc: 0.7500 loss_cls: 2.2026 2023/03/17 17:53:55 - mmengine - INFO - Epoch(train) [7][ 660/1320] lr: 2.0000e-02 eta: 5:21:35 time: 0.3359 data_time: 0.0111 memory: 18752 grad_norm: 4.5540 loss: 2.2795 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2795 2023/03/17 17:54:02 - mmengine - INFO - Epoch(train) [7][ 680/1320] lr: 2.0000e-02 eta: 5:21:28 time: 0.3356 data_time: 0.0114 memory: 18752 grad_norm: 4.4186 loss: 2.5025 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.5025 2023/03/17 17:54:09 - mmengine - INFO - Epoch(train) [7][ 700/1320] lr: 2.0000e-02 eta: 5:21:21 time: 0.3354 data_time: 0.0113 memory: 18752 grad_norm: 4.4923 loss: 2.2717 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.2717 2023/03/17 17:54:15 - mmengine - INFO - Epoch(train) [7][ 720/1320] lr: 2.0000e-02 eta: 5:21:14 time: 0.3351 data_time: 0.0111 memory: 18752 grad_norm: 4.5917 loss: 2.0831 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0831 2023/03/17 17:54:22 - mmengine - INFO - Epoch(train) [7][ 740/1320] lr: 2.0000e-02 eta: 5:21:08 time: 0.3356 data_time: 0.0114 memory: 18752 grad_norm: 4.4235 loss: 2.1712 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1712 2023/03/17 17:54:29 - mmengine - INFO - Epoch(train) [7][ 760/1320] lr: 2.0000e-02 eta: 5:21:01 time: 0.3355 data_time: 0.0114 memory: 18752 grad_norm: 4.5485 loss: 2.1763 top1_acc: 0.1250 top5_acc: 0.6875 loss_cls: 2.1763 2023/03/17 17:54:35 - mmengine - INFO - Epoch(train) [7][ 780/1320] lr: 2.0000e-02 eta: 5:20:54 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.4924 loss: 2.2890 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2890 2023/03/17 17:54:42 - mmengine - INFO - Epoch(train) [7][ 800/1320] lr: 2.0000e-02 eta: 5:20:47 time: 0.3349 data_time: 0.0122 memory: 18752 grad_norm: 4.5313 loss: 2.2272 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2272 2023/03/17 17:54:49 - mmengine - INFO - Epoch(train) [7][ 820/1320] lr: 2.0000e-02 eta: 5:20:40 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 4.4180 loss: 2.2053 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2053 2023/03/17 17:54:56 - mmengine - INFO - Epoch(train) [7][ 840/1320] lr: 2.0000e-02 eta: 5:20:34 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.3651 loss: 2.6021 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.6021 2023/03/17 17:55:02 - mmengine - INFO - Epoch(train) [7][ 860/1320] lr: 2.0000e-02 eta: 5:20:27 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.4148 loss: 2.2752 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2752 2023/03/17 17:55:09 - mmengine - INFO - Epoch(train) [7][ 880/1320] lr: 2.0000e-02 eta: 5:20:20 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.4756 loss: 2.3483 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3483 2023/03/17 17:55:16 - mmengine - INFO - Epoch(train) [7][ 900/1320] lr: 2.0000e-02 eta: 5:20:13 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.4767 loss: 2.4209 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.4209 2023/03/17 17:55:22 - mmengine - INFO - Epoch(train) [7][ 920/1320] lr: 2.0000e-02 eta: 5:20:06 time: 0.3351 data_time: 0.0123 memory: 18752 grad_norm: 4.3020 loss: 2.2028 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2028 2023/03/17 17:55:29 - mmengine - INFO - Epoch(train) [7][ 940/1320] lr: 2.0000e-02 eta: 5:19:59 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 4.2842 loss: 2.1506 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1506 2023/03/17 17:55:36 - mmengine - INFO - Epoch(train) [7][ 960/1320] lr: 2.0000e-02 eta: 5:19:53 time: 0.3351 data_time: 0.0122 memory: 18752 grad_norm: 4.4853 loss: 2.2770 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2770 2023/03/17 17:55:43 - mmengine - INFO - Epoch(train) [7][ 980/1320] lr: 2.0000e-02 eta: 5:19:46 time: 0.3366 data_time: 0.0122 memory: 18752 grad_norm: 4.4303 loss: 2.0706 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0706 2023/03/17 17:55:49 - mmengine - INFO - Epoch(train) [7][1000/1320] lr: 2.0000e-02 eta: 5:19:39 time: 0.3353 data_time: 0.0126 memory: 18752 grad_norm: 4.3859 loss: 2.2330 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2330 2023/03/17 17:55:56 - mmengine - INFO - Epoch(train) [7][1020/1320] lr: 2.0000e-02 eta: 5:19:32 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.4906 loss: 2.1223 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1223 2023/03/17 17:56:03 - mmengine - INFO - Epoch(train) [7][1040/1320] lr: 2.0000e-02 eta: 5:19:25 time: 0.3350 data_time: 0.0123 memory: 18752 grad_norm: 4.4477 loss: 2.1709 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1709 2023/03/17 17:56:09 - mmengine - INFO - Epoch(train) [7][1060/1320] lr: 2.0000e-02 eta: 5:19:19 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 4.5802 loss: 2.2303 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2303 2023/03/17 17:56:16 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:56:16 - mmengine - INFO - Epoch(train) [7][1080/1320] lr: 2.0000e-02 eta: 5:19:12 time: 0.3349 data_time: 0.0119 memory: 18752 grad_norm: 4.5463 loss: 2.3380 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3380 2023/03/17 17:56:23 - mmengine - INFO - Epoch(train) [7][1100/1320] lr: 2.0000e-02 eta: 5:19:05 time: 0.3356 data_time: 0.0127 memory: 18752 grad_norm: 4.5359 loss: 2.4583 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4583 2023/03/17 17:56:30 - mmengine - INFO - Epoch(train) [7][1120/1320] lr: 2.0000e-02 eta: 5:18:58 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.3804 loss: 2.2494 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2494 2023/03/17 17:56:36 - mmengine - INFO - Epoch(train) [7][1140/1320] lr: 2.0000e-02 eta: 5:18:51 time: 0.3350 data_time: 0.0123 memory: 18752 grad_norm: 4.2801 loss: 2.2214 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2214 2023/03/17 17:56:43 - mmengine - INFO - Epoch(train) [7][1160/1320] lr: 2.0000e-02 eta: 5:18:45 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.4534 loss: 2.3035 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3035 2023/03/17 17:56:50 - mmengine - INFO - Epoch(train) [7][1180/1320] lr: 2.0000e-02 eta: 5:18:38 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.4302 loss: 2.4159 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.4159 2023/03/17 17:56:56 - mmengine - INFO - Epoch(train) [7][1200/1320] lr: 2.0000e-02 eta: 5:18:31 time: 0.3355 data_time: 0.0126 memory: 18752 grad_norm: 4.4305 loss: 2.2500 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2500 2023/03/17 17:57:03 - mmengine - INFO - Epoch(train) [7][1220/1320] lr: 2.0000e-02 eta: 5:18:24 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.3688 loss: 2.1680 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1680 2023/03/17 17:57:10 - mmengine - INFO - Epoch(train) [7][1240/1320] lr: 2.0000e-02 eta: 5:18:18 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 4.3335 loss: 2.2260 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2260 2023/03/17 17:57:17 - mmengine - INFO - Epoch(train) [7][1260/1320] lr: 2.0000e-02 eta: 5:18:11 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.4769 loss: 2.3422 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.3422 2023/03/17 17:57:23 - mmengine - INFO - Epoch(train) [7][1280/1320] lr: 2.0000e-02 eta: 5:18:04 time: 0.3363 data_time: 0.0113 memory: 18752 grad_norm: 4.5319 loss: 2.1942 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1942 2023/03/17 17:57:30 - mmengine - INFO - Epoch(train) [7][1300/1320] lr: 2.0000e-02 eta: 5:17:58 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.4899 loss: 2.1936 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1936 2023/03/17 17:57:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 17:57:37 - mmengine - INFO - Epoch(train) [7][1320/1320] lr: 2.0000e-02 eta: 5:17:50 time: 0.3303 data_time: 0.0117 memory: 18752 grad_norm: 4.4124 loss: 1.8741 top1_acc: 0.5455 top5_acc: 0.8182 loss_cls: 1.8741 2023/03/17 17:57:39 - mmengine - INFO - Epoch(val) [7][ 20/194] eta: 0:00:22 time: 0.1316 data_time: 0.0451 memory: 2112 2023/03/17 17:57:41 - mmengine - INFO - Epoch(val) [7][ 40/194] eta: 0:00:17 time: 0.0979 data_time: 0.0119 memory: 2112 2023/03/17 17:57:43 - mmengine - INFO - Epoch(val) [7][ 60/194] eta: 0:00:14 time: 0.0969 data_time: 0.0111 memory: 2112 2023/03/17 17:57:45 - mmengine - INFO - Epoch(val) [7][ 80/194] eta: 0:00:12 time: 0.0963 data_time: 0.0107 memory: 2112 2023/03/17 17:57:47 - mmengine - INFO - Epoch(val) [7][100/194] eta: 0:00:09 time: 0.0973 data_time: 0.0110 memory: 2112 2023/03/17 17:57:49 - mmengine - INFO - Epoch(val) [7][120/194] eta: 0:00:07 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 17:57:51 - mmengine - INFO - Epoch(val) [7][140/194] eta: 0:00:05 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 17:57:53 - mmengine - INFO - Epoch(val) [7][160/194] eta: 0:00:03 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 17:57:55 - mmengine - INFO - Epoch(val) [7][180/194] eta: 0:00:01 time: 0.0974 data_time: 0.0115 memory: 2112 2023/03/17 17:57:58 - mmengine - INFO - Epoch(val) [7][194/194] acc/top1: 0.4187 acc/top5: 0.7037 acc/mean1: 0.3483 2023/03/17 17:57:58 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_3.pth is removed 2023/03/17 17:57:59 - mmengine - INFO - The best checkpoint with 0.4187 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2023/03/17 17:58:07 - mmengine - INFO - Epoch(train) [8][ 20/1320] lr: 2.0000e-02 eta: 5:17:47 time: 0.3685 data_time: 0.0368 memory: 18752 grad_norm: 4.4678 loss: 2.0464 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0464 2023/03/17 17:58:14 - mmengine - INFO - Epoch(train) [8][ 40/1320] lr: 2.0000e-02 eta: 5:17:41 time: 0.3366 data_time: 0.0120 memory: 18752 grad_norm: 4.4482 loss: 2.2319 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2319 2023/03/17 17:58:20 - mmengine - INFO - Epoch(train) [8][ 60/1320] lr: 2.0000e-02 eta: 5:17:34 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 4.5615 loss: 2.2061 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2061 2023/03/17 17:58:27 - mmengine - INFO - Epoch(train) [8][ 80/1320] lr: 2.0000e-02 eta: 5:17:27 time: 0.3351 data_time: 0.0111 memory: 18752 grad_norm: 4.3929 loss: 2.2105 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2105 2023/03/17 17:58:34 - mmengine - INFO - Epoch(train) [8][ 100/1320] lr: 2.0000e-02 eta: 5:17:20 time: 0.3359 data_time: 0.0115 memory: 18752 grad_norm: 4.4628 loss: 2.0950 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.0950 2023/03/17 17:58:40 - mmengine - INFO - Epoch(train) [8][ 120/1320] lr: 2.0000e-02 eta: 5:17:14 time: 0.3365 data_time: 0.0115 memory: 18752 grad_norm: 4.3938 loss: 2.1044 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1044 2023/03/17 17:58:47 - mmengine - INFO - Epoch(train) [8][ 140/1320] lr: 2.0000e-02 eta: 5:17:07 time: 0.3364 data_time: 0.0115 memory: 18752 grad_norm: 4.4685 loss: 2.3350 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3350 2023/03/17 17:58:54 - mmengine - INFO - Epoch(train) [8][ 160/1320] lr: 2.0000e-02 eta: 5:17:00 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.5576 loss: 1.9972 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9972 2023/03/17 17:59:01 - mmengine - INFO - Epoch(train) [8][ 180/1320] lr: 2.0000e-02 eta: 5:16:54 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.4262 loss: 2.0683 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0683 2023/03/17 17:59:07 - mmengine - INFO - Epoch(train) [8][ 200/1320] lr: 2.0000e-02 eta: 5:16:47 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 4.4613 loss: 2.2159 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.2159 2023/03/17 17:59:14 - mmengine - INFO - Epoch(train) [8][ 220/1320] lr: 2.0000e-02 eta: 5:16:40 time: 0.3366 data_time: 0.0115 memory: 18752 grad_norm: 4.3838 loss: 2.3263 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.3263 2023/03/17 17:59:21 - mmengine - INFO - Epoch(train) [8][ 240/1320] lr: 2.0000e-02 eta: 5:16:33 time: 0.3352 data_time: 0.0122 memory: 18752 grad_norm: 4.4811 loss: 2.0279 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0279 2023/03/17 17:59:27 - mmengine - INFO - Epoch(train) [8][ 260/1320] lr: 2.0000e-02 eta: 5:16:27 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.6620 loss: 2.4005 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4005 2023/03/17 17:59:34 - mmengine - INFO - Epoch(train) [8][ 280/1320] lr: 2.0000e-02 eta: 5:16:20 time: 0.3365 data_time: 0.0115 memory: 18752 grad_norm: 4.4520 loss: 2.3292 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3292 2023/03/17 17:59:41 - mmengine - INFO - Epoch(train) [8][ 300/1320] lr: 2.0000e-02 eta: 5:16:13 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.5140 loss: 2.4537 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4537 2023/03/17 17:59:48 - mmengine - INFO - Epoch(train) [8][ 320/1320] lr: 2.0000e-02 eta: 5:16:06 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.5476 loss: 1.9993 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9993 2023/03/17 17:59:54 - mmengine - INFO - Epoch(train) [8][ 340/1320] lr: 2.0000e-02 eta: 5:16:00 time: 0.3375 data_time: 0.0122 memory: 18752 grad_norm: 4.5632 loss: 2.2051 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2051 2023/03/17 18:00:01 - mmengine - INFO - Epoch(train) [8][ 360/1320] lr: 2.0000e-02 eta: 5:15:53 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 4.5205 loss: 2.3778 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 2.3778 2023/03/17 18:00:08 - mmengine - INFO - Epoch(train) [8][ 380/1320] lr: 2.0000e-02 eta: 5:15:46 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.6345 loss: 2.3468 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3468 2023/03/17 18:00:15 - mmengine - INFO - Epoch(train) [8][ 400/1320] lr: 2.0000e-02 eta: 5:15:40 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.4415 loss: 2.1752 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1752 2023/03/17 18:00:21 - mmengine - INFO - Epoch(train) [8][ 420/1320] lr: 2.0000e-02 eta: 5:15:33 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 4.3967 loss: 2.3503 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3503 2023/03/17 18:00:28 - mmengine - INFO - Epoch(train) [8][ 440/1320] lr: 2.0000e-02 eta: 5:15:26 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.6074 loss: 2.3009 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3009 2023/03/17 18:00:35 - mmengine - INFO - Epoch(train) [8][ 460/1320] lr: 2.0000e-02 eta: 5:15:19 time: 0.3365 data_time: 0.0118 memory: 18752 grad_norm: 4.5375 loss: 2.1922 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.1922 2023/03/17 18:00:41 - mmengine - INFO - Epoch(train) [8][ 480/1320] lr: 2.0000e-02 eta: 5:15:13 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.4631 loss: 2.0870 top1_acc: 0.0625 top5_acc: 0.5625 loss_cls: 2.0870 2023/03/17 18:00:48 - mmengine - INFO - Epoch(train) [8][ 500/1320] lr: 2.0000e-02 eta: 5:15:06 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.6492 loss: 2.2527 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.2527 2023/03/17 18:00:55 - mmengine - INFO - Epoch(train) [8][ 520/1320] lr: 2.0000e-02 eta: 5:14:59 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.2987 loss: 2.3167 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3167 2023/03/17 18:01:02 - mmengine - INFO - Epoch(train) [8][ 540/1320] lr: 2.0000e-02 eta: 5:14:52 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.2071 loss: 2.1536 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1536 2023/03/17 18:01:08 - mmengine - INFO - Epoch(train) [8][ 560/1320] lr: 2.0000e-02 eta: 5:14:46 time: 0.3357 data_time: 0.0127 memory: 18752 grad_norm: 4.3292 loss: 2.0983 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0983 2023/03/17 18:01:15 - mmengine - INFO - Epoch(train) [8][ 580/1320] lr: 2.0000e-02 eta: 5:14:39 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.4007 loss: 2.2124 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2124 2023/03/17 18:01:22 - mmengine - INFO - Epoch(train) [8][ 600/1320] lr: 2.0000e-02 eta: 5:14:32 time: 0.3367 data_time: 0.0132 memory: 18752 grad_norm: 4.4119 loss: 2.2757 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2757 2023/03/17 18:01:28 - mmengine - INFO - Epoch(train) [8][ 620/1320] lr: 2.0000e-02 eta: 5:14:25 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 4.3964 loss: 2.2137 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2137 2023/03/17 18:01:35 - mmengine - INFO - Epoch(train) [8][ 640/1320] lr: 2.0000e-02 eta: 5:14:19 time: 0.3352 data_time: 0.0125 memory: 18752 grad_norm: 4.3825 loss: 2.1644 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1644 2023/03/17 18:01:42 - mmengine - INFO - Epoch(train) [8][ 660/1320] lr: 2.0000e-02 eta: 5:14:12 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.4090 loss: 2.0481 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0481 2023/03/17 18:01:49 - mmengine - INFO - Epoch(train) [8][ 680/1320] lr: 2.0000e-02 eta: 5:14:05 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.3790 loss: 1.9993 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9993 2023/03/17 18:01:55 - mmengine - INFO - Epoch(train) [8][ 700/1320] lr: 2.0000e-02 eta: 5:13:58 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5338 loss: 2.1403 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1403 2023/03/17 18:02:02 - mmengine - INFO - Epoch(train) [8][ 720/1320] lr: 2.0000e-02 eta: 5:13:51 time: 0.3349 data_time: 0.0118 memory: 18752 grad_norm: 4.6061 loss: 2.2814 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.2814 2023/03/17 18:02:09 - mmengine - INFO - Epoch(train) [8][ 740/1320] lr: 2.0000e-02 eta: 5:13:45 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.4542 loss: 2.1582 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1582 2023/03/17 18:02:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:02:15 - mmengine - INFO - Epoch(train) [8][ 760/1320] lr: 2.0000e-02 eta: 5:13:38 time: 0.3360 data_time: 0.0127 memory: 18752 grad_norm: 4.3144 loss: 2.2155 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2155 2023/03/17 18:02:22 - mmengine - INFO - Epoch(train) [8][ 780/1320] lr: 2.0000e-02 eta: 5:13:31 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.4589 loss: 2.0482 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0482 2023/03/17 18:02:29 - mmengine - INFO - Epoch(train) [8][ 800/1320] lr: 2.0000e-02 eta: 5:13:24 time: 0.3358 data_time: 0.0113 memory: 18752 grad_norm: 4.5909 loss: 2.1924 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1924 2023/03/17 18:02:36 - mmengine - INFO - Epoch(train) [8][ 820/1320] lr: 2.0000e-02 eta: 5:13:17 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.4478 loss: 2.1355 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.1355 2023/03/17 18:02:42 - mmengine - INFO - Epoch(train) [8][ 840/1320] lr: 2.0000e-02 eta: 5:13:11 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.4212 loss: 2.3312 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.3312 2023/03/17 18:02:49 - mmengine - INFO - Epoch(train) [8][ 860/1320] lr: 2.0000e-02 eta: 5:13:04 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 4.4127 loss: 2.3437 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3437 2023/03/17 18:02:56 - mmengine - INFO - Epoch(train) [8][ 880/1320] lr: 2.0000e-02 eta: 5:12:57 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.3693 loss: 2.1362 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1362 2023/03/17 18:03:02 - mmengine - INFO - Epoch(train) [8][ 900/1320] lr: 2.0000e-02 eta: 5:12:50 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 4.6049 loss: 2.2502 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2502 2023/03/17 18:03:09 - mmengine - INFO - Epoch(train) [8][ 920/1320] lr: 2.0000e-02 eta: 5:12:44 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.5219 loss: 2.1577 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.1577 2023/03/17 18:03:16 - mmengine - INFO - Epoch(train) [8][ 940/1320] lr: 2.0000e-02 eta: 5:12:37 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.6074 loss: 2.2050 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.2050 2023/03/17 18:03:23 - mmengine - INFO - Epoch(train) [8][ 960/1320] lr: 2.0000e-02 eta: 5:12:30 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.4395 loss: 2.3694 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3694 2023/03/17 18:03:29 - mmengine - INFO - Epoch(train) [8][ 980/1320] lr: 2.0000e-02 eta: 5:12:23 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.5120 loss: 2.1693 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1693 2023/03/17 18:03:36 - mmengine - INFO - Epoch(train) [8][1000/1320] lr: 2.0000e-02 eta: 5:12:16 time: 0.3351 data_time: 0.0122 memory: 18752 grad_norm: 4.2769 loss: 2.2653 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2653 2023/03/17 18:03:43 - mmengine - INFO - Epoch(train) [8][1020/1320] lr: 2.0000e-02 eta: 5:12:10 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.3344 loss: 2.1048 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.1048 2023/03/17 18:03:49 - mmengine - INFO - Epoch(train) [8][1040/1320] lr: 2.0000e-02 eta: 5:12:03 time: 0.3354 data_time: 0.0126 memory: 18752 grad_norm: 4.6457 loss: 2.2202 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2202 2023/03/17 18:03:56 - mmengine - INFO - Epoch(train) [8][1060/1320] lr: 2.0000e-02 eta: 5:11:58 time: 0.3499 data_time: 0.0119 memory: 18752 grad_norm: 4.3933 loss: 1.9672 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9672 2023/03/17 18:04:03 - mmengine - INFO - Epoch(train) [8][1080/1320] lr: 2.0000e-02 eta: 5:11:51 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.4953 loss: 2.2938 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2938 2023/03/17 18:04:10 - mmengine - INFO - Epoch(train) [8][1100/1320] lr: 2.0000e-02 eta: 5:11:44 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.4727 loss: 2.3351 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3351 2023/03/17 18:04:16 - mmengine - INFO - Epoch(train) [8][1120/1320] lr: 2.0000e-02 eta: 5:11:37 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 4.5479 loss: 2.0433 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0433 2023/03/17 18:04:23 - mmengine - INFO - Epoch(train) [8][1140/1320] lr: 2.0000e-02 eta: 5:11:31 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.4193 loss: 2.1415 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1415 2023/03/17 18:04:30 - mmengine - INFO - Epoch(train) [8][1160/1320] lr: 2.0000e-02 eta: 5:11:24 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 4.4751 loss: 2.1893 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1893 2023/03/17 18:04:37 - mmengine - INFO - Epoch(train) [8][1180/1320] lr: 2.0000e-02 eta: 5:11:17 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.3627 loss: 2.1924 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1924 2023/03/17 18:04:43 - mmengine - INFO - Epoch(train) [8][1200/1320] lr: 2.0000e-02 eta: 5:11:10 time: 0.3350 data_time: 0.0115 memory: 18752 grad_norm: 4.5371 loss: 2.0846 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0846 2023/03/17 18:04:50 - mmengine - INFO - Epoch(train) [8][1220/1320] lr: 2.0000e-02 eta: 5:11:03 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.5163 loss: 2.2421 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2421 2023/03/17 18:04:57 - mmengine - INFO - Epoch(train) [8][1240/1320] lr: 2.0000e-02 eta: 5:10:57 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 4.6180 loss: 2.1658 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1658 2023/03/17 18:05:03 - mmengine - INFO - Epoch(train) [8][1260/1320] lr: 2.0000e-02 eta: 5:10:50 time: 0.3352 data_time: 0.0115 memory: 18752 grad_norm: 4.5944 loss: 2.3182 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3182 2023/03/17 18:05:10 - mmengine - INFO - Epoch(train) [8][1280/1320] lr: 2.0000e-02 eta: 5:10:43 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.4158 loss: 2.0544 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0544 2023/03/17 18:05:17 - mmengine - INFO - Epoch(train) [8][1300/1320] lr: 2.0000e-02 eta: 5:10:36 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 4.5192 loss: 2.4678 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4678 2023/03/17 18:05:23 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:05:23 - mmengine - INFO - Epoch(train) [8][1320/1320] lr: 2.0000e-02 eta: 5:10:29 time: 0.3305 data_time: 0.0125 memory: 18752 grad_norm: 4.4606 loss: 2.1853 top1_acc: 0.5455 top5_acc: 0.9091 loss_cls: 2.1853 2023/03/17 18:05:26 - mmengine - INFO - Epoch(val) [8][ 20/194] eta: 0:00:22 time: 0.1278 data_time: 0.0411 memory: 2112 2023/03/17 18:05:28 - mmengine - INFO - Epoch(val) [8][ 40/194] eta: 0:00:17 time: 0.0956 data_time: 0.0099 memory: 2112 2023/03/17 18:05:30 - mmengine - INFO - Epoch(val) [8][ 60/194] eta: 0:00:14 time: 0.0962 data_time: 0.0105 memory: 2112 2023/03/17 18:05:32 - mmengine - INFO - Epoch(val) [8][ 80/194] eta: 0:00:11 time: 0.0965 data_time: 0.0105 memory: 2112 2023/03/17 18:05:34 - mmengine - INFO - Epoch(val) [8][100/194] eta: 0:00:09 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 18:05:36 - mmengine - INFO - Epoch(val) [8][120/194] eta: 0:00:07 time: 0.0976 data_time: 0.0118 memory: 2112 2023/03/17 18:05:38 - mmengine - INFO - Epoch(val) [8][140/194] eta: 0:00:05 time: 0.0968 data_time: 0.0110 memory: 2112 2023/03/17 18:05:40 - mmengine - INFO - Epoch(val) [8][160/194] eta: 0:00:03 time: 0.0969 data_time: 0.0111 memory: 2112 2023/03/17 18:05:42 - mmengine - INFO - Epoch(val) [8][180/194] eta: 0:00:01 time: 0.0967 data_time: 0.0108 memory: 2112 2023/03/17 18:05:45 - mmengine - INFO - Epoch(val) [8][194/194] acc/top1: 0.4191 acc/top5: 0.7147 acc/mean1: 0.3538 2023/03/17 18:05:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_7.pth is removed 2023/03/17 18:05:47 - mmengine - INFO - The best checkpoint with 0.4191 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2023/03/17 18:05:54 - mmengine - INFO - Epoch(train) [9][ 20/1320] lr: 2.0000e-02 eta: 5:10:26 time: 0.3672 data_time: 0.0366 memory: 18752 grad_norm: 4.4837 loss: 2.0083 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0083 2023/03/17 18:06:01 - mmengine - INFO - Epoch(train) [9][ 40/1320] lr: 2.0000e-02 eta: 5:10:19 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.3840 loss: 2.3172 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3172 2023/03/17 18:06:07 - mmengine - INFO - Epoch(train) [9][ 60/1320] lr: 2.0000e-02 eta: 5:10:12 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.4850 loss: 1.9725 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9725 2023/03/17 18:06:14 - mmengine - INFO - Epoch(train) [9][ 80/1320] lr: 2.0000e-02 eta: 5:10:05 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.4638 loss: 2.2607 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2607 2023/03/17 18:06:21 - mmengine - INFO - Epoch(train) [9][ 100/1320] lr: 2.0000e-02 eta: 5:09:58 time: 0.3354 data_time: 0.0114 memory: 18752 grad_norm: 4.4510 loss: 2.2022 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2022 2023/03/17 18:06:27 - mmengine - INFO - Epoch(train) [9][ 120/1320] lr: 2.0000e-02 eta: 5:09:52 time: 0.3352 data_time: 0.0126 memory: 18752 grad_norm: 4.5632 loss: 2.2684 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2684 2023/03/17 18:06:34 - mmengine - INFO - Epoch(train) [9][ 140/1320] lr: 2.0000e-02 eta: 5:09:45 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 4.6423 loss: 2.1605 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1605 2023/03/17 18:06:41 - mmengine - INFO - Epoch(train) [9][ 160/1320] lr: 2.0000e-02 eta: 5:09:38 time: 0.3356 data_time: 0.0127 memory: 18752 grad_norm: 4.5594 loss: 2.1387 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1387 2023/03/17 18:06:48 - mmengine - INFO - Epoch(train) [9][ 180/1320] lr: 2.0000e-02 eta: 5:09:31 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.4356 loss: 2.1696 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 2.1696 2023/03/17 18:06:54 - mmengine - INFO - Epoch(train) [9][ 200/1320] lr: 2.0000e-02 eta: 5:09:25 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 4.3939 loss: 2.4070 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.4070 2023/03/17 18:07:01 - mmengine - INFO - Epoch(train) [9][ 220/1320] lr: 2.0000e-02 eta: 5:09:18 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.4176 loss: 2.2582 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2582 2023/03/17 18:07:08 - mmengine - INFO - Epoch(train) [9][ 240/1320] lr: 2.0000e-02 eta: 5:09:11 time: 0.3364 data_time: 0.0128 memory: 18752 grad_norm: 4.5272 loss: 2.0848 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.0848 2023/03/17 18:07:14 - mmengine - INFO - Epoch(train) [9][ 260/1320] lr: 2.0000e-02 eta: 5:09:05 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.5547 loss: 2.1120 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1120 2023/03/17 18:07:21 - mmengine - INFO - Epoch(train) [9][ 280/1320] lr: 2.0000e-02 eta: 5:08:58 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.4651 loss: 2.1652 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1652 2023/03/17 18:07:28 - mmengine - INFO - Epoch(train) [9][ 300/1320] lr: 2.0000e-02 eta: 5:08:51 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.4843 loss: 2.2098 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.2098 2023/03/17 18:07:35 - mmengine - INFO - Epoch(train) [9][ 320/1320] lr: 2.0000e-02 eta: 5:08:44 time: 0.3356 data_time: 0.0114 memory: 18752 grad_norm: 4.3413 loss: 2.0318 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0318 2023/03/17 18:07:41 - mmengine - INFO - Epoch(train) [9][ 340/1320] lr: 2.0000e-02 eta: 5:08:38 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.4143 loss: 2.2721 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2721 2023/03/17 18:07:48 - mmengine - INFO - Epoch(train) [9][ 360/1320] lr: 2.0000e-02 eta: 5:08:31 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.3878 loss: 2.2388 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.2388 2023/03/17 18:07:55 - mmengine - INFO - Epoch(train) [9][ 380/1320] lr: 2.0000e-02 eta: 5:08:24 time: 0.3360 data_time: 0.0114 memory: 18752 grad_norm: 4.5509 loss: 2.0928 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0928 2023/03/17 18:08:01 - mmengine - INFO - Epoch(train) [9][ 400/1320] lr: 2.0000e-02 eta: 5:08:17 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.4104 loss: 2.0374 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0374 2023/03/17 18:08:08 - mmengine - INFO - Epoch(train) [9][ 420/1320] lr: 2.0000e-02 eta: 5:08:11 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 4.5349 loss: 1.9983 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9983 2023/03/17 18:08:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:08:15 - mmengine - INFO - Epoch(train) [9][ 440/1320] lr: 2.0000e-02 eta: 5:08:04 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.7563 loss: 2.0712 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0712 2023/03/17 18:08:22 - mmengine - INFO - Epoch(train) [9][ 460/1320] lr: 2.0000e-02 eta: 5:07:57 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.4488 loss: 2.1269 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1269 2023/03/17 18:08:28 - mmengine - INFO - Epoch(train) [9][ 480/1320] lr: 2.0000e-02 eta: 5:07:50 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.3352 loss: 2.4255 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4255 2023/03/17 18:08:35 - mmengine - INFO - Epoch(train) [9][ 500/1320] lr: 2.0000e-02 eta: 5:07:44 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.5098 loss: 2.1791 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1791 2023/03/17 18:08:42 - mmengine - INFO - Epoch(train) [9][ 520/1320] lr: 2.0000e-02 eta: 5:07:37 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.7620 loss: 2.1018 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1018 2023/03/17 18:08:49 - mmengine - INFO - Epoch(train) [9][ 540/1320] lr: 2.0000e-02 eta: 5:07:30 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.5809 loss: 2.2109 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2109 2023/03/17 18:08:55 - mmengine - INFO - Epoch(train) [9][ 560/1320] lr: 2.0000e-02 eta: 5:07:23 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5452 loss: 2.0743 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 2.0743 2023/03/17 18:09:02 - mmengine - INFO - Epoch(train) [9][ 580/1320] lr: 2.0000e-02 eta: 5:07:17 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.2867 loss: 2.0836 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0836 2023/03/17 18:09:09 - mmengine - INFO - Epoch(train) [9][ 600/1320] lr: 2.0000e-02 eta: 5:07:10 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.6246 loss: 2.1854 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.1854 2023/03/17 18:09:15 - mmengine - INFO - Epoch(train) [9][ 620/1320] lr: 2.0000e-02 eta: 5:07:03 time: 0.3359 data_time: 0.0113 memory: 18752 grad_norm: 4.5038 loss: 2.1499 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1499 2023/03/17 18:09:22 - mmengine - INFO - Epoch(train) [9][ 640/1320] lr: 2.0000e-02 eta: 5:06:56 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.3718 loss: 1.9665 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9665 2023/03/17 18:09:29 - mmengine - INFO - Epoch(train) [9][ 660/1320] lr: 2.0000e-02 eta: 5:06:50 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 4.6143 loss: 2.2133 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2133 2023/03/17 18:09:36 - mmengine - INFO - Epoch(train) [9][ 680/1320] lr: 2.0000e-02 eta: 5:06:43 time: 0.3359 data_time: 0.0127 memory: 18752 grad_norm: 4.5140 loss: 2.0270 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0270 2023/03/17 18:09:42 - mmengine - INFO - Epoch(train) [9][ 700/1320] lr: 2.0000e-02 eta: 5:06:36 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.4648 loss: 2.1670 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.1670 2023/03/17 18:09:49 - mmengine - INFO - Epoch(train) [9][ 720/1320] lr: 2.0000e-02 eta: 5:06:29 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.5606 loss: 2.0265 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0265 2023/03/17 18:09:56 - mmengine - INFO - Epoch(train) [9][ 740/1320] lr: 2.0000e-02 eta: 5:06:23 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.4867 loss: 2.1643 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1643 2023/03/17 18:10:02 - mmengine - INFO - Epoch(train) [9][ 760/1320] lr: 2.0000e-02 eta: 5:06:16 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.4467 loss: 2.1298 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1298 2023/03/17 18:10:09 - mmengine - INFO - Epoch(train) [9][ 780/1320] lr: 2.0000e-02 eta: 5:06:09 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.3469 loss: 2.1431 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1431 2023/03/17 18:10:16 - mmengine - INFO - Epoch(train) [9][ 800/1320] lr: 2.0000e-02 eta: 5:06:02 time: 0.3356 data_time: 0.0128 memory: 18752 grad_norm: 4.3447 loss: 2.0369 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0369 2023/03/17 18:10:22 - mmengine - INFO - Epoch(train) [9][ 820/1320] lr: 2.0000e-02 eta: 5:05:56 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.6989 loss: 2.2175 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2175 2023/03/17 18:10:29 - mmengine - INFO - Epoch(train) [9][ 840/1320] lr: 2.0000e-02 eta: 5:05:49 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 4.4812 loss: 2.3499 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3499 2023/03/17 18:10:36 - mmengine - INFO - Epoch(train) [9][ 860/1320] lr: 2.0000e-02 eta: 5:05:42 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.5639 loss: 2.1231 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1231 2023/03/17 18:10:43 - mmengine - INFO - Epoch(train) [9][ 880/1320] lr: 2.0000e-02 eta: 5:05:35 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 4.3678 loss: 2.0824 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0824 2023/03/17 18:10:49 - mmengine - INFO - Epoch(train) [9][ 900/1320] lr: 2.0000e-02 eta: 5:05:28 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 4.5024 loss: 2.1098 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 2.1098 2023/03/17 18:10:56 - mmengine - INFO - Epoch(train) [9][ 920/1320] lr: 2.0000e-02 eta: 5:05:22 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.5387 loss: 2.1449 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1449 2023/03/17 18:11:03 - mmengine - INFO - Epoch(train) [9][ 940/1320] lr: 2.0000e-02 eta: 5:05:15 time: 0.3354 data_time: 0.0113 memory: 18752 grad_norm: 4.3859 loss: 2.1461 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.1461 2023/03/17 18:11:09 - mmengine - INFO - Epoch(train) [9][ 960/1320] lr: 2.0000e-02 eta: 5:05:08 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.3784 loss: 2.1663 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1663 2023/03/17 18:11:16 - mmengine - INFO - Epoch(train) [9][ 980/1320] lr: 2.0000e-02 eta: 5:05:01 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.3984 loss: 2.2842 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2842 2023/03/17 18:11:23 - mmengine - INFO - Epoch(train) [9][1000/1320] lr: 2.0000e-02 eta: 5:04:54 time: 0.3354 data_time: 0.0128 memory: 18752 grad_norm: 4.4474 loss: 1.9845 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9845 2023/03/17 18:11:30 - mmengine - INFO - Epoch(train) [9][1020/1320] lr: 2.0000e-02 eta: 5:04:48 time: 0.3358 data_time: 0.0131 memory: 18752 grad_norm: 4.5403 loss: 2.1680 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.1680 2023/03/17 18:11:36 - mmengine - INFO - Epoch(train) [9][1040/1320] lr: 2.0000e-02 eta: 5:04:41 time: 0.3346 data_time: 0.0121 memory: 18752 grad_norm: 4.5139 loss: 2.1639 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.1639 2023/03/17 18:11:43 - mmengine - INFO - Epoch(train) [9][1060/1320] lr: 2.0000e-02 eta: 5:04:34 time: 0.3366 data_time: 0.0138 memory: 18752 grad_norm: 4.5171 loss: 2.0500 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0500 2023/03/17 18:11:50 - mmengine - INFO - Epoch(train) [9][1080/1320] lr: 2.0000e-02 eta: 5:04:27 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 4.4424 loss: 2.2042 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2042 2023/03/17 18:11:56 - mmengine - INFO - Epoch(train) [9][1100/1320] lr: 2.0000e-02 eta: 5:04:21 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.4576 loss: 2.1059 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1059 2023/03/17 18:12:03 - mmengine - INFO - Epoch(train) [9][1120/1320] lr: 2.0000e-02 eta: 5:04:14 time: 0.3351 data_time: 0.0124 memory: 18752 grad_norm: 4.5194 loss: 1.9845 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9845 2023/03/17 18:12:10 - mmengine - INFO - Epoch(train) [9][1140/1320] lr: 2.0000e-02 eta: 5:04:07 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 4.4129 loss: 2.3195 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3195 2023/03/17 18:12:17 - mmengine - INFO - Epoch(train) [9][1160/1320] lr: 2.0000e-02 eta: 5:04:00 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.5315 loss: 1.9808 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9808 2023/03/17 18:12:23 - mmengine - INFO - Epoch(train) [9][1180/1320] lr: 2.0000e-02 eta: 5:03:54 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.4342 loss: 2.2725 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 2.2725 2023/03/17 18:12:30 - mmengine - INFO - Epoch(train) [9][1200/1320] lr: 2.0000e-02 eta: 5:03:47 time: 0.3350 data_time: 0.0122 memory: 18752 grad_norm: 4.4099 loss: 2.0307 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0307 2023/03/17 18:12:37 - mmengine - INFO - Epoch(train) [9][1220/1320] lr: 2.0000e-02 eta: 5:03:40 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.4518 loss: 2.4148 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4148 2023/03/17 18:12:43 - mmengine - INFO - Epoch(train) [9][1240/1320] lr: 2.0000e-02 eta: 5:03:33 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.4364 loss: 2.3381 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.3381 2023/03/17 18:12:50 - mmengine - INFO - Epoch(train) [9][1260/1320] lr: 2.0000e-02 eta: 5:03:26 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.3788 loss: 2.0560 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0560 2023/03/17 18:12:57 - mmengine - INFO - Epoch(train) [9][1280/1320] lr: 2.0000e-02 eta: 5:03:19 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5371 loss: 1.9840 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9840 2023/03/17 18:13:03 - mmengine - INFO - Epoch(train) [9][1300/1320] lr: 2.0000e-02 eta: 5:03:13 time: 0.3349 data_time: 0.0124 memory: 18752 grad_norm: 4.3670 loss: 1.9951 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.9951 2023/03/17 18:13:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:13:10 - mmengine - INFO - Epoch(train) [9][1320/1320] lr: 2.0000e-02 eta: 5:03:05 time: 0.3298 data_time: 0.0122 memory: 18752 grad_norm: 4.4294 loss: 2.0763 top1_acc: 0.4545 top5_acc: 0.6364 loss_cls: 2.0763 2023/03/17 18:13:10 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/03/17 18:13:15 - mmengine - INFO - Epoch(val) [9][ 20/194] eta: 0:00:22 time: 0.1269 data_time: 0.0403 memory: 2112 2023/03/17 18:13:17 - mmengine - INFO - Epoch(val) [9][ 40/194] eta: 0:00:17 time: 0.0967 data_time: 0.0105 memory: 2112 2023/03/17 18:13:19 - mmengine - INFO - Epoch(val) [9][ 60/194] eta: 0:00:14 time: 0.0969 data_time: 0.0111 memory: 2112 2023/03/17 18:13:21 - mmengine - INFO - Epoch(val) [9][ 80/194] eta: 0:00:11 time: 0.0967 data_time: 0.0110 memory: 2112 2023/03/17 18:13:23 - mmengine - INFO - Epoch(val) [9][100/194] eta: 0:00:09 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 18:13:25 - mmengine - INFO - Epoch(val) [9][120/194] eta: 0:00:07 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 18:13:27 - mmengine - INFO - Epoch(val) [9][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 18:13:29 - mmengine - INFO - Epoch(val) [9][160/194] eta: 0:00:03 time: 0.0970 data_time: 0.0112 memory: 2112 2023/03/17 18:13:31 - mmengine - INFO - Epoch(val) [9][180/194] eta: 0:00:01 time: 0.0957 data_time: 0.0100 memory: 2112 2023/03/17 18:13:33 - mmengine - INFO - Epoch(val) [9][194/194] acc/top1: 0.4160 acc/top5: 0.7150 acc/mean1: 0.3452 2023/03/17 18:13:41 - mmengine - INFO - Epoch(train) [10][ 20/1320] lr: 2.0000e-02 eta: 5:03:02 time: 0.3733 data_time: 0.0416 memory: 18752 grad_norm: 4.4662 loss: 2.0741 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0741 2023/03/17 18:13:48 - mmengine - INFO - Epoch(train) [10][ 40/1320] lr: 2.0000e-02 eta: 5:02:55 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.4135 loss: 2.0391 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0391 2023/03/17 18:13:55 - mmengine - INFO - Epoch(train) [10][ 60/1320] lr: 2.0000e-02 eta: 5:02:50 time: 0.3535 data_time: 0.0251 memory: 18752 grad_norm: 4.4512 loss: 2.0327 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0327 2023/03/17 18:14:01 - mmengine - INFO - Epoch(train) [10][ 80/1320] lr: 2.0000e-02 eta: 5:02:43 time: 0.3356 data_time: 0.0115 memory: 18752 grad_norm: 4.4137 loss: 2.1601 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1601 2023/03/17 18:14:08 - mmengine - INFO - Epoch(train) [10][ 100/1320] lr: 2.0000e-02 eta: 5:02:37 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.3732 loss: 2.2869 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.2869 2023/03/17 18:14:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:14:15 - mmengine - INFO - Epoch(train) [10][ 120/1320] lr: 2.0000e-02 eta: 5:02:30 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.4311 loss: 1.7846 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7846 2023/03/17 18:14:22 - mmengine - INFO - Epoch(train) [10][ 140/1320] lr: 2.0000e-02 eta: 5:02:23 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.4768 loss: 2.1603 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.1603 2023/03/17 18:14:28 - mmengine - INFO - Epoch(train) [10][ 160/1320] lr: 2.0000e-02 eta: 5:02:16 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 4.4169 loss: 1.9613 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9613 2023/03/17 18:14:35 - mmengine - INFO - Epoch(train) [10][ 180/1320] lr: 2.0000e-02 eta: 5:02:09 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.4173 loss: 2.0657 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.0657 2023/03/17 18:14:42 - mmengine - INFO - Epoch(train) [10][ 200/1320] lr: 2.0000e-02 eta: 5:02:03 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 4.5362 loss: 2.3337 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3337 2023/03/17 18:14:48 - mmengine - INFO - Epoch(train) [10][ 220/1320] lr: 2.0000e-02 eta: 5:01:56 time: 0.3352 data_time: 0.0115 memory: 18752 grad_norm: 4.4996 loss: 2.1124 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1124 2023/03/17 18:14:55 - mmengine - INFO - Epoch(train) [10][ 240/1320] lr: 2.0000e-02 eta: 5:01:49 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 4.4972 loss: 2.3486 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3486 2023/03/17 18:15:02 - mmengine - INFO - Epoch(train) [10][ 260/1320] lr: 2.0000e-02 eta: 5:01:42 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.5615 loss: 2.1454 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1454 2023/03/17 18:15:08 - mmengine - INFO - Epoch(train) [10][ 280/1320] lr: 2.0000e-02 eta: 5:01:35 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.4564 loss: 2.3243 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.3243 2023/03/17 18:15:15 - mmengine - INFO - Epoch(train) [10][ 300/1320] lr: 2.0000e-02 eta: 5:01:29 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.4722 loss: 2.1396 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1396 2023/03/17 18:15:22 - mmengine - INFO - Epoch(train) [10][ 320/1320] lr: 2.0000e-02 eta: 5:01:22 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5141 loss: 2.0293 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0293 2023/03/17 18:15:29 - mmengine - INFO - Epoch(train) [10][ 340/1320] lr: 2.0000e-02 eta: 5:01:15 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.6023 loss: 2.0901 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0901 2023/03/17 18:15:35 - mmengine - INFO - Epoch(train) [10][ 360/1320] lr: 2.0000e-02 eta: 5:01:08 time: 0.3351 data_time: 0.0127 memory: 18752 grad_norm: 4.5418 loss: 2.2861 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2861 2023/03/17 18:15:42 - mmengine - INFO - Epoch(train) [10][ 380/1320] lr: 2.0000e-02 eta: 5:01:01 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.4954 loss: 2.1139 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1139 2023/03/17 18:15:49 - mmengine - INFO - Epoch(train) [10][ 400/1320] lr: 2.0000e-02 eta: 5:00:55 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5184 loss: 2.1009 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1009 2023/03/17 18:15:55 - mmengine - INFO - Epoch(train) [10][ 420/1320] lr: 2.0000e-02 eta: 5:00:48 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.4964 loss: 2.1224 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1224 2023/03/17 18:16:02 - mmengine - INFO - Epoch(train) [10][ 440/1320] lr: 2.0000e-02 eta: 5:00:41 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.6369 loss: 1.8781 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8781 2023/03/17 18:16:09 - mmengine - INFO - Epoch(train) [10][ 460/1320] lr: 2.0000e-02 eta: 5:00:34 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4702 loss: 2.0491 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0491 2023/03/17 18:16:16 - mmengine - INFO - Epoch(train) [10][ 480/1320] lr: 2.0000e-02 eta: 5:00:27 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.5092 loss: 2.2332 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2332 2023/03/17 18:16:22 - mmengine - INFO - Epoch(train) [10][ 500/1320] lr: 2.0000e-02 eta: 5:00:21 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5557 loss: 2.1444 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1444 2023/03/17 18:16:29 - mmengine - INFO - Epoch(train) [10][ 520/1320] lr: 2.0000e-02 eta: 5:00:14 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.4840 loss: 2.2587 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2587 2023/03/17 18:16:36 - mmengine - INFO - Epoch(train) [10][ 540/1320] lr: 2.0000e-02 eta: 5:00:07 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5106 loss: 2.1816 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1816 2023/03/17 18:16:42 - mmengine - INFO - Epoch(train) [10][ 560/1320] lr: 2.0000e-02 eta: 5:00:00 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.3730 loss: 2.0932 top1_acc: 0.1250 top5_acc: 0.6875 loss_cls: 2.0932 2023/03/17 18:16:49 - mmengine - INFO - Epoch(train) [10][ 580/1320] lr: 2.0000e-02 eta: 4:59:53 time: 0.3349 data_time: 0.0121 memory: 18752 grad_norm: 4.5623 loss: 2.1552 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1552 2023/03/17 18:16:56 - mmengine - INFO - Epoch(train) [10][ 600/1320] lr: 2.0000e-02 eta: 4:59:47 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.3997 loss: 1.9765 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9765 2023/03/17 18:17:03 - mmengine - INFO - Epoch(train) [10][ 620/1320] lr: 2.0000e-02 eta: 4:59:40 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.4091 loss: 1.9929 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9929 2023/03/17 18:17:09 - mmengine - INFO - Epoch(train) [10][ 640/1320] lr: 2.0000e-02 eta: 4:59:33 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.3634 loss: 2.0355 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0355 2023/03/17 18:17:16 - mmengine - INFO - Epoch(train) [10][ 660/1320] lr: 2.0000e-02 eta: 4:59:26 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.4987 loss: 2.1386 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1386 2023/03/17 18:17:23 - mmengine - INFO - Epoch(train) [10][ 680/1320] lr: 2.0000e-02 eta: 4:59:19 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.3903 loss: 1.9472 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9472 2023/03/17 18:17:29 - mmengine - INFO - Epoch(train) [10][ 700/1320] lr: 2.0000e-02 eta: 4:59:13 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.4847 loss: 2.0773 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0773 2023/03/17 18:17:36 - mmengine - INFO - Epoch(train) [10][ 720/1320] lr: 2.0000e-02 eta: 4:59:06 time: 0.3348 data_time: 0.0120 memory: 18752 grad_norm: 4.4689 loss: 2.2615 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2615 2023/03/17 18:17:43 - mmengine - INFO - Epoch(train) [10][ 740/1320] lr: 2.0000e-02 eta: 4:58:59 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.5710 loss: 2.2274 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.2274 2023/03/17 18:17:49 - mmengine - INFO - Epoch(train) [10][ 760/1320] lr: 2.0000e-02 eta: 4:58:52 time: 0.3351 data_time: 0.0124 memory: 18752 grad_norm: 4.3840 loss: 2.1864 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1864 2023/03/17 18:17:56 - mmengine - INFO - Epoch(train) [10][ 780/1320] lr: 2.0000e-02 eta: 4:58:45 time: 0.3351 data_time: 0.0122 memory: 18752 grad_norm: 4.3592 loss: 2.0872 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0872 2023/03/17 18:18:03 - mmengine - INFO - Epoch(train) [10][ 800/1320] lr: 2.0000e-02 eta: 4:58:39 time: 0.3349 data_time: 0.0125 memory: 18752 grad_norm: 4.5833 loss: 2.0874 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0874 2023/03/17 18:18:10 - mmengine - INFO - Epoch(train) [10][ 820/1320] lr: 2.0000e-02 eta: 4:58:32 time: 0.3351 data_time: 0.0123 memory: 18752 grad_norm: 4.6085 loss: 2.1033 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1033 2023/03/17 18:18:16 - mmengine - INFO - Epoch(train) [10][ 840/1320] lr: 2.0000e-02 eta: 4:58:25 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.5377 loss: 2.2356 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.2356 2023/03/17 18:18:23 - mmengine - INFO - Epoch(train) [10][ 860/1320] lr: 2.0000e-02 eta: 4:58:18 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.5072 loss: 2.1937 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1937 2023/03/17 18:18:30 - mmengine - INFO - Epoch(train) [10][ 880/1320] lr: 2.0000e-02 eta: 4:58:12 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.4859 loss: 2.2459 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.2459 2023/03/17 18:18:36 - mmengine - INFO - Epoch(train) [10][ 900/1320] lr: 2.0000e-02 eta: 4:58:05 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.4560 loss: 2.0869 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0869 2023/03/17 18:18:43 - mmengine - INFO - Epoch(train) [10][ 920/1320] lr: 2.0000e-02 eta: 4:57:58 time: 0.3352 data_time: 0.0122 memory: 18752 grad_norm: 4.3816 loss: 2.1170 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1170 2023/03/17 18:18:50 - mmengine - INFO - Epoch(train) [10][ 940/1320] lr: 2.0000e-02 eta: 4:57:51 time: 0.3356 data_time: 0.0127 memory: 18752 grad_norm: 4.5772 loss: 2.0457 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0457 2023/03/17 18:18:57 - mmengine - INFO - Epoch(train) [10][ 960/1320] lr: 2.0000e-02 eta: 4:57:44 time: 0.3354 data_time: 0.0125 memory: 18752 grad_norm: 4.4960 loss: 2.0738 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0738 2023/03/17 18:19:03 - mmengine - INFO - Epoch(train) [10][ 980/1320] lr: 2.0000e-02 eta: 4:57:38 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 4.5582 loss: 2.1585 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1585 2023/03/17 18:19:10 - mmengine - INFO - Epoch(train) [10][1000/1320] lr: 2.0000e-02 eta: 4:57:31 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.4854 loss: 2.0944 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.0944 2023/03/17 18:19:17 - mmengine - INFO - Epoch(train) [10][1020/1320] lr: 2.0000e-02 eta: 4:57:24 time: 0.3350 data_time: 0.0123 memory: 18752 grad_norm: 4.3554 loss: 2.1496 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1496 2023/03/17 18:19:23 - mmengine - INFO - Epoch(train) [10][1040/1320] lr: 2.0000e-02 eta: 4:57:17 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 4.4270 loss: 2.0934 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0934 2023/03/17 18:19:30 - mmengine - INFO - Epoch(train) [10][1060/1320] lr: 2.0000e-02 eta: 4:57:11 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 4.4618 loss: 2.2485 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2485 2023/03/17 18:19:37 - mmengine - INFO - Epoch(train) [10][1080/1320] lr: 2.0000e-02 eta: 4:57:04 time: 0.3352 data_time: 0.0128 memory: 18752 grad_norm: 4.3321 loss: 2.1024 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1024 2023/03/17 18:19:44 - mmengine - INFO - Epoch(train) [10][1100/1320] lr: 2.0000e-02 eta: 4:56:57 time: 0.3369 data_time: 0.0129 memory: 18752 grad_norm: 4.4201 loss: 2.0399 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0399 2023/03/17 18:19:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:19:50 - mmengine - INFO - Epoch(train) [10][1120/1320] lr: 2.0000e-02 eta: 4:56:50 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.3683 loss: 2.3102 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.3102 2023/03/17 18:19:57 - mmengine - INFO - Epoch(train) [10][1140/1320] lr: 2.0000e-02 eta: 4:56:44 time: 0.3357 data_time: 0.0128 memory: 18752 grad_norm: 4.3807 loss: 2.3164 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3164 2023/03/17 18:20:04 - mmengine - INFO - Epoch(train) [10][1160/1320] lr: 2.0000e-02 eta: 4:56:37 time: 0.3350 data_time: 0.0124 memory: 18752 grad_norm: 4.4746 loss: 2.0524 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0524 2023/03/17 18:20:10 - mmengine - INFO - Epoch(train) [10][1180/1320] lr: 2.0000e-02 eta: 4:56:30 time: 0.3356 data_time: 0.0126 memory: 18752 grad_norm: 4.4432 loss: 2.0286 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0286 2023/03/17 18:20:17 - mmengine - INFO - Epoch(train) [10][1200/1320] lr: 2.0000e-02 eta: 4:56:23 time: 0.3348 data_time: 0.0122 memory: 18752 grad_norm: 4.4860 loss: 2.1602 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1602 2023/03/17 18:20:24 - mmengine - INFO - Epoch(train) [10][1220/1320] lr: 2.0000e-02 eta: 4:56:17 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.5229 loss: 2.1194 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1194 2023/03/17 18:20:30 - mmengine - INFO - Epoch(train) [10][1240/1320] lr: 2.0000e-02 eta: 4:56:10 time: 0.3354 data_time: 0.0129 memory: 18752 grad_norm: 4.5097 loss: 2.1637 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1637 2023/03/17 18:20:37 - mmengine - INFO - Epoch(train) [10][1260/1320] lr: 2.0000e-02 eta: 4:56:03 time: 0.3355 data_time: 0.0127 memory: 18752 grad_norm: 4.5671 loss: 2.1439 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1439 2023/03/17 18:20:44 - mmengine - INFO - Epoch(train) [10][1280/1320] lr: 2.0000e-02 eta: 4:55:56 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.4683 loss: 2.1473 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1473 2023/03/17 18:20:51 - mmengine - INFO - Epoch(train) [10][1300/1320] lr: 2.0000e-02 eta: 4:55:50 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 4.3706 loss: 2.0805 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0805 2023/03/17 18:20:57 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:20:57 - mmengine - INFO - Epoch(train) [10][1320/1320] lr: 2.0000e-02 eta: 4:55:42 time: 0.3302 data_time: 0.0124 memory: 18752 grad_norm: 4.4075 loss: 1.9862 top1_acc: 0.2727 top5_acc: 0.7273 loss_cls: 1.9862 2023/03/17 18:21:00 - mmengine - INFO - Epoch(val) [10][ 20/194] eta: 0:00:22 time: 0.1289 data_time: 0.0423 memory: 2112 2023/03/17 18:21:02 - mmengine - INFO - Epoch(val) [10][ 40/194] eta: 0:00:17 time: 0.0965 data_time: 0.0109 memory: 2112 2023/03/17 18:21:04 - mmengine - INFO - Epoch(val) [10][ 60/194] eta: 0:00:14 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 18:21:06 - mmengine - INFO - Epoch(val) [10][ 80/194] eta: 0:00:11 time: 0.0980 data_time: 0.0112 memory: 2112 2023/03/17 18:21:08 - mmengine - INFO - Epoch(val) [10][100/194] eta: 0:00:09 time: 0.0972 data_time: 0.0111 memory: 2112 2023/03/17 18:21:10 - mmengine - INFO - Epoch(val) [10][120/194] eta: 0:00:07 time: 0.0965 data_time: 0.0106 memory: 2112 2023/03/17 18:21:11 - mmengine - INFO - Epoch(val) [10][140/194] eta: 0:00:05 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 18:21:13 - mmengine - INFO - Epoch(val) [10][160/194] eta: 0:00:03 time: 0.0973 data_time: 0.0111 memory: 2112 2023/03/17 18:21:15 - mmengine - INFO - Epoch(val) [10][180/194] eta: 0:00:01 time: 0.0975 data_time: 0.0115 memory: 2112 2023/03/17 18:21:19 - mmengine - INFO - Epoch(val) [10][194/194] acc/top1: 0.4399 acc/top5: 0.7381 acc/mean1: 0.3772 2023/03/17 18:21:19 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_8.pth is removed 2023/03/17 18:21:21 - mmengine - INFO - The best checkpoint with 0.4399 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2023/03/17 18:21:28 - mmengine - INFO - Epoch(train) [11][ 20/1320] lr: 2.0000e-02 eta: 4:55:38 time: 0.3670 data_time: 0.0365 memory: 18752 grad_norm: 4.4685 loss: 2.1166 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1166 2023/03/17 18:21:35 - mmengine - INFO - Epoch(train) [11][ 40/1320] lr: 2.0000e-02 eta: 4:55:31 time: 0.3352 data_time: 0.0125 memory: 18752 grad_norm: 4.4267 loss: 2.0871 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0871 2023/03/17 18:21:41 - mmengine - INFO - Epoch(train) [11][ 60/1320] lr: 2.0000e-02 eta: 4:55:24 time: 0.3351 data_time: 0.0125 memory: 18752 grad_norm: 4.4887 loss: 2.1367 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1367 2023/03/17 18:21:48 - mmengine - INFO - Epoch(train) [11][ 80/1320] lr: 2.0000e-02 eta: 4:55:18 time: 0.3345 data_time: 0.0123 memory: 18752 grad_norm: 4.5142 loss: 2.1097 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1097 2023/03/17 18:21:55 - mmengine - INFO - Epoch(train) [11][ 100/1320] lr: 2.0000e-02 eta: 4:55:11 time: 0.3347 data_time: 0.0117 memory: 18752 grad_norm: 4.4376 loss: 2.0002 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0002 2023/03/17 18:22:02 - mmengine - INFO - Epoch(train) [11][ 120/1320] lr: 2.0000e-02 eta: 4:55:04 time: 0.3346 data_time: 0.0123 memory: 18752 grad_norm: 4.6100 loss: 2.0648 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0648 2023/03/17 18:22:08 - mmengine - INFO - Epoch(train) [11][ 140/1320] lr: 2.0000e-02 eta: 4:54:57 time: 0.3347 data_time: 0.0123 memory: 18752 grad_norm: 4.3891 loss: 2.0043 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0043 2023/03/17 18:22:15 - mmengine - INFO - Epoch(train) [11][ 160/1320] lr: 2.0000e-02 eta: 4:54:50 time: 0.3348 data_time: 0.0117 memory: 18752 grad_norm: 4.4093 loss: 2.0804 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0804 2023/03/17 18:22:22 - mmengine - INFO - Epoch(train) [11][ 180/1320] lr: 2.0000e-02 eta: 4:54:44 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 4.3459 loss: 2.0289 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0289 2023/03/17 18:22:28 - mmengine - INFO - Epoch(train) [11][ 200/1320] lr: 2.0000e-02 eta: 4:54:37 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 4.4047 loss: 1.9917 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9917 2023/03/17 18:22:35 - mmengine - INFO - Epoch(train) [11][ 220/1320] lr: 2.0000e-02 eta: 4:54:30 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.4063 loss: 2.1255 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1255 2023/03/17 18:22:42 - mmengine - INFO - Epoch(train) [11][ 240/1320] lr: 2.0000e-02 eta: 4:54:23 time: 0.3346 data_time: 0.0120 memory: 18752 grad_norm: 4.5215 loss: 2.0598 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0598 2023/03/17 18:22:48 - mmengine - INFO - Epoch(train) [11][ 260/1320] lr: 2.0000e-02 eta: 4:54:16 time: 0.3351 data_time: 0.0122 memory: 18752 grad_norm: 4.6612 loss: 2.1294 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1294 2023/03/17 18:22:55 - mmengine - INFO - Epoch(train) [11][ 280/1320] lr: 2.0000e-02 eta: 4:54:09 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 4.5105 loss: 2.0195 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0195 2023/03/17 18:23:02 - mmengine - INFO - Epoch(train) [11][ 300/1320] lr: 2.0000e-02 eta: 4:54:03 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.3687 loss: 2.3345 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.3345 2023/03/17 18:23:09 - mmengine - INFO - Epoch(train) [11][ 320/1320] lr: 2.0000e-02 eta: 4:53:56 time: 0.3349 data_time: 0.0120 memory: 18752 grad_norm: 4.4481 loss: 1.9393 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.9393 2023/03/17 18:23:15 - mmengine - INFO - Epoch(train) [11][ 340/1320] lr: 2.0000e-02 eta: 4:53:49 time: 0.3351 data_time: 0.0115 memory: 18752 grad_norm: 4.4969 loss: 2.1489 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.1489 2023/03/17 18:23:22 - mmengine - INFO - Epoch(train) [11][ 360/1320] lr: 2.0000e-02 eta: 4:53:42 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.4262 loss: 2.0278 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0278 2023/03/17 18:23:29 - mmengine - INFO - Epoch(train) [11][ 380/1320] lr: 2.0000e-02 eta: 4:53:36 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.4680 loss: 2.2390 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2390 2023/03/17 18:23:35 - mmengine - INFO - Epoch(train) [11][ 400/1320] lr: 2.0000e-02 eta: 4:53:29 time: 0.3348 data_time: 0.0120 memory: 18752 grad_norm: 4.5855 loss: 1.9475 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9475 2023/03/17 18:23:42 - mmengine - INFO - Epoch(train) [11][ 420/1320] lr: 2.0000e-02 eta: 4:53:22 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.5682 loss: 1.8811 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8811 2023/03/17 18:23:49 - mmengine - INFO - Epoch(train) [11][ 440/1320] lr: 2.0000e-02 eta: 4:53:15 time: 0.3365 data_time: 0.0117 memory: 18752 grad_norm: 4.5150 loss: 1.9827 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9827 2023/03/17 18:23:56 - mmengine - INFO - Epoch(train) [11][ 460/1320] lr: 2.0000e-02 eta: 4:53:09 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 4.3033 loss: 2.1433 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1433 2023/03/17 18:24:02 - mmengine - INFO - Epoch(train) [11][ 480/1320] lr: 2.0000e-02 eta: 4:53:02 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.5447 loss: 2.1133 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1133 2023/03/17 18:24:09 - mmengine - INFO - Epoch(train) [11][ 500/1320] lr: 2.0000e-02 eta: 4:52:55 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.4934 loss: 2.0476 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.0476 2023/03/17 18:24:16 - mmengine - INFO - Epoch(train) [11][ 520/1320] lr: 2.0000e-02 eta: 4:52:48 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.4663 loss: 2.0555 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0555 2023/03/17 18:24:22 - mmengine - INFO - Epoch(train) [11][ 540/1320] lr: 2.0000e-02 eta: 4:52:42 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.5794 loss: 2.0388 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0388 2023/03/17 18:24:29 - mmengine - INFO - Epoch(train) [11][ 560/1320] lr: 2.0000e-02 eta: 4:52:35 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.5823 loss: 2.0382 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0382 2023/03/17 18:24:36 - mmengine - INFO - Epoch(train) [11][ 580/1320] lr: 2.0000e-02 eta: 4:52:28 time: 0.3366 data_time: 0.0125 memory: 18752 grad_norm: 4.4981 loss: 2.1282 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1282 2023/03/17 18:24:43 - mmengine - INFO - Epoch(train) [11][ 600/1320] lr: 2.0000e-02 eta: 4:52:21 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.6280 loss: 1.9678 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9678 2023/03/17 18:24:49 - mmengine - INFO - Epoch(train) [11][ 620/1320] lr: 2.0000e-02 eta: 4:52:15 time: 0.3366 data_time: 0.0124 memory: 18752 grad_norm: 4.4055 loss: 2.1025 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1025 2023/03/17 18:24:56 - mmengine - INFO - Epoch(train) [11][ 640/1320] lr: 2.0000e-02 eta: 4:52:08 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 4.4009 loss: 2.0968 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0968 2023/03/17 18:25:03 - mmengine - INFO - Epoch(train) [11][ 660/1320] lr: 2.0000e-02 eta: 4:52:01 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5264 loss: 2.1311 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1311 2023/03/17 18:25:09 - mmengine - INFO - Epoch(train) [11][ 680/1320] lr: 2.0000e-02 eta: 4:51:54 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.4413 loss: 2.0621 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0621 2023/03/17 18:25:16 - mmengine - INFO - Epoch(train) [11][ 700/1320] lr: 2.0000e-02 eta: 4:51:48 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.5403 loss: 2.1274 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1274 2023/03/17 18:25:23 - mmengine - INFO - Epoch(train) [11][ 720/1320] lr: 2.0000e-02 eta: 4:51:41 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.3654 loss: 2.0685 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0685 2023/03/17 18:25:30 - mmengine - INFO - Epoch(train) [11][ 740/1320] lr: 2.0000e-02 eta: 4:51:34 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.5602 loss: 1.9894 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.9894 2023/03/17 18:25:36 - mmengine - INFO - Epoch(train) [11][ 760/1320] lr: 2.0000e-02 eta: 4:51:28 time: 0.3364 data_time: 0.0119 memory: 18752 grad_norm: 4.5343 loss: 2.2462 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2462 2023/03/17 18:25:43 - mmengine - INFO - Epoch(train) [11][ 780/1320] lr: 2.0000e-02 eta: 4:51:21 time: 0.3367 data_time: 0.0122 memory: 18752 grad_norm: 4.3730 loss: 2.1390 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1390 2023/03/17 18:25:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:25:50 - mmengine - INFO - Epoch(train) [11][ 800/1320] lr: 2.0000e-02 eta: 4:51:14 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.5729 loss: 2.1986 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1986 2023/03/17 18:25:56 - mmengine - INFO - Epoch(train) [11][ 820/1320] lr: 2.0000e-02 eta: 4:51:07 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.4541 loss: 2.3779 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.3779 2023/03/17 18:26:03 - mmengine - INFO - Epoch(train) [11][ 840/1320] lr: 2.0000e-02 eta: 4:51:01 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.3842 loss: 2.1360 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 2.1360 2023/03/17 18:26:10 - mmengine - INFO - Epoch(train) [11][ 860/1320] lr: 2.0000e-02 eta: 4:50:54 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.3323 loss: 2.2993 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2993 2023/03/17 18:26:17 - mmengine - INFO - Epoch(train) [11][ 880/1320] lr: 2.0000e-02 eta: 4:50:47 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.5588 loss: 2.2093 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.2093 2023/03/17 18:26:23 - mmengine - INFO - Epoch(train) [11][ 900/1320] lr: 2.0000e-02 eta: 4:50:40 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 4.5758 loss: 1.9335 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.9335 2023/03/17 18:26:30 - mmengine - INFO - Epoch(train) [11][ 920/1320] lr: 2.0000e-02 eta: 4:50:34 time: 0.3360 data_time: 0.0129 memory: 18752 grad_norm: 4.4903 loss: 2.1078 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1078 2023/03/17 18:26:37 - mmengine - INFO - Epoch(train) [11][ 940/1320] lr: 2.0000e-02 eta: 4:50:27 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.5792 loss: 2.0334 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.0334 2023/03/17 18:26:43 - mmengine - INFO - Epoch(train) [11][ 960/1320] lr: 2.0000e-02 eta: 4:50:20 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.3909 loss: 2.0697 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 2.0697 2023/03/17 18:26:50 - mmengine - INFO - Epoch(train) [11][ 980/1320] lr: 2.0000e-02 eta: 4:50:13 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.3409 loss: 2.1378 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.1378 2023/03/17 18:26:57 - mmengine - INFO - Epoch(train) [11][1000/1320] lr: 2.0000e-02 eta: 4:50:07 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 4.5033 loss: 1.9282 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9282 2023/03/17 18:27:04 - mmengine - INFO - Epoch(train) [11][1020/1320] lr: 2.0000e-02 eta: 4:50:00 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.3862 loss: 2.1095 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1095 2023/03/17 18:27:10 - mmengine - INFO - Epoch(train) [11][1040/1320] lr: 2.0000e-02 eta: 4:49:53 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.5312 loss: 2.0795 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.0795 2023/03/17 18:27:17 - mmengine - INFO - Epoch(train) [11][1060/1320] lr: 2.0000e-02 eta: 4:49:46 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.3728 loss: 1.9288 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9288 2023/03/17 18:27:24 - mmengine - INFO - Epoch(train) [11][1080/1320] lr: 2.0000e-02 eta: 4:49:40 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.2216 loss: 1.8456 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8456 2023/03/17 18:27:30 - mmengine - INFO - Epoch(train) [11][1100/1320] lr: 2.0000e-02 eta: 4:49:33 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.3075 loss: 2.1962 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1962 2023/03/17 18:27:37 - mmengine - INFO - Epoch(train) [11][1120/1320] lr: 2.0000e-02 eta: 4:49:26 time: 0.3349 data_time: 0.0117 memory: 18752 grad_norm: 4.3615 loss: 2.1815 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1815 2023/03/17 18:27:44 - mmengine - INFO - Epoch(train) [11][1140/1320] lr: 2.0000e-02 eta: 4:49:19 time: 0.3355 data_time: 0.0114 memory: 18752 grad_norm: 4.4725 loss: 1.9871 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9871 2023/03/17 18:27:51 - mmengine - INFO - Epoch(train) [11][1160/1320] lr: 2.0000e-02 eta: 4:49:13 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.4099 loss: 1.9722 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9722 2023/03/17 18:27:57 - mmengine - INFO - Epoch(train) [11][1180/1320] lr: 2.0000e-02 eta: 4:49:06 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.5212 loss: 2.0547 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0547 2023/03/17 18:28:04 - mmengine - INFO - Epoch(train) [11][1200/1320] lr: 2.0000e-02 eta: 4:48:59 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.3408 loss: 2.0270 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0270 2023/03/17 18:28:11 - mmengine - INFO - Epoch(train) [11][1220/1320] lr: 2.0000e-02 eta: 4:48:52 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 4.6114 loss: 2.1567 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1567 2023/03/17 18:28:17 - mmengine - INFO - Epoch(train) [11][1240/1320] lr: 2.0000e-02 eta: 4:48:45 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.6611 loss: 2.0763 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0763 2023/03/17 18:28:24 - mmengine - INFO - Epoch(train) [11][1260/1320] lr: 2.0000e-02 eta: 4:48:39 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 4.4365 loss: 1.9024 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9024 2023/03/17 18:28:31 - mmengine - INFO - Epoch(train) [11][1280/1320] lr: 2.0000e-02 eta: 4:48:32 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.5203 loss: 2.3231 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3231 2023/03/17 18:28:38 - mmengine - INFO - Epoch(train) [11][1300/1320] lr: 2.0000e-02 eta: 4:48:25 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.3943 loss: 2.1019 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1019 2023/03/17 18:28:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:28:44 - mmengine - INFO - Epoch(train) [11][1320/1320] lr: 2.0000e-02 eta: 4:48:18 time: 0.3309 data_time: 0.0125 memory: 18752 grad_norm: 4.3908 loss: 2.0335 top1_acc: 0.4545 top5_acc: 0.6364 loss_cls: 2.0335 2023/03/17 18:28:47 - mmengine - INFO - Epoch(val) [11][ 20/194] eta: 0:00:22 time: 0.1308 data_time: 0.0447 memory: 2112 2023/03/17 18:28:49 - mmengine - INFO - Epoch(val) [11][ 40/194] eta: 0:00:17 time: 0.0965 data_time: 0.0103 memory: 2112 2023/03/17 18:28:51 - mmengine - INFO - Epoch(val) [11][ 60/194] eta: 0:00:14 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 18:28:53 - mmengine - INFO - Epoch(val) [11][ 80/194] eta: 0:00:12 time: 0.0976 data_time: 0.0113 memory: 2112 2023/03/17 18:28:55 - mmengine - INFO - Epoch(val) [11][100/194] eta: 0:00:09 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 18:28:56 - mmengine - INFO - Epoch(val) [11][120/194] eta: 0:00:07 time: 0.0970 data_time: 0.0113 memory: 2112 2023/03/17 18:28:58 - mmengine - INFO - Epoch(val) [11][140/194] eta: 0:00:05 time: 0.0973 data_time: 0.0112 memory: 2112 2023/03/17 18:29:00 - mmengine - INFO - Epoch(val) [11][160/194] eta: 0:00:03 time: 0.0974 data_time: 0.0110 memory: 2112 2023/03/17 18:29:02 - mmengine - INFO - Epoch(val) [11][180/194] eta: 0:00:01 time: 0.0984 data_time: 0.0123 memory: 2112 2023/03/17 18:29:06 - mmengine - INFO - Epoch(val) [11][194/194] acc/top1: 0.4462 acc/top5: 0.7408 acc/mean1: 0.3796 2023/03/17 18:29:06 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_10.pth is removed 2023/03/17 18:29:07 - mmengine - INFO - The best checkpoint with 0.4462 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2023/03/17 18:29:15 - mmengine - INFO - Epoch(train) [12][ 20/1320] lr: 2.0000e-02 eta: 4:48:14 time: 0.3703 data_time: 0.0386 memory: 18752 grad_norm: 4.3745 loss: 2.0904 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0904 2023/03/17 18:29:21 - mmengine - INFO - Epoch(train) [12][ 40/1320] lr: 2.0000e-02 eta: 4:48:07 time: 0.3368 data_time: 0.0124 memory: 18752 grad_norm: 4.5333 loss: 2.0034 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0034 2023/03/17 18:29:28 - mmengine - INFO - Epoch(train) [12][ 60/1320] lr: 2.0000e-02 eta: 4:48:00 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 4.5770 loss: 2.2112 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 2.2112 2023/03/17 18:29:35 - mmengine - INFO - Epoch(train) [12][ 80/1320] lr: 2.0000e-02 eta: 4:47:54 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.4393 loss: 1.9868 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9868 2023/03/17 18:29:42 - mmengine - INFO - Epoch(train) [12][ 100/1320] lr: 2.0000e-02 eta: 4:47:47 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.3891 loss: 2.0588 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0588 2023/03/17 18:29:48 - mmengine - INFO - Epoch(train) [12][ 120/1320] lr: 2.0000e-02 eta: 4:47:40 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.3413 loss: 2.0950 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0950 2023/03/17 18:29:55 - mmengine - INFO - Epoch(train) [12][ 140/1320] lr: 2.0000e-02 eta: 4:47:33 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5347 loss: 1.8879 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8879 2023/03/17 18:30:02 - mmengine - INFO - Epoch(train) [12][ 160/1320] lr: 2.0000e-02 eta: 4:47:27 time: 0.3351 data_time: 0.0123 memory: 18752 grad_norm: 4.4448 loss: 1.9453 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9453 2023/03/17 18:30:08 - mmengine - INFO - Epoch(train) [12][ 180/1320] lr: 2.0000e-02 eta: 4:47:20 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.3943 loss: 2.1546 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1546 2023/03/17 18:30:15 - mmengine - INFO - Epoch(train) [12][ 200/1320] lr: 2.0000e-02 eta: 4:47:13 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 4.3943 loss: 2.1445 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1445 2023/03/17 18:30:22 - mmengine - INFO - Epoch(train) [12][ 220/1320] lr: 2.0000e-02 eta: 4:47:06 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.5639 loss: 1.8301 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8301 2023/03/17 18:30:29 - mmengine - INFO - Epoch(train) [12][ 240/1320] lr: 2.0000e-02 eta: 4:47:00 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.3935 loss: 1.9253 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9253 2023/03/17 18:30:35 - mmengine - INFO - Epoch(train) [12][ 260/1320] lr: 2.0000e-02 eta: 4:46:53 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 4.4289 loss: 1.9717 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9717 2023/03/17 18:30:42 - mmengine - INFO - Epoch(train) [12][ 280/1320] lr: 2.0000e-02 eta: 4:46:46 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 4.4688 loss: 1.9134 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9134 2023/03/17 18:30:49 - mmengine - INFO - Epoch(train) [12][ 300/1320] lr: 2.0000e-02 eta: 4:46:39 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 4.4417 loss: 2.0802 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0802 2023/03/17 18:30:56 - mmengine - INFO - Epoch(train) [12][ 320/1320] lr: 2.0000e-02 eta: 4:46:33 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.7392 loss: 2.0675 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0675 2023/03/17 18:31:02 - mmengine - INFO - Epoch(train) [12][ 340/1320] lr: 2.0000e-02 eta: 4:46:26 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.3557 loss: 2.0456 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0456 2023/03/17 18:31:09 - mmengine - INFO - Epoch(train) [12][ 360/1320] lr: 2.0000e-02 eta: 4:46:19 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.4265 loss: 2.0780 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0780 2023/03/17 18:31:16 - mmengine - INFO - Epoch(train) [12][ 380/1320] lr: 2.0000e-02 eta: 4:46:12 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.6103 loss: 2.0063 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0063 2023/03/17 18:31:22 - mmengine - INFO - Epoch(train) [12][ 400/1320] lr: 2.0000e-02 eta: 4:46:06 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.6051 loss: 1.8853 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8853 2023/03/17 18:31:29 - mmengine - INFO - Epoch(train) [12][ 420/1320] lr: 2.0000e-02 eta: 4:45:59 time: 0.3379 data_time: 0.0129 memory: 18752 grad_norm: 4.6489 loss: 2.0512 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0512 2023/03/17 18:31:36 - mmengine - INFO - Epoch(train) [12][ 440/1320] lr: 2.0000e-02 eta: 4:45:52 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 4.6091 loss: 2.0785 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0785 2023/03/17 18:31:43 - mmengine - INFO - Epoch(train) [12][ 460/1320] lr: 2.0000e-02 eta: 4:45:46 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.3530 loss: 1.9625 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9625 2023/03/17 18:31:49 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:31:49 - mmengine - INFO - Epoch(train) [12][ 480/1320] lr: 2.0000e-02 eta: 4:45:39 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.4912 loss: 2.0438 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0438 2023/03/17 18:31:56 - mmengine - INFO - Epoch(train) [12][ 500/1320] lr: 2.0000e-02 eta: 4:45:32 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.4793 loss: 1.9616 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9616 2023/03/17 18:32:03 - mmengine - INFO - Epoch(train) [12][ 520/1320] lr: 2.0000e-02 eta: 4:45:25 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.4751 loss: 1.9332 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9332 2023/03/17 18:32:09 - mmengine - INFO - Epoch(train) [12][ 540/1320] lr: 2.0000e-02 eta: 4:45:19 time: 0.3356 data_time: 0.0128 memory: 18752 grad_norm: 4.4490 loss: 2.0528 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0528 2023/03/17 18:32:16 - mmengine - INFO - Epoch(train) [12][ 560/1320] lr: 2.0000e-02 eta: 4:45:12 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 4.5369 loss: 2.0200 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0200 2023/03/17 18:32:23 - mmengine - INFO - Epoch(train) [12][ 580/1320] lr: 2.0000e-02 eta: 4:45:05 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 4.3734 loss: 1.8533 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8533 2023/03/17 18:32:30 - mmengine - INFO - Epoch(train) [12][ 600/1320] lr: 2.0000e-02 eta: 4:44:58 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 4.5179 loss: 2.3117 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.3117 2023/03/17 18:32:36 - mmengine - INFO - Epoch(train) [12][ 620/1320] lr: 2.0000e-02 eta: 4:44:52 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.4973 loss: 2.0401 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0401 2023/03/17 18:32:43 - mmengine - INFO - Epoch(train) [12][ 640/1320] lr: 2.0000e-02 eta: 4:44:45 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.4371 loss: 1.8868 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8868 2023/03/17 18:32:50 - mmengine - INFO - Epoch(train) [12][ 660/1320] lr: 2.0000e-02 eta: 4:44:38 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 4.5068 loss: 2.0068 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0068 2023/03/17 18:32:56 - mmengine - INFO - Epoch(train) [12][ 680/1320] lr: 2.0000e-02 eta: 4:44:31 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 4.5450 loss: 1.9601 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9601 2023/03/17 18:33:03 - mmengine - INFO - Epoch(train) [12][ 700/1320] lr: 2.0000e-02 eta: 4:44:25 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.6021 loss: 2.2238 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2238 2023/03/17 18:33:10 - mmengine - INFO - Epoch(train) [12][ 720/1320] lr: 2.0000e-02 eta: 4:44:18 time: 0.3371 data_time: 0.0131 memory: 18752 grad_norm: 4.4574 loss: 1.9889 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9889 2023/03/17 18:33:17 - mmengine - INFO - Epoch(train) [12][ 740/1320] lr: 2.0000e-02 eta: 4:44:11 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 4.3619 loss: 1.8985 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.8985 2023/03/17 18:33:23 - mmengine - INFO - Epoch(train) [12][ 760/1320] lr: 2.0000e-02 eta: 4:44:05 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 4.4435 loss: 2.0865 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0865 2023/03/17 18:33:30 - mmengine - INFO - Epoch(train) [12][ 780/1320] lr: 2.0000e-02 eta: 4:43:58 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.4809 loss: 2.0401 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.0401 2023/03/17 18:33:37 - mmengine - INFO - Epoch(train) [12][ 800/1320] lr: 2.0000e-02 eta: 4:43:51 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 4.4946 loss: 1.9771 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9771 2023/03/17 18:33:43 - mmengine - INFO - Epoch(train) [12][ 820/1320] lr: 2.0000e-02 eta: 4:43:44 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 4.5076 loss: 1.9807 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9807 2023/03/17 18:33:50 - mmengine - INFO - Epoch(train) [12][ 840/1320] lr: 2.0000e-02 eta: 4:43:38 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.4309 loss: 2.1475 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1475 2023/03/17 18:33:57 - mmengine - INFO - Epoch(train) [12][ 860/1320] lr: 2.0000e-02 eta: 4:43:31 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.4490 loss: 1.9691 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9691 2023/03/17 18:34:04 - mmengine - INFO - Epoch(train) [12][ 880/1320] lr: 2.0000e-02 eta: 4:43:24 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.6364 loss: 1.9975 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9975 2023/03/17 18:34:10 - mmengine - INFO - Epoch(train) [12][ 900/1320] lr: 2.0000e-02 eta: 4:43:17 time: 0.3354 data_time: 0.0125 memory: 18752 grad_norm: 4.5404 loss: 1.9794 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9794 2023/03/17 18:34:17 - mmengine - INFO - Epoch(train) [12][ 920/1320] lr: 2.0000e-02 eta: 4:43:11 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 4.4964 loss: 2.1092 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 2.1092 2023/03/17 18:34:24 - mmengine - INFO - Epoch(train) [12][ 940/1320] lr: 2.0000e-02 eta: 4:43:04 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 4.5042 loss: 2.0963 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0963 2023/03/17 18:34:31 - mmengine - INFO - Epoch(train) [12][ 960/1320] lr: 2.0000e-02 eta: 4:42:57 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.4954 loss: 2.1941 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1941 2023/03/17 18:34:37 - mmengine - INFO - Epoch(train) [12][ 980/1320] lr: 2.0000e-02 eta: 4:42:51 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 4.4167 loss: 1.9499 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9499 2023/03/17 18:34:44 - mmengine - INFO - Epoch(train) [12][1000/1320] lr: 2.0000e-02 eta: 4:42:44 time: 0.3367 data_time: 0.0125 memory: 18752 grad_norm: 4.4396 loss: 2.1852 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1852 2023/03/17 18:34:51 - mmengine - INFO - Epoch(train) [12][1020/1320] lr: 2.0000e-02 eta: 4:42:37 time: 0.3371 data_time: 0.0132 memory: 18752 grad_norm: 4.4106 loss: 2.1460 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1460 2023/03/17 18:34:57 - mmengine - INFO - Epoch(train) [12][1040/1320] lr: 2.0000e-02 eta: 4:42:31 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.3920 loss: 2.1748 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1748 2023/03/17 18:35:04 - mmengine - INFO - Epoch(train) [12][1060/1320] lr: 2.0000e-02 eta: 4:42:24 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 4.4483 loss: 2.2446 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 2.2446 2023/03/17 18:35:11 - mmengine - INFO - Epoch(train) [12][1080/1320] lr: 2.0000e-02 eta: 4:42:17 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.3875 loss: 1.8164 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8164 2023/03/17 18:35:18 - mmengine - INFO - Epoch(train) [12][1100/1320] lr: 2.0000e-02 eta: 4:42:10 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 4.4282 loss: 2.2480 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2480 2023/03/17 18:35:24 - mmengine - INFO - Epoch(train) [12][1120/1320] lr: 2.0000e-02 eta: 4:42:04 time: 0.3359 data_time: 0.0117 memory: 18752 grad_norm: 4.4624 loss: 2.0911 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0911 2023/03/17 18:35:31 - mmengine - INFO - Epoch(train) [12][1140/1320] lr: 2.0000e-02 eta: 4:41:57 time: 0.3373 data_time: 0.0120 memory: 18752 grad_norm: 4.3694 loss: 2.0918 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0918 2023/03/17 18:35:38 - mmengine - INFO - Epoch(train) [12][1160/1320] lr: 2.0000e-02 eta: 4:41:50 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 4.5787 loss: 2.1257 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1257 2023/03/17 18:35:45 - mmengine - INFO - Epoch(train) [12][1180/1320] lr: 2.0000e-02 eta: 4:41:44 time: 0.3365 data_time: 0.0115 memory: 18752 grad_norm: 4.3846 loss: 2.0795 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0795 2023/03/17 18:35:51 - mmengine - INFO - Epoch(train) [12][1200/1320] lr: 2.0000e-02 eta: 4:41:37 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 4.4669 loss: 2.1437 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1437 2023/03/17 18:35:58 - mmengine - INFO - Epoch(train) [12][1220/1320] lr: 2.0000e-02 eta: 4:41:30 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.4044 loss: 2.0576 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0576 2023/03/17 18:36:05 - mmengine - INFO - Epoch(train) [12][1240/1320] lr: 2.0000e-02 eta: 4:41:23 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.4387 loss: 2.2181 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2181 2023/03/17 18:36:11 - mmengine - INFO - Epoch(train) [12][1260/1320] lr: 2.0000e-02 eta: 4:41:17 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.5511 loss: 1.8621 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.8621 2023/03/17 18:36:18 - mmengine - INFO - Epoch(train) [12][1280/1320] lr: 2.0000e-02 eta: 4:41:10 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.4966 loss: 2.1713 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1713 2023/03/17 18:36:25 - mmengine - INFO - Epoch(train) [12][1300/1320] lr: 2.0000e-02 eta: 4:41:03 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.4266 loss: 2.0786 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0786 2023/03/17 18:36:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:36:31 - mmengine - INFO - Epoch(train) [12][1320/1320] lr: 2.0000e-02 eta: 4:40:56 time: 0.3307 data_time: 0.0123 memory: 18752 grad_norm: 4.6454 loss: 2.1011 top1_acc: 0.5455 top5_acc: 0.7273 loss_cls: 2.1011 2023/03/17 18:36:31 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/03/17 18:36:37 - mmengine - INFO - Epoch(val) [12][ 20/194] eta: 0:00:22 time: 0.1293 data_time: 0.0422 memory: 2112 2023/03/17 18:36:39 - mmengine - INFO - Epoch(val) [12][ 40/194] eta: 0:00:17 time: 0.0966 data_time: 0.0108 memory: 2112 2023/03/17 18:36:41 - mmengine - INFO - Epoch(val) [12][ 60/194] eta: 0:00:14 time: 0.0972 data_time: 0.0112 memory: 2112 2023/03/17 18:36:43 - mmengine - INFO - Epoch(val) [12][ 80/194] eta: 0:00:11 time: 0.0969 data_time: 0.0108 memory: 2112 2023/03/17 18:36:45 - mmengine - INFO - Epoch(val) [12][100/194] eta: 0:00:09 time: 0.0972 data_time: 0.0109 memory: 2112 2023/03/17 18:36:47 - mmengine - INFO - Epoch(val) [12][120/194] eta: 0:00:07 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 18:36:48 - mmengine - INFO - Epoch(val) [12][140/194] eta: 0:00:05 time: 0.0976 data_time: 0.0111 memory: 2112 2023/03/17 18:36:50 - mmengine - INFO - Epoch(val) [12][160/194] eta: 0:00:03 time: 0.0964 data_time: 0.0106 memory: 2112 2023/03/17 18:36:52 - mmengine - INFO - Epoch(val) [12][180/194] eta: 0:00:01 time: 0.0965 data_time: 0.0102 memory: 2112 2023/03/17 18:36:55 - mmengine - INFO - Epoch(val) [12][194/194] acc/top1: 0.4511 acc/top5: 0.7440 acc/mean1: 0.3825 2023/03/17 18:36:55 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_11.pth is removed 2023/03/17 18:36:56 - mmengine - INFO - The best checkpoint with 0.4511 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2023/03/17 18:37:04 - mmengine - INFO - Epoch(train) [13][ 20/1320] lr: 2.0000e-02 eta: 4:40:52 time: 0.3718 data_time: 0.0413 memory: 18752 grad_norm: 4.4051 loss: 2.0281 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0281 2023/03/17 18:37:11 - mmengine - INFO - Epoch(train) [13][ 40/1320] lr: 2.0000e-02 eta: 4:40:45 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.5023 loss: 1.8361 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8361 2023/03/17 18:37:17 - mmengine - INFO - Epoch(train) [13][ 60/1320] lr: 2.0000e-02 eta: 4:40:38 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.5799 loss: 2.1515 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1515 2023/03/17 18:37:24 - mmengine - INFO - Epoch(train) [13][ 80/1320] lr: 2.0000e-02 eta: 4:40:31 time: 0.3345 data_time: 0.0118 memory: 18752 grad_norm: 4.4316 loss: 1.9510 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9510 2023/03/17 18:37:31 - mmengine - INFO - Epoch(train) [13][ 100/1320] lr: 2.0000e-02 eta: 4:40:25 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.5076 loss: 2.0254 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0254 2023/03/17 18:37:37 - mmengine - INFO - Epoch(train) [13][ 120/1320] lr: 2.0000e-02 eta: 4:40:18 time: 0.3349 data_time: 0.0119 memory: 18752 grad_norm: 4.4953 loss: 1.7995 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7995 2023/03/17 18:37:44 - mmengine - INFO - Epoch(train) [13][ 140/1320] lr: 2.0000e-02 eta: 4:40:11 time: 0.3349 data_time: 0.0119 memory: 18752 grad_norm: 4.4923 loss: 2.0142 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0142 2023/03/17 18:37:51 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:37:51 - mmengine - INFO - Epoch(train) [13][ 160/1320] lr: 2.0000e-02 eta: 4:40:04 time: 0.3347 data_time: 0.0117 memory: 18752 grad_norm: 4.3869 loss: 2.0258 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0258 2023/03/17 18:37:57 - mmengine - INFO - Epoch(train) [13][ 180/1320] lr: 2.0000e-02 eta: 4:39:57 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.4883 loss: 2.0317 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.0317 2023/03/17 18:38:04 - mmengine - INFO - Epoch(train) [13][ 200/1320] lr: 2.0000e-02 eta: 4:39:51 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.5737 loss: 1.8173 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8173 2023/03/17 18:38:11 - mmengine - INFO - Epoch(train) [13][ 220/1320] lr: 2.0000e-02 eta: 4:39:44 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 4.5800 loss: 2.1900 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1900 2023/03/17 18:38:18 - mmengine - INFO - Epoch(train) [13][ 240/1320] lr: 2.0000e-02 eta: 4:39:37 time: 0.3369 data_time: 0.0123 memory: 18752 grad_norm: 4.5830 loss: 1.9856 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9856 2023/03/17 18:38:24 - mmengine - INFO - Epoch(train) [13][ 260/1320] lr: 2.0000e-02 eta: 4:39:30 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.5900 loss: 2.1635 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.1635 2023/03/17 18:38:31 - mmengine - INFO - Epoch(train) [13][ 280/1320] lr: 2.0000e-02 eta: 4:39:24 time: 0.3359 data_time: 0.0117 memory: 18752 grad_norm: 4.5959 loss: 2.1674 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1674 2023/03/17 18:38:38 - mmengine - INFO - Epoch(train) [13][ 300/1320] lr: 2.0000e-02 eta: 4:39:17 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.6613 loss: 2.1244 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.1244 2023/03/17 18:38:44 - mmengine - INFO - Epoch(train) [13][ 320/1320] lr: 2.0000e-02 eta: 4:39:10 time: 0.3363 data_time: 0.0116 memory: 18752 grad_norm: 4.5265 loss: 1.9551 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9551 2023/03/17 18:38:51 - mmengine - INFO - Epoch(train) [13][ 340/1320] lr: 2.0000e-02 eta: 4:39:03 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.3369 loss: 1.9060 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9060 2023/03/17 18:38:58 - mmengine - INFO - Epoch(train) [13][ 360/1320] lr: 2.0000e-02 eta: 4:38:57 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.4248 loss: 2.0178 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0178 2023/03/17 18:39:05 - mmengine - INFO - Epoch(train) [13][ 380/1320] lr: 2.0000e-02 eta: 4:38:50 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.4478 loss: 1.9649 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9649 2023/03/17 18:39:11 - mmengine - INFO - Epoch(train) [13][ 400/1320] lr: 2.0000e-02 eta: 4:38:43 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.5676 loss: 2.1993 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1993 2023/03/17 18:39:18 - mmengine - INFO - Epoch(train) [13][ 420/1320] lr: 2.0000e-02 eta: 4:38:37 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 4.4600 loss: 1.9914 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9914 2023/03/17 18:39:25 - mmengine - INFO - Epoch(train) [13][ 440/1320] lr: 2.0000e-02 eta: 4:38:30 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.5327 loss: 1.9708 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9708 2023/03/17 18:39:31 - mmengine - INFO - Epoch(train) [13][ 460/1320] lr: 2.0000e-02 eta: 4:38:23 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 4.5051 loss: 1.9361 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.9361 2023/03/17 18:39:38 - mmengine - INFO - Epoch(train) [13][ 480/1320] lr: 2.0000e-02 eta: 4:38:16 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.4942 loss: 1.9829 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9829 2023/03/17 18:39:45 - mmengine - INFO - Epoch(train) [13][ 500/1320] lr: 2.0000e-02 eta: 4:38:10 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4801 loss: 1.8379 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8379 2023/03/17 18:39:52 - mmengine - INFO - Epoch(train) [13][ 520/1320] lr: 2.0000e-02 eta: 4:38:03 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.4770 loss: 1.9006 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9006 2023/03/17 18:39:58 - mmengine - INFO - Epoch(train) [13][ 540/1320] lr: 2.0000e-02 eta: 4:37:56 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5294 loss: 2.0253 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0253 2023/03/17 18:40:05 - mmengine - INFO - Epoch(train) [13][ 560/1320] lr: 2.0000e-02 eta: 4:37:49 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5785 loss: 2.1899 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1899 2023/03/17 18:40:12 - mmengine - INFO - Epoch(train) [13][ 580/1320] lr: 2.0000e-02 eta: 4:37:42 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.3027 loss: 2.0606 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0606 2023/03/17 18:40:18 - mmengine - INFO - Epoch(train) [13][ 600/1320] lr: 2.0000e-02 eta: 4:37:36 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.3528 loss: 1.8913 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8913 2023/03/17 18:40:25 - mmengine - INFO - Epoch(train) [13][ 620/1320] lr: 2.0000e-02 eta: 4:37:29 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.6125 loss: 2.0914 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0914 2023/03/17 18:40:32 - mmengine - INFO - Epoch(train) [13][ 640/1320] lr: 2.0000e-02 eta: 4:37:22 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.4786 loss: 2.2099 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2099 2023/03/17 18:40:39 - mmengine - INFO - Epoch(train) [13][ 660/1320] lr: 2.0000e-02 eta: 4:37:15 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.4900 loss: 2.0147 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0147 2023/03/17 18:40:45 - mmengine - INFO - Epoch(train) [13][ 680/1320] lr: 2.0000e-02 eta: 4:37:09 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5438 loss: 2.1130 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1130 2023/03/17 18:40:52 - mmengine - INFO - Epoch(train) [13][ 700/1320] lr: 2.0000e-02 eta: 4:37:02 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.4735 loss: 2.0148 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0148 2023/03/17 18:40:59 - mmengine - INFO - Epoch(train) [13][ 720/1320] lr: 2.0000e-02 eta: 4:36:55 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.5943 loss: 2.1247 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1247 2023/03/17 18:41:05 - mmengine - INFO - Epoch(train) [13][ 740/1320] lr: 2.0000e-02 eta: 4:36:49 time: 0.3362 data_time: 0.0126 memory: 18752 grad_norm: 4.6148 loss: 2.0675 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0675 2023/03/17 18:41:12 - mmengine - INFO - Epoch(train) [13][ 760/1320] lr: 2.0000e-02 eta: 4:36:42 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.4940 loss: 1.9302 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 1.9302 2023/03/17 18:41:19 - mmengine - INFO - Epoch(train) [13][ 780/1320] lr: 2.0000e-02 eta: 4:36:35 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 4.4470 loss: 1.9642 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9642 2023/03/17 18:41:26 - mmengine - INFO - Epoch(train) [13][ 800/1320] lr: 2.0000e-02 eta: 4:36:28 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5183 loss: 2.1815 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1815 2023/03/17 18:41:32 - mmengine - INFO - Epoch(train) [13][ 820/1320] lr: 2.0000e-02 eta: 4:36:21 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.3317 loss: 2.0048 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0048 2023/03/17 18:41:39 - mmengine - INFO - Epoch(train) [13][ 840/1320] lr: 2.0000e-02 eta: 4:36:15 time: 0.3357 data_time: 0.0128 memory: 18752 grad_norm: 4.4369 loss: 2.0947 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0947 2023/03/17 18:41:46 - mmengine - INFO - Epoch(train) [13][ 860/1320] lr: 2.0000e-02 eta: 4:36:08 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.3750 loss: 2.1423 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1423 2023/03/17 18:41:52 - mmengine - INFO - Epoch(train) [13][ 880/1320] lr: 2.0000e-02 eta: 4:36:01 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 4.4160 loss: 2.0189 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0189 2023/03/17 18:41:59 - mmengine - INFO - Epoch(train) [13][ 900/1320] lr: 2.0000e-02 eta: 4:35:55 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.3989 loss: 1.8979 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8979 2023/03/17 18:42:06 - mmengine - INFO - Epoch(train) [13][ 920/1320] lr: 2.0000e-02 eta: 4:35:48 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.4715 loss: 1.9506 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9506 2023/03/17 18:42:13 - mmengine - INFO - Epoch(train) [13][ 940/1320] lr: 2.0000e-02 eta: 4:35:41 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5210 loss: 2.0452 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0452 2023/03/17 18:42:19 - mmengine - INFO - Epoch(train) [13][ 960/1320] lr: 2.0000e-02 eta: 4:35:34 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.5871 loss: 1.8614 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.8614 2023/03/17 18:42:26 - mmengine - INFO - Epoch(train) [13][ 980/1320] lr: 2.0000e-02 eta: 4:35:28 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.6034 loss: 1.9643 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9643 2023/03/17 18:42:33 - mmengine - INFO - Epoch(train) [13][1000/1320] lr: 2.0000e-02 eta: 4:35:21 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.6417 loss: 2.1176 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1176 2023/03/17 18:42:39 - mmengine - INFO - Epoch(train) [13][1020/1320] lr: 2.0000e-02 eta: 4:35:14 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.5949 loss: 2.1658 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1658 2023/03/17 18:42:46 - mmengine - INFO - Epoch(train) [13][1040/1320] lr: 2.0000e-02 eta: 4:35:07 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.4822 loss: 2.2008 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2008 2023/03/17 18:42:53 - mmengine - INFO - Epoch(train) [13][1060/1320] lr: 2.0000e-02 eta: 4:35:01 time: 0.3358 data_time: 0.0129 memory: 18752 grad_norm: 4.5161 loss: 2.1393 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1393 2023/03/17 18:43:00 - mmengine - INFO - Epoch(train) [13][1080/1320] lr: 2.0000e-02 eta: 4:34:54 time: 0.3350 data_time: 0.0121 memory: 18752 grad_norm: 4.3987 loss: 2.1030 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1030 2023/03/17 18:43:06 - mmengine - INFO - Epoch(train) [13][1100/1320] lr: 2.0000e-02 eta: 4:34:47 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 4.5089 loss: 2.1714 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1714 2023/03/17 18:43:13 - mmengine - INFO - Epoch(train) [13][1120/1320] lr: 2.0000e-02 eta: 4:34:40 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.4983 loss: 2.0943 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.0943 2023/03/17 18:43:20 - mmengine - INFO - Epoch(train) [13][1140/1320] lr: 2.0000e-02 eta: 4:34:34 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.6497 loss: 2.1038 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1038 2023/03/17 18:43:26 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:43:26 - mmengine - INFO - Epoch(train) [13][1160/1320] lr: 2.0000e-02 eta: 4:34:27 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.3982 loss: 2.0796 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0796 2023/03/17 18:43:33 - mmengine - INFO - Epoch(train) [13][1180/1320] lr: 2.0000e-02 eta: 4:34:20 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.4572 loss: 2.2265 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.2265 2023/03/17 18:43:40 - mmengine - INFO - Epoch(train) [13][1200/1320] lr: 2.0000e-02 eta: 4:34:13 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.4847 loss: 2.2551 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.2551 2023/03/17 18:43:47 - mmengine - INFO - Epoch(train) [13][1220/1320] lr: 2.0000e-02 eta: 4:34:07 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.5431 loss: 2.1613 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1613 2023/03/17 18:43:53 - mmengine - INFO - Epoch(train) [13][1240/1320] lr: 2.0000e-02 eta: 4:34:00 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 4.4625 loss: 2.2430 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2430 2023/03/17 18:44:00 - mmengine - INFO - Epoch(train) [13][1260/1320] lr: 2.0000e-02 eta: 4:33:53 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.5518 loss: 2.0371 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.0371 2023/03/17 18:44:07 - mmengine - INFO - Epoch(train) [13][1280/1320] lr: 2.0000e-02 eta: 4:33:46 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.3883 loss: 1.9221 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.9221 2023/03/17 18:44:13 - mmengine - INFO - Epoch(train) [13][1300/1320] lr: 2.0000e-02 eta: 4:33:40 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 4.4977 loss: 2.0296 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.0296 2023/03/17 18:44:20 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:44:20 - mmengine - INFO - Epoch(train) [13][1320/1320] lr: 2.0000e-02 eta: 4:33:33 time: 0.3310 data_time: 0.0122 memory: 18752 grad_norm: 4.4698 loss: 1.9978 top1_acc: 0.4545 top5_acc: 0.5455 loss_cls: 1.9978 2023/03/17 18:44:23 - mmengine - INFO - Epoch(val) [13][ 20/194] eta: 0:00:22 time: 0.1302 data_time: 0.0437 memory: 2112 2023/03/17 18:44:25 - mmengine - INFO - Epoch(val) [13][ 40/194] eta: 0:00:17 time: 0.0958 data_time: 0.0101 memory: 2112 2023/03/17 18:44:27 - mmengine - INFO - Epoch(val) [13][ 60/194] eta: 0:00:14 time: 0.0971 data_time: 0.0107 memory: 2112 2023/03/17 18:44:28 - mmengine - INFO - Epoch(val) [13][ 80/194] eta: 0:00:11 time: 0.0966 data_time: 0.0106 memory: 2112 2023/03/17 18:44:30 - mmengine - INFO - Epoch(val) [13][100/194] eta: 0:00:09 time: 0.0972 data_time: 0.0112 memory: 2112 2023/03/17 18:44:32 - mmengine - INFO - Epoch(val) [13][120/194] eta: 0:00:07 time: 0.0976 data_time: 0.0117 memory: 2112 2023/03/17 18:44:34 - mmengine - INFO - Epoch(val) [13][140/194] eta: 0:00:05 time: 0.0978 data_time: 0.0116 memory: 2112 2023/03/17 18:44:36 - mmengine - INFO - Epoch(val) [13][160/194] eta: 0:00:03 time: 0.0976 data_time: 0.0114 memory: 2112 2023/03/17 18:44:38 - mmengine - INFO - Epoch(val) [13][180/194] eta: 0:00:01 time: 0.0966 data_time: 0.0109 memory: 2112 2023/03/17 18:44:42 - mmengine - INFO - Epoch(val) [13][194/194] acc/top1: 0.4573 acc/top5: 0.7540 acc/mean1: 0.3892 2023/03/17 18:44:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_12.pth is removed 2023/03/17 18:44:43 - mmengine - INFO - The best checkpoint with 0.4573 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2023/03/17 18:44:50 - mmengine - INFO - Epoch(train) [14][ 20/1320] lr: 2.0000e-02 eta: 4:33:28 time: 0.3640 data_time: 0.0345 memory: 18752 grad_norm: 4.3894 loss: 1.8532 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.8532 2023/03/17 18:44:57 - mmengine - INFO - Epoch(train) [14][ 40/1320] lr: 2.0000e-02 eta: 4:33:21 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.6454 loss: 2.0488 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 2.0488 2023/03/17 18:45:04 - mmengine - INFO - Epoch(train) [14][ 60/1320] lr: 2.0000e-02 eta: 4:33:14 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.6376 loss: 1.9917 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9917 2023/03/17 18:45:10 - mmengine - INFO - Epoch(train) [14][ 80/1320] lr: 2.0000e-02 eta: 4:33:07 time: 0.3344 data_time: 0.0115 memory: 18752 grad_norm: 4.5447 loss: 1.9264 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9264 2023/03/17 18:45:17 - mmengine - INFO - Epoch(train) [14][ 100/1320] lr: 2.0000e-02 eta: 4:33:00 time: 0.3352 data_time: 0.0114 memory: 18752 grad_norm: 4.5389 loss: 2.0449 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0449 2023/03/17 18:45:24 - mmengine - INFO - Epoch(train) [14][ 120/1320] lr: 2.0000e-02 eta: 4:32:54 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.4256 loss: 1.8118 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8118 2023/03/17 18:45:31 - mmengine - INFO - Epoch(train) [14][ 140/1320] lr: 2.0000e-02 eta: 4:32:47 time: 0.3351 data_time: 0.0122 memory: 18752 grad_norm: 4.5519 loss: 1.9297 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9297 2023/03/17 18:45:37 - mmengine - INFO - Epoch(train) [14][ 160/1320] lr: 2.0000e-02 eta: 4:32:40 time: 0.3345 data_time: 0.0119 memory: 18752 grad_norm: 4.5229 loss: 2.0879 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0879 2023/03/17 18:45:44 - mmengine - INFO - Epoch(train) [14][ 180/1320] lr: 2.0000e-02 eta: 4:32:33 time: 0.3349 data_time: 0.0123 memory: 18752 grad_norm: 4.6363 loss: 1.8828 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8828 2023/03/17 18:45:51 - mmengine - INFO - Epoch(train) [14][ 200/1320] lr: 2.0000e-02 eta: 4:32:26 time: 0.3353 data_time: 0.0124 memory: 18752 grad_norm: 4.5279 loss: 2.0452 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0452 2023/03/17 18:45:57 - mmengine - INFO - Epoch(train) [14][ 220/1320] lr: 2.0000e-02 eta: 4:32:20 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.5974 loss: 2.0373 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0373 2023/03/17 18:46:04 - mmengine - INFO - Epoch(train) [14][ 240/1320] lr: 2.0000e-02 eta: 4:32:13 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.5576 loss: 2.0877 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0877 2023/03/17 18:46:11 - mmengine - INFO - Epoch(train) [14][ 260/1320] lr: 2.0000e-02 eta: 4:32:06 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.4537 loss: 1.9097 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.9097 2023/03/17 18:46:18 - mmengine - INFO - Epoch(train) [14][ 280/1320] lr: 2.0000e-02 eta: 4:31:59 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.5331 loss: 2.0616 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 2.0616 2023/03/17 18:46:24 - mmengine - INFO - Epoch(train) [14][ 300/1320] lr: 2.0000e-02 eta: 4:31:53 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.4799 loss: 1.8634 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8634 2023/03/17 18:46:31 - mmengine - INFO - Epoch(train) [14][ 320/1320] lr: 2.0000e-02 eta: 4:31:46 time: 0.3355 data_time: 0.0129 memory: 18752 grad_norm: 4.5640 loss: 2.1181 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1181 2023/03/17 18:46:38 - mmengine - INFO - Epoch(train) [14][ 340/1320] lr: 2.0000e-02 eta: 4:31:39 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 4.4723 loss: 1.9714 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9714 2023/03/17 18:46:44 - mmengine - INFO - Epoch(train) [14][ 360/1320] lr: 2.0000e-02 eta: 4:31:32 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 4.5502 loss: 1.8265 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8265 2023/03/17 18:46:51 - mmengine - INFO - Epoch(train) [14][ 380/1320] lr: 2.0000e-02 eta: 4:31:26 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 4.5205 loss: 2.0838 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0838 2023/03/17 18:46:58 - mmengine - INFO - Epoch(train) [14][ 400/1320] lr: 2.0000e-02 eta: 4:31:19 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 4.5482 loss: 2.0717 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0717 2023/03/17 18:47:05 - mmengine - INFO - Epoch(train) [14][ 420/1320] lr: 2.0000e-02 eta: 4:31:12 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.5585 loss: 2.0333 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0333 2023/03/17 18:47:11 - mmengine - INFO - Epoch(train) [14][ 440/1320] lr: 2.0000e-02 eta: 4:31:06 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.5345 loss: 1.7545 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7545 2023/03/17 18:47:18 - mmengine - INFO - Epoch(train) [14][ 460/1320] lr: 2.0000e-02 eta: 4:30:59 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 4.4925 loss: 1.9790 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9790 2023/03/17 18:47:25 - mmengine - INFO - Epoch(train) [14][ 480/1320] lr: 2.0000e-02 eta: 4:30:52 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 4.5474 loss: 1.9767 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.9767 2023/03/17 18:47:31 - mmengine - INFO - Epoch(train) [14][ 500/1320] lr: 2.0000e-02 eta: 4:30:45 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 4.6490 loss: 2.0005 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0005 2023/03/17 18:47:38 - mmengine - INFO - Epoch(train) [14][ 520/1320] lr: 2.0000e-02 eta: 4:30:39 time: 0.3366 data_time: 0.0118 memory: 18752 grad_norm: 4.5580 loss: 2.0045 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0045 2023/03/17 18:47:45 - mmengine - INFO - Epoch(train) [14][ 540/1320] lr: 2.0000e-02 eta: 4:30:32 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.3344 loss: 2.0049 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.0049 2023/03/17 18:47:52 - mmengine - INFO - Epoch(train) [14][ 560/1320] lr: 2.0000e-02 eta: 4:30:25 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5476 loss: 1.9743 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9743 2023/03/17 18:47:58 - mmengine - INFO - Epoch(train) [14][ 580/1320] lr: 2.0000e-02 eta: 4:30:18 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.5321 loss: 2.0764 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0764 2023/03/17 18:48:05 - mmengine - INFO - Epoch(train) [14][ 600/1320] lr: 2.0000e-02 eta: 4:30:12 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.3481 loss: 2.1169 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1169 2023/03/17 18:48:12 - mmengine - INFO - Epoch(train) [14][ 620/1320] lr: 2.0000e-02 eta: 4:30:05 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.2290 loss: 1.9189 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9189 2023/03/17 18:48:18 - mmengine - INFO - Epoch(train) [14][ 640/1320] lr: 2.0000e-02 eta: 4:29:58 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.5392 loss: 2.1203 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1203 2023/03/17 18:48:25 - mmengine - INFO - Epoch(train) [14][ 660/1320] lr: 2.0000e-02 eta: 4:29:51 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5310 loss: 1.9891 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9891 2023/03/17 18:48:32 - mmengine - INFO - Epoch(train) [14][ 680/1320] lr: 2.0000e-02 eta: 4:29:45 time: 0.3361 data_time: 0.0114 memory: 18752 grad_norm: 4.4091 loss: 1.9933 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9933 2023/03/17 18:48:39 - mmengine - INFO - Epoch(train) [14][ 700/1320] lr: 2.0000e-02 eta: 4:29:38 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.4157 loss: 1.8873 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8873 2023/03/17 18:48:45 - mmengine - INFO - Epoch(train) [14][ 720/1320] lr: 2.0000e-02 eta: 4:29:31 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5014 loss: 2.1444 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1444 2023/03/17 18:48:52 - mmengine - INFO - Epoch(train) [14][ 740/1320] lr: 2.0000e-02 eta: 4:29:25 time: 0.3362 data_time: 0.0116 memory: 18752 grad_norm: 4.4960 loss: 1.9073 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9073 2023/03/17 18:48:59 - mmengine - INFO - Epoch(train) [14][ 760/1320] lr: 2.0000e-02 eta: 4:29:18 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 4.5515 loss: 2.1558 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1558 2023/03/17 18:49:05 - mmengine - INFO - Epoch(train) [14][ 780/1320] lr: 2.0000e-02 eta: 4:29:11 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.4738 loss: 1.8402 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.8402 2023/03/17 18:49:12 - mmengine - INFO - Epoch(train) [14][ 800/1320] lr: 2.0000e-02 eta: 4:29:04 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.5631 loss: 2.1141 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1141 2023/03/17 18:49:19 - mmengine - INFO - Epoch(train) [14][ 820/1320] lr: 2.0000e-02 eta: 4:28:58 time: 0.3371 data_time: 0.0131 memory: 18752 grad_norm: 4.5506 loss: 1.9829 top1_acc: 0.3125 top5_acc: 0.9375 loss_cls: 1.9829 2023/03/17 18:49:26 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:49:26 - mmengine - INFO - Epoch(train) [14][ 840/1320] lr: 2.0000e-02 eta: 4:28:51 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.3739 loss: 1.8300 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8300 2023/03/17 18:49:32 - mmengine - INFO - Epoch(train) [14][ 860/1320] lr: 2.0000e-02 eta: 4:28:44 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.5078 loss: 2.0492 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.0492 2023/03/17 18:49:39 - mmengine - INFO - Epoch(train) [14][ 880/1320] lr: 2.0000e-02 eta: 4:28:37 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5148 loss: 2.2017 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2017 2023/03/17 18:49:46 - mmengine - INFO - Epoch(train) [14][ 900/1320] lr: 2.0000e-02 eta: 4:28:31 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.4522 loss: 2.0018 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0018 2023/03/17 18:49:53 - mmengine - INFO - Epoch(train) [14][ 920/1320] lr: 2.0000e-02 eta: 4:28:24 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.5130 loss: 1.8758 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8758 2023/03/17 18:49:59 - mmengine - INFO - Epoch(train) [14][ 940/1320] lr: 2.0000e-02 eta: 4:28:17 time: 0.3360 data_time: 0.0116 memory: 18752 grad_norm: 4.5138 loss: 2.0638 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0638 2023/03/17 18:50:06 - mmengine - INFO - Epoch(train) [14][ 960/1320] lr: 2.0000e-02 eta: 4:28:11 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5376 loss: 1.8245 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8245 2023/03/17 18:50:13 - mmengine - INFO - Epoch(train) [14][ 980/1320] lr: 2.0000e-02 eta: 4:28:04 time: 0.3365 data_time: 0.0130 memory: 18752 grad_norm: 4.3122 loss: 1.7863 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7863 2023/03/17 18:50:19 - mmengine - INFO - Epoch(train) [14][1000/1320] lr: 2.0000e-02 eta: 4:27:57 time: 0.3350 data_time: 0.0114 memory: 18752 grad_norm: 4.5235 loss: 1.8688 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8688 2023/03/17 18:50:26 - mmengine - INFO - Epoch(train) [14][1020/1320] lr: 2.0000e-02 eta: 4:27:50 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 4.5322 loss: 2.0573 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0573 2023/03/17 18:50:33 - mmengine - INFO - Epoch(train) [14][1040/1320] lr: 2.0000e-02 eta: 4:27:44 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.4737 loss: 2.1405 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.1405 2023/03/17 18:50:40 - mmengine - INFO - Epoch(train) [14][1060/1320] lr: 2.0000e-02 eta: 4:27:37 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.4785 loss: 2.0993 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0993 2023/03/17 18:50:46 - mmengine - INFO - Epoch(train) [14][1080/1320] lr: 2.0000e-02 eta: 4:27:30 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5472 loss: 2.1471 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1471 2023/03/17 18:50:53 - mmengine - INFO - Epoch(train) [14][1100/1320] lr: 2.0000e-02 eta: 4:27:23 time: 0.3359 data_time: 0.0115 memory: 18752 grad_norm: 4.3668 loss: 2.0741 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0741 2023/03/17 18:51:00 - mmengine - INFO - Epoch(train) [14][1120/1320] lr: 2.0000e-02 eta: 4:27:17 time: 0.3358 data_time: 0.0114 memory: 18752 grad_norm: 4.6346 loss: 2.2052 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2052 2023/03/17 18:51:06 - mmengine - INFO - Epoch(train) [14][1140/1320] lr: 2.0000e-02 eta: 4:27:10 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.3530 loss: 2.1348 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.1348 2023/03/17 18:51:13 - mmengine - INFO - Epoch(train) [14][1160/1320] lr: 2.0000e-02 eta: 4:27:03 time: 0.3365 data_time: 0.0136 memory: 18752 grad_norm: 4.4543 loss: 1.9449 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9449 2023/03/17 18:51:20 - mmengine - INFO - Epoch(train) [14][1180/1320] lr: 2.0000e-02 eta: 4:26:56 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.5607 loss: 2.0118 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0118 2023/03/17 18:51:27 - mmengine - INFO - Epoch(train) [14][1200/1320] lr: 2.0000e-02 eta: 4:26:50 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 4.5451 loss: 2.1251 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1251 2023/03/17 18:51:33 - mmengine - INFO - Epoch(train) [14][1220/1320] lr: 2.0000e-02 eta: 4:26:43 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.6714 loss: 1.9793 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.9793 2023/03/17 18:51:40 - mmengine - INFO - Epoch(train) [14][1240/1320] lr: 2.0000e-02 eta: 4:26:36 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5312 loss: 1.9881 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9881 2023/03/17 18:51:47 - mmengine - INFO - Epoch(train) [14][1260/1320] lr: 2.0000e-02 eta: 4:26:30 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 4.6590 loss: 1.9201 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9201 2023/03/17 18:51:53 - mmengine - INFO - Epoch(train) [14][1280/1320] lr: 2.0000e-02 eta: 4:26:23 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.4759 loss: 2.0769 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0769 2023/03/17 18:52:00 - mmengine - INFO - Epoch(train) [14][1300/1320] lr: 2.0000e-02 eta: 4:26:16 time: 0.3364 data_time: 0.0126 memory: 18752 grad_norm: 4.4621 loss: 1.9564 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9564 2023/03/17 18:52:07 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:52:07 - mmengine - INFO - Epoch(train) [14][1320/1320] lr: 2.0000e-02 eta: 4:26:09 time: 0.3305 data_time: 0.0119 memory: 18752 grad_norm: 4.5569 loss: 2.2546 top1_acc: 0.0909 top5_acc: 0.5455 loss_cls: 2.2546 2023/03/17 18:52:09 - mmengine - INFO - Epoch(val) [14][ 20/194] eta: 0:00:22 time: 0.1269 data_time: 0.0407 memory: 2112 2023/03/17 18:52:11 - mmengine - INFO - Epoch(val) [14][ 40/194] eta: 0:00:17 time: 0.0969 data_time: 0.0108 memory: 2112 2023/03/17 18:52:13 - mmengine - INFO - Epoch(val) [14][ 60/194] eta: 0:00:14 time: 0.0962 data_time: 0.0102 memory: 2112 2023/03/17 18:52:15 - mmengine - INFO - Epoch(val) [14][ 80/194] eta: 0:00:11 time: 0.0964 data_time: 0.0105 memory: 2112 2023/03/17 18:52:17 - mmengine - INFO - Epoch(val) [14][100/194] eta: 0:00:09 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 18:52:19 - mmengine - INFO - Epoch(val) [14][120/194] eta: 0:00:07 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 18:52:21 - mmengine - INFO - Epoch(val) [14][140/194] eta: 0:00:05 time: 0.0969 data_time: 0.0112 memory: 2112 2023/03/17 18:52:23 - mmengine - INFO - Epoch(val) [14][160/194] eta: 0:00:03 time: 0.0961 data_time: 0.0101 memory: 2112 2023/03/17 18:52:25 - mmengine - INFO - Epoch(val) [14][180/194] eta: 0:00:01 time: 0.0969 data_time: 0.0112 memory: 2112 2023/03/17 18:52:28 - mmengine - INFO - Epoch(val) [14][194/194] acc/top1: 0.4550 acc/top5: 0.7525 acc/mean1: 0.3771 2023/03/17 18:52:35 - mmengine - INFO - Epoch(train) [15][ 20/1320] lr: 2.0000e-02 eta: 4:26:04 time: 0.3706 data_time: 0.0415 memory: 18752 grad_norm: 4.3780 loss: 2.1918 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1918 2023/03/17 18:52:42 - mmengine - INFO - Epoch(train) [15][ 40/1320] lr: 2.0000e-02 eta: 4:25:57 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.3831 loss: 2.0318 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.0318 2023/03/17 18:52:49 - mmengine - INFO - Epoch(train) [15][ 60/1320] lr: 2.0000e-02 eta: 4:25:51 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4112 loss: 1.8454 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.8454 2023/03/17 18:52:56 - mmengine - INFO - Epoch(train) [15][ 80/1320] lr: 2.0000e-02 eta: 4:25:44 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 4.4318 loss: 1.9593 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9593 2023/03/17 18:53:02 - mmengine - INFO - Epoch(train) [15][ 100/1320] lr: 2.0000e-02 eta: 4:25:37 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.6331 loss: 2.0403 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0403 2023/03/17 18:53:09 - mmengine - INFO - Epoch(train) [15][ 120/1320] lr: 2.0000e-02 eta: 4:25:30 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.4778 loss: 1.9364 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9364 2023/03/17 18:53:16 - mmengine - INFO - Epoch(train) [15][ 140/1320] lr: 2.0000e-02 eta: 4:25:24 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5951 loss: 1.8340 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8340 2023/03/17 18:53:22 - mmengine - INFO - Epoch(train) [15][ 160/1320] lr: 2.0000e-02 eta: 4:25:17 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 4.5294 loss: 1.8821 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8821 2023/03/17 18:53:29 - mmengine - INFO - Epoch(train) [15][ 180/1320] lr: 2.0000e-02 eta: 4:25:10 time: 0.3350 data_time: 0.0112 memory: 18752 grad_norm: 4.4172 loss: 1.8777 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8777 2023/03/17 18:53:36 - mmengine - INFO - Epoch(train) [15][ 200/1320] lr: 2.0000e-02 eta: 4:25:03 time: 0.3350 data_time: 0.0120 memory: 18752 grad_norm: 4.6240 loss: 1.7577 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7577 2023/03/17 18:53:43 - mmengine - INFO - Epoch(train) [15][ 220/1320] lr: 2.0000e-02 eta: 4:24:57 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5696 loss: 2.0652 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0652 2023/03/17 18:53:49 - mmengine - INFO - Epoch(train) [15][ 240/1320] lr: 2.0000e-02 eta: 4:24:50 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 4.5761 loss: 1.9958 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9958 2023/03/17 18:53:56 - mmengine - INFO - Epoch(train) [15][ 260/1320] lr: 2.0000e-02 eta: 4:24:43 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.5616 loss: 2.1837 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1837 2023/03/17 18:54:03 - mmengine - INFO - Epoch(train) [15][ 280/1320] lr: 2.0000e-02 eta: 4:24:36 time: 0.3347 data_time: 0.0115 memory: 18752 grad_norm: 4.5120 loss: 1.8463 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8463 2023/03/17 18:54:09 - mmengine - INFO - Epoch(train) [15][ 300/1320] lr: 2.0000e-02 eta: 4:24:29 time: 0.3347 data_time: 0.0115 memory: 18752 grad_norm: 4.6862 loss: 1.8607 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8607 2023/03/17 18:54:16 - mmengine - INFO - Epoch(train) [15][ 320/1320] lr: 2.0000e-02 eta: 4:24:23 time: 0.3351 data_time: 0.0115 memory: 18752 grad_norm: 4.4893 loss: 2.0246 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0246 2023/03/17 18:54:23 - mmengine - INFO - Epoch(train) [15][ 340/1320] lr: 2.0000e-02 eta: 4:24:16 time: 0.3354 data_time: 0.0110 memory: 18752 grad_norm: 4.6485 loss: 1.9923 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9923 2023/03/17 18:54:29 - mmengine - INFO - Epoch(train) [15][ 360/1320] lr: 2.0000e-02 eta: 4:24:09 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.4381 loss: 1.9513 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9513 2023/03/17 18:54:36 - mmengine - INFO - Epoch(train) [15][ 380/1320] lr: 2.0000e-02 eta: 4:24:02 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.5353 loss: 1.9353 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9353 2023/03/17 18:54:43 - mmengine - INFO - Epoch(train) [15][ 400/1320] lr: 2.0000e-02 eta: 4:23:56 time: 0.3345 data_time: 0.0115 memory: 18752 grad_norm: 4.4503 loss: 1.8753 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8753 2023/03/17 18:54:50 - mmengine - INFO - Epoch(train) [15][ 420/1320] lr: 2.0000e-02 eta: 4:23:49 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.5616 loss: 1.9697 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9697 2023/03/17 18:54:56 - mmengine - INFO - Epoch(train) [15][ 440/1320] lr: 2.0000e-02 eta: 4:23:42 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.4993 loss: 1.8355 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8355 2023/03/17 18:55:03 - mmengine - INFO - Epoch(train) [15][ 460/1320] lr: 2.0000e-02 eta: 4:23:35 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.4565 loss: 2.0779 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0779 2023/03/17 18:55:10 - mmengine - INFO - Epoch(train) [15][ 480/1320] lr: 2.0000e-02 eta: 4:23:28 time: 0.3348 data_time: 0.0114 memory: 18752 grad_norm: 4.4532 loss: 2.1930 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1930 2023/03/17 18:55:16 - mmengine - INFO - Epoch(train) [15][ 500/1320] lr: 2.0000e-02 eta: 4:23:22 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.4574 loss: 2.0233 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0233 2023/03/17 18:55:23 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:55:23 - mmengine - INFO - Epoch(train) [15][ 520/1320] lr: 2.0000e-02 eta: 4:23:15 time: 0.3394 data_time: 0.0118 memory: 18752 grad_norm: 4.6483 loss: 1.9642 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9642 2023/03/17 18:55:30 - mmengine - INFO - Epoch(train) [15][ 540/1320] lr: 2.0000e-02 eta: 4:23:08 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5406 loss: 2.1574 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1574 2023/03/17 18:55:37 - mmengine - INFO - Epoch(train) [15][ 560/1320] lr: 2.0000e-02 eta: 4:23:02 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.3960 loss: 1.9461 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9461 2023/03/17 18:55:43 - mmengine - INFO - Epoch(train) [15][ 580/1320] lr: 2.0000e-02 eta: 4:22:55 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.4801 loss: 2.0652 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0652 2023/03/17 18:55:50 - mmengine - INFO - Epoch(train) [15][ 600/1320] lr: 2.0000e-02 eta: 4:22:48 time: 0.3364 data_time: 0.0126 memory: 18752 grad_norm: 4.4389 loss: 1.8970 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8970 2023/03/17 18:55:57 - mmengine - INFO - Epoch(train) [15][ 620/1320] lr: 2.0000e-02 eta: 4:22:41 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.5283 loss: 2.0176 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0176 2023/03/17 18:56:03 - mmengine - INFO - Epoch(train) [15][ 640/1320] lr: 2.0000e-02 eta: 4:22:35 time: 0.3364 data_time: 0.0119 memory: 18752 grad_norm: 4.6001 loss: 1.9548 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9548 2023/03/17 18:56:10 - mmengine - INFO - Epoch(train) [15][ 660/1320] lr: 2.0000e-02 eta: 4:22:28 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 4.4934 loss: 1.7903 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7903 2023/03/17 18:56:17 - mmengine - INFO - Epoch(train) [15][ 680/1320] lr: 2.0000e-02 eta: 4:22:21 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 4.6245 loss: 1.9602 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9602 2023/03/17 18:56:24 - mmengine - INFO - Epoch(train) [15][ 700/1320] lr: 2.0000e-02 eta: 4:22:15 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.4981 loss: 1.8462 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.8462 2023/03/17 18:56:30 - mmengine - INFO - Epoch(train) [15][ 720/1320] lr: 2.0000e-02 eta: 4:22:08 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.6394 loss: 2.0169 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0169 2023/03/17 18:56:37 - mmengine - INFO - Epoch(train) [15][ 740/1320] lr: 2.0000e-02 eta: 4:22:01 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.5725 loss: 2.0095 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 2.0095 2023/03/17 18:56:44 - mmengine - INFO - Epoch(train) [15][ 760/1320] lr: 2.0000e-02 eta: 4:21:54 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.4788 loss: 2.1785 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.1785 2023/03/17 18:56:50 - mmengine - INFO - Epoch(train) [15][ 780/1320] lr: 2.0000e-02 eta: 4:21:48 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.3507 loss: 2.0159 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0159 2023/03/17 18:56:57 - mmengine - INFO - Epoch(train) [15][ 800/1320] lr: 2.0000e-02 eta: 4:21:41 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 4.4556 loss: 2.0158 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0158 2023/03/17 18:57:04 - mmengine - INFO - Epoch(train) [15][ 820/1320] lr: 2.0000e-02 eta: 4:21:34 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.4606 loss: 1.9269 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9269 2023/03/17 18:57:11 - mmengine - INFO - Epoch(train) [15][ 840/1320] lr: 2.0000e-02 eta: 4:21:27 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.5204 loss: 2.1268 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1268 2023/03/17 18:57:17 - mmengine - INFO - Epoch(train) [15][ 860/1320] lr: 2.0000e-02 eta: 4:21:21 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.4560 loss: 2.1324 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1324 2023/03/17 18:57:24 - mmengine - INFO - Epoch(train) [15][ 880/1320] lr: 2.0000e-02 eta: 4:21:14 time: 0.3370 data_time: 0.0116 memory: 18752 grad_norm: 4.5498 loss: 1.9610 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9610 2023/03/17 18:57:31 - mmengine - INFO - Epoch(train) [15][ 900/1320] lr: 2.0000e-02 eta: 4:21:07 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.5657 loss: 2.1503 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1503 2023/03/17 18:57:37 - mmengine - INFO - Epoch(train) [15][ 920/1320] lr: 2.0000e-02 eta: 4:21:00 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.6539 loss: 2.0036 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0036 2023/03/17 18:57:44 - mmengine - INFO - Epoch(train) [15][ 940/1320] lr: 2.0000e-02 eta: 4:20:54 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 4.7073 loss: 2.0814 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 2.0814 2023/03/17 18:57:51 - mmengine - INFO - Epoch(train) [15][ 960/1320] lr: 2.0000e-02 eta: 4:20:47 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5995 loss: 1.8131 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8131 2023/03/17 18:57:58 - mmengine - INFO - Epoch(train) [15][ 980/1320] lr: 2.0000e-02 eta: 4:20:40 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 4.6309 loss: 2.1050 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1050 2023/03/17 18:58:04 - mmengine - INFO - Epoch(train) [15][1000/1320] lr: 2.0000e-02 eta: 4:20:34 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 4.4956 loss: 2.1391 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1391 2023/03/17 18:58:11 - mmengine - INFO - Epoch(train) [15][1020/1320] lr: 2.0000e-02 eta: 4:20:27 time: 0.3367 data_time: 0.0121 memory: 18752 grad_norm: 4.4472 loss: 1.9191 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9191 2023/03/17 18:58:18 - mmengine - INFO - Epoch(train) [15][1040/1320] lr: 2.0000e-02 eta: 4:20:20 time: 0.3359 data_time: 0.0117 memory: 18752 grad_norm: 4.5468 loss: 2.2625 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2625 2023/03/17 18:58:25 - mmengine - INFO - Epoch(train) [15][1060/1320] lr: 2.0000e-02 eta: 4:20:13 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 4.5296 loss: 2.0027 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0027 2023/03/17 18:58:31 - mmengine - INFO - Epoch(train) [15][1080/1320] lr: 2.0000e-02 eta: 4:20:07 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.4090 loss: 1.8912 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8912 2023/03/17 18:58:38 - mmengine - INFO - Epoch(train) [15][1100/1320] lr: 2.0000e-02 eta: 4:20:00 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.5924 loss: 1.8244 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8244 2023/03/17 18:58:45 - mmengine - INFO - Epoch(train) [15][1120/1320] lr: 2.0000e-02 eta: 4:19:53 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.5937 loss: 1.9412 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9412 2023/03/17 18:58:51 - mmengine - INFO - Epoch(train) [15][1140/1320] lr: 2.0000e-02 eta: 4:19:46 time: 0.3355 data_time: 0.0113 memory: 18752 grad_norm: 4.5046 loss: 1.8653 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8653 2023/03/17 18:58:58 - mmengine - INFO - Epoch(train) [15][1160/1320] lr: 2.0000e-02 eta: 4:19:40 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.3059 loss: 1.8132 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8132 2023/03/17 18:59:05 - mmengine - INFO - Epoch(train) [15][1180/1320] lr: 2.0000e-02 eta: 4:19:33 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 4.6115 loss: 2.0639 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0639 2023/03/17 18:59:12 - mmengine - INFO - Epoch(train) [15][1200/1320] lr: 2.0000e-02 eta: 4:19:26 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.4403 loss: 1.7942 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7942 2023/03/17 18:59:18 - mmengine - INFO - Epoch(train) [15][1220/1320] lr: 2.0000e-02 eta: 4:19:19 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.4668 loss: 2.0563 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0563 2023/03/17 18:59:25 - mmengine - INFO - Epoch(train) [15][1240/1320] lr: 2.0000e-02 eta: 4:19:13 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.5668 loss: 1.9365 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9365 2023/03/17 18:59:32 - mmengine - INFO - Epoch(train) [15][1260/1320] lr: 2.0000e-02 eta: 4:19:06 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.3806 loss: 1.9756 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9756 2023/03/17 18:59:38 - mmengine - INFO - Epoch(train) [15][1280/1320] lr: 2.0000e-02 eta: 4:18:59 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.5617 loss: 2.0364 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0364 2023/03/17 18:59:45 - mmengine - INFO - Epoch(train) [15][1300/1320] lr: 2.0000e-02 eta: 4:18:53 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 4.4016 loss: 2.0014 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0014 2023/03/17 18:59:52 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 18:59:52 - mmengine - INFO - Epoch(train) [15][1320/1320] lr: 2.0000e-02 eta: 4:18:46 time: 0.3306 data_time: 0.0119 memory: 18752 grad_norm: 4.5035 loss: 2.1391 top1_acc: 0.5455 top5_acc: 0.7273 loss_cls: 2.1391 2023/03/17 18:59:52 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/03/17 18:59:57 - mmengine - INFO - Epoch(val) [15][ 20/194] eta: 0:00:22 time: 0.1288 data_time: 0.0418 memory: 2112 2023/03/17 18:59:59 - mmengine - INFO - Epoch(val) [15][ 40/194] eta: 0:00:17 time: 0.0996 data_time: 0.0137 memory: 2112 2023/03/17 19:00:01 - mmengine - INFO - Epoch(val) [15][ 60/194] eta: 0:00:14 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 19:00:03 - mmengine - INFO - Epoch(val) [15][ 80/194] eta: 0:00:12 time: 0.0973 data_time: 0.0109 memory: 2112 2023/03/17 19:00:05 - mmengine - INFO - Epoch(val) [15][100/194] eta: 0:00:09 time: 0.0961 data_time: 0.0103 memory: 2112 2023/03/17 19:00:07 - mmengine - INFO - Epoch(val) [15][120/194] eta: 0:00:07 time: 0.0966 data_time: 0.0105 memory: 2112 2023/03/17 19:00:09 - mmengine - INFO - Epoch(val) [15][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0108 memory: 2112 2023/03/17 19:00:11 - mmengine - INFO - Epoch(val) [15][160/194] eta: 0:00:03 time: 0.0979 data_time: 0.0117 memory: 2112 2023/03/17 19:00:13 - mmengine - INFO - Epoch(val) [15][180/194] eta: 0:00:01 time: 0.0963 data_time: 0.0101 memory: 2112 2023/03/17 19:00:16 - mmengine - INFO - Epoch(val) [15][194/194] acc/top1: 0.4561 acc/top5: 0.7476 acc/mean1: 0.3863 2023/03/17 19:00:23 - mmengine - INFO - Epoch(train) [16][ 20/1320] lr: 2.0000e-02 eta: 4:18:41 time: 0.3723 data_time: 0.0406 memory: 18752 grad_norm: 4.3713 loss: 1.9166 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9166 2023/03/17 19:00:30 - mmengine - INFO - Epoch(train) [16][ 40/1320] lr: 2.0000e-02 eta: 4:18:34 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 4.4808 loss: 1.8536 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8536 2023/03/17 19:00:37 - mmengine - INFO - Epoch(train) [16][ 60/1320] lr: 2.0000e-02 eta: 4:18:27 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 4.6487 loss: 2.1181 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1181 2023/03/17 19:00:43 - mmengine - INFO - Epoch(train) [16][ 80/1320] lr: 2.0000e-02 eta: 4:18:20 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 4.4187 loss: 1.9875 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.9875 2023/03/17 19:00:50 - mmengine - INFO - Epoch(train) [16][ 100/1320] lr: 2.0000e-02 eta: 4:18:14 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.3289 loss: 1.9717 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9717 2023/03/17 19:00:57 - mmengine - INFO - Epoch(train) [16][ 120/1320] lr: 2.0000e-02 eta: 4:18:07 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.3399 loss: 2.0219 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0219 2023/03/17 19:01:04 - mmengine - INFO - Epoch(train) [16][ 140/1320] lr: 2.0000e-02 eta: 4:18:00 time: 0.3353 data_time: 0.0114 memory: 18752 grad_norm: 4.4676 loss: 1.8823 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.8823 2023/03/17 19:01:10 - mmengine - INFO - Epoch(train) [16][ 160/1320] lr: 2.0000e-02 eta: 4:17:53 time: 0.3347 data_time: 0.0118 memory: 18752 grad_norm: 4.5895 loss: 1.9619 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9619 2023/03/17 19:01:17 - mmengine - INFO - Epoch(train) [16][ 180/1320] lr: 2.0000e-02 eta: 4:17:47 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.4800 loss: 1.9658 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9658 2023/03/17 19:01:24 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:01:24 - mmengine - INFO - Epoch(train) [16][ 200/1320] lr: 2.0000e-02 eta: 4:17:40 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.4552 loss: 1.8632 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8632 2023/03/17 19:01:30 - mmengine - INFO - Epoch(train) [16][ 220/1320] lr: 2.0000e-02 eta: 4:17:33 time: 0.3355 data_time: 0.0126 memory: 18752 grad_norm: 4.5686 loss: 2.1370 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1370 2023/03/17 19:01:37 - mmengine - INFO - Epoch(train) [16][ 240/1320] lr: 2.0000e-02 eta: 4:17:26 time: 0.3351 data_time: 0.0126 memory: 18752 grad_norm: 4.6403 loss: 2.0499 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0499 2023/03/17 19:01:44 - mmengine - INFO - Epoch(train) [16][ 260/1320] lr: 2.0000e-02 eta: 4:17:20 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.4561 loss: 2.0838 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0838 2023/03/17 19:01:50 - mmengine - INFO - Epoch(train) [16][ 280/1320] lr: 2.0000e-02 eta: 4:17:13 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 4.5004 loss: 1.9785 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9785 2023/03/17 19:01:57 - mmengine - INFO - Epoch(train) [16][ 300/1320] lr: 2.0000e-02 eta: 4:17:06 time: 0.3352 data_time: 0.0115 memory: 18752 grad_norm: 4.4996 loss: 1.9523 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9523 2023/03/17 19:02:04 - mmengine - INFO - Epoch(train) [16][ 320/1320] lr: 2.0000e-02 eta: 4:16:59 time: 0.3356 data_time: 0.0125 memory: 18752 grad_norm: 4.5408 loss: 1.8331 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8331 2023/03/17 19:02:11 - mmengine - INFO - Epoch(train) [16][ 340/1320] lr: 2.0000e-02 eta: 4:16:53 time: 0.3351 data_time: 0.0122 memory: 18752 grad_norm: 4.4840 loss: 1.8651 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8651 2023/03/17 19:02:17 - mmengine - INFO - Epoch(train) [16][ 360/1320] lr: 2.0000e-02 eta: 4:16:46 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 4.5700 loss: 1.9666 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9666 2023/03/17 19:02:24 - mmengine - INFO - Epoch(train) [16][ 380/1320] lr: 2.0000e-02 eta: 4:16:39 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 4.5975 loss: 2.1060 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1060 2023/03/17 19:02:31 - mmengine - INFO - Epoch(train) [16][ 400/1320] lr: 2.0000e-02 eta: 4:16:32 time: 0.3352 data_time: 0.0113 memory: 18752 grad_norm: 4.5933 loss: 1.8414 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8414 2023/03/17 19:02:37 - mmengine - INFO - Epoch(train) [16][ 420/1320] lr: 2.0000e-02 eta: 4:16:26 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.5887 loss: 1.8809 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8809 2023/03/17 19:02:44 - mmengine - INFO - Epoch(train) [16][ 440/1320] lr: 2.0000e-02 eta: 4:16:19 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.6252 loss: 1.9965 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9965 2023/03/17 19:02:51 - mmengine - INFO - Epoch(train) [16][ 460/1320] lr: 2.0000e-02 eta: 4:16:12 time: 0.3352 data_time: 0.0113 memory: 18752 grad_norm: 4.5218 loss: 1.8577 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8577 2023/03/17 19:02:58 - mmengine - INFO - Epoch(train) [16][ 480/1320] lr: 2.0000e-02 eta: 4:16:05 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.5007 loss: 2.0878 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0878 2023/03/17 19:03:04 - mmengine - INFO - Epoch(train) [16][ 500/1320] lr: 2.0000e-02 eta: 4:15:59 time: 0.3358 data_time: 0.0116 memory: 18752 grad_norm: 4.5657 loss: 2.1033 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1033 2023/03/17 19:03:11 - mmengine - INFO - Epoch(train) [16][ 520/1320] lr: 2.0000e-02 eta: 4:15:52 time: 0.3353 data_time: 0.0112 memory: 18752 grad_norm: 4.5023 loss: 2.0090 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0090 2023/03/17 19:03:18 - mmengine - INFO - Epoch(train) [16][ 540/1320] lr: 2.0000e-02 eta: 4:15:45 time: 0.3357 data_time: 0.0114 memory: 18752 grad_norm: 4.5143 loss: 1.7566 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 1.7566 2023/03/17 19:03:24 - mmengine - INFO - Epoch(train) [16][ 560/1320] lr: 2.0000e-02 eta: 4:15:38 time: 0.3358 data_time: 0.0116 memory: 18752 grad_norm: 4.5795 loss: 2.0973 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0973 2023/03/17 19:03:31 - mmengine - INFO - Epoch(train) [16][ 580/1320] lr: 2.0000e-02 eta: 4:15:32 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.4990 loss: 2.0266 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0266 2023/03/17 19:03:38 - mmengine - INFO - Epoch(train) [16][ 600/1320] lr: 2.0000e-02 eta: 4:15:25 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.4619 loss: 1.7963 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7963 2023/03/17 19:03:45 - mmengine - INFO - Epoch(train) [16][ 620/1320] lr: 2.0000e-02 eta: 4:15:18 time: 0.3365 data_time: 0.0115 memory: 18752 grad_norm: 4.6175 loss: 2.0146 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0146 2023/03/17 19:03:51 - mmengine - INFO - Epoch(train) [16][ 640/1320] lr: 2.0000e-02 eta: 4:15:11 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5737 loss: 1.9201 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9201 2023/03/17 19:03:58 - mmengine - INFO - Epoch(train) [16][ 660/1320] lr: 2.0000e-02 eta: 4:15:05 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 4.7346 loss: 1.8220 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.8220 2023/03/17 19:04:05 - mmengine - INFO - Epoch(train) [16][ 680/1320] lr: 2.0000e-02 eta: 4:14:58 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.4922 loss: 1.8382 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.8382 2023/03/17 19:04:11 - mmengine - INFO - Epoch(train) [16][ 700/1320] lr: 2.0000e-02 eta: 4:14:51 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.4635 loss: 1.8764 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8764 2023/03/17 19:04:18 - mmengine - INFO - Epoch(train) [16][ 720/1320] lr: 2.0000e-02 eta: 4:14:44 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.5847 loss: 2.0047 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0047 2023/03/17 19:04:25 - mmengine - INFO - Epoch(train) [16][ 740/1320] lr: 2.0000e-02 eta: 4:14:38 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.4458 loss: 1.8920 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8920 2023/03/17 19:04:32 - mmengine - INFO - Epoch(train) [16][ 760/1320] lr: 2.0000e-02 eta: 4:14:31 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.5277 loss: 1.8919 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8919 2023/03/17 19:04:38 - mmengine - INFO - Epoch(train) [16][ 780/1320] lr: 2.0000e-02 eta: 4:14:24 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5060 loss: 1.9431 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.9431 2023/03/17 19:04:45 - mmengine - INFO - Epoch(train) [16][ 800/1320] lr: 2.0000e-02 eta: 4:14:17 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.7120 loss: 2.1216 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.1216 2023/03/17 19:04:52 - mmengine - INFO - Epoch(train) [16][ 820/1320] lr: 2.0000e-02 eta: 4:14:11 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 4.4386 loss: 1.7409 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7409 2023/03/17 19:04:58 - mmengine - INFO - Epoch(train) [16][ 840/1320] lr: 2.0000e-02 eta: 4:14:04 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.3599 loss: 1.9894 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9894 2023/03/17 19:05:05 - mmengine - INFO - Epoch(train) [16][ 860/1320] lr: 2.0000e-02 eta: 4:13:57 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5349 loss: 1.8796 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8796 2023/03/17 19:05:12 - mmengine - INFO - Epoch(train) [16][ 880/1320] lr: 2.0000e-02 eta: 4:13:50 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.5311 loss: 1.9148 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9148 2023/03/17 19:05:19 - mmengine - INFO - Epoch(train) [16][ 900/1320] lr: 2.0000e-02 eta: 4:13:44 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.5774 loss: 2.1155 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1155 2023/03/17 19:05:25 - mmengine - INFO - Epoch(train) [16][ 920/1320] lr: 2.0000e-02 eta: 4:13:37 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.4341 loss: 1.9923 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9923 2023/03/17 19:05:32 - mmengine - INFO - Epoch(train) [16][ 940/1320] lr: 2.0000e-02 eta: 4:13:30 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.5321 loss: 1.8898 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8898 2023/03/17 19:05:39 - mmengine - INFO - Epoch(train) [16][ 960/1320] lr: 2.0000e-02 eta: 4:13:24 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 4.6080 loss: 2.0096 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0096 2023/03/17 19:05:45 - mmengine - INFO - Epoch(train) [16][ 980/1320] lr: 2.0000e-02 eta: 4:13:17 time: 0.3366 data_time: 0.0133 memory: 18752 grad_norm: 4.5583 loss: 1.9275 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9275 2023/03/17 19:05:52 - mmengine - INFO - Epoch(train) [16][1000/1320] lr: 2.0000e-02 eta: 4:13:10 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5581 loss: 1.9053 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9053 2023/03/17 19:05:59 - mmengine - INFO - Epoch(train) [16][1020/1320] lr: 2.0000e-02 eta: 4:13:03 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.5546 loss: 1.8364 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8364 2023/03/17 19:06:06 - mmengine - INFO - Epoch(train) [16][1040/1320] lr: 2.0000e-02 eta: 4:12:57 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5463 loss: 1.8552 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8552 2023/03/17 19:06:12 - mmengine - INFO - Epoch(train) [16][1060/1320] lr: 2.0000e-02 eta: 4:12:50 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 4.3977 loss: 1.8315 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8315 2023/03/17 19:06:19 - mmengine - INFO - Epoch(train) [16][1080/1320] lr: 2.0000e-02 eta: 4:12:43 time: 0.3369 data_time: 0.0120 memory: 18752 grad_norm: 4.4697 loss: 1.8665 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.8665 2023/03/17 19:06:26 - mmengine - INFO - Epoch(train) [16][1100/1320] lr: 2.0000e-02 eta: 4:12:36 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 4.5210 loss: 1.8125 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8125 2023/03/17 19:06:32 - mmengine - INFO - Epoch(train) [16][1120/1320] lr: 2.0000e-02 eta: 4:12:30 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 4.5079 loss: 1.9648 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9648 2023/03/17 19:06:39 - mmengine - INFO - Epoch(train) [16][1140/1320] lr: 2.0000e-02 eta: 4:12:23 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.5063 loss: 1.9985 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9985 2023/03/17 19:06:46 - mmengine - INFO - Epoch(train) [16][1160/1320] lr: 2.0000e-02 eta: 4:12:16 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5585 loss: 1.9167 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9167 2023/03/17 19:06:53 - mmengine - INFO - Epoch(train) [16][1180/1320] lr: 2.0000e-02 eta: 4:12:10 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.7530 loss: 1.9613 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.9613 2023/03/17 19:06:59 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:06:59 - mmengine - INFO - Epoch(train) [16][1200/1320] lr: 2.0000e-02 eta: 4:12:03 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5278 loss: 2.0074 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0074 2023/03/17 19:07:06 - mmengine - INFO - Epoch(train) [16][1220/1320] lr: 2.0000e-02 eta: 4:11:56 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5325 loss: 2.0371 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0371 2023/03/17 19:07:13 - mmengine - INFO - Epoch(train) [16][1240/1320] lr: 2.0000e-02 eta: 4:11:49 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.4489 loss: 2.0414 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0414 2023/03/17 19:07:19 - mmengine - INFO - Epoch(train) [16][1260/1320] lr: 2.0000e-02 eta: 4:11:43 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 4.5596 loss: 2.0374 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0374 2023/03/17 19:07:26 - mmengine - INFO - Epoch(train) [16][1280/1320] lr: 2.0000e-02 eta: 4:11:36 time: 0.3359 data_time: 0.0129 memory: 18752 grad_norm: 4.5127 loss: 1.8348 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8348 2023/03/17 19:07:33 - mmengine - INFO - Epoch(train) [16][1300/1320] lr: 2.0000e-02 eta: 4:11:29 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 4.5579 loss: 2.1680 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1680 2023/03/17 19:07:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:07:39 - mmengine - INFO - Epoch(train) [16][1320/1320] lr: 2.0000e-02 eta: 4:11:22 time: 0.3302 data_time: 0.0120 memory: 18752 grad_norm: 4.5380 loss: 2.1153 top1_acc: 0.4545 top5_acc: 0.5455 loss_cls: 2.1153 2023/03/17 19:07:42 - mmengine - INFO - Epoch(val) [16][ 20/194] eta: 0:00:22 time: 0.1268 data_time: 0.0405 memory: 2112 2023/03/17 19:07:44 - mmengine - INFO - Epoch(val) [16][ 40/194] eta: 0:00:17 time: 0.0959 data_time: 0.0098 memory: 2112 2023/03/17 19:07:46 - mmengine - INFO - Epoch(val) [16][ 60/194] eta: 0:00:14 time: 0.0973 data_time: 0.0113 memory: 2112 2023/03/17 19:07:48 - mmengine - INFO - Epoch(val) [16][ 80/194] eta: 0:00:11 time: 0.0973 data_time: 0.0113 memory: 2112 2023/03/17 19:07:50 - mmengine - INFO - Epoch(val) [16][100/194] eta: 0:00:09 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 19:07:52 - mmengine - INFO - Epoch(val) [16][120/194] eta: 0:00:07 time: 0.0959 data_time: 0.0101 memory: 2112 2023/03/17 19:07:54 - mmengine - INFO - Epoch(val) [16][140/194] eta: 0:00:05 time: 0.0980 data_time: 0.0119 memory: 2112 2023/03/17 19:07:56 - mmengine - INFO - Epoch(val) [16][160/194] eta: 0:00:03 time: 0.0964 data_time: 0.0104 memory: 2112 2023/03/17 19:07:58 - mmengine - INFO - Epoch(val) [16][180/194] eta: 0:00:01 time: 0.0967 data_time: 0.0108 memory: 2112 2023/03/17 19:08:01 - mmengine - INFO - Epoch(val) [16][194/194] acc/top1: 0.4486 acc/top5: 0.7450 acc/mean1: 0.3892 2023/03/17 19:08:08 - mmengine - INFO - Epoch(train) [17][ 20/1320] lr: 2.0000e-02 eta: 4:11:17 time: 0.3717 data_time: 0.0422 memory: 18752 grad_norm: 4.3147 loss: 1.8622 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8622 2023/03/17 19:08:15 - mmengine - INFO - Epoch(train) [17][ 40/1320] lr: 2.0000e-02 eta: 4:11:10 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.4253 loss: 1.8227 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8227 2023/03/17 19:08:22 - mmengine - INFO - Epoch(train) [17][ 60/1320] lr: 2.0000e-02 eta: 4:11:03 time: 0.3355 data_time: 0.0128 memory: 18752 grad_norm: 4.5530 loss: 1.8767 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8767 2023/03/17 19:08:28 - mmengine - INFO - Epoch(train) [17][ 80/1320] lr: 2.0000e-02 eta: 4:10:57 time: 0.3349 data_time: 0.0118 memory: 18752 grad_norm: 4.4976 loss: 1.7922 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7922 2023/03/17 19:08:35 - mmengine - INFO - Epoch(train) [17][ 100/1320] lr: 2.0000e-02 eta: 4:10:50 time: 0.3348 data_time: 0.0117 memory: 18752 grad_norm: 4.5704 loss: 1.8639 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8639 2023/03/17 19:08:42 - mmengine - INFO - Epoch(train) [17][ 120/1320] lr: 2.0000e-02 eta: 4:10:43 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.4240 loss: 2.0359 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0359 2023/03/17 19:08:48 - mmengine - INFO - Epoch(train) [17][ 140/1320] lr: 2.0000e-02 eta: 4:10:36 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.4900 loss: 1.8294 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8294 2023/03/17 19:08:55 - mmengine - INFO - Epoch(train) [17][ 160/1320] lr: 2.0000e-02 eta: 4:10:30 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.5235 loss: 1.8837 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8837 2023/03/17 19:09:02 - mmengine - INFO - Epoch(train) [17][ 180/1320] lr: 2.0000e-02 eta: 4:10:23 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.5705 loss: 1.9068 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9068 2023/03/17 19:09:09 - mmengine - INFO - Epoch(train) [17][ 200/1320] lr: 2.0000e-02 eta: 4:10:16 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.4966 loss: 1.7703 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.7703 2023/03/17 19:09:15 - mmengine - INFO - Epoch(train) [17][ 220/1320] lr: 2.0000e-02 eta: 4:10:09 time: 0.3356 data_time: 0.0129 memory: 18752 grad_norm: 4.5183 loss: 1.9521 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9521 2023/03/17 19:09:22 - mmengine - INFO - Epoch(train) [17][ 240/1320] lr: 2.0000e-02 eta: 4:10:03 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.4585 loss: 2.0108 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0108 2023/03/17 19:09:29 - mmengine - INFO - Epoch(train) [17][ 260/1320] lr: 2.0000e-02 eta: 4:09:56 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.7385 loss: 2.1060 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1060 2023/03/17 19:09:35 - mmengine - INFO - Epoch(train) [17][ 280/1320] lr: 2.0000e-02 eta: 4:09:49 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.4633 loss: 1.8113 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8113 2023/03/17 19:09:42 - mmengine - INFO - Epoch(train) [17][ 300/1320] lr: 2.0000e-02 eta: 4:09:42 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 4.4903 loss: 2.1851 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1851 2023/03/17 19:09:49 - mmengine - INFO - Epoch(train) [17][ 320/1320] lr: 2.0000e-02 eta: 4:09:36 time: 0.3350 data_time: 0.0116 memory: 18752 grad_norm: 4.3712 loss: 1.7818 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7818 2023/03/17 19:09:56 - mmengine - INFO - Epoch(train) [17][ 340/1320] lr: 2.0000e-02 eta: 4:09:29 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 4.6354 loss: 1.7956 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.7956 2023/03/17 19:10:02 - mmengine - INFO - Epoch(train) [17][ 360/1320] lr: 2.0000e-02 eta: 4:09:22 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.5103 loss: 1.7201 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7201 2023/03/17 19:10:09 - mmengine - INFO - Epoch(train) [17][ 380/1320] lr: 2.0000e-02 eta: 4:09:15 time: 0.3359 data_time: 0.0129 memory: 18752 grad_norm: 4.6218 loss: 2.0477 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0477 2023/03/17 19:10:16 - mmengine - INFO - Epoch(train) [17][ 400/1320] lr: 2.0000e-02 eta: 4:09:09 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.6382 loss: 1.7362 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7362 2023/03/17 19:10:22 - mmengine - INFO - Epoch(train) [17][ 420/1320] lr: 2.0000e-02 eta: 4:09:02 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.6106 loss: 1.9234 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9234 2023/03/17 19:10:29 - mmengine - INFO - Epoch(train) [17][ 440/1320] lr: 2.0000e-02 eta: 4:08:55 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 4.5707 loss: 1.8201 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8201 2023/03/17 19:10:36 - mmengine - INFO - Epoch(train) [17][ 460/1320] lr: 2.0000e-02 eta: 4:08:48 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.6940 loss: 2.1125 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1125 2023/03/17 19:10:43 - mmengine - INFO - Epoch(train) [17][ 480/1320] lr: 2.0000e-02 eta: 4:08:42 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.4458 loss: 1.7923 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7923 2023/03/17 19:10:49 - mmengine - INFO - Epoch(train) [17][ 500/1320] lr: 2.0000e-02 eta: 4:08:35 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 4.4016 loss: 1.7518 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7518 2023/03/17 19:10:56 - mmengine - INFO - Epoch(train) [17][ 520/1320] lr: 2.0000e-02 eta: 4:08:28 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.5168 loss: 1.7406 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7406 2023/03/17 19:11:03 - mmengine - INFO - Epoch(train) [17][ 540/1320] lr: 2.0000e-02 eta: 4:08:21 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5810 loss: 1.9201 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.9201 2023/03/17 19:11:09 - mmengine - INFO - Epoch(train) [17][ 560/1320] lr: 2.0000e-02 eta: 4:08:15 time: 0.3353 data_time: 0.0114 memory: 18752 grad_norm: 4.5455 loss: 1.9185 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9185 2023/03/17 19:11:16 - mmengine - INFO - Epoch(train) [17][ 580/1320] lr: 2.0000e-02 eta: 4:08:08 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.4824 loss: 1.7956 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7956 2023/03/17 19:11:23 - mmengine - INFO - Epoch(train) [17][ 600/1320] lr: 2.0000e-02 eta: 4:08:01 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.4301 loss: 2.0771 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0771 2023/03/17 19:11:30 - mmengine - INFO - Epoch(train) [17][ 620/1320] lr: 2.0000e-02 eta: 4:07:54 time: 0.3346 data_time: 0.0117 memory: 18752 grad_norm: 4.4388 loss: 1.7444 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7444 2023/03/17 19:11:36 - mmengine - INFO - Epoch(train) [17][ 640/1320] lr: 2.0000e-02 eta: 4:07:48 time: 0.3349 data_time: 0.0118 memory: 18752 grad_norm: 4.5167 loss: 1.9562 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9562 2023/03/17 19:11:43 - mmengine - INFO - Epoch(train) [17][ 660/1320] lr: 2.0000e-02 eta: 4:07:41 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.6786 loss: 1.9080 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9080 2023/03/17 19:11:50 - mmengine - INFO - Epoch(train) [17][ 680/1320] lr: 2.0000e-02 eta: 4:07:34 time: 0.3364 data_time: 0.0133 memory: 18752 grad_norm: 4.5368 loss: 1.9826 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9826 2023/03/17 19:11:56 - mmengine - INFO - Epoch(train) [17][ 700/1320] lr: 2.0000e-02 eta: 4:07:27 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 4.5332 loss: 1.9312 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9312 2023/03/17 19:12:03 - mmengine - INFO - Epoch(train) [17][ 720/1320] lr: 2.0000e-02 eta: 4:07:21 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.5299 loss: 1.9450 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9450 2023/03/17 19:12:10 - mmengine - INFO - Epoch(train) [17][ 740/1320] lr: 2.0000e-02 eta: 4:07:14 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.5054 loss: 1.9586 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9586 2023/03/17 19:12:16 - mmengine - INFO - Epoch(train) [17][ 760/1320] lr: 2.0000e-02 eta: 4:07:07 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5520 loss: 1.9585 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.9585 2023/03/17 19:12:23 - mmengine - INFO - Epoch(train) [17][ 780/1320] lr: 2.0000e-02 eta: 4:07:00 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.5883 loss: 2.0610 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0610 2023/03/17 19:12:30 - mmengine - INFO - Epoch(train) [17][ 800/1320] lr: 2.0000e-02 eta: 4:06:54 time: 0.3358 data_time: 0.0128 memory: 18752 grad_norm: 4.5520 loss: 1.8627 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8627 2023/03/17 19:12:37 - mmengine - INFO - Epoch(train) [17][ 820/1320] lr: 2.0000e-02 eta: 4:06:47 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 4.5017 loss: 1.9258 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9258 2023/03/17 19:12:43 - mmengine - INFO - Epoch(train) [17][ 840/1320] lr: 2.0000e-02 eta: 4:06:40 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5512 loss: 1.9426 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9426 2023/03/17 19:12:50 - mmengine - INFO - Epoch(train) [17][ 860/1320] lr: 2.0000e-02 eta: 4:06:33 time: 0.3351 data_time: 0.0130 memory: 18752 grad_norm: 4.5979 loss: 1.9978 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9978 2023/03/17 19:12:57 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:12:57 - mmengine - INFO - Epoch(train) [17][ 880/1320] lr: 2.0000e-02 eta: 4:06:27 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.6008 loss: 1.8453 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.8453 2023/03/17 19:13:03 - mmengine - INFO - Epoch(train) [17][ 900/1320] lr: 2.0000e-02 eta: 4:06:20 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5781 loss: 1.9614 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9614 2023/03/17 19:13:10 - mmengine - INFO - Epoch(train) [17][ 920/1320] lr: 2.0000e-02 eta: 4:06:13 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.6019 loss: 2.0580 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0580 2023/03/17 19:13:17 - mmengine - INFO - Epoch(train) [17][ 940/1320] lr: 2.0000e-02 eta: 4:06:06 time: 0.3349 data_time: 0.0121 memory: 18752 grad_norm: 4.5802 loss: 1.9456 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9456 2023/03/17 19:13:24 - mmengine - INFO - Epoch(train) [17][ 960/1320] lr: 2.0000e-02 eta: 4:06:00 time: 0.3346 data_time: 0.0121 memory: 18752 grad_norm: 4.4796 loss: 1.9577 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9577 2023/03/17 19:13:30 - mmengine - INFO - Epoch(train) [17][ 980/1320] lr: 2.0000e-02 eta: 4:05:53 time: 0.3350 data_time: 0.0127 memory: 18752 grad_norm: 4.5324 loss: 1.9124 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9124 2023/03/17 19:13:37 - mmengine - INFO - Epoch(train) [17][1000/1320] lr: 2.0000e-02 eta: 4:05:46 time: 0.3352 data_time: 0.0125 memory: 18752 grad_norm: 4.5515 loss: 1.9688 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9688 2023/03/17 19:13:44 - mmengine - INFO - Epoch(train) [17][1020/1320] lr: 2.0000e-02 eta: 4:05:39 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.6409 loss: 1.7997 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7997 2023/03/17 19:13:50 - mmengine - INFO - Epoch(train) [17][1040/1320] lr: 2.0000e-02 eta: 4:05:33 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.5393 loss: 1.9415 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9415 2023/03/17 19:13:57 - mmengine - INFO - Epoch(train) [17][1060/1320] lr: 2.0000e-02 eta: 4:05:26 time: 0.3350 data_time: 0.0120 memory: 18752 grad_norm: 4.4859 loss: 1.8592 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8592 2023/03/17 19:14:04 - mmengine - INFO - Epoch(train) [17][1080/1320] lr: 2.0000e-02 eta: 4:05:19 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.6151 loss: 2.1348 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1348 2023/03/17 19:14:11 - mmengine - INFO - Epoch(train) [17][1100/1320] lr: 2.0000e-02 eta: 4:05:12 time: 0.3350 data_time: 0.0120 memory: 18752 grad_norm: 4.3805 loss: 1.7504 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7504 2023/03/17 19:14:17 - mmengine - INFO - Epoch(train) [17][1120/1320] lr: 2.0000e-02 eta: 4:05:06 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.5177 loss: 1.7833 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7833 2023/03/17 19:14:24 - mmengine - INFO - Epoch(train) [17][1140/1320] lr: 2.0000e-02 eta: 4:04:59 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.5562 loss: 1.8822 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8822 2023/03/17 19:14:31 - mmengine - INFO - Epoch(train) [17][1160/1320] lr: 2.0000e-02 eta: 4:04:52 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.5009 loss: 1.9811 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.9811 2023/03/17 19:14:37 - mmengine - INFO - Epoch(train) [17][1180/1320] lr: 2.0000e-02 eta: 4:04:45 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.4378 loss: 2.0044 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0044 2023/03/17 19:14:44 - mmengine - INFO - Epoch(train) [17][1200/1320] lr: 2.0000e-02 eta: 4:04:39 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.3318 loss: 1.8838 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8838 2023/03/17 19:14:51 - mmengine - INFO - Epoch(train) [17][1220/1320] lr: 2.0000e-02 eta: 4:04:32 time: 0.3349 data_time: 0.0118 memory: 18752 grad_norm: 4.4501 loss: 1.7461 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7461 2023/03/17 19:14:57 - mmengine - INFO - Epoch(train) [17][1240/1320] lr: 2.0000e-02 eta: 4:04:25 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5212 loss: 1.7991 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7991 2023/03/17 19:15:04 - mmengine - INFO - Epoch(train) [17][1260/1320] lr: 2.0000e-02 eta: 4:04:18 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.4942 loss: 1.9328 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 1.9328 2023/03/17 19:15:11 - mmengine - INFO - Epoch(train) [17][1280/1320] lr: 2.0000e-02 eta: 4:04:12 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.5090 loss: 1.9184 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9184 2023/03/17 19:15:18 - mmengine - INFO - Epoch(train) [17][1300/1320] lr: 2.0000e-02 eta: 4:04:05 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.5605 loss: 1.7953 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7953 2023/03/17 19:15:24 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:15:24 - mmengine - INFO - Epoch(train) [17][1320/1320] lr: 2.0000e-02 eta: 4:03:58 time: 0.3302 data_time: 0.0116 memory: 18752 grad_norm: 4.5082 loss: 1.8346 top1_acc: 0.4545 top5_acc: 0.9091 loss_cls: 1.8346 2023/03/17 19:15:27 - mmengine - INFO - Epoch(val) [17][ 20/194] eta: 0:00:22 time: 0.1285 data_time: 0.0422 memory: 2112 2023/03/17 19:15:29 - mmengine - INFO - Epoch(val) [17][ 40/194] eta: 0:00:17 time: 0.0963 data_time: 0.0102 memory: 2112 2023/03/17 19:15:31 - mmengine - INFO - Epoch(val) [17][ 60/194] eta: 0:00:14 time: 0.0974 data_time: 0.0116 memory: 2112 2023/03/17 19:15:33 - mmengine - INFO - Epoch(val) [17][ 80/194] eta: 0:00:11 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 19:15:35 - mmengine - INFO - Epoch(val) [17][100/194] eta: 0:00:09 time: 0.0968 data_time: 0.0112 memory: 2112 2023/03/17 19:15:36 - mmengine - INFO - Epoch(val) [17][120/194] eta: 0:00:07 time: 0.0973 data_time: 0.0114 memory: 2112 2023/03/17 19:15:38 - mmengine - INFO - Epoch(val) [17][140/194] eta: 0:00:05 time: 0.0973 data_time: 0.0114 memory: 2112 2023/03/17 19:15:40 - mmengine - INFO - Epoch(val) [17][160/194] eta: 0:00:03 time: 0.0965 data_time: 0.0106 memory: 2112 2023/03/17 19:15:42 - mmengine - INFO - Epoch(val) [17][180/194] eta: 0:00:01 time: 0.0968 data_time: 0.0109 memory: 2112 2023/03/17 19:15:46 - mmengine - INFO - Epoch(val) [17][194/194] acc/top1: 0.4688 acc/top5: 0.7538 acc/mean1: 0.4003 2023/03/17 19:15:46 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_13.pth is removed 2023/03/17 19:15:48 - mmengine - INFO - The best checkpoint with 0.4688 acc/top1 at 17 epoch is saved to best_acc/top1_epoch_17.pth. 2023/03/17 19:15:55 - mmengine - INFO - Epoch(train) [18][ 20/1320] lr: 2.0000e-02 eta: 4:03:52 time: 0.3672 data_time: 0.0377 memory: 18752 grad_norm: 4.4323 loss: 1.8525 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8525 2023/03/17 19:16:02 - mmengine - INFO - Epoch(train) [18][ 40/1320] lr: 2.0000e-02 eta: 4:03:46 time: 0.3367 data_time: 0.0117 memory: 18752 grad_norm: 4.6052 loss: 1.8969 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8969 2023/03/17 19:16:09 - mmengine - INFO - Epoch(train) [18][ 60/1320] lr: 2.0000e-02 eta: 4:03:39 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.5466 loss: 1.8883 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8883 2023/03/17 19:16:15 - mmengine - INFO - Epoch(train) [18][ 80/1320] lr: 2.0000e-02 eta: 4:03:32 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 4.6371 loss: 1.8877 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8877 2023/03/17 19:16:22 - mmengine - INFO - Epoch(train) [18][ 100/1320] lr: 2.0000e-02 eta: 4:03:26 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.4268 loss: 2.0258 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.0258 2023/03/17 19:16:29 - mmengine - INFO - Epoch(train) [18][ 120/1320] lr: 2.0000e-02 eta: 4:03:19 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 4.5628 loss: 1.9426 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9426 2023/03/17 19:16:36 - mmengine - INFO - Epoch(train) [18][ 140/1320] lr: 2.0000e-02 eta: 4:03:12 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.4823 loss: 1.8438 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8438 2023/03/17 19:16:42 - mmengine - INFO - Epoch(train) [18][ 160/1320] lr: 2.0000e-02 eta: 4:03:05 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.4997 loss: 1.9310 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9310 2023/03/17 19:16:49 - mmengine - INFO - Epoch(train) [18][ 180/1320] lr: 2.0000e-02 eta: 4:02:59 time: 0.3362 data_time: 0.0116 memory: 18752 grad_norm: 4.6612 loss: 1.8653 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8653 2023/03/17 19:16:56 - mmengine - INFO - Epoch(train) [18][ 200/1320] lr: 2.0000e-02 eta: 4:02:52 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 4.6510 loss: 1.8223 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8223 2023/03/17 19:17:02 - mmengine - INFO - Epoch(train) [18][ 220/1320] lr: 2.0000e-02 eta: 4:02:45 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 4.6745 loss: 1.6849 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6849 2023/03/17 19:17:09 - mmengine - INFO - Epoch(train) [18][ 240/1320] lr: 2.0000e-02 eta: 4:02:38 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.4858 loss: 2.0670 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0670 2023/03/17 19:17:16 - mmengine - INFO - Epoch(train) [18][ 260/1320] lr: 2.0000e-02 eta: 4:02:32 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 4.4580 loss: 1.8125 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8125 2023/03/17 19:17:23 - mmengine - INFO - Epoch(train) [18][ 280/1320] lr: 2.0000e-02 eta: 4:02:25 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 4.5661 loss: 2.0656 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0656 2023/03/17 19:17:29 - mmengine - INFO - Epoch(train) [18][ 300/1320] lr: 2.0000e-02 eta: 4:02:18 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.4233 loss: 2.0325 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0325 2023/03/17 19:17:36 - mmengine - INFO - Epoch(train) [18][ 320/1320] lr: 2.0000e-02 eta: 4:02:11 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.5720 loss: 1.9246 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9246 2023/03/17 19:17:43 - mmengine - INFO - Epoch(train) [18][ 340/1320] lr: 2.0000e-02 eta: 4:02:05 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.5580 loss: 1.8040 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8040 2023/03/17 19:17:49 - mmengine - INFO - Epoch(train) [18][ 360/1320] lr: 2.0000e-02 eta: 4:01:58 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.5184 loss: 1.8226 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8226 2023/03/17 19:17:56 - mmengine - INFO - Epoch(train) [18][ 380/1320] lr: 2.0000e-02 eta: 4:01:51 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.4854 loss: 2.0173 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0173 2023/03/17 19:18:03 - mmengine - INFO - Epoch(train) [18][ 400/1320] lr: 2.0000e-02 eta: 4:01:44 time: 0.3344 data_time: 0.0120 memory: 18752 grad_norm: 4.5937 loss: 1.9051 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9051 2023/03/17 19:18:09 - mmengine - INFO - Epoch(train) [18][ 420/1320] lr: 2.0000e-02 eta: 4:01:38 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.5103 loss: 1.9678 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9678 2023/03/17 19:18:16 - mmengine - INFO - Epoch(train) [18][ 440/1320] lr: 2.0000e-02 eta: 4:01:31 time: 0.3348 data_time: 0.0119 memory: 18752 grad_norm: 4.4999 loss: 1.7348 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7348 2023/03/17 19:18:23 - mmengine - INFO - Epoch(train) [18][ 460/1320] lr: 2.0000e-02 eta: 4:01:24 time: 0.3348 data_time: 0.0119 memory: 18752 grad_norm: 4.4330 loss: 1.8977 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8977 2023/03/17 19:18:30 - mmengine - INFO - Epoch(train) [18][ 480/1320] lr: 2.0000e-02 eta: 4:01:17 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.5680 loss: 2.1738 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1738 2023/03/17 19:18:36 - mmengine - INFO - Epoch(train) [18][ 500/1320] lr: 2.0000e-02 eta: 4:01:11 time: 0.3350 data_time: 0.0116 memory: 18752 grad_norm: 4.6134 loss: 1.9963 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9963 2023/03/17 19:18:43 - mmengine - INFO - Epoch(train) [18][ 520/1320] lr: 2.0000e-02 eta: 4:01:04 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 4.4072 loss: 1.8676 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.8676 2023/03/17 19:18:50 - mmengine - INFO - Epoch(train) [18][ 540/1320] lr: 2.0000e-02 eta: 4:00:57 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 4.5704 loss: 1.9300 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9300 2023/03/17 19:18:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:18:56 - mmengine - INFO - Epoch(train) [18][ 560/1320] lr: 2.0000e-02 eta: 4:00:50 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.4288 loss: 1.7820 top1_acc: 0.1875 top5_acc: 0.7500 loss_cls: 1.7820 2023/03/17 19:19:03 - mmengine - INFO - Epoch(train) [18][ 580/1320] lr: 2.0000e-02 eta: 4:00:44 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.4605 loss: 1.8299 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8299 2023/03/17 19:19:10 - mmengine - INFO - Epoch(train) [18][ 600/1320] lr: 2.0000e-02 eta: 4:00:37 time: 0.3354 data_time: 0.0125 memory: 18752 grad_norm: 4.3399 loss: 1.8464 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8464 2023/03/17 19:19:17 - mmengine - INFO - Epoch(train) [18][ 620/1320] lr: 2.0000e-02 eta: 4:00:30 time: 0.3349 data_time: 0.0119 memory: 18752 grad_norm: 4.6572 loss: 1.9141 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9141 2023/03/17 19:19:23 - mmengine - INFO - Epoch(train) [18][ 640/1320] lr: 2.0000e-02 eta: 4:00:23 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5455 loss: 1.8587 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8587 2023/03/17 19:19:30 - mmengine - INFO - Epoch(train) [18][ 660/1320] lr: 2.0000e-02 eta: 4:00:17 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.3079 loss: 1.8199 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8199 2023/03/17 19:19:37 - mmengine - INFO - Epoch(train) [18][ 680/1320] lr: 2.0000e-02 eta: 4:00:10 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.5687 loss: 1.9507 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9507 2023/03/17 19:19:43 - mmengine - INFO - Epoch(train) [18][ 700/1320] lr: 2.0000e-02 eta: 4:00:03 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 4.6346 loss: 1.9687 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9687 2023/03/17 19:19:50 - mmengine - INFO - Epoch(train) [18][ 720/1320] lr: 2.0000e-02 eta: 3:59:57 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 4.6001 loss: 1.8209 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8209 2023/03/17 19:19:57 - mmengine - INFO - Epoch(train) [18][ 740/1320] lr: 2.0000e-02 eta: 3:59:50 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.5222 loss: 1.9145 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9145 2023/03/17 19:20:04 - mmengine - INFO - Epoch(train) [18][ 760/1320] lr: 2.0000e-02 eta: 3:59:43 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 4.4551 loss: 1.7605 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7605 2023/03/17 19:20:10 - mmengine - INFO - Epoch(train) [18][ 780/1320] lr: 2.0000e-02 eta: 3:59:36 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.6735 loss: 1.9602 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9602 2023/03/17 19:20:17 - mmengine - INFO - Epoch(train) [18][ 800/1320] lr: 2.0000e-02 eta: 3:59:30 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5475 loss: 2.0210 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.0210 2023/03/17 19:20:24 - mmengine - INFO - Epoch(train) [18][ 820/1320] lr: 2.0000e-02 eta: 3:59:23 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.4773 loss: 1.8611 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8611 2023/03/17 19:20:30 - mmengine - INFO - Epoch(train) [18][ 840/1320] lr: 2.0000e-02 eta: 3:59:16 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.5641 loss: 1.8910 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8910 2023/03/17 19:20:37 - mmengine - INFO - Epoch(train) [18][ 860/1320] lr: 2.0000e-02 eta: 3:59:09 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.5469 loss: 1.8852 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8852 2023/03/17 19:20:44 - mmengine - INFO - Epoch(train) [18][ 880/1320] lr: 2.0000e-02 eta: 3:59:03 time: 0.3362 data_time: 0.0127 memory: 18752 grad_norm: 4.6258 loss: 1.8588 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8588 2023/03/17 19:20:51 - mmengine - INFO - Epoch(train) [18][ 900/1320] lr: 2.0000e-02 eta: 3:58:56 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.5500 loss: 2.0437 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0437 2023/03/17 19:20:57 - mmengine - INFO - Epoch(train) [18][ 920/1320] lr: 2.0000e-02 eta: 3:58:49 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.3140 loss: 1.8838 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8838 2023/03/17 19:21:04 - mmengine - INFO - Epoch(train) [18][ 940/1320] lr: 2.0000e-02 eta: 3:58:42 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.5489 loss: 1.8461 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8461 2023/03/17 19:21:11 - mmengine - INFO - Epoch(train) [18][ 960/1320] lr: 2.0000e-02 eta: 3:58:36 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.5555 loss: 1.7816 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.7816 2023/03/17 19:21:17 - mmengine - INFO - Epoch(train) [18][ 980/1320] lr: 2.0000e-02 eta: 3:58:29 time: 0.3351 data_time: 0.0115 memory: 18752 grad_norm: 4.6485 loss: 1.9506 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9506 2023/03/17 19:21:24 - mmengine - INFO - Epoch(train) [18][1000/1320] lr: 2.0000e-02 eta: 3:58:22 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.5753 loss: 1.9883 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9883 2023/03/17 19:21:31 - mmengine - INFO - Epoch(train) [18][1020/1320] lr: 2.0000e-02 eta: 3:58:15 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 4.4967 loss: 1.9932 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9932 2023/03/17 19:21:38 - mmengine - INFO - Epoch(train) [18][1040/1320] lr: 2.0000e-02 eta: 3:58:09 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.4548 loss: 1.9245 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9245 2023/03/17 19:21:44 - mmengine - INFO - Epoch(train) [18][1060/1320] lr: 2.0000e-02 eta: 3:58:02 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.5976 loss: 2.0860 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.0860 2023/03/17 19:21:51 - mmengine - INFO - Epoch(train) [18][1080/1320] lr: 2.0000e-02 eta: 3:57:55 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.6050 loss: 1.8139 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8139 2023/03/17 19:21:58 - mmengine - INFO - Epoch(train) [18][1100/1320] lr: 2.0000e-02 eta: 3:57:49 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.6284 loss: 1.7042 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 1.7042 2023/03/17 19:22:04 - mmengine - INFO - Epoch(train) [18][1120/1320] lr: 2.0000e-02 eta: 3:57:42 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5362 loss: 2.0963 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0963 2023/03/17 19:22:11 - mmengine - INFO - Epoch(train) [18][1140/1320] lr: 2.0000e-02 eta: 3:57:35 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.3579 loss: 1.9425 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9425 2023/03/17 19:22:18 - mmengine - INFO - Epoch(train) [18][1160/1320] lr: 2.0000e-02 eta: 3:57:28 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.3798 loss: 2.0054 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0054 2023/03/17 19:22:25 - mmengine - INFO - Epoch(train) [18][1180/1320] lr: 2.0000e-02 eta: 3:57:22 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.3461 loss: 1.9369 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9369 2023/03/17 19:22:31 - mmengine - INFO - Epoch(train) [18][1200/1320] lr: 2.0000e-02 eta: 3:57:15 time: 0.3355 data_time: 0.0115 memory: 18752 grad_norm: 4.3479 loss: 1.6584 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6584 2023/03/17 19:22:38 - mmengine - INFO - Epoch(train) [18][1220/1320] lr: 2.0000e-02 eta: 3:57:08 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.4238 loss: 1.9012 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9012 2023/03/17 19:22:45 - mmengine - INFO - Epoch(train) [18][1240/1320] lr: 2.0000e-02 eta: 3:57:01 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.5181 loss: 1.8563 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8563 2023/03/17 19:22:51 - mmengine - INFO - Epoch(train) [18][1260/1320] lr: 2.0000e-02 eta: 3:56:55 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.5701 loss: 2.0960 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0960 2023/03/17 19:22:58 - mmengine - INFO - Epoch(train) [18][1280/1320] lr: 2.0000e-02 eta: 3:56:48 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5751 loss: 1.8610 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8610 2023/03/17 19:23:05 - mmengine - INFO - Epoch(train) [18][1300/1320] lr: 2.0000e-02 eta: 3:56:41 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.3816 loss: 1.7772 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7772 2023/03/17 19:23:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:23:11 - mmengine - INFO - Epoch(train) [18][1320/1320] lr: 2.0000e-02 eta: 3:56:34 time: 0.3304 data_time: 0.0117 memory: 18752 grad_norm: 4.4967 loss: 2.0627 top1_acc: 0.6364 top5_acc: 0.9091 loss_cls: 2.0627 2023/03/17 19:23:11 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/03/17 19:23:17 - mmengine - INFO - Epoch(val) [18][ 20/194] eta: 0:00:22 time: 0.1289 data_time: 0.0414 memory: 2112 2023/03/17 19:23:19 - mmengine - INFO - Epoch(val) [18][ 40/194] eta: 0:00:17 time: 0.0943 data_time: 0.0089 memory: 2112 2023/03/17 19:23:21 - mmengine - INFO - Epoch(val) [18][ 60/194] eta: 0:00:14 time: 0.0963 data_time: 0.0104 memory: 2112 2023/03/17 19:23:23 - mmengine - INFO - Epoch(val) [18][ 80/194] eta: 0:00:11 time: 0.0960 data_time: 0.0103 memory: 2112 2023/03/17 19:23:25 - mmengine - INFO - Epoch(val) [18][100/194] eta: 0:00:09 time: 0.0958 data_time: 0.0102 memory: 2112 2023/03/17 19:23:27 - mmengine - INFO - Epoch(val) [18][120/194] eta: 0:00:07 time: 0.0967 data_time: 0.0107 memory: 2112 2023/03/17 19:23:29 - mmengine - INFO - Epoch(val) [18][140/194] eta: 0:00:05 time: 0.0973 data_time: 0.0111 memory: 2112 2023/03/17 19:23:30 - mmengine - INFO - Epoch(val) [18][160/194] eta: 0:00:03 time: 0.0966 data_time: 0.0105 memory: 2112 2023/03/17 19:23:32 - mmengine - INFO - Epoch(val) [18][180/194] eta: 0:00:01 time: 0.0955 data_time: 0.0096 memory: 2112 2023/03/17 19:23:35 - mmengine - INFO - Epoch(val) [18][194/194] acc/top1: 0.4656 acc/top5: 0.7601 acc/mean1: 0.3991 2023/03/17 19:23:42 - mmengine - INFO - Epoch(train) [19][ 20/1320] lr: 2.0000e-02 eta: 3:56:29 time: 0.3742 data_time: 0.0393 memory: 18752 grad_norm: 4.3818 loss: 1.9447 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9447 2023/03/17 19:23:49 - mmengine - INFO - Epoch(train) [19][ 40/1320] lr: 2.0000e-02 eta: 3:56:22 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.5269 loss: 1.9890 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9890 2023/03/17 19:23:56 - mmengine - INFO - Epoch(train) [19][ 60/1320] lr: 2.0000e-02 eta: 3:56:15 time: 0.3365 data_time: 0.0126 memory: 18752 grad_norm: 4.6722 loss: 1.8382 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8382 2023/03/17 19:24:02 - mmengine - INFO - Epoch(train) [19][ 80/1320] lr: 2.0000e-02 eta: 3:56:09 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5546 loss: 1.7645 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7645 2023/03/17 19:24:09 - mmengine - INFO - Epoch(train) [19][ 100/1320] lr: 2.0000e-02 eta: 3:56:02 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.5579 loss: 1.7154 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7154 2023/03/17 19:24:16 - mmengine - INFO - Epoch(train) [19][ 120/1320] lr: 2.0000e-02 eta: 3:55:55 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.6165 loss: 1.7570 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7570 2023/03/17 19:24:22 - mmengine - INFO - Epoch(train) [19][ 140/1320] lr: 2.0000e-02 eta: 3:55:48 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.6363 loss: 1.7502 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7502 2023/03/17 19:24:29 - mmengine - INFO - Epoch(train) [19][ 160/1320] lr: 2.0000e-02 eta: 3:55:42 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 4.5320 loss: 1.9361 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9361 2023/03/17 19:24:36 - mmengine - INFO - Epoch(train) [19][ 180/1320] lr: 2.0000e-02 eta: 3:55:35 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.6808 loss: 1.9355 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9355 2023/03/17 19:24:43 - mmengine - INFO - Epoch(train) [19][ 200/1320] lr: 2.0000e-02 eta: 3:55:28 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.7820 loss: 1.9265 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9265 2023/03/17 19:24:49 - mmengine - INFO - Epoch(train) [19][ 220/1320] lr: 2.0000e-02 eta: 3:55:21 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.4673 loss: 1.9906 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9906 2023/03/17 19:24:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:24:56 - mmengine - INFO - Epoch(train) [19][ 240/1320] lr: 2.0000e-02 eta: 3:55:15 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.5546 loss: 1.8984 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8984 2023/03/17 19:25:03 - mmengine - INFO - Epoch(train) [19][ 260/1320] lr: 2.0000e-02 eta: 3:55:08 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.5323 loss: 1.8435 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8435 2023/03/17 19:25:09 - mmengine - INFO - Epoch(train) [19][ 280/1320] lr: 2.0000e-02 eta: 3:55:01 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.5427 loss: 2.0789 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0789 2023/03/17 19:25:16 - mmengine - INFO - Epoch(train) [19][ 300/1320] lr: 2.0000e-02 eta: 3:54:55 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.5934 loss: 1.8768 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8768 2023/03/17 19:25:23 - mmengine - INFO - Epoch(train) [19][ 320/1320] lr: 2.0000e-02 eta: 3:54:48 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.5103 loss: 2.0164 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0164 2023/03/17 19:25:29 - mmengine - INFO - Epoch(train) [19][ 340/1320] lr: 2.0000e-02 eta: 3:54:41 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.6380 loss: 1.9431 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9431 2023/03/17 19:25:36 - mmengine - INFO - Epoch(train) [19][ 360/1320] lr: 2.0000e-02 eta: 3:54:34 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5825 loss: 1.7962 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7962 2023/03/17 19:25:43 - mmengine - INFO - Epoch(train) [19][ 380/1320] lr: 2.0000e-02 eta: 3:54:28 time: 0.3355 data_time: 0.0114 memory: 18752 grad_norm: 4.6981 loss: 2.1223 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1223 2023/03/17 19:25:50 - mmengine - INFO - Epoch(train) [19][ 400/1320] lr: 2.0000e-02 eta: 3:54:21 time: 0.3349 data_time: 0.0117 memory: 18752 grad_norm: 4.5123 loss: 1.8704 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.8704 2023/03/17 19:25:56 - mmengine - INFO - Epoch(train) [19][ 420/1320] lr: 2.0000e-02 eta: 3:54:14 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5361 loss: 1.7264 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7264 2023/03/17 19:26:03 - mmengine - INFO - Epoch(train) [19][ 440/1320] lr: 2.0000e-02 eta: 3:54:07 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.5372 loss: 1.8736 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8736 2023/03/17 19:26:10 - mmengine - INFO - Epoch(train) [19][ 460/1320] lr: 2.0000e-02 eta: 3:54:01 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.6222 loss: 1.8789 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8789 2023/03/17 19:26:16 - mmengine - INFO - Epoch(train) [19][ 480/1320] lr: 2.0000e-02 eta: 3:53:54 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.5385 loss: 1.9064 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9064 2023/03/17 19:26:23 - mmengine - INFO - Epoch(train) [19][ 500/1320] lr: 2.0000e-02 eta: 3:53:47 time: 0.3349 data_time: 0.0118 memory: 18752 grad_norm: 4.4951 loss: 1.9023 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9023 2023/03/17 19:26:30 - mmengine - INFO - Epoch(train) [19][ 520/1320] lr: 2.0000e-02 eta: 3:53:40 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.5507 loss: 1.8848 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8848 2023/03/17 19:26:37 - mmengine - INFO - Epoch(train) [19][ 540/1320] lr: 2.0000e-02 eta: 3:53:34 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.6635 loss: 1.8456 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8456 2023/03/17 19:26:43 - mmengine - INFO - Epoch(train) [19][ 560/1320] lr: 2.0000e-02 eta: 3:53:27 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.4728 loss: 1.9807 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9807 2023/03/17 19:26:50 - mmengine - INFO - Epoch(train) [19][ 580/1320] lr: 2.0000e-02 eta: 3:53:20 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 4.5905 loss: 1.8202 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8202 2023/03/17 19:26:57 - mmengine - INFO - Epoch(train) [19][ 600/1320] lr: 2.0000e-02 eta: 3:53:13 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.4627 loss: 1.9790 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9790 2023/03/17 19:27:03 - mmengine - INFO - Epoch(train) [19][ 620/1320] lr: 2.0000e-02 eta: 3:53:07 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.3009 loss: 1.8918 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8918 2023/03/17 19:27:10 - mmengine - INFO - Epoch(train) [19][ 640/1320] lr: 2.0000e-02 eta: 3:53:00 time: 0.3348 data_time: 0.0115 memory: 18752 grad_norm: 4.4834 loss: 1.7513 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7513 2023/03/17 19:27:17 - mmengine - INFO - Epoch(train) [19][ 660/1320] lr: 2.0000e-02 eta: 3:52:53 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.5620 loss: 1.8749 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8749 2023/03/17 19:27:24 - mmengine - INFO - Epoch(train) [19][ 680/1320] lr: 2.0000e-02 eta: 3:52:46 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.4582 loss: 2.0955 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0955 2023/03/17 19:27:30 - mmengine - INFO - Epoch(train) [19][ 700/1320] lr: 2.0000e-02 eta: 3:52:40 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.5013 loss: 1.8873 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8873 2023/03/17 19:27:37 - mmengine - INFO - Epoch(train) [19][ 720/1320] lr: 2.0000e-02 eta: 3:52:33 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.5110 loss: 1.7868 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7868 2023/03/17 19:27:44 - mmengine - INFO - Epoch(train) [19][ 740/1320] lr: 2.0000e-02 eta: 3:52:26 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5695 loss: 1.8814 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8814 2023/03/17 19:27:50 - mmengine - INFO - Epoch(train) [19][ 760/1320] lr: 2.0000e-02 eta: 3:52:19 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.4345 loss: 1.9898 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9898 2023/03/17 19:27:57 - mmengine - INFO - Epoch(train) [19][ 780/1320] lr: 2.0000e-02 eta: 3:52:13 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 4.6698 loss: 1.9136 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9136 2023/03/17 19:28:04 - mmengine - INFO - Epoch(train) [19][ 800/1320] lr: 2.0000e-02 eta: 3:52:06 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.5248 loss: 1.8976 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8976 2023/03/17 19:28:11 - mmengine - INFO - Epoch(train) [19][ 820/1320] lr: 2.0000e-02 eta: 3:51:59 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.3370 loss: 1.8720 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8720 2023/03/17 19:28:17 - mmengine - INFO - Epoch(train) [19][ 840/1320] lr: 2.0000e-02 eta: 3:51:53 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.4376 loss: 2.0477 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.0477 2023/03/17 19:28:24 - mmengine - INFO - Epoch(train) [19][ 860/1320] lr: 2.0000e-02 eta: 3:51:46 time: 0.3360 data_time: 0.0114 memory: 18752 grad_norm: 4.5177 loss: 1.9139 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9139 2023/03/17 19:28:31 - mmengine - INFO - Epoch(train) [19][ 880/1320] lr: 2.0000e-02 eta: 3:51:39 time: 0.3355 data_time: 0.0114 memory: 18752 grad_norm: 4.4770 loss: 1.9648 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9648 2023/03/17 19:28:37 - mmengine - INFO - Epoch(train) [19][ 900/1320] lr: 2.0000e-02 eta: 3:51:32 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.6796 loss: 1.8754 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8754 2023/03/17 19:28:44 - mmengine - INFO - Epoch(train) [19][ 920/1320] lr: 2.0000e-02 eta: 3:51:26 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.6156 loss: 1.8389 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8389 2023/03/17 19:28:51 - mmengine - INFO - Epoch(train) [19][ 940/1320] lr: 2.0000e-02 eta: 3:51:19 time: 0.3357 data_time: 0.0111 memory: 18752 grad_norm: 4.5488 loss: 1.9046 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9046 2023/03/17 19:28:58 - mmengine - INFO - Epoch(train) [19][ 960/1320] lr: 2.0000e-02 eta: 3:51:12 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.5822 loss: 2.0674 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0674 2023/03/17 19:29:04 - mmengine - INFO - Epoch(train) [19][ 980/1320] lr: 2.0000e-02 eta: 3:51:05 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 4.5062 loss: 1.9169 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9169 2023/03/17 19:29:11 - mmengine - INFO - Epoch(train) [19][1000/1320] lr: 2.0000e-02 eta: 3:50:59 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.6435 loss: 1.8149 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8149 2023/03/17 19:29:18 - mmengine - INFO - Epoch(train) [19][1020/1320] lr: 2.0000e-02 eta: 3:50:52 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.6178 loss: 2.0277 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0277 2023/03/17 19:29:24 - mmengine - INFO - Epoch(train) [19][1040/1320] lr: 2.0000e-02 eta: 3:50:45 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 4.6065 loss: 1.8579 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8579 2023/03/17 19:29:31 - mmengine - INFO - Epoch(train) [19][1060/1320] lr: 2.0000e-02 eta: 3:50:38 time: 0.3358 data_time: 0.0114 memory: 18752 grad_norm: 4.4052 loss: 1.7032 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7032 2023/03/17 19:29:38 - mmengine - INFO - Epoch(train) [19][1080/1320] lr: 2.0000e-02 eta: 3:50:32 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 4.4780 loss: 1.8115 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8115 2023/03/17 19:29:45 - mmengine - INFO - Epoch(train) [19][1100/1320] lr: 2.0000e-02 eta: 3:50:25 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5506 loss: 1.8340 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8340 2023/03/17 19:29:51 - mmengine - INFO - Epoch(train) [19][1120/1320] lr: 2.0000e-02 eta: 3:50:18 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.5438 loss: 1.7843 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7843 2023/03/17 19:29:58 - mmengine - INFO - Epoch(train) [19][1140/1320] lr: 2.0000e-02 eta: 3:50:12 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.5652 loss: 1.9803 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9803 2023/03/17 19:30:05 - mmengine - INFO - Epoch(train) [19][1160/1320] lr: 2.0000e-02 eta: 3:50:05 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 4.6210 loss: 2.0642 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0642 2023/03/17 19:30:11 - mmengine - INFO - Epoch(train) [19][1180/1320] lr: 2.0000e-02 eta: 3:49:58 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.4807 loss: 1.9270 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9270 2023/03/17 19:30:18 - mmengine - INFO - Epoch(train) [19][1200/1320] lr: 2.0000e-02 eta: 3:49:51 time: 0.3359 data_time: 0.0113 memory: 18752 grad_norm: 4.4654 loss: 1.9095 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9095 2023/03/17 19:30:25 - mmengine - INFO - Epoch(train) [19][1220/1320] lr: 2.0000e-02 eta: 3:49:45 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 4.4619 loss: 1.7594 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7594 2023/03/17 19:30:32 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:30:32 - mmengine - INFO - Epoch(train) [19][1240/1320] lr: 2.0000e-02 eta: 3:49:38 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.4309 loss: 1.9845 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9845 2023/03/17 19:30:38 - mmengine - INFO - Epoch(train) [19][1260/1320] lr: 2.0000e-02 eta: 3:49:31 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 4.4662 loss: 1.8576 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8576 2023/03/17 19:30:45 - mmengine - INFO - Epoch(train) [19][1280/1320] lr: 2.0000e-02 eta: 3:49:25 time: 0.3366 data_time: 0.0119 memory: 18752 grad_norm: 4.5004 loss: 1.8636 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8636 2023/03/17 19:30:52 - mmengine - INFO - Epoch(train) [19][1300/1320] lr: 2.0000e-02 eta: 3:49:18 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.5271 loss: 1.9704 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9704 2023/03/17 19:30:58 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:30:58 - mmengine - INFO - Epoch(train) [19][1320/1320] lr: 2.0000e-02 eta: 3:49:11 time: 0.3325 data_time: 0.0127 memory: 18752 grad_norm: 4.5800 loss: 1.9549 top1_acc: 0.3636 top5_acc: 0.5455 loss_cls: 1.9549 2023/03/17 19:31:01 - mmengine - INFO - Epoch(val) [19][ 20/194] eta: 0:00:22 time: 0.1306 data_time: 0.0439 memory: 2112 2023/03/17 19:31:03 - mmengine - INFO - Epoch(val) [19][ 40/194] eta: 0:00:17 time: 0.0966 data_time: 0.0102 memory: 2112 2023/03/17 19:31:05 - mmengine - INFO - Epoch(val) [19][ 60/194] eta: 0:00:14 time: 0.0975 data_time: 0.0115 memory: 2112 2023/03/17 19:31:07 - mmengine - INFO - Epoch(val) [19][ 80/194] eta: 0:00:12 time: 0.0964 data_time: 0.0104 memory: 2112 2023/03/17 19:31:09 - mmengine - INFO - Epoch(val) [19][100/194] eta: 0:00:09 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 19:31:11 - mmengine - INFO - Epoch(val) [19][120/194] eta: 0:00:07 time: 0.0963 data_time: 0.0102 memory: 2112 2023/03/17 19:31:13 - mmengine - INFO - Epoch(val) [19][140/194] eta: 0:00:05 time: 0.0971 data_time: 0.0110 memory: 2112 2023/03/17 19:31:15 - mmengine - INFO - Epoch(val) [19][160/194] eta: 0:00:03 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 19:31:17 - mmengine - INFO - Epoch(val) [19][180/194] eta: 0:00:01 time: 0.0968 data_time: 0.0106 memory: 2112 2023/03/17 19:31:20 - mmengine - INFO - Epoch(val) [19][194/194] acc/top1: 0.4706 acc/top5: 0.7634 acc/mean1: 0.4037 2023/03/17 19:31:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_17.pth is removed 2023/03/17 19:31:21 - mmengine - INFO - The best checkpoint with 0.4706 acc/top1 at 19 epoch is saved to best_acc/top1_epoch_19.pth. 2023/03/17 19:31:29 - mmengine - INFO - Epoch(train) [20][ 20/1320] lr: 2.0000e-02 eta: 3:49:05 time: 0.3668 data_time: 0.0362 memory: 18752 grad_norm: 4.4619 loss: 1.6820 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6820 2023/03/17 19:31:35 - mmengine - INFO - Epoch(train) [20][ 40/1320] lr: 2.0000e-02 eta: 3:48:59 time: 0.3354 data_time: 0.0126 memory: 18752 grad_norm: 4.3163 loss: 1.6389 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6389 2023/03/17 19:31:42 - mmengine - INFO - Epoch(train) [20][ 60/1320] lr: 2.0000e-02 eta: 3:48:52 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.7010 loss: 1.8987 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8987 2023/03/17 19:31:49 - mmengine - INFO - Epoch(train) [20][ 80/1320] lr: 2.0000e-02 eta: 3:48:45 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5695 loss: 1.8833 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.8833 2023/03/17 19:31:56 - mmengine - INFO - Epoch(train) [20][ 100/1320] lr: 2.0000e-02 eta: 3:48:38 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 4.5618 loss: 2.0849 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0849 2023/03/17 19:32:02 - mmengine - INFO - Epoch(train) [20][ 120/1320] lr: 2.0000e-02 eta: 3:48:32 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5396 loss: 2.1697 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1697 2023/03/17 19:32:09 - mmengine - INFO - Epoch(train) [20][ 140/1320] lr: 2.0000e-02 eta: 3:48:25 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.5143 loss: 1.7959 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7959 2023/03/17 19:32:16 - mmengine - INFO - Epoch(train) [20][ 160/1320] lr: 2.0000e-02 eta: 3:48:18 time: 0.3346 data_time: 0.0117 memory: 18752 grad_norm: 4.4805 loss: 1.7979 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7979 2023/03/17 19:32:22 - mmengine - INFO - Epoch(train) [20][ 180/1320] lr: 2.0000e-02 eta: 3:48:11 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.3686 loss: 1.8549 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8549 2023/03/17 19:32:29 - mmengine - INFO - Epoch(train) [20][ 200/1320] lr: 2.0000e-02 eta: 3:48:05 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.4594 loss: 1.8111 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8111 2023/03/17 19:32:36 - mmengine - INFO - Epoch(train) [20][ 220/1320] lr: 2.0000e-02 eta: 3:47:58 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.6462 loss: 1.8062 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.8062 2023/03/17 19:32:43 - mmengine - INFO - Epoch(train) [20][ 240/1320] lr: 2.0000e-02 eta: 3:47:51 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 4.5029 loss: 1.8695 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8695 2023/03/17 19:32:49 - mmengine - INFO - Epoch(train) [20][ 260/1320] lr: 2.0000e-02 eta: 3:47:44 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.5403 loss: 2.0126 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0126 2023/03/17 19:32:56 - mmengine - INFO - Epoch(train) [20][ 280/1320] lr: 2.0000e-02 eta: 3:47:38 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.4825 loss: 1.9630 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9630 2023/03/17 19:33:03 - mmengine - INFO - Epoch(train) [20][ 300/1320] lr: 2.0000e-02 eta: 3:47:31 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 4.5709 loss: 1.8329 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8329 2023/03/17 19:33:09 - mmengine - INFO - Epoch(train) [20][ 320/1320] lr: 2.0000e-02 eta: 3:47:24 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.5707 loss: 1.9227 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9227 2023/03/17 19:33:16 - mmengine - INFO - Epoch(train) [20][ 340/1320] lr: 2.0000e-02 eta: 3:47:17 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.5340 loss: 1.7147 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7147 2023/03/17 19:33:23 - mmengine - INFO - Epoch(train) [20][ 360/1320] lr: 2.0000e-02 eta: 3:47:11 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.5875 loss: 2.0054 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0054 2023/03/17 19:33:30 - mmengine - INFO - Epoch(train) [20][ 380/1320] lr: 2.0000e-02 eta: 3:47:04 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5174 loss: 1.8898 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8898 2023/03/17 19:33:36 - mmengine - INFO - Epoch(train) [20][ 400/1320] lr: 2.0000e-02 eta: 3:46:57 time: 0.3349 data_time: 0.0120 memory: 18752 grad_norm: 4.5099 loss: 1.8410 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8410 2023/03/17 19:33:43 - mmengine - INFO - Epoch(train) [20][ 420/1320] lr: 2.0000e-02 eta: 3:46:50 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.4674 loss: 1.8896 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.8896 2023/03/17 19:33:50 - mmengine - INFO - Epoch(train) [20][ 440/1320] lr: 2.0000e-02 eta: 3:46:44 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.6188 loss: 1.8957 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8957 2023/03/17 19:33:56 - mmengine - INFO - Epoch(train) [20][ 460/1320] lr: 2.0000e-02 eta: 3:46:37 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.6408 loss: 2.0218 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0218 2023/03/17 19:34:03 - mmengine - INFO - Epoch(train) [20][ 480/1320] lr: 2.0000e-02 eta: 3:46:30 time: 0.3353 data_time: 0.0125 memory: 18752 grad_norm: 4.6967 loss: 1.7429 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7429 2023/03/17 19:34:10 - mmengine - INFO - Epoch(train) [20][ 500/1320] lr: 2.0000e-02 eta: 3:46:23 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.5374 loss: 1.7976 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7976 2023/03/17 19:34:17 - mmengine - INFO - Epoch(train) [20][ 520/1320] lr: 2.0000e-02 eta: 3:46:17 time: 0.3351 data_time: 0.0124 memory: 18752 grad_norm: 4.5192 loss: 1.8931 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.8931 2023/03/17 19:34:23 - mmengine - INFO - Epoch(train) [20][ 540/1320] lr: 2.0000e-02 eta: 3:46:10 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.4886 loss: 2.0016 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.0016 2023/03/17 19:34:30 - mmengine - INFO - Epoch(train) [20][ 560/1320] lr: 2.0000e-02 eta: 3:46:03 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.4793 loss: 2.0356 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0356 2023/03/17 19:34:37 - mmengine - INFO - Epoch(train) [20][ 580/1320] lr: 2.0000e-02 eta: 3:45:56 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.3777 loss: 1.7993 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7993 2023/03/17 19:34:43 - mmengine - INFO - Epoch(train) [20][ 600/1320] lr: 2.0000e-02 eta: 3:45:50 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.6345 loss: 1.9762 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9762 2023/03/17 19:34:50 - mmengine - INFO - Epoch(train) [20][ 620/1320] lr: 2.0000e-02 eta: 3:45:43 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5354 loss: 2.0260 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0260 2023/03/17 19:34:57 - mmengine - INFO - Epoch(train) [20][ 640/1320] lr: 2.0000e-02 eta: 3:45:36 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.3863 loss: 1.7820 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7820 2023/03/17 19:35:04 - mmengine - INFO - Epoch(train) [20][ 660/1320] lr: 2.0000e-02 eta: 3:45:30 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.4995 loss: 1.9594 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9594 2023/03/17 19:35:10 - mmengine - INFO - Epoch(train) [20][ 680/1320] lr: 2.0000e-02 eta: 3:45:23 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.4310 loss: 1.7069 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7069 2023/03/17 19:35:17 - mmengine - INFO - Epoch(train) [20][ 700/1320] lr: 2.0000e-02 eta: 3:45:16 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 4.5099 loss: 1.9988 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9988 2023/03/17 19:35:24 - mmengine - INFO - Epoch(train) [20][ 720/1320] lr: 2.0000e-02 eta: 3:45:09 time: 0.3356 data_time: 0.0127 memory: 18752 grad_norm: 4.5424 loss: 1.7851 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7851 2023/03/17 19:35:30 - mmengine - INFO - Epoch(train) [20][ 740/1320] lr: 2.0000e-02 eta: 3:45:03 time: 0.3364 data_time: 0.0129 memory: 18752 grad_norm: 4.4653 loss: 1.7281 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7281 2023/03/17 19:35:37 - mmengine - INFO - Epoch(train) [20][ 760/1320] lr: 2.0000e-02 eta: 3:44:56 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 4.5569 loss: 1.9166 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9166 2023/03/17 19:35:44 - mmengine - INFO - Epoch(train) [20][ 780/1320] lr: 2.0000e-02 eta: 3:44:49 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.6430 loss: 2.1181 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1181 2023/03/17 19:35:51 - mmengine - INFO - Epoch(train) [20][ 800/1320] lr: 2.0000e-02 eta: 3:44:42 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.3910 loss: 1.7628 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7628 2023/03/17 19:35:57 - mmengine - INFO - Epoch(train) [20][ 820/1320] lr: 2.0000e-02 eta: 3:44:36 time: 0.3371 data_time: 0.0124 memory: 18752 grad_norm: 4.5308 loss: 1.9635 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 1.9635 2023/03/17 19:36:04 - mmengine - INFO - Epoch(train) [20][ 840/1320] lr: 2.0000e-02 eta: 3:44:29 time: 0.3359 data_time: 0.0127 memory: 18752 grad_norm: 4.5420 loss: 1.7987 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7987 2023/03/17 19:36:11 - mmengine - INFO - Epoch(train) [20][ 860/1320] lr: 2.0000e-02 eta: 3:44:22 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.7290 loss: 1.8944 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8944 2023/03/17 19:36:17 - mmengine - INFO - Epoch(train) [20][ 880/1320] lr: 2.0000e-02 eta: 3:44:16 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.4897 loss: 1.7538 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7538 2023/03/17 19:36:24 - mmengine - INFO - Epoch(train) [20][ 900/1320] lr: 2.0000e-02 eta: 3:44:09 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 4.4182 loss: 1.8770 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8770 2023/03/17 19:36:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:36:31 - mmengine - INFO - Epoch(train) [20][ 920/1320] lr: 2.0000e-02 eta: 3:44:02 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.5349 loss: 1.8542 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8542 2023/03/17 19:36:38 - mmengine - INFO - Epoch(train) [20][ 940/1320] lr: 2.0000e-02 eta: 3:43:56 time: 0.3394 data_time: 0.0122 memory: 18752 grad_norm: 4.5801 loss: 1.8775 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8775 2023/03/17 19:36:44 - mmengine - INFO - Epoch(train) [20][ 960/1320] lr: 2.0000e-02 eta: 3:43:49 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 4.4632 loss: 1.8498 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8498 2023/03/17 19:36:51 - mmengine - INFO - Epoch(train) [20][ 980/1320] lr: 2.0000e-02 eta: 3:43:42 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.5358 loss: 2.0330 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0330 2023/03/17 19:36:58 - mmengine - INFO - Epoch(train) [20][1000/1320] lr: 2.0000e-02 eta: 3:43:35 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 4.3884 loss: 1.6554 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6554 2023/03/17 19:37:05 - mmengine - INFO - Epoch(train) [20][1020/1320] lr: 2.0000e-02 eta: 3:43:29 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 4.5333 loss: 1.9354 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9354 2023/03/17 19:37:11 - mmengine - INFO - Epoch(train) [20][1040/1320] lr: 2.0000e-02 eta: 3:43:22 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 4.3648 loss: 1.7662 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7662 2023/03/17 19:37:18 - mmengine - INFO - Epoch(train) [20][1060/1320] lr: 2.0000e-02 eta: 3:43:15 time: 0.3363 data_time: 0.0128 memory: 18752 grad_norm: 4.4259 loss: 1.9486 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9486 2023/03/17 19:37:25 - mmengine - INFO - Epoch(train) [20][1080/1320] lr: 2.0000e-02 eta: 3:43:08 time: 0.3362 data_time: 0.0127 memory: 18752 grad_norm: 4.4008 loss: 2.0758 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0758 2023/03/17 19:37:31 - mmengine - INFO - Epoch(train) [20][1100/1320] lr: 2.0000e-02 eta: 3:43:02 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5301 loss: 1.8098 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8098 2023/03/17 19:37:38 - mmengine - INFO - Epoch(train) [20][1120/1320] lr: 2.0000e-02 eta: 3:42:55 time: 0.3366 data_time: 0.0124 memory: 18752 grad_norm: 4.5634 loss: 1.9161 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9161 2023/03/17 19:37:45 - mmengine - INFO - Epoch(train) [20][1140/1320] lr: 2.0000e-02 eta: 3:42:48 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.5233 loss: 1.8976 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8976 2023/03/17 19:37:52 - mmengine - INFO - Epoch(train) [20][1160/1320] lr: 2.0000e-02 eta: 3:42:42 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 4.6376 loss: 1.9022 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9022 2023/03/17 19:37:58 - mmengine - INFO - Epoch(train) [20][1180/1320] lr: 2.0000e-02 eta: 3:42:35 time: 0.3366 data_time: 0.0116 memory: 18752 grad_norm: 4.4404 loss: 1.8275 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8275 2023/03/17 19:38:05 - mmengine - INFO - Epoch(train) [20][1200/1320] lr: 2.0000e-02 eta: 3:42:28 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.5861 loss: 1.8320 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8320 2023/03/17 19:38:12 - mmengine - INFO - Epoch(train) [20][1220/1320] lr: 2.0000e-02 eta: 3:42:21 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 4.5095 loss: 1.8991 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8991 2023/03/17 19:38:18 - mmengine - INFO - Epoch(train) [20][1240/1320] lr: 2.0000e-02 eta: 3:42:15 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 4.3973 loss: 1.9619 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.9619 2023/03/17 19:38:25 - mmengine - INFO - Epoch(train) [20][1260/1320] lr: 2.0000e-02 eta: 3:42:08 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 4.5204 loss: 1.6610 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6610 2023/03/17 19:38:32 - mmengine - INFO - Epoch(train) [20][1280/1320] lr: 2.0000e-02 eta: 3:42:01 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 4.5443 loss: 1.9180 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.9180 2023/03/17 19:38:39 - mmengine - INFO - Epoch(train) [20][1300/1320] lr: 2.0000e-02 eta: 3:41:55 time: 0.3367 data_time: 0.0122 memory: 18752 grad_norm: 4.5878 loss: 2.0661 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0661 2023/03/17 19:38:45 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:38:45 - mmengine - INFO - Epoch(train) [20][1320/1320] lr: 2.0000e-02 eta: 3:41:48 time: 0.3320 data_time: 0.0132 memory: 18752 grad_norm: 4.4781 loss: 1.8537 top1_acc: 0.6364 top5_acc: 0.8182 loss_cls: 1.8537 2023/03/17 19:38:48 - mmengine - INFO - Epoch(val) [20][ 20/194] eta: 0:00:22 time: 0.1275 data_time: 0.0410 memory: 2112 2023/03/17 19:38:50 - mmengine - INFO - Epoch(val) [20][ 40/194] eta: 0:00:17 time: 0.0950 data_time: 0.0092 memory: 2112 2023/03/17 19:38:52 - mmengine - INFO - Epoch(val) [20][ 60/194] eta: 0:00:14 time: 0.0967 data_time: 0.0108 memory: 2112 2023/03/17 19:38:54 - mmengine - INFO - Epoch(val) [20][ 80/194] eta: 0:00:11 time: 0.0968 data_time: 0.0110 memory: 2112 2023/03/17 19:38:56 - mmengine - INFO - Epoch(val) [20][100/194] eta: 0:00:09 time: 0.0960 data_time: 0.0101 memory: 2112 2023/03/17 19:38:58 - mmengine - INFO - Epoch(val) [20][120/194] eta: 0:00:07 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 19:38:59 - mmengine - INFO - Epoch(val) [20][140/194] eta: 0:00:05 time: 0.0974 data_time: 0.0108 memory: 2112 2023/03/17 19:39:01 - mmengine - INFO - Epoch(val) [20][160/194] eta: 0:00:03 time: 0.0973 data_time: 0.0111 memory: 2112 2023/03/17 19:39:03 - mmengine - INFO - Epoch(val) [20][180/194] eta: 0:00:01 time: 0.0970 data_time: 0.0112 memory: 2112 2023/03/17 19:39:07 - mmengine - INFO - Epoch(val) [20][194/194] acc/top1: 0.4721 acc/top5: 0.7707 acc/mean1: 0.4017 2023/03/17 19:39:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_19.pth is removed 2023/03/17 19:39:09 - mmengine - INFO - The best checkpoint with 0.4721 acc/top1 at 20 epoch is saved to best_acc/top1_epoch_20.pth. 2023/03/17 19:39:16 - mmengine - INFO - Epoch(train) [21][ 20/1320] lr: 2.0000e-02 eta: 3:41:42 time: 0.3669 data_time: 0.0369 memory: 18752 grad_norm: 4.4514 loss: 1.7543 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7543 2023/03/17 19:39:23 - mmengine - INFO - Epoch(train) [21][ 40/1320] lr: 2.0000e-02 eta: 3:41:35 time: 0.3366 data_time: 0.0118 memory: 18752 grad_norm: 4.6432 loss: 1.8282 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8282 2023/03/17 19:39:29 - mmengine - INFO - Epoch(train) [21][ 60/1320] lr: 2.0000e-02 eta: 3:41:28 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5533 loss: 1.8678 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8678 2023/03/17 19:39:36 - mmengine - INFO - Epoch(train) [21][ 80/1320] lr: 2.0000e-02 eta: 3:41:22 time: 0.3362 data_time: 0.0116 memory: 18752 grad_norm: 4.5023 loss: 1.8490 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8490 2023/03/17 19:39:43 - mmengine - INFO - Epoch(train) [21][ 100/1320] lr: 2.0000e-02 eta: 3:41:15 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 4.6800 loss: 1.8587 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8587 2023/03/17 19:39:50 - mmengine - INFO - Epoch(train) [21][ 120/1320] lr: 2.0000e-02 eta: 3:41:08 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 4.5226 loss: 2.0106 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0106 2023/03/17 19:39:56 - mmengine - INFO - Epoch(train) [21][ 140/1320] lr: 2.0000e-02 eta: 3:41:02 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 4.4120 loss: 1.7138 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7138 2023/03/17 19:40:03 - mmengine - INFO - Epoch(train) [21][ 160/1320] lr: 2.0000e-02 eta: 3:40:55 time: 0.3356 data_time: 0.0114 memory: 18752 grad_norm: 4.4488 loss: 1.9098 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9098 2023/03/17 19:40:10 - mmengine - INFO - Epoch(train) [21][ 180/1320] lr: 2.0000e-02 eta: 3:40:48 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.6681 loss: 1.7594 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7594 2023/03/17 19:40:16 - mmengine - INFO - Epoch(train) [21][ 200/1320] lr: 2.0000e-02 eta: 3:40:41 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.6308 loss: 1.8457 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8457 2023/03/17 19:40:23 - mmengine - INFO - Epoch(train) [21][ 220/1320] lr: 2.0000e-02 eta: 3:40:35 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.6202 loss: 2.0675 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0675 2023/03/17 19:40:30 - mmengine - INFO - Epoch(train) [21][ 240/1320] lr: 2.0000e-02 eta: 3:40:28 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.6663 loss: 1.8217 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8217 2023/03/17 19:40:37 - mmengine - INFO - Epoch(train) [21][ 260/1320] lr: 2.0000e-02 eta: 3:40:21 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.5422 loss: 1.8888 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8888 2023/03/17 19:40:43 - mmengine - INFO - Epoch(train) [21][ 280/1320] lr: 2.0000e-02 eta: 3:40:14 time: 0.3362 data_time: 0.0126 memory: 18752 grad_norm: 4.6295 loss: 1.8331 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8331 2023/03/17 19:40:50 - mmengine - INFO - Epoch(train) [21][ 300/1320] lr: 2.0000e-02 eta: 3:40:08 time: 0.3357 data_time: 0.0128 memory: 18752 grad_norm: 4.6417 loss: 1.9454 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9454 2023/03/17 19:40:57 - mmengine - INFO - Epoch(train) [21][ 320/1320] lr: 2.0000e-02 eta: 3:40:01 time: 0.3352 data_time: 0.0124 memory: 18752 grad_norm: 4.5379 loss: 1.7800 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.7800 2023/03/17 19:41:03 - mmengine - INFO - Epoch(train) [21][ 340/1320] lr: 2.0000e-02 eta: 3:39:54 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.5911 loss: 1.9380 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9380 2023/03/17 19:41:10 - mmengine - INFO - Epoch(train) [21][ 360/1320] lr: 2.0000e-02 eta: 3:39:47 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.6052 loss: 1.8482 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8482 2023/03/17 19:41:17 - mmengine - INFO - Epoch(train) [21][ 380/1320] lr: 2.0000e-02 eta: 3:39:41 time: 0.3349 data_time: 0.0118 memory: 18752 grad_norm: 4.7139 loss: 1.9941 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9941 2023/03/17 19:41:23 - mmengine - INFO - Epoch(train) [21][ 400/1320] lr: 2.0000e-02 eta: 3:39:34 time: 0.3353 data_time: 0.0125 memory: 18752 grad_norm: 4.6020 loss: 1.9850 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9850 2023/03/17 19:41:30 - mmengine - INFO - Epoch(train) [21][ 420/1320] lr: 2.0000e-02 eta: 3:39:27 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.4903 loss: 1.8894 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8894 2023/03/17 19:41:37 - mmengine - INFO - Epoch(train) [21][ 440/1320] lr: 2.0000e-02 eta: 3:39:20 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.5643 loss: 1.7638 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7638 2023/03/17 19:41:44 - mmengine - INFO - Epoch(train) [21][ 460/1320] lr: 2.0000e-02 eta: 3:39:14 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.6277 loss: 1.8430 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8430 2023/03/17 19:41:50 - mmengine - INFO - Epoch(train) [21][ 480/1320] lr: 2.0000e-02 eta: 3:39:07 time: 0.3356 data_time: 0.0131 memory: 18752 grad_norm: 4.6045 loss: 1.8500 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8500 2023/03/17 19:41:57 - mmengine - INFO - Epoch(train) [21][ 500/1320] lr: 2.0000e-02 eta: 3:39:00 time: 0.3360 data_time: 0.0128 memory: 18752 grad_norm: 4.5850 loss: 1.7944 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7944 2023/03/17 19:42:04 - mmengine - INFO - Epoch(train) [21][ 520/1320] lr: 2.0000e-02 eta: 3:38:54 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.6392 loss: 1.8876 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8876 2023/03/17 19:42:10 - mmengine - INFO - Epoch(train) [21][ 540/1320] lr: 2.0000e-02 eta: 3:38:47 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.6120 loss: 1.9763 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9763 2023/03/17 19:42:17 - mmengine - INFO - Epoch(train) [21][ 560/1320] lr: 2.0000e-02 eta: 3:38:40 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.4712 loss: 1.7334 top1_acc: 0.1875 top5_acc: 0.7500 loss_cls: 1.7334 2023/03/17 19:42:24 - mmengine - INFO - Epoch(train) [21][ 580/1320] lr: 2.0000e-02 eta: 3:38:33 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.6023 loss: 1.7002 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.7002 2023/03/17 19:42:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:42:31 - mmengine - INFO - Epoch(train) [21][ 600/1320] lr: 2.0000e-02 eta: 3:38:27 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5056 loss: 1.8098 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8098 2023/03/17 19:42:37 - mmengine - INFO - Epoch(train) [21][ 620/1320] lr: 2.0000e-02 eta: 3:38:20 time: 0.3348 data_time: 0.0117 memory: 18752 grad_norm: 4.6065 loss: 1.9760 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9760 2023/03/17 19:42:44 - mmengine - INFO - Epoch(train) [21][ 640/1320] lr: 2.0000e-02 eta: 3:38:13 time: 0.3358 data_time: 0.0128 memory: 18752 grad_norm: 4.6007 loss: 1.9465 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9465 2023/03/17 19:42:51 - mmengine - INFO - Epoch(train) [21][ 660/1320] lr: 2.0000e-02 eta: 3:38:06 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.4741 loss: 1.9358 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9358 2023/03/17 19:42:57 - mmengine - INFO - Epoch(train) [21][ 680/1320] lr: 2.0000e-02 eta: 3:38:00 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5572 loss: 1.8882 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8882 2023/03/17 19:43:04 - mmengine - INFO - Epoch(train) [21][ 700/1320] lr: 2.0000e-02 eta: 3:37:53 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 4.5016 loss: 1.7984 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7984 2023/03/17 19:43:11 - mmengine - INFO - Epoch(train) [21][ 720/1320] lr: 2.0000e-02 eta: 3:37:46 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.5372 loss: 1.8804 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8804 2023/03/17 19:43:18 - mmengine - INFO - Epoch(train) [21][ 740/1320] lr: 2.0000e-02 eta: 3:37:39 time: 0.3349 data_time: 0.0122 memory: 18752 grad_norm: 4.5123 loss: 1.9239 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9239 2023/03/17 19:43:24 - mmengine - INFO - Epoch(train) [21][ 760/1320] lr: 2.0000e-02 eta: 3:37:33 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.4814 loss: 2.0221 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0221 2023/03/17 19:43:31 - mmengine - INFO - Epoch(train) [21][ 780/1320] lr: 2.0000e-02 eta: 3:37:26 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.6230 loss: 1.8166 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.8166 2023/03/17 19:43:38 - mmengine - INFO - Epoch(train) [21][ 800/1320] lr: 2.0000e-02 eta: 3:37:19 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.6012 loss: 1.7890 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7890 2023/03/17 19:43:44 - mmengine - INFO - Epoch(train) [21][ 820/1320] lr: 2.0000e-02 eta: 3:37:12 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.5752 loss: 1.6262 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6262 2023/03/17 19:43:51 - mmengine - INFO - Epoch(train) [21][ 840/1320] lr: 2.0000e-02 eta: 3:37:06 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.6155 loss: 2.0235 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0235 2023/03/17 19:43:58 - mmengine - INFO - Epoch(train) [21][ 860/1320] lr: 2.0000e-02 eta: 3:36:59 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.5566 loss: 1.9404 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.9404 2023/03/17 19:44:05 - mmengine - INFO - Epoch(train) [21][ 880/1320] lr: 2.0000e-02 eta: 3:36:52 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.6073 loss: 1.8017 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8017 2023/03/17 19:44:11 - mmengine - INFO - Epoch(train) [21][ 900/1320] lr: 2.0000e-02 eta: 3:36:45 time: 0.3352 data_time: 0.0126 memory: 18752 grad_norm: 4.5222 loss: 1.7382 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.7382 2023/03/17 19:44:18 - mmengine - INFO - Epoch(train) [21][ 920/1320] lr: 2.0000e-02 eta: 3:36:39 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.4114 loss: 1.9340 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.9340 2023/03/17 19:44:25 - mmengine - INFO - Epoch(train) [21][ 940/1320] lr: 2.0000e-02 eta: 3:36:32 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.4693 loss: 2.0252 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0252 2023/03/17 19:44:31 - mmengine - INFO - Epoch(train) [21][ 960/1320] lr: 2.0000e-02 eta: 3:36:25 time: 0.3348 data_time: 0.0120 memory: 18752 grad_norm: 4.5946 loss: 1.9391 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9391 2023/03/17 19:44:38 - mmengine - INFO - Epoch(train) [21][ 980/1320] lr: 2.0000e-02 eta: 3:36:19 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.4683 loss: 1.9801 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9801 2023/03/17 19:44:45 - mmengine - INFO - Epoch(train) [21][1000/1320] lr: 2.0000e-02 eta: 3:36:12 time: 0.3352 data_time: 0.0122 memory: 18752 grad_norm: 4.5519 loss: 1.8525 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.8525 2023/03/17 19:44:51 - mmengine - INFO - Epoch(train) [21][1020/1320] lr: 2.0000e-02 eta: 3:36:05 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.5124 loss: 1.9693 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9693 2023/03/17 19:44:58 - mmengine - INFO - Epoch(train) [21][1040/1320] lr: 2.0000e-02 eta: 3:35:58 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.5934 loss: 1.8558 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8558 2023/03/17 19:45:05 - mmengine - INFO - Epoch(train) [21][1060/1320] lr: 2.0000e-02 eta: 3:35:52 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.5861 loss: 1.9136 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9136 2023/03/17 19:45:12 - mmengine - INFO - Epoch(train) [21][1080/1320] lr: 2.0000e-02 eta: 3:35:45 time: 0.3350 data_time: 0.0124 memory: 18752 grad_norm: 4.6008 loss: 1.8488 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8488 2023/03/17 19:45:18 - mmengine - INFO - Epoch(train) [21][1100/1320] lr: 2.0000e-02 eta: 3:35:38 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.5115 loss: 1.9401 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9401 2023/03/17 19:45:25 - mmengine - INFO - Epoch(train) [21][1120/1320] lr: 2.0000e-02 eta: 3:35:31 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.3456 loss: 2.0263 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0263 2023/03/17 19:45:32 - mmengine - INFO - Epoch(train) [21][1140/1320] lr: 2.0000e-02 eta: 3:35:25 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.5193 loss: 1.8893 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8893 2023/03/17 19:45:38 - mmengine - INFO - Epoch(train) [21][1160/1320] lr: 2.0000e-02 eta: 3:35:18 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.3606 loss: 1.7336 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.7336 2023/03/17 19:45:45 - mmengine - INFO - Epoch(train) [21][1180/1320] lr: 2.0000e-02 eta: 3:35:11 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.4327 loss: 1.8413 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8413 2023/03/17 19:45:52 - mmengine - INFO - Epoch(train) [21][1200/1320] lr: 2.0000e-02 eta: 3:35:04 time: 0.3348 data_time: 0.0118 memory: 18752 grad_norm: 4.5330 loss: 1.8311 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8311 2023/03/17 19:45:59 - mmengine - INFO - Epoch(train) [21][1220/1320] lr: 2.0000e-02 eta: 3:34:58 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.4660 loss: 1.9878 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9878 2023/03/17 19:46:05 - mmengine - INFO - Epoch(train) [21][1240/1320] lr: 2.0000e-02 eta: 3:34:51 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 4.5000 loss: 1.7691 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7691 2023/03/17 19:46:12 - mmengine - INFO - Epoch(train) [21][1260/1320] lr: 2.0000e-02 eta: 3:34:44 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 4.4534 loss: 2.0102 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0102 2023/03/17 19:46:19 - mmengine - INFO - Epoch(train) [21][1280/1320] lr: 2.0000e-02 eta: 3:34:37 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.4073 loss: 1.8839 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8839 2023/03/17 19:46:25 - mmengine - INFO - Epoch(train) [21][1300/1320] lr: 2.0000e-02 eta: 3:34:31 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.4537 loss: 1.8505 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.8505 2023/03/17 19:46:32 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:46:32 - mmengine - INFO - Epoch(train) [21][1320/1320] lr: 2.0000e-02 eta: 3:34:24 time: 0.3300 data_time: 0.0114 memory: 18752 grad_norm: 4.4758 loss: 1.8125 top1_acc: 0.4545 top5_acc: 0.9091 loss_cls: 1.8125 2023/03/17 19:46:32 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/03/17 19:46:37 - mmengine - INFO - Epoch(val) [21][ 20/194] eta: 0:00:22 time: 0.1289 data_time: 0.0422 memory: 2112 2023/03/17 19:46:39 - mmengine - INFO - Epoch(val) [21][ 40/194] eta: 0:00:17 time: 0.0956 data_time: 0.0096 memory: 2112 2023/03/17 19:46:41 - mmengine - INFO - Epoch(val) [21][ 60/194] eta: 0:00:14 time: 0.0966 data_time: 0.0105 memory: 2112 2023/03/17 19:46:43 - mmengine - INFO - Epoch(val) [21][ 80/194] eta: 0:00:11 time: 0.0980 data_time: 0.0116 memory: 2112 2023/03/17 19:46:45 - mmengine - INFO - Epoch(val) [21][100/194] eta: 0:00:09 time: 0.0967 data_time: 0.0108 memory: 2112 2023/03/17 19:46:47 - mmengine - INFO - Epoch(val) [21][120/194] eta: 0:00:07 time: 0.0970 data_time: 0.0114 memory: 2112 2023/03/17 19:46:49 - mmengine - INFO - Epoch(val) [21][140/194] eta: 0:00:05 time: 0.0967 data_time: 0.0107 memory: 2112 2023/03/17 19:46:51 - mmengine - INFO - Epoch(val) [21][160/194] eta: 0:00:03 time: 0.0966 data_time: 0.0108 memory: 2112 2023/03/17 19:46:53 - mmengine - INFO - Epoch(val) [21][180/194] eta: 0:00:01 time: 0.0953 data_time: 0.0095 memory: 2112 2023/03/17 19:46:56 - mmengine - INFO - Epoch(val) [21][194/194] acc/top1: 0.4687 acc/top5: 0.7694 acc/mean1: 0.4073 2023/03/17 19:47:03 - mmengine - INFO - Epoch(train) [22][ 20/1320] lr: 2.0000e-02 eta: 3:34:18 time: 0.3723 data_time: 0.0415 memory: 18752 grad_norm: 4.6346 loss: 1.6755 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6755 2023/03/17 19:47:10 - mmengine - INFO - Epoch(train) [22][ 40/1320] lr: 2.0000e-02 eta: 3:34:11 time: 0.3361 data_time: 0.0115 memory: 18752 grad_norm: 4.6449 loss: 1.9100 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9100 2023/03/17 19:47:17 - mmengine - INFO - Epoch(train) [22][ 60/1320] lr: 2.0000e-02 eta: 3:34:05 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.5582 loss: 1.8922 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8922 2023/03/17 19:47:23 - mmengine - INFO - Epoch(train) [22][ 80/1320] lr: 2.0000e-02 eta: 3:33:58 time: 0.3351 data_time: 0.0116 memory: 18752 grad_norm: 4.4888 loss: 1.9661 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9661 2023/03/17 19:47:30 - mmengine - INFO - Epoch(train) [22][ 100/1320] lr: 2.0000e-02 eta: 3:33:51 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.6238 loss: 1.7410 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7410 2023/03/17 19:47:37 - mmengine - INFO - Epoch(train) [22][ 120/1320] lr: 2.0000e-02 eta: 3:33:44 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.8147 loss: 2.0584 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0584 2023/03/17 19:47:44 - mmengine - INFO - Epoch(train) [22][ 140/1320] lr: 2.0000e-02 eta: 3:33:38 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.5785 loss: 2.1078 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.1078 2023/03/17 19:47:50 - mmengine - INFO - Epoch(train) [22][ 160/1320] lr: 2.0000e-02 eta: 3:33:31 time: 0.3345 data_time: 0.0116 memory: 18752 grad_norm: 4.6316 loss: 1.8696 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8696 2023/03/17 19:47:57 - mmengine - INFO - Epoch(train) [22][ 180/1320] lr: 2.0000e-02 eta: 3:33:24 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.5880 loss: 1.8880 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8880 2023/03/17 19:48:04 - mmengine - INFO - Epoch(train) [22][ 200/1320] lr: 2.0000e-02 eta: 3:33:17 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.5693 loss: 1.7835 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7835 2023/03/17 19:48:10 - mmengine - INFO - Epoch(train) [22][ 220/1320] lr: 2.0000e-02 eta: 3:33:11 time: 0.3350 data_time: 0.0120 memory: 18752 grad_norm: 4.5263 loss: 1.8663 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8663 2023/03/17 19:48:17 - mmengine - INFO - Epoch(train) [22][ 240/1320] lr: 2.0000e-02 eta: 3:33:04 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.6643 loss: 1.8695 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8695 2023/03/17 19:48:24 - mmengine - INFO - Epoch(train) [22][ 260/1320] lr: 2.0000e-02 eta: 3:32:57 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.5371 loss: 1.8600 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8600 2023/03/17 19:48:30 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:48:30 - mmengine - INFO - Epoch(train) [22][ 280/1320] lr: 2.0000e-02 eta: 3:32:50 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.5941 loss: 1.7871 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7871 2023/03/17 19:48:37 - mmengine - INFO - Epoch(train) [22][ 300/1320] lr: 2.0000e-02 eta: 3:32:44 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 4.5397 loss: 1.8032 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.8032 2023/03/17 19:48:44 - mmengine - INFO - Epoch(train) [22][ 320/1320] lr: 2.0000e-02 eta: 3:32:37 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.6020 loss: 1.7129 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7129 2023/03/17 19:48:51 - mmengine - INFO - Epoch(train) [22][ 340/1320] lr: 2.0000e-02 eta: 3:32:30 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.6565 loss: 1.7877 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7877 2023/03/17 19:48:57 - mmengine - INFO - Epoch(train) [22][ 360/1320] lr: 2.0000e-02 eta: 3:32:23 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 4.4684 loss: 1.7584 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7584 2023/03/17 19:49:04 - mmengine - INFO - Epoch(train) [22][ 380/1320] lr: 2.0000e-02 eta: 3:32:17 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5880 loss: 1.8766 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8766 2023/03/17 19:49:11 - mmengine - INFO - Epoch(train) [22][ 400/1320] lr: 2.0000e-02 eta: 3:32:10 time: 0.3350 data_time: 0.0115 memory: 18752 grad_norm: 4.4695 loss: 1.8587 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8587 2023/03/17 19:49:17 - mmengine - INFO - Epoch(train) [22][ 420/1320] lr: 2.0000e-02 eta: 3:32:03 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.5639 loss: 1.8638 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8638 2023/03/17 19:49:24 - mmengine - INFO - Epoch(train) [22][ 440/1320] lr: 2.0000e-02 eta: 3:31:56 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 4.5337 loss: 1.8349 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8349 2023/03/17 19:49:31 - mmengine - INFO - Epoch(train) [22][ 460/1320] lr: 2.0000e-02 eta: 3:31:50 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.4459 loss: 1.8235 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8235 2023/03/17 19:49:38 - mmengine - INFO - Epoch(train) [22][ 480/1320] lr: 2.0000e-02 eta: 3:31:43 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.6028 loss: 1.7776 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7776 2023/03/17 19:49:44 - mmengine - INFO - Epoch(train) [22][ 500/1320] lr: 2.0000e-02 eta: 3:31:36 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.6341 loss: 1.8437 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 1.8437 2023/03/17 19:49:51 - mmengine - INFO - Epoch(train) [22][ 520/1320] lr: 2.0000e-02 eta: 3:31:30 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 4.6042 loss: 1.7676 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7676 2023/03/17 19:49:58 - mmengine - INFO - Epoch(train) [22][ 540/1320] lr: 2.0000e-02 eta: 3:31:23 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.5272 loss: 1.7402 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7402 2023/03/17 19:50:04 - mmengine - INFO - Epoch(train) [22][ 560/1320] lr: 2.0000e-02 eta: 3:31:16 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.5623 loss: 1.7276 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7276 2023/03/17 19:50:11 - mmengine - INFO - Epoch(train) [22][ 580/1320] lr: 2.0000e-02 eta: 3:31:09 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.5825 loss: 1.7484 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7484 2023/03/17 19:50:18 - mmengine - INFO - Epoch(train) [22][ 600/1320] lr: 2.0000e-02 eta: 3:31:03 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5662 loss: 1.7534 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.7534 2023/03/17 19:50:25 - mmengine - INFO - Epoch(train) [22][ 620/1320] lr: 2.0000e-02 eta: 3:30:56 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.6664 loss: 1.8669 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8669 2023/03/17 19:50:31 - mmengine - INFO - Epoch(train) [22][ 640/1320] lr: 2.0000e-02 eta: 3:30:49 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.6234 loss: 1.9608 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9608 2023/03/17 19:50:38 - mmengine - INFO - Epoch(train) [22][ 660/1320] lr: 2.0000e-02 eta: 3:30:42 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.5026 loss: 1.6786 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6786 2023/03/17 19:50:45 - mmengine - INFO - Epoch(train) [22][ 680/1320] lr: 2.0000e-02 eta: 3:30:36 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.6768 loss: 1.8706 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8706 2023/03/17 19:50:51 - mmengine - INFO - Epoch(train) [22][ 700/1320] lr: 2.0000e-02 eta: 3:30:29 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5356 loss: 1.8573 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8573 2023/03/17 19:50:58 - mmengine - INFO - Epoch(train) [22][ 720/1320] lr: 2.0000e-02 eta: 3:30:22 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.6530 loss: 1.8172 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8172 2023/03/17 19:51:05 - mmengine - INFO - Epoch(train) [22][ 740/1320] lr: 2.0000e-02 eta: 3:30:15 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.5553 loss: 1.9153 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9153 2023/03/17 19:51:12 - mmengine - INFO - Epoch(train) [22][ 760/1320] lr: 2.0000e-02 eta: 3:30:09 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 4.6315 loss: 1.6676 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6676 2023/03/17 19:51:18 - mmengine - INFO - Epoch(train) [22][ 780/1320] lr: 2.0000e-02 eta: 3:30:02 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.6729 loss: 1.9699 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9699 2023/03/17 19:51:25 - mmengine - INFO - Epoch(train) [22][ 800/1320] lr: 2.0000e-02 eta: 3:29:55 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.4846 loss: 1.7681 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7681 2023/03/17 19:51:32 - mmengine - INFO - Epoch(train) [22][ 820/1320] lr: 2.0000e-02 eta: 3:29:49 time: 0.3356 data_time: 0.0126 memory: 18752 grad_norm: 4.4907 loss: 1.7928 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7928 2023/03/17 19:51:38 - mmengine - INFO - Epoch(train) [22][ 840/1320] lr: 2.0000e-02 eta: 3:29:42 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.5323 loss: 1.8553 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8553 2023/03/17 19:51:45 - mmengine - INFO - Epoch(train) [22][ 860/1320] lr: 2.0000e-02 eta: 3:29:35 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 4.7427 loss: 1.7928 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7928 2023/03/17 19:51:52 - mmengine - INFO - Epoch(train) [22][ 880/1320] lr: 2.0000e-02 eta: 3:29:28 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.6573 loss: 2.0123 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0123 2023/03/17 19:51:59 - mmengine - INFO - Epoch(train) [22][ 900/1320] lr: 2.0000e-02 eta: 3:29:22 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.4413 loss: 1.9761 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9761 2023/03/17 19:52:05 - mmengine - INFO - Epoch(train) [22][ 920/1320] lr: 2.0000e-02 eta: 3:29:15 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5700 loss: 1.7018 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7018 2023/03/17 19:52:12 - mmengine - INFO - Epoch(train) [22][ 940/1320] lr: 2.0000e-02 eta: 3:29:08 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 4.4789 loss: 1.9933 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9933 2023/03/17 19:52:19 - mmengine - INFO - Epoch(train) [22][ 960/1320] lr: 2.0000e-02 eta: 3:29:01 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.5838 loss: 1.7275 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7275 2023/03/17 19:52:25 - mmengine - INFO - Epoch(train) [22][ 980/1320] lr: 2.0000e-02 eta: 3:28:55 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 4.6294 loss: 1.9247 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9247 2023/03/17 19:52:32 - mmengine - INFO - Epoch(train) [22][1000/1320] lr: 2.0000e-02 eta: 3:28:48 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 4.5876 loss: 1.7649 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7649 2023/03/17 19:52:39 - mmengine - INFO - Epoch(train) [22][1020/1320] lr: 2.0000e-02 eta: 3:28:41 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 4.5339 loss: 1.8305 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8305 2023/03/17 19:52:46 - mmengine - INFO - Epoch(train) [22][1040/1320] lr: 2.0000e-02 eta: 3:28:35 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 4.5951 loss: 1.8316 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8316 2023/03/17 19:52:52 - mmengine - INFO - Epoch(train) [22][1060/1320] lr: 2.0000e-02 eta: 3:28:28 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 4.5516 loss: 1.8707 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8707 2023/03/17 19:52:59 - mmengine - INFO - Epoch(train) [22][1080/1320] lr: 2.0000e-02 eta: 3:28:21 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.6204 loss: 1.9395 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9395 2023/03/17 19:53:06 - mmengine - INFO - Epoch(train) [22][1100/1320] lr: 2.0000e-02 eta: 3:28:14 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.5111 loss: 1.5725 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5725 2023/03/17 19:53:12 - mmengine - INFO - Epoch(train) [22][1120/1320] lr: 2.0000e-02 eta: 3:28:08 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.6791 loss: 1.9481 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9481 2023/03/17 19:53:19 - mmengine - INFO - Epoch(train) [22][1140/1320] lr: 2.0000e-02 eta: 3:28:01 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.6524 loss: 2.1465 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1465 2023/03/17 19:53:26 - mmengine - INFO - Epoch(train) [22][1160/1320] lr: 2.0000e-02 eta: 3:27:54 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.3950 loss: 1.8183 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8183 2023/03/17 19:53:33 - mmengine - INFO - Epoch(train) [22][1180/1320] lr: 2.0000e-02 eta: 3:27:47 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.5128 loss: 1.8987 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8987 2023/03/17 19:53:39 - mmengine - INFO - Epoch(train) [22][1200/1320] lr: 2.0000e-02 eta: 3:27:41 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.6003 loss: 1.7694 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7694 2023/03/17 19:53:46 - mmengine - INFO - Epoch(train) [22][1220/1320] lr: 2.0000e-02 eta: 3:27:34 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.6081 loss: 1.8112 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 1.8112 2023/03/17 19:53:53 - mmengine - INFO - Epoch(train) [22][1240/1320] lr: 2.0000e-02 eta: 3:27:27 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.4962 loss: 1.9524 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9524 2023/03/17 19:53:59 - mmengine - INFO - Epoch(train) [22][1260/1320] lr: 2.0000e-02 eta: 3:27:20 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.5072 loss: 1.7944 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7944 2023/03/17 19:54:06 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:54:06 - mmengine - INFO - Epoch(train) [22][1280/1320] lr: 2.0000e-02 eta: 3:27:14 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.5140 loss: 1.8553 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8553 2023/03/17 19:54:13 - mmengine - INFO - Epoch(train) [22][1300/1320] lr: 2.0000e-02 eta: 3:27:07 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5620 loss: 1.8256 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8256 2023/03/17 19:54:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 19:54:19 - mmengine - INFO - Epoch(train) [22][1320/1320] lr: 2.0000e-02 eta: 3:27:00 time: 0.3302 data_time: 0.0120 memory: 18752 grad_norm: 4.6229 loss: 1.8880 top1_acc: 0.4545 top5_acc: 0.6364 loss_cls: 1.8880 2023/03/17 19:54:22 - mmengine - INFO - Epoch(val) [22][ 20/194] eta: 0:00:21 time: 0.1264 data_time: 0.0400 memory: 2112 2023/03/17 19:54:24 - mmengine - INFO - Epoch(val) [22][ 40/194] eta: 0:00:17 time: 0.0957 data_time: 0.0096 memory: 2112 2023/03/17 19:54:26 - mmengine - INFO - Epoch(val) [22][ 60/194] eta: 0:00:14 time: 0.0961 data_time: 0.0105 memory: 2112 2023/03/17 19:54:28 - mmengine - INFO - Epoch(val) [22][ 80/194] eta: 0:00:11 time: 0.0964 data_time: 0.0104 memory: 2112 2023/03/17 19:54:30 - mmengine - INFO - Epoch(val) [22][100/194] eta: 0:00:09 time: 0.0966 data_time: 0.0108 memory: 2112 2023/03/17 19:54:32 - mmengine - INFO - Epoch(val) [22][120/194] eta: 0:00:07 time: 0.0961 data_time: 0.0105 memory: 2112 2023/03/17 19:54:34 - mmengine - INFO - Epoch(val) [22][140/194] eta: 0:00:05 time: 0.0960 data_time: 0.0102 memory: 2112 2023/03/17 19:54:36 - mmengine - INFO - Epoch(val) [22][160/194] eta: 0:00:03 time: 0.0967 data_time: 0.0108 memory: 2112 2023/03/17 19:54:37 - mmengine - INFO - Epoch(val) [22][180/194] eta: 0:00:01 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 19:54:41 - mmengine - INFO - Epoch(val) [22][194/194] acc/top1: 0.4635 acc/top5: 0.7602 acc/mean1: 0.3967 2023/03/17 19:54:49 - mmengine - INFO - Epoch(train) [23][ 20/1320] lr: 2.0000e-02 eta: 3:26:54 time: 0.3712 data_time: 0.0411 memory: 18752 grad_norm: 4.3753 loss: 2.0016 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0016 2023/03/17 19:54:55 - mmengine - INFO - Epoch(train) [23][ 40/1320] lr: 2.0000e-02 eta: 3:26:48 time: 0.3367 data_time: 0.0124 memory: 18752 grad_norm: 4.4483 loss: 1.7336 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7336 2023/03/17 19:55:02 - mmengine - INFO - Epoch(train) [23][ 60/1320] lr: 2.0000e-02 eta: 3:26:41 time: 0.3350 data_time: 0.0122 memory: 18752 grad_norm: 4.4301 loss: 1.7205 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7205 2023/03/17 19:55:09 - mmengine - INFO - Epoch(train) [23][ 80/1320] lr: 2.0000e-02 eta: 3:26:34 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.5089 loss: 1.9014 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9014 2023/03/17 19:55:15 - mmengine - INFO - Epoch(train) [23][ 100/1320] lr: 2.0000e-02 eta: 3:26:27 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.5560 loss: 1.9462 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9462 2023/03/17 19:55:22 - mmengine - INFO - Epoch(train) [23][ 120/1320] lr: 2.0000e-02 eta: 3:26:21 time: 0.3365 data_time: 0.0118 memory: 18752 grad_norm: 4.5205 loss: 1.7716 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.7716 2023/03/17 19:55:29 - mmengine - INFO - Epoch(train) [23][ 140/1320] lr: 2.0000e-02 eta: 3:26:14 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 4.6035 loss: 1.7686 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7686 2023/03/17 19:55:36 - mmengine - INFO - Epoch(train) [23][ 160/1320] lr: 2.0000e-02 eta: 3:26:07 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.7034 loss: 2.0795 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.0795 2023/03/17 19:55:42 - mmengine - INFO - Epoch(train) [23][ 180/1320] lr: 2.0000e-02 eta: 3:26:00 time: 0.3348 data_time: 0.0117 memory: 18752 grad_norm: 4.6117 loss: 1.8950 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8950 2023/03/17 19:55:49 - mmengine - INFO - Epoch(train) [23][ 200/1320] lr: 2.0000e-02 eta: 3:25:54 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.5250 loss: 1.8448 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8448 2023/03/17 19:55:56 - mmengine - INFO - Epoch(train) [23][ 220/1320] lr: 2.0000e-02 eta: 3:25:47 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.5160 loss: 1.7416 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7416 2023/03/17 19:56:02 - mmengine - INFO - Epoch(train) [23][ 240/1320] lr: 2.0000e-02 eta: 3:25:40 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5220 loss: 1.8980 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8980 2023/03/17 19:56:09 - mmengine - INFO - Epoch(train) [23][ 260/1320] lr: 2.0000e-02 eta: 3:25:34 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.6656 loss: 1.9984 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9984 2023/03/17 19:56:16 - mmengine - INFO - Epoch(train) [23][ 280/1320] lr: 2.0000e-02 eta: 3:25:27 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 4.6706 loss: 1.9510 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9510 2023/03/17 19:56:23 - mmengine - INFO - Epoch(train) [23][ 300/1320] lr: 2.0000e-02 eta: 3:25:20 time: 0.3351 data_time: 0.0117 memory: 18752 grad_norm: 4.6863 loss: 1.8401 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8401 2023/03/17 19:56:29 - mmengine - INFO - Epoch(train) [23][ 320/1320] lr: 2.0000e-02 eta: 3:25:13 time: 0.3350 data_time: 0.0117 memory: 18752 grad_norm: 4.4698 loss: 1.8242 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8242 2023/03/17 19:56:36 - mmengine - INFO - Epoch(train) [23][ 340/1320] lr: 2.0000e-02 eta: 3:25:07 time: 0.3350 data_time: 0.0121 memory: 18752 grad_norm: 4.5244 loss: 1.8833 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8833 2023/03/17 19:56:43 - mmengine - INFO - Epoch(train) [23][ 360/1320] lr: 2.0000e-02 eta: 3:25:00 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5981 loss: 1.6406 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6406 2023/03/17 19:56:49 - mmengine - INFO - Epoch(train) [23][ 380/1320] lr: 2.0000e-02 eta: 3:24:53 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5293 loss: 1.8917 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8917 2023/03/17 19:56:56 - mmengine - INFO - Epoch(train) [23][ 400/1320] lr: 2.0000e-02 eta: 3:24:46 time: 0.3354 data_time: 0.0128 memory: 18752 grad_norm: 4.5869 loss: 2.1576 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1576 2023/03/17 19:57:03 - mmengine - INFO - Epoch(train) [23][ 420/1320] lr: 2.0000e-02 eta: 3:24:40 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.4792 loss: 1.9377 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9377 2023/03/17 19:57:10 - mmengine - INFO - Epoch(train) [23][ 440/1320] lr: 2.0000e-02 eta: 3:24:33 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.6206 loss: 1.7858 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7858 2023/03/17 19:57:16 - mmengine - INFO - Epoch(train) [23][ 460/1320] lr: 2.0000e-02 eta: 3:24:26 time: 0.3370 data_time: 0.0121 memory: 18752 grad_norm: 4.7367 loss: 1.8220 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8220 2023/03/17 19:57:23 - mmengine - INFO - Epoch(train) [23][ 480/1320] lr: 2.0000e-02 eta: 3:24:19 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 4.5431 loss: 1.7997 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7997 2023/03/17 19:57:30 - mmengine - INFO - Epoch(train) [23][ 500/1320] lr: 2.0000e-02 eta: 3:24:13 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 4.5324 loss: 1.8971 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8971 2023/03/17 19:57:36 - mmengine - INFO - Epoch(train) [23][ 520/1320] lr: 2.0000e-02 eta: 3:24:06 time: 0.3349 data_time: 0.0120 memory: 18752 grad_norm: 4.6328 loss: 2.0206 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0206 2023/03/17 19:57:43 - mmengine - INFO - Epoch(train) [23][ 540/1320] lr: 2.0000e-02 eta: 3:23:59 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.5692 loss: 1.9508 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9508 2023/03/17 19:57:50 - mmengine - INFO - Epoch(train) [23][ 560/1320] lr: 2.0000e-02 eta: 3:23:52 time: 0.3350 data_time: 0.0123 memory: 18752 grad_norm: 4.5303 loss: 1.8990 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8990 2023/03/17 19:57:57 - mmengine - INFO - Epoch(train) [23][ 580/1320] lr: 2.0000e-02 eta: 3:23:46 time: 0.3350 data_time: 0.0122 memory: 18752 grad_norm: 4.5713 loss: 1.8899 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8899 2023/03/17 19:58:03 - mmengine - INFO - Epoch(train) [23][ 600/1320] lr: 2.0000e-02 eta: 3:23:39 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.6396 loss: 1.7978 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7978 2023/03/17 19:58:10 - mmengine - INFO - Epoch(train) [23][ 620/1320] lr: 2.0000e-02 eta: 3:23:32 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.6528 loss: 1.9043 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9043 2023/03/17 19:58:17 - mmengine - INFO - Epoch(train) [23][ 640/1320] lr: 2.0000e-02 eta: 3:23:26 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.5413 loss: 1.9468 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9468 2023/03/17 19:58:23 - mmengine - INFO - Epoch(train) [23][ 660/1320] lr: 2.0000e-02 eta: 3:23:19 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.4795 loss: 1.8858 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8858 2023/03/17 19:58:30 - mmengine - INFO - Epoch(train) [23][ 680/1320] lr: 2.0000e-02 eta: 3:23:12 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.6311 loss: 1.8319 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.8319 2023/03/17 19:58:37 - mmengine - INFO - Epoch(train) [23][ 700/1320] lr: 2.0000e-02 eta: 3:23:05 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.6335 loss: 1.9173 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9173 2023/03/17 19:58:43 - mmengine - INFO - Epoch(train) [23][ 720/1320] lr: 2.0000e-02 eta: 3:22:59 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.6924 loss: 1.8424 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8424 2023/03/17 19:58:50 - mmengine - INFO - Epoch(train) [23][ 740/1320] lr: 2.0000e-02 eta: 3:22:52 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.5392 loss: 1.7858 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.7858 2023/03/17 19:58:57 - mmengine - INFO - Epoch(train) [23][ 760/1320] lr: 2.0000e-02 eta: 3:22:45 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.7440 loss: 1.9019 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9019 2023/03/17 19:59:04 - mmengine - INFO - Epoch(train) [23][ 780/1320] lr: 2.0000e-02 eta: 3:22:38 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 4.4304 loss: 2.0872 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0872 2023/03/17 19:59:10 - mmengine - INFO - Epoch(train) [23][ 800/1320] lr: 2.0000e-02 eta: 3:22:32 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 4.5127 loss: 2.0018 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0018 2023/03/17 19:59:17 - mmengine - INFO - Epoch(train) [23][ 820/1320] lr: 2.0000e-02 eta: 3:22:25 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4839 loss: 1.8634 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8634 2023/03/17 19:59:24 - mmengine - INFO - Epoch(train) [23][ 840/1320] lr: 2.0000e-02 eta: 3:22:18 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 4.6066 loss: 1.8873 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8873 2023/03/17 19:59:30 - mmengine - INFO - Epoch(train) [23][ 860/1320] lr: 2.0000e-02 eta: 3:22:11 time: 0.3354 data_time: 0.0128 memory: 18752 grad_norm: 4.6328 loss: 1.8739 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8739 2023/03/17 19:59:37 - mmengine - INFO - Epoch(train) [23][ 880/1320] lr: 2.0000e-02 eta: 3:22:05 time: 0.3353 data_time: 0.0126 memory: 18752 grad_norm: 4.5137 loss: 1.7357 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7357 2023/03/17 19:59:44 - mmengine - INFO - Epoch(train) [23][ 900/1320] lr: 2.0000e-02 eta: 3:21:58 time: 0.3362 data_time: 0.0124 memory: 18752 grad_norm: 4.4885 loss: 1.9395 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9395 2023/03/17 19:59:51 - mmengine - INFO - Epoch(train) [23][ 920/1320] lr: 2.0000e-02 eta: 3:21:51 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.6957 loss: 1.9372 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9372 2023/03/17 19:59:57 - mmengine - INFO - Epoch(train) [23][ 940/1320] lr: 2.0000e-02 eta: 3:21:45 time: 0.3370 data_time: 0.0122 memory: 18752 grad_norm: 4.5740 loss: 1.9297 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9297 2023/03/17 20:00:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:00:04 - mmengine - INFO - Epoch(train) [23][ 960/1320] lr: 2.0000e-02 eta: 3:21:38 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 4.3870 loss: 1.8812 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8812 2023/03/17 20:00:11 - mmengine - INFO - Epoch(train) [23][ 980/1320] lr: 2.0000e-02 eta: 3:21:31 time: 0.3379 data_time: 0.0123 memory: 18752 grad_norm: 4.4341 loss: 1.7501 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7501 2023/03/17 20:00:18 - mmengine - INFO - Epoch(train) [23][1000/1320] lr: 2.0000e-02 eta: 3:21:24 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 4.5779 loss: 1.7173 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7173 2023/03/17 20:00:24 - mmengine - INFO - Epoch(train) [23][1020/1320] lr: 2.0000e-02 eta: 3:21:18 time: 0.3366 data_time: 0.0123 memory: 18752 grad_norm: 4.3831 loss: 1.7570 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7570 2023/03/17 20:00:31 - mmengine - INFO - Epoch(train) [23][1040/1320] lr: 2.0000e-02 eta: 3:21:11 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 4.5784 loss: 1.8244 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8244 2023/03/17 20:00:38 - mmengine - INFO - Epoch(train) [23][1060/1320] lr: 2.0000e-02 eta: 3:21:04 time: 0.3367 data_time: 0.0124 memory: 18752 grad_norm: 4.6465 loss: 1.8257 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8257 2023/03/17 20:00:44 - mmengine - INFO - Epoch(train) [23][1080/1320] lr: 2.0000e-02 eta: 3:20:58 time: 0.3359 data_time: 0.0126 memory: 18752 grad_norm: 4.4968 loss: 1.8456 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8456 2023/03/17 20:00:51 - mmengine - INFO - Epoch(train) [23][1100/1320] lr: 2.0000e-02 eta: 3:20:51 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 4.5939 loss: 1.9878 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9878 2023/03/17 20:00:58 - mmengine - INFO - Epoch(train) [23][1120/1320] lr: 2.0000e-02 eta: 3:20:44 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.6179 loss: 1.9203 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9203 2023/03/17 20:01:05 - mmengine - INFO - Epoch(train) [23][1140/1320] lr: 2.0000e-02 eta: 3:20:37 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 4.5722 loss: 1.8005 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8005 2023/03/17 20:01:11 - mmengine - INFO - Epoch(train) [23][1160/1320] lr: 2.0000e-02 eta: 3:20:31 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.5737 loss: 1.8766 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8766 2023/03/17 20:01:18 - mmengine - INFO - Epoch(train) [23][1180/1320] lr: 2.0000e-02 eta: 3:20:24 time: 0.3362 data_time: 0.0129 memory: 18752 grad_norm: 4.5094 loss: 1.7878 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7878 2023/03/17 20:01:25 - mmengine - INFO - Epoch(train) [23][1200/1320] lr: 2.0000e-02 eta: 3:20:17 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 4.5187 loss: 1.7386 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.7386 2023/03/17 20:01:32 - mmengine - INFO - Epoch(train) [23][1220/1320] lr: 2.0000e-02 eta: 3:20:11 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 4.5836 loss: 2.0645 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0645 2023/03/17 20:01:38 - mmengine - INFO - Epoch(train) [23][1240/1320] lr: 2.0000e-02 eta: 3:20:04 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.5189 loss: 1.8983 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8983 2023/03/17 20:01:45 - mmengine - INFO - Epoch(train) [23][1260/1320] lr: 2.0000e-02 eta: 3:19:57 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.5394 loss: 2.0049 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0049 2023/03/17 20:01:52 - mmengine - INFO - Epoch(train) [23][1280/1320] lr: 2.0000e-02 eta: 3:19:50 time: 0.3371 data_time: 0.0126 memory: 18752 grad_norm: 4.6338 loss: 1.7093 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7093 2023/03/17 20:01:58 - mmengine - INFO - Epoch(train) [23][1300/1320] lr: 2.0000e-02 eta: 3:19:44 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.5283 loss: 2.0212 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.0212 2023/03/17 20:02:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:02:05 - mmengine - INFO - Epoch(train) [23][1320/1320] lr: 2.0000e-02 eta: 3:19:37 time: 0.3308 data_time: 0.0124 memory: 18752 grad_norm: 4.5760 loss: 1.9010 top1_acc: 0.2727 top5_acc: 0.8182 loss_cls: 1.9010 2023/03/17 20:02:08 - mmengine - INFO - Epoch(val) [23][ 20/194] eta: 0:00:22 time: 0.1302 data_time: 0.0437 memory: 2112 2023/03/17 20:02:10 - mmengine - INFO - Epoch(val) [23][ 40/194] eta: 0:00:17 time: 0.0964 data_time: 0.0100 memory: 2112 2023/03/17 20:02:11 - mmengine - INFO - Epoch(val) [23][ 60/194] eta: 0:00:14 time: 0.0961 data_time: 0.0102 memory: 2112 2023/03/17 20:02:13 - mmengine - INFO - Epoch(val) [23][ 80/194] eta: 0:00:11 time: 0.0964 data_time: 0.0104 memory: 2112 2023/03/17 20:02:15 - mmengine - INFO - Epoch(val) [23][100/194] eta: 0:00:09 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 20:02:17 - mmengine - INFO - Epoch(val) [23][120/194] eta: 0:00:07 time: 0.0966 data_time: 0.0108 memory: 2112 2023/03/17 20:02:19 - mmengine - INFO - Epoch(val) [23][140/194] eta: 0:00:05 time: 0.0972 data_time: 0.0113 memory: 2112 2023/03/17 20:02:21 - mmengine - INFO - Epoch(val) [23][160/194] eta: 0:00:03 time: 0.0970 data_time: 0.0109 memory: 2112 2023/03/17 20:02:23 - mmengine - INFO - Epoch(val) [23][180/194] eta: 0:00:01 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 20:02:26 - mmengine - INFO - Epoch(val) [23][194/194] acc/top1: 0.4718 acc/top5: 0.7641 acc/mean1: 0.3973 2023/03/17 20:02:34 - mmengine - INFO - Epoch(train) [24][ 20/1320] lr: 2.0000e-02 eta: 3:19:31 time: 0.3721 data_time: 0.0411 memory: 18752 grad_norm: 4.4116 loss: 1.8601 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8601 2023/03/17 20:02:41 - mmengine - INFO - Epoch(train) [24][ 40/1320] lr: 2.0000e-02 eta: 3:19:24 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.3591 loss: 1.9254 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9254 2023/03/17 20:02:47 - mmengine - INFO - Epoch(train) [24][ 60/1320] lr: 2.0000e-02 eta: 3:19:17 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 4.6092 loss: 1.7800 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7800 2023/03/17 20:02:54 - mmengine - INFO - Epoch(train) [24][ 80/1320] lr: 2.0000e-02 eta: 3:19:11 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.5573 loss: 1.6880 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6880 2023/03/17 20:03:01 - mmengine - INFO - Epoch(train) [24][ 100/1320] lr: 2.0000e-02 eta: 3:19:04 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.6926 loss: 1.8838 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8838 2023/03/17 20:03:08 - mmengine - INFO - Epoch(train) [24][ 120/1320] lr: 2.0000e-02 eta: 3:18:57 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.6085 loss: 1.8506 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8506 2023/03/17 20:03:14 - mmengine - INFO - Epoch(train) [24][ 140/1320] lr: 2.0000e-02 eta: 3:18:51 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.5116 loss: 1.9426 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9426 2023/03/17 20:03:21 - mmengine - INFO - Epoch(train) [24][ 160/1320] lr: 2.0000e-02 eta: 3:18:44 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.6109 loss: 1.5725 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5725 2023/03/17 20:03:28 - mmengine - INFO - Epoch(train) [24][ 180/1320] lr: 2.0000e-02 eta: 3:18:37 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 4.4007 loss: 1.8275 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8275 2023/03/17 20:03:34 - mmengine - INFO - Epoch(train) [24][ 200/1320] lr: 2.0000e-02 eta: 3:18:30 time: 0.3362 data_time: 0.0114 memory: 18752 grad_norm: 4.4871 loss: 1.6249 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6249 2023/03/17 20:03:41 - mmengine - INFO - Epoch(train) [24][ 220/1320] lr: 2.0000e-02 eta: 3:18:24 time: 0.3356 data_time: 0.0115 memory: 18752 grad_norm: 4.5764 loss: 1.9023 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.9023 2023/03/17 20:03:48 - mmengine - INFO - Epoch(train) [24][ 240/1320] lr: 2.0000e-02 eta: 3:18:17 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 4.6147 loss: 1.7620 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7620 2023/03/17 20:03:55 - mmengine - INFO - Epoch(train) [24][ 260/1320] lr: 2.0000e-02 eta: 3:18:10 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.4952 loss: 1.7947 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7947 2023/03/17 20:04:01 - mmengine - INFO - Epoch(train) [24][ 280/1320] lr: 2.0000e-02 eta: 3:18:03 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.6334 loss: 1.8778 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8778 2023/03/17 20:04:08 - mmengine - INFO - Epoch(train) [24][ 300/1320] lr: 2.0000e-02 eta: 3:17:57 time: 0.3361 data_time: 0.0113 memory: 18752 grad_norm: 4.6046 loss: 1.8636 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8636 2023/03/17 20:04:15 - mmengine - INFO - Epoch(train) [24][ 320/1320] lr: 2.0000e-02 eta: 3:17:50 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.7119 loss: 1.8391 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8391 2023/03/17 20:04:21 - mmengine - INFO - Epoch(train) [24][ 340/1320] lr: 2.0000e-02 eta: 3:17:43 time: 0.3356 data_time: 0.0127 memory: 18752 grad_norm: 4.6442 loss: 1.7603 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7603 2023/03/17 20:04:28 - mmengine - INFO - Epoch(train) [24][ 360/1320] lr: 2.0000e-02 eta: 3:17:36 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.6062 loss: 1.7372 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7372 2023/03/17 20:04:35 - mmengine - INFO - Epoch(train) [24][ 380/1320] lr: 2.0000e-02 eta: 3:17:30 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.6233 loss: 1.8468 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8468 2023/03/17 20:04:42 - mmengine - INFO - Epoch(train) [24][ 400/1320] lr: 2.0000e-02 eta: 3:17:23 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 4.4973 loss: 1.9938 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9938 2023/03/17 20:04:48 - mmengine - INFO - Epoch(train) [24][ 420/1320] lr: 2.0000e-02 eta: 3:17:16 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.4567 loss: 1.8392 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8392 2023/03/17 20:04:55 - mmengine - INFO - Epoch(train) [24][ 440/1320] lr: 2.0000e-02 eta: 3:17:10 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.5479 loss: 1.7741 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7741 2023/03/17 20:05:02 - mmengine - INFO - Epoch(train) [24][ 460/1320] lr: 2.0000e-02 eta: 3:17:03 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.5754 loss: 1.9100 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9100 2023/03/17 20:05:08 - mmengine - INFO - Epoch(train) [24][ 480/1320] lr: 2.0000e-02 eta: 3:16:56 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 4.5418 loss: 1.9554 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9554 2023/03/17 20:05:15 - mmengine - INFO - Epoch(train) [24][ 500/1320] lr: 2.0000e-02 eta: 3:16:49 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 4.4821 loss: 1.8604 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8604 2023/03/17 20:05:22 - mmengine - INFO - Epoch(train) [24][ 520/1320] lr: 2.0000e-02 eta: 3:16:43 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.6376 loss: 1.9759 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9759 2023/03/17 20:05:29 - mmengine - INFO - Epoch(train) [24][ 540/1320] lr: 2.0000e-02 eta: 3:16:36 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.5729 loss: 1.7280 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7280 2023/03/17 20:05:35 - mmengine - INFO - Epoch(train) [24][ 560/1320] lr: 2.0000e-02 eta: 3:16:29 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.5217 loss: 1.8540 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8540 2023/03/17 20:05:42 - mmengine - INFO - Epoch(train) [24][ 580/1320] lr: 2.0000e-02 eta: 3:16:22 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.6439 loss: 1.8877 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8877 2023/03/17 20:05:49 - mmengine - INFO - Epoch(train) [24][ 600/1320] lr: 2.0000e-02 eta: 3:16:16 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 4.6251 loss: 2.0155 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0155 2023/03/17 20:05:55 - mmengine - INFO - Epoch(train) [24][ 620/1320] lr: 2.0000e-02 eta: 3:16:09 time: 0.3356 data_time: 0.0114 memory: 18752 grad_norm: 4.5899 loss: 1.8046 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.8046 2023/03/17 20:06:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:06:02 - mmengine - INFO - Epoch(train) [24][ 640/1320] lr: 2.0000e-02 eta: 3:16:02 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 4.6524 loss: 1.9712 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9712 2023/03/17 20:06:09 - mmengine - INFO - Epoch(train) [24][ 660/1320] lr: 2.0000e-02 eta: 3:15:56 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.5419 loss: 1.8888 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8888 2023/03/17 20:06:16 - mmengine - INFO - Epoch(train) [24][ 680/1320] lr: 2.0000e-02 eta: 3:15:49 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 4.6065 loss: 1.9135 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9135 2023/03/17 20:06:22 - mmengine - INFO - Epoch(train) [24][ 700/1320] lr: 2.0000e-02 eta: 3:15:42 time: 0.3351 data_time: 0.0114 memory: 18752 grad_norm: 4.4878 loss: 1.9809 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9809 2023/03/17 20:06:29 - mmengine - INFO - Epoch(train) [24][ 720/1320] lr: 2.0000e-02 eta: 3:15:35 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 4.5252 loss: 1.7622 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7622 2023/03/17 20:06:36 - mmengine - INFO - Epoch(train) [24][ 740/1320] lr: 2.0000e-02 eta: 3:15:29 time: 0.3364 data_time: 0.0117 memory: 18752 grad_norm: 4.6805 loss: 1.6570 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6570 2023/03/17 20:06:42 - mmengine - INFO - Epoch(train) [24][ 760/1320] lr: 2.0000e-02 eta: 3:15:22 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.7974 loss: 2.0386 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0386 2023/03/17 20:06:49 - mmengine - INFO - Epoch(train) [24][ 780/1320] lr: 2.0000e-02 eta: 3:15:15 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 4.5887 loss: 1.6754 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6754 2023/03/17 20:06:56 - mmengine - INFO - Epoch(train) [24][ 800/1320] lr: 2.0000e-02 eta: 3:15:08 time: 0.3350 data_time: 0.0121 memory: 18752 grad_norm: 4.7549 loss: 1.9322 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9322 2023/03/17 20:07:03 - mmengine - INFO - Epoch(train) [24][ 820/1320] lr: 2.0000e-02 eta: 3:15:02 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.5561 loss: 1.7094 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7094 2023/03/17 20:07:09 - mmengine - INFO - Epoch(train) [24][ 840/1320] lr: 2.0000e-02 eta: 3:14:55 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.5483 loss: 1.7366 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7366 2023/03/17 20:07:16 - mmengine - INFO - Epoch(train) [24][ 860/1320] lr: 2.0000e-02 eta: 3:14:48 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.6294 loss: 1.9320 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9320 2023/03/17 20:07:23 - mmengine - INFO - Epoch(train) [24][ 880/1320] lr: 2.0000e-02 eta: 3:14:42 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.6937 loss: 2.0118 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0118 2023/03/17 20:07:29 - mmengine - INFO - Epoch(train) [24][ 900/1320] lr: 2.0000e-02 eta: 3:14:35 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5501 loss: 1.7472 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7472 2023/03/17 20:07:36 - mmengine - INFO - Epoch(train) [24][ 920/1320] lr: 2.0000e-02 eta: 3:14:28 time: 0.3350 data_time: 0.0120 memory: 18752 grad_norm: 4.5152 loss: 1.7452 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7452 2023/03/17 20:07:43 - mmengine - INFO - Epoch(train) [24][ 940/1320] lr: 2.0000e-02 eta: 3:14:21 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 4.4340 loss: 1.8761 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.8761 2023/03/17 20:07:50 - mmengine - INFO - Epoch(train) [24][ 960/1320] lr: 2.0000e-02 eta: 3:14:15 time: 0.3352 data_time: 0.0114 memory: 18752 grad_norm: 4.5794 loss: 1.8482 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8482 2023/03/17 20:07:56 - mmengine - INFO - Epoch(train) [24][ 980/1320] lr: 2.0000e-02 eta: 3:14:08 time: 0.3356 data_time: 0.0115 memory: 18752 grad_norm: 4.6138 loss: 1.9316 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9316 2023/03/17 20:08:03 - mmengine - INFO - Epoch(train) [24][1000/1320] lr: 2.0000e-02 eta: 3:14:01 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.5785 loss: 1.8266 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8266 2023/03/17 20:08:10 - mmengine - INFO - Epoch(train) [24][1020/1320] lr: 2.0000e-02 eta: 3:13:54 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 4.5421 loss: 2.1001 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1001 2023/03/17 20:08:16 - mmengine - INFO - Epoch(train) [24][1040/1320] lr: 2.0000e-02 eta: 3:13:48 time: 0.3360 data_time: 0.0117 memory: 18752 grad_norm: 4.6466 loss: 1.8454 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8454 2023/03/17 20:08:23 - mmengine - INFO - Epoch(train) [24][1060/1320] lr: 2.0000e-02 eta: 3:13:41 time: 0.3358 data_time: 0.0129 memory: 18752 grad_norm: 4.5911 loss: 1.8108 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8108 2023/03/17 20:08:30 - mmengine - INFO - Epoch(train) [24][1080/1320] lr: 2.0000e-02 eta: 3:13:34 time: 0.3371 data_time: 0.0124 memory: 18752 grad_norm: 4.4971 loss: 1.6843 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6843 2023/03/17 20:08:37 - mmengine - INFO - Epoch(train) [24][1100/1320] lr: 2.0000e-02 eta: 3:13:27 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.6546 loss: 1.9583 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9583 2023/03/17 20:08:43 - mmengine - INFO - Epoch(train) [24][1120/1320] lr: 2.0000e-02 eta: 3:13:21 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.5033 loss: 1.9372 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9372 2023/03/17 20:08:50 - mmengine - INFO - Epoch(train) [24][1140/1320] lr: 2.0000e-02 eta: 3:13:14 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.5268 loss: 1.7873 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7873 2023/03/17 20:08:57 - mmengine - INFO - Epoch(train) [24][1160/1320] lr: 2.0000e-02 eta: 3:13:07 time: 0.3365 data_time: 0.0118 memory: 18752 grad_norm: 4.4365 loss: 1.9386 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9386 2023/03/17 20:09:03 - mmengine - INFO - Epoch(train) [24][1180/1320] lr: 2.0000e-02 eta: 3:13:01 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5194 loss: 1.7565 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7565 2023/03/17 20:09:10 - mmengine - INFO - Epoch(train) [24][1200/1320] lr: 2.0000e-02 eta: 3:12:54 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.6191 loss: 1.8028 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8028 2023/03/17 20:09:17 - mmengine - INFO - Epoch(train) [24][1220/1320] lr: 2.0000e-02 eta: 3:12:47 time: 0.3355 data_time: 0.0115 memory: 18752 grad_norm: 4.6107 loss: 1.7846 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7846 2023/03/17 20:09:24 - mmengine - INFO - Epoch(train) [24][1240/1320] lr: 2.0000e-02 eta: 3:12:40 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.7289 loss: 1.9133 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.9133 2023/03/17 20:09:30 - mmengine - INFO - Epoch(train) [24][1260/1320] lr: 2.0000e-02 eta: 3:12:34 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.6076 loss: 1.9906 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9906 2023/03/17 20:09:37 - mmengine - INFO - Epoch(train) [24][1280/1320] lr: 2.0000e-02 eta: 3:12:27 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.5142 loss: 1.7947 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7947 2023/03/17 20:09:44 - mmengine - INFO - Epoch(train) [24][1300/1320] lr: 2.0000e-02 eta: 3:12:20 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 4.4390 loss: 1.6938 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6938 2023/03/17 20:09:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:09:50 - mmengine - INFO - Epoch(train) [24][1320/1320] lr: 2.0000e-02 eta: 3:12:13 time: 0.3309 data_time: 0.0119 memory: 18752 grad_norm: 4.5299 loss: 1.9887 top1_acc: 0.5455 top5_acc: 1.0000 loss_cls: 1.9887 2023/03/17 20:09:50 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/03/17 20:09:56 - mmengine - INFO - Epoch(val) [24][ 20/194] eta: 0:00:22 time: 0.1265 data_time: 0.0395 memory: 2112 2023/03/17 20:09:58 - mmengine - INFO - Epoch(val) [24][ 40/194] eta: 0:00:17 time: 0.1024 data_time: 0.0163 memory: 2112 2023/03/17 20:10:00 - mmengine - INFO - Epoch(val) [24][ 60/194] eta: 0:00:14 time: 0.0966 data_time: 0.0109 memory: 2112 2023/03/17 20:10:02 - mmengine - INFO - Epoch(val) [24][ 80/194] eta: 0:00:12 time: 0.0963 data_time: 0.0106 memory: 2112 2023/03/17 20:10:04 - mmengine - INFO - Epoch(val) [24][100/194] eta: 0:00:09 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 20:10:06 - mmengine - INFO - Epoch(val) [24][120/194] eta: 0:00:07 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 20:10:08 - mmengine - INFO - Epoch(val) [24][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 20:10:10 - mmengine - INFO - Epoch(val) [24][160/194] eta: 0:00:03 time: 0.0968 data_time: 0.0108 memory: 2112 2023/03/17 20:10:12 - mmengine - INFO - Epoch(val) [24][180/194] eta: 0:00:01 time: 0.0954 data_time: 0.0095 memory: 2112 2023/03/17 20:10:14 - mmengine - INFO - Epoch(val) [24][194/194] acc/top1: 0.4853 acc/top5: 0.7830 acc/mean1: 0.4278 2023/03/17 20:10:14 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_20.pth is removed 2023/03/17 20:10:15 - mmengine - INFO - The best checkpoint with 0.4853 acc/top1 at 24 epoch is saved to best_acc/top1_epoch_24.pth. 2023/03/17 20:10:23 - mmengine - INFO - Epoch(train) [25][ 20/1320] lr: 2.0000e-02 eta: 3:12:07 time: 0.3672 data_time: 0.0364 memory: 18752 grad_norm: 4.4817 loss: 1.6892 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6892 2023/03/17 20:10:29 - mmengine - INFO - Epoch(train) [25][ 40/1320] lr: 2.0000e-02 eta: 3:12:01 time: 0.3362 data_time: 0.0116 memory: 18752 grad_norm: 4.5553 loss: 1.5672 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5672 2023/03/17 20:10:36 - mmengine - INFO - Epoch(train) [25][ 60/1320] lr: 2.0000e-02 eta: 3:11:54 time: 0.3358 data_time: 0.0113 memory: 18752 grad_norm: 4.5241 loss: 1.7137 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.7137 2023/03/17 20:10:43 - mmengine - INFO - Epoch(train) [25][ 80/1320] lr: 2.0000e-02 eta: 3:11:47 time: 0.3349 data_time: 0.0112 memory: 18752 grad_norm: 4.5485 loss: 1.6684 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6684 2023/03/17 20:10:50 - mmengine - INFO - Epoch(train) [25][ 100/1320] lr: 2.0000e-02 eta: 3:11:40 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.5196 loss: 1.8619 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8619 2023/03/17 20:10:56 - mmengine - INFO - Epoch(train) [25][ 120/1320] lr: 2.0000e-02 eta: 3:11:34 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.5795 loss: 1.7620 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7620 2023/03/17 20:11:03 - mmengine - INFO - Epoch(train) [25][ 140/1320] lr: 2.0000e-02 eta: 3:11:27 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 4.5950 loss: 1.8943 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8943 2023/03/17 20:11:10 - mmengine - INFO - Epoch(train) [25][ 160/1320] lr: 2.0000e-02 eta: 3:11:20 time: 0.3353 data_time: 0.0113 memory: 18752 grad_norm: 4.4193 loss: 1.7759 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7759 2023/03/17 20:11:16 - mmengine - INFO - Epoch(train) [25][ 180/1320] lr: 2.0000e-02 eta: 3:11:13 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.5277 loss: 1.7486 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7486 2023/03/17 20:11:23 - mmengine - INFO - Epoch(train) [25][ 200/1320] lr: 2.0000e-02 eta: 3:11:07 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.5529 loss: 1.9654 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9654 2023/03/17 20:11:30 - mmengine - INFO - Epoch(train) [25][ 220/1320] lr: 2.0000e-02 eta: 3:11:00 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.7619 loss: 1.7501 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7501 2023/03/17 20:11:37 - mmengine - INFO - Epoch(train) [25][ 240/1320] lr: 2.0000e-02 eta: 3:10:53 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.6746 loss: 1.7910 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7910 2023/03/17 20:11:43 - mmengine - INFO - Epoch(train) [25][ 260/1320] lr: 2.0000e-02 eta: 3:10:47 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.5864 loss: 1.7623 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7623 2023/03/17 20:11:50 - mmengine - INFO - Epoch(train) [25][ 280/1320] lr: 2.0000e-02 eta: 3:10:40 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5235 loss: 1.7778 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7778 2023/03/17 20:11:57 - mmengine - INFO - Epoch(train) [25][ 300/1320] lr: 2.0000e-02 eta: 3:10:33 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.5141 loss: 1.8696 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8696 2023/03/17 20:12:03 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:12:03 - mmengine - INFO - Epoch(train) [25][ 320/1320] lr: 2.0000e-02 eta: 3:10:26 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.6874 loss: 1.7587 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7587 2023/03/17 20:12:10 - mmengine - INFO - Epoch(train) [25][ 340/1320] lr: 2.0000e-02 eta: 3:10:20 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.7090 loss: 1.5838 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5838 2023/03/17 20:12:17 - mmengine - INFO - Epoch(train) [25][ 360/1320] lr: 2.0000e-02 eta: 3:10:13 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5627 loss: 1.8695 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8695 2023/03/17 20:12:23 - mmengine - INFO - Epoch(train) [25][ 380/1320] lr: 2.0000e-02 eta: 3:10:06 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.6079 loss: 1.8738 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.8738 2023/03/17 20:12:30 - mmengine - INFO - Epoch(train) [25][ 400/1320] lr: 2.0000e-02 eta: 3:09:59 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 4.4246 loss: 1.6772 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6772 2023/03/17 20:12:37 - mmengine - INFO - Epoch(train) [25][ 420/1320] lr: 2.0000e-02 eta: 3:09:53 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.5849 loss: 1.7022 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7022 2023/03/17 20:12:44 - mmengine - INFO - Epoch(train) [25][ 440/1320] lr: 2.0000e-02 eta: 3:09:46 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.7785 loss: 1.8559 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8559 2023/03/17 20:12:50 - mmengine - INFO - Epoch(train) [25][ 460/1320] lr: 2.0000e-02 eta: 3:09:39 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5768 loss: 1.9920 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9920 2023/03/17 20:12:57 - mmengine - INFO - Epoch(train) [25][ 480/1320] lr: 2.0000e-02 eta: 3:09:32 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.5839 loss: 1.9804 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9804 2023/03/17 20:13:04 - mmengine - INFO - Epoch(train) [25][ 500/1320] lr: 2.0000e-02 eta: 3:09:26 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 4.5005 loss: 1.8490 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8490 2023/03/17 20:13:10 - mmengine - INFO - Epoch(train) [25][ 520/1320] lr: 2.0000e-02 eta: 3:09:19 time: 0.3353 data_time: 0.0114 memory: 18752 grad_norm: 4.3546 loss: 1.7478 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7478 2023/03/17 20:13:17 - mmengine - INFO - Epoch(train) [25][ 540/1320] lr: 2.0000e-02 eta: 3:09:12 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 4.3913 loss: 1.8249 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8249 2023/03/17 20:13:24 - mmengine - INFO - Epoch(train) [25][ 560/1320] lr: 2.0000e-02 eta: 3:09:06 time: 0.3400 data_time: 0.0119 memory: 18752 grad_norm: 4.5760 loss: 1.7041 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7041 2023/03/17 20:13:31 - mmengine - INFO - Epoch(train) [25][ 580/1320] lr: 2.0000e-02 eta: 3:08:59 time: 0.3370 data_time: 0.0121 memory: 18752 grad_norm: 4.5051 loss: 1.8826 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8826 2023/03/17 20:13:37 - mmengine - INFO - Epoch(train) [25][ 600/1320] lr: 2.0000e-02 eta: 3:08:52 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5199 loss: 2.0371 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 2.0371 2023/03/17 20:13:44 - mmengine - INFO - Epoch(train) [25][ 620/1320] lr: 2.0000e-02 eta: 3:08:45 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.6447 loss: 1.7826 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.7826 2023/03/17 20:13:51 - mmengine - INFO - Epoch(train) [25][ 640/1320] lr: 2.0000e-02 eta: 3:08:39 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.6896 loss: 1.8413 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8413 2023/03/17 20:13:58 - mmengine - INFO - Epoch(train) [25][ 660/1320] lr: 2.0000e-02 eta: 3:08:32 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5565 loss: 1.8633 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8633 2023/03/17 20:14:04 - mmengine - INFO - Epoch(train) [25][ 680/1320] lr: 2.0000e-02 eta: 3:08:25 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.5192 loss: 1.8012 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8012 2023/03/17 20:14:11 - mmengine - INFO - Epoch(train) [25][ 700/1320] lr: 2.0000e-02 eta: 3:08:19 time: 0.3483 data_time: 0.0116 memory: 18752 grad_norm: 4.6944 loss: 1.8193 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8193 2023/03/17 20:14:18 - mmengine - INFO - Epoch(train) [25][ 720/1320] lr: 2.0000e-02 eta: 3:08:12 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 4.7895 loss: 2.0272 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.0272 2023/03/17 20:14:25 - mmengine - INFO - Epoch(train) [25][ 740/1320] lr: 2.0000e-02 eta: 3:08:05 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.5875 loss: 1.8850 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8850 2023/03/17 20:14:31 - mmengine - INFO - Epoch(train) [25][ 760/1320] lr: 2.0000e-02 eta: 3:07:59 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.6303 loss: 1.9018 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9018 2023/03/17 20:14:38 - mmengine - INFO - Epoch(train) [25][ 780/1320] lr: 2.0000e-02 eta: 3:07:52 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.6061 loss: 1.9306 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9306 2023/03/17 20:14:45 - mmengine - INFO - Epoch(train) [25][ 800/1320] lr: 2.0000e-02 eta: 3:07:45 time: 0.3353 data_time: 0.0124 memory: 18752 grad_norm: 4.6175 loss: 1.7709 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7709 2023/03/17 20:14:51 - mmengine - INFO - Epoch(train) [25][ 820/1320] lr: 2.0000e-02 eta: 3:07:38 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 4.5782 loss: 1.8657 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8657 2023/03/17 20:14:58 - mmengine - INFO - Epoch(train) [25][ 840/1320] lr: 2.0000e-02 eta: 3:07:32 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.7514 loss: 1.9135 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9135 2023/03/17 20:15:05 - mmengine - INFO - Epoch(train) [25][ 860/1320] lr: 2.0000e-02 eta: 3:07:25 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.6497 loss: 1.7951 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7951 2023/03/17 20:15:12 - mmengine - INFO - Epoch(train) [25][ 880/1320] lr: 2.0000e-02 eta: 3:07:18 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.7513 loss: 1.8293 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.8293 2023/03/17 20:15:18 - mmengine - INFO - Epoch(train) [25][ 900/1320] lr: 2.0000e-02 eta: 3:07:11 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.6591 loss: 1.9690 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9690 2023/03/17 20:15:25 - mmengine - INFO - Epoch(train) [25][ 920/1320] lr: 2.0000e-02 eta: 3:07:05 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.5384 loss: 1.9649 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 1.9649 2023/03/17 20:15:32 - mmengine - INFO - Epoch(train) [25][ 940/1320] lr: 2.0000e-02 eta: 3:06:58 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 4.6345 loss: 2.1601 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1601 2023/03/17 20:15:39 - mmengine - INFO - Epoch(train) [25][ 960/1320] lr: 2.0000e-02 eta: 3:06:51 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.7352 loss: 2.0117 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0117 2023/03/17 20:15:45 - mmengine - INFO - Epoch(train) [25][ 980/1320] lr: 2.0000e-02 eta: 3:06:45 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.7284 loss: 1.9375 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9375 2023/03/17 20:15:52 - mmengine - INFO - Epoch(train) [25][1000/1320] lr: 2.0000e-02 eta: 3:06:38 time: 0.3357 data_time: 0.0129 memory: 18752 grad_norm: 4.4729 loss: 2.0858 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.0858 2023/03/17 20:15:59 - mmengine - INFO - Epoch(train) [25][1020/1320] lr: 2.0000e-02 eta: 3:06:31 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.4427 loss: 1.7939 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7939 2023/03/17 20:16:05 - mmengine - INFO - Epoch(train) [25][1040/1320] lr: 2.0000e-02 eta: 3:06:24 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5883 loss: 1.8728 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8728 2023/03/17 20:16:12 - mmengine - INFO - Epoch(train) [25][1060/1320] lr: 2.0000e-02 eta: 3:06:18 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.5388 loss: 1.8013 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.8013 2023/03/17 20:16:19 - mmengine - INFO - Epoch(train) [25][1080/1320] lr: 2.0000e-02 eta: 3:06:11 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.3989 loss: 1.8046 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8046 2023/03/17 20:16:26 - mmengine - INFO - Epoch(train) [25][1100/1320] lr: 2.0000e-02 eta: 3:06:04 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.6744 loss: 2.0483 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0483 2023/03/17 20:16:32 - mmengine - INFO - Epoch(train) [25][1120/1320] lr: 2.0000e-02 eta: 3:05:57 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.5829 loss: 2.0198 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0198 2023/03/17 20:16:39 - mmengine - INFO - Epoch(train) [25][1140/1320] lr: 2.0000e-02 eta: 3:05:51 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.5621 loss: 1.8481 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8481 2023/03/17 20:16:46 - mmengine - INFO - Epoch(train) [25][1160/1320] lr: 2.0000e-02 eta: 3:05:44 time: 0.3352 data_time: 0.0122 memory: 18752 grad_norm: 4.5990 loss: 1.9033 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9033 2023/03/17 20:16:52 - mmengine - INFO - Epoch(train) [25][1180/1320] lr: 2.0000e-02 eta: 3:05:37 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.6268 loss: 2.0293 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0293 2023/03/17 20:16:59 - mmengine - INFO - Epoch(train) [25][1200/1320] lr: 2.0000e-02 eta: 3:05:30 time: 0.3358 data_time: 0.0129 memory: 18752 grad_norm: 4.4124 loss: 1.8961 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8961 2023/03/17 20:17:06 - mmengine - INFO - Epoch(train) [25][1220/1320] lr: 2.0000e-02 eta: 3:05:24 time: 0.3372 data_time: 0.0137 memory: 18752 grad_norm: 4.5749 loss: 1.8390 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8390 2023/03/17 20:17:13 - mmengine - INFO - Epoch(train) [25][1240/1320] lr: 2.0000e-02 eta: 3:05:17 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.7103 loss: 1.8233 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8233 2023/03/17 20:17:19 - mmengine - INFO - Epoch(train) [25][1260/1320] lr: 2.0000e-02 eta: 3:05:10 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 4.6056 loss: 2.1470 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1470 2023/03/17 20:17:26 - mmengine - INFO - Epoch(train) [25][1280/1320] lr: 2.0000e-02 eta: 3:05:04 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.5074 loss: 1.8015 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8015 2023/03/17 20:17:33 - mmengine - INFO - Epoch(train) [25][1300/1320] lr: 2.0000e-02 eta: 3:04:57 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.5622 loss: 1.7075 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.7075 2023/03/17 20:17:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:17:39 - mmengine - INFO - Epoch(train) [25][1320/1320] lr: 2.0000e-02 eta: 3:04:50 time: 0.3309 data_time: 0.0116 memory: 18752 grad_norm: 4.7800 loss: 1.5240 top1_acc: 0.6364 top5_acc: 0.8182 loss_cls: 1.5240 2023/03/17 20:17:42 - mmengine - INFO - Epoch(val) [25][ 20/194] eta: 0:00:22 time: 0.1309 data_time: 0.0446 memory: 2112 2023/03/17 20:17:44 - mmengine - INFO - Epoch(val) [25][ 40/194] eta: 0:00:17 time: 0.0968 data_time: 0.0109 memory: 2112 2023/03/17 20:17:46 - mmengine - INFO - Epoch(val) [25][ 60/194] eta: 0:00:14 time: 0.0971 data_time: 0.0111 memory: 2112 2023/03/17 20:17:48 - mmengine - INFO - Epoch(val) [25][ 80/194] eta: 0:00:12 time: 0.0967 data_time: 0.0108 memory: 2112 2023/03/17 20:17:50 - mmengine - INFO - Epoch(val) [25][100/194] eta: 0:00:09 time: 0.0978 data_time: 0.0115 memory: 2112 2023/03/17 20:17:52 - mmengine - INFO - Epoch(val) [25][120/194] eta: 0:00:07 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 20:17:54 - mmengine - INFO - Epoch(val) [25][140/194] eta: 0:00:05 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 20:17:56 - mmengine - INFO - Epoch(val) [25][160/194] eta: 0:00:03 time: 0.0971 data_time: 0.0106 memory: 2112 2023/03/17 20:17:57 - mmengine - INFO - Epoch(val) [25][180/194] eta: 0:00:01 time: 0.0962 data_time: 0.0103 memory: 2112 2023/03/17 20:18:01 - mmengine - INFO - Epoch(val) [25][194/194] acc/top1: 0.4729 acc/top5: 0.7600 acc/mean1: 0.4029 2023/03/17 20:18:08 - mmengine - INFO - Epoch(train) [26][ 20/1320] lr: 2.0000e-03 eta: 3:04:44 time: 0.3737 data_time: 0.0385 memory: 18752 grad_norm: 4.5846 loss: 1.6711 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6711 2023/03/17 20:18:15 - mmengine - INFO - Epoch(train) [26][ 40/1320] lr: 2.0000e-03 eta: 3:04:37 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 4.2892 loss: 1.8806 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8806 2023/03/17 20:18:22 - mmengine - INFO - Epoch(train) [26][ 60/1320] lr: 2.0000e-03 eta: 3:04:31 time: 0.3361 data_time: 0.0114 memory: 18752 grad_norm: 4.2902 loss: 1.4816 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4816 2023/03/17 20:18:28 - mmengine - INFO - Epoch(train) [26][ 80/1320] lr: 2.0000e-03 eta: 3:04:24 time: 0.3355 data_time: 0.0113 memory: 18752 grad_norm: 4.1741 loss: 1.5934 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5934 2023/03/17 20:18:35 - mmengine - INFO - Epoch(train) [26][ 100/1320] lr: 2.0000e-03 eta: 3:04:17 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.2803 loss: 1.4126 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4126 2023/03/17 20:18:42 - mmengine - INFO - Epoch(train) [26][ 120/1320] lr: 2.0000e-03 eta: 3:04:10 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.2642 loss: 1.3723 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3723 2023/03/17 20:18:49 - mmengine - INFO - Epoch(train) [26][ 140/1320] lr: 2.0000e-03 eta: 3:04:04 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.1739 loss: 1.5780 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5780 2023/03/17 20:18:55 - mmengine - INFO - Epoch(train) [26][ 160/1320] lr: 2.0000e-03 eta: 3:03:57 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 4.1259 loss: 1.3545 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3545 2023/03/17 20:19:02 - mmengine - INFO - Epoch(train) [26][ 180/1320] lr: 2.0000e-03 eta: 3:03:50 time: 0.3356 data_time: 0.0113 memory: 18752 grad_norm: 4.2463 loss: 1.5677 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5677 2023/03/17 20:19:09 - mmengine - INFO - Epoch(train) [26][ 200/1320] lr: 2.0000e-03 eta: 3:03:44 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 4.1115 loss: 1.4777 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4777 2023/03/17 20:19:15 - mmengine - INFO - Epoch(train) [26][ 220/1320] lr: 2.0000e-03 eta: 3:03:37 time: 0.3368 data_time: 0.0130 memory: 18752 grad_norm: 4.1319 loss: 1.5003 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5003 2023/03/17 20:19:22 - mmengine - INFO - Epoch(train) [26][ 240/1320] lr: 2.0000e-03 eta: 3:03:30 time: 0.3348 data_time: 0.0117 memory: 18752 grad_norm: 4.2304 loss: 1.3314 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3314 2023/03/17 20:19:29 - mmengine - INFO - Epoch(train) [26][ 260/1320] lr: 2.0000e-03 eta: 3:03:23 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.1464 loss: 1.5498 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5498 2023/03/17 20:19:36 - mmengine - INFO - Epoch(train) [26][ 280/1320] lr: 2.0000e-03 eta: 3:03:17 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.2882 loss: 1.4751 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4751 2023/03/17 20:19:42 - mmengine - INFO - Epoch(train) [26][ 300/1320] lr: 2.0000e-03 eta: 3:03:10 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.1635 loss: 1.3669 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3669 2023/03/17 20:19:49 - mmengine - INFO - Epoch(train) [26][ 320/1320] lr: 2.0000e-03 eta: 3:03:03 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 4.2899 loss: 1.5576 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.5576 2023/03/17 20:19:56 - mmengine - INFO - Epoch(train) [26][ 340/1320] lr: 2.0000e-03 eta: 3:02:56 time: 0.3355 data_time: 0.0129 memory: 18752 grad_norm: 4.2090 loss: 1.2906 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2906 2023/03/17 20:20:02 - mmengine - INFO - Epoch(train) [26][ 360/1320] lr: 2.0000e-03 eta: 3:02:50 time: 0.3373 data_time: 0.0127 memory: 18752 grad_norm: 4.2638 loss: 1.4106 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.4106 2023/03/17 20:20:09 - mmengine - INFO - Epoch(train) [26][ 380/1320] lr: 2.0000e-03 eta: 3:02:43 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 4.2884 loss: 1.3723 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3723 2023/03/17 20:20:16 - mmengine - INFO - Epoch(train) [26][ 400/1320] lr: 2.0000e-03 eta: 3:02:36 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.2647 loss: 1.3961 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3961 2023/03/17 20:20:23 - mmengine - INFO - Epoch(train) [26][ 420/1320] lr: 2.0000e-03 eta: 3:02:30 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 4.2140 loss: 1.5887 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5887 2023/03/17 20:20:29 - mmengine - INFO - Epoch(train) [26][ 440/1320] lr: 2.0000e-03 eta: 3:02:23 time: 0.3358 data_time: 0.0130 memory: 18752 grad_norm: 4.2605 loss: 1.5082 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.5082 2023/03/17 20:20:36 - mmengine - INFO - Epoch(train) [26][ 460/1320] lr: 2.0000e-03 eta: 3:02:16 time: 0.3363 data_time: 0.0130 memory: 18752 grad_norm: 4.1919 loss: 1.3113 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3113 2023/03/17 20:20:43 - mmengine - INFO - Epoch(train) [26][ 480/1320] lr: 2.0000e-03 eta: 3:02:09 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 4.2299 loss: 1.4541 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4541 2023/03/17 20:20:49 - mmengine - INFO - Epoch(train) [26][ 500/1320] lr: 2.0000e-03 eta: 3:02:03 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.3645 loss: 1.3675 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3675 2023/03/17 20:20:56 - mmengine - INFO - Epoch(train) [26][ 520/1320] lr: 2.0000e-03 eta: 3:01:56 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 4.1680 loss: 1.4677 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4677 2023/03/17 20:21:03 - mmengine - INFO - Epoch(train) [26][ 540/1320] lr: 2.0000e-03 eta: 3:01:49 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 4.2068 loss: 1.4568 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4568 2023/03/17 20:21:10 - mmengine - INFO - Epoch(train) [26][ 560/1320] lr: 2.0000e-03 eta: 3:01:42 time: 0.3353 data_time: 0.0131 memory: 18752 grad_norm: 4.1768 loss: 1.4364 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4364 2023/03/17 20:21:16 - mmengine - INFO - Epoch(train) [26][ 580/1320] lr: 2.0000e-03 eta: 3:01:36 time: 0.3355 data_time: 0.0128 memory: 18752 grad_norm: 4.3458 loss: 1.4120 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4120 2023/03/17 20:21:23 - mmengine - INFO - Epoch(train) [26][ 600/1320] lr: 2.0000e-03 eta: 3:01:29 time: 0.3361 data_time: 0.0129 memory: 18752 grad_norm: 4.3754 loss: 1.4226 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4226 2023/03/17 20:21:30 - mmengine - INFO - Epoch(train) [26][ 620/1320] lr: 2.0000e-03 eta: 3:01:22 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 4.2916 loss: 1.5035 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5035 2023/03/17 20:21:36 - mmengine - INFO - Epoch(train) [26][ 640/1320] lr: 2.0000e-03 eta: 3:01:15 time: 0.3357 data_time: 0.0128 memory: 18752 grad_norm: 4.2885 loss: 1.3780 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3780 2023/03/17 20:21:43 - mmengine - INFO - Epoch(train) [26][ 660/1320] lr: 2.0000e-03 eta: 3:01:09 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 4.2907 loss: 1.4563 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.4563 2023/03/17 20:21:50 - mmengine - INFO - Epoch(train) [26][ 680/1320] lr: 2.0000e-03 eta: 3:01:02 time: 0.3359 data_time: 0.0127 memory: 18752 grad_norm: 4.1506 loss: 1.3030 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3030 2023/03/17 20:21:57 - mmengine - INFO - Epoch(train) [26][ 700/1320] lr: 2.0000e-03 eta: 3:00:55 time: 0.3364 data_time: 0.0130 memory: 18752 grad_norm: 4.2306 loss: 1.2351 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2351 2023/03/17 20:22:03 - mmengine - INFO - Epoch(train) [26][ 720/1320] lr: 2.0000e-03 eta: 3:00:49 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.5080 loss: 1.5092 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5092 2023/03/17 20:22:10 - mmengine - INFO - Epoch(train) [26][ 740/1320] lr: 2.0000e-03 eta: 3:00:42 time: 0.3354 data_time: 0.0128 memory: 18752 grad_norm: 4.2850 loss: 1.3528 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3528 2023/03/17 20:22:17 - mmengine - INFO - Epoch(train) [26][ 760/1320] lr: 2.0000e-03 eta: 3:00:35 time: 0.3352 data_time: 0.0133 memory: 18752 grad_norm: 4.3468 loss: 1.3087 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3087 2023/03/17 20:22:23 - mmengine - INFO - Epoch(train) [26][ 780/1320] lr: 2.0000e-03 eta: 3:00:28 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 4.2257 loss: 1.2762 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2762 2023/03/17 20:22:30 - mmengine - INFO - Epoch(train) [26][ 800/1320] lr: 2.0000e-03 eta: 3:00:22 time: 0.3349 data_time: 0.0126 memory: 18752 grad_norm: 4.4161 loss: 1.4780 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4780 2023/03/17 20:22:37 - mmengine - INFO - Epoch(train) [26][ 820/1320] lr: 2.0000e-03 eta: 3:00:15 time: 0.3358 data_time: 0.0129 memory: 18752 grad_norm: 4.3261 loss: 1.3273 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3273 2023/03/17 20:22:44 - mmengine - INFO - Epoch(train) [26][ 840/1320] lr: 2.0000e-03 eta: 3:00:08 time: 0.3355 data_time: 0.0128 memory: 18752 grad_norm: 4.3252 loss: 1.3532 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3532 2023/03/17 20:22:50 - mmengine - INFO - Epoch(train) [26][ 860/1320] lr: 2.0000e-03 eta: 3:00:01 time: 0.3357 data_time: 0.0132 memory: 18752 grad_norm: 4.3563 loss: 1.4926 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4926 2023/03/17 20:22:57 - mmengine - INFO - Epoch(train) [26][ 880/1320] lr: 2.0000e-03 eta: 2:59:55 time: 0.3354 data_time: 0.0130 memory: 18752 grad_norm: 4.2936 loss: 1.3060 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3060 2023/03/17 20:23:04 - mmengine - INFO - Epoch(train) [26][ 900/1320] lr: 2.0000e-03 eta: 2:59:48 time: 0.3350 data_time: 0.0126 memory: 18752 grad_norm: 4.3209 loss: 1.1838 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1838 2023/03/17 20:23:10 - mmengine - INFO - Epoch(train) [26][ 920/1320] lr: 2.0000e-03 eta: 2:59:41 time: 0.3352 data_time: 0.0127 memory: 18752 grad_norm: 4.2874 loss: 1.2599 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.2599 2023/03/17 20:23:17 - mmengine - INFO - Epoch(train) [26][ 940/1320] lr: 2.0000e-03 eta: 2:59:35 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 4.3732 loss: 1.4020 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4020 2023/03/17 20:23:24 - mmengine - INFO - Epoch(train) [26][ 960/1320] lr: 2.0000e-03 eta: 2:59:28 time: 0.3353 data_time: 0.0128 memory: 18752 grad_norm: 4.4649 loss: 1.4617 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4617 2023/03/17 20:23:31 - mmengine - INFO - Epoch(train) [26][ 980/1320] lr: 2.0000e-03 eta: 2:59:21 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 4.3964 loss: 1.3475 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3475 2023/03/17 20:23:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:23:37 - mmengine - INFO - Epoch(train) [26][1000/1320] lr: 2.0000e-03 eta: 2:59:14 time: 0.3351 data_time: 0.0125 memory: 18752 grad_norm: 4.4567 loss: 1.3369 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3369 2023/03/17 20:23:44 - mmengine - INFO - Epoch(train) [26][1020/1320] lr: 2.0000e-03 eta: 2:59:08 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 4.2856 loss: 1.3230 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3230 2023/03/17 20:23:51 - mmengine - INFO - Epoch(train) [26][1040/1320] lr: 2.0000e-03 eta: 2:59:01 time: 0.3350 data_time: 0.0125 memory: 18752 grad_norm: 4.3197 loss: 1.1763 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1763 2023/03/17 20:23:57 - mmengine - INFO - Epoch(train) [26][1060/1320] lr: 2.0000e-03 eta: 2:58:54 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 4.4385 loss: 1.3741 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3741 2023/03/17 20:24:04 - mmengine - INFO - Epoch(train) [26][1080/1320] lr: 2.0000e-03 eta: 2:58:47 time: 0.3365 data_time: 0.0118 memory: 18752 grad_norm: 4.4204 loss: 1.4369 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4369 2023/03/17 20:24:16 - mmengine - INFO - Epoch(train) [26][1100/1320] lr: 2.0000e-03 eta: 2:58:46 time: 0.6016 data_time: 0.0245 memory: 18752 grad_norm: 4.4517 loss: 1.2492 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2492 2023/03/17 20:24:23 - mmengine - INFO - Epoch(train) [26][1120/1320] lr: 2.0000e-03 eta: 2:58:39 time: 0.3338 data_time: 0.0087 memory: 18752 grad_norm: 4.2938 loss: 1.1868 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1868 2023/03/17 20:24:30 - mmengine - INFO - Epoch(train) [26][1140/1320] lr: 2.0000e-03 eta: 2:58:32 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 4.2745 loss: 1.3116 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3116 2023/03/17 20:24:36 - mmengine - INFO - Epoch(train) [26][1160/1320] lr: 2.0000e-03 eta: 2:58:25 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.3550 loss: 1.2920 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2920 2023/03/17 20:24:43 - mmengine - INFO - Epoch(train) [26][1180/1320] lr: 2.0000e-03 eta: 2:58:19 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 4.4540 loss: 1.4207 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4207 2023/03/17 20:24:50 - mmengine - INFO - Epoch(train) [26][1200/1320] lr: 2.0000e-03 eta: 2:58:12 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 4.3947 loss: 1.3888 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3888 2023/03/17 20:24:56 - mmengine - INFO - Epoch(train) [26][1220/1320] lr: 2.0000e-03 eta: 2:58:05 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 4.3491 loss: 1.2231 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2231 2023/03/17 20:25:03 - mmengine - INFO - Epoch(train) [26][1240/1320] lr: 2.0000e-03 eta: 2:57:58 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4366 loss: 1.5362 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5362 2023/03/17 20:25:10 - mmengine - INFO - Epoch(train) [26][1260/1320] lr: 2.0000e-03 eta: 2:57:52 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 4.5141 loss: 1.2940 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2940 2023/03/17 20:25:17 - mmengine - INFO - Epoch(train) [26][1280/1320] lr: 2.0000e-03 eta: 2:57:45 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.3304 loss: 1.2927 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2927 2023/03/17 20:25:23 - mmengine - INFO - Epoch(train) [26][1300/1320] lr: 2.0000e-03 eta: 2:57:38 time: 0.3360 data_time: 0.0117 memory: 18752 grad_norm: 4.4332 loss: 1.4665 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4665 2023/03/17 20:25:30 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:25:30 - mmengine - INFO - Epoch(train) [26][1320/1320] lr: 2.0000e-03 eta: 2:57:31 time: 0.3301 data_time: 0.0117 memory: 18752 grad_norm: 4.4452 loss: 1.3963 top1_acc: 0.6364 top5_acc: 1.0000 loss_cls: 1.3963 2023/03/17 20:25:32 - mmengine - INFO - Epoch(val) [26][ 20/194] eta: 0:00:22 time: 0.1268 data_time: 0.0404 memory: 2112 2023/03/17 20:25:34 - mmengine - INFO - Epoch(val) [26][ 40/194] eta: 0:00:17 time: 0.0955 data_time: 0.0095 memory: 2112 2023/03/17 20:25:36 - mmengine - INFO - Epoch(val) [26][ 60/194] eta: 0:00:14 time: 0.0967 data_time: 0.0109 memory: 2112 2023/03/17 20:25:38 - mmengine - INFO - Epoch(val) [26][ 80/194] eta: 0:00:11 time: 0.0968 data_time: 0.0108 memory: 2112 2023/03/17 20:25:40 - mmengine - INFO - Epoch(val) [26][100/194] eta: 0:00:09 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 20:25:42 - mmengine - INFO - Epoch(val) [26][120/194] eta: 0:00:07 time: 0.0960 data_time: 0.0103 memory: 2112 2023/03/17 20:25:44 - mmengine - INFO - Epoch(val) [26][140/194] eta: 0:00:05 time: 0.0973 data_time: 0.0115 memory: 2112 2023/03/17 20:25:46 - mmengine - INFO - Epoch(val) [26][160/194] eta: 0:00:03 time: 0.0973 data_time: 0.0114 memory: 2112 2023/03/17 20:25:48 - mmengine - INFO - Epoch(val) [26][180/194] eta: 0:00:01 time: 0.0973 data_time: 0.0113 memory: 2112 2023/03/17 20:25:51 - mmengine - INFO - Epoch(val) [26][194/194] acc/top1: 0.5869 acc/top5: 0.8508 acc/mean1: 0.5197 2023/03/17 20:25:51 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_24.pth is removed 2023/03/17 20:25:53 - mmengine - INFO - The best checkpoint with 0.5869 acc/top1 at 26 epoch is saved to best_acc/top1_epoch_26.pth. 2023/03/17 20:26:00 - mmengine - INFO - Epoch(train) [27][ 20/1320] lr: 2.0000e-03 eta: 2:57:25 time: 0.3685 data_time: 0.0386 memory: 18752 grad_norm: 4.3445 loss: 1.3150 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3150 2023/03/17 20:26:07 - mmengine - INFO - Epoch(train) [27][ 40/1320] lr: 2.0000e-03 eta: 2:57:19 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 4.4101 loss: 1.3566 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3566 2023/03/17 20:26:14 - mmengine - INFO - Epoch(train) [27][ 60/1320] lr: 2.0000e-03 eta: 2:57:12 time: 0.3362 data_time: 0.0113 memory: 18752 grad_norm: 4.2779 loss: 1.3241 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3241 2023/03/17 20:26:20 - mmengine - INFO - Epoch(train) [27][ 80/1320] lr: 2.0000e-03 eta: 2:57:05 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 4.4423 loss: 1.2104 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2104 2023/03/17 20:26:27 - mmengine - INFO - Epoch(train) [27][ 100/1320] lr: 2.0000e-03 eta: 2:56:58 time: 0.3363 data_time: 0.0114 memory: 18752 grad_norm: 4.4186 loss: 1.2422 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2422 2023/03/17 20:26:34 - mmengine - INFO - Epoch(train) [27][ 120/1320] lr: 2.0000e-03 eta: 2:56:52 time: 0.3363 data_time: 0.0112 memory: 18752 grad_norm: 4.5899 loss: 1.2184 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2184 2023/03/17 20:26:41 - mmengine - INFO - Epoch(train) [27][ 140/1320] lr: 2.0000e-03 eta: 2:56:45 time: 0.3355 data_time: 0.0114 memory: 18752 grad_norm: 4.3933 loss: 1.1648 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1648 2023/03/17 20:26:47 - mmengine - INFO - Epoch(train) [27][ 160/1320] lr: 2.0000e-03 eta: 2:56:38 time: 0.3355 data_time: 0.0113 memory: 18752 grad_norm: 4.4346 loss: 1.4329 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4329 2023/03/17 20:26:54 - mmengine - INFO - Epoch(train) [27][ 180/1320] lr: 2.0000e-03 eta: 2:56:31 time: 0.3363 data_time: 0.0115 memory: 18752 grad_norm: 4.4199 loss: 1.2456 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2456 2023/03/17 20:27:01 - mmengine - INFO - Epoch(train) [27][ 200/1320] lr: 2.0000e-03 eta: 2:56:25 time: 0.3367 data_time: 0.0123 memory: 18752 grad_norm: 4.5061 loss: 1.3219 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3219 2023/03/17 20:27:08 - mmengine - INFO - Epoch(train) [27][ 220/1320] lr: 2.0000e-03 eta: 2:56:18 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.3629 loss: 1.2421 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2421 2023/03/17 20:27:14 - mmengine - INFO - Epoch(train) [27][ 240/1320] lr: 2.0000e-03 eta: 2:56:11 time: 0.3353 data_time: 0.0113 memory: 18752 grad_norm: 4.5045 loss: 1.3450 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3450 2023/03/17 20:27:21 - mmengine - INFO - Epoch(train) [27][ 260/1320] lr: 2.0000e-03 eta: 2:56:05 time: 0.3357 data_time: 0.0114 memory: 18752 grad_norm: 4.5026 loss: 1.4354 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.4354 2023/03/17 20:27:28 - mmengine - INFO - Epoch(train) [27][ 280/1320] lr: 2.0000e-03 eta: 2:55:58 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 4.4120 loss: 1.3288 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3288 2023/03/17 20:27:34 - mmengine - INFO - Epoch(train) [27][ 300/1320] lr: 2.0000e-03 eta: 2:55:51 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.3524 loss: 1.3341 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3341 2023/03/17 20:27:41 - mmengine - INFO - Epoch(train) [27][ 320/1320] lr: 2.0000e-03 eta: 2:55:44 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 4.4857 loss: 1.4050 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4050 2023/03/17 20:27:48 - mmengine - INFO - Epoch(train) [27][ 340/1320] lr: 2.0000e-03 eta: 2:55:38 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 4.5574 loss: 1.3956 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3956 2023/03/17 20:27:55 - mmengine - INFO - Epoch(train) [27][ 360/1320] lr: 2.0000e-03 eta: 2:55:31 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.4902 loss: 1.2953 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2953 2023/03/17 20:28:01 - mmengine - INFO - Epoch(train) [27][ 380/1320] lr: 2.0000e-03 eta: 2:55:24 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 4.4429 loss: 1.2050 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2050 2023/03/17 20:28:08 - mmengine - INFO - Epoch(train) [27][ 400/1320] lr: 2.0000e-03 eta: 2:55:17 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4910 loss: 1.1585 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1585 2023/03/17 20:28:15 - mmengine - INFO - Epoch(train) [27][ 420/1320] lr: 2.0000e-03 eta: 2:55:11 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.3964 loss: 1.4070 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4070 2023/03/17 20:28:21 - mmengine - INFO - Epoch(train) [27][ 440/1320] lr: 2.0000e-03 eta: 2:55:04 time: 0.3363 data_time: 0.0128 memory: 18752 grad_norm: 4.5544 loss: 1.1970 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1970 2023/03/17 20:28:28 - mmengine - INFO - Epoch(train) [27][ 460/1320] lr: 2.0000e-03 eta: 2:54:57 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.3396 loss: 1.1015 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1015 2023/03/17 20:28:35 - mmengine - INFO - Epoch(train) [27][ 480/1320] lr: 2.0000e-03 eta: 2:54:50 time: 0.3352 data_time: 0.0116 memory: 18752 grad_norm: 4.4085 loss: 1.1309 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1309 2023/03/17 20:28:41 - mmengine - INFO - Epoch(train) [27][ 500/1320] lr: 2.0000e-03 eta: 2:54:44 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.3982 loss: 1.2489 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2489 2023/03/17 20:28:48 - mmengine - INFO - Epoch(train) [27][ 520/1320] lr: 2.0000e-03 eta: 2:54:37 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 4.4132 loss: 1.2011 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2011 2023/03/17 20:28:55 - mmengine - INFO - Epoch(train) [27][ 540/1320] lr: 2.0000e-03 eta: 2:54:30 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 4.4691 loss: 1.3428 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.3428 2023/03/17 20:29:02 - mmengine - INFO - Epoch(train) [27][ 560/1320] lr: 2.0000e-03 eta: 2:54:23 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.4011 loss: 1.3383 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3383 2023/03/17 20:29:08 - mmengine - INFO - Epoch(train) [27][ 580/1320] lr: 2.0000e-03 eta: 2:54:17 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 4.5886 loss: 1.3922 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3922 2023/03/17 20:29:15 - mmengine - INFO - Epoch(train) [27][ 600/1320] lr: 2.0000e-03 eta: 2:54:10 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.5558 loss: 1.3555 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3555 2023/03/17 20:29:22 - mmengine - INFO - Epoch(train) [27][ 620/1320] lr: 2.0000e-03 eta: 2:54:03 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.5680 loss: 1.2782 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2782 2023/03/17 20:29:28 - mmengine - INFO - Epoch(train) [27][ 640/1320] lr: 2.0000e-03 eta: 2:53:57 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.5539 loss: 1.2798 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2798 2023/03/17 20:29:35 - mmengine - INFO - Epoch(train) [27][ 660/1320] lr: 2.0000e-03 eta: 2:53:50 time: 0.3365 data_time: 0.0119 memory: 18752 grad_norm: 4.5356 loss: 1.2310 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2310 2023/03/17 20:29:42 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:29:42 - mmengine - INFO - Epoch(train) [27][ 680/1320] lr: 2.0000e-03 eta: 2:53:43 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 4.5238 loss: 1.2170 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2170 2023/03/17 20:29:49 - mmengine - INFO - Epoch(train) [27][ 700/1320] lr: 2.0000e-03 eta: 2:53:36 time: 0.3366 data_time: 0.0120 memory: 18752 grad_norm: 4.5556 loss: 1.2483 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2483 2023/03/17 20:29:55 - mmengine - INFO - Epoch(train) [27][ 720/1320] lr: 2.0000e-03 eta: 2:53:30 time: 0.3358 data_time: 0.0116 memory: 18752 grad_norm: 4.6144 loss: 1.2563 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2563 2023/03/17 20:30:02 - mmengine - INFO - Epoch(train) [27][ 740/1320] lr: 2.0000e-03 eta: 2:53:23 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 4.4264 loss: 1.2084 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2084 2023/03/17 20:30:09 - mmengine - INFO - Epoch(train) [27][ 760/1320] lr: 2.0000e-03 eta: 2:53:16 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.5088 loss: 1.2178 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2178 2023/03/17 20:30:16 - mmengine - INFO - Epoch(train) [27][ 780/1320] lr: 2.0000e-03 eta: 2:53:09 time: 0.3367 data_time: 0.0117 memory: 18752 grad_norm: 4.5928 loss: 1.2514 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2514 2023/03/17 20:30:22 - mmengine - INFO - Epoch(train) [27][ 800/1320] lr: 2.0000e-03 eta: 2:53:03 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.4364 loss: 1.2625 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2625 2023/03/17 20:30:29 - mmengine - INFO - Epoch(train) [27][ 820/1320] lr: 2.0000e-03 eta: 2:52:56 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.5804 loss: 1.1503 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1503 2023/03/17 20:30:36 - mmengine - INFO - Epoch(train) [27][ 840/1320] lr: 2.0000e-03 eta: 2:52:49 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 4.5220 loss: 1.3879 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3879 2023/03/17 20:30:42 - mmengine - INFO - Epoch(train) [27][ 860/1320] lr: 2.0000e-03 eta: 2:52:43 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 4.5838 loss: 1.3022 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3022 2023/03/17 20:30:49 - mmengine - INFO - Epoch(train) [27][ 880/1320] lr: 2.0000e-03 eta: 2:52:36 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5090 loss: 1.2102 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2102 2023/03/17 20:30:56 - mmengine - INFO - Epoch(train) [27][ 900/1320] lr: 2.0000e-03 eta: 2:52:29 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.5583 loss: 1.2044 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2044 2023/03/17 20:31:03 - mmengine - INFO - Epoch(train) [27][ 920/1320] lr: 2.0000e-03 eta: 2:52:22 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.4329 loss: 1.0731 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0731 2023/03/17 20:31:09 - mmengine - INFO - Epoch(train) [27][ 940/1320] lr: 2.0000e-03 eta: 2:52:16 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.5474 loss: 1.3091 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3091 2023/03/17 20:31:16 - mmengine - INFO - Epoch(train) [27][ 960/1320] lr: 2.0000e-03 eta: 2:52:09 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.5544 loss: 1.2401 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2401 2023/03/17 20:31:23 - mmengine - INFO - Epoch(train) [27][ 980/1320] lr: 2.0000e-03 eta: 2:52:02 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.5212 loss: 1.2171 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2171 2023/03/17 20:31:29 - mmengine - INFO - Epoch(train) [27][1000/1320] lr: 2.0000e-03 eta: 2:51:55 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.6171 loss: 1.1065 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1065 2023/03/17 20:31:36 - mmengine - INFO - Epoch(train) [27][1020/1320] lr: 2.0000e-03 eta: 2:51:49 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.6853 loss: 1.2037 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2037 2023/03/17 20:31:43 - mmengine - INFO - Epoch(train) [27][1040/1320] lr: 2.0000e-03 eta: 2:51:42 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 4.5820 loss: 1.3365 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3365 2023/03/17 20:31:50 - mmengine - INFO - Epoch(train) [27][1060/1320] lr: 2.0000e-03 eta: 2:51:35 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.6567 loss: 1.2205 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2205 2023/03/17 20:31:56 - mmengine - INFO - Epoch(train) [27][1080/1320] lr: 2.0000e-03 eta: 2:51:28 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.5161 loss: 1.3115 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3115 2023/03/17 20:32:03 - mmengine - INFO - Epoch(train) [27][1100/1320] lr: 2.0000e-03 eta: 2:51:22 time: 0.3367 data_time: 0.0117 memory: 18752 grad_norm: 4.5753 loss: 1.2713 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2713 2023/03/17 20:32:10 - mmengine - INFO - Epoch(train) [27][1120/1320] lr: 2.0000e-03 eta: 2:51:15 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 4.7475 loss: 1.3445 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3445 2023/03/17 20:32:16 - mmengine - INFO - Epoch(train) [27][1140/1320] lr: 2.0000e-03 eta: 2:51:08 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 4.7610 loss: 1.3684 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3684 2023/03/17 20:32:23 - mmengine - INFO - Epoch(train) [27][1160/1320] lr: 2.0000e-03 eta: 2:51:01 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 4.6071 loss: 1.3892 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3892 2023/03/17 20:32:30 - mmengine - INFO - Epoch(train) [27][1180/1320] lr: 2.0000e-03 eta: 2:50:55 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 4.5043 loss: 1.4500 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4500 2023/03/17 20:32:37 - mmengine - INFO - Epoch(train) [27][1200/1320] lr: 2.0000e-03 eta: 2:50:48 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 4.6902 loss: 1.2456 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2456 2023/03/17 20:32:43 - mmengine - INFO - Epoch(train) [27][1220/1320] lr: 2.0000e-03 eta: 2:50:41 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.6720 loss: 1.1953 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1953 2023/03/17 20:32:50 - mmengine - INFO - Epoch(train) [27][1240/1320] lr: 2.0000e-03 eta: 2:50:35 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 4.5197 loss: 0.9717 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9717 2023/03/17 20:32:57 - mmengine - INFO - Epoch(train) [27][1260/1320] lr: 2.0000e-03 eta: 2:50:28 time: 0.3360 data_time: 0.0117 memory: 18752 grad_norm: 4.6334 loss: 1.3877 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3877 2023/03/17 20:33:03 - mmengine - INFO - Epoch(train) [27][1280/1320] lr: 2.0000e-03 eta: 2:50:21 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 4.5461 loss: 1.1379 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1379 2023/03/17 20:33:10 - mmengine - INFO - Epoch(train) [27][1300/1320] lr: 2.0000e-03 eta: 2:50:14 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.5987 loss: 1.2892 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2892 2023/03/17 20:33:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:33:17 - mmengine - INFO - Epoch(train) [27][1320/1320] lr: 2.0000e-03 eta: 2:50:07 time: 0.3304 data_time: 0.0121 memory: 18752 grad_norm: 4.4488 loss: 1.2000 top1_acc: 0.7273 top5_acc: 0.8182 loss_cls: 1.2000 2023/03/17 20:33:17 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/03/17 20:33:22 - mmengine - INFO - Epoch(val) [27][ 20/194] eta: 0:00:22 time: 0.1289 data_time: 0.0418 memory: 2112 2023/03/17 20:33:24 - mmengine - INFO - Epoch(val) [27][ 40/194] eta: 0:00:17 time: 0.0960 data_time: 0.0099 memory: 2112 2023/03/17 20:33:26 - mmengine - INFO - Epoch(val) [27][ 60/194] eta: 0:00:14 time: 0.0981 data_time: 0.0117 memory: 2112 2023/03/17 20:33:28 - mmengine - INFO - Epoch(val) [27][ 80/194] eta: 0:00:11 time: 0.0973 data_time: 0.0111 memory: 2112 2023/03/17 20:33:30 - mmengine - INFO - Epoch(val) [27][100/194] eta: 0:00:09 time: 0.0968 data_time: 0.0108 memory: 2112 2023/03/17 20:33:32 - mmengine - INFO - Epoch(val) [27][120/194] eta: 0:00:07 time: 0.0964 data_time: 0.0105 memory: 2112 2023/03/17 20:33:34 - mmengine - INFO - Epoch(val) [27][140/194] eta: 0:00:05 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 20:33:36 - mmengine - INFO - Epoch(val) [27][160/194] eta: 0:00:03 time: 0.0953 data_time: 0.0098 memory: 2112 2023/03/17 20:33:38 - mmengine - INFO - Epoch(val) [27][180/194] eta: 0:00:01 time: 0.0953 data_time: 0.0094 memory: 2112 2023/03/17 20:33:40 - mmengine - INFO - Epoch(val) [27][194/194] acc/top1: 0.5945 acc/top5: 0.8563 acc/mean1: 0.5268 2023/03/17 20:33:40 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_26.pth is removed 2023/03/17 20:33:42 - mmengine - INFO - The best checkpoint with 0.5945 acc/top1 at 27 epoch is saved to best_acc/top1_epoch_27.pth. 2023/03/17 20:33:49 - mmengine - INFO - Epoch(train) [28][ 20/1320] lr: 2.0000e-03 eta: 2:50:01 time: 0.3654 data_time: 0.0349 memory: 18752 grad_norm: 4.7003 loss: 1.2209 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2209 2023/03/17 20:33:56 - mmengine - INFO - Epoch(train) [28][ 40/1320] lr: 2.0000e-03 eta: 2:49:55 time: 0.3365 data_time: 0.0129 memory: 18752 grad_norm: 4.5841 loss: 1.2948 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2948 2023/03/17 20:34:03 - mmengine - INFO - Epoch(train) [28][ 60/1320] lr: 2.0000e-03 eta: 2:49:48 time: 0.3357 data_time: 0.0113 memory: 18752 grad_norm: 4.6975 loss: 1.3907 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3907 2023/03/17 20:34:09 - mmengine - INFO - Epoch(train) [28][ 80/1320] lr: 2.0000e-03 eta: 2:49:41 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 4.4753 loss: 1.3100 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.3100 2023/03/17 20:34:16 - mmengine - INFO - Epoch(train) [28][ 100/1320] lr: 2.0000e-03 eta: 2:49:35 time: 0.3477 data_time: 0.0117 memory: 18752 grad_norm: 4.6663 loss: 1.1952 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1952 2023/03/17 20:34:23 - mmengine - INFO - Epoch(train) [28][ 120/1320] lr: 2.0000e-03 eta: 2:49:28 time: 0.3364 data_time: 0.0119 memory: 18752 grad_norm: 4.5212 loss: 1.1955 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1955 2023/03/17 20:34:30 - mmengine - INFO - Epoch(train) [28][ 140/1320] lr: 2.0000e-03 eta: 2:49:21 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 4.5295 loss: 1.0647 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.0647 2023/03/17 20:34:36 - mmengine - INFO - Epoch(train) [28][ 160/1320] lr: 2.0000e-03 eta: 2:49:14 time: 0.3364 data_time: 0.0130 memory: 18752 grad_norm: 4.5319 loss: 1.3551 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3551 2023/03/17 20:34:43 - mmengine - INFO - Epoch(train) [28][ 180/1320] lr: 2.0000e-03 eta: 2:49:08 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 4.5581 loss: 1.1205 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1205 2023/03/17 20:34:50 - mmengine - INFO - Epoch(train) [28][ 200/1320] lr: 2.0000e-03 eta: 2:49:01 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.5811 loss: 1.1145 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1145 2023/03/17 20:34:57 - mmengine - INFO - Epoch(train) [28][ 220/1320] lr: 2.0000e-03 eta: 2:48:54 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 4.5679 loss: 1.2251 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2251 2023/03/17 20:35:03 - mmengine - INFO - Epoch(train) [28][ 240/1320] lr: 2.0000e-03 eta: 2:48:47 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.6564 loss: 1.0928 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0928 2023/03/17 20:35:10 - mmengine - INFO - Epoch(train) [28][ 260/1320] lr: 2.0000e-03 eta: 2:48:41 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 4.6920 loss: 1.2399 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2399 2023/03/17 20:35:17 - mmengine - INFO - Epoch(train) [28][ 280/1320] lr: 2.0000e-03 eta: 2:48:34 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.6393 loss: 1.1822 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1822 2023/03/17 20:35:23 - mmengine - INFO - Epoch(train) [28][ 300/1320] lr: 2.0000e-03 eta: 2:48:27 time: 0.3368 data_time: 0.0123 memory: 18752 grad_norm: 4.7176 loss: 1.3256 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3256 2023/03/17 20:35:30 - mmengine - INFO - Epoch(train) [28][ 320/1320] lr: 2.0000e-03 eta: 2:48:20 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.5496 loss: 1.2395 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2395 2023/03/17 20:35:37 - mmengine - INFO - Epoch(train) [28][ 340/1320] lr: 2.0000e-03 eta: 2:48:14 time: 0.3364 data_time: 0.0116 memory: 18752 grad_norm: 4.7341 loss: 1.2541 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2541 2023/03/17 20:35:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:35:44 - mmengine - INFO - Epoch(train) [28][ 360/1320] lr: 2.0000e-03 eta: 2:48:07 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 4.5439 loss: 1.2942 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2942 2023/03/17 20:35:50 - mmengine - INFO - Epoch(train) [28][ 380/1320] lr: 2.0000e-03 eta: 2:48:00 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.5771 loss: 1.1458 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1458 2023/03/17 20:35:57 - mmengine - INFO - Epoch(train) [28][ 400/1320] lr: 2.0000e-03 eta: 2:47:54 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.6404 loss: 1.2481 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2481 2023/03/17 20:36:04 - mmengine - INFO - Epoch(train) [28][ 420/1320] lr: 2.0000e-03 eta: 2:47:47 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.7755 loss: 1.2585 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2585 2023/03/17 20:36:10 - mmengine - INFO - Epoch(train) [28][ 440/1320] lr: 2.0000e-03 eta: 2:47:40 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.7190 loss: 1.3899 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3899 2023/03/17 20:36:17 - mmengine - INFO - Epoch(train) [28][ 460/1320] lr: 2.0000e-03 eta: 2:47:33 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 4.5135 loss: 1.2717 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2717 2023/03/17 20:36:24 - mmengine - INFO - Epoch(train) [28][ 480/1320] lr: 2.0000e-03 eta: 2:47:27 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.6442 loss: 1.3809 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3809 2023/03/17 20:36:31 - mmengine - INFO - Epoch(train) [28][ 500/1320] lr: 2.0000e-03 eta: 2:47:20 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 4.6770 loss: 1.2502 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2502 2023/03/17 20:36:37 - mmengine - INFO - Epoch(train) [28][ 520/1320] lr: 2.0000e-03 eta: 2:47:13 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 4.6775 loss: 1.1560 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1560 2023/03/17 20:36:44 - mmengine - INFO - Epoch(train) [28][ 540/1320] lr: 2.0000e-03 eta: 2:47:06 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.6662 loss: 1.1325 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1325 2023/03/17 20:36:51 - mmengine - INFO - Epoch(train) [28][ 560/1320] lr: 2.0000e-03 eta: 2:47:00 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 4.5873 loss: 1.2341 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2341 2023/03/17 20:36:57 - mmengine - INFO - Epoch(train) [28][ 580/1320] lr: 2.0000e-03 eta: 2:46:53 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.8897 loss: 1.3241 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3241 2023/03/17 20:37:04 - mmengine - INFO - Epoch(train) [28][ 600/1320] lr: 2.0000e-03 eta: 2:46:46 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.6850 loss: 1.3277 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3277 2023/03/17 20:37:11 - mmengine - INFO - Epoch(train) [28][ 620/1320] lr: 2.0000e-03 eta: 2:46:40 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.8189 loss: 1.2160 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2160 2023/03/17 20:37:18 - mmengine - INFO - Epoch(train) [28][ 640/1320] lr: 2.0000e-03 eta: 2:46:33 time: 0.3350 data_time: 0.0123 memory: 18752 grad_norm: 4.6930 loss: 1.2066 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2066 2023/03/17 20:37:24 - mmengine - INFO - Epoch(train) [28][ 660/1320] lr: 2.0000e-03 eta: 2:46:26 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.6464 loss: 1.1258 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1258 2023/03/17 20:37:31 - mmengine - INFO - Epoch(train) [28][ 680/1320] lr: 2.0000e-03 eta: 2:46:19 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 4.6149 loss: 1.3365 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3365 2023/03/17 20:37:38 - mmengine - INFO - Epoch(train) [28][ 700/1320] lr: 2.0000e-03 eta: 2:46:13 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.6773 loss: 1.1825 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1825 2023/03/17 20:37:44 - mmengine - INFO - Epoch(train) [28][ 720/1320] lr: 2.0000e-03 eta: 2:46:06 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 4.5634 loss: 1.1851 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1851 2023/03/17 20:37:51 - mmengine - INFO - Epoch(train) [28][ 740/1320] lr: 2.0000e-03 eta: 2:45:59 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.7232 loss: 1.2396 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2396 2023/03/17 20:37:58 - mmengine - INFO - Epoch(train) [28][ 760/1320] lr: 2.0000e-03 eta: 2:45:52 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 4.7378 loss: 1.2705 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2705 2023/03/17 20:38:05 - mmengine - INFO - Epoch(train) [28][ 780/1320] lr: 2.0000e-03 eta: 2:45:46 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 4.6610 loss: 1.1722 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1722 2023/03/17 20:38:11 - mmengine - INFO - Epoch(train) [28][ 800/1320] lr: 2.0000e-03 eta: 2:45:39 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.6179 loss: 1.1045 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1045 2023/03/17 20:38:18 - mmengine - INFO - Epoch(train) [28][ 820/1320] lr: 2.0000e-03 eta: 2:45:32 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.7162 loss: 1.2218 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2218 2023/03/17 20:38:25 - mmengine - INFO - Epoch(train) [28][ 840/1320] lr: 2.0000e-03 eta: 2:45:25 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.8029 loss: 1.3007 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3007 2023/03/17 20:38:32 - mmengine - INFO - Epoch(train) [28][ 860/1320] lr: 2.0000e-03 eta: 2:45:19 time: 0.3367 data_time: 0.0124 memory: 18752 grad_norm: 4.6609 loss: 1.2771 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2771 2023/03/17 20:38:38 - mmengine - INFO - Epoch(train) [28][ 880/1320] lr: 2.0000e-03 eta: 2:45:12 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.6992 loss: 1.3595 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3595 2023/03/17 20:38:45 - mmengine - INFO - Epoch(train) [28][ 900/1320] lr: 2.0000e-03 eta: 2:45:05 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 4.6280 loss: 1.1375 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1375 2023/03/17 20:38:52 - mmengine - INFO - Epoch(train) [28][ 920/1320] lr: 2.0000e-03 eta: 2:44:59 time: 0.3365 data_time: 0.0125 memory: 18752 grad_norm: 4.7923 loss: 1.2317 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2317 2023/03/17 20:38:58 - mmengine - INFO - Epoch(train) [28][ 940/1320] lr: 2.0000e-03 eta: 2:44:52 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 4.6412 loss: 1.2998 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2998 2023/03/17 20:39:05 - mmengine - INFO - Epoch(train) [28][ 960/1320] lr: 2.0000e-03 eta: 2:44:45 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 4.7144 loss: 1.2577 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2577 2023/03/17 20:39:12 - mmengine - INFO - Epoch(train) [28][ 980/1320] lr: 2.0000e-03 eta: 2:44:38 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 4.7573 loss: 1.3239 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3239 2023/03/17 20:39:19 - mmengine - INFO - Epoch(train) [28][1000/1320] lr: 2.0000e-03 eta: 2:44:32 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 4.6593 loss: 1.2664 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2664 2023/03/17 20:39:25 - mmengine - INFO - Epoch(train) [28][1020/1320] lr: 2.0000e-03 eta: 2:44:25 time: 0.3363 data_time: 0.0127 memory: 18752 grad_norm: 4.7189 loss: 1.2686 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2686 2023/03/17 20:39:32 - mmengine - INFO - Epoch(train) [28][1040/1320] lr: 2.0000e-03 eta: 2:44:18 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.7399 loss: 1.2784 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2784 2023/03/17 20:39:39 - mmengine - INFO - Epoch(train) [28][1060/1320] lr: 2.0000e-03 eta: 2:44:11 time: 0.3359 data_time: 0.0128 memory: 18752 grad_norm: 4.7135 loss: 1.2133 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2133 2023/03/17 20:39:45 - mmengine - INFO - Epoch(train) [28][1080/1320] lr: 2.0000e-03 eta: 2:44:05 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.8267 loss: 1.3539 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3539 2023/03/17 20:39:52 - mmengine - INFO - Epoch(train) [28][1100/1320] lr: 2.0000e-03 eta: 2:43:58 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.6951 loss: 1.2157 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2157 2023/03/17 20:39:59 - mmengine - INFO - Epoch(train) [28][1120/1320] lr: 2.0000e-03 eta: 2:43:51 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.7556 loss: 1.2812 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2812 2023/03/17 20:40:06 - mmengine - INFO - Epoch(train) [28][1140/1320] lr: 2.0000e-03 eta: 2:43:45 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.6928 loss: 1.2504 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2504 2023/03/17 20:40:12 - mmengine - INFO - Epoch(train) [28][1160/1320] lr: 2.0000e-03 eta: 2:43:38 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 4.6327 loss: 1.0614 top1_acc: 0.4375 top5_acc: 1.0000 loss_cls: 1.0614 2023/03/17 20:40:19 - mmengine - INFO - Epoch(train) [28][1180/1320] lr: 2.0000e-03 eta: 2:43:31 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 4.7007 loss: 1.2291 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2291 2023/03/17 20:40:26 - mmengine - INFO - Epoch(train) [28][1200/1320] lr: 2.0000e-03 eta: 2:43:24 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.7586 loss: 1.2071 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2071 2023/03/17 20:40:32 - mmengine - INFO - Epoch(train) [28][1220/1320] lr: 2.0000e-03 eta: 2:43:18 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.7236 loss: 1.1889 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1889 2023/03/17 20:40:39 - mmengine - INFO - Epoch(train) [28][1240/1320] lr: 2.0000e-03 eta: 2:43:11 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 4.7508 loss: 1.2398 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2398 2023/03/17 20:40:46 - mmengine - INFO - Epoch(train) [28][1260/1320] lr: 2.0000e-03 eta: 2:43:04 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 5.0109 loss: 1.2763 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2763 2023/03/17 20:40:53 - mmengine - INFO - Epoch(train) [28][1280/1320] lr: 2.0000e-03 eta: 2:42:57 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 4.8827 loss: 1.1222 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1222 2023/03/17 20:40:59 - mmengine - INFO - Epoch(train) [28][1300/1320] lr: 2.0000e-03 eta: 2:42:51 time: 0.3353 data_time: 0.0125 memory: 18752 grad_norm: 4.6683 loss: 1.2326 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2326 2023/03/17 20:41:06 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:41:06 - mmengine - INFO - Epoch(train) [28][1320/1320] lr: 2.0000e-03 eta: 2:42:44 time: 0.3302 data_time: 0.0124 memory: 18752 grad_norm: 4.9062 loss: 1.1889 top1_acc: 0.7273 top5_acc: 1.0000 loss_cls: 1.1889 2023/03/17 20:41:08 - mmengine - INFO - Epoch(val) [28][ 20/194] eta: 0:00:21 time: 0.1262 data_time: 0.0399 memory: 2112 2023/03/17 20:41:10 - mmengine - INFO - Epoch(val) [28][ 40/194] eta: 0:00:17 time: 0.0952 data_time: 0.0094 memory: 2112 2023/03/17 20:41:12 - mmengine - INFO - Epoch(val) [28][ 60/194] eta: 0:00:14 time: 0.0963 data_time: 0.0106 memory: 2112 2023/03/17 20:41:14 - mmengine - INFO - Epoch(val) [28][ 80/194] eta: 0:00:11 time: 0.0968 data_time: 0.0108 memory: 2112 2023/03/17 20:41:16 - mmengine - INFO - Epoch(val) [28][100/194] eta: 0:00:09 time: 0.0963 data_time: 0.0105 memory: 2112 2023/03/17 20:41:18 - mmengine - INFO - Epoch(val) [28][120/194] eta: 0:00:07 time: 0.0967 data_time: 0.0109 memory: 2112 2023/03/17 20:41:20 - mmengine - INFO - Epoch(val) [28][140/194] eta: 0:00:05 time: 0.0971 data_time: 0.0112 memory: 2112 2023/03/17 20:41:22 - mmengine - INFO - Epoch(val) [28][160/194] eta: 0:00:03 time: 0.0977 data_time: 0.0117 memory: 2112 2023/03/17 20:41:24 - mmengine - INFO - Epoch(val) [28][180/194] eta: 0:00:01 time: 0.0969 data_time: 0.0106 memory: 2112 2023/03/17 20:41:27 - mmengine - INFO - Epoch(val) [28][194/194] acc/top1: 0.5996 acc/top5: 0.8593 acc/mean1: 0.5348 2023/03/17 20:41:27 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_27.pth is removed 2023/03/17 20:41:29 - mmengine - INFO - The best checkpoint with 0.5996 acc/top1 at 28 epoch is saved to best_acc/top1_epoch_28.pth. 2023/03/17 20:41:36 - mmengine - INFO - Epoch(train) [29][ 20/1320] lr: 2.0000e-03 eta: 2:42:38 time: 0.3673 data_time: 0.0369 memory: 18752 grad_norm: 4.9056 loss: 1.2962 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2962 2023/03/17 20:41:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:41:43 - mmengine - INFO - Epoch(train) [29][ 40/1320] lr: 2.0000e-03 eta: 2:42:31 time: 0.3352 data_time: 0.0130 memory: 18752 grad_norm: 4.6707 loss: 1.2589 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2589 2023/03/17 20:41:50 - mmengine - INFO - Epoch(train) [29][ 60/1320] lr: 2.0000e-03 eta: 2:42:24 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 4.5197 loss: 1.2146 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2146 2023/03/17 20:41:56 - mmengine - INFO - Epoch(train) [29][ 80/1320] lr: 2.0000e-03 eta: 2:42:17 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.7515 loss: 1.1770 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1770 2023/03/17 20:42:03 - mmengine - INFO - Epoch(train) [29][ 100/1320] lr: 2.0000e-03 eta: 2:42:11 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 4.6379 loss: 1.2616 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2616 2023/03/17 20:42:10 - mmengine - INFO - Epoch(train) [29][ 120/1320] lr: 2.0000e-03 eta: 2:42:04 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.7185 loss: 1.1632 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1632 2023/03/17 20:42:17 - mmengine - INFO - Epoch(train) [29][ 140/1320] lr: 2.0000e-03 eta: 2:41:57 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.8875 loss: 1.0854 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0854 2023/03/17 20:42:23 - mmengine - INFO - Epoch(train) [29][ 160/1320] lr: 2.0000e-03 eta: 2:41:50 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 4.6789 loss: 1.1420 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1420 2023/03/17 20:42:30 - mmengine - INFO - Epoch(train) [29][ 180/1320] lr: 2.0000e-03 eta: 2:41:44 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 4.8295 loss: 1.0638 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0638 2023/03/17 20:42:37 - mmengine - INFO - Epoch(train) [29][ 200/1320] lr: 2.0000e-03 eta: 2:41:37 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.7126 loss: 1.0536 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0536 2023/03/17 20:42:43 - mmengine - INFO - Epoch(train) [29][ 220/1320] lr: 2.0000e-03 eta: 2:41:30 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 4.8862 loss: 1.0447 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0447 2023/03/17 20:42:50 - mmengine - INFO - Epoch(train) [29][ 240/1320] lr: 2.0000e-03 eta: 2:41:24 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 4.7624 loss: 1.0690 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0690 2023/03/17 20:42:57 - mmengine - INFO - Epoch(train) [29][ 260/1320] lr: 2.0000e-03 eta: 2:41:17 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.8143 loss: 1.1844 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1844 2023/03/17 20:43:04 - mmengine - INFO - Epoch(train) [29][ 280/1320] lr: 2.0000e-03 eta: 2:41:10 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 4.7344 loss: 1.2830 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2830 2023/03/17 20:43:10 - mmengine - INFO - Epoch(train) [29][ 300/1320] lr: 2.0000e-03 eta: 2:41:03 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.8508 loss: 1.2094 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2094 2023/03/17 20:43:17 - mmengine - INFO - Epoch(train) [29][ 320/1320] lr: 2.0000e-03 eta: 2:40:57 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 4.7716 loss: 1.2364 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2364 2023/03/17 20:43:24 - mmengine - INFO - Epoch(train) [29][ 340/1320] lr: 2.0000e-03 eta: 2:40:50 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.8129 loss: 1.2663 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2663 2023/03/17 20:43:30 - mmengine - INFO - Epoch(train) [29][ 360/1320] lr: 2.0000e-03 eta: 2:40:43 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.7985 loss: 1.0513 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0513 2023/03/17 20:43:37 - mmengine - INFO - Epoch(train) [29][ 380/1320] lr: 2.0000e-03 eta: 2:40:36 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 4.7973 loss: 1.1989 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1989 2023/03/17 20:43:44 - mmengine - INFO - Epoch(train) [29][ 400/1320] lr: 2.0000e-03 eta: 2:40:30 time: 0.3353 data_time: 0.0124 memory: 18752 grad_norm: 4.7605 loss: 1.2519 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2519 2023/03/17 20:43:51 - mmengine - INFO - Epoch(train) [29][ 420/1320] lr: 2.0000e-03 eta: 2:40:23 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 4.7306 loss: 1.1122 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1122 2023/03/17 20:43:57 - mmengine - INFO - Epoch(train) [29][ 440/1320] lr: 2.0000e-03 eta: 2:40:16 time: 0.3351 data_time: 0.0123 memory: 18752 grad_norm: 4.7730 loss: 1.2118 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2118 2023/03/17 20:44:04 - mmengine - INFO - Epoch(train) [29][ 460/1320] lr: 2.0000e-03 eta: 2:40:09 time: 0.3361 data_time: 0.0138 memory: 18752 grad_norm: 4.7936 loss: 1.1701 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1701 2023/03/17 20:44:11 - mmengine - INFO - Epoch(train) [29][ 480/1320] lr: 2.0000e-03 eta: 2:40:03 time: 0.3350 data_time: 0.0124 memory: 18752 grad_norm: 4.5856 loss: 1.0836 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0836 2023/03/17 20:44:17 - mmengine - INFO - Epoch(train) [29][ 500/1320] lr: 2.0000e-03 eta: 2:39:56 time: 0.3371 data_time: 0.0122 memory: 18752 grad_norm: 4.8526 loss: 1.1001 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1001 2023/03/17 20:44:24 - mmengine - INFO - Epoch(train) [29][ 520/1320] lr: 2.0000e-03 eta: 2:39:49 time: 0.3422 data_time: 0.0116 memory: 18752 grad_norm: 4.9852 loss: 1.2105 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2105 2023/03/17 20:44:31 - mmengine - INFO - Epoch(train) [29][ 540/1320] lr: 2.0000e-03 eta: 2:39:43 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.9174 loss: 1.2223 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2223 2023/03/17 20:44:38 - mmengine - INFO - Epoch(train) [29][ 560/1320] lr: 2.0000e-03 eta: 2:39:36 time: 0.3412 data_time: 0.0128 memory: 18752 grad_norm: 4.8621 loss: 1.2128 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2128 2023/03/17 20:44:44 - mmengine - INFO - Epoch(train) [29][ 580/1320] lr: 2.0000e-03 eta: 2:39:29 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.8110 loss: 1.1978 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.1978 2023/03/17 20:44:51 - mmengine - INFO - Epoch(train) [29][ 600/1320] lr: 2.0000e-03 eta: 2:39:22 time: 0.3358 data_time: 0.0116 memory: 18752 grad_norm: 4.7288 loss: 1.0270 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0270 2023/03/17 20:44:58 - mmengine - INFO - Epoch(train) [29][ 620/1320] lr: 2.0000e-03 eta: 2:39:16 time: 0.3351 data_time: 0.0116 memory: 18752 grad_norm: 4.8068 loss: 1.2488 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2488 2023/03/17 20:45:05 - mmengine - INFO - Epoch(train) [29][ 640/1320] lr: 2.0000e-03 eta: 2:39:09 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.8278 loss: 1.2213 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2213 2023/03/17 20:45:11 - mmengine - INFO - Epoch(train) [29][ 660/1320] lr: 2.0000e-03 eta: 2:39:02 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.7969 loss: 1.2645 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2645 2023/03/17 20:45:18 - mmengine - INFO - Epoch(train) [29][ 680/1320] lr: 2.0000e-03 eta: 2:38:56 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.8211 loss: 1.1100 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1100 2023/03/17 20:45:25 - mmengine - INFO - Epoch(train) [29][ 700/1320] lr: 2.0000e-03 eta: 2:38:49 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 4.8723 loss: 1.0333 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0333 2023/03/17 20:45:31 - mmengine - INFO - Epoch(train) [29][ 720/1320] lr: 2.0000e-03 eta: 2:38:42 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 4.8990 loss: 1.2743 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2743 2023/03/17 20:45:38 - mmengine - INFO - Epoch(train) [29][ 740/1320] lr: 2.0000e-03 eta: 2:38:35 time: 0.3360 data_time: 0.0116 memory: 18752 grad_norm: 4.8526 loss: 1.1282 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1282 2023/03/17 20:45:45 - mmengine - INFO - Epoch(train) [29][ 760/1320] lr: 2.0000e-03 eta: 2:38:29 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 4.8836 loss: 1.3172 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3172 2023/03/17 20:45:52 - mmengine - INFO - Epoch(train) [29][ 780/1320] lr: 2.0000e-03 eta: 2:38:22 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 4.9272 loss: 1.1157 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1157 2023/03/17 20:45:58 - mmengine - INFO - Epoch(train) [29][ 800/1320] lr: 2.0000e-03 eta: 2:38:15 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 4.8783 loss: 1.1175 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1175 2023/03/17 20:46:05 - mmengine - INFO - Epoch(train) [29][ 820/1320] lr: 2.0000e-03 eta: 2:38:08 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 4.9098 loss: 1.0617 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0617 2023/03/17 20:46:12 - mmengine - INFO - Epoch(train) [29][ 840/1320] lr: 2.0000e-03 eta: 2:38:02 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 4.8985 loss: 1.3230 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.3230 2023/03/17 20:46:19 - mmengine - INFO - Epoch(train) [29][ 860/1320] lr: 2.0000e-03 eta: 2:37:55 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 4.8014 loss: 1.2543 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.2543 2023/03/17 20:46:25 - mmengine - INFO - Epoch(train) [29][ 880/1320] lr: 2.0000e-03 eta: 2:37:48 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.6602 loss: 1.1474 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1474 2023/03/17 20:46:32 - mmengine - INFO - Epoch(train) [29][ 900/1320] lr: 2.0000e-03 eta: 2:37:42 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 4.8382 loss: 1.1837 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1837 2023/03/17 20:46:39 - mmengine - INFO - Epoch(train) [29][ 920/1320] lr: 2.0000e-03 eta: 2:37:35 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.8848 loss: 1.1057 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1057 2023/03/17 20:46:45 - mmengine - INFO - Epoch(train) [29][ 940/1320] lr: 2.0000e-03 eta: 2:37:28 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 4.8825 loss: 0.9845 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9845 2023/03/17 20:46:52 - mmengine - INFO - Epoch(train) [29][ 960/1320] lr: 2.0000e-03 eta: 2:37:21 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 4.9732 loss: 1.2878 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2878 2023/03/17 20:46:59 - mmengine - INFO - Epoch(train) [29][ 980/1320] lr: 2.0000e-03 eta: 2:37:15 time: 0.3365 data_time: 0.0124 memory: 18752 grad_norm: 4.9685 loss: 1.3928 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3928 2023/03/17 20:47:06 - mmengine - INFO - Epoch(train) [29][1000/1320] lr: 2.0000e-03 eta: 2:37:08 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 4.8587 loss: 1.0952 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0952 2023/03/17 20:47:12 - mmengine - INFO - Epoch(train) [29][1020/1320] lr: 2.0000e-03 eta: 2:37:01 time: 0.3362 data_time: 0.0132 memory: 18752 grad_norm: 4.7325 loss: 1.1909 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1909 2023/03/17 20:47:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:47:19 - mmengine - INFO - Epoch(train) [29][1040/1320] lr: 2.0000e-03 eta: 2:36:54 time: 0.3365 data_time: 0.0130 memory: 18752 grad_norm: 4.7038 loss: 1.0919 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0919 2023/03/17 20:47:26 - mmengine - INFO - Epoch(train) [29][1060/1320] lr: 2.0000e-03 eta: 2:36:48 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 4.9426 loss: 1.2530 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2530 2023/03/17 20:47:32 - mmengine - INFO - Epoch(train) [29][1080/1320] lr: 2.0000e-03 eta: 2:36:41 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 4.9683 loss: 1.2463 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2463 2023/03/17 20:47:39 - mmengine - INFO - Epoch(train) [29][1100/1320] lr: 2.0000e-03 eta: 2:36:34 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 4.7429 loss: 1.0386 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0386 2023/03/17 20:47:46 - mmengine - INFO - Epoch(train) [29][1120/1320] lr: 2.0000e-03 eta: 2:36:28 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 4.9829 loss: 1.0636 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0636 2023/03/17 20:47:53 - mmengine - INFO - Epoch(train) [29][1140/1320] lr: 2.0000e-03 eta: 2:36:21 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 4.9041 loss: 1.1614 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1614 2023/03/17 20:47:59 - mmengine - INFO - Epoch(train) [29][1160/1320] lr: 2.0000e-03 eta: 2:36:14 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.9210 loss: 1.1460 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1460 2023/03/17 20:48:06 - mmengine - INFO - Epoch(train) [29][1180/1320] lr: 2.0000e-03 eta: 2:36:07 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 4.9660 loss: 1.0958 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0958 2023/03/17 20:48:13 - mmengine - INFO - Epoch(train) [29][1200/1320] lr: 2.0000e-03 eta: 2:36:01 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 4.8470 loss: 1.1337 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1337 2023/03/17 20:48:20 - mmengine - INFO - Epoch(train) [29][1220/1320] lr: 2.0000e-03 eta: 2:35:54 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 4.9738 loss: 1.0941 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0941 2023/03/17 20:48:26 - mmengine - INFO - Epoch(train) [29][1240/1320] lr: 2.0000e-03 eta: 2:35:47 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 4.8459 loss: 1.1645 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1645 2023/03/17 20:48:33 - mmengine - INFO - Epoch(train) [29][1260/1320] lr: 2.0000e-03 eta: 2:35:40 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 4.9411 loss: 0.9669 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9669 2023/03/17 20:48:40 - mmengine - INFO - Epoch(train) [29][1280/1320] lr: 2.0000e-03 eta: 2:35:34 time: 0.3358 data_time: 0.0116 memory: 18752 grad_norm: 4.8753 loss: 1.2210 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2210 2023/03/17 20:48:46 - mmengine - INFO - Epoch(train) [29][1300/1320] lr: 2.0000e-03 eta: 2:35:27 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 4.9409 loss: 1.1123 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1123 2023/03/17 20:48:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:48:53 - mmengine - INFO - Epoch(train) [29][1320/1320] lr: 2.0000e-03 eta: 2:35:20 time: 0.3310 data_time: 0.0123 memory: 18752 grad_norm: 5.0652 loss: 1.1912 top1_acc: 0.9091 top5_acc: 1.0000 loss_cls: 1.1912 2023/03/17 20:48:56 - mmengine - INFO - Epoch(val) [29][ 20/194] eta: 0:00:22 time: 0.1286 data_time: 0.0424 memory: 2112 2023/03/17 20:48:58 - mmengine - INFO - Epoch(val) [29][ 40/194] eta: 0:00:17 time: 0.0963 data_time: 0.0106 memory: 2112 2023/03/17 20:48:59 - mmengine - INFO - Epoch(val) [29][ 60/194] eta: 0:00:14 time: 0.0962 data_time: 0.0105 memory: 2112 2023/03/17 20:49:01 - mmengine - INFO - Epoch(val) [29][ 80/194] eta: 0:00:11 time: 0.0974 data_time: 0.0112 memory: 2112 2023/03/17 20:49:03 - mmengine - INFO - Epoch(val) [29][100/194] eta: 0:00:09 time: 0.0989 data_time: 0.0122 memory: 2112 2023/03/17 20:49:05 - mmengine - INFO - Epoch(val) [29][120/194] eta: 0:00:07 time: 0.0978 data_time: 0.0118 memory: 2112 2023/03/17 20:49:07 - mmengine - INFO - Epoch(val) [29][140/194] eta: 0:00:05 time: 0.0963 data_time: 0.0103 memory: 2112 2023/03/17 20:49:09 - mmengine - INFO - Epoch(val) [29][160/194] eta: 0:00:03 time: 0.0969 data_time: 0.0109 memory: 2112 2023/03/17 20:49:11 - mmengine - INFO - Epoch(val) [29][180/194] eta: 0:00:01 time: 0.0969 data_time: 0.0113 memory: 2112 2023/03/17 20:49:15 - mmengine - INFO - Epoch(val) [29][194/194] acc/top1: 0.5986 acc/top5: 0.8592 acc/mean1: 0.5327 2023/03/17 20:49:22 - mmengine - INFO - Epoch(train) [30][ 20/1320] lr: 2.0000e-03 eta: 2:35:14 time: 0.3760 data_time: 0.0429 memory: 18752 grad_norm: 4.8115 loss: 1.1608 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1608 2023/03/17 20:49:29 - mmengine - INFO - Epoch(train) [30][ 40/1320] lr: 2.0000e-03 eta: 2:35:07 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.7207 loss: 1.0597 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0597 2023/03/17 20:49:36 - mmengine - INFO - Epoch(train) [30][ 60/1320] lr: 2.0000e-03 eta: 2:35:01 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 4.9479 loss: 1.2261 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2261 2023/03/17 20:49:42 - mmengine - INFO - Epoch(train) [30][ 80/1320] lr: 2.0000e-03 eta: 2:34:54 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 4.9061 loss: 1.2757 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2757 2023/03/17 20:49:49 - mmengine - INFO - Epoch(train) [30][ 100/1320] lr: 2.0000e-03 eta: 2:34:47 time: 0.3370 data_time: 0.0121 memory: 18752 grad_norm: 4.8763 loss: 1.0352 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0352 2023/03/17 20:49:56 - mmengine - INFO - Epoch(train) [30][ 120/1320] lr: 2.0000e-03 eta: 2:34:40 time: 0.3368 data_time: 0.0121 memory: 18752 grad_norm: 4.8444 loss: 1.1758 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1758 2023/03/17 20:50:02 - mmengine - INFO - Epoch(train) [30][ 140/1320] lr: 2.0000e-03 eta: 2:34:34 time: 0.3363 data_time: 0.0127 memory: 18752 grad_norm: 5.0027 loss: 1.2334 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2334 2023/03/17 20:50:09 - mmengine - INFO - Epoch(train) [30][ 160/1320] lr: 2.0000e-03 eta: 2:34:27 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 4.8934 loss: 1.1464 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1464 2023/03/17 20:50:16 - mmengine - INFO - Epoch(train) [30][ 180/1320] lr: 2.0000e-03 eta: 2:34:20 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 4.9620 loss: 1.1250 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1250 2023/03/17 20:50:23 - mmengine - INFO - Epoch(train) [30][ 200/1320] lr: 2.0000e-03 eta: 2:34:13 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 4.9392 loss: 1.1502 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1502 2023/03/17 20:50:29 - mmengine - INFO - Epoch(train) [30][ 220/1320] lr: 2.0000e-03 eta: 2:34:07 time: 0.3368 data_time: 0.0123 memory: 18752 grad_norm: 4.9920 loss: 1.1107 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1107 2023/03/17 20:50:36 - mmengine - INFO - Epoch(train) [30][ 240/1320] lr: 2.0000e-03 eta: 2:34:00 time: 0.3352 data_time: 0.0122 memory: 18752 grad_norm: 5.0692 loss: 1.1730 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1730 2023/03/17 20:50:43 - mmengine - INFO - Epoch(train) [30][ 260/1320] lr: 2.0000e-03 eta: 2:33:53 time: 0.3366 data_time: 0.0119 memory: 18752 grad_norm: 4.8167 loss: 1.1696 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1696 2023/03/17 20:50:50 - mmengine - INFO - Epoch(train) [30][ 280/1320] lr: 2.0000e-03 eta: 2:33:47 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 4.8856 loss: 1.0950 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0950 2023/03/17 20:50:56 - mmengine - INFO - Epoch(train) [30][ 300/1320] lr: 2.0000e-03 eta: 2:33:40 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 5.0228 loss: 1.2040 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2040 2023/03/17 20:51:03 - mmengine - INFO - Epoch(train) [30][ 320/1320] lr: 2.0000e-03 eta: 2:33:33 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 5.1119 loss: 1.3547 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3547 2023/03/17 20:51:10 - mmengine - INFO - Epoch(train) [30][ 340/1320] lr: 2.0000e-03 eta: 2:33:26 time: 0.3363 data_time: 0.0113 memory: 18752 grad_norm: 4.9945 loss: 1.0626 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0626 2023/03/17 20:51:16 - mmengine - INFO - Epoch(train) [30][ 360/1320] lr: 2.0000e-03 eta: 2:33:20 time: 0.3359 data_time: 0.0115 memory: 18752 grad_norm: 4.7646 loss: 1.0734 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0734 2023/03/17 20:51:23 - mmengine - INFO - Epoch(train) [30][ 380/1320] lr: 2.0000e-03 eta: 2:33:13 time: 0.3362 data_time: 0.0127 memory: 18752 grad_norm: 4.7532 loss: 1.2020 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2020 2023/03/17 20:51:30 - mmengine - INFO - Epoch(train) [30][ 400/1320] lr: 2.0000e-03 eta: 2:33:06 time: 0.3350 data_time: 0.0118 memory: 18752 grad_norm: 4.8905 loss: 1.1011 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1011 2023/03/17 20:51:37 - mmengine - INFO - Epoch(train) [30][ 420/1320] lr: 2.0000e-03 eta: 2:32:59 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 4.8900 loss: 1.0279 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0279 2023/03/17 20:51:43 - mmengine - INFO - Epoch(train) [30][ 440/1320] lr: 2.0000e-03 eta: 2:32:53 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 5.0470 loss: 1.0676 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0676 2023/03/17 20:51:50 - mmengine - INFO - Epoch(train) [30][ 460/1320] lr: 2.0000e-03 eta: 2:32:46 time: 0.3359 data_time: 0.0129 memory: 18752 grad_norm: 4.7766 loss: 1.3196 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3196 2023/03/17 20:51:57 - mmengine - INFO - Epoch(train) [30][ 480/1320] lr: 2.0000e-03 eta: 2:32:39 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.9408 loss: 1.1909 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1909 2023/03/17 20:52:03 - mmengine - INFO - Epoch(train) [30][ 500/1320] lr: 2.0000e-03 eta: 2:32:33 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 5.0138 loss: 1.0586 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0586 2023/03/17 20:52:10 - mmengine - INFO - Epoch(train) [30][ 520/1320] lr: 2.0000e-03 eta: 2:32:26 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 4.9799 loss: 1.2658 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2658 2023/03/17 20:52:17 - mmengine - INFO - Epoch(train) [30][ 540/1320] lr: 2.0000e-03 eta: 2:32:19 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 5.0105 loss: 1.1343 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1343 2023/03/17 20:52:24 - mmengine - INFO - Epoch(train) [30][ 560/1320] lr: 2.0000e-03 eta: 2:32:12 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 5.1124 loss: 1.0645 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0645 2023/03/17 20:52:30 - mmengine - INFO - Epoch(train) [30][ 580/1320] lr: 2.0000e-03 eta: 2:32:06 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 4.9035 loss: 1.1094 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1094 2023/03/17 20:52:37 - mmengine - INFO - Epoch(train) [30][ 600/1320] lr: 2.0000e-03 eta: 2:31:59 time: 0.3363 data_time: 0.0115 memory: 18752 grad_norm: 5.0766 loss: 1.1837 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1837 2023/03/17 20:52:44 - mmengine - INFO - Epoch(train) [30][ 620/1320] lr: 2.0000e-03 eta: 2:31:52 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 5.0271 loss: 1.1471 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1471 2023/03/17 20:52:51 - mmengine - INFO - Epoch(train) [30][ 640/1320] lr: 2.0000e-03 eta: 2:31:45 time: 0.3364 data_time: 0.0117 memory: 18752 grad_norm: 5.0958 loss: 1.1130 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1130 2023/03/17 20:52:57 - mmengine - INFO - Epoch(train) [30][ 660/1320] lr: 2.0000e-03 eta: 2:31:39 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 4.8607 loss: 1.2025 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2025 2023/03/17 20:53:04 - mmengine - INFO - Epoch(train) [30][ 680/1320] lr: 2.0000e-03 eta: 2:31:32 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 4.9385 loss: 1.3406 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3406 2023/03/17 20:53:11 - mmengine - INFO - Epoch(train) [30][ 700/1320] lr: 2.0000e-03 eta: 2:31:25 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 5.2311 loss: 1.3670 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3670 2023/03/17 20:53:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:53:17 - mmengine - INFO - Epoch(train) [30][ 720/1320] lr: 2.0000e-03 eta: 2:31:19 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 4.9886 loss: 1.1722 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1722 2023/03/17 20:53:24 - mmengine - INFO - Epoch(train) [30][ 740/1320] lr: 2.0000e-03 eta: 2:31:12 time: 0.3368 data_time: 0.0119 memory: 18752 grad_norm: 5.0832 loss: 1.1923 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.1923 2023/03/17 20:53:31 - mmengine - INFO - Epoch(train) [30][ 760/1320] lr: 2.0000e-03 eta: 2:31:05 time: 0.3360 data_time: 0.0117 memory: 18752 grad_norm: 5.0551 loss: 1.1086 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1086 2023/03/17 20:53:38 - mmengine - INFO - Epoch(train) [30][ 780/1320] lr: 2.0000e-03 eta: 2:30:58 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 4.9595 loss: 1.1744 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1744 2023/03/17 20:53:44 - mmengine - INFO - Epoch(train) [30][ 800/1320] lr: 2.0000e-03 eta: 2:30:52 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 5.0559 loss: 1.2023 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2023 2023/03/17 20:53:51 - mmengine - INFO - Epoch(train) [30][ 820/1320] lr: 2.0000e-03 eta: 2:30:45 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 4.8693 loss: 1.1708 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1708 2023/03/17 20:53:58 - mmengine - INFO - Epoch(train) [30][ 840/1320] lr: 2.0000e-03 eta: 2:30:38 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 5.0551 loss: 1.2142 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2142 2023/03/17 20:54:04 - mmengine - INFO - Epoch(train) [30][ 860/1320] lr: 2.0000e-03 eta: 2:30:31 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 4.9606 loss: 1.0255 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0255 2023/03/17 20:54:11 - mmengine - INFO - Epoch(train) [30][ 880/1320] lr: 2.0000e-03 eta: 2:30:25 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 5.1481 loss: 1.1254 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1254 2023/03/17 20:54:18 - mmengine - INFO - Epoch(train) [30][ 900/1320] lr: 2.0000e-03 eta: 2:30:18 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 5.0528 loss: 1.2808 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2808 2023/03/17 20:54:25 - mmengine - INFO - Epoch(train) [30][ 920/1320] lr: 2.0000e-03 eta: 2:30:11 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 5.1284 loss: 1.1946 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1946 2023/03/17 20:54:31 - mmengine - INFO - Epoch(train) [30][ 940/1320] lr: 2.0000e-03 eta: 2:30:05 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 5.0456 loss: 1.2113 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2113 2023/03/17 20:54:38 - mmengine - INFO - Epoch(train) [30][ 960/1320] lr: 2.0000e-03 eta: 2:29:58 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 5.0642 loss: 1.1588 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1588 2023/03/17 20:54:45 - mmengine - INFO - Epoch(train) [30][ 980/1320] lr: 2.0000e-03 eta: 2:29:51 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 5.1352 loss: 1.2615 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2615 2023/03/17 20:54:52 - mmengine - INFO - Epoch(train) [30][1000/1320] lr: 2.0000e-03 eta: 2:29:44 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 5.0225 loss: 1.1033 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1033 2023/03/17 20:54:58 - mmengine - INFO - Epoch(train) [30][1020/1320] lr: 2.0000e-03 eta: 2:29:38 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 5.1077 loss: 1.1635 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1635 2023/03/17 20:55:05 - mmengine - INFO - Epoch(train) [30][1040/1320] lr: 2.0000e-03 eta: 2:29:31 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 4.9958 loss: 1.1231 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1231 2023/03/17 20:55:12 - mmengine - INFO - Epoch(train) [30][1060/1320] lr: 2.0000e-03 eta: 2:29:24 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 4.9674 loss: 1.1209 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1209 2023/03/17 20:55:18 - mmengine - INFO - Epoch(train) [30][1080/1320] lr: 2.0000e-03 eta: 2:29:17 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 5.1481 loss: 1.2120 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2120 2023/03/17 20:55:25 - mmengine - INFO - Epoch(train) [30][1100/1320] lr: 2.0000e-03 eta: 2:29:11 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 5.0373 loss: 1.0918 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0918 2023/03/17 20:55:32 - mmengine - INFO - Epoch(train) [30][1120/1320] lr: 2.0000e-03 eta: 2:29:04 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 5.1391 loss: 0.9795 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9795 2023/03/17 20:55:39 - mmengine - INFO - Epoch(train) [30][1140/1320] lr: 2.0000e-03 eta: 2:28:57 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 4.9009 loss: 1.1449 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1449 2023/03/17 20:55:45 - mmengine - INFO - Epoch(train) [30][1160/1320] lr: 2.0000e-03 eta: 2:28:51 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 5.0968 loss: 1.1770 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1770 2023/03/17 20:55:52 - mmengine - INFO - Epoch(train) [30][1180/1320] lr: 2.0000e-03 eta: 2:28:44 time: 0.3369 data_time: 0.0123 memory: 18752 grad_norm: 5.0237 loss: 1.1513 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1513 2023/03/17 20:55:59 - mmengine - INFO - Epoch(train) [30][1200/1320] lr: 2.0000e-03 eta: 2:28:37 time: 0.3367 data_time: 0.0123 memory: 18752 grad_norm: 5.2232 loss: 1.2682 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2682 2023/03/17 20:56:06 - mmengine - INFO - Epoch(train) [30][1220/1320] lr: 2.0000e-03 eta: 2:28:30 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 5.1079 loss: 1.0626 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0626 2023/03/17 20:56:12 - mmengine - INFO - Epoch(train) [30][1240/1320] lr: 2.0000e-03 eta: 2:28:24 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 4.9643 loss: 1.2241 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2241 2023/03/17 20:56:19 - mmengine - INFO - Epoch(train) [30][1260/1320] lr: 2.0000e-03 eta: 2:28:17 time: 0.3360 data_time: 0.0131 memory: 18752 grad_norm: 5.2397 loss: 1.1796 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1796 2023/03/17 20:56:26 - mmengine - INFO - Epoch(train) [30][1280/1320] lr: 2.0000e-03 eta: 2:28:10 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 5.1260 loss: 1.0191 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0191 2023/03/17 20:56:32 - mmengine - INFO - Epoch(train) [30][1300/1320] lr: 2.0000e-03 eta: 2:28:03 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 5.1743 loss: 1.1104 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1104 2023/03/17 20:56:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:56:39 - mmengine - INFO - Epoch(train) [30][1320/1320] lr: 2.0000e-03 eta: 2:27:57 time: 0.3318 data_time: 0.0134 memory: 18752 grad_norm: 5.0855 loss: 1.1437 top1_acc: 0.8182 top5_acc: 1.0000 loss_cls: 1.1437 2023/03/17 20:56:39 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/03/17 20:56:45 - mmengine - INFO - Epoch(val) [30][ 20/194] eta: 0:00:22 time: 0.1286 data_time: 0.0417 memory: 2112 2023/03/17 20:56:47 - mmengine - INFO - Epoch(val) [30][ 40/194] eta: 0:00:17 time: 0.0960 data_time: 0.0102 memory: 2112 2023/03/17 20:56:48 - mmengine - INFO - Epoch(val) [30][ 60/194] eta: 0:00:14 time: 0.0975 data_time: 0.0113 memory: 2112 2023/03/17 20:56:50 - mmengine - INFO - Epoch(val) [30][ 80/194] eta: 0:00:11 time: 0.0965 data_time: 0.0104 memory: 2112 2023/03/17 20:56:52 - mmengine - INFO - Epoch(val) [30][100/194] eta: 0:00:09 time: 0.0971 data_time: 0.0111 memory: 2112 2023/03/17 20:56:54 - mmengine - INFO - Epoch(val) [30][120/194] eta: 0:00:07 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 20:56:56 - mmengine - INFO - Epoch(val) [30][140/194] eta: 0:00:05 time: 0.0972 data_time: 0.0111 memory: 2112 2023/03/17 20:56:58 - mmengine - INFO - Epoch(val) [30][160/194] eta: 0:00:03 time: 0.0961 data_time: 0.0105 memory: 2112 2023/03/17 20:57:00 - mmengine - INFO - Epoch(val) [30][180/194] eta: 0:00:01 time: 0.0961 data_time: 0.0102 memory: 2112 2023/03/17 20:57:03 - mmengine - INFO - Epoch(val) [30][194/194] acc/top1: 0.5987 acc/top5: 0.8616 acc/mean1: 0.5384 2023/03/17 20:57:10 - mmengine - INFO - Epoch(train) [31][ 20/1320] lr: 2.0000e-03 eta: 2:27:50 time: 0.3755 data_time: 0.0414 memory: 18752 grad_norm: 5.1040 loss: 1.2456 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2456 2023/03/17 20:57:17 - mmengine - INFO - Epoch(train) [31][ 40/1320] lr: 2.0000e-03 eta: 2:27:44 time: 0.3371 data_time: 0.0114 memory: 18752 grad_norm: 4.9042 loss: 1.0031 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0031 2023/03/17 20:57:24 - mmengine - INFO - Epoch(train) [31][ 60/1320] lr: 2.0000e-03 eta: 2:27:37 time: 0.3357 data_time: 0.0113 memory: 18752 grad_norm: 4.8832 loss: 1.0473 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0473 2023/03/17 20:57:31 - mmengine - INFO - Epoch(train) [31][ 80/1320] lr: 2.0000e-03 eta: 2:27:30 time: 0.3351 data_time: 0.0120 memory: 18752 grad_norm: 5.0105 loss: 0.9397 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9397 2023/03/17 20:57:37 - mmengine - INFO - Epoch(train) [31][ 100/1320] lr: 2.0000e-03 eta: 2:27:24 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 5.0559 loss: 1.1906 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1906 2023/03/17 20:57:44 - mmengine - INFO - Epoch(train) [31][ 120/1320] lr: 2.0000e-03 eta: 2:27:17 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 5.0771 loss: 1.1669 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1669 2023/03/17 20:57:51 - mmengine - INFO - Epoch(train) [31][ 140/1320] lr: 2.0000e-03 eta: 2:27:10 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 5.0404 loss: 1.0969 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0969 2023/03/17 20:57:57 - mmengine - INFO - Epoch(train) [31][ 160/1320] lr: 2.0000e-03 eta: 2:27:03 time: 0.3350 data_time: 0.0120 memory: 18752 grad_norm: 5.2263 loss: 1.0938 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.0938 2023/03/17 20:58:04 - mmengine - INFO - Epoch(train) [31][ 180/1320] lr: 2.0000e-03 eta: 2:26:57 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 5.1770 loss: 1.1634 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1634 2023/03/17 20:58:11 - mmengine - INFO - Epoch(train) [31][ 200/1320] lr: 2.0000e-03 eta: 2:26:50 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.1941 loss: 1.1012 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1012 2023/03/17 20:58:18 - mmengine - INFO - Epoch(train) [31][ 220/1320] lr: 2.0000e-03 eta: 2:26:43 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 5.1781 loss: 1.2033 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2033 2023/03/17 20:58:24 - mmengine - INFO - Epoch(train) [31][ 240/1320] lr: 2.0000e-03 eta: 2:26:36 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 5.1717 loss: 1.2020 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2020 2023/03/17 20:58:31 - mmengine - INFO - Epoch(train) [31][ 260/1320] lr: 2.0000e-03 eta: 2:26:30 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 4.9877 loss: 1.0710 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0710 2023/03/17 20:58:38 - mmengine - INFO - Epoch(train) [31][ 280/1320] lr: 2.0000e-03 eta: 2:26:23 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 5.1120 loss: 1.2887 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2887 2023/03/17 20:58:44 - mmengine - INFO - Epoch(train) [31][ 300/1320] lr: 2.0000e-03 eta: 2:26:16 time: 0.3354 data_time: 0.0116 memory: 18752 grad_norm: 5.1463 loss: 1.1084 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1084 2023/03/17 20:58:51 - mmengine - INFO - Epoch(train) [31][ 320/1320] lr: 2.0000e-03 eta: 2:26:09 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 5.1796 loss: 1.0129 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0129 2023/03/17 20:58:58 - mmengine - INFO - Epoch(train) [31][ 340/1320] lr: 2.0000e-03 eta: 2:26:03 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 4.9693 loss: 1.0676 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0676 2023/03/17 20:59:05 - mmengine - INFO - Epoch(train) [31][ 360/1320] lr: 2.0000e-03 eta: 2:25:56 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 5.0256 loss: 1.0961 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0961 2023/03/17 20:59:11 - mmengine - INFO - Epoch(train) [31][ 380/1320] lr: 2.0000e-03 eta: 2:25:49 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 5.2249 loss: 1.0870 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0870 2023/03/17 20:59:18 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 20:59:18 - mmengine - INFO - Epoch(train) [31][ 400/1320] lr: 2.0000e-03 eta: 2:25:43 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 5.1391 loss: 1.2135 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2135 2023/03/17 20:59:25 - mmengine - INFO - Epoch(train) [31][ 420/1320] lr: 2.0000e-03 eta: 2:25:36 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 5.1509 loss: 1.1254 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1254 2023/03/17 20:59:31 - mmengine - INFO - Epoch(train) [31][ 440/1320] lr: 2.0000e-03 eta: 2:25:29 time: 0.3357 data_time: 0.0129 memory: 18752 grad_norm: 5.2662 loss: 1.2290 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2290 2023/03/17 20:59:38 - mmengine - INFO - Epoch(train) [31][ 460/1320] lr: 2.0000e-03 eta: 2:25:22 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 5.1514 loss: 1.2392 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2392 2023/03/17 20:59:45 - mmengine - INFO - Epoch(train) [31][ 480/1320] lr: 2.0000e-03 eta: 2:25:16 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 5.2004 loss: 1.1315 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1315 2023/03/17 20:59:52 - mmengine - INFO - Epoch(train) [31][ 500/1320] lr: 2.0000e-03 eta: 2:25:09 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 5.0860 loss: 1.0914 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0914 2023/03/17 20:59:58 - mmengine - INFO - Epoch(train) [31][ 520/1320] lr: 2.0000e-03 eta: 2:25:02 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 5.2539 loss: 1.1862 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1862 2023/03/17 21:00:05 - mmengine - INFO - Epoch(train) [31][ 540/1320] lr: 2.0000e-03 eta: 2:24:55 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 5.1836 loss: 1.1628 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1628 2023/03/17 21:00:12 - mmengine - INFO - Epoch(train) [31][ 560/1320] lr: 2.0000e-03 eta: 2:24:49 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 5.1166 loss: 1.1920 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1920 2023/03/17 21:00:18 - mmengine - INFO - Epoch(train) [31][ 580/1320] lr: 2.0000e-03 eta: 2:24:42 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 5.1650 loss: 1.0065 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0065 2023/03/17 21:00:25 - mmengine - INFO - Epoch(train) [31][ 600/1320] lr: 2.0000e-03 eta: 2:24:35 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 5.1884 loss: 1.1998 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1998 2023/03/17 21:00:32 - mmengine - INFO - Epoch(train) [31][ 620/1320] lr: 2.0000e-03 eta: 2:24:28 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.2523 loss: 1.1982 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1982 2023/03/17 21:00:39 - mmengine - INFO - Epoch(train) [31][ 640/1320] lr: 2.0000e-03 eta: 2:24:22 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 5.1329 loss: 1.1693 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1693 2023/03/17 21:00:45 - mmengine - INFO - Epoch(train) [31][ 660/1320] lr: 2.0000e-03 eta: 2:24:15 time: 0.3362 data_time: 0.0128 memory: 18752 grad_norm: 5.1626 loss: 1.2296 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2296 2023/03/17 21:00:52 - mmengine - INFO - Epoch(train) [31][ 680/1320] lr: 2.0000e-03 eta: 2:24:08 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 5.0758 loss: 1.1334 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1334 2023/03/17 21:00:59 - mmengine - INFO - Epoch(train) [31][ 700/1320] lr: 2.0000e-03 eta: 2:24:02 time: 0.3369 data_time: 0.0124 memory: 18752 grad_norm: 5.1419 loss: 1.1802 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1802 2023/03/17 21:01:06 - mmengine - INFO - Epoch(train) [31][ 720/1320] lr: 2.0000e-03 eta: 2:23:55 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 5.1533 loss: 1.2327 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2327 2023/03/17 21:01:12 - mmengine - INFO - Epoch(train) [31][ 740/1320] lr: 2.0000e-03 eta: 2:23:48 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 5.5738 loss: 1.3659 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3659 2023/03/17 21:01:19 - mmengine - INFO - Epoch(train) [31][ 760/1320] lr: 2.0000e-03 eta: 2:23:41 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 5.2227 loss: 1.1768 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1768 2023/03/17 21:01:26 - mmengine - INFO - Epoch(train) [31][ 780/1320] lr: 2.0000e-03 eta: 2:23:35 time: 0.3368 data_time: 0.0121 memory: 18752 grad_norm: 5.2270 loss: 1.2616 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2616 2023/03/17 21:01:32 - mmengine - INFO - Epoch(train) [31][ 800/1320] lr: 2.0000e-03 eta: 2:23:28 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 5.1605 loss: 1.1117 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1117 2023/03/17 21:01:39 - mmengine - INFO - Epoch(train) [31][ 820/1320] lr: 2.0000e-03 eta: 2:23:21 time: 0.3355 data_time: 0.0127 memory: 18752 grad_norm: 5.1345 loss: 1.0679 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0679 2023/03/17 21:01:46 - mmengine - INFO - Epoch(train) [31][ 840/1320] lr: 2.0000e-03 eta: 2:23:14 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 5.3168 loss: 1.0316 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0316 2023/03/17 21:01:53 - mmengine - INFO - Epoch(train) [31][ 860/1320] lr: 2.0000e-03 eta: 2:23:08 time: 0.3367 data_time: 0.0124 memory: 18752 grad_norm: 5.1633 loss: 1.2077 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2077 2023/03/17 21:01:59 - mmengine - INFO - Epoch(train) [31][ 880/1320] lr: 2.0000e-03 eta: 2:23:01 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 5.3626 loss: 1.0952 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0952 2023/03/17 21:02:06 - mmengine - INFO - Epoch(train) [31][ 900/1320] lr: 2.0000e-03 eta: 2:22:54 time: 0.3372 data_time: 0.0127 memory: 18752 grad_norm: 5.3325 loss: 1.1215 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.1215 2023/03/17 21:02:13 - mmengine - INFO - Epoch(train) [31][ 920/1320] lr: 2.0000e-03 eta: 2:22:48 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 5.4031 loss: 1.1907 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1907 2023/03/17 21:02:19 - mmengine - INFO - Epoch(train) [31][ 940/1320] lr: 2.0000e-03 eta: 2:22:41 time: 0.3360 data_time: 0.0128 memory: 18752 grad_norm: 4.9788 loss: 1.0032 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0032 2023/03/17 21:02:26 - mmengine - INFO - Epoch(train) [31][ 960/1320] lr: 2.0000e-03 eta: 2:22:34 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 5.0780 loss: 1.1004 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1004 2023/03/17 21:02:33 - mmengine - INFO - Epoch(train) [31][ 980/1320] lr: 2.0000e-03 eta: 2:22:27 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 5.1289 loss: 1.0696 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0696 2023/03/17 21:02:40 - mmengine - INFO - Epoch(train) [31][1000/1320] lr: 2.0000e-03 eta: 2:22:21 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 5.2390 loss: 1.0646 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0646 2023/03/17 21:02:46 - mmengine - INFO - Epoch(train) [31][1020/1320] lr: 2.0000e-03 eta: 2:22:14 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 5.1668 loss: 1.1930 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1930 2023/03/17 21:02:53 - mmengine - INFO - Epoch(train) [31][1040/1320] lr: 2.0000e-03 eta: 2:22:07 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 5.0825 loss: 1.0617 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0617 2023/03/17 21:03:00 - mmengine - INFO - Epoch(train) [31][1060/1320] lr: 2.0000e-03 eta: 2:22:00 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 5.3031 loss: 1.1411 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1411 2023/03/17 21:03:06 - mmengine - INFO - Epoch(train) [31][1080/1320] lr: 2.0000e-03 eta: 2:21:54 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 5.2192 loss: 1.0994 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0994 2023/03/17 21:03:13 - mmengine - INFO - Epoch(train) [31][1100/1320] lr: 2.0000e-03 eta: 2:21:47 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 5.3064 loss: 1.1561 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1561 2023/03/17 21:03:20 - mmengine - INFO - Epoch(train) [31][1120/1320] lr: 2.0000e-03 eta: 2:21:40 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 5.3131 loss: 1.1157 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1157 2023/03/17 21:03:27 - mmengine - INFO - Epoch(train) [31][1140/1320] lr: 2.0000e-03 eta: 2:21:33 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 5.4369 loss: 1.0863 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0863 2023/03/17 21:03:33 - mmengine - INFO - Epoch(train) [31][1160/1320] lr: 2.0000e-03 eta: 2:21:27 time: 0.3362 data_time: 0.0127 memory: 18752 grad_norm: 5.3272 loss: 1.2116 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2116 2023/03/17 21:03:40 - mmengine - INFO - Epoch(train) [31][1180/1320] lr: 2.0000e-03 eta: 2:21:20 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 5.3807 loss: 1.2096 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2096 2023/03/17 21:03:47 - mmengine - INFO - Epoch(train) [31][1200/1320] lr: 2.0000e-03 eta: 2:21:13 time: 0.3361 data_time: 0.0132 memory: 18752 grad_norm: 5.2721 loss: 1.1961 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1961 2023/03/17 21:03:53 - mmengine - INFO - Epoch(train) [31][1220/1320] lr: 2.0000e-03 eta: 2:21:07 time: 0.3361 data_time: 0.0133 memory: 18752 grad_norm: 5.3174 loss: 1.0838 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0838 2023/03/17 21:04:00 - mmengine - INFO - Epoch(train) [31][1240/1320] lr: 2.0000e-03 eta: 2:21:00 time: 0.3360 data_time: 0.0128 memory: 18752 grad_norm: 5.3831 loss: 1.2626 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2626 2023/03/17 21:04:07 - mmengine - INFO - Epoch(train) [31][1260/1320] lr: 2.0000e-03 eta: 2:20:53 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 5.4037 loss: 1.2179 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2179 2023/03/17 21:04:14 - mmengine - INFO - Epoch(train) [31][1280/1320] lr: 2.0000e-03 eta: 2:20:46 time: 0.3364 data_time: 0.0130 memory: 18752 grad_norm: 5.2318 loss: 1.2286 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2286 2023/03/17 21:04:21 - mmengine - INFO - Epoch(train) [31][1300/1320] lr: 2.0000e-03 eta: 2:20:40 time: 0.3446 data_time: 0.0122 memory: 18752 grad_norm: 5.3255 loss: 1.0746 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0746 2023/03/17 21:04:27 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:04:27 - mmengine - INFO - Epoch(train) [31][1320/1320] lr: 2.0000e-03 eta: 2:20:33 time: 0.3414 data_time: 0.0129 memory: 18752 grad_norm: 5.2680 loss: 1.0879 top1_acc: 0.5455 top5_acc: 0.9091 loss_cls: 1.0879 2023/03/17 21:04:35 - mmengine - INFO - Epoch(val) [31][ 20/194] eta: 0:01:02 time: 0.3608 data_time: 0.2733 memory: 2112 2023/03/17 21:04:37 - mmengine - INFO - Epoch(val) [31][ 40/194] eta: 0:00:35 time: 0.0977 data_time: 0.0118 memory: 2112 2023/03/17 21:04:39 - mmengine - INFO - Epoch(val) [31][ 60/194] eta: 0:00:24 time: 0.0969 data_time: 0.0109 memory: 2112 2023/03/17 21:04:40 - mmengine - INFO - Epoch(val) [31][ 80/194] eta: 0:00:18 time: 0.0964 data_time: 0.0107 memory: 2112 2023/03/17 21:04:42 - mmengine - INFO - Epoch(val) [31][100/194] eta: 0:00:14 time: 0.0970 data_time: 0.0113 memory: 2112 2023/03/17 21:04:44 - mmengine - INFO - Epoch(val) [31][120/194] eta: 0:00:10 time: 0.0964 data_time: 0.0106 memory: 2112 2023/03/17 21:04:46 - mmengine - INFO - Epoch(val) [31][140/194] eta: 0:00:07 time: 0.0965 data_time: 0.0107 memory: 2112 2023/03/17 21:04:48 - mmengine - INFO - Epoch(val) [31][160/194] eta: 0:00:04 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 21:04:50 - mmengine - INFO - Epoch(val) [31][180/194] eta: 0:00:01 time: 0.0968 data_time: 0.0107 memory: 2112 2023/03/17 21:04:54 - mmengine - INFO - Epoch(val) [31][194/194] acc/top1: 0.6058 acc/top5: 0.8630 acc/mean1: 0.5420 2023/03/17 21:04:54 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_28.pth is removed 2023/03/17 21:04:55 - mmengine - INFO - The best checkpoint with 0.6058 acc/top1 at 31 epoch is saved to best_acc/top1_epoch_31.pth. 2023/03/17 21:05:06 - mmengine - INFO - Epoch(train) [32][ 20/1320] lr: 2.0000e-03 eta: 2:20:29 time: 0.5178 data_time: 0.0376 memory: 18752 grad_norm: 5.1787 loss: 1.2095 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2095 2023/03/17 21:05:12 - mmengine - INFO - Epoch(train) [32][ 40/1320] lr: 2.0000e-03 eta: 2:20:22 time: 0.3364 data_time: 0.0126 memory: 18752 grad_norm: 5.2330 loss: 1.0905 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0905 2023/03/17 21:05:19 - mmengine - INFO - Epoch(train) [32][ 60/1320] lr: 2.0000e-03 eta: 2:20:15 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 5.1914 loss: 1.1923 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1923 2023/03/17 21:05:26 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:05:26 - mmengine - INFO - Epoch(train) [32][ 80/1320] lr: 2.0000e-03 eta: 2:20:08 time: 0.3358 data_time: 0.0131 memory: 18752 grad_norm: 5.2336 loss: 1.0293 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0293 2023/03/17 21:05:33 - mmengine - INFO - Epoch(train) [32][ 100/1320] lr: 2.0000e-03 eta: 2:20:02 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 5.1697 loss: 1.1160 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1160 2023/03/17 21:05:39 - mmengine - INFO - Epoch(train) [32][ 120/1320] lr: 2.0000e-03 eta: 2:19:55 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 5.2569 loss: 0.9861 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9861 2023/03/17 21:05:46 - mmengine - INFO - Epoch(train) [32][ 140/1320] lr: 2.0000e-03 eta: 2:19:48 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 5.1788 loss: 1.0477 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0477 2023/03/17 21:05:53 - mmengine - INFO - Epoch(train) [32][ 160/1320] lr: 2.0000e-03 eta: 2:19:41 time: 0.3357 data_time: 0.0130 memory: 18752 grad_norm: 5.2647 loss: 1.0398 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0398 2023/03/17 21:05:59 - mmengine - INFO - Epoch(train) [32][ 180/1320] lr: 2.0000e-03 eta: 2:19:35 time: 0.3357 data_time: 0.0130 memory: 18752 grad_norm: 5.1626 loss: 1.0578 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0578 2023/03/17 21:06:06 - mmengine - INFO - Epoch(train) [32][ 200/1320] lr: 2.0000e-03 eta: 2:19:28 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 5.2967 loss: 1.1303 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1303 2023/03/17 21:06:13 - mmengine - INFO - Epoch(train) [32][ 220/1320] lr: 2.0000e-03 eta: 2:19:21 time: 0.3354 data_time: 0.0125 memory: 18752 grad_norm: 5.2738 loss: 1.0505 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0505 2023/03/17 21:06:20 - mmengine - INFO - Epoch(train) [32][ 240/1320] lr: 2.0000e-03 eta: 2:19:15 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 5.3701 loss: 1.1113 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1113 2023/03/17 21:06:26 - mmengine - INFO - Epoch(train) [32][ 260/1320] lr: 2.0000e-03 eta: 2:19:08 time: 0.3364 data_time: 0.0127 memory: 18752 grad_norm: 5.4023 loss: 1.1513 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1513 2023/03/17 21:06:33 - mmengine - INFO - Epoch(train) [32][ 280/1320] lr: 2.0000e-03 eta: 2:19:01 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 5.2135 loss: 0.9855 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9855 2023/03/17 21:06:40 - mmengine - INFO - Epoch(train) [32][ 300/1320] lr: 2.0000e-03 eta: 2:18:54 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 5.3974 loss: 1.2360 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2360 2023/03/17 21:06:46 - mmengine - INFO - Epoch(train) [32][ 320/1320] lr: 2.0000e-03 eta: 2:18:48 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 5.3399 loss: 1.1524 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1524 2023/03/17 21:06:53 - mmengine - INFO - Epoch(train) [32][ 340/1320] lr: 2.0000e-03 eta: 2:18:41 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 5.1890 loss: 0.9990 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9990 2023/03/17 21:07:00 - mmengine - INFO - Epoch(train) [32][ 360/1320] lr: 2.0000e-03 eta: 2:18:34 time: 0.3369 data_time: 0.0125 memory: 18752 grad_norm: 5.2929 loss: 1.0812 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0812 2023/03/17 21:07:07 - mmengine - INFO - Epoch(train) [32][ 380/1320] lr: 2.0000e-03 eta: 2:18:27 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 5.3041 loss: 0.9882 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9882 2023/03/17 21:07:13 - mmengine - INFO - Epoch(train) [32][ 400/1320] lr: 2.0000e-03 eta: 2:18:21 time: 0.3355 data_time: 0.0128 memory: 18752 grad_norm: 5.1420 loss: 1.1135 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.1135 2023/03/17 21:07:20 - mmengine - INFO - Epoch(train) [32][ 420/1320] lr: 2.0000e-03 eta: 2:18:14 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 5.3327 loss: 1.1305 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1305 2023/03/17 21:07:27 - mmengine - INFO - Epoch(train) [32][ 440/1320] lr: 2.0000e-03 eta: 2:18:07 time: 0.3366 data_time: 0.0124 memory: 18752 grad_norm: 5.2212 loss: 0.9943 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9943 2023/03/17 21:07:33 - mmengine - INFO - Epoch(train) [32][ 460/1320] lr: 2.0000e-03 eta: 2:18:01 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 5.2071 loss: 1.1398 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1398 2023/03/17 21:07:40 - mmengine - INFO - Epoch(train) [32][ 480/1320] lr: 2.0000e-03 eta: 2:17:54 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 5.5046 loss: 1.0724 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0724 2023/03/17 21:07:47 - mmengine - INFO - Epoch(train) [32][ 500/1320] lr: 2.0000e-03 eta: 2:17:47 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 5.1344 loss: 1.0044 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0044 2023/03/17 21:07:54 - mmengine - INFO - Epoch(train) [32][ 520/1320] lr: 2.0000e-03 eta: 2:17:40 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 5.3880 loss: 1.1860 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1860 2023/03/17 21:08:00 - mmengine - INFO - Epoch(train) [32][ 540/1320] lr: 2.0000e-03 eta: 2:17:34 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 5.1273 loss: 1.0344 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0344 2023/03/17 21:08:07 - mmengine - INFO - Epoch(train) [32][ 560/1320] lr: 2.0000e-03 eta: 2:17:27 time: 0.3354 data_time: 0.0133 memory: 18752 grad_norm: 5.2748 loss: 1.2409 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2409 2023/03/17 21:08:14 - mmengine - INFO - Epoch(train) [32][ 580/1320] lr: 2.0000e-03 eta: 2:17:20 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 5.3399 loss: 1.1935 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1935 2023/03/17 21:08:20 - mmengine - INFO - Epoch(train) [32][ 600/1320] lr: 2.0000e-03 eta: 2:17:13 time: 0.3356 data_time: 0.0126 memory: 18752 grad_norm: 5.3865 loss: 1.2151 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2151 2023/03/17 21:08:27 - mmengine - INFO - Epoch(train) [32][ 620/1320] lr: 2.0000e-03 eta: 2:17:07 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 5.2565 loss: 1.0175 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0175 2023/03/17 21:08:34 - mmengine - INFO - Epoch(train) [32][ 640/1320] lr: 2.0000e-03 eta: 2:17:00 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 5.5924 loss: 1.0990 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0990 2023/03/17 21:08:41 - mmengine - INFO - Epoch(train) [32][ 660/1320] lr: 2.0000e-03 eta: 2:16:53 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 5.4880 loss: 1.1057 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1057 2023/03/17 21:08:47 - mmengine - INFO - Epoch(train) [32][ 680/1320] lr: 2.0000e-03 eta: 2:16:46 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 5.4722 loss: 1.1588 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1588 2023/03/17 21:08:54 - mmengine - INFO - Epoch(train) [32][ 700/1320] lr: 2.0000e-03 eta: 2:16:40 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 5.2947 loss: 1.1695 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1695 2023/03/17 21:09:01 - mmengine - INFO - Epoch(train) [32][ 720/1320] lr: 2.0000e-03 eta: 2:16:33 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 5.2483 loss: 1.0948 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0948 2023/03/17 21:09:08 - mmengine - INFO - Epoch(train) [32][ 740/1320] lr: 2.0000e-03 eta: 2:16:26 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 5.4171 loss: 1.0529 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0529 2023/03/17 21:09:14 - mmengine - INFO - Epoch(train) [32][ 760/1320] lr: 2.0000e-03 eta: 2:16:20 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.3631 loss: 1.3295 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3295 2023/03/17 21:09:21 - mmengine - INFO - Epoch(train) [32][ 780/1320] lr: 2.0000e-03 eta: 2:16:13 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.1389 loss: 1.0902 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0902 2023/03/17 21:09:28 - mmengine - INFO - Epoch(train) [32][ 800/1320] lr: 2.0000e-03 eta: 2:16:06 time: 0.3355 data_time: 0.0127 memory: 18752 grad_norm: 5.1718 loss: 0.9896 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9896 2023/03/17 21:09:34 - mmengine - INFO - Epoch(train) [32][ 820/1320] lr: 2.0000e-03 eta: 2:15:59 time: 0.3361 data_time: 0.0130 memory: 18752 grad_norm: 5.2935 loss: 0.9305 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9305 2023/03/17 21:09:41 - mmengine - INFO - Epoch(train) [32][ 840/1320] lr: 2.0000e-03 eta: 2:15:53 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 5.4203 loss: 1.0766 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0766 2023/03/17 21:09:48 - mmengine - INFO - Epoch(train) [32][ 860/1320] lr: 2.0000e-03 eta: 2:15:46 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 5.4653 loss: 1.1008 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1008 2023/03/17 21:09:55 - mmengine - INFO - Epoch(train) [32][ 880/1320] lr: 2.0000e-03 eta: 2:15:39 time: 0.3353 data_time: 0.0125 memory: 18752 grad_norm: 5.3678 loss: 1.1924 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1924 2023/03/17 21:10:01 - mmengine - INFO - Epoch(train) [32][ 900/1320] lr: 2.0000e-03 eta: 2:15:32 time: 0.3353 data_time: 0.0124 memory: 18752 grad_norm: 5.4518 loss: 1.0813 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0813 2023/03/17 21:10:08 - mmengine - INFO - Epoch(train) [32][ 920/1320] lr: 2.0000e-03 eta: 2:15:26 time: 0.3360 data_time: 0.0127 memory: 18752 grad_norm: 5.1292 loss: 1.1040 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1040 2023/03/17 21:10:15 - mmengine - INFO - Epoch(train) [32][ 940/1320] lr: 2.0000e-03 eta: 2:15:19 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 5.3898 loss: 1.1281 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1281 2023/03/17 21:10:21 - mmengine - INFO - Epoch(train) [32][ 960/1320] lr: 2.0000e-03 eta: 2:15:12 time: 0.3358 data_time: 0.0128 memory: 18752 grad_norm: 5.4801 loss: 1.2472 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2472 2023/03/17 21:10:28 - mmengine - INFO - Epoch(train) [32][ 980/1320] lr: 2.0000e-03 eta: 2:15:05 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 5.2890 loss: 1.2856 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2856 2023/03/17 21:10:35 - mmengine - INFO - Epoch(train) [32][1000/1320] lr: 2.0000e-03 eta: 2:14:59 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 5.4482 loss: 0.9757 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9757 2023/03/17 21:10:42 - mmengine - INFO - Epoch(train) [32][1020/1320] lr: 2.0000e-03 eta: 2:14:52 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 5.3198 loss: 1.0726 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0726 2023/03/17 21:10:48 - mmengine - INFO - Epoch(train) [32][1040/1320] lr: 2.0000e-03 eta: 2:14:45 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 5.3465 loss: 1.0890 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0890 2023/03/17 21:10:55 - mmengine - INFO - Epoch(train) [32][1060/1320] lr: 2.0000e-03 eta: 2:14:39 time: 0.3356 data_time: 0.0126 memory: 18752 grad_norm: 5.3837 loss: 1.1304 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1304 2023/03/17 21:11:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:11:02 - mmengine - INFO - Epoch(train) [32][1080/1320] lr: 2.0000e-03 eta: 2:14:32 time: 0.3359 data_time: 0.0127 memory: 18752 grad_norm: 5.4520 loss: 1.2657 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2657 2023/03/17 21:11:08 - mmengine - INFO - Epoch(train) [32][1100/1320] lr: 2.0000e-03 eta: 2:14:25 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 5.4337 loss: 1.1377 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1377 2023/03/17 21:11:15 - mmengine - INFO - Epoch(train) [32][1120/1320] lr: 2.0000e-03 eta: 2:14:18 time: 0.3354 data_time: 0.0125 memory: 18752 grad_norm: 5.4081 loss: 1.2563 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2563 2023/03/17 21:11:22 - mmengine - INFO - Epoch(train) [32][1140/1320] lr: 2.0000e-03 eta: 2:14:12 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 5.2711 loss: 1.1097 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1097 2023/03/17 21:11:29 - mmengine - INFO - Epoch(train) [32][1160/1320] lr: 2.0000e-03 eta: 2:14:05 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.4560 loss: 1.1550 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1550 2023/03/17 21:11:35 - mmengine - INFO - Epoch(train) [32][1180/1320] lr: 2.0000e-03 eta: 2:13:58 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 5.3546 loss: 1.0056 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0056 2023/03/17 21:11:42 - mmengine - INFO - Epoch(train) [32][1200/1320] lr: 2.0000e-03 eta: 2:13:51 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 5.4538 loss: 1.1029 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1029 2023/03/17 21:11:49 - mmengine - INFO - Epoch(train) [32][1220/1320] lr: 2.0000e-03 eta: 2:13:45 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.3794 loss: 1.1485 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1485 2023/03/17 21:11:55 - mmengine - INFO - Epoch(train) [32][1240/1320] lr: 2.0000e-03 eta: 2:13:38 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 5.5499 loss: 1.2333 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2333 2023/03/17 21:12:02 - mmengine - INFO - Epoch(train) [32][1260/1320] lr: 2.0000e-03 eta: 2:13:31 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 5.4701 loss: 1.3587 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.3587 2023/03/17 21:12:09 - mmengine - INFO - Epoch(train) [32][1280/1320] lr: 2.0000e-03 eta: 2:13:24 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 5.4190 loss: 1.0925 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0925 2023/03/17 21:12:16 - mmengine - INFO - Epoch(train) [32][1300/1320] lr: 2.0000e-03 eta: 2:13:18 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 5.3439 loss: 1.0005 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0005 2023/03/17 21:12:22 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:12:22 - mmengine - INFO - Epoch(train) [32][1320/1320] lr: 2.0000e-03 eta: 2:13:11 time: 0.3312 data_time: 0.0128 memory: 18752 grad_norm: 5.4855 loss: 1.3967 top1_acc: 0.7273 top5_acc: 0.8182 loss_cls: 1.3967 2023/03/17 21:12:25 - mmengine - INFO - Epoch(val) [32][ 20/194] eta: 0:00:22 time: 0.1273 data_time: 0.0408 memory: 2112 2023/03/17 21:12:27 - mmengine - INFO - Epoch(val) [32][ 40/194] eta: 0:00:17 time: 0.0950 data_time: 0.0094 memory: 2112 2023/03/17 21:12:29 - mmengine - INFO - Epoch(val) [32][ 60/194] eta: 0:00:14 time: 0.0971 data_time: 0.0113 memory: 2112 2023/03/17 21:12:31 - mmengine - INFO - Epoch(val) [32][ 80/194] eta: 0:00:11 time: 0.0970 data_time: 0.0109 memory: 2112 2023/03/17 21:12:33 - mmengine - INFO - Epoch(val) [32][100/194] eta: 0:00:09 time: 0.0976 data_time: 0.0113 memory: 2112 2023/03/17 21:12:35 - mmengine - INFO - Epoch(val) [32][120/194] eta: 0:00:07 time: 0.0972 data_time: 0.0112 memory: 2112 2023/03/17 21:12:36 - mmengine - INFO - Epoch(val) [32][140/194] eta: 0:00:05 time: 0.0971 data_time: 0.0109 memory: 2112 2023/03/17 21:12:38 - mmengine - INFO - Epoch(val) [32][160/194] eta: 0:00:03 time: 0.0973 data_time: 0.0110 memory: 2112 2023/03/17 21:12:40 - mmengine - INFO - Epoch(val) [32][180/194] eta: 0:00:01 time: 0.0965 data_time: 0.0106 memory: 2112 2023/03/17 21:12:44 - mmengine - INFO - Epoch(val) [32][194/194] acc/top1: 0.6102 acc/top5: 0.8654 acc/mean1: 0.5472 2023/03/17 21:12:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_31.pth is removed 2023/03/17 21:12:46 - mmengine - INFO - The best checkpoint with 0.6102 acc/top1 at 32 epoch is saved to best_acc/top1_epoch_32.pth. 2023/03/17 21:12:53 - mmengine - INFO - Epoch(train) [33][ 20/1320] lr: 2.0000e-03 eta: 2:13:05 time: 0.3676 data_time: 0.0352 memory: 18752 grad_norm: 5.3468 loss: 1.1730 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1730 2023/03/17 21:13:00 - mmengine - INFO - Epoch(train) [33][ 40/1320] lr: 2.0000e-03 eta: 2:12:58 time: 0.3354 data_time: 0.0125 memory: 18752 grad_norm: 5.4561 loss: 1.1573 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1573 2023/03/17 21:13:07 - mmengine - INFO - Epoch(train) [33][ 60/1320] lr: 2.0000e-03 eta: 2:12:51 time: 0.3353 data_time: 0.0126 memory: 18752 grad_norm: 5.3043 loss: 1.1498 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1498 2023/03/17 21:13:14 - mmengine - INFO - Epoch(train) [33][ 80/1320] lr: 2.0000e-03 eta: 2:12:44 time: 0.3350 data_time: 0.0124 memory: 18752 grad_norm: 5.3579 loss: 1.2025 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2025 2023/03/17 21:13:20 - mmengine - INFO - Epoch(train) [33][ 100/1320] lr: 2.0000e-03 eta: 2:12:38 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 5.5114 loss: 1.1357 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1357 2023/03/17 21:13:27 - mmengine - INFO - Epoch(train) [33][ 120/1320] lr: 2.0000e-03 eta: 2:12:31 time: 0.3354 data_time: 0.0125 memory: 18752 grad_norm: 5.2780 loss: 1.0808 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0808 2023/03/17 21:13:34 - mmengine - INFO - Epoch(train) [33][ 140/1320] lr: 2.0000e-03 eta: 2:12:24 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 5.2188 loss: 0.9908 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9908 2023/03/17 21:13:40 - mmengine - INFO - Epoch(train) [33][ 160/1320] lr: 2.0000e-03 eta: 2:12:17 time: 0.3351 data_time: 0.0123 memory: 18752 grad_norm: 5.2244 loss: 1.0369 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0369 2023/03/17 21:13:47 - mmengine - INFO - Epoch(train) [33][ 180/1320] lr: 2.0000e-03 eta: 2:12:11 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 5.2277 loss: 1.1366 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1366 2023/03/17 21:13:54 - mmengine - INFO - Epoch(train) [33][ 200/1320] lr: 2.0000e-03 eta: 2:12:04 time: 0.3356 data_time: 0.0126 memory: 18752 grad_norm: 5.4930 loss: 1.0839 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0839 2023/03/17 21:14:01 - mmengine - INFO - Epoch(train) [33][ 220/1320] lr: 2.0000e-03 eta: 2:11:57 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 5.4359 loss: 1.1414 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1414 2023/03/17 21:14:07 - mmengine - INFO - Epoch(train) [33][ 240/1320] lr: 2.0000e-03 eta: 2:11:50 time: 0.3348 data_time: 0.0121 memory: 18752 grad_norm: 5.6598 loss: 1.1857 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1857 2023/03/17 21:14:14 - mmengine - INFO - Epoch(train) [33][ 260/1320] lr: 2.0000e-03 eta: 2:11:44 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 5.4062 loss: 1.1023 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1023 2023/03/17 21:14:21 - mmengine - INFO - Epoch(train) [33][ 280/1320] lr: 2.0000e-03 eta: 2:11:37 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 5.4036 loss: 1.0791 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0791 2023/03/17 21:14:27 - mmengine - INFO - Epoch(train) [33][ 300/1320] lr: 2.0000e-03 eta: 2:11:30 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 5.4216 loss: 1.0896 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0896 2023/03/17 21:14:34 - mmengine - INFO - Epoch(train) [33][ 320/1320] lr: 2.0000e-03 eta: 2:11:24 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 5.3614 loss: 1.0718 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0718 2023/03/17 21:14:41 - mmengine - INFO - Epoch(train) [33][ 340/1320] lr: 2.0000e-03 eta: 2:11:17 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 5.3327 loss: 1.0560 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0560 2023/03/17 21:14:48 - mmengine - INFO - Epoch(train) [33][ 360/1320] lr: 2.0000e-03 eta: 2:11:10 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 5.3329 loss: 1.0015 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0015 2023/03/17 21:14:54 - mmengine - INFO - Epoch(train) [33][ 380/1320] lr: 2.0000e-03 eta: 2:11:03 time: 0.3367 data_time: 0.0117 memory: 18752 grad_norm: 5.4615 loss: 1.1159 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1159 2023/03/17 21:15:01 - mmengine - INFO - Epoch(train) [33][ 400/1320] lr: 2.0000e-03 eta: 2:10:57 time: 0.3351 data_time: 0.0118 memory: 18752 grad_norm: 5.2321 loss: 1.0309 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0309 2023/03/17 21:15:08 - mmengine - INFO - Epoch(train) [33][ 420/1320] lr: 2.0000e-03 eta: 2:10:50 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 5.4310 loss: 1.2183 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2183 2023/03/17 21:15:14 - mmengine - INFO - Epoch(train) [33][ 440/1320] lr: 2.0000e-03 eta: 2:10:43 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 5.3475 loss: 1.1921 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1921 2023/03/17 21:15:21 - mmengine - INFO - Epoch(train) [33][ 460/1320] lr: 2.0000e-03 eta: 2:10:36 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 5.5708 loss: 1.1420 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1420 2023/03/17 21:15:28 - mmengine - INFO - Epoch(train) [33][ 480/1320] lr: 2.0000e-03 eta: 2:10:30 time: 0.3358 data_time: 0.0127 memory: 18752 grad_norm: 5.3339 loss: 1.0020 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0020 2023/03/17 21:15:35 - mmengine - INFO - Epoch(train) [33][ 500/1320] lr: 2.0000e-03 eta: 2:10:23 time: 0.3358 data_time: 0.0127 memory: 18752 grad_norm: 5.5723 loss: 1.2046 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2046 2023/03/17 21:15:41 - mmengine - INFO - Epoch(train) [33][ 520/1320] lr: 2.0000e-03 eta: 2:10:16 time: 0.3356 data_time: 0.0126 memory: 18752 grad_norm: 5.4232 loss: 1.1461 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1461 2023/03/17 21:15:48 - mmengine - INFO - Epoch(train) [33][ 540/1320] lr: 2.0000e-03 eta: 2:10:09 time: 0.3372 data_time: 0.0127 memory: 18752 grad_norm: 5.3344 loss: 1.1191 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.1191 2023/03/17 21:15:55 - mmengine - INFO - Epoch(train) [33][ 560/1320] lr: 2.0000e-03 eta: 2:10:03 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 5.5044 loss: 1.1819 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1819 2023/03/17 21:16:01 - mmengine - INFO - Epoch(train) [33][ 580/1320] lr: 2.0000e-03 eta: 2:09:56 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 5.5403 loss: 1.1068 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1068 2023/03/17 21:16:08 - mmengine - INFO - Epoch(train) [33][ 600/1320] lr: 2.0000e-03 eta: 2:09:49 time: 0.3361 data_time: 0.0115 memory: 18752 grad_norm: 5.5804 loss: 1.0879 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0879 2023/03/17 21:16:15 - mmengine - INFO - Epoch(train) [33][ 620/1320] lr: 2.0000e-03 eta: 2:09:43 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 5.5856 loss: 1.1031 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1031 2023/03/17 21:16:22 - mmengine - INFO - Epoch(train) [33][ 640/1320] lr: 2.0000e-03 eta: 2:09:36 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 5.6846 loss: 0.9366 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9366 2023/03/17 21:16:28 - mmengine - INFO - Epoch(train) [33][ 660/1320] lr: 2.0000e-03 eta: 2:09:29 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 5.3012 loss: 1.0907 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0907 2023/03/17 21:16:35 - mmengine - INFO - Epoch(train) [33][ 680/1320] lr: 2.0000e-03 eta: 2:09:22 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 5.4240 loss: 1.1252 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1252 2023/03/17 21:16:42 - mmengine - INFO - Epoch(train) [33][ 700/1320] lr: 2.0000e-03 eta: 2:09:16 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 5.4008 loss: 1.1667 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1667 2023/03/17 21:16:49 - mmengine - INFO - Epoch(train) [33][ 720/1320] lr: 2.0000e-03 eta: 2:09:09 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 5.6229 loss: 0.9935 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9935 2023/03/17 21:16:55 - mmengine - INFO - Epoch(train) [33][ 740/1320] lr: 2.0000e-03 eta: 2:09:02 time: 0.3357 data_time: 0.0129 memory: 18752 grad_norm: 5.5289 loss: 1.0966 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0966 2023/03/17 21:17:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:17:02 - mmengine - INFO - Epoch(train) [33][ 760/1320] lr: 2.0000e-03 eta: 2:08:55 time: 0.3364 data_time: 0.0130 memory: 18752 grad_norm: 5.5470 loss: 1.1420 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1420 2023/03/17 21:17:09 - mmengine - INFO - Epoch(train) [33][ 780/1320] lr: 2.0000e-03 eta: 2:08:49 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 5.4374 loss: 1.0885 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0885 2023/03/17 21:17:15 - mmengine - INFO - Epoch(train) [33][ 800/1320] lr: 2.0000e-03 eta: 2:08:42 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 5.5992 loss: 1.0958 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0958 2023/03/17 21:17:22 - mmengine - INFO - Epoch(train) [33][ 820/1320] lr: 2.0000e-03 eta: 2:08:35 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 5.4898 loss: 1.1294 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1294 2023/03/17 21:17:29 - mmengine - INFO - Epoch(train) [33][ 840/1320] lr: 2.0000e-03 eta: 2:08:29 time: 0.3355 data_time: 0.0126 memory: 18752 grad_norm: 5.6278 loss: 1.2547 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2547 2023/03/17 21:17:36 - mmengine - INFO - Epoch(train) [33][ 860/1320] lr: 2.0000e-03 eta: 2:08:22 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 5.5420 loss: 1.2384 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2384 2023/03/17 21:17:42 - mmengine - INFO - Epoch(train) [33][ 880/1320] lr: 2.0000e-03 eta: 2:08:15 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 5.5917 loss: 1.0404 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0404 2023/03/17 21:17:49 - mmengine - INFO - Epoch(train) [33][ 900/1320] lr: 2.0000e-03 eta: 2:08:08 time: 0.3354 data_time: 0.0126 memory: 18752 grad_norm: 5.5885 loss: 1.0144 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0144 2023/03/17 21:17:56 - mmengine - INFO - Epoch(train) [33][ 920/1320] lr: 2.0000e-03 eta: 2:08:02 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 5.5838 loss: 1.1712 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1712 2023/03/17 21:18:02 - mmengine - INFO - Epoch(train) [33][ 940/1320] lr: 2.0000e-03 eta: 2:07:55 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 5.5189 loss: 0.9277 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9277 2023/03/17 21:18:09 - mmengine - INFO - Epoch(train) [33][ 960/1320] lr: 2.0000e-03 eta: 2:07:48 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 5.6113 loss: 1.2163 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2163 2023/03/17 21:18:16 - mmengine - INFO - Epoch(train) [33][ 980/1320] lr: 2.0000e-03 eta: 2:07:41 time: 0.3362 data_time: 0.0127 memory: 18752 grad_norm: 5.6012 loss: 1.0070 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0070 2023/03/17 21:18:23 - mmengine - INFO - Epoch(train) [33][1000/1320] lr: 2.0000e-03 eta: 2:07:35 time: 0.3359 data_time: 0.0126 memory: 18752 grad_norm: 5.6378 loss: 0.9417 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9417 2023/03/17 21:18:29 - mmengine - INFO - Epoch(train) [33][1020/1320] lr: 2.0000e-03 eta: 2:07:28 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 5.7612 loss: 0.9105 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9105 2023/03/17 21:18:36 - mmengine - INFO - Epoch(train) [33][1040/1320] lr: 2.0000e-03 eta: 2:07:21 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 5.5604 loss: 1.0122 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0122 2023/03/17 21:18:43 - mmengine - INFO - Epoch(train) [33][1060/1320] lr: 2.0000e-03 eta: 2:07:14 time: 0.3351 data_time: 0.0119 memory: 18752 grad_norm: 5.6712 loss: 1.1237 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1237 2023/03/17 21:18:49 - mmengine - INFO - Epoch(train) [33][1080/1320] lr: 2.0000e-03 eta: 2:07:08 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 5.5617 loss: 1.1120 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1120 2023/03/17 21:18:56 - mmengine - INFO - Epoch(train) [33][1100/1320] lr: 2.0000e-03 eta: 2:07:01 time: 0.3359 data_time: 0.0130 memory: 18752 grad_norm: 5.6734 loss: 1.1586 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1586 2023/03/17 21:19:03 - mmengine - INFO - Epoch(train) [33][1120/1320] lr: 2.0000e-03 eta: 2:06:54 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 5.7313 loss: 1.2098 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2098 2023/03/17 21:19:10 - mmengine - INFO - Epoch(train) [33][1140/1320] lr: 2.0000e-03 eta: 2:06:48 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 5.7113 loss: 1.0817 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0817 2023/03/17 21:19:16 - mmengine - INFO - Epoch(train) [33][1160/1320] lr: 2.0000e-03 eta: 2:06:41 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 5.5516 loss: 1.0715 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0715 2023/03/17 21:19:23 - mmengine - INFO - Epoch(train) [33][1180/1320] lr: 2.0000e-03 eta: 2:06:34 time: 0.3367 data_time: 0.0122 memory: 18752 grad_norm: 5.6714 loss: 1.1084 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1084 2023/03/17 21:19:30 - mmengine - INFO - Epoch(train) [33][1200/1320] lr: 2.0000e-03 eta: 2:06:27 time: 0.3359 data_time: 0.0126 memory: 18752 grad_norm: 5.5356 loss: 1.0004 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0004 2023/03/17 21:19:36 - mmengine - INFO - Epoch(train) [33][1220/1320] lr: 2.0000e-03 eta: 2:06:21 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 5.8388 loss: 1.1075 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1075 2023/03/17 21:19:43 - mmengine - INFO - Epoch(train) [33][1240/1320] lr: 2.0000e-03 eta: 2:06:14 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 5.6139 loss: 1.1323 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1323 2023/03/17 21:19:50 - mmengine - INFO - Epoch(train) [33][1260/1320] lr: 2.0000e-03 eta: 2:06:07 time: 0.3359 data_time: 0.0127 memory: 18752 grad_norm: 5.5708 loss: 1.0236 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0236 2023/03/17 21:19:57 - mmengine - INFO - Epoch(train) [33][1280/1320] lr: 2.0000e-03 eta: 2:06:00 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 5.7391 loss: 1.0645 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0645 2023/03/17 21:20:03 - mmengine - INFO - Epoch(train) [33][1300/1320] lr: 2.0000e-03 eta: 2:05:54 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 5.5922 loss: 1.0084 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0084 2023/03/17 21:20:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:20:10 - mmengine - INFO - Epoch(train) [33][1320/1320] lr: 2.0000e-03 eta: 2:05:47 time: 0.3313 data_time: 0.0131 memory: 18752 grad_norm: 5.7486 loss: 1.2054 top1_acc: 0.6364 top5_acc: 1.0000 loss_cls: 1.2054 2023/03/17 21:20:10 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/03/17 21:20:16 - mmengine - INFO - Epoch(val) [33][ 20/194] eta: 0:00:22 time: 0.1273 data_time: 0.0403 memory: 2112 2023/03/17 21:20:18 - mmengine - INFO - Epoch(val) [33][ 40/194] eta: 0:00:17 time: 0.0952 data_time: 0.0092 memory: 2112 2023/03/17 21:20:20 - mmengine - INFO - Epoch(val) [33][ 60/194] eta: 0:00:14 time: 0.0985 data_time: 0.0116 memory: 2112 2023/03/17 21:20:21 - mmengine - INFO - Epoch(val) [33][ 80/194] eta: 0:00:11 time: 0.0969 data_time: 0.0106 memory: 2112 2023/03/17 21:20:23 - mmengine - INFO - Epoch(val) [33][100/194] eta: 0:00:09 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 21:20:25 - mmengine - INFO - Epoch(val) [33][120/194] eta: 0:00:07 time: 0.0963 data_time: 0.0107 memory: 2112 2023/03/17 21:20:27 - mmengine - INFO - Epoch(val) [33][140/194] eta: 0:00:05 time: 0.0975 data_time: 0.0116 memory: 2112 2023/03/17 21:20:29 - mmengine - INFO - Epoch(val) [33][160/194] eta: 0:00:03 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 21:20:31 - mmengine - INFO - Epoch(val) [33][180/194] eta: 0:00:01 time: 0.0973 data_time: 0.0106 memory: 2112 2023/03/17 21:20:35 - mmengine - INFO - Epoch(val) [33][194/194] acc/top1: 0.6051 acc/top5: 0.8629 acc/mean1: 0.5444 2023/03/17 21:20:42 - mmengine - INFO - Epoch(train) [34][ 20/1320] lr: 2.0000e-03 eta: 2:05:41 time: 0.3793 data_time: 0.0406 memory: 18752 grad_norm: 5.6711 loss: 0.9422 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9422 2023/03/17 21:20:49 - mmengine - INFO - Epoch(train) [34][ 40/1320] lr: 2.0000e-03 eta: 2:05:34 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 5.5742 loss: 1.0340 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0340 2023/03/17 21:20:56 - mmengine - INFO - Epoch(train) [34][ 60/1320] lr: 2.0000e-03 eta: 2:05:27 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 5.5279 loss: 0.9586 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9586 2023/03/17 21:21:03 - mmengine - INFO - Epoch(train) [34][ 80/1320] lr: 2.0000e-03 eta: 2:05:20 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 5.6684 loss: 1.2087 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2087 2023/03/17 21:21:09 - mmengine - INFO - Epoch(train) [34][ 100/1320] lr: 2.0000e-03 eta: 2:05:14 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 5.5211 loss: 1.1509 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1509 2023/03/17 21:21:16 - mmengine - INFO - Epoch(train) [34][ 120/1320] lr: 2.0000e-03 eta: 2:05:07 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 5.4679 loss: 1.1902 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1902 2023/03/17 21:21:23 - mmengine - INFO - Epoch(train) [34][ 140/1320] lr: 2.0000e-03 eta: 2:05:00 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 5.5466 loss: 1.0547 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0547 2023/03/17 21:21:29 - mmengine - INFO - Epoch(train) [34][ 160/1320] lr: 2.0000e-03 eta: 2:04:53 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 5.5354 loss: 1.0959 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.0959 2023/03/17 21:21:36 - mmengine - INFO - Epoch(train) [34][ 180/1320] lr: 2.0000e-03 eta: 2:04:47 time: 0.3349 data_time: 0.0121 memory: 18752 grad_norm: 5.6615 loss: 1.0888 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0888 2023/03/17 21:21:43 - mmengine - INFO - Epoch(train) [34][ 200/1320] lr: 2.0000e-03 eta: 2:04:40 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 5.7732 loss: 1.1333 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1333 2023/03/17 21:21:50 - mmengine - INFO - Epoch(train) [34][ 220/1320] lr: 2.0000e-03 eta: 2:04:33 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 5.5633 loss: 1.0515 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0515 2023/03/17 21:21:56 - mmengine - INFO - Epoch(train) [34][ 240/1320] lr: 2.0000e-03 eta: 2:04:27 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 5.6395 loss: 1.1370 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.1370 2023/03/17 21:22:03 - mmengine - INFO - Epoch(train) [34][ 260/1320] lr: 2.0000e-03 eta: 2:04:20 time: 0.3368 data_time: 0.0117 memory: 18752 grad_norm: 5.6454 loss: 1.0031 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0031 2023/03/17 21:22:10 - mmengine - INFO - Epoch(train) [34][ 280/1320] lr: 2.0000e-03 eta: 2:04:13 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 5.8007 loss: 1.0669 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0669 2023/03/17 21:22:17 - mmengine - INFO - Epoch(train) [34][ 300/1320] lr: 2.0000e-03 eta: 2:04:06 time: 0.3365 data_time: 0.0121 memory: 18752 grad_norm: 5.7322 loss: 1.0707 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0707 2023/03/17 21:22:23 - mmengine - INFO - Epoch(train) [34][ 320/1320] lr: 2.0000e-03 eta: 2:04:00 time: 0.3361 data_time: 0.0130 memory: 18752 grad_norm: 5.5694 loss: 1.1097 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1097 2023/03/17 21:22:30 - mmengine - INFO - Epoch(train) [34][ 340/1320] lr: 2.0000e-03 eta: 2:03:53 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 5.6429 loss: 1.0922 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0922 2023/03/17 21:22:37 - mmengine - INFO - Epoch(train) [34][ 360/1320] lr: 2.0000e-03 eta: 2:03:46 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 5.5087 loss: 1.0368 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0368 2023/03/17 21:22:43 - mmengine - INFO - Epoch(train) [34][ 380/1320] lr: 2.0000e-03 eta: 2:03:39 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 5.6133 loss: 1.0111 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0111 2023/03/17 21:22:50 - mmengine - INFO - Epoch(train) [34][ 400/1320] lr: 2.0000e-03 eta: 2:03:33 time: 0.3353 data_time: 0.0124 memory: 18752 grad_norm: 5.6654 loss: 0.9918 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9918 2023/03/17 21:22:57 - mmengine - INFO - Epoch(train) [34][ 420/1320] lr: 2.0000e-03 eta: 2:03:26 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 5.7535 loss: 1.0540 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0540 2023/03/17 21:23:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:23:04 - mmengine - INFO - Epoch(train) [34][ 440/1320] lr: 2.0000e-03 eta: 2:03:19 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 5.6897 loss: 0.9995 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9995 2023/03/17 21:23:10 - mmengine - INFO - Epoch(train) [34][ 460/1320] lr: 2.0000e-03 eta: 2:03:12 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 5.5757 loss: 0.9490 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9490 2023/03/17 21:23:17 - mmengine - INFO - Epoch(train) [34][ 480/1320] lr: 2.0000e-03 eta: 2:03:06 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 5.7361 loss: 1.1317 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1317 2023/03/17 21:23:24 - mmengine - INFO - Epoch(train) [34][ 500/1320] lr: 2.0000e-03 eta: 2:02:59 time: 0.3360 data_time: 0.0116 memory: 18752 grad_norm: 5.8030 loss: 1.1890 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1890 2023/03/17 21:23:30 - mmengine - INFO - Epoch(train) [34][ 520/1320] lr: 2.0000e-03 eta: 2:02:52 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 5.5976 loss: 0.9862 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9862 2023/03/17 21:23:37 - mmengine - INFO - Epoch(train) [34][ 540/1320] lr: 2.0000e-03 eta: 2:02:46 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 5.6163 loss: 1.0816 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0816 2023/03/17 21:23:44 - mmengine - INFO - Epoch(train) [34][ 560/1320] lr: 2.0000e-03 eta: 2:02:39 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 5.6771 loss: 1.0852 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0852 2023/03/17 21:23:51 - mmengine - INFO - Epoch(train) [34][ 580/1320] lr: 2.0000e-03 eta: 2:02:32 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 5.6010 loss: 1.1175 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1175 2023/03/17 21:23:57 - mmengine - INFO - Epoch(train) [34][ 600/1320] lr: 2.0000e-03 eta: 2:02:25 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 5.7925 loss: 1.0897 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0897 2023/03/17 21:24:04 - mmengine - INFO - Epoch(train) [34][ 620/1320] lr: 2.0000e-03 eta: 2:02:19 time: 0.3365 data_time: 0.0117 memory: 18752 grad_norm: 5.5880 loss: 1.1360 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1360 2023/03/17 21:24:11 - mmengine - INFO - Epoch(train) [34][ 640/1320] lr: 2.0000e-03 eta: 2:02:12 time: 0.3363 data_time: 0.0128 memory: 18752 grad_norm: 5.6169 loss: 1.0190 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0190 2023/03/17 21:24:18 - mmengine - INFO - Epoch(train) [34][ 660/1320] lr: 2.0000e-03 eta: 2:02:05 time: 0.3358 data_time: 0.0124 memory: 18752 grad_norm: 5.6293 loss: 1.2214 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2214 2023/03/17 21:24:24 - mmengine - INFO - Epoch(train) [34][ 680/1320] lr: 2.0000e-03 eta: 2:01:58 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 5.5575 loss: 1.0709 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0709 2023/03/17 21:24:31 - mmengine - INFO - Epoch(train) [34][ 700/1320] lr: 2.0000e-03 eta: 2:01:52 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 5.6917 loss: 1.0679 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0679 2023/03/17 21:24:38 - mmengine - INFO - Epoch(train) [34][ 720/1320] lr: 2.0000e-03 eta: 2:01:45 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 5.5963 loss: 1.0770 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0770 2023/03/17 21:24:44 - mmengine - INFO - Epoch(train) [34][ 740/1320] lr: 2.0000e-03 eta: 2:01:38 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 5.5323 loss: 0.8971 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8971 2023/03/17 21:24:51 - mmengine - INFO - Epoch(train) [34][ 760/1320] lr: 2.0000e-03 eta: 2:01:32 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 5.6480 loss: 1.0485 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0485 2023/03/17 21:24:58 - mmengine - INFO - Epoch(train) [34][ 780/1320] lr: 2.0000e-03 eta: 2:01:25 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 5.7740 loss: 0.9435 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9435 2023/03/17 21:25:05 - mmengine - INFO - Epoch(train) [34][ 800/1320] lr: 2.0000e-03 eta: 2:01:18 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 5.8219 loss: 1.1288 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1288 2023/03/17 21:25:11 - mmengine - INFO - Epoch(train) [34][ 820/1320] lr: 2.0000e-03 eta: 2:01:11 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 5.8118 loss: 1.1225 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1225 2023/03/17 21:25:18 - mmengine - INFO - Epoch(train) [34][ 840/1320] lr: 2.0000e-03 eta: 2:01:05 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 5.6843 loss: 0.9968 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9968 2023/03/17 21:25:25 - mmengine - INFO - Epoch(train) [34][ 860/1320] lr: 2.0000e-03 eta: 2:00:58 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 5.5890 loss: 1.2547 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2547 2023/03/17 21:25:31 - mmengine - INFO - Epoch(train) [34][ 880/1320] lr: 2.0000e-03 eta: 2:00:51 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 5.8080 loss: 1.1680 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1680 2023/03/17 21:25:38 - mmengine - INFO - Epoch(train) [34][ 900/1320] lr: 2.0000e-03 eta: 2:00:44 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 5.5922 loss: 1.0422 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0422 2023/03/17 21:25:45 - mmengine - INFO - Epoch(train) [34][ 920/1320] lr: 2.0000e-03 eta: 2:00:38 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 5.8658 loss: 1.0792 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0792 2023/03/17 21:25:52 - mmengine - INFO - Epoch(train) [34][ 940/1320] lr: 2.0000e-03 eta: 2:00:31 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 5.8194 loss: 1.1052 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1052 2023/03/17 21:25:58 - mmengine - INFO - Epoch(train) [34][ 960/1320] lr: 2.0000e-03 eta: 2:00:24 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 5.6893 loss: 1.0063 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0063 2023/03/17 21:26:05 - mmengine - INFO - Epoch(train) [34][ 980/1320] lr: 2.0000e-03 eta: 2:00:17 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 5.6672 loss: 1.0024 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0024 2023/03/17 21:26:12 - mmengine - INFO - Epoch(train) [34][1000/1320] lr: 2.0000e-03 eta: 2:00:11 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 5.7509 loss: 1.3108 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3108 2023/03/17 21:26:18 - mmengine - INFO - Epoch(train) [34][1020/1320] lr: 2.0000e-03 eta: 2:00:04 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 5.8237 loss: 1.0914 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0914 2023/03/17 21:26:25 - mmengine - INFO - Epoch(train) [34][1040/1320] lr: 2.0000e-03 eta: 1:59:57 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 5.7966 loss: 0.8922 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.8922 2023/03/17 21:26:32 - mmengine - INFO - Epoch(train) [34][1060/1320] lr: 2.0000e-03 eta: 1:59:51 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 5.6316 loss: 1.2682 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2682 2023/03/17 21:26:39 - mmengine - INFO - Epoch(train) [34][1080/1320] lr: 2.0000e-03 eta: 1:59:44 time: 0.3362 data_time: 0.0124 memory: 18752 grad_norm: 5.6107 loss: 1.1020 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1020 2023/03/17 21:26:45 - mmengine - INFO - Epoch(train) [34][1100/1320] lr: 2.0000e-03 eta: 1:59:37 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 5.7084 loss: 1.0134 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0134 2023/03/17 21:26:52 - mmengine - INFO - Epoch(train) [34][1120/1320] lr: 2.0000e-03 eta: 1:59:30 time: 0.3356 data_time: 0.0129 memory: 18752 grad_norm: 5.6876 loss: 1.1927 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.1927 2023/03/17 21:26:59 - mmengine - INFO - Epoch(train) [34][1140/1320] lr: 2.0000e-03 eta: 1:59:24 time: 0.3359 data_time: 0.0129 memory: 18752 grad_norm: 5.8035 loss: 1.1114 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1114 2023/03/17 21:27:05 - mmengine - INFO - Epoch(train) [34][1160/1320] lr: 2.0000e-03 eta: 1:59:17 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.6915 loss: 1.2229 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2229 2023/03/17 21:27:12 - mmengine - INFO - Epoch(train) [34][1180/1320] lr: 2.0000e-03 eta: 1:59:10 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 5.6570 loss: 1.0760 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0760 2023/03/17 21:27:19 - mmengine - INFO - Epoch(train) [34][1200/1320] lr: 2.0000e-03 eta: 1:59:03 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 5.7910 loss: 1.1328 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1328 2023/03/17 21:27:26 - mmengine - INFO - Epoch(train) [34][1220/1320] lr: 2.0000e-03 eta: 1:58:57 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 5.7861 loss: 1.0401 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.0401 2023/03/17 21:27:32 - mmengine - INFO - Epoch(train) [34][1240/1320] lr: 2.0000e-03 eta: 1:58:50 time: 0.3366 data_time: 0.0122 memory: 18752 grad_norm: 5.8767 loss: 1.0732 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0732 2023/03/17 21:27:39 - mmengine - INFO - Epoch(train) [34][1260/1320] lr: 2.0000e-03 eta: 1:58:43 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 5.7214 loss: 1.1909 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1909 2023/03/17 21:27:46 - mmengine - INFO - Epoch(train) [34][1280/1320] lr: 2.0000e-03 eta: 1:58:37 time: 0.3359 data_time: 0.0128 memory: 18752 grad_norm: 5.6961 loss: 0.9430 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9430 2023/03/17 21:27:53 - mmengine - INFO - Epoch(train) [34][1300/1320] lr: 2.0000e-03 eta: 1:58:30 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 5.8496 loss: 1.0648 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0648 2023/03/17 21:27:59 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:27:59 - mmengine - INFO - Epoch(train) [34][1320/1320] lr: 2.0000e-03 eta: 1:58:23 time: 0.3310 data_time: 0.0120 memory: 18752 grad_norm: 5.8857 loss: 1.1422 top1_acc: 0.8182 top5_acc: 0.8182 loss_cls: 1.1422 2023/03/17 21:28:02 - mmengine - INFO - Epoch(val) [34][ 20/194] eta: 0:00:22 time: 0.1271 data_time: 0.0403 memory: 2112 2023/03/17 21:28:04 - mmengine - INFO - Epoch(val) [34][ 40/194] eta: 0:00:17 time: 0.0969 data_time: 0.0105 memory: 2112 2023/03/17 21:28:06 - mmengine - INFO - Epoch(val) [34][ 60/194] eta: 0:00:14 time: 0.0976 data_time: 0.0111 memory: 2112 2023/03/17 21:28:08 - mmengine - INFO - Epoch(val) [34][ 80/194] eta: 0:00:11 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 21:28:09 - mmengine - INFO - Epoch(val) [34][100/194] eta: 0:00:09 time: 0.0968 data_time: 0.0109 memory: 2112 2023/03/17 21:28:11 - mmengine - INFO - Epoch(val) [34][120/194] eta: 0:00:07 time: 0.0964 data_time: 0.0106 memory: 2112 2023/03/17 21:28:13 - mmengine - INFO - Epoch(val) [34][140/194] eta: 0:00:05 time: 0.0968 data_time: 0.0109 memory: 2112 2023/03/17 21:28:15 - mmengine - INFO - Epoch(val) [34][160/194] eta: 0:00:03 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 21:28:17 - mmengine - INFO - Epoch(val) [34][180/194] eta: 0:00:01 time: 0.0973 data_time: 0.0113 memory: 2112 2023/03/17 21:28:20 - mmengine - INFO - Epoch(val) [34][194/194] acc/top1: 0.6054 acc/top5: 0.8646 acc/mean1: 0.5420 2023/03/17 21:28:28 - mmengine - INFO - Epoch(train) [35][ 20/1320] lr: 2.0000e-03 eta: 1:58:17 time: 0.3725 data_time: 0.0419 memory: 18752 grad_norm: 5.5254 loss: 1.0759 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0759 2023/03/17 21:28:35 - mmengine - INFO - Epoch(train) [35][ 40/1320] lr: 2.0000e-03 eta: 1:58:10 time: 0.3360 data_time: 0.0128 memory: 18752 grad_norm: 5.8547 loss: 1.0424 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0424 2023/03/17 21:28:41 - mmengine - INFO - Epoch(train) [35][ 60/1320] lr: 2.0000e-03 eta: 1:58:03 time: 0.3366 data_time: 0.0129 memory: 18752 grad_norm: 5.6973 loss: 1.0637 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0637 2023/03/17 21:28:48 - mmengine - INFO - Epoch(train) [35][ 80/1320] lr: 2.0000e-03 eta: 1:57:56 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 5.9104 loss: 0.9156 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9156 2023/03/17 21:28:55 - mmengine - INFO - Epoch(train) [35][ 100/1320] lr: 2.0000e-03 eta: 1:57:50 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 5.8770 loss: 1.1557 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1557 2023/03/17 21:29:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:29:02 - mmengine - INFO - Epoch(train) [35][ 120/1320] lr: 2.0000e-03 eta: 1:57:43 time: 0.3359 data_time: 0.0127 memory: 18752 grad_norm: 5.8025 loss: 1.0282 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0282 2023/03/17 21:29:08 - mmengine - INFO - Epoch(train) [35][ 140/1320] lr: 2.0000e-03 eta: 1:57:36 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 5.7741 loss: 1.0817 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0817 2023/03/17 21:29:15 - mmengine - INFO - Epoch(train) [35][ 160/1320] lr: 2.0000e-03 eta: 1:57:30 time: 0.3356 data_time: 0.0128 memory: 18752 grad_norm: 5.7770 loss: 0.9555 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9555 2023/03/17 21:29:22 - mmengine - INFO - Epoch(train) [35][ 180/1320] lr: 2.0000e-03 eta: 1:57:23 time: 0.3357 data_time: 0.0115 memory: 18752 grad_norm: 5.8754 loss: 1.0640 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0640 2023/03/17 21:29:28 - mmengine - INFO - Epoch(train) [35][ 200/1320] lr: 2.0000e-03 eta: 1:57:16 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 5.7300 loss: 1.2558 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2558 2023/03/17 21:29:35 - mmengine - INFO - Epoch(train) [35][ 220/1320] lr: 2.0000e-03 eta: 1:57:09 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 5.8029 loss: 1.0820 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0820 2023/03/17 21:29:42 - mmengine - INFO - Epoch(train) [35][ 240/1320] lr: 2.0000e-03 eta: 1:57:03 time: 0.3370 data_time: 0.0120 memory: 18752 grad_norm: 5.6830 loss: 1.0087 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0087 2023/03/17 21:29:49 - mmengine - INFO - Epoch(train) [35][ 260/1320] lr: 2.0000e-03 eta: 1:56:56 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 5.8333 loss: 0.9617 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9617 2023/03/17 21:29:55 - mmengine - INFO - Epoch(train) [35][ 280/1320] lr: 2.0000e-03 eta: 1:56:49 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 5.8846 loss: 1.1170 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1170 2023/03/17 21:30:02 - mmengine - INFO - Epoch(train) [35][ 300/1320] lr: 2.0000e-03 eta: 1:56:42 time: 0.3353 data_time: 0.0124 memory: 18752 grad_norm: 5.8939 loss: 1.1659 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1659 2023/03/17 21:30:09 - mmengine - INFO - Epoch(train) [35][ 320/1320] lr: 2.0000e-03 eta: 1:56:36 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 5.7031 loss: 1.0412 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0412 2023/03/17 21:30:15 - mmengine - INFO - Epoch(train) [35][ 340/1320] lr: 2.0000e-03 eta: 1:56:29 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 5.7843 loss: 0.9900 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9900 2023/03/17 21:30:22 - mmengine - INFO - Epoch(train) [35][ 360/1320] lr: 2.0000e-03 eta: 1:56:22 time: 0.3362 data_time: 0.0126 memory: 18752 grad_norm: 5.7397 loss: 1.0532 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0532 2023/03/17 21:30:29 - mmengine - INFO - Epoch(train) [35][ 380/1320] lr: 2.0000e-03 eta: 1:56:15 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 5.6946 loss: 0.9785 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9785 2023/03/17 21:30:36 - mmengine - INFO - Epoch(train) [35][ 400/1320] lr: 2.0000e-03 eta: 1:56:09 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 5.8784 loss: 0.9762 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9762 2023/03/17 21:30:42 - mmengine - INFO - Epoch(train) [35][ 420/1320] lr: 2.0000e-03 eta: 1:56:02 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 5.8933 loss: 1.0557 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0557 2023/03/17 21:30:49 - mmengine - INFO - Epoch(train) [35][ 440/1320] lr: 2.0000e-03 eta: 1:55:55 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 5.8822 loss: 1.2476 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2476 2023/03/17 21:30:56 - mmengine - INFO - Epoch(train) [35][ 460/1320] lr: 2.0000e-03 eta: 1:55:49 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 5.8484 loss: 0.9678 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9678 2023/03/17 21:31:02 - mmengine - INFO - Epoch(train) [35][ 480/1320] lr: 2.0000e-03 eta: 1:55:42 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 5.6279 loss: 1.0544 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0544 2023/03/17 21:31:09 - mmengine - INFO - Epoch(train) [35][ 500/1320] lr: 2.0000e-03 eta: 1:55:35 time: 0.3357 data_time: 0.0114 memory: 18752 grad_norm: 5.9749 loss: 0.9737 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9737 2023/03/17 21:31:16 - mmengine - INFO - Epoch(train) [35][ 520/1320] lr: 2.0000e-03 eta: 1:55:28 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 5.7736 loss: 0.9694 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9694 2023/03/17 21:31:23 - mmengine - INFO - Epoch(train) [35][ 540/1320] lr: 2.0000e-03 eta: 1:55:22 time: 0.3363 data_time: 0.0128 memory: 18752 grad_norm: 6.0066 loss: 1.0232 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0232 2023/03/17 21:31:29 - mmengine - INFO - Epoch(train) [35][ 560/1320] lr: 2.0000e-03 eta: 1:55:15 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 5.7891 loss: 0.9724 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9724 2023/03/17 21:31:36 - mmengine - INFO - Epoch(train) [35][ 580/1320] lr: 2.0000e-03 eta: 1:55:08 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 6.0066 loss: 1.1059 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1059 2023/03/17 21:31:43 - mmengine - INFO - Epoch(train) [35][ 600/1320] lr: 2.0000e-03 eta: 1:55:01 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 5.7477 loss: 1.0904 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0904 2023/03/17 21:31:50 - mmengine - INFO - Epoch(train) [35][ 620/1320] lr: 2.0000e-03 eta: 1:54:55 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 5.9945 loss: 1.1745 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1745 2023/03/17 21:31:56 - mmengine - INFO - Epoch(train) [35][ 640/1320] lr: 2.0000e-03 eta: 1:54:48 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 5.7256 loss: 0.9040 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9040 2023/03/17 21:32:03 - mmengine - INFO - Epoch(train) [35][ 660/1320] lr: 2.0000e-03 eta: 1:54:41 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 5.7720 loss: 1.1064 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1064 2023/03/17 21:32:10 - mmengine - INFO - Epoch(train) [35][ 680/1320] lr: 2.0000e-03 eta: 1:54:35 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 6.0164 loss: 1.0259 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0259 2023/03/17 21:32:16 - mmengine - INFO - Epoch(train) [35][ 700/1320] lr: 2.0000e-03 eta: 1:54:28 time: 0.3353 data_time: 0.0117 memory: 18752 grad_norm: 5.7275 loss: 1.0664 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0664 2023/03/17 21:32:23 - mmengine - INFO - Epoch(train) [35][ 720/1320] lr: 2.0000e-03 eta: 1:54:21 time: 0.3352 data_time: 0.0125 memory: 18752 grad_norm: 6.0151 loss: 1.3286 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3286 2023/03/17 21:32:30 - mmengine - INFO - Epoch(train) [35][ 740/1320] lr: 2.0000e-03 eta: 1:54:14 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 5.8846 loss: 1.0950 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0950 2023/03/17 21:32:37 - mmengine - INFO - Epoch(train) [35][ 760/1320] lr: 2.0000e-03 eta: 1:54:08 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 5.8374 loss: 0.9021 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9021 2023/03/17 21:32:43 - mmengine - INFO - Epoch(train) [35][ 780/1320] lr: 2.0000e-03 eta: 1:54:01 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 6.0025 loss: 1.1334 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1334 2023/03/17 21:32:50 - mmengine - INFO - Epoch(train) [35][ 800/1320] lr: 2.0000e-03 eta: 1:53:54 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 5.9374 loss: 1.0751 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0751 2023/03/17 21:32:57 - mmengine - INFO - Epoch(train) [35][ 820/1320] lr: 2.0000e-03 eta: 1:53:47 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 5.9851 loss: 1.0844 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0844 2023/03/17 21:33:03 - mmengine - INFO - Epoch(train) [35][ 840/1320] lr: 2.0000e-03 eta: 1:53:41 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 5.8367 loss: 1.0810 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0810 2023/03/17 21:33:10 - mmengine - INFO - Epoch(train) [35][ 860/1320] lr: 2.0000e-03 eta: 1:53:34 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 5.8799 loss: 1.0258 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0258 2023/03/17 21:33:17 - mmengine - INFO - Epoch(train) [35][ 880/1320] lr: 2.0000e-03 eta: 1:53:27 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 6.0089 loss: 0.9402 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9402 2023/03/17 21:33:24 - mmengine - INFO - Epoch(train) [35][ 900/1320] lr: 2.0000e-03 eta: 1:53:20 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 5.9049 loss: 1.0846 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0846 2023/03/17 21:33:30 - mmengine - INFO - Epoch(train) [35][ 920/1320] lr: 2.0000e-03 eta: 1:53:14 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 6.1282 loss: 1.2231 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.2231 2023/03/17 21:33:37 - mmengine - INFO - Epoch(train) [35][ 940/1320] lr: 2.0000e-03 eta: 1:53:07 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.1129 loss: 1.0418 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0418 2023/03/17 21:33:44 - mmengine - INFO - Epoch(train) [35][ 960/1320] lr: 2.0000e-03 eta: 1:53:00 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 5.8989 loss: 1.1264 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1264 2023/03/17 21:33:50 - mmengine - INFO - Epoch(train) [35][ 980/1320] lr: 2.0000e-03 eta: 1:52:54 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 5.7850 loss: 1.2225 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2225 2023/03/17 21:33:57 - mmengine - INFO - Epoch(train) [35][1000/1320] lr: 2.0000e-03 eta: 1:52:47 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 5.8748 loss: 0.9250 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9250 2023/03/17 21:34:04 - mmengine - INFO - Epoch(train) [35][1020/1320] lr: 2.0000e-03 eta: 1:52:40 time: 0.3355 data_time: 0.0130 memory: 18752 grad_norm: 5.8156 loss: 1.0074 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0074 2023/03/17 21:34:11 - mmengine - INFO - Epoch(train) [35][1040/1320] lr: 2.0000e-03 eta: 1:52:33 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 6.0551 loss: 1.1181 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1181 2023/03/17 21:34:17 - mmengine - INFO - Epoch(train) [35][1060/1320] lr: 2.0000e-03 eta: 1:52:27 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 6.0884 loss: 1.0163 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0163 2023/03/17 21:34:24 - mmengine - INFO - Epoch(train) [35][1080/1320] lr: 2.0000e-03 eta: 1:52:20 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 5.9641 loss: 1.1775 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1775 2023/03/17 21:34:31 - mmengine - INFO - Epoch(train) [35][1100/1320] lr: 2.0000e-03 eta: 1:52:13 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 5.8773 loss: 1.0702 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0702 2023/03/17 21:34:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:34:37 - mmengine - INFO - Epoch(train) [35][1120/1320] lr: 2.0000e-03 eta: 1:52:06 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 5.7329 loss: 0.8926 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8926 2023/03/17 21:34:44 - mmengine - INFO - Epoch(train) [35][1140/1320] lr: 2.0000e-03 eta: 1:52:00 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 5.7464 loss: 0.9599 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9599 2023/03/17 21:34:51 - mmengine - INFO - Epoch(train) [35][1160/1320] lr: 2.0000e-03 eta: 1:51:53 time: 0.3358 data_time: 0.0127 memory: 18752 grad_norm: 5.8471 loss: 0.9566 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9566 2023/03/17 21:34:58 - mmengine - INFO - Epoch(train) [35][1180/1320] lr: 2.0000e-03 eta: 1:51:46 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 6.0032 loss: 1.1541 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1541 2023/03/17 21:35:04 - mmengine - INFO - Epoch(train) [35][1200/1320] lr: 2.0000e-03 eta: 1:51:39 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 5.9047 loss: 1.0730 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0730 2023/03/17 21:35:11 - mmengine - INFO - Epoch(train) [35][1220/1320] lr: 2.0000e-03 eta: 1:51:33 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 5.9660 loss: 0.8081 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8081 2023/03/17 21:35:18 - mmengine - INFO - Epoch(train) [35][1240/1320] lr: 2.0000e-03 eta: 1:51:26 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 5.9783 loss: 1.2165 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2165 2023/03/17 21:35:24 - mmengine - INFO - Epoch(train) [35][1260/1320] lr: 2.0000e-03 eta: 1:51:19 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 6.1174 loss: 1.0242 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0242 2023/03/17 21:35:31 - mmengine - INFO - Epoch(train) [35][1280/1320] lr: 2.0000e-03 eta: 1:51:13 time: 0.3367 data_time: 0.0116 memory: 18752 grad_norm: 6.0893 loss: 1.1915 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1915 2023/03/17 21:35:38 - mmengine - INFO - Epoch(train) [35][1300/1320] lr: 2.0000e-03 eta: 1:51:06 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 5.8812 loss: 1.1504 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1504 2023/03/17 21:35:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:35:44 - mmengine - INFO - Epoch(train) [35][1320/1320] lr: 2.0000e-03 eta: 1:50:59 time: 0.3304 data_time: 0.0118 memory: 18752 grad_norm: 6.1306 loss: 1.1562 top1_acc: 0.7273 top5_acc: 0.9091 loss_cls: 1.1562 2023/03/17 21:35:47 - mmengine - INFO - Epoch(val) [35][ 20/194] eta: 0:00:23 time: 0.1322 data_time: 0.0454 memory: 2112 2023/03/17 21:35:49 - mmengine - INFO - Epoch(val) [35][ 40/194] eta: 0:00:17 time: 0.0979 data_time: 0.0119 memory: 2112 2023/03/17 21:35:51 - mmengine - INFO - Epoch(val) [35][ 60/194] eta: 0:00:14 time: 0.0968 data_time: 0.0108 memory: 2112 2023/03/17 21:35:53 - mmengine - INFO - Epoch(val) [35][ 80/194] eta: 0:00:12 time: 0.0971 data_time: 0.0110 memory: 2112 2023/03/17 21:35:55 - mmengine - INFO - Epoch(val) [35][100/194] eta: 0:00:09 time: 0.0971 data_time: 0.0115 memory: 2112 2023/03/17 21:35:57 - mmengine - INFO - Epoch(val) [35][120/194] eta: 0:00:07 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 21:35:59 - mmengine - INFO - Epoch(val) [35][140/194] eta: 0:00:05 time: 0.0967 data_time: 0.0104 memory: 2112 2023/03/17 21:36:01 - mmengine - INFO - Epoch(val) [35][160/194] eta: 0:00:03 time: 0.0963 data_time: 0.0105 memory: 2112 2023/03/17 21:36:03 - mmengine - INFO - Epoch(val) [35][180/194] eta: 0:00:01 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 21:36:06 - mmengine - INFO - Epoch(val) [35][194/194] acc/top1: 0.6042 acc/top5: 0.8634 acc/mean1: 0.5422 2023/03/17 21:36:13 - mmengine - INFO - Epoch(train) [36][ 20/1320] lr: 2.0000e-03 eta: 1:50:53 time: 0.3748 data_time: 0.0408 memory: 18752 grad_norm: 5.8989 loss: 1.0911 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0911 2023/03/17 21:36:20 - mmengine - INFO - Epoch(train) [36][ 40/1320] lr: 2.0000e-03 eta: 1:50:46 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 5.9017 loss: 1.0188 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0188 2023/03/17 21:36:27 - mmengine - INFO - Epoch(train) [36][ 60/1320] lr: 2.0000e-03 eta: 1:50:39 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 6.0738 loss: 1.1668 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1668 2023/03/17 21:36:34 - mmengine - INFO - Epoch(train) [36][ 80/1320] lr: 2.0000e-03 eta: 1:50:32 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 5.8818 loss: 0.9716 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9716 2023/03/17 21:36:40 - mmengine - INFO - Epoch(train) [36][ 100/1320] lr: 2.0000e-03 eta: 1:50:26 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 5.9047 loss: 0.9273 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9273 2023/03/17 21:36:47 - mmengine - INFO - Epoch(train) [36][ 120/1320] lr: 2.0000e-03 eta: 1:50:19 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 5.9539 loss: 1.1117 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1117 2023/03/17 21:36:54 - mmengine - INFO - Epoch(train) [36][ 140/1320] lr: 2.0000e-03 eta: 1:50:12 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 5.8753 loss: 0.9677 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9677 2023/03/17 21:37:00 - mmengine - INFO - Epoch(train) [36][ 160/1320] lr: 2.0000e-03 eta: 1:50:06 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 5.9347 loss: 0.9653 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9653 2023/03/17 21:37:07 - mmengine - INFO - Epoch(train) [36][ 180/1320] lr: 2.0000e-03 eta: 1:49:59 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 5.9462 loss: 1.1654 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1654 2023/03/17 21:37:14 - mmengine - INFO - Epoch(train) [36][ 200/1320] lr: 2.0000e-03 eta: 1:49:52 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 5.9132 loss: 0.9928 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9928 2023/03/17 21:37:21 - mmengine - INFO - Epoch(train) [36][ 220/1320] lr: 2.0000e-03 eta: 1:49:45 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 5.9381 loss: 1.1065 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1065 2023/03/17 21:37:27 - mmengine - INFO - Epoch(train) [36][ 240/1320] lr: 2.0000e-03 eta: 1:49:39 time: 0.3369 data_time: 0.0119 memory: 18752 grad_norm: 6.0415 loss: 0.8871 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8871 2023/03/17 21:37:34 - mmengine - INFO - Epoch(train) [36][ 260/1320] lr: 2.0000e-03 eta: 1:49:32 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 5.8641 loss: 1.0100 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0100 2023/03/17 21:37:41 - mmengine - INFO - Epoch(train) [36][ 280/1320] lr: 2.0000e-03 eta: 1:49:25 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 6.0642 loss: 1.1196 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1196 2023/03/17 21:37:47 - mmengine - INFO - Epoch(train) [36][ 300/1320] lr: 2.0000e-03 eta: 1:49:18 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 6.1004 loss: 1.0134 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0134 2023/03/17 21:37:54 - mmengine - INFO - Epoch(train) [36][ 320/1320] lr: 2.0000e-03 eta: 1:49:12 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 6.2554 loss: 1.0655 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0655 2023/03/17 21:38:01 - mmengine - INFO - Epoch(train) [36][ 340/1320] lr: 2.0000e-03 eta: 1:49:05 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 5.9515 loss: 0.9210 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9210 2023/03/17 21:38:08 - mmengine - INFO - Epoch(train) [36][ 360/1320] lr: 2.0000e-03 eta: 1:48:58 time: 0.3364 data_time: 0.0116 memory: 18752 grad_norm: 5.7755 loss: 1.1692 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1692 2023/03/17 21:38:14 - mmengine - INFO - Epoch(train) [36][ 380/1320] lr: 2.0000e-03 eta: 1:48:51 time: 0.3350 data_time: 0.0119 memory: 18752 grad_norm: 6.2347 loss: 1.0487 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0487 2023/03/17 21:38:21 - mmengine - INFO - Epoch(train) [36][ 400/1320] lr: 2.0000e-03 eta: 1:48:45 time: 0.3358 data_time: 0.0116 memory: 18752 grad_norm: 5.9666 loss: 0.9870 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9870 2023/03/17 21:38:28 - mmengine - INFO - Epoch(train) [36][ 420/1320] lr: 2.0000e-03 eta: 1:48:38 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 6.1543 loss: 1.0735 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0735 2023/03/17 21:38:34 - mmengine - INFO - Epoch(train) [36][ 440/1320] lr: 2.0000e-03 eta: 1:48:31 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 6.0185 loss: 1.0288 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0288 2023/03/17 21:38:41 - mmengine - INFO - Epoch(train) [36][ 460/1320] lr: 2.0000e-03 eta: 1:48:25 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 5.8497 loss: 0.9658 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9658 2023/03/17 21:38:48 - mmengine - INFO - Epoch(train) [36][ 480/1320] lr: 2.0000e-03 eta: 1:48:18 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 6.0646 loss: 0.8821 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8821 2023/03/17 21:38:55 - mmengine - INFO - Epoch(train) [36][ 500/1320] lr: 2.0000e-03 eta: 1:48:11 time: 0.3363 data_time: 0.0127 memory: 18752 grad_norm: 5.9054 loss: 1.0400 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0400 2023/03/17 21:39:01 - mmengine - INFO - Epoch(train) [36][ 520/1320] lr: 2.0000e-03 eta: 1:48:04 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 5.9512 loss: 1.0504 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0504 2023/03/17 21:39:08 - mmengine - INFO - Epoch(train) [36][ 540/1320] lr: 2.0000e-03 eta: 1:47:58 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 5.9024 loss: 1.0552 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0552 2023/03/17 21:39:15 - mmengine - INFO - Epoch(train) [36][ 560/1320] lr: 2.0000e-03 eta: 1:47:51 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 6.0235 loss: 1.1206 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1206 2023/03/17 21:39:22 - mmengine - INFO - Epoch(train) [36][ 580/1320] lr: 2.0000e-03 eta: 1:47:44 time: 0.3363 data_time: 0.0127 memory: 18752 grad_norm: 6.2140 loss: 1.0031 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0031 2023/03/17 21:39:28 - mmengine - INFO - Epoch(train) [36][ 600/1320] lr: 2.0000e-03 eta: 1:47:37 time: 0.3366 data_time: 0.0123 memory: 18752 grad_norm: 6.1321 loss: 1.0994 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0994 2023/03/17 21:39:35 - mmengine - INFO - Epoch(train) [36][ 620/1320] lr: 2.0000e-03 eta: 1:47:31 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 6.0393 loss: 1.0109 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0109 2023/03/17 21:39:42 - mmengine - INFO - Epoch(train) [36][ 640/1320] lr: 2.0000e-03 eta: 1:47:24 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 6.0127 loss: 1.0159 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0159 2023/03/17 21:39:48 - mmengine - INFO - Epoch(train) [36][ 660/1320] lr: 2.0000e-03 eta: 1:47:17 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 5.9174 loss: 1.1816 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1816 2023/03/17 21:39:55 - mmengine - INFO - Epoch(train) [36][ 680/1320] lr: 2.0000e-03 eta: 1:47:11 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 5.9577 loss: 0.8080 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8080 2023/03/17 21:40:02 - mmengine - INFO - Epoch(train) [36][ 700/1320] lr: 2.0000e-03 eta: 1:47:04 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 6.0181 loss: 1.1030 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1030 2023/03/17 21:40:09 - mmengine - INFO - Epoch(train) [36][ 720/1320] lr: 2.0000e-03 eta: 1:46:57 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.0481 loss: 0.8732 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8732 2023/03/17 21:40:15 - mmengine - INFO - Epoch(train) [36][ 740/1320] lr: 2.0000e-03 eta: 1:46:50 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 6.0900 loss: 1.2415 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2415 2023/03/17 21:40:22 - mmengine - INFO - Epoch(train) [36][ 760/1320] lr: 2.0000e-03 eta: 1:46:44 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 6.0427 loss: 1.1274 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1274 2023/03/17 21:40:29 - mmengine - INFO - Epoch(train) [36][ 780/1320] lr: 2.0000e-03 eta: 1:46:37 time: 0.3362 data_time: 0.0116 memory: 18752 grad_norm: 6.2079 loss: 1.0552 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0552 2023/03/17 21:40:35 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:40:35 - mmengine - INFO - Epoch(train) [36][ 800/1320] lr: 2.0000e-03 eta: 1:46:30 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 5.9735 loss: 0.9887 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9887 2023/03/17 21:40:42 - mmengine - INFO - Epoch(train) [36][ 820/1320] lr: 2.0000e-03 eta: 1:46:23 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 6.0686 loss: 1.1118 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1118 2023/03/17 21:40:49 - mmengine - INFO - Epoch(train) [36][ 840/1320] lr: 2.0000e-03 eta: 1:46:17 time: 0.3367 data_time: 0.0119 memory: 18752 grad_norm: 6.1783 loss: 1.0241 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0241 2023/03/17 21:40:56 - mmengine - INFO - Epoch(train) [36][ 860/1320] lr: 2.0000e-03 eta: 1:46:10 time: 0.3364 data_time: 0.0128 memory: 18752 grad_norm: 6.2024 loss: 1.1515 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1515 2023/03/17 21:41:02 - mmengine - INFO - Epoch(train) [36][ 880/1320] lr: 2.0000e-03 eta: 1:46:03 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 5.8638 loss: 1.0016 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.0016 2023/03/17 21:41:09 - mmengine - INFO - Epoch(train) [36][ 900/1320] lr: 2.0000e-03 eta: 1:45:56 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 6.1924 loss: 0.9661 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9661 2023/03/17 21:41:16 - mmengine - INFO - Epoch(train) [36][ 920/1320] lr: 2.0000e-03 eta: 1:45:50 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 5.9758 loss: 0.9440 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9440 2023/03/17 21:41:23 - mmengine - INFO - Epoch(train) [36][ 940/1320] lr: 2.0000e-03 eta: 1:45:43 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 6.0332 loss: 0.9091 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9091 2023/03/17 21:41:29 - mmengine - INFO - Epoch(train) [36][ 960/1320] lr: 2.0000e-03 eta: 1:45:36 time: 0.3355 data_time: 0.0116 memory: 18752 grad_norm: 6.2800 loss: 1.0832 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0832 2023/03/17 21:41:36 - mmengine - INFO - Epoch(train) [36][ 980/1320] lr: 2.0000e-03 eta: 1:45:30 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 6.0966 loss: 0.8995 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8995 2023/03/17 21:41:43 - mmengine - INFO - Epoch(train) [36][1000/1320] lr: 2.0000e-03 eta: 1:45:23 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 6.0591 loss: 1.1332 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1332 2023/03/17 21:41:49 - mmengine - INFO - Epoch(train) [36][1020/1320] lr: 2.0000e-03 eta: 1:45:16 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 6.0522 loss: 1.0346 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0346 2023/03/17 21:41:56 - mmengine - INFO - Epoch(train) [36][1040/1320] lr: 2.0000e-03 eta: 1:45:09 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 6.0748 loss: 0.8555 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8555 2023/03/17 21:42:03 - mmengine - INFO - Epoch(train) [36][1060/1320] lr: 2.0000e-03 eta: 1:45:03 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 6.2621 loss: 1.2604 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2604 2023/03/17 21:42:10 - mmengine - INFO - Epoch(train) [36][1080/1320] lr: 2.0000e-03 eta: 1:44:56 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 6.0091 loss: 1.0435 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0435 2023/03/17 21:42:16 - mmengine - INFO - Epoch(train) [36][1100/1320] lr: 2.0000e-03 eta: 1:44:49 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 6.0362 loss: 1.1416 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1416 2023/03/17 21:42:23 - mmengine - INFO - Epoch(train) [36][1120/1320] lr: 2.0000e-03 eta: 1:44:42 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.1623 loss: 1.0280 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0280 2023/03/17 21:42:30 - mmengine - INFO - Epoch(train) [36][1140/1320] lr: 2.0000e-03 eta: 1:44:36 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 6.0760 loss: 1.0601 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0601 2023/03/17 21:42:36 - mmengine - INFO - Epoch(train) [36][1160/1320] lr: 2.0000e-03 eta: 1:44:29 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 6.2710 loss: 1.1112 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1112 2023/03/17 21:42:43 - mmengine - INFO - Epoch(train) [36][1180/1320] lr: 2.0000e-03 eta: 1:44:22 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 6.0730 loss: 1.0446 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0446 2023/03/17 21:42:50 - mmengine - INFO - Epoch(train) [36][1200/1320] lr: 2.0000e-03 eta: 1:44:16 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 5.9955 loss: 1.0414 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0414 2023/03/17 21:42:57 - mmengine - INFO - Epoch(train) [36][1220/1320] lr: 2.0000e-03 eta: 1:44:09 time: 0.3364 data_time: 0.0117 memory: 18752 grad_norm: 6.2285 loss: 1.1087 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1087 2023/03/17 21:43:03 - mmengine - INFO - Epoch(train) [36][1240/1320] lr: 2.0000e-03 eta: 1:44:02 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 6.1603 loss: 1.1446 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1446 2023/03/17 21:43:10 - mmengine - INFO - Epoch(train) [36][1260/1320] lr: 2.0000e-03 eta: 1:43:55 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 5.9200 loss: 1.0518 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0518 2023/03/17 21:43:17 - mmengine - INFO - Epoch(train) [36][1280/1320] lr: 2.0000e-03 eta: 1:43:49 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 5.9467 loss: 0.8523 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8523 2023/03/17 21:43:23 - mmengine - INFO - Epoch(train) [36][1300/1320] lr: 2.0000e-03 eta: 1:43:42 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 6.0828 loss: 1.0734 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0734 2023/03/17 21:43:30 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:43:30 - mmengine - INFO - Epoch(train) [36][1320/1320] lr: 2.0000e-03 eta: 1:43:35 time: 0.3303 data_time: 0.0123 memory: 18752 grad_norm: 6.0609 loss: 0.8817 top1_acc: 0.8182 top5_acc: 1.0000 loss_cls: 0.8817 2023/03/17 21:43:30 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/03/17 21:43:36 - mmengine - INFO - Epoch(val) [36][ 20/194] eta: 0:00:21 time: 0.1255 data_time: 0.0389 memory: 2112 2023/03/17 21:43:38 - mmengine - INFO - Epoch(val) [36][ 40/194] eta: 0:00:17 time: 0.0962 data_time: 0.0105 memory: 2112 2023/03/17 21:43:39 - mmengine - INFO - Epoch(val) [36][ 60/194] eta: 0:00:14 time: 0.0965 data_time: 0.0105 memory: 2112 2023/03/17 21:43:41 - mmengine - INFO - Epoch(val) [36][ 80/194] eta: 0:00:11 time: 0.0967 data_time: 0.0104 memory: 2112 2023/03/17 21:43:43 - mmengine - INFO - Epoch(val) [36][100/194] eta: 0:00:09 time: 0.0963 data_time: 0.0105 memory: 2112 2023/03/17 21:43:45 - mmengine - INFO - Epoch(val) [36][120/194] eta: 0:00:07 time: 0.0971 data_time: 0.0110 memory: 2112 2023/03/17 21:43:47 - mmengine - INFO - Epoch(val) [36][140/194] eta: 0:00:05 time: 0.0974 data_time: 0.0115 memory: 2112 2023/03/17 21:43:49 - mmengine - INFO - Epoch(val) [36][160/194] eta: 0:00:03 time: 0.0970 data_time: 0.0109 memory: 2112 2023/03/17 21:43:51 - mmengine - INFO - Epoch(val) [36][180/194] eta: 0:00:01 time: 0.0955 data_time: 0.0096 memory: 2112 2023/03/17 21:43:54 - mmengine - INFO - Epoch(val) [36][194/194] acc/top1: 0.6052 acc/top5: 0.8614 acc/mean1: 0.5460 2023/03/17 21:44:01 - mmengine - INFO - Epoch(train) [37][ 20/1320] lr: 2.0000e-03 eta: 1:43:29 time: 0.3700 data_time: 0.0398 memory: 18752 grad_norm: 5.8843 loss: 1.0058 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0058 2023/03/17 21:44:08 - mmengine - INFO - Epoch(train) [37][ 40/1320] lr: 2.0000e-03 eta: 1:43:22 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 6.2705 loss: 1.0120 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0120 2023/03/17 21:44:14 - mmengine - INFO - Epoch(train) [37][ 60/1320] lr: 2.0000e-03 eta: 1:43:15 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 6.1103 loss: 1.0302 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0302 2023/03/17 21:44:21 - mmengine - INFO - Epoch(train) [37][ 80/1320] lr: 2.0000e-03 eta: 1:43:08 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.0488 loss: 1.0743 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0743 2023/03/17 21:44:28 - mmengine - INFO - Epoch(train) [37][ 100/1320] lr: 2.0000e-03 eta: 1:43:02 time: 0.3368 data_time: 0.0132 memory: 18752 grad_norm: 6.2349 loss: 0.9110 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9110 2023/03/17 21:44:35 - mmengine - INFO - Epoch(train) [37][ 120/1320] lr: 2.0000e-03 eta: 1:42:55 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 6.1439 loss: 1.0588 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0588 2023/03/17 21:44:41 - mmengine - INFO - Epoch(train) [37][ 140/1320] lr: 2.0000e-03 eta: 1:42:48 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 6.3166 loss: 0.9742 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9742 2023/03/17 21:44:48 - mmengine - INFO - Epoch(train) [37][ 160/1320] lr: 2.0000e-03 eta: 1:42:42 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.1998 loss: 1.0848 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.0848 2023/03/17 21:44:55 - mmengine - INFO - Epoch(train) [37][ 180/1320] lr: 2.0000e-03 eta: 1:42:35 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 6.1445 loss: 0.9530 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9530 2023/03/17 21:45:02 - mmengine - INFO - Epoch(train) [37][ 200/1320] lr: 2.0000e-03 eta: 1:42:28 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 6.0993 loss: 0.8527 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8527 2023/03/17 21:45:08 - mmengine - INFO - Epoch(train) [37][ 220/1320] lr: 2.0000e-03 eta: 1:42:21 time: 0.3456 data_time: 0.0114 memory: 18752 grad_norm: 6.2790 loss: 1.0180 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0180 2023/03/17 21:45:15 - mmengine - INFO - Epoch(train) [37][ 240/1320] lr: 2.0000e-03 eta: 1:42:15 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 6.2425 loss: 1.1086 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1086 2023/03/17 21:45:22 - mmengine - INFO - Epoch(train) [37][ 260/1320] lr: 2.0000e-03 eta: 1:42:08 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 5.9123 loss: 1.0517 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0517 2023/03/17 21:45:29 - mmengine - INFO - Epoch(train) [37][ 280/1320] lr: 2.0000e-03 eta: 1:42:01 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 6.1673 loss: 0.9552 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9552 2023/03/17 21:45:35 - mmengine - INFO - Epoch(train) [37][ 300/1320] lr: 2.0000e-03 eta: 1:41:54 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 6.1025 loss: 0.9102 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9102 2023/03/17 21:45:42 - mmengine - INFO - Epoch(train) [37][ 320/1320] lr: 2.0000e-03 eta: 1:41:48 time: 0.3352 data_time: 0.0117 memory: 18752 grad_norm: 6.3003 loss: 0.9621 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9621 2023/03/17 21:45:49 - mmengine - INFO - Epoch(train) [37][ 340/1320] lr: 2.0000e-03 eta: 1:41:41 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 5.9874 loss: 0.8613 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8613 2023/03/17 21:45:55 - mmengine - INFO - Epoch(train) [37][ 360/1320] lr: 2.0000e-03 eta: 1:41:34 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 6.1632 loss: 0.9901 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9901 2023/03/17 21:46:02 - mmengine - INFO - Epoch(train) [37][ 380/1320] lr: 2.0000e-03 eta: 1:41:28 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 6.1116 loss: 0.8785 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8785 2023/03/17 21:46:09 - mmengine - INFO - Epoch(train) [37][ 400/1320] lr: 2.0000e-03 eta: 1:41:21 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 6.0341 loss: 1.0744 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0744 2023/03/17 21:46:16 - mmengine - INFO - Epoch(train) [37][ 420/1320] lr: 2.0000e-03 eta: 1:41:14 time: 0.3354 data_time: 0.0115 memory: 18752 grad_norm: 6.2348 loss: 1.0457 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0457 2023/03/17 21:46:22 - mmengine - INFO - Epoch(train) [37][ 440/1320] lr: 2.0000e-03 eta: 1:41:07 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 6.1486 loss: 1.0449 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0449 2023/03/17 21:46:29 - mmengine - INFO - Epoch(train) [37][ 460/1320] lr: 2.0000e-03 eta: 1:41:01 time: 0.3359 data_time: 0.0115 memory: 18752 grad_norm: 6.1427 loss: 1.2259 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2259 2023/03/17 21:46:36 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:46:36 - mmengine - INFO - Epoch(train) [37][ 480/1320] lr: 2.0000e-03 eta: 1:40:54 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 6.0364 loss: 0.8679 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8679 2023/03/17 21:46:42 - mmengine - INFO - Epoch(train) [37][ 500/1320] lr: 2.0000e-03 eta: 1:40:47 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 6.1472 loss: 1.0251 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0251 2023/03/17 21:46:49 - mmengine - INFO - Epoch(train) [37][ 520/1320] lr: 2.0000e-03 eta: 1:40:40 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 6.2704 loss: 1.1135 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1135 2023/03/17 21:46:56 - mmengine - INFO - Epoch(train) [37][ 540/1320] lr: 2.0000e-03 eta: 1:40:34 time: 0.3359 data_time: 0.0115 memory: 18752 grad_norm: 6.3510 loss: 1.0590 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0590 2023/03/17 21:47:03 - mmengine - INFO - Epoch(train) [37][ 560/1320] lr: 2.0000e-03 eta: 1:40:27 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 6.4513 loss: 1.0695 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0695 2023/03/17 21:47:09 - mmengine - INFO - Epoch(train) [37][ 580/1320] lr: 2.0000e-03 eta: 1:40:20 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 6.1600 loss: 0.9799 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9799 2023/03/17 21:47:16 - mmengine - INFO - Epoch(train) [37][ 600/1320] lr: 2.0000e-03 eta: 1:40:13 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 6.5052 loss: 1.0521 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0521 2023/03/17 21:47:23 - mmengine - INFO - Epoch(train) [37][ 620/1320] lr: 2.0000e-03 eta: 1:40:07 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 6.2478 loss: 1.0570 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0570 2023/03/17 21:47:29 - mmengine - INFO - Epoch(train) [37][ 640/1320] lr: 2.0000e-03 eta: 1:40:00 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 6.2717 loss: 0.9888 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9888 2023/03/17 21:47:36 - mmengine - INFO - Epoch(train) [37][ 660/1320] lr: 2.0000e-03 eta: 1:39:53 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 6.2628 loss: 1.1538 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1538 2023/03/17 21:47:43 - mmengine - INFO - Epoch(train) [37][ 680/1320] lr: 2.0000e-03 eta: 1:39:47 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 6.3011 loss: 1.1480 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1480 2023/03/17 21:47:50 - mmengine - INFO - Epoch(train) [37][ 700/1320] lr: 2.0000e-03 eta: 1:39:40 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 6.1017 loss: 1.0236 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0236 2023/03/17 21:47:56 - mmengine - INFO - Epoch(train) [37][ 720/1320] lr: 2.0000e-03 eta: 1:39:33 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 6.2854 loss: 1.0747 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.0747 2023/03/17 21:48:03 - mmengine - INFO - Epoch(train) [37][ 740/1320] lr: 2.0000e-03 eta: 1:39:26 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 6.7103 loss: 1.0908 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0908 2023/03/17 21:48:10 - mmengine - INFO - Epoch(train) [37][ 760/1320] lr: 2.0000e-03 eta: 1:39:20 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 6.2142 loss: 1.0271 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0271 2023/03/17 21:48:17 - mmengine - INFO - Epoch(train) [37][ 780/1320] lr: 2.0000e-03 eta: 1:39:13 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 6.3659 loss: 1.1923 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1923 2023/03/17 21:48:23 - mmengine - INFO - Epoch(train) [37][ 800/1320] lr: 2.0000e-03 eta: 1:39:06 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 6.4064 loss: 1.0785 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0785 2023/03/17 21:48:30 - mmengine - INFO - Epoch(train) [37][ 820/1320] lr: 2.0000e-03 eta: 1:38:59 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 6.3532 loss: 0.9289 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9289 2023/03/17 21:48:37 - mmengine - INFO - Epoch(train) [37][ 840/1320] lr: 2.0000e-03 eta: 1:38:53 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 6.2276 loss: 1.0531 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0531 2023/03/17 21:48:43 - mmengine - INFO - Epoch(train) [37][ 860/1320] lr: 2.0000e-03 eta: 1:38:46 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 6.3819 loss: 1.0515 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0515 2023/03/17 21:48:50 - mmengine - INFO - Epoch(train) [37][ 880/1320] lr: 2.0000e-03 eta: 1:38:39 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 6.3905 loss: 1.1265 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1265 2023/03/17 21:48:57 - mmengine - INFO - Epoch(train) [37][ 900/1320] lr: 2.0000e-03 eta: 1:38:33 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 6.2418 loss: 0.9610 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9610 2023/03/17 21:49:04 - mmengine - INFO - Epoch(train) [37][ 920/1320] lr: 2.0000e-03 eta: 1:38:26 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 6.2811 loss: 0.8553 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8553 2023/03/17 21:49:10 - mmengine - INFO - Epoch(train) [37][ 940/1320] lr: 2.0000e-03 eta: 1:38:19 time: 0.3373 data_time: 0.0121 memory: 18752 grad_norm: 6.1208 loss: 1.1783 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1783 2023/03/17 21:49:17 - mmengine - INFO - Epoch(train) [37][ 960/1320] lr: 2.0000e-03 eta: 1:38:12 time: 0.3370 data_time: 0.0123 memory: 18752 grad_norm: 6.4090 loss: 1.0049 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0049 2023/03/17 21:49:24 - mmengine - INFO - Epoch(train) [37][ 980/1320] lr: 2.0000e-03 eta: 1:38:06 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 6.3350 loss: 0.9976 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9976 2023/03/17 21:49:31 - mmengine - INFO - Epoch(train) [37][1000/1320] lr: 2.0000e-03 eta: 1:37:59 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 6.2217 loss: 1.0732 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0732 2023/03/17 21:49:37 - mmengine - INFO - Epoch(train) [37][1020/1320] lr: 2.0000e-03 eta: 1:37:52 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 6.2939 loss: 1.0829 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0829 2023/03/17 21:49:44 - mmengine - INFO - Epoch(train) [37][1040/1320] lr: 2.0000e-03 eta: 1:37:45 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 6.3438 loss: 0.9862 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 0.9862 2023/03/17 21:49:51 - mmengine - INFO - Epoch(train) [37][1060/1320] lr: 2.0000e-03 eta: 1:37:39 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 6.3923 loss: 0.9659 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9659 2023/03/17 21:49:57 - mmengine - INFO - Epoch(train) [37][1080/1320] lr: 2.0000e-03 eta: 1:37:32 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 6.1214 loss: 0.8557 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8557 2023/03/17 21:50:04 - mmengine - INFO - Epoch(train) [37][1100/1320] lr: 2.0000e-03 eta: 1:37:25 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 6.3460 loss: 0.9565 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9565 2023/03/17 21:50:11 - mmengine - INFO - Epoch(train) [37][1120/1320] lr: 2.0000e-03 eta: 1:37:19 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.1640 loss: 1.0135 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0135 2023/03/17 21:50:18 - mmengine - INFO - Epoch(train) [37][1140/1320] lr: 2.0000e-03 eta: 1:37:12 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 6.3512 loss: 1.1156 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1156 2023/03/17 21:50:24 - mmengine - INFO - Epoch(train) [37][1160/1320] lr: 2.0000e-03 eta: 1:37:05 time: 0.3360 data_time: 0.0132 memory: 18752 grad_norm: 6.2162 loss: 1.0565 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0565 2023/03/17 21:50:31 - mmengine - INFO - Epoch(train) [37][1180/1320] lr: 2.0000e-03 eta: 1:36:58 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 6.2099 loss: 1.0259 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0259 2023/03/17 21:50:38 - mmengine - INFO - Epoch(train) [37][1200/1320] lr: 2.0000e-03 eta: 1:36:52 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 6.2219 loss: 1.1293 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1293 2023/03/17 21:50:45 - mmengine - INFO - Epoch(train) [37][1220/1320] lr: 2.0000e-03 eta: 1:36:45 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 6.2071 loss: 1.0424 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0424 2023/03/17 21:50:51 - mmengine - INFO - Epoch(train) [37][1240/1320] lr: 2.0000e-03 eta: 1:36:38 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 6.2073 loss: 0.9302 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9302 2023/03/17 21:50:58 - mmengine - INFO - Epoch(train) [37][1260/1320] lr: 2.0000e-03 eta: 1:36:31 time: 0.3363 data_time: 0.0128 memory: 18752 grad_norm: 6.1517 loss: 1.1462 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1462 2023/03/17 21:51:05 - mmengine - INFO - Epoch(train) [37][1280/1320] lr: 2.0000e-03 eta: 1:36:25 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 6.3200 loss: 1.0322 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0322 2023/03/17 21:51:11 - mmengine - INFO - Epoch(train) [37][1300/1320] lr: 2.0000e-03 eta: 1:36:18 time: 0.3367 data_time: 0.0129 memory: 18752 grad_norm: 6.0985 loss: 0.9834 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9834 2023/03/17 21:51:18 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:51:18 - mmengine - INFO - Epoch(train) [37][1320/1320] lr: 2.0000e-03 eta: 1:36:11 time: 0.3308 data_time: 0.0130 memory: 18752 grad_norm: 6.5816 loss: 1.2367 top1_acc: 0.7273 top5_acc: 0.8182 loss_cls: 1.2367 2023/03/17 21:51:21 - mmengine - INFO - Epoch(val) [37][ 20/194] eta: 0:00:22 time: 0.1289 data_time: 0.0423 memory: 2112 2023/03/17 21:51:23 - mmengine - INFO - Epoch(val) [37][ 40/194] eta: 0:00:17 time: 0.0961 data_time: 0.0103 memory: 2112 2023/03/17 21:51:25 - mmengine - INFO - Epoch(val) [37][ 60/194] eta: 0:00:14 time: 0.0972 data_time: 0.0111 memory: 2112 2023/03/17 21:51:26 - mmengine - INFO - Epoch(val) [37][ 80/194] eta: 0:00:11 time: 0.0983 data_time: 0.0122 memory: 2112 2023/03/17 21:51:28 - mmengine - INFO - Epoch(val) [37][100/194] eta: 0:00:09 time: 0.0976 data_time: 0.0114 memory: 2112 2023/03/17 21:51:30 - mmengine - INFO - Epoch(val) [37][120/194] eta: 0:00:07 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 21:51:32 - mmengine - INFO - Epoch(val) [37][140/194] eta: 0:00:05 time: 0.0961 data_time: 0.0103 memory: 2112 2023/03/17 21:51:34 - mmengine - INFO - Epoch(val) [37][160/194] eta: 0:00:03 time: 0.0972 data_time: 0.0109 memory: 2112 2023/03/17 21:51:36 - mmengine - INFO - Epoch(val) [37][180/194] eta: 0:00:01 time: 0.0983 data_time: 0.0117 memory: 2112 2023/03/17 21:51:39 - mmengine - INFO - Epoch(val) [37][194/194] acc/top1: 0.6036 acc/top5: 0.8630 acc/mean1: 0.5437 2023/03/17 21:51:47 - mmengine - INFO - Epoch(train) [38][ 20/1320] lr: 2.0000e-03 eta: 1:36:05 time: 0.3717 data_time: 0.0413 memory: 18752 grad_norm: 6.2735 loss: 1.1275 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1275 2023/03/17 21:51:54 - mmengine - INFO - Epoch(train) [38][ 40/1320] lr: 2.0000e-03 eta: 1:35:58 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 6.1628 loss: 1.0666 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0666 2023/03/17 21:52:00 - mmengine - INFO - Epoch(train) [38][ 60/1320] lr: 2.0000e-03 eta: 1:35:51 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 6.1513 loss: 1.1186 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1186 2023/03/17 21:52:07 - mmengine - INFO - Epoch(train) [38][ 80/1320] lr: 2.0000e-03 eta: 1:35:45 time: 0.3367 data_time: 0.0124 memory: 18752 grad_norm: 6.2297 loss: 1.0361 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0361 2023/03/17 21:52:14 - mmengine - INFO - Epoch(train) [38][ 100/1320] lr: 2.0000e-03 eta: 1:35:38 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 6.4013 loss: 0.9981 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9981 2023/03/17 21:52:20 - mmengine - INFO - Epoch(train) [38][ 120/1320] lr: 2.0000e-03 eta: 1:35:31 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 6.2032 loss: 1.0962 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0962 2023/03/17 21:52:27 - mmengine - INFO - Epoch(train) [38][ 140/1320] lr: 2.0000e-03 eta: 1:35:24 time: 0.3360 data_time: 0.0114 memory: 18752 grad_norm: 5.9337 loss: 0.8764 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8764 2023/03/17 21:52:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:52:34 - mmengine - INFO - Epoch(train) [38][ 160/1320] lr: 2.0000e-03 eta: 1:35:18 time: 0.3364 data_time: 0.0115 memory: 18752 grad_norm: 6.1015 loss: 1.0029 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0029 2023/03/17 21:52:41 - mmengine - INFO - Epoch(train) [38][ 180/1320] lr: 2.0000e-03 eta: 1:35:11 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 6.4128 loss: 0.9447 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9447 2023/03/17 21:52:47 - mmengine - INFO - Epoch(train) [38][ 200/1320] lr: 2.0000e-03 eta: 1:35:04 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 6.5157 loss: 1.0072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0072 2023/03/17 21:52:54 - mmengine - INFO - Epoch(train) [38][ 220/1320] lr: 2.0000e-03 eta: 1:34:57 time: 0.3368 data_time: 0.0116 memory: 18752 grad_norm: 6.4015 loss: 1.2013 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2013 2023/03/17 21:53:01 - mmengine - INFO - Epoch(train) [38][ 240/1320] lr: 2.0000e-03 eta: 1:34:51 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 6.3042 loss: 1.0761 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0761 2023/03/17 21:53:08 - mmengine - INFO - Epoch(train) [38][ 260/1320] lr: 2.0000e-03 eta: 1:34:44 time: 0.3366 data_time: 0.0124 memory: 18752 grad_norm: 6.3382 loss: 1.1181 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1181 2023/03/17 21:53:14 - mmengine - INFO - Epoch(train) [38][ 280/1320] lr: 2.0000e-03 eta: 1:34:37 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 6.3145 loss: 0.9948 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9948 2023/03/17 21:53:21 - mmengine - INFO - Epoch(train) [38][ 300/1320] lr: 2.0000e-03 eta: 1:34:31 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 6.1516 loss: 0.9302 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9302 2023/03/17 21:53:28 - mmengine - INFO - Epoch(train) [38][ 320/1320] lr: 2.0000e-03 eta: 1:34:24 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 6.4273 loss: 1.0936 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0936 2023/03/17 21:53:34 - mmengine - INFO - Epoch(train) [38][ 340/1320] lr: 2.0000e-03 eta: 1:34:17 time: 0.3362 data_time: 0.0128 memory: 18752 grad_norm: 6.3668 loss: 1.1212 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1212 2023/03/17 21:53:41 - mmengine - INFO - Epoch(train) [38][ 360/1320] lr: 2.0000e-03 eta: 1:34:10 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 6.5407 loss: 1.0146 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0146 2023/03/17 21:53:48 - mmengine - INFO - Epoch(train) [38][ 380/1320] lr: 2.0000e-03 eta: 1:34:04 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 6.3015 loss: 0.9350 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9350 2023/03/17 21:53:55 - mmengine - INFO - Epoch(train) [38][ 400/1320] lr: 2.0000e-03 eta: 1:33:57 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 6.4316 loss: 1.1399 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1399 2023/03/17 21:54:01 - mmengine - INFO - Epoch(train) [38][ 420/1320] lr: 2.0000e-03 eta: 1:33:50 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 6.4784 loss: 1.0379 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0379 2023/03/17 21:54:08 - mmengine - INFO - Epoch(train) [38][ 440/1320] lr: 2.0000e-03 eta: 1:33:43 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 6.2899 loss: 1.0857 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0857 2023/03/17 21:54:15 - mmengine - INFO - Epoch(train) [38][ 460/1320] lr: 2.0000e-03 eta: 1:33:37 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 6.4085 loss: 1.0748 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0748 2023/03/17 21:54:21 - mmengine - INFO - Epoch(train) [38][ 480/1320] lr: 2.0000e-03 eta: 1:33:30 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.4287 loss: 1.0218 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0218 2023/03/17 21:54:28 - mmengine - INFO - Epoch(train) [38][ 500/1320] lr: 2.0000e-03 eta: 1:33:23 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 6.3565 loss: 1.1181 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1181 2023/03/17 21:54:35 - mmengine - INFO - Epoch(train) [38][ 520/1320] lr: 2.0000e-03 eta: 1:33:17 time: 0.3360 data_time: 0.0127 memory: 18752 grad_norm: 6.6453 loss: 1.0746 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0746 2023/03/17 21:54:42 - mmengine - INFO - Epoch(train) [38][ 540/1320] lr: 2.0000e-03 eta: 1:33:10 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 6.5899 loss: 1.0665 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0665 2023/03/17 21:54:48 - mmengine - INFO - Epoch(train) [38][ 560/1320] lr: 2.0000e-03 eta: 1:33:03 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 6.4697 loss: 1.1275 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1275 2023/03/17 21:54:55 - mmengine - INFO - Epoch(train) [38][ 580/1320] lr: 2.0000e-03 eta: 1:32:56 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 6.2843 loss: 1.0277 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0277 2023/03/17 21:55:02 - mmengine - INFO - Epoch(train) [38][ 600/1320] lr: 2.0000e-03 eta: 1:32:50 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 6.3486 loss: 1.0295 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0295 2023/03/17 21:55:08 - mmengine - INFO - Epoch(train) [38][ 620/1320] lr: 2.0000e-03 eta: 1:32:43 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 6.5010 loss: 0.9702 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9702 2023/03/17 21:55:15 - mmengine - INFO - Epoch(train) [38][ 640/1320] lr: 2.0000e-03 eta: 1:32:36 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 6.5284 loss: 0.9939 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9939 2023/03/17 21:55:22 - mmengine - INFO - Epoch(train) [38][ 660/1320] lr: 2.0000e-03 eta: 1:32:29 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 6.3921 loss: 1.1845 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1845 2023/03/17 21:55:29 - mmengine - INFO - Epoch(train) [38][ 680/1320] lr: 2.0000e-03 eta: 1:32:23 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 6.6301 loss: 0.9415 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9415 2023/03/17 21:55:35 - mmengine - INFO - Epoch(train) [38][ 700/1320] lr: 2.0000e-03 eta: 1:32:16 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 6.4449 loss: 1.1498 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1498 2023/03/17 21:55:42 - mmengine - INFO - Epoch(train) [38][ 720/1320] lr: 2.0000e-03 eta: 1:32:09 time: 0.3354 data_time: 0.0130 memory: 18752 grad_norm: 6.5240 loss: 1.0075 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.0075 2023/03/17 21:55:49 - mmengine - INFO - Epoch(train) [38][ 740/1320] lr: 2.0000e-03 eta: 1:32:02 time: 0.3362 data_time: 0.0130 memory: 18752 grad_norm: 6.2834 loss: 1.0250 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0250 2023/03/17 21:55:55 - mmengine - INFO - Epoch(train) [38][ 760/1320] lr: 2.0000e-03 eta: 1:31:56 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 6.4895 loss: 0.9771 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9771 2023/03/17 21:56:02 - mmengine - INFO - Epoch(train) [38][ 780/1320] lr: 2.0000e-03 eta: 1:31:49 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 6.4539 loss: 1.0488 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0488 2023/03/17 21:56:09 - mmengine - INFO - Epoch(train) [38][ 800/1320] lr: 2.0000e-03 eta: 1:31:42 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 6.3706 loss: 1.0057 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0057 2023/03/17 21:56:16 - mmengine - INFO - Epoch(train) [38][ 820/1320] lr: 2.0000e-03 eta: 1:31:36 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 6.4672 loss: 0.9088 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9088 2023/03/17 21:56:22 - mmengine - INFO - Epoch(train) [38][ 840/1320] lr: 2.0000e-03 eta: 1:31:29 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 6.5198 loss: 0.8985 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8985 2023/03/17 21:56:29 - mmengine - INFO - Epoch(train) [38][ 860/1320] lr: 2.0000e-03 eta: 1:31:22 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 6.4105 loss: 1.0281 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0281 2023/03/17 21:56:36 - mmengine - INFO - Epoch(train) [38][ 880/1320] lr: 2.0000e-03 eta: 1:31:15 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 6.5844 loss: 1.1269 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1269 2023/03/17 21:56:43 - mmengine - INFO - Epoch(train) [38][ 900/1320] lr: 2.0000e-03 eta: 1:31:09 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 6.1793 loss: 1.0097 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0097 2023/03/17 21:56:49 - mmengine - INFO - Epoch(train) [38][ 920/1320] lr: 2.0000e-03 eta: 1:31:02 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 6.3848 loss: 0.9289 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9289 2023/03/17 21:56:56 - mmengine - INFO - Epoch(train) [38][ 940/1320] lr: 2.0000e-03 eta: 1:30:55 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 6.4349 loss: 1.2781 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.2781 2023/03/17 21:57:03 - mmengine - INFO - Epoch(train) [38][ 960/1320] lr: 2.0000e-03 eta: 1:30:48 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 6.6044 loss: 0.9734 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9734 2023/03/17 21:57:09 - mmengine - INFO - Epoch(train) [38][ 980/1320] lr: 2.0000e-03 eta: 1:30:42 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 6.4337 loss: 1.1348 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1348 2023/03/17 21:57:16 - mmengine - INFO - Epoch(train) [38][1000/1320] lr: 2.0000e-03 eta: 1:30:35 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 6.3150 loss: 0.9784 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9784 2023/03/17 21:57:23 - mmengine - INFO - Epoch(train) [38][1020/1320] lr: 2.0000e-03 eta: 1:30:28 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 6.4339 loss: 0.9556 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9556 2023/03/17 21:57:30 - mmengine - INFO - Epoch(train) [38][1040/1320] lr: 2.0000e-03 eta: 1:30:22 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.3784 loss: 0.9063 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9063 2023/03/17 21:57:36 - mmengine - INFO - Epoch(train) [38][1060/1320] lr: 2.0000e-03 eta: 1:30:15 time: 0.3369 data_time: 0.0119 memory: 18752 grad_norm: 6.2312 loss: 1.0560 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.0560 2023/03/17 21:57:43 - mmengine - INFO - Epoch(train) [38][1080/1320] lr: 2.0000e-03 eta: 1:30:08 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 6.4861 loss: 1.1054 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1054 2023/03/17 21:57:50 - mmengine - INFO - Epoch(train) [38][1100/1320] lr: 2.0000e-03 eta: 1:30:01 time: 0.3365 data_time: 0.0117 memory: 18752 grad_norm: 6.3067 loss: 0.9835 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9835 2023/03/17 21:57:57 - mmengine - INFO - Epoch(train) [38][1120/1320] lr: 2.0000e-03 eta: 1:29:55 time: 0.3356 data_time: 0.0119 memory: 18752 grad_norm: 6.4503 loss: 1.1180 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1180 2023/03/17 21:58:03 - mmengine - INFO - Epoch(train) [38][1140/1320] lr: 2.0000e-03 eta: 1:29:48 time: 0.3363 data_time: 0.0114 memory: 18752 grad_norm: 6.4489 loss: 1.1132 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1132 2023/03/17 21:58:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:58:10 - mmengine - INFO - Epoch(train) [38][1160/1320] lr: 2.0000e-03 eta: 1:29:41 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 6.6831 loss: 1.0436 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0436 2023/03/17 21:58:17 - mmengine - INFO - Epoch(train) [38][1180/1320] lr: 2.0000e-03 eta: 1:29:34 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 6.3307 loss: 1.0759 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0759 2023/03/17 21:58:23 - mmengine - INFO - Epoch(train) [38][1200/1320] lr: 2.0000e-03 eta: 1:29:28 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 6.5475 loss: 0.9277 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9277 2023/03/17 21:58:30 - mmengine - INFO - Epoch(train) [38][1220/1320] lr: 2.0000e-03 eta: 1:29:21 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 6.5233 loss: 0.8919 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8919 2023/03/17 21:58:37 - mmengine - INFO - Epoch(train) [38][1240/1320] lr: 2.0000e-03 eta: 1:29:14 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 6.3662 loss: 0.9814 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9814 2023/03/17 21:58:44 - mmengine - INFO - Epoch(train) [38][1260/1320] lr: 2.0000e-03 eta: 1:29:08 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 6.6026 loss: 1.3024 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3024 2023/03/17 21:58:50 - mmengine - INFO - Epoch(train) [38][1280/1320] lr: 2.0000e-03 eta: 1:29:01 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 6.6988 loss: 1.0117 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0117 2023/03/17 21:58:57 - mmengine - INFO - Epoch(train) [38][1300/1320] lr: 2.0000e-03 eta: 1:28:54 time: 0.3368 data_time: 0.0109 memory: 18752 grad_norm: 6.7289 loss: 0.9938 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9938 2023/03/17 21:59:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 21:59:04 - mmengine - INFO - Epoch(train) [38][1320/1320] lr: 2.0000e-03 eta: 1:28:47 time: 0.3319 data_time: 0.0132 memory: 18752 grad_norm: 6.3738 loss: 1.0942 top1_acc: 0.9091 top5_acc: 0.9091 loss_cls: 1.0942 2023/03/17 21:59:06 - mmengine - INFO - Epoch(val) [38][ 20/194] eta: 0:00:22 time: 0.1273 data_time: 0.0409 memory: 2112 2023/03/17 21:59:08 - mmengine - INFO - Epoch(val) [38][ 40/194] eta: 0:00:17 time: 0.0963 data_time: 0.0104 memory: 2112 2023/03/17 21:59:10 - mmengine - INFO - Epoch(val) [38][ 60/194] eta: 0:00:14 time: 0.0973 data_time: 0.0113 memory: 2112 2023/03/17 21:59:12 - mmengine - INFO - Epoch(val) [38][ 80/194] eta: 0:00:11 time: 0.0970 data_time: 0.0112 memory: 2112 2023/03/17 21:59:14 - mmengine - INFO - Epoch(val) [38][100/194] eta: 0:00:09 time: 0.0974 data_time: 0.0114 memory: 2112 2023/03/17 21:59:16 - mmengine - INFO - Epoch(val) [38][120/194] eta: 0:00:07 time: 0.0969 data_time: 0.0110 memory: 2112 2023/03/17 21:59:18 - mmengine - INFO - Epoch(val) [38][140/194] eta: 0:00:05 time: 0.0973 data_time: 0.0114 memory: 2112 2023/03/17 21:59:20 - mmengine - INFO - Epoch(val) [38][160/194] eta: 0:00:03 time: 0.0971 data_time: 0.0110 memory: 2112 2023/03/17 21:59:22 - mmengine - INFO - Epoch(val) [38][180/194] eta: 0:00:01 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 21:59:25 - mmengine - INFO - Epoch(val) [38][194/194] acc/top1: 0.6004 acc/top5: 0.8615 acc/mean1: 0.5390 2023/03/17 21:59:33 - mmengine - INFO - Epoch(train) [39][ 20/1320] lr: 2.0000e-03 eta: 1:28:41 time: 0.3756 data_time: 0.0418 memory: 18752 grad_norm: 6.4400 loss: 1.0560 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0560 2023/03/17 21:59:39 - mmengine - INFO - Epoch(train) [39][ 40/1320] lr: 2.0000e-03 eta: 1:28:34 time: 0.3367 data_time: 0.0119 memory: 18752 grad_norm: 6.3791 loss: 0.9278 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9278 2023/03/17 21:59:46 - mmengine - INFO - Epoch(train) [39][ 60/1320] lr: 2.0000e-03 eta: 1:28:27 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 6.4732 loss: 0.9050 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9050 2023/03/17 21:59:53 - mmengine - INFO - Epoch(train) [39][ 80/1320] lr: 2.0000e-03 eta: 1:28:21 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 6.3722 loss: 1.0248 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0248 2023/03/17 22:00:00 - mmengine - INFO - Epoch(train) [39][ 100/1320] lr: 2.0000e-03 eta: 1:28:14 time: 0.3367 data_time: 0.0136 memory: 18752 grad_norm: 6.2621 loss: 0.9082 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9082 2023/03/17 22:00:06 - mmengine - INFO - Epoch(train) [39][ 120/1320] lr: 2.0000e-03 eta: 1:28:07 time: 0.3366 data_time: 0.0130 memory: 18752 grad_norm: 6.3556 loss: 0.9410 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9410 2023/03/17 22:00:13 - mmengine - INFO - Epoch(train) [39][ 140/1320] lr: 2.0000e-03 eta: 1:28:00 time: 0.3367 data_time: 0.0131 memory: 18752 grad_norm: 6.5859 loss: 1.1638 top1_acc: 0.4375 top5_acc: 1.0000 loss_cls: 1.1638 2023/03/17 22:00:20 - mmengine - INFO - Epoch(train) [39][ 160/1320] lr: 2.0000e-03 eta: 1:27:54 time: 0.3360 data_time: 0.0127 memory: 18752 grad_norm: 6.4456 loss: 0.9986 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9986 2023/03/17 22:00:27 - mmengine - INFO - Epoch(train) [39][ 180/1320] lr: 2.0000e-03 eta: 1:27:47 time: 0.3369 data_time: 0.0127 memory: 18752 grad_norm: 6.4062 loss: 0.9391 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9391 2023/03/17 22:00:33 - mmengine - INFO - Epoch(train) [39][ 200/1320] lr: 2.0000e-03 eta: 1:27:40 time: 0.3369 data_time: 0.0131 memory: 18752 grad_norm: 6.3839 loss: 0.9960 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9960 2023/03/17 22:00:40 - mmengine - INFO - Epoch(train) [39][ 220/1320] lr: 2.0000e-03 eta: 1:27:34 time: 0.3365 data_time: 0.0130 memory: 18752 grad_norm: 6.4099 loss: 0.8181 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8181 2023/03/17 22:00:47 - mmengine - INFO - Epoch(train) [39][ 240/1320] lr: 2.0000e-03 eta: 1:27:27 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 6.5636 loss: 0.9952 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9952 2023/03/17 22:00:53 - mmengine - INFO - Epoch(train) [39][ 260/1320] lr: 2.0000e-03 eta: 1:27:20 time: 0.3368 data_time: 0.0117 memory: 18752 grad_norm: 6.5699 loss: 1.0442 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0442 2023/03/17 22:01:00 - mmengine - INFO - Epoch(train) [39][ 280/1320] lr: 2.0000e-03 eta: 1:27:13 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 6.6393 loss: 1.0356 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0356 2023/03/17 22:01:07 - mmengine - INFO - Epoch(train) [39][ 300/1320] lr: 2.0000e-03 eta: 1:27:07 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 6.4841 loss: 0.8162 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8162 2023/03/17 22:01:14 - mmengine - INFO - Epoch(train) [39][ 320/1320] lr: 2.0000e-03 eta: 1:27:00 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 6.4076 loss: 0.9625 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9625 2023/03/17 22:01:20 - mmengine - INFO - Epoch(train) [39][ 340/1320] lr: 2.0000e-03 eta: 1:26:53 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 6.6428 loss: 1.1089 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1089 2023/03/17 22:01:27 - mmengine - INFO - Epoch(train) [39][ 360/1320] lr: 2.0000e-03 eta: 1:26:46 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 6.6691 loss: 0.9306 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9306 2023/03/17 22:01:34 - mmengine - INFO - Epoch(train) [39][ 380/1320] lr: 2.0000e-03 eta: 1:26:40 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 6.6099 loss: 1.0975 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0975 2023/03/17 22:01:41 - mmengine - INFO - Epoch(train) [39][ 400/1320] lr: 2.0000e-03 eta: 1:26:33 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 6.5519 loss: 1.0584 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0584 2023/03/17 22:01:47 - mmengine - INFO - Epoch(train) [39][ 420/1320] lr: 2.0000e-03 eta: 1:26:26 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 6.3979 loss: 0.7159 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7159 2023/03/17 22:01:54 - mmengine - INFO - Epoch(train) [39][ 440/1320] lr: 2.0000e-03 eta: 1:26:20 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 6.5518 loss: 1.0836 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0836 2023/03/17 22:02:01 - mmengine - INFO - Epoch(train) [39][ 460/1320] lr: 2.0000e-03 eta: 1:26:13 time: 0.3365 data_time: 0.0124 memory: 18752 grad_norm: 6.3634 loss: 1.0713 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0713 2023/03/17 22:02:07 - mmengine - INFO - Epoch(train) [39][ 480/1320] lr: 2.0000e-03 eta: 1:26:06 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 6.6275 loss: 1.0591 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0591 2023/03/17 22:02:14 - mmengine - INFO - Epoch(train) [39][ 500/1320] lr: 2.0000e-03 eta: 1:25:59 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 6.6562 loss: 0.9725 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9725 2023/03/17 22:02:21 - mmengine - INFO - Epoch(train) [39][ 520/1320] lr: 2.0000e-03 eta: 1:25:53 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 6.4769 loss: 1.0261 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0261 2023/03/17 22:02:28 - mmengine - INFO - Epoch(train) [39][ 540/1320] lr: 2.0000e-03 eta: 1:25:46 time: 0.3360 data_time: 0.0127 memory: 18752 grad_norm: 6.2851 loss: 0.8760 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8760 2023/03/17 22:02:34 - mmengine - INFO - Epoch(train) [39][ 560/1320] lr: 2.0000e-03 eta: 1:25:39 time: 0.3360 data_time: 0.0126 memory: 18752 grad_norm: 6.7398 loss: 1.1923 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1923 2023/03/17 22:02:41 - mmengine - INFO - Epoch(train) [39][ 580/1320] lr: 2.0000e-03 eta: 1:25:32 time: 0.3361 data_time: 0.0128 memory: 18752 grad_norm: 6.6604 loss: 1.1023 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1023 2023/03/17 22:02:48 - mmengine - INFO - Epoch(train) [39][ 600/1320] lr: 2.0000e-03 eta: 1:25:26 time: 0.3355 data_time: 0.0126 memory: 18752 grad_norm: 6.6181 loss: 1.0160 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0160 2023/03/17 22:02:55 - mmengine - INFO - Epoch(train) [39][ 620/1320] lr: 2.0000e-03 eta: 1:25:19 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 6.5850 loss: 1.0622 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0622 2023/03/17 22:03:01 - mmengine - INFO - Epoch(train) [39][ 640/1320] lr: 2.0000e-03 eta: 1:25:12 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 6.4644 loss: 0.9776 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9776 2023/03/17 22:03:08 - mmengine - INFO - Epoch(train) [39][ 660/1320] lr: 2.0000e-03 eta: 1:25:06 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 6.5336 loss: 1.0429 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0429 2023/03/17 22:03:15 - mmengine - INFO - Epoch(train) [39][ 680/1320] lr: 2.0000e-03 eta: 1:24:59 time: 0.3362 data_time: 0.0124 memory: 18752 grad_norm: 6.5379 loss: 1.0193 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0193 2023/03/17 22:03:21 - mmengine - INFO - Epoch(train) [39][ 700/1320] lr: 2.0000e-03 eta: 1:24:52 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 6.6808 loss: 1.0103 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0103 2023/03/17 22:03:28 - mmengine - INFO - Epoch(train) [39][ 720/1320] lr: 2.0000e-03 eta: 1:24:45 time: 0.3356 data_time: 0.0127 memory: 18752 grad_norm: 6.6963 loss: 1.0307 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0307 2023/03/17 22:03:35 - mmengine - INFO - Epoch(train) [39][ 740/1320] lr: 2.0000e-03 eta: 1:24:39 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 6.7586 loss: 0.9639 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9639 2023/03/17 22:03:42 - mmengine - INFO - Epoch(train) [39][ 760/1320] lr: 2.0000e-03 eta: 1:24:32 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 6.6962 loss: 0.8924 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8924 2023/03/17 22:03:48 - mmengine - INFO - Epoch(train) [39][ 780/1320] lr: 2.0000e-03 eta: 1:24:25 time: 0.3370 data_time: 0.0122 memory: 18752 grad_norm: 6.6977 loss: 1.0237 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0237 2023/03/17 22:03:55 - mmengine - INFO - Epoch(train) [39][ 800/1320] lr: 2.0000e-03 eta: 1:24:18 time: 0.3356 data_time: 0.0124 memory: 18752 grad_norm: 6.5551 loss: 0.9800 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9800 2023/03/17 22:04:02 - mmengine - INFO - Epoch(train) [39][ 820/1320] lr: 2.0000e-03 eta: 1:24:12 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 6.4497 loss: 1.0485 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0485 2023/03/17 22:04:08 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:04:08 - mmengine - INFO - Epoch(train) [39][ 840/1320] lr: 2.0000e-03 eta: 1:24:05 time: 0.3365 data_time: 0.0125 memory: 18752 grad_norm: 6.8089 loss: 0.9870 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9870 2023/03/17 22:04:15 - mmengine - INFO - Epoch(train) [39][ 860/1320] lr: 2.0000e-03 eta: 1:23:58 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 6.7054 loss: 1.0055 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0055 2023/03/17 22:04:22 - mmengine - INFO - Epoch(train) [39][ 880/1320] lr: 2.0000e-03 eta: 1:23:52 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 6.6026 loss: 1.0363 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0363 2023/03/17 22:04:29 - mmengine - INFO - Epoch(train) [39][ 900/1320] lr: 2.0000e-03 eta: 1:23:45 time: 0.3607 data_time: 0.0200 memory: 18752 grad_norm: 6.4951 loss: 1.0478 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0478 2023/03/17 22:04:36 - mmengine - INFO - Epoch(train) [39][ 920/1320] lr: 2.0000e-03 eta: 1:23:38 time: 0.3386 data_time: 0.0111 memory: 18752 grad_norm: 6.4824 loss: 0.9947 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9947 2023/03/17 22:04:43 - mmengine - INFO - Epoch(train) [39][ 940/1320] lr: 2.0000e-03 eta: 1:23:32 time: 0.3365 data_time: 0.0127 memory: 18752 grad_norm: 6.7187 loss: 1.1718 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1718 2023/03/17 22:04:49 - mmengine - INFO - Epoch(train) [39][ 960/1320] lr: 2.0000e-03 eta: 1:23:25 time: 0.3359 data_time: 0.0126 memory: 18752 grad_norm: 6.8313 loss: 0.9035 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9035 2023/03/17 22:04:56 - mmengine - INFO - Epoch(train) [39][ 980/1320] lr: 2.0000e-03 eta: 1:23:18 time: 0.3362 data_time: 0.0129 memory: 18752 grad_norm: 6.7012 loss: 0.9256 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9256 2023/03/17 22:05:03 - mmengine - INFO - Epoch(train) [39][1000/1320] lr: 2.0000e-03 eta: 1:23:11 time: 0.3360 data_time: 0.0128 memory: 18752 grad_norm: 6.7795 loss: 1.0074 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0074 2023/03/17 22:05:10 - mmengine - INFO - Epoch(train) [39][1020/1320] lr: 2.0000e-03 eta: 1:23:05 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 6.6896 loss: 1.0370 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0370 2023/03/17 22:05:16 - mmengine - INFO - Epoch(train) [39][1040/1320] lr: 2.0000e-03 eta: 1:22:58 time: 0.3360 data_time: 0.0133 memory: 18752 grad_norm: 6.4797 loss: 1.0742 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0742 2023/03/17 22:05:23 - mmengine - INFO - Epoch(train) [39][1060/1320] lr: 2.0000e-03 eta: 1:22:51 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 6.7571 loss: 1.1291 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1291 2023/03/17 22:05:30 - mmengine - INFO - Epoch(train) [39][1080/1320] lr: 2.0000e-03 eta: 1:22:44 time: 0.3355 data_time: 0.0128 memory: 18752 grad_norm: 6.5946 loss: 0.9787 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9787 2023/03/17 22:05:36 - mmengine - INFO - Epoch(train) [39][1100/1320] lr: 2.0000e-03 eta: 1:22:38 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 6.6165 loss: 0.9032 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9032 2023/03/17 22:05:43 - mmengine - INFO - Epoch(train) [39][1120/1320] lr: 2.0000e-03 eta: 1:22:31 time: 0.3356 data_time: 0.0125 memory: 18752 grad_norm: 6.5844 loss: 0.9507 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9507 2023/03/17 22:05:50 - mmengine - INFO - Epoch(train) [39][1140/1320] lr: 2.0000e-03 eta: 1:22:24 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 6.6966 loss: 1.0324 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0324 2023/03/17 22:05:57 - mmengine - INFO - Epoch(train) [39][1160/1320] lr: 2.0000e-03 eta: 1:22:17 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 6.7398 loss: 0.9067 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9067 2023/03/17 22:06:03 - mmengine - INFO - Epoch(train) [39][1180/1320] lr: 2.0000e-03 eta: 1:22:11 time: 0.3366 data_time: 0.0124 memory: 18752 grad_norm: 6.6962 loss: 1.0065 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0065 2023/03/17 22:06:10 - mmengine - INFO - Epoch(train) [39][1200/1320] lr: 2.0000e-03 eta: 1:22:04 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 6.6302 loss: 0.9384 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9384 2023/03/17 22:06:17 - mmengine - INFO - Epoch(train) [39][1220/1320] lr: 2.0000e-03 eta: 1:21:57 time: 0.3364 data_time: 0.0126 memory: 18752 grad_norm: 6.7536 loss: 0.8945 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8945 2023/03/17 22:06:23 - mmengine - INFO - Epoch(train) [39][1240/1320] lr: 2.0000e-03 eta: 1:21:51 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 6.8377 loss: 1.0993 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0993 2023/03/17 22:06:30 - mmengine - INFO - Epoch(train) [39][1260/1320] lr: 2.0000e-03 eta: 1:21:44 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 6.4777 loss: 0.9945 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9945 2023/03/17 22:06:37 - mmengine - INFO - Epoch(train) [39][1280/1320] lr: 2.0000e-03 eta: 1:21:37 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 6.7621 loss: 1.1734 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1734 2023/03/17 22:06:44 - mmengine - INFO - Epoch(train) [39][1300/1320] lr: 2.0000e-03 eta: 1:21:30 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 6.7575 loss: 0.9684 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9684 2023/03/17 22:06:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:06:50 - mmengine - INFO - Epoch(train) [39][1320/1320] lr: 2.0000e-03 eta: 1:21:24 time: 0.3306 data_time: 0.0122 memory: 18752 grad_norm: 6.8990 loss: 0.9754 top1_acc: 0.9091 top5_acc: 1.0000 loss_cls: 0.9754 2023/03/17 22:06:50 - mmengine - INFO - Saving checkpoint at 39 epochs 2023/03/17 22:06:55 - mmengine - INFO - Epoch(val) [39][ 20/194] eta: 0:00:21 time: 0.1236 data_time: 0.0368 memory: 2112 2023/03/17 22:06:57 - mmengine - INFO - Epoch(val) [39][ 40/194] eta: 0:00:16 time: 0.0942 data_time: 0.0086 memory: 2112 2023/03/17 22:06:59 - mmengine - INFO - Epoch(val) [39][ 60/194] eta: 0:00:14 time: 0.0973 data_time: 0.0112 memory: 2112 2023/03/17 22:07:01 - mmengine - INFO - Epoch(val) [39][ 80/194] eta: 0:00:11 time: 0.0971 data_time: 0.0111 memory: 2112 2023/03/17 22:07:03 - mmengine - INFO - Epoch(val) [39][100/194] eta: 0:00:09 time: 0.0967 data_time: 0.0111 memory: 2112 2023/03/17 22:07:05 - mmengine - INFO - Epoch(val) [39][120/194] eta: 0:00:07 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 22:07:07 - mmengine - INFO - Epoch(val) [39][140/194] eta: 0:00:05 time: 0.0967 data_time: 0.0109 memory: 2112 2023/03/17 22:07:09 - mmengine - INFO - Epoch(val) [39][160/194] eta: 0:00:03 time: 0.0972 data_time: 0.0111 memory: 2112 2023/03/17 22:07:11 - mmengine - INFO - Epoch(val) [39][180/194] eta: 0:00:01 time: 0.0971 data_time: 0.0109 memory: 2112 2023/03/17 22:07:16 - mmengine - INFO - Epoch(val) [39][194/194] acc/top1: 0.6029 acc/top5: 0.8628 acc/mean1: 0.5410 2023/03/17 22:07:23 - mmengine - INFO - Epoch(train) [40][ 20/1320] lr: 2.0000e-03 eta: 1:21:17 time: 0.3717 data_time: 0.0418 memory: 18752 grad_norm: 6.6709 loss: 0.9895 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9895 2023/03/17 22:07:30 - mmengine - INFO - Epoch(train) [40][ 40/1320] lr: 2.0000e-03 eta: 1:21:10 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 6.7802 loss: 1.1427 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1427 2023/03/17 22:07:37 - mmengine - INFO - Epoch(train) [40][ 60/1320] lr: 2.0000e-03 eta: 1:21:04 time: 0.3354 data_time: 0.0112 memory: 18752 grad_norm: 6.5722 loss: 1.0626 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0626 2023/03/17 22:07:43 - mmengine - INFO - Epoch(train) [40][ 80/1320] lr: 2.0000e-03 eta: 1:20:57 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 6.3724 loss: 0.9948 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9948 2023/03/17 22:07:50 - mmengine - INFO - Epoch(train) [40][ 100/1320] lr: 2.0000e-03 eta: 1:20:50 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 6.6135 loss: 1.0877 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0877 2023/03/17 22:07:57 - mmengine - INFO - Epoch(train) [40][ 120/1320] lr: 2.0000e-03 eta: 1:20:43 time: 0.3365 data_time: 0.0115 memory: 18752 grad_norm: 6.6617 loss: 0.9985 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9985 2023/03/17 22:08:03 - mmengine - INFO - Epoch(train) [40][ 140/1320] lr: 2.0000e-03 eta: 1:20:37 time: 0.3368 data_time: 0.0119 memory: 18752 grad_norm: 6.3151 loss: 0.9255 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9255 2023/03/17 22:08:10 - mmengine - INFO - Epoch(train) [40][ 160/1320] lr: 2.0000e-03 eta: 1:20:30 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 6.7270 loss: 0.8520 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8520 2023/03/17 22:08:17 - mmengine - INFO - Epoch(train) [40][ 180/1320] lr: 2.0000e-03 eta: 1:20:23 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 6.5216 loss: 1.0146 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0146 2023/03/17 22:08:24 - mmengine - INFO - Epoch(train) [40][ 200/1320] lr: 2.0000e-03 eta: 1:20:17 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 6.7282 loss: 0.8294 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8294 2023/03/17 22:08:30 - mmengine - INFO - Epoch(train) [40][ 220/1320] lr: 2.0000e-03 eta: 1:20:10 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 6.7043 loss: 1.0151 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0151 2023/03/17 22:08:37 - mmengine - INFO - Epoch(train) [40][ 240/1320] lr: 2.0000e-03 eta: 1:20:03 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 6.7831 loss: 1.0670 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0670 2023/03/17 22:08:44 - mmengine - INFO - Epoch(train) [40][ 260/1320] lr: 2.0000e-03 eta: 1:19:56 time: 0.3365 data_time: 0.0119 memory: 18752 grad_norm: 6.6527 loss: 1.0006 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0006 2023/03/17 22:08:51 - mmengine - INFO - Epoch(train) [40][ 280/1320] lr: 2.0000e-03 eta: 1:19:50 time: 0.3359 data_time: 0.0117 memory: 18752 grad_norm: 6.4965 loss: 1.0003 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0003 2023/03/17 22:08:57 - mmengine - INFO - Epoch(train) [40][ 300/1320] lr: 2.0000e-03 eta: 1:19:43 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 6.4762 loss: 0.9295 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9295 2023/03/17 22:09:04 - mmengine - INFO - Epoch(train) [40][ 320/1320] lr: 2.0000e-03 eta: 1:19:36 time: 0.3359 data_time: 0.0117 memory: 18752 grad_norm: 6.5972 loss: 0.9956 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9956 2023/03/17 22:09:11 - mmengine - INFO - Epoch(train) [40][ 340/1320] lr: 2.0000e-03 eta: 1:19:29 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 6.6007 loss: 0.8947 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8947 2023/03/17 22:09:17 - mmengine - INFO - Epoch(train) [40][ 360/1320] lr: 2.0000e-03 eta: 1:19:23 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 6.8133 loss: 1.0502 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0502 2023/03/17 22:09:24 - mmengine - INFO - Epoch(train) [40][ 380/1320] lr: 2.0000e-03 eta: 1:19:16 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 6.7320 loss: 0.9181 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9181 2023/03/17 22:09:31 - mmengine - INFO - Epoch(train) [40][ 400/1320] lr: 2.0000e-03 eta: 1:19:09 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 6.8108 loss: 0.9806 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9806 2023/03/17 22:09:38 - mmengine - INFO - Epoch(train) [40][ 420/1320] lr: 2.0000e-03 eta: 1:19:02 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 6.7637 loss: 0.9944 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9944 2023/03/17 22:09:44 - mmengine - INFO - Epoch(train) [40][ 440/1320] lr: 2.0000e-03 eta: 1:18:56 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 6.7855 loss: 1.0542 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0542 2023/03/17 22:09:51 - mmengine - INFO - Epoch(train) [40][ 460/1320] lr: 2.0000e-03 eta: 1:18:49 time: 0.3374 data_time: 0.0120 memory: 18752 grad_norm: 6.8077 loss: 0.9650 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9650 2023/03/17 22:09:58 - mmengine - INFO - Epoch(train) [40][ 480/1320] lr: 2.0000e-03 eta: 1:18:42 time: 0.3351 data_time: 0.0116 memory: 18752 grad_norm: 6.9285 loss: 1.1012 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1012 2023/03/17 22:10:04 - mmengine - INFO - Epoch(train) [40][ 500/1320] lr: 2.0000e-03 eta: 1:18:36 time: 0.3353 data_time: 0.0115 memory: 18752 grad_norm: 6.7311 loss: 1.0661 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0661 2023/03/17 22:10:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:10:11 - mmengine - INFO - Epoch(train) [40][ 520/1320] lr: 2.0000e-03 eta: 1:18:29 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 6.5222 loss: 0.9702 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9702 2023/03/17 22:10:18 - mmengine - INFO - Epoch(train) [40][ 540/1320] lr: 2.0000e-03 eta: 1:18:22 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 6.7085 loss: 1.0243 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0243 2023/03/17 22:10:25 - mmengine - INFO - Epoch(train) [40][ 560/1320] lr: 2.0000e-03 eta: 1:18:15 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 6.7182 loss: 0.9941 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9941 2023/03/17 22:10:31 - mmengine - INFO - Epoch(train) [40][ 580/1320] lr: 2.0000e-03 eta: 1:18:09 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 6.6392 loss: 1.0475 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0475 2023/03/17 22:10:38 - mmengine - INFO - Epoch(train) [40][ 600/1320] lr: 2.0000e-03 eta: 1:18:02 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 6.6204 loss: 1.1464 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1464 2023/03/17 22:10:45 - mmengine - INFO - Epoch(train) [40][ 620/1320] lr: 2.0000e-03 eta: 1:17:55 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 6.5430 loss: 1.0750 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0750 2023/03/17 22:10:51 - mmengine - INFO - Epoch(train) [40][ 640/1320] lr: 2.0000e-03 eta: 1:17:48 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 6.8960 loss: 0.9542 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9542 2023/03/17 22:10:58 - mmengine - INFO - Epoch(train) [40][ 660/1320] lr: 2.0000e-03 eta: 1:17:42 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 6.7311 loss: 1.0052 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0052 2023/03/17 22:11:05 - mmengine - INFO - Epoch(train) [40][ 680/1320] lr: 2.0000e-03 eta: 1:17:35 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 6.8556 loss: 1.0386 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0386 2023/03/17 22:11:12 - mmengine - INFO - Epoch(train) [40][ 700/1320] lr: 2.0000e-03 eta: 1:17:28 time: 0.3360 data_time: 0.0117 memory: 18752 grad_norm: 6.8423 loss: 0.9125 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9125 2023/03/17 22:11:18 - mmengine - INFO - Epoch(train) [40][ 720/1320] lr: 2.0000e-03 eta: 1:17:22 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 6.8918 loss: 1.0684 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0684 2023/03/17 22:11:25 - mmengine - INFO - Epoch(train) [40][ 740/1320] lr: 2.0000e-03 eta: 1:17:15 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 6.6619 loss: 0.8709 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8709 2023/03/17 22:11:32 - mmengine - INFO - Epoch(train) [40][ 760/1320] lr: 2.0000e-03 eta: 1:17:08 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 6.6966 loss: 1.0343 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0343 2023/03/17 22:11:38 - mmengine - INFO - Epoch(train) [40][ 780/1320] lr: 2.0000e-03 eta: 1:17:01 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 6.9534 loss: 1.0850 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0850 2023/03/17 22:11:45 - mmengine - INFO - Epoch(train) [40][ 800/1320] lr: 2.0000e-03 eta: 1:16:55 time: 0.3351 data_time: 0.0121 memory: 18752 grad_norm: 6.8348 loss: 1.0453 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.0453 2023/03/17 22:11:52 - mmengine - INFO - Epoch(train) [40][ 820/1320] lr: 2.0000e-03 eta: 1:16:48 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 6.9946 loss: 1.0514 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0514 2023/03/17 22:11:59 - mmengine - INFO - Epoch(train) [40][ 840/1320] lr: 2.0000e-03 eta: 1:16:41 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 6.9975 loss: 0.9573 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9573 2023/03/17 22:12:05 - mmengine - INFO - Epoch(train) [40][ 860/1320] lr: 2.0000e-03 eta: 1:16:34 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 6.7962 loss: 0.8437 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8437 2023/03/17 22:12:12 - mmengine - INFO - Epoch(train) [40][ 880/1320] lr: 2.0000e-03 eta: 1:16:28 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 6.8625 loss: 0.9542 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9542 2023/03/17 22:12:19 - mmengine - INFO - Epoch(train) [40][ 900/1320] lr: 2.0000e-03 eta: 1:16:21 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 6.6735 loss: 0.8663 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8663 2023/03/17 22:12:25 - mmengine - INFO - Epoch(train) [40][ 920/1320] lr: 2.0000e-03 eta: 1:16:14 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.0101 loss: 1.0978 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0978 2023/03/17 22:12:32 - mmengine - INFO - Epoch(train) [40][ 940/1320] lr: 2.0000e-03 eta: 1:16:07 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 6.5732 loss: 0.9925 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9925 2023/03/17 22:12:39 - mmengine - INFO - Epoch(train) [40][ 960/1320] lr: 2.0000e-03 eta: 1:16:01 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 6.8950 loss: 0.9260 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9260 2023/03/17 22:12:46 - mmengine - INFO - Epoch(train) [40][ 980/1320] lr: 2.0000e-03 eta: 1:15:54 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 6.7736 loss: 1.0310 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0310 2023/03/17 22:12:52 - mmengine - INFO - Epoch(train) [40][1000/1320] lr: 2.0000e-03 eta: 1:15:47 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 6.8921 loss: 1.0751 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0751 2023/03/17 22:12:59 - mmengine - INFO - Epoch(train) [40][1020/1320] lr: 2.0000e-03 eta: 1:15:41 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 6.4761 loss: 1.0280 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0280 2023/03/17 22:13:06 - mmengine - INFO - Epoch(train) [40][1040/1320] lr: 2.0000e-03 eta: 1:15:34 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 6.8471 loss: 1.0647 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0647 2023/03/17 22:13:13 - mmengine - INFO - Epoch(train) [40][1060/1320] lr: 2.0000e-03 eta: 1:15:27 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 6.8069 loss: 1.0137 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0137 2023/03/17 22:13:19 - mmengine - INFO - Epoch(train) [40][1080/1320] lr: 2.0000e-03 eta: 1:15:20 time: 0.3357 data_time: 0.0126 memory: 18752 grad_norm: 7.1180 loss: 1.0116 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0116 2023/03/17 22:13:26 - mmengine - INFO - Epoch(train) [40][1100/1320] lr: 2.0000e-03 eta: 1:15:14 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 6.8964 loss: 1.1057 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1057 2023/03/17 22:13:33 - mmengine - INFO - Epoch(train) [40][1120/1320] lr: 2.0000e-03 eta: 1:15:07 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 7.0725 loss: 1.0733 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0733 2023/03/17 22:13:39 - mmengine - INFO - Epoch(train) [40][1140/1320] lr: 2.0000e-03 eta: 1:15:00 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 7.0165 loss: 1.0215 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0215 2023/03/17 22:13:46 - mmengine - INFO - Epoch(train) [40][1160/1320] lr: 2.0000e-03 eta: 1:14:53 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 6.8071 loss: 1.0055 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0055 2023/03/17 22:13:53 - mmengine - INFO - Epoch(train) [40][1180/1320] lr: 2.0000e-03 eta: 1:14:47 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 6.8483 loss: 0.8841 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8841 2023/03/17 22:14:00 - mmengine - INFO - Epoch(train) [40][1200/1320] lr: 2.0000e-03 eta: 1:14:40 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 7.0085 loss: 1.0771 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0771 2023/03/17 22:14:06 - mmengine - INFO - Epoch(train) [40][1220/1320] lr: 2.0000e-03 eta: 1:14:33 time: 0.3370 data_time: 0.0119 memory: 18752 grad_norm: 7.0352 loss: 1.0852 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0852 2023/03/17 22:14:13 - mmengine - INFO - Epoch(train) [40][1240/1320] lr: 2.0000e-03 eta: 1:14:27 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 6.7335 loss: 1.0863 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0863 2023/03/17 22:14:20 - mmengine - INFO - Epoch(train) [40][1260/1320] lr: 2.0000e-03 eta: 1:14:20 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 6.8134 loss: 1.1146 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1146 2023/03/17 22:14:27 - mmengine - INFO - Epoch(train) [40][1280/1320] lr: 2.0000e-03 eta: 1:14:13 time: 0.3369 data_time: 0.0120 memory: 18752 grad_norm: 7.2422 loss: 0.9173 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9173 2023/03/17 22:14:33 - mmengine - INFO - Epoch(train) [40][1300/1320] lr: 2.0000e-03 eta: 1:14:06 time: 0.3453 data_time: 0.0113 memory: 18752 grad_norm: 6.8900 loss: 0.9804 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9804 2023/03/17 22:14:40 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:14:40 - mmengine - INFO - Epoch(train) [40][1320/1320] lr: 2.0000e-03 eta: 1:14:00 time: 0.3321 data_time: 0.0127 memory: 18752 grad_norm: 7.0631 loss: 1.1280 top1_acc: 0.6364 top5_acc: 0.9091 loss_cls: 1.1280 2023/03/17 22:14:43 - mmengine - INFO - Epoch(val) [40][ 20/194] eta: 0:00:22 time: 0.1268 data_time: 0.0404 memory: 2112 2023/03/17 22:14:45 - mmengine - INFO - Epoch(val) [40][ 40/194] eta: 0:00:17 time: 0.0954 data_time: 0.0095 memory: 2112 2023/03/17 22:14:46 - mmengine - INFO - Epoch(val) [40][ 60/194] eta: 0:00:14 time: 0.0968 data_time: 0.0108 memory: 2112 2023/03/17 22:14:48 - mmengine - INFO - Epoch(val) [40][ 80/194] eta: 0:00:11 time: 0.0971 data_time: 0.0110 memory: 2112 2023/03/17 22:14:50 - mmengine - INFO - Epoch(val) [40][100/194] eta: 0:00:09 time: 0.0981 data_time: 0.0117 memory: 2112 2023/03/17 22:14:52 - mmengine - INFO - Epoch(val) [40][120/194] eta: 0:00:07 time: 0.0970 data_time: 0.0108 memory: 2112 2023/03/17 22:14:54 - mmengine - INFO - Epoch(val) [40][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 22:14:56 - mmengine - INFO - Epoch(val) [40][160/194] eta: 0:00:03 time: 0.0975 data_time: 0.0114 memory: 2112 2023/03/17 22:14:58 - mmengine - INFO - Epoch(val) [40][180/194] eta: 0:00:01 time: 0.0976 data_time: 0.0115 memory: 2112 2023/03/17 22:15:01 - mmengine - INFO - Epoch(val) [40][194/194] acc/top1: 0.5988 acc/top5: 0.8605 acc/mean1: 0.5389 2023/03/17 22:15:09 - mmengine - INFO - Epoch(train) [41][ 20/1320] lr: 2.0000e-03 eta: 1:13:53 time: 0.3775 data_time: 0.0411 memory: 18752 grad_norm: 6.8545 loss: 0.8244 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8244 2023/03/17 22:15:16 - mmengine - INFO - Epoch(train) [41][ 40/1320] lr: 2.0000e-03 eta: 1:13:46 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 6.5007 loss: 0.9489 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9489 2023/03/17 22:15:22 - mmengine - INFO - Epoch(train) [41][ 60/1320] lr: 2.0000e-03 eta: 1:13:40 time: 0.3364 data_time: 0.0116 memory: 18752 grad_norm: 6.8412 loss: 1.0320 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0320 2023/03/17 22:15:29 - mmengine - INFO - Epoch(train) [41][ 80/1320] lr: 2.0000e-03 eta: 1:13:33 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 6.9663 loss: 0.9592 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9592 2023/03/17 22:15:36 - mmengine - INFO - Epoch(train) [41][ 100/1320] lr: 2.0000e-03 eta: 1:13:26 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 6.7551 loss: 1.0026 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0026 2023/03/17 22:15:43 - mmengine - INFO - Epoch(train) [41][ 120/1320] lr: 2.0000e-03 eta: 1:13:19 time: 0.3363 data_time: 0.0127 memory: 18752 grad_norm: 6.5202 loss: 0.9606 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9606 2023/03/17 22:15:49 - mmengine - INFO - Epoch(train) [41][ 140/1320] lr: 2.0000e-03 eta: 1:13:13 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 6.8232 loss: 1.0873 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0873 2023/03/17 22:15:56 - mmengine - INFO - Epoch(train) [41][ 160/1320] lr: 2.0000e-03 eta: 1:13:06 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 6.8541 loss: 1.0383 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0383 2023/03/17 22:16:03 - mmengine - INFO - Epoch(train) [41][ 180/1320] lr: 2.0000e-03 eta: 1:12:59 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 6.8362 loss: 0.8944 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8944 2023/03/17 22:16:09 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:16:09 - mmengine - INFO - Epoch(train) [41][ 200/1320] lr: 2.0000e-03 eta: 1:12:53 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 6.6818 loss: 0.9785 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9785 2023/03/17 22:16:16 - mmengine - INFO - Epoch(train) [41][ 220/1320] lr: 2.0000e-03 eta: 1:12:46 time: 0.3363 data_time: 0.0116 memory: 18752 grad_norm: 6.8561 loss: 0.9484 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9484 2023/03/17 22:16:23 - mmengine - INFO - Epoch(train) [41][ 240/1320] lr: 2.0000e-03 eta: 1:12:39 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 6.9154 loss: 0.8963 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8963 2023/03/17 22:16:30 - mmengine - INFO - Epoch(train) [41][ 260/1320] lr: 2.0000e-03 eta: 1:12:32 time: 0.3367 data_time: 0.0121 memory: 18752 grad_norm: 6.8794 loss: 1.0608 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0608 2023/03/17 22:16:36 - mmengine - INFO - Epoch(train) [41][ 280/1320] lr: 2.0000e-03 eta: 1:12:26 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.1210 loss: 0.9676 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 0.9676 2023/03/17 22:16:43 - mmengine - INFO - Epoch(train) [41][ 300/1320] lr: 2.0000e-03 eta: 1:12:19 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 6.9059 loss: 0.9140 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 0.9140 2023/03/17 22:16:50 - mmengine - INFO - Epoch(train) [41][ 320/1320] lr: 2.0000e-03 eta: 1:12:12 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 6.8575 loss: 0.9477 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9477 2023/03/17 22:16:57 - mmengine - INFO - Epoch(train) [41][ 340/1320] lr: 2.0000e-03 eta: 1:12:05 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 6.9938 loss: 0.9355 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9355 2023/03/17 22:17:03 - mmengine - INFO - Epoch(train) [41][ 360/1320] lr: 2.0000e-03 eta: 1:11:59 time: 0.3367 data_time: 0.0119 memory: 18752 grad_norm: 6.8781 loss: 0.8723 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8723 2023/03/17 22:17:10 - mmengine - INFO - Epoch(train) [41][ 380/1320] lr: 2.0000e-03 eta: 1:11:52 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 6.8057 loss: 1.0230 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0230 2023/03/17 22:17:17 - mmengine - INFO - Epoch(train) [41][ 400/1320] lr: 2.0000e-03 eta: 1:11:45 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 7.0466 loss: 0.8990 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8990 2023/03/17 22:17:23 - mmengine - INFO - Epoch(train) [41][ 420/1320] lr: 2.0000e-03 eta: 1:11:39 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 7.1128 loss: 1.0086 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0086 2023/03/17 22:17:30 - mmengine - INFO - Epoch(train) [41][ 440/1320] lr: 2.0000e-03 eta: 1:11:32 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 6.9807 loss: 1.0504 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0504 2023/03/17 22:17:37 - mmengine - INFO - Epoch(train) [41][ 460/1320] lr: 2.0000e-03 eta: 1:11:25 time: 0.3356 data_time: 0.0116 memory: 18752 grad_norm: 6.8560 loss: 0.7988 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7988 2023/03/17 22:17:44 - mmengine - INFO - Epoch(train) [41][ 480/1320] lr: 2.0000e-03 eta: 1:11:18 time: 0.3353 data_time: 0.0116 memory: 18752 grad_norm: 6.7554 loss: 0.9414 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9414 2023/03/17 22:17:50 - mmengine - INFO - Epoch(train) [41][ 500/1320] lr: 2.0000e-03 eta: 1:11:12 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 6.9133 loss: 1.0508 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0508 2023/03/17 22:17:57 - mmengine - INFO - Epoch(train) [41][ 520/1320] lr: 2.0000e-03 eta: 1:11:05 time: 0.3352 data_time: 0.0121 memory: 18752 grad_norm: 6.9233 loss: 0.9699 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9699 2023/03/17 22:18:04 - mmengine - INFO - Epoch(train) [41][ 540/1320] lr: 2.0000e-03 eta: 1:10:58 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 6.9447 loss: 0.9677 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9677 2023/03/17 22:18:10 - mmengine - INFO - Epoch(train) [41][ 560/1320] lr: 2.0000e-03 eta: 1:10:51 time: 0.3350 data_time: 0.0116 memory: 18752 grad_norm: 6.9851 loss: 0.9169 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9169 2023/03/17 22:18:17 - mmengine - INFO - Epoch(train) [41][ 580/1320] lr: 2.0000e-03 eta: 1:10:45 time: 0.3353 data_time: 0.0127 memory: 18752 grad_norm: 6.8011 loss: 0.9665 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9665 2023/03/17 22:18:24 - mmengine - INFO - Epoch(train) [41][ 600/1320] lr: 2.0000e-03 eta: 1:10:38 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 6.9964 loss: 1.0147 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0147 2023/03/17 22:18:30 - mmengine - INFO - Epoch(train) [41][ 620/1320] lr: 2.0000e-03 eta: 1:10:31 time: 0.3353 data_time: 0.0123 memory: 18752 grad_norm: 7.2121 loss: 0.8829 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8829 2023/03/17 22:18:37 - mmengine - INFO - Epoch(train) [41][ 640/1320] lr: 2.0000e-03 eta: 1:10:24 time: 0.3354 data_time: 0.0121 memory: 18752 grad_norm: 6.9600 loss: 1.0808 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0808 2023/03/17 22:18:44 - mmengine - INFO - Epoch(train) [41][ 660/1320] lr: 2.0000e-03 eta: 1:10:18 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 6.9049 loss: 1.2275 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2275 2023/03/17 22:18:51 - mmengine - INFO - Epoch(train) [41][ 680/1320] lr: 2.0000e-03 eta: 1:10:11 time: 0.3354 data_time: 0.0122 memory: 18752 grad_norm: 6.8997 loss: 1.0061 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0061 2023/03/17 22:18:57 - mmengine - INFO - Epoch(train) [41][ 700/1320] lr: 2.0000e-03 eta: 1:10:04 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 6.9152 loss: 1.1053 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1053 2023/03/17 22:19:04 - mmengine - INFO - Epoch(train) [41][ 720/1320] lr: 2.0000e-03 eta: 1:09:58 time: 0.3353 data_time: 0.0122 memory: 18752 grad_norm: 7.1144 loss: 0.9970 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.9970 2023/03/17 22:19:11 - mmengine - INFO - Epoch(train) [41][ 740/1320] lr: 2.0000e-03 eta: 1:09:51 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 6.9791 loss: 1.0991 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.0991 2023/03/17 22:19:17 - mmengine - INFO - Epoch(train) [41][ 760/1320] lr: 2.0000e-03 eta: 1:09:44 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 6.8586 loss: 0.9385 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9385 2023/03/17 22:19:24 - mmengine - INFO - Epoch(train) [41][ 780/1320] lr: 2.0000e-03 eta: 1:09:37 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 7.1317 loss: 0.9363 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9363 2023/03/17 22:19:31 - mmengine - INFO - Epoch(train) [41][ 800/1320] lr: 2.0000e-03 eta: 1:09:31 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.0500 loss: 1.0025 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0025 2023/03/17 22:19:38 - mmengine - INFO - Epoch(train) [41][ 820/1320] lr: 2.0000e-03 eta: 1:09:24 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 7.0376 loss: 1.0050 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0050 2023/03/17 22:19:44 - mmengine - INFO - Epoch(train) [41][ 840/1320] lr: 2.0000e-03 eta: 1:09:17 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 6.9787 loss: 0.8925 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8925 2023/03/17 22:19:51 - mmengine - INFO - Epoch(train) [41][ 860/1320] lr: 2.0000e-03 eta: 1:09:10 time: 0.3363 data_time: 0.0116 memory: 18752 grad_norm: 6.8403 loss: 0.9271 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9271 2023/03/17 22:19:58 - mmengine - INFO - Epoch(train) [41][ 880/1320] lr: 2.0000e-03 eta: 1:09:04 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 6.9444 loss: 0.9571 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9571 2023/03/17 22:20:05 - mmengine - INFO - Epoch(train) [41][ 900/1320] lr: 2.0000e-03 eta: 1:08:57 time: 0.3359 data_time: 0.0127 memory: 18752 grad_norm: 6.8367 loss: 0.9495 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9495 2023/03/17 22:20:11 - mmengine - INFO - Epoch(train) [41][ 920/1320] lr: 2.0000e-03 eta: 1:08:50 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 7.1328 loss: 0.9469 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9469 2023/03/17 22:20:18 - mmengine - INFO - Epoch(train) [41][ 940/1320] lr: 2.0000e-03 eta: 1:08:44 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 7.0344 loss: 0.9829 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 0.9829 2023/03/17 22:20:25 - mmengine - INFO - Epoch(train) [41][ 960/1320] lr: 2.0000e-03 eta: 1:08:37 time: 0.3364 data_time: 0.0127 memory: 18752 grad_norm: 7.0692 loss: 0.9810 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9810 2023/03/17 22:20:31 - mmengine - INFO - Epoch(train) [41][ 980/1320] lr: 2.0000e-03 eta: 1:08:30 time: 0.3363 data_time: 0.0126 memory: 18752 grad_norm: 6.6775 loss: 0.8617 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8617 2023/03/17 22:20:38 - mmengine - INFO - Epoch(train) [41][1000/1320] lr: 2.0000e-03 eta: 1:08:23 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 6.9204 loss: 1.0374 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0374 2023/03/17 22:20:45 - mmengine - INFO - Epoch(train) [41][1020/1320] lr: 2.0000e-03 eta: 1:08:17 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 6.8905 loss: 1.0682 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0682 2023/03/17 22:20:52 - mmengine - INFO - Epoch(train) [41][1040/1320] lr: 2.0000e-03 eta: 1:08:10 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 7.2206 loss: 1.1107 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1107 2023/03/17 22:20:58 - mmengine - INFO - Epoch(train) [41][1060/1320] lr: 2.0000e-03 eta: 1:08:03 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 7.0237 loss: 0.9428 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9428 2023/03/17 22:21:05 - mmengine - INFO - Epoch(train) [41][1080/1320] lr: 2.0000e-03 eta: 1:07:56 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 6.9005 loss: 0.9305 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9305 2023/03/17 22:21:12 - mmengine - INFO - Epoch(train) [41][1100/1320] lr: 2.0000e-03 eta: 1:07:50 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.0722 loss: 1.0271 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0271 2023/03/17 22:21:18 - mmengine - INFO - Epoch(train) [41][1120/1320] lr: 2.0000e-03 eta: 1:07:43 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 6.8935 loss: 0.9346 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9346 2023/03/17 22:21:25 - mmengine - INFO - Epoch(train) [41][1140/1320] lr: 2.0000e-03 eta: 1:07:36 time: 0.3362 data_time: 0.0126 memory: 18752 grad_norm: 7.0188 loss: 0.9877 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9877 2023/03/17 22:21:32 - mmengine - INFO - Epoch(train) [41][1160/1320] lr: 2.0000e-03 eta: 1:07:30 time: 0.3363 data_time: 0.0128 memory: 18752 grad_norm: 7.0194 loss: 1.0943 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0943 2023/03/17 22:21:39 - mmengine - INFO - Epoch(train) [41][1180/1320] lr: 2.0000e-03 eta: 1:07:23 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 6.9661 loss: 0.9062 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9062 2023/03/17 22:21:45 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:21:45 - mmengine - INFO - Epoch(train) [41][1200/1320] lr: 2.0000e-03 eta: 1:07:16 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 6.9738 loss: 0.9748 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.9748 2023/03/17 22:21:52 - mmengine - INFO - Epoch(train) [41][1220/1320] lr: 2.0000e-03 eta: 1:07:09 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 7.0527 loss: 0.9250 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9250 2023/03/17 22:21:59 - mmengine - INFO - Epoch(train) [41][1240/1320] lr: 2.0000e-03 eta: 1:07:03 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.1411 loss: 0.9721 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9721 2023/03/17 22:22:05 - mmengine - INFO - Epoch(train) [41][1260/1320] lr: 2.0000e-03 eta: 1:06:56 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 6.9567 loss: 0.8973 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8973 2023/03/17 22:22:12 - mmengine - INFO - Epoch(train) [41][1280/1320] lr: 2.0000e-03 eta: 1:06:49 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 6.9197 loss: 0.8692 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8692 2023/03/17 22:22:19 - mmengine - INFO - Epoch(train) [41][1300/1320] lr: 2.0000e-03 eta: 1:06:42 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 6.9175 loss: 0.8767 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8767 2023/03/17 22:22:26 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:22:26 - mmengine - INFO - Epoch(train) [41][1320/1320] lr: 2.0000e-03 eta: 1:06:36 time: 0.3306 data_time: 0.0120 memory: 18752 grad_norm: 7.0686 loss: 0.9508 top1_acc: 0.7273 top5_acc: 0.9091 loss_cls: 0.9508 2023/03/17 22:22:28 - mmengine - INFO - Epoch(val) [41][ 20/194] eta: 0:00:22 time: 0.1286 data_time: 0.0423 memory: 2112 2023/03/17 22:22:30 - mmengine - INFO - Epoch(val) [41][ 40/194] eta: 0:00:17 time: 0.0959 data_time: 0.0104 memory: 2112 2023/03/17 22:22:32 - mmengine - INFO - Epoch(val) [41][ 60/194] eta: 0:00:14 time: 0.0972 data_time: 0.0114 memory: 2112 2023/03/17 22:22:34 - mmengine - INFO - Epoch(val) [41][ 80/194] eta: 0:00:11 time: 0.0975 data_time: 0.0111 memory: 2112 2023/03/17 22:22:36 - mmengine - INFO - Epoch(val) [41][100/194] eta: 0:00:09 time: 0.0973 data_time: 0.0113 memory: 2112 2023/03/17 22:22:38 - mmengine - INFO - Epoch(val) [41][120/194] eta: 0:00:07 time: 0.0965 data_time: 0.0105 memory: 2112 2023/03/17 22:22:40 - mmengine - INFO - Epoch(val) [41][140/194] eta: 0:00:05 time: 0.0977 data_time: 0.0116 memory: 2112 2023/03/17 22:22:42 - mmengine - INFO - Epoch(val) [41][160/194] eta: 0:00:03 time: 0.0972 data_time: 0.0113 memory: 2112 2023/03/17 22:22:44 - mmengine - INFO - Epoch(val) [41][180/194] eta: 0:00:01 time: 0.0979 data_time: 0.0118 memory: 2112 2023/03/17 22:22:47 - mmengine - INFO - Epoch(val) [41][194/194] acc/top1: 0.6046 acc/top5: 0.8655 acc/mean1: 0.5452 2023/03/17 22:22:54 - mmengine - INFO - Epoch(train) [42][ 20/1320] lr: 2.0000e-03 eta: 1:06:29 time: 0.3703 data_time: 0.0400 memory: 18752 grad_norm: 6.7932 loss: 1.0485 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0485 2023/03/17 22:23:01 - mmengine - INFO - Epoch(train) [42][ 40/1320] lr: 2.0000e-03 eta: 1:06:22 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 6.8887 loss: 0.7474 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7474 2023/03/17 22:23:08 - mmengine - INFO - Epoch(train) [42][ 60/1320] lr: 2.0000e-03 eta: 1:06:16 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 6.8886 loss: 0.9579 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9579 2023/03/17 22:23:14 - mmengine - INFO - Epoch(train) [42][ 80/1320] lr: 2.0000e-03 eta: 1:06:09 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 7.0713 loss: 1.0250 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.0250 2023/03/17 22:23:21 - mmengine - INFO - Epoch(train) [42][ 100/1320] lr: 2.0000e-03 eta: 1:06:02 time: 0.3357 data_time: 0.0117 memory: 18752 grad_norm: 7.1620 loss: 0.8616 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8616 2023/03/17 22:23:28 - mmengine - INFO - Epoch(train) [42][ 120/1320] lr: 2.0000e-03 eta: 1:05:55 time: 0.3357 data_time: 0.0124 memory: 18752 grad_norm: 6.9717 loss: 0.8114 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8114 2023/03/17 22:23:35 - mmengine - INFO - Epoch(train) [42][ 140/1320] lr: 2.0000e-03 eta: 1:05:49 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 6.9575 loss: 0.9342 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9342 2023/03/17 22:23:41 - mmengine - INFO - Epoch(train) [42][ 160/1320] lr: 2.0000e-03 eta: 1:05:42 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 7.0033 loss: 1.0361 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0361 2023/03/17 22:23:48 - mmengine - INFO - Epoch(train) [42][ 180/1320] lr: 2.0000e-03 eta: 1:05:35 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 7.0864 loss: 0.8331 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8331 2023/03/17 22:23:55 - mmengine - INFO - Epoch(train) [42][ 200/1320] lr: 2.0000e-03 eta: 1:05:29 time: 0.3366 data_time: 0.0122 memory: 18752 grad_norm: 7.0614 loss: 1.0116 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0116 2023/03/17 22:24:01 - mmengine - INFO - Epoch(train) [42][ 220/1320] lr: 2.0000e-03 eta: 1:05:22 time: 0.3362 data_time: 0.0116 memory: 18752 grad_norm: 7.0035 loss: 0.9243 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9243 2023/03/17 22:24:08 - mmengine - INFO - Epoch(train) [42][ 240/1320] lr: 2.0000e-03 eta: 1:05:15 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 7.0561 loss: 0.7896 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7896 2023/03/17 22:24:15 - mmengine - INFO - Epoch(train) [42][ 260/1320] lr: 2.0000e-03 eta: 1:05:08 time: 0.3361 data_time: 0.0127 memory: 18752 grad_norm: 6.9219 loss: 0.9292 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9292 2023/03/17 22:24:22 - mmengine - INFO - Epoch(train) [42][ 280/1320] lr: 2.0000e-03 eta: 1:05:02 time: 0.3357 data_time: 0.0122 memory: 18752 grad_norm: 6.8876 loss: 0.8468 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8468 2023/03/17 22:24:28 - mmengine - INFO - Epoch(train) [42][ 300/1320] lr: 2.0000e-03 eta: 1:04:55 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 6.9807 loss: 0.8524 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8524 2023/03/17 22:24:35 - mmengine - INFO - Epoch(train) [42][ 320/1320] lr: 2.0000e-03 eta: 1:04:48 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.2222 loss: 1.0038 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0038 2023/03/17 22:24:42 - mmengine - INFO - Epoch(train) [42][ 340/1320] lr: 2.0000e-03 eta: 1:04:41 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.2229 loss: 0.9490 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9490 2023/03/17 22:24:49 - mmengine - INFO - Epoch(train) [42][ 360/1320] lr: 2.0000e-03 eta: 1:04:35 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 7.2466 loss: 0.8841 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8841 2023/03/17 22:24:55 - mmengine - INFO - Epoch(train) [42][ 380/1320] lr: 2.0000e-03 eta: 1:04:28 time: 0.3362 data_time: 0.0124 memory: 18752 grad_norm: 7.1318 loss: 0.8981 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8981 2023/03/17 22:25:02 - mmengine - INFO - Epoch(train) [42][ 400/1320] lr: 2.0000e-03 eta: 1:04:21 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 6.8868 loss: 0.9458 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9458 2023/03/17 22:25:09 - mmengine - INFO - Epoch(train) [42][ 420/1320] lr: 2.0000e-03 eta: 1:04:14 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.1137 loss: 0.9987 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9987 2023/03/17 22:25:15 - mmengine - INFO - Epoch(train) [42][ 440/1320] lr: 2.0000e-03 eta: 1:04:08 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.0834 loss: 1.0093 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0093 2023/03/17 22:25:22 - mmengine - INFO - Epoch(train) [42][ 460/1320] lr: 2.0000e-03 eta: 1:04:01 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 7.1037 loss: 0.8845 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8845 2023/03/17 22:25:29 - mmengine - INFO - Epoch(train) [42][ 480/1320] lr: 2.0000e-03 eta: 1:03:54 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.1050 loss: 1.0017 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0017 2023/03/17 22:25:36 - mmengine - INFO - Epoch(train) [42][ 500/1320] lr: 2.0000e-03 eta: 1:03:48 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 7.0718 loss: 0.9475 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9475 2023/03/17 22:25:42 - mmengine - INFO - Epoch(train) [42][ 520/1320] lr: 2.0000e-03 eta: 1:03:41 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 7.1012 loss: 0.9144 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9144 2023/03/17 22:25:49 - mmengine - INFO - Epoch(train) [42][ 540/1320] lr: 2.0000e-03 eta: 1:03:34 time: 0.3366 data_time: 0.0126 memory: 18752 grad_norm: 7.2594 loss: 1.0640 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0640 2023/03/17 22:25:56 - mmengine - INFO - Epoch(train) [42][ 560/1320] lr: 2.0000e-03 eta: 1:03:27 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 7.1874 loss: 1.0094 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0094 2023/03/17 22:26:02 - mmengine - INFO - Epoch(train) [42][ 580/1320] lr: 2.0000e-03 eta: 1:03:21 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 7.1468 loss: 1.0843 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0843 2023/03/17 22:26:09 - mmengine - INFO - Epoch(train) [42][ 600/1320] lr: 2.0000e-03 eta: 1:03:14 time: 0.3361 data_time: 0.0125 memory: 18752 grad_norm: 7.0266 loss: 0.9069 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9069 2023/03/17 22:26:16 - mmengine - INFO - Epoch(train) [42][ 620/1320] lr: 2.0000e-03 eta: 1:03:07 time: 0.3365 data_time: 0.0121 memory: 18752 grad_norm: 7.0853 loss: 0.9928 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9928 2023/03/17 22:26:23 - mmengine - INFO - Epoch(train) [42][ 640/1320] lr: 2.0000e-03 eta: 1:03:00 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 7.3202 loss: 0.9702 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9702 2023/03/17 22:26:29 - mmengine - INFO - Epoch(train) [42][ 660/1320] lr: 2.0000e-03 eta: 1:02:54 time: 0.3370 data_time: 0.0117 memory: 18752 grad_norm: 7.0489 loss: 0.8519 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8519 2023/03/17 22:26:36 - mmengine - INFO - Epoch(train) [42][ 680/1320] lr: 2.0000e-03 eta: 1:02:47 time: 0.3372 data_time: 0.0126 memory: 18752 grad_norm: 7.1049 loss: 0.8915 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8915 2023/03/17 22:26:43 - mmengine - INFO - Epoch(train) [42][ 700/1320] lr: 2.0000e-03 eta: 1:02:40 time: 0.3367 data_time: 0.0122 memory: 18752 grad_norm: 7.3940 loss: 1.0816 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.0816 2023/03/17 22:26:50 - mmengine - INFO - Epoch(train) [42][ 720/1320] lr: 2.0000e-03 eta: 1:02:34 time: 0.3367 data_time: 0.0123 memory: 18752 grad_norm: 7.2811 loss: 0.9953 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9953 2023/03/17 22:26:56 - mmengine - INFO - Epoch(train) [42][ 740/1320] lr: 2.0000e-03 eta: 1:02:27 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.0978 loss: 0.8535 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8535 2023/03/17 22:27:03 - mmengine - INFO - Epoch(train) [42][ 760/1320] lr: 2.0000e-03 eta: 1:02:20 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 7.4736 loss: 1.2150 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2150 2023/03/17 22:27:10 - mmengine - INFO - Epoch(train) [42][ 780/1320] lr: 2.0000e-03 eta: 1:02:13 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 7.6038 loss: 1.0569 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0569 2023/03/17 22:27:17 - mmengine - INFO - Epoch(train) [42][ 800/1320] lr: 2.0000e-03 eta: 1:02:07 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 7.2431 loss: 1.0705 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0705 2023/03/17 22:27:23 - mmengine - INFO - Epoch(train) [42][ 820/1320] lr: 2.0000e-03 eta: 1:02:00 time: 0.3362 data_time: 0.0124 memory: 18752 grad_norm: 7.1984 loss: 0.9918 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9918 2023/03/17 22:27:30 - mmengine - INFO - Epoch(train) [42][ 840/1320] lr: 2.0000e-03 eta: 1:01:53 time: 0.3368 data_time: 0.0123 memory: 18752 grad_norm: 7.1362 loss: 0.8401 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8401 2023/03/17 22:27:37 - mmengine - INFO - Epoch(train) [42][ 860/1320] lr: 2.0000e-03 eta: 1:01:46 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 7.2283 loss: 0.8288 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8288 2023/03/17 22:27:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:27:43 - mmengine - INFO - Epoch(train) [42][ 880/1320] lr: 2.0000e-03 eta: 1:01:40 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 7.1793 loss: 1.0085 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0085 2023/03/17 22:27:50 - mmengine - INFO - Epoch(train) [42][ 900/1320] lr: 2.0000e-03 eta: 1:01:33 time: 0.3370 data_time: 0.0130 memory: 18752 grad_norm: 7.1614 loss: 0.9365 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9365 2023/03/17 22:27:57 - mmengine - INFO - Epoch(train) [42][ 920/1320] lr: 2.0000e-03 eta: 1:01:26 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 7.0082 loss: 0.9671 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9671 2023/03/17 22:28:04 - mmengine - INFO - Epoch(train) [42][ 940/1320] lr: 2.0000e-03 eta: 1:01:20 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 7.1888 loss: 0.9105 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9105 2023/03/17 22:28:10 - mmengine - INFO - Epoch(train) [42][ 960/1320] lr: 2.0000e-03 eta: 1:01:13 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 7.0899 loss: 0.9716 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9716 2023/03/17 22:28:17 - mmengine - INFO - Epoch(train) [42][ 980/1320] lr: 2.0000e-03 eta: 1:01:06 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 7.2366 loss: 0.9114 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9114 2023/03/17 22:28:24 - mmengine - INFO - Epoch(train) [42][1000/1320] lr: 2.0000e-03 eta: 1:00:59 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.0284 loss: 1.0199 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0199 2023/03/17 22:28:31 - mmengine - INFO - Epoch(train) [42][1020/1320] lr: 2.0000e-03 eta: 1:00:53 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 6.8956 loss: 0.7685 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7685 2023/03/17 22:28:37 - mmengine - INFO - Epoch(train) [42][1040/1320] lr: 2.0000e-03 eta: 1:00:46 time: 0.3365 data_time: 0.0127 memory: 18752 grad_norm: 7.3949 loss: 0.8594 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8594 2023/03/17 22:28:44 - mmengine - INFO - Epoch(train) [42][1060/1320] lr: 2.0000e-03 eta: 1:00:39 time: 0.3363 data_time: 0.0127 memory: 18752 grad_norm: 7.3924 loss: 0.9413 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9413 2023/03/17 22:28:51 - mmengine - INFO - Epoch(train) [42][1080/1320] lr: 2.0000e-03 eta: 1:00:32 time: 0.3367 data_time: 0.0123 memory: 18752 grad_norm: 7.0404 loss: 1.0007 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0007 2023/03/17 22:28:57 - mmengine - INFO - Epoch(train) [42][1100/1320] lr: 2.0000e-03 eta: 1:00:26 time: 0.3365 data_time: 0.0119 memory: 18752 grad_norm: 7.4070 loss: 1.1725 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1725 2023/03/17 22:29:04 - mmengine - INFO - Epoch(train) [42][1120/1320] lr: 2.0000e-03 eta: 1:00:19 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 7.0599 loss: 0.9850 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9850 2023/03/17 22:29:11 - mmengine - INFO - Epoch(train) [42][1140/1320] lr: 2.0000e-03 eta: 1:00:12 time: 0.3368 data_time: 0.0119 memory: 18752 grad_norm: 7.6136 loss: 0.9783 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9783 2023/03/17 22:29:18 - mmengine - INFO - Epoch(train) [42][1160/1320] lr: 2.0000e-03 eta: 1:00:06 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 7.2877 loss: 0.9908 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9908 2023/03/17 22:29:24 - mmengine - INFO - Epoch(train) [42][1180/1320] lr: 2.0000e-03 eta: 0:59:59 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 7.3659 loss: 0.9239 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9239 2023/03/17 22:29:31 - mmengine - INFO - Epoch(train) [42][1200/1320] lr: 2.0000e-03 eta: 0:59:52 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 7.2634 loss: 1.0132 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0132 2023/03/17 22:29:38 - mmengine - INFO - Epoch(train) [42][1220/1320] lr: 2.0000e-03 eta: 0:59:45 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.4162 loss: 1.0018 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0018 2023/03/17 22:29:45 - mmengine - INFO - Epoch(train) [42][1240/1320] lr: 2.0000e-03 eta: 0:59:39 time: 0.3369 data_time: 0.0130 memory: 18752 grad_norm: 7.2990 loss: 0.8795 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8795 2023/03/17 22:29:51 - mmengine - INFO - Epoch(train) [42][1260/1320] lr: 2.0000e-03 eta: 0:59:32 time: 0.3373 data_time: 0.0125 memory: 18752 grad_norm: 7.1801 loss: 0.9898 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9898 2023/03/17 22:29:58 - mmengine - INFO - Epoch(train) [42][1280/1320] lr: 2.0000e-03 eta: 0:59:25 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 7.4030 loss: 1.1699 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1699 2023/03/17 22:30:05 - mmengine - INFO - Epoch(train) [42][1300/1320] lr: 2.0000e-03 eta: 0:59:18 time: 0.3368 data_time: 0.0120 memory: 18752 grad_norm: 7.1140 loss: 0.8778 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8778 2023/03/17 22:30:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:30:11 - mmengine - INFO - Epoch(train) [42][1320/1320] lr: 2.0000e-03 eta: 0:59:12 time: 0.3307 data_time: 0.0123 memory: 18752 grad_norm: 7.3561 loss: 0.9262 top1_acc: 0.7273 top5_acc: 0.7273 loss_cls: 0.9262 2023/03/17 22:30:11 - mmengine - INFO - Saving checkpoint at 42 epochs 2023/03/17 22:30:17 - mmengine - INFO - Epoch(val) [42][ 20/194] eta: 0:00:22 time: 0.1272 data_time: 0.0405 memory: 2112 2023/03/17 22:30:19 - mmengine - INFO - Epoch(val) [42][ 40/194] eta: 0:00:17 time: 0.0976 data_time: 0.0114 memory: 2112 2023/03/17 22:30:21 - mmengine - INFO - Epoch(val) [42][ 60/194] eta: 0:00:14 time: 0.0965 data_time: 0.0107 memory: 2112 2023/03/17 22:30:23 - mmengine - INFO - Epoch(val) [42][ 80/194] eta: 0:00:11 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 22:30:25 - mmengine - INFO - Epoch(val) [42][100/194] eta: 0:00:09 time: 0.0981 data_time: 0.0119 memory: 2112 2023/03/17 22:30:27 - mmengine - INFO - Epoch(val) [42][120/194] eta: 0:00:07 time: 0.0974 data_time: 0.0112 memory: 2112 2023/03/17 22:30:29 - mmengine - INFO - Epoch(val) [42][140/194] eta: 0:00:05 time: 0.0966 data_time: 0.0105 memory: 2112 2023/03/17 22:30:31 - mmengine - INFO - Epoch(val) [42][160/194] eta: 0:00:03 time: 0.0958 data_time: 0.0102 memory: 2112 2023/03/17 22:30:32 - mmengine - INFO - Epoch(val) [42][180/194] eta: 0:00:01 time: 0.0958 data_time: 0.0099 memory: 2112 2023/03/17 22:30:35 - mmengine - INFO - Epoch(val) [42][194/194] acc/top1: 0.5984 acc/top5: 0.8617 acc/mean1: 0.5391 2023/03/17 22:30:43 - mmengine - INFO - Epoch(train) [43][ 20/1320] lr: 2.0000e-03 eta: 0:59:05 time: 0.3747 data_time: 0.0421 memory: 18752 grad_norm: 6.9489 loss: 0.9611 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9611 2023/03/17 22:30:50 - mmengine - INFO - Epoch(train) [43][ 40/1320] lr: 2.0000e-03 eta: 0:58:58 time: 0.3372 data_time: 0.0123 memory: 18752 grad_norm: 7.2773 loss: 0.8609 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8609 2023/03/17 22:30:56 - mmengine - INFO - Epoch(train) [43][ 60/1320] lr: 2.0000e-03 eta: 0:58:52 time: 0.3362 data_time: 0.0110 memory: 18752 grad_norm: 7.0636 loss: 0.9365 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9365 2023/03/17 22:31:03 - mmengine - INFO - Epoch(train) [43][ 80/1320] lr: 2.0000e-03 eta: 0:58:45 time: 0.3359 data_time: 0.0114 memory: 18752 grad_norm: 7.1671 loss: 0.8710 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8710 2023/03/17 22:31:10 - mmengine - INFO - Epoch(train) [43][ 100/1320] lr: 2.0000e-03 eta: 0:58:38 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 7.3552 loss: 1.0020 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0020 2023/03/17 22:31:17 - mmengine - INFO - Epoch(train) [43][ 120/1320] lr: 2.0000e-03 eta: 0:58:32 time: 0.3368 data_time: 0.0119 memory: 18752 grad_norm: 6.9260 loss: 0.9205 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9205 2023/03/17 22:31:23 - mmengine - INFO - Epoch(train) [43][ 140/1320] lr: 2.0000e-03 eta: 0:58:25 time: 0.3371 data_time: 0.0116 memory: 18752 grad_norm: 7.1373 loss: 0.9835 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9835 2023/03/17 22:31:30 - mmengine - INFO - Epoch(train) [43][ 160/1320] lr: 2.0000e-03 eta: 0:58:18 time: 0.3367 data_time: 0.0133 memory: 18752 grad_norm: 7.1834 loss: 1.0214 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0214 2023/03/17 22:31:37 - mmengine - INFO - Epoch(train) [43][ 180/1320] lr: 2.0000e-03 eta: 0:58:11 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 7.1500 loss: 0.9973 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9973 2023/03/17 22:31:43 - mmengine - INFO - Epoch(train) [43][ 200/1320] lr: 2.0000e-03 eta: 0:58:05 time: 0.3360 data_time: 0.0114 memory: 18752 grad_norm: 7.0787 loss: 0.7785 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7785 2023/03/17 22:31:50 - mmengine - INFO - Epoch(train) [43][ 220/1320] lr: 2.0000e-03 eta: 0:57:58 time: 0.3359 data_time: 0.0118 memory: 18752 grad_norm: 7.4311 loss: 0.8732 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8732 2023/03/17 22:31:57 - mmengine - INFO - Epoch(train) [43][ 240/1320] lr: 2.0000e-03 eta: 0:57:51 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 7.1440 loss: 0.8504 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8504 2023/03/17 22:32:04 - mmengine - INFO - Epoch(train) [43][ 260/1320] lr: 2.0000e-03 eta: 0:57:44 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 7.0982 loss: 1.0154 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0154 2023/03/17 22:32:10 - mmengine - INFO - Epoch(train) [43][ 280/1320] lr: 2.0000e-03 eta: 0:57:38 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 7.1474 loss: 0.8574 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8574 2023/03/17 22:32:17 - mmengine - INFO - Epoch(train) [43][ 300/1320] lr: 2.0000e-03 eta: 0:57:31 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 7.1379 loss: 0.9673 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9673 2023/03/17 22:32:24 - mmengine - INFO - Epoch(train) [43][ 320/1320] lr: 2.0000e-03 eta: 0:57:24 time: 0.3357 data_time: 0.0119 memory: 18752 grad_norm: 7.4593 loss: 1.0908 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0908 2023/03/17 22:32:30 - mmengine - INFO - Epoch(train) [43][ 340/1320] lr: 2.0000e-03 eta: 0:57:17 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 7.0866 loss: 0.8955 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8955 2023/03/17 22:32:37 - mmengine - INFO - Epoch(train) [43][ 360/1320] lr: 2.0000e-03 eta: 0:57:11 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 7.2481 loss: 0.8952 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8952 2023/03/17 22:32:44 - mmengine - INFO - Epoch(train) [43][ 380/1320] lr: 2.0000e-03 eta: 0:57:04 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 7.4879 loss: 1.0266 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0266 2023/03/17 22:32:51 - mmengine - INFO - Epoch(train) [43][ 400/1320] lr: 2.0000e-03 eta: 0:56:57 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 7.3965 loss: 0.9325 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9325 2023/03/17 22:32:57 - mmengine - INFO - Epoch(train) [43][ 420/1320] lr: 2.0000e-03 eta: 0:56:51 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 7.1623 loss: 0.8839 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8839 2023/03/17 22:33:04 - mmengine - INFO - Epoch(train) [43][ 440/1320] lr: 2.0000e-03 eta: 0:56:44 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.4396 loss: 0.8634 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8634 2023/03/17 22:33:11 - mmengine - INFO - Epoch(train) [43][ 460/1320] lr: 2.0000e-03 eta: 0:56:37 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.2632 loss: 0.8752 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8752 2023/03/17 22:33:18 - mmengine - INFO - Epoch(train) [43][ 480/1320] lr: 2.0000e-03 eta: 0:56:30 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 7.3253 loss: 0.8868 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8868 2023/03/17 22:33:24 - mmengine - INFO - Epoch(train) [43][ 500/1320] lr: 2.0000e-03 eta: 0:56:24 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 7.1502 loss: 0.8410 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8410 2023/03/17 22:33:31 - mmengine - INFO - Epoch(train) [43][ 520/1320] lr: 2.0000e-03 eta: 0:56:17 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.3252 loss: 0.9158 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9158 2023/03/17 22:33:38 - mmengine - INFO - Epoch(train) [43][ 540/1320] lr: 2.0000e-03 eta: 0:56:10 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 7.5425 loss: 0.9616 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9616 2023/03/17 22:33:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:33:44 - mmengine - INFO - Epoch(train) [43][ 560/1320] lr: 2.0000e-03 eta: 0:56:03 time: 0.3356 data_time: 0.0122 memory: 18752 grad_norm: 7.4144 loss: 1.0336 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0336 2023/03/17 22:33:51 - mmengine - INFO - Epoch(train) [43][ 580/1320] lr: 2.0000e-03 eta: 0:55:57 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 7.3487 loss: 0.9457 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9457 2023/03/17 22:33:58 - mmengine - INFO - Epoch(train) [43][ 600/1320] lr: 2.0000e-03 eta: 0:55:50 time: 0.3353 data_time: 0.0125 memory: 18752 grad_norm: 7.4978 loss: 0.9674 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9674 2023/03/17 22:34:05 - mmengine - INFO - Epoch(train) [43][ 620/1320] lr: 2.0000e-03 eta: 0:55:43 time: 0.3364 data_time: 0.0119 memory: 18752 grad_norm: 7.5464 loss: 0.9592 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9592 2023/03/17 22:34:11 - mmengine - INFO - Epoch(train) [43][ 640/1320] lr: 2.0000e-03 eta: 0:55:37 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 7.4670 loss: 0.8772 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8772 2023/03/17 22:34:18 - mmengine - INFO - Epoch(train) [43][ 660/1320] lr: 2.0000e-03 eta: 0:55:30 time: 0.3367 data_time: 0.0122 memory: 18752 grad_norm: 7.1513 loss: 0.9393 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9393 2023/03/17 22:34:25 - mmengine - INFO - Epoch(train) [43][ 680/1320] lr: 2.0000e-03 eta: 0:55:23 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 7.2900 loss: 1.1223 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1223 2023/03/17 22:34:32 - mmengine - INFO - Epoch(train) [43][ 700/1320] lr: 2.0000e-03 eta: 0:55:16 time: 0.3447 data_time: 0.0174 memory: 18752 grad_norm: 7.0391 loss: 0.9997 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9997 2023/03/17 22:34:38 - mmengine - INFO - Epoch(train) [43][ 720/1320] lr: 2.0000e-03 eta: 0:55:10 time: 0.3367 data_time: 0.0106 memory: 18752 grad_norm: 7.2102 loss: 0.9791 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9791 2023/03/17 22:34:45 - mmengine - INFO - Epoch(train) [43][ 740/1320] lr: 2.0000e-03 eta: 0:55:03 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 7.6871 loss: 0.9567 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9567 2023/03/17 22:34:52 - mmengine - INFO - Epoch(train) [43][ 760/1320] lr: 2.0000e-03 eta: 0:54:56 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.2323 loss: 1.0182 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0182 2023/03/17 22:34:59 - mmengine - INFO - Epoch(train) [43][ 780/1320] lr: 2.0000e-03 eta: 0:54:49 time: 0.3365 data_time: 0.0124 memory: 18752 grad_norm: 7.3956 loss: 0.9771 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9771 2023/03/17 22:35:05 - mmengine - INFO - Epoch(train) [43][ 800/1320] lr: 2.0000e-03 eta: 0:54:43 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.3770 loss: 1.0308 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0308 2023/03/17 22:35:12 - mmengine - INFO - Epoch(train) [43][ 820/1320] lr: 2.0000e-03 eta: 0:54:36 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 7.2829 loss: 1.0793 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0793 2023/03/17 22:35:19 - mmengine - INFO - Epoch(train) [43][ 840/1320] lr: 2.0000e-03 eta: 0:54:29 time: 0.3477 data_time: 0.0126 memory: 18752 grad_norm: 7.0641 loss: 0.9971 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9971 2023/03/17 22:35:26 - mmengine - INFO - Epoch(train) [43][ 860/1320] lr: 2.0000e-03 eta: 0:54:23 time: 0.3355 data_time: 0.0125 memory: 18752 grad_norm: 7.1111 loss: 0.9192 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9192 2023/03/17 22:35:32 - mmengine - INFO - Epoch(train) [43][ 880/1320] lr: 2.0000e-03 eta: 0:54:16 time: 0.3352 data_time: 0.0118 memory: 18752 grad_norm: 7.3502 loss: 0.9225 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9225 2023/03/17 22:35:39 - mmengine - INFO - Epoch(train) [43][ 900/1320] lr: 2.0000e-03 eta: 0:54:09 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 7.5625 loss: 0.9282 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9282 2023/03/17 22:35:46 - mmengine - INFO - Epoch(train) [43][ 920/1320] lr: 2.0000e-03 eta: 0:54:02 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 7.5087 loss: 0.9136 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9136 2023/03/17 22:35:53 - mmengine - INFO - Epoch(train) [43][ 940/1320] lr: 2.0000e-03 eta: 0:53:56 time: 0.3364 data_time: 0.0117 memory: 18752 grad_norm: 7.3639 loss: 1.1695 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1695 2023/03/17 22:35:59 - mmengine - INFO - Epoch(train) [43][ 960/1320] lr: 2.0000e-03 eta: 0:53:49 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 7.4599 loss: 0.9191 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9191 2023/03/17 22:36:06 - mmengine - INFO - Epoch(train) [43][ 980/1320] lr: 2.0000e-03 eta: 0:53:42 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.3575 loss: 0.9940 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9940 2023/03/17 22:36:13 - mmengine - INFO - Epoch(train) [43][1000/1320] lr: 2.0000e-03 eta: 0:53:35 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.6290 loss: 1.0260 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0260 2023/03/17 22:36:19 - mmengine - INFO - Epoch(train) [43][1020/1320] lr: 2.0000e-03 eta: 0:53:29 time: 0.3374 data_time: 0.0118 memory: 18752 grad_norm: 7.3877 loss: 0.8885 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8885 2023/03/17 22:36:26 - mmengine - INFO - Epoch(train) [43][1040/1320] lr: 2.0000e-03 eta: 0:53:22 time: 0.3368 data_time: 0.0122 memory: 18752 grad_norm: 7.2347 loss: 0.9152 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9152 2023/03/17 22:36:33 - mmengine - INFO - Epoch(train) [43][1060/1320] lr: 2.0000e-03 eta: 0:53:15 time: 0.3365 data_time: 0.0125 memory: 18752 grad_norm: 7.2992 loss: 0.8846 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8846 2023/03/17 22:36:40 - mmengine - INFO - Epoch(train) [43][1080/1320] lr: 2.0000e-03 eta: 0:53:09 time: 0.3365 data_time: 0.0125 memory: 18752 grad_norm: 7.4279 loss: 0.9655 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9655 2023/03/17 22:36:46 - mmengine - INFO - Epoch(train) [43][1100/1320] lr: 2.0000e-03 eta: 0:53:02 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 7.3389 loss: 0.9409 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9409 2023/03/17 22:36:53 - mmengine - INFO - Epoch(train) [43][1120/1320] lr: 2.0000e-03 eta: 0:52:55 time: 0.3358 data_time: 0.0117 memory: 18752 grad_norm: 7.3461 loss: 0.9708 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9708 2023/03/17 22:37:00 - mmengine - INFO - Epoch(train) [43][1140/1320] lr: 2.0000e-03 eta: 0:52:48 time: 0.3370 data_time: 0.0121 memory: 18752 grad_norm: 7.3205 loss: 0.9130 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9130 2023/03/17 22:37:07 - mmengine - INFO - Epoch(train) [43][1160/1320] lr: 2.0000e-03 eta: 0:52:42 time: 0.3367 data_time: 0.0121 memory: 18752 grad_norm: 7.2406 loss: 1.0399 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0399 2023/03/17 22:37:13 - mmengine - INFO - Epoch(train) [43][1180/1320] lr: 2.0000e-03 eta: 0:52:35 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.3204 loss: 1.0482 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0482 2023/03/17 22:37:20 - mmengine - INFO - Epoch(train) [43][1200/1320] lr: 2.0000e-03 eta: 0:52:28 time: 0.3366 data_time: 0.0126 memory: 18752 grad_norm: 7.4745 loss: 0.7913 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7913 2023/03/17 22:37:27 - mmengine - INFO - Epoch(train) [43][1220/1320] lr: 2.0000e-03 eta: 0:52:21 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 7.6231 loss: 1.1060 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1060 2023/03/17 22:37:33 - mmengine - INFO - Epoch(train) [43][1240/1320] lr: 2.0000e-03 eta: 0:52:15 time: 0.3353 data_time: 0.0120 memory: 18752 grad_norm: 7.5242 loss: 0.8725 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8725 2023/03/17 22:37:40 - mmengine - INFO - Epoch(train) [43][1260/1320] lr: 2.0000e-03 eta: 0:52:08 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 7.2363 loss: 0.8693 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8693 2023/03/17 22:37:47 - mmengine - INFO - Epoch(train) [43][1280/1320] lr: 2.0000e-03 eta: 0:52:01 time: 0.3365 data_time: 0.0125 memory: 18752 grad_norm: 7.2769 loss: 1.1144 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1144 2023/03/17 22:37:54 - mmengine - INFO - Epoch(train) [43][1300/1320] lr: 2.0000e-03 eta: 0:51:55 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.3826 loss: 0.8734 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8734 2023/03/17 22:38:00 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:38:00 - mmengine - INFO - Epoch(train) [43][1320/1320] lr: 2.0000e-03 eta: 0:51:48 time: 0.3309 data_time: 0.0120 memory: 18752 grad_norm: 7.5302 loss: 0.9857 top1_acc: 0.8182 top5_acc: 0.9091 loss_cls: 0.9857 2023/03/17 22:38:03 - mmengine - INFO - Epoch(val) [43][ 20/194] eta: 0:00:22 time: 0.1293 data_time: 0.0430 memory: 2112 2023/03/17 22:38:05 - mmengine - INFO - Epoch(val) [43][ 40/194] eta: 0:00:17 time: 0.0958 data_time: 0.0098 memory: 2112 2023/03/17 22:38:07 - mmengine - INFO - Epoch(val) [43][ 60/194] eta: 0:00:14 time: 0.0966 data_time: 0.0110 memory: 2112 2023/03/17 22:38:09 - mmengine - INFO - Epoch(val) [43][ 80/194] eta: 0:00:11 time: 0.0972 data_time: 0.0109 memory: 2112 2023/03/17 22:38:11 - mmengine - INFO - Epoch(val) [43][100/194] eta: 0:00:09 time: 0.0971 data_time: 0.0110 memory: 2112 2023/03/17 22:38:13 - mmengine - INFO - Epoch(val) [43][120/194] eta: 0:00:07 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 22:38:14 - mmengine - INFO - Epoch(val) [43][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 22:38:16 - mmengine - INFO - Epoch(val) [43][160/194] eta: 0:00:03 time: 0.0970 data_time: 0.0109 memory: 2112 2023/03/17 22:38:18 - mmengine - INFO - Epoch(val) [43][180/194] eta: 0:00:01 time: 0.0967 data_time: 0.0110 memory: 2112 2023/03/17 22:38:22 - mmengine - INFO - Epoch(val) [43][194/194] acc/top1: 0.6002 acc/top5: 0.8616 acc/mean1: 0.5385 2023/03/17 22:38:29 - mmengine - INFO - Epoch(train) [44][ 20/1320] lr: 2.0000e-03 eta: 0:51:41 time: 0.3729 data_time: 0.0392 memory: 18752 grad_norm: 7.1829 loss: 1.0419 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0419 2023/03/17 22:38:36 - mmengine - INFO - Epoch(train) [44][ 40/1320] lr: 2.0000e-03 eta: 0:51:34 time: 0.3355 data_time: 0.0117 memory: 18752 grad_norm: 7.0297 loss: 1.0783 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0783 2023/03/17 22:38:43 - mmengine - INFO - Epoch(train) [44][ 60/1320] lr: 2.0000e-03 eta: 0:51:28 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 7.1787 loss: 0.9174 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9174 2023/03/17 22:38:49 - mmengine - INFO - Epoch(train) [44][ 80/1320] lr: 2.0000e-03 eta: 0:51:21 time: 0.3372 data_time: 0.0118 memory: 18752 grad_norm: 7.2485 loss: 0.8895 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8895 2023/03/17 22:38:56 - mmengine - INFO - Epoch(train) [44][ 100/1320] lr: 2.0000e-03 eta: 0:51:14 time: 0.3363 data_time: 0.0129 memory: 18752 grad_norm: 7.2047 loss: 0.8852 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8852 2023/03/17 22:39:03 - mmengine - INFO - Epoch(train) [44][ 120/1320] lr: 2.0000e-03 eta: 0:51:08 time: 0.3351 data_time: 0.0125 memory: 18752 grad_norm: 7.2427 loss: 0.8828 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8828 2023/03/17 22:39:09 - mmengine - INFO - Epoch(train) [44][ 140/1320] lr: 2.0000e-03 eta: 0:51:01 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 7.1706 loss: 0.8636 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8636 2023/03/17 22:39:16 - mmengine - INFO - Epoch(train) [44][ 160/1320] lr: 2.0000e-03 eta: 0:50:54 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 7.3672 loss: 1.0289 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0289 2023/03/17 22:39:23 - mmengine - INFO - Epoch(train) [44][ 180/1320] lr: 2.0000e-03 eta: 0:50:47 time: 0.3366 data_time: 0.0118 memory: 18752 grad_norm: 7.0858 loss: 1.1000 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1000 2023/03/17 22:39:30 - mmengine - INFO - Epoch(train) [44][ 200/1320] lr: 2.0000e-03 eta: 0:50:41 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 7.1864 loss: 0.9686 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9686 2023/03/17 22:39:36 - mmengine - INFO - Epoch(train) [44][ 220/1320] lr: 2.0000e-03 eta: 0:50:34 time: 0.3371 data_time: 0.0125 memory: 18752 grad_norm: 7.2099 loss: 1.0397 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0397 2023/03/17 22:39:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:39:43 - mmengine - INFO - Epoch(train) [44][ 240/1320] lr: 2.0000e-03 eta: 0:50:27 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.3598 loss: 0.7930 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7930 2023/03/17 22:39:50 - mmengine - INFO - Epoch(train) [44][ 260/1320] lr: 2.0000e-03 eta: 0:50:20 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 7.3160 loss: 0.8433 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8433 2023/03/17 22:39:57 - mmengine - INFO - Epoch(train) [44][ 280/1320] lr: 2.0000e-03 eta: 0:50:14 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.5231 loss: 0.8657 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8657 2023/03/17 22:40:03 - mmengine - INFO - Epoch(train) [44][ 300/1320] lr: 2.0000e-03 eta: 0:50:07 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 7.5046 loss: 0.8884 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8884 2023/03/17 22:40:10 - mmengine - INFO - Epoch(train) [44][ 320/1320] lr: 2.0000e-03 eta: 0:50:00 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 7.4425 loss: 0.9858 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9858 2023/03/17 22:40:17 - mmengine - INFO - Epoch(train) [44][ 340/1320] lr: 2.0000e-03 eta: 0:49:54 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.5045 loss: 0.9220 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9220 2023/03/17 22:40:23 - mmengine - INFO - Epoch(train) [44][ 360/1320] lr: 2.0000e-03 eta: 0:49:47 time: 0.3362 data_time: 0.0126 memory: 18752 grad_norm: 7.6925 loss: 1.0654 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0654 2023/03/17 22:40:30 - mmengine - INFO - Epoch(train) [44][ 380/1320] lr: 2.0000e-03 eta: 0:49:40 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.5518 loss: 0.9713 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9713 2023/03/17 22:40:37 - mmengine - INFO - Epoch(train) [44][ 400/1320] lr: 2.0000e-03 eta: 0:49:33 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 7.2504 loss: 0.8772 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8772 2023/03/17 22:40:44 - mmengine - INFO - Epoch(train) [44][ 420/1320] lr: 2.0000e-03 eta: 0:49:27 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 7.4176 loss: 1.0144 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0144 2023/03/17 22:40:50 - mmengine - INFO - Epoch(train) [44][ 440/1320] lr: 2.0000e-03 eta: 0:49:20 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 7.3077 loss: 0.7912 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7912 2023/03/17 22:40:57 - mmengine - INFO - Epoch(train) [44][ 460/1320] lr: 2.0000e-03 eta: 0:49:13 time: 0.3363 data_time: 0.0120 memory: 18752 grad_norm: 7.3485 loss: 0.9926 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.9926 2023/03/17 22:41:04 - mmengine - INFO - Epoch(train) [44][ 480/1320] lr: 2.0000e-03 eta: 0:49:06 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 7.3894 loss: 1.0702 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0702 2023/03/17 22:41:11 - mmengine - INFO - Epoch(train) [44][ 500/1320] lr: 2.0000e-03 eta: 0:49:00 time: 0.3359 data_time: 0.0117 memory: 18752 grad_norm: 7.4528 loss: 0.9847 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9847 2023/03/17 22:41:17 - mmengine - INFO - Epoch(train) [44][ 520/1320] lr: 2.0000e-03 eta: 0:48:53 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 7.6674 loss: 1.0024 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0024 2023/03/17 22:41:24 - mmengine - INFO - Epoch(train) [44][ 540/1320] lr: 2.0000e-03 eta: 0:48:46 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.3040 loss: 1.0315 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0315 2023/03/17 22:41:31 - mmengine - INFO - Epoch(train) [44][ 560/1320] lr: 2.0000e-03 eta: 0:48:40 time: 0.3354 data_time: 0.0120 memory: 18752 grad_norm: 7.4993 loss: 0.8898 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8898 2023/03/17 22:41:37 - mmengine - INFO - Epoch(train) [44][ 580/1320] lr: 2.0000e-03 eta: 0:48:33 time: 0.3365 data_time: 0.0117 memory: 18752 grad_norm: 7.5126 loss: 0.9357 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9357 2023/03/17 22:41:44 - mmengine - INFO - Epoch(train) [44][ 600/1320] lr: 2.0000e-03 eta: 0:48:26 time: 0.3366 data_time: 0.0118 memory: 18752 grad_norm: 7.2853 loss: 0.8978 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8978 2023/03/17 22:41:51 - mmengine - INFO - Epoch(train) [44][ 620/1320] lr: 2.0000e-03 eta: 0:48:19 time: 0.3364 data_time: 0.0119 memory: 18752 grad_norm: 7.4252 loss: 0.9337 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9337 2023/03/17 22:41:58 - mmengine - INFO - Epoch(train) [44][ 640/1320] lr: 2.0000e-03 eta: 0:48:13 time: 0.3364 data_time: 0.0117 memory: 18752 grad_norm: 7.3645 loss: 0.8253 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8253 2023/03/17 22:42:04 - mmengine - INFO - Epoch(train) [44][ 660/1320] lr: 2.0000e-03 eta: 0:48:06 time: 0.3358 data_time: 0.0115 memory: 18752 grad_norm: 7.4004 loss: 1.0003 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0003 2023/03/17 22:42:11 - mmengine - INFO - Epoch(train) [44][ 680/1320] lr: 2.0000e-03 eta: 0:47:59 time: 0.3358 data_time: 0.0119 memory: 18752 grad_norm: 7.5804 loss: 1.0679 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0679 2023/03/17 22:42:18 - mmengine - INFO - Epoch(train) [44][ 700/1320] lr: 2.0000e-03 eta: 0:47:52 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 7.6635 loss: 1.0318 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0318 2023/03/17 22:42:24 - mmengine - INFO - Epoch(train) [44][ 720/1320] lr: 2.0000e-03 eta: 0:47:46 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 7.9156 loss: 0.9514 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9514 2023/03/17 22:42:31 - mmengine - INFO - Epoch(train) [44][ 740/1320] lr: 2.0000e-03 eta: 0:47:39 time: 0.3353 data_time: 0.0119 memory: 18752 grad_norm: 7.5260 loss: 1.0195 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.0195 2023/03/17 22:42:38 - mmengine - INFO - Epoch(train) [44][ 760/1320] lr: 2.0000e-03 eta: 0:47:32 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 7.6292 loss: 0.8225 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8225 2023/03/17 22:42:45 - mmengine - INFO - Epoch(train) [44][ 780/1320] lr: 2.0000e-03 eta: 0:47:25 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 7.6826 loss: 0.9145 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9145 2023/03/17 22:42:51 - mmengine - INFO - Epoch(train) [44][ 800/1320] lr: 2.0000e-03 eta: 0:47:19 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 7.3603 loss: 0.8777 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8777 2023/03/17 22:42:58 - mmengine - INFO - Epoch(train) [44][ 820/1320] lr: 2.0000e-03 eta: 0:47:12 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 7.4201 loss: 0.8401 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8401 2023/03/17 22:43:05 - mmengine - INFO - Epoch(train) [44][ 840/1320] lr: 2.0000e-03 eta: 0:47:05 time: 0.3356 data_time: 0.0115 memory: 18752 grad_norm: 7.3909 loss: 0.9673 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9673 2023/03/17 22:43:11 - mmengine - INFO - Epoch(train) [44][ 860/1320] lr: 2.0000e-03 eta: 0:46:59 time: 0.3362 data_time: 0.0117 memory: 18752 grad_norm: 7.4398 loss: 0.8639 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8639 2023/03/17 22:43:18 - mmengine - INFO - Epoch(train) [44][ 880/1320] lr: 2.0000e-03 eta: 0:46:52 time: 0.3353 data_time: 0.0118 memory: 18752 grad_norm: 7.5592 loss: 0.9824 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9824 2023/03/17 22:43:25 - mmengine - INFO - Epoch(train) [44][ 900/1320] lr: 2.0000e-03 eta: 0:46:45 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.4997 loss: 0.9223 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9223 2023/03/17 22:43:32 - mmengine - INFO - Epoch(train) [44][ 920/1320] lr: 2.0000e-03 eta: 0:46:38 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 7.5403 loss: 0.8431 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8431 2023/03/17 22:43:38 - mmengine - INFO - Epoch(train) [44][ 940/1320] lr: 2.0000e-03 eta: 0:46:32 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 7.6549 loss: 0.9343 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9343 2023/03/17 22:43:45 - mmengine - INFO - Epoch(train) [44][ 960/1320] lr: 2.0000e-03 eta: 0:46:25 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 7.4077 loss: 1.1109 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1109 2023/03/17 22:43:52 - mmengine - INFO - Epoch(train) [44][ 980/1320] lr: 2.0000e-03 eta: 0:46:18 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 7.4075 loss: 1.0229 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0229 2023/03/17 22:43:58 - mmengine - INFO - Epoch(train) [44][1000/1320] lr: 2.0000e-03 eta: 0:46:11 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.4211 loss: 0.8627 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8627 2023/03/17 22:44:05 - mmengine - INFO - Epoch(train) [44][1020/1320] lr: 2.0000e-03 eta: 0:46:05 time: 0.3363 data_time: 0.0124 memory: 18752 grad_norm: 7.5290 loss: 0.9395 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9395 2023/03/17 22:44:12 - mmengine - INFO - Epoch(train) [44][1040/1320] lr: 2.0000e-03 eta: 0:45:58 time: 0.3368 data_time: 0.0116 memory: 18752 grad_norm: 7.5589 loss: 0.9845 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9845 2023/03/17 22:44:19 - mmengine - INFO - Epoch(train) [44][1060/1320] lr: 2.0000e-03 eta: 0:45:51 time: 0.3366 data_time: 0.0129 memory: 18752 grad_norm: 7.3153 loss: 0.8695 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8695 2023/03/17 22:44:25 - mmengine - INFO - Epoch(train) [44][1080/1320] lr: 2.0000e-03 eta: 0:45:45 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 7.6780 loss: 0.9417 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9417 2023/03/17 22:44:32 - mmengine - INFO - Epoch(train) [44][1100/1320] lr: 2.0000e-03 eta: 0:45:38 time: 0.3367 data_time: 0.0122 memory: 18752 grad_norm: 7.5130 loss: 0.8254 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8254 2023/03/17 22:44:39 - mmengine - INFO - Epoch(train) [44][1120/1320] lr: 2.0000e-03 eta: 0:45:31 time: 0.3473 data_time: 0.0118 memory: 18752 grad_norm: 7.4576 loss: 1.0435 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0435 2023/03/17 22:44:46 - mmengine - INFO - Epoch(train) [44][1140/1320] lr: 2.0000e-03 eta: 0:45:24 time: 0.3368 data_time: 0.0120 memory: 18752 grad_norm: 7.7037 loss: 0.9715 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9715 2023/03/17 22:44:53 - mmengine - INFO - Epoch(train) [44][1160/1320] lr: 2.0000e-03 eta: 0:45:18 time: 0.3371 data_time: 0.0122 memory: 18752 grad_norm: 7.5293 loss: 1.0498 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0498 2023/03/17 22:44:59 - mmengine - INFO - Epoch(train) [44][1180/1320] lr: 2.0000e-03 eta: 0:45:11 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 7.6672 loss: 1.0831 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0831 2023/03/17 22:45:06 - mmengine - INFO - Epoch(train) [44][1200/1320] lr: 2.0000e-03 eta: 0:45:04 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.4809 loss: 0.9717 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9717 2023/03/17 22:45:13 - mmengine - INFO - Epoch(train) [44][1220/1320] lr: 2.0000e-03 eta: 0:44:57 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.5481 loss: 0.9574 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9574 2023/03/17 22:45:20 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:45:20 - mmengine - INFO - Epoch(train) [44][1240/1320] lr: 2.0000e-03 eta: 0:44:51 time: 0.3373 data_time: 0.0123 memory: 18752 grad_norm: 7.5300 loss: 1.1245 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1245 2023/03/17 22:45:26 - mmengine - INFO - Epoch(train) [44][1260/1320] lr: 2.0000e-03 eta: 0:44:44 time: 0.3371 data_time: 0.0125 memory: 18752 grad_norm: 7.4495 loss: 0.9998 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9998 2023/03/17 22:45:33 - mmengine - INFO - Epoch(train) [44][1280/1320] lr: 2.0000e-03 eta: 0:44:37 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.4657 loss: 0.8632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8632 2023/03/17 22:45:40 - mmengine - INFO - Epoch(train) [44][1300/1320] lr: 2.0000e-03 eta: 0:44:31 time: 0.3365 data_time: 0.0117 memory: 18752 grad_norm: 7.6796 loss: 1.1890 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1890 2023/03/17 22:45:46 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:45:46 - mmengine - INFO - Epoch(train) [44][1320/1320] lr: 2.0000e-03 eta: 0:44:24 time: 0.3309 data_time: 0.0117 memory: 18752 grad_norm: 7.5214 loss: 1.1015 top1_acc: 0.8182 top5_acc: 0.9091 loss_cls: 1.1015 2023/03/17 22:45:49 - mmengine - INFO - Epoch(val) [44][ 20/194] eta: 0:00:22 time: 0.1272 data_time: 0.0408 memory: 2112 2023/03/17 22:45:51 - mmengine - INFO - Epoch(val) [44][ 40/194] eta: 0:00:17 time: 0.0961 data_time: 0.0100 memory: 2112 2023/03/17 22:45:53 - mmengine - INFO - Epoch(val) [44][ 60/194] eta: 0:00:14 time: 0.0966 data_time: 0.0104 memory: 2112 2023/03/17 22:45:55 - mmengine - INFO - Epoch(val) [44][ 80/194] eta: 0:00:11 time: 0.0982 data_time: 0.0117 memory: 2112 2023/03/17 22:45:57 - mmengine - INFO - Epoch(val) [44][100/194] eta: 0:00:09 time: 0.0981 data_time: 0.0122 memory: 2112 2023/03/17 22:45:59 - mmengine - INFO - Epoch(val) [44][120/194] eta: 0:00:07 time: 0.0971 data_time: 0.0109 memory: 2112 2023/03/17 22:46:01 - mmengine - INFO - Epoch(val) [44][140/194] eta: 0:00:05 time: 0.0962 data_time: 0.0105 memory: 2112 2023/03/17 22:46:03 - mmengine - INFO - Epoch(val) [44][160/194] eta: 0:00:03 time: 0.1011 data_time: 0.0151 memory: 2112 2023/03/17 22:46:05 - mmengine - INFO - Epoch(val) [44][180/194] eta: 0:00:01 time: 0.0966 data_time: 0.0107 memory: 2112 2023/03/17 22:46:08 - mmengine - INFO - Epoch(val) [44][194/194] acc/top1: 0.6006 acc/top5: 0.8597 acc/mean1: 0.5437 2023/03/17 22:46:15 - mmengine - INFO - Epoch(train) [45][ 20/1320] lr: 2.0000e-03 eta: 0:44:17 time: 0.3753 data_time: 0.0419 memory: 18752 grad_norm: 7.2743 loss: 0.9202 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9202 2023/03/17 22:46:22 - mmengine - INFO - Epoch(train) [45][ 40/1320] lr: 2.0000e-03 eta: 0:44:10 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 7.4051 loss: 0.9320 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9320 2023/03/17 22:46:28 - mmengine - INFO - Epoch(train) [45][ 60/1320] lr: 2.0000e-03 eta: 0:44:04 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 7.2136 loss: 0.9886 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9886 2023/03/17 22:46:35 - mmengine - INFO - Epoch(train) [45][ 80/1320] lr: 2.0000e-03 eta: 0:43:57 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 7.3247 loss: 0.9009 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9009 2023/03/17 22:46:42 - mmengine - INFO - Epoch(train) [45][ 100/1320] lr: 2.0000e-03 eta: 0:43:50 time: 0.3359 data_time: 0.0126 memory: 18752 grad_norm: 7.3711 loss: 0.8253 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8253 2023/03/17 22:46:49 - mmengine - INFO - Epoch(train) [45][ 120/1320] lr: 2.0000e-03 eta: 0:43:44 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 7.5505 loss: 0.8968 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8968 2023/03/17 22:46:55 - mmengine - INFO - Epoch(train) [45][ 140/1320] lr: 2.0000e-03 eta: 0:43:37 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 7.3966 loss: 0.7921 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7921 2023/03/17 22:47:02 - mmengine - INFO - Epoch(train) [45][ 160/1320] lr: 2.0000e-03 eta: 0:43:30 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 7.4333 loss: 0.9219 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9219 2023/03/17 22:47:09 - mmengine - INFO - Epoch(train) [45][ 180/1320] lr: 2.0000e-03 eta: 0:43:23 time: 0.3362 data_time: 0.0127 memory: 18752 grad_norm: 7.3377 loss: 0.8648 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8648 2023/03/17 22:47:16 - mmengine - INFO - Epoch(train) [45][ 200/1320] lr: 2.0000e-03 eta: 0:43:17 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 7.6498 loss: 1.0540 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0540 2023/03/17 22:47:22 - mmengine - INFO - Epoch(train) [45][ 220/1320] lr: 2.0000e-03 eta: 0:43:10 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 7.3591 loss: 0.7850 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7850 2023/03/17 22:47:29 - mmengine - INFO - Epoch(train) [45][ 240/1320] lr: 2.0000e-03 eta: 0:43:03 time: 0.3352 data_time: 0.0120 memory: 18752 grad_norm: 7.7293 loss: 0.9312 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9312 2023/03/17 22:47:36 - mmengine - INFO - Epoch(train) [45][ 260/1320] lr: 2.0000e-03 eta: 0:42:56 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 7.3459 loss: 0.9072 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9072 2023/03/17 22:47:42 - mmengine - INFO - Epoch(train) [45][ 280/1320] lr: 2.0000e-03 eta: 0:42:50 time: 0.3354 data_time: 0.0126 memory: 18752 grad_norm: 7.5072 loss: 0.8967 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8967 2023/03/17 22:47:49 - mmengine - INFO - Epoch(train) [45][ 300/1320] lr: 2.0000e-03 eta: 0:42:43 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 7.6525 loss: 0.9648 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9648 2023/03/17 22:47:56 - mmengine - INFO - Epoch(train) [45][ 320/1320] lr: 2.0000e-03 eta: 0:42:36 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.6757 loss: 0.9412 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9412 2023/03/17 22:48:03 - mmengine - INFO - Epoch(train) [45][ 340/1320] lr: 2.0000e-03 eta: 0:42:30 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 7.4417 loss: 0.9942 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9942 2023/03/17 22:48:09 - mmengine - INFO - Epoch(train) [45][ 360/1320] lr: 2.0000e-03 eta: 0:42:23 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 7.4512 loss: 1.0158 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0158 2023/03/17 22:48:16 - mmengine - INFO - Epoch(train) [45][ 380/1320] lr: 2.0000e-03 eta: 0:42:16 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 7.7535 loss: 0.8577 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8577 2023/03/17 22:48:23 - mmengine - INFO - Epoch(train) [45][ 400/1320] lr: 2.0000e-03 eta: 0:42:09 time: 0.3360 data_time: 0.0118 memory: 18752 grad_norm: 7.3602 loss: 0.9216 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9216 2023/03/17 22:48:29 - mmengine - INFO - Epoch(train) [45][ 420/1320] lr: 2.0000e-03 eta: 0:42:03 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.4921 loss: 0.9278 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9278 2023/03/17 22:48:36 - mmengine - INFO - Epoch(train) [45][ 440/1320] lr: 2.0000e-03 eta: 0:41:56 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.3979 loss: 0.7748 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7748 2023/03/17 22:48:43 - mmengine - INFO - Epoch(train) [45][ 460/1320] lr: 2.0000e-03 eta: 0:41:49 time: 0.3358 data_time: 0.0116 memory: 18752 grad_norm: 7.8588 loss: 1.1649 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1649 2023/03/17 22:48:50 - mmengine - INFO - Epoch(train) [45][ 480/1320] lr: 2.0000e-03 eta: 0:41:42 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 7.9691 loss: 0.9518 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9518 2023/03/17 22:48:56 - mmengine - INFO - Epoch(train) [45][ 500/1320] lr: 2.0000e-03 eta: 0:41:36 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 7.5864 loss: 0.9269 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9269 2023/03/17 22:49:03 - mmengine - INFO - Epoch(train) [45][ 520/1320] lr: 2.0000e-03 eta: 0:41:29 time: 0.3356 data_time: 0.0118 memory: 18752 grad_norm: 7.7385 loss: 0.9287 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9287 2023/03/17 22:49:10 - mmengine - INFO - Epoch(train) [45][ 540/1320] lr: 2.0000e-03 eta: 0:41:22 time: 0.3360 data_time: 0.0115 memory: 18752 grad_norm: 7.7589 loss: 1.0247 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0247 2023/03/17 22:49:16 - mmengine - INFO - Epoch(train) [45][ 560/1320] lr: 2.0000e-03 eta: 0:41:16 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 7.4055 loss: 1.0016 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0016 2023/03/17 22:49:23 - mmengine - INFO - Epoch(train) [45][ 580/1320] lr: 2.0000e-03 eta: 0:41:09 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.7895 loss: 0.9849 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.9849 2023/03/17 22:49:30 - mmengine - INFO - Epoch(train) [45][ 600/1320] lr: 2.0000e-03 eta: 0:41:02 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.2536 loss: 0.8819 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8819 2023/03/17 22:49:37 - mmengine - INFO - Epoch(train) [45][ 620/1320] lr: 2.0000e-03 eta: 0:40:55 time: 0.3365 data_time: 0.0118 memory: 18752 grad_norm: 7.4461 loss: 0.9866 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 0.9866 2023/03/17 22:49:43 - mmengine - INFO - Epoch(train) [45][ 640/1320] lr: 2.0000e-03 eta: 0:40:49 time: 0.3361 data_time: 0.0127 memory: 18752 grad_norm: 7.2682 loss: 1.0625 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0625 2023/03/17 22:49:50 - mmengine - INFO - Epoch(train) [45][ 660/1320] lr: 2.0000e-03 eta: 0:40:42 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.6092 loss: 0.8795 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8795 2023/03/17 22:49:57 - mmengine - INFO - Epoch(train) [45][ 680/1320] lr: 2.0000e-03 eta: 0:40:35 time: 0.3360 data_time: 0.0127 memory: 18752 grad_norm: 7.6436 loss: 0.9355 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9355 2023/03/17 22:50:04 - mmengine - INFO - Epoch(train) [45][ 700/1320] lr: 2.0000e-03 eta: 0:40:28 time: 0.3363 data_time: 0.0126 memory: 18752 grad_norm: 7.9208 loss: 0.9865 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9865 2023/03/17 22:50:10 - mmengine - INFO - Epoch(train) [45][ 720/1320] lr: 2.0000e-03 eta: 0:40:22 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 7.7127 loss: 1.0960 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0960 2023/03/17 22:50:17 - mmengine - INFO - Epoch(train) [45][ 740/1320] lr: 2.0000e-03 eta: 0:40:15 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.8373 loss: 0.8892 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8892 2023/03/17 22:50:24 - mmengine - INFO - Epoch(train) [45][ 760/1320] lr: 2.0000e-03 eta: 0:40:08 time: 0.3366 data_time: 0.0127 memory: 18752 grad_norm: 7.6651 loss: 0.9134 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9134 2023/03/17 22:50:30 - mmengine - INFO - Epoch(train) [45][ 780/1320] lr: 2.0000e-03 eta: 0:40:01 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 7.7171 loss: 0.8715 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8715 2023/03/17 22:50:37 - mmengine - INFO - Epoch(train) [45][ 800/1320] lr: 2.0000e-03 eta: 0:39:55 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 7.5058 loss: 0.9477 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9477 2023/03/17 22:50:44 - mmengine - INFO - Epoch(train) [45][ 820/1320] lr: 2.0000e-03 eta: 0:39:48 time: 0.3369 data_time: 0.0121 memory: 18752 grad_norm: 7.7510 loss: 0.9369 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9369 2023/03/17 22:50:51 - mmengine - INFO - Epoch(train) [45][ 840/1320] lr: 2.0000e-03 eta: 0:39:41 time: 0.3367 data_time: 0.0125 memory: 18752 grad_norm: 7.7081 loss: 1.0827 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0827 2023/03/17 22:50:57 - mmengine - INFO - Epoch(train) [45][ 860/1320] lr: 2.0000e-03 eta: 0:39:35 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 7.7134 loss: 1.0194 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0194 2023/03/17 22:51:04 - mmengine - INFO - Epoch(train) [45][ 880/1320] lr: 2.0000e-03 eta: 0:39:28 time: 0.3358 data_time: 0.0125 memory: 18752 grad_norm: 7.9027 loss: 1.1153 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1153 2023/03/17 22:51:11 - mmengine - INFO - Epoch(train) [45][ 900/1320] lr: 2.0000e-03 eta: 0:39:21 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 7.5411 loss: 1.0178 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0178 2023/03/17 22:51:18 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:51:18 - mmengine - INFO - Epoch(train) [45][ 920/1320] lr: 2.0000e-03 eta: 0:39:14 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.4666 loss: 0.8843 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8843 2023/03/17 22:51:24 - mmengine - INFO - Epoch(train) [45][ 940/1320] lr: 2.0000e-03 eta: 0:39:08 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 7.4320 loss: 0.8774 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8774 2023/03/17 22:51:31 - mmengine - INFO - Epoch(train) [45][ 960/1320] lr: 2.0000e-03 eta: 0:39:01 time: 0.3365 data_time: 0.0129 memory: 18752 grad_norm: 7.6148 loss: 0.8516 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8516 2023/03/17 22:51:38 - mmengine - INFO - Epoch(train) [45][ 980/1320] lr: 2.0000e-03 eta: 0:38:54 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.8112 loss: 1.0478 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0478 2023/03/17 22:51:44 - mmengine - INFO - Epoch(train) [45][1000/1320] lr: 2.0000e-03 eta: 0:38:47 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.3916 loss: 0.9603 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9603 2023/03/17 22:51:51 - mmengine - INFO - Epoch(train) [45][1020/1320] lr: 2.0000e-03 eta: 0:38:41 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.5944 loss: 0.8289 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8289 2023/03/17 22:51:58 - mmengine - INFO - Epoch(train) [45][1040/1320] lr: 2.0000e-03 eta: 0:38:34 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.9045 loss: 0.9927 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9927 2023/03/17 22:52:05 - mmengine - INFO - Epoch(train) [45][1060/1320] lr: 2.0000e-03 eta: 0:38:27 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 7.7424 loss: 0.8787 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8787 2023/03/17 22:52:11 - mmengine - INFO - Epoch(train) [45][1080/1320] lr: 2.0000e-03 eta: 0:38:21 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 7.8048 loss: 1.0312 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0312 2023/03/17 22:52:18 - mmengine - INFO - Epoch(train) [45][1100/1320] lr: 2.0000e-03 eta: 0:38:14 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 7.7208 loss: 1.0150 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0150 2023/03/17 22:52:25 - mmengine - INFO - Epoch(train) [45][1120/1320] lr: 2.0000e-03 eta: 0:38:07 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 8.0615 loss: 0.8501 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8501 2023/03/17 22:52:31 - mmengine - INFO - Epoch(train) [45][1140/1320] lr: 2.0000e-03 eta: 0:38:00 time: 0.3366 data_time: 0.0117 memory: 18752 grad_norm: 7.8385 loss: 1.0108 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0108 2023/03/17 22:52:38 - mmengine - INFO - Epoch(train) [45][1160/1320] lr: 2.0000e-03 eta: 0:37:54 time: 0.3362 data_time: 0.0124 memory: 18752 grad_norm: 7.4944 loss: 0.8213 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8213 2023/03/17 22:52:45 - mmengine - INFO - Epoch(train) [45][1180/1320] lr: 2.0000e-03 eta: 0:37:47 time: 0.3367 data_time: 0.0123 memory: 18752 grad_norm: 7.7958 loss: 0.8680 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8680 2023/03/17 22:52:52 - mmengine - INFO - Epoch(train) [45][1200/1320] lr: 2.0000e-03 eta: 0:37:40 time: 0.3357 data_time: 0.0116 memory: 18752 grad_norm: 7.7145 loss: 1.1228 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1228 2023/03/17 22:52:58 - mmengine - INFO - Epoch(train) [45][1220/1320] lr: 2.0000e-03 eta: 0:37:33 time: 0.3356 data_time: 0.0117 memory: 18752 grad_norm: 7.8536 loss: 0.8663 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8663 2023/03/17 22:53:05 - mmengine - INFO - Epoch(train) [45][1240/1320] lr: 2.0000e-03 eta: 0:37:27 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 7.7087 loss: 0.9003 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9003 2023/03/17 22:53:12 - mmengine - INFO - Epoch(train) [45][1260/1320] lr: 2.0000e-03 eta: 0:37:20 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 7.8976 loss: 1.0293 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0293 2023/03/17 22:53:19 - mmengine - INFO - Epoch(train) [45][1280/1320] lr: 2.0000e-03 eta: 0:37:13 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 7.5898 loss: 0.9059 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9059 2023/03/17 22:53:25 - mmengine - INFO - Epoch(train) [45][1300/1320] lr: 2.0000e-03 eta: 0:37:07 time: 0.3358 data_time: 0.0123 memory: 18752 grad_norm: 7.4174 loss: 0.8233 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8233 2023/03/17 22:53:32 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:53:32 - mmengine - INFO - Epoch(train) [45][1320/1320] lr: 2.0000e-03 eta: 0:37:00 time: 0.3305 data_time: 0.0121 memory: 18752 grad_norm: 7.8406 loss: 0.9391 top1_acc: 0.7273 top5_acc: 0.9091 loss_cls: 0.9391 2023/03/17 22:53:32 - mmengine - INFO - Saving checkpoint at 45 epochs 2023/03/17 22:53:37 - mmengine - INFO - Epoch(val) [45][ 20/194] eta: 0:00:22 time: 0.1267 data_time: 0.0402 memory: 2112 2023/03/17 22:53:39 - mmengine - INFO - Epoch(val) [45][ 40/194] eta: 0:00:17 time: 0.0966 data_time: 0.0108 memory: 2112 2023/03/17 22:53:41 - mmengine - INFO - Epoch(val) [45][ 60/194] eta: 0:00:14 time: 0.0966 data_time: 0.0108 memory: 2112 2023/03/17 22:53:43 - mmengine - INFO - Epoch(val) [45][ 80/194] eta: 0:00:11 time: 0.0963 data_time: 0.0107 memory: 2112 2023/03/17 22:53:45 - mmengine - INFO - Epoch(val) [45][100/194] eta: 0:00:09 time: 0.0965 data_time: 0.0108 memory: 2112 2023/03/17 22:53:47 - mmengine - INFO - Epoch(val) [45][120/194] eta: 0:00:07 time: 0.0970 data_time: 0.0108 memory: 2112 2023/03/17 22:53:49 - mmengine - INFO - Epoch(val) [45][140/194] eta: 0:00:05 time: 0.0971 data_time: 0.0111 memory: 2112 2023/03/17 22:53:51 - mmengine - INFO - Epoch(val) [45][160/194] eta: 0:00:03 time: 0.0962 data_time: 0.0106 memory: 2112 2023/03/17 22:53:53 - mmengine - INFO - Epoch(val) [45][180/194] eta: 0:00:01 time: 0.0954 data_time: 0.0099 memory: 2112 2023/03/17 22:53:55 - mmengine - INFO - Epoch(val) [45][194/194] acc/top1: 0.5984 acc/top5: 0.8580 acc/mean1: 0.5403 2023/03/17 22:54:02 - mmengine - INFO - Epoch(train) [46][ 20/1320] lr: 2.0000e-04 eta: 0:36:53 time: 0.3733 data_time: 0.0413 memory: 18752 grad_norm: 7.6530 loss: 0.9909 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9909 2023/03/17 22:54:09 - mmengine - INFO - Epoch(train) [46][ 40/1320] lr: 2.0000e-04 eta: 0:36:46 time: 0.3362 data_time: 0.0114 memory: 18752 grad_norm: 7.5501 loss: 0.8880 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.8880 2023/03/17 22:54:16 - mmengine - INFO - Epoch(train) [46][ 60/1320] lr: 2.0000e-04 eta: 0:36:40 time: 0.3362 data_time: 0.0116 memory: 18752 grad_norm: 7.2591 loss: 0.9862 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9862 2023/03/17 22:54:23 - mmengine - INFO - Epoch(train) [46][ 80/1320] lr: 2.0000e-04 eta: 0:36:33 time: 0.3364 data_time: 0.0126 memory: 18752 grad_norm: 7.4129 loss: 0.8694 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8694 2023/03/17 22:54:29 - mmengine - INFO - Epoch(train) [46][ 100/1320] lr: 2.0000e-04 eta: 0:36:26 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 7.4299 loss: 0.9916 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9916 2023/03/17 22:54:36 - mmengine - INFO - Epoch(train) [46][ 120/1320] lr: 2.0000e-04 eta: 0:36:20 time: 0.3354 data_time: 0.0119 memory: 18752 grad_norm: 7.2676 loss: 0.8490 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8490 2023/03/17 22:54:43 - mmengine - INFO - Epoch(train) [46][ 140/1320] lr: 2.0000e-04 eta: 0:36:13 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 7.4882 loss: 0.8041 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8041 2023/03/17 22:54:50 - mmengine - INFO - Epoch(train) [46][ 160/1320] lr: 2.0000e-04 eta: 0:36:06 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 7.3586 loss: 0.9911 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9911 2023/03/17 22:54:56 - mmengine - INFO - Epoch(train) [46][ 180/1320] lr: 2.0000e-04 eta: 0:35:59 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.2990 loss: 0.8905 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8905 2023/03/17 22:55:03 - mmengine - INFO - Epoch(train) [46][ 200/1320] lr: 2.0000e-04 eta: 0:35:53 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 7.2795 loss: 0.8140 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8140 2023/03/17 22:55:10 - mmengine - INFO - Epoch(train) [46][ 220/1320] lr: 2.0000e-04 eta: 0:35:46 time: 0.3358 data_time: 0.0126 memory: 18752 grad_norm: 7.2611 loss: 0.9875 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9875 2023/03/17 22:55:16 - mmengine - INFO - Epoch(train) [46][ 240/1320] lr: 2.0000e-04 eta: 0:35:39 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 7.4680 loss: 0.9872 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9872 2023/03/17 22:55:23 - mmengine - INFO - Epoch(train) [46][ 260/1320] lr: 2.0000e-04 eta: 0:35:32 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 7.2821 loss: 0.8813 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8813 2023/03/17 22:55:30 - mmengine - INFO - Epoch(train) [46][ 280/1320] lr: 2.0000e-04 eta: 0:35:26 time: 0.3355 data_time: 0.0122 memory: 18752 grad_norm: 7.2104 loss: 0.7982 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7982 2023/03/17 22:55:37 - mmengine - INFO - Epoch(train) [46][ 300/1320] lr: 2.0000e-04 eta: 0:35:19 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 7.1119 loss: 0.7816 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7816 2023/03/17 22:55:43 - mmengine - INFO - Epoch(train) [46][ 320/1320] lr: 2.0000e-04 eta: 0:35:12 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 7.0434 loss: 0.8489 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8489 2023/03/17 22:55:50 - mmengine - INFO - Epoch(train) [46][ 340/1320] lr: 2.0000e-04 eta: 0:35:05 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 7.7317 loss: 0.8235 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8235 2023/03/17 22:55:57 - mmengine - INFO - Epoch(train) [46][ 360/1320] lr: 2.0000e-04 eta: 0:34:59 time: 0.3358 data_time: 0.0128 memory: 18752 grad_norm: 7.4531 loss: 0.7781 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7781 2023/03/17 22:56:03 - mmengine - INFO - Epoch(train) [46][ 380/1320] lr: 2.0000e-04 eta: 0:34:52 time: 0.3367 data_time: 0.0120 memory: 18752 grad_norm: 7.1602 loss: 0.8880 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8880 2023/03/17 22:56:10 - mmengine - INFO - Epoch(train) [46][ 400/1320] lr: 2.0000e-04 eta: 0:34:45 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 7.2347 loss: 0.6964 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6964 2023/03/17 22:56:17 - mmengine - INFO - Epoch(train) [46][ 420/1320] lr: 2.0000e-04 eta: 0:34:39 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 7.2564 loss: 0.7701 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7701 2023/03/17 22:56:24 - mmengine - INFO - Epoch(train) [46][ 440/1320] lr: 2.0000e-04 eta: 0:34:32 time: 0.3355 data_time: 0.0123 memory: 18752 grad_norm: 7.3633 loss: 0.9739 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9739 2023/03/17 22:56:30 - mmengine - INFO - Epoch(train) [46][ 460/1320] lr: 2.0000e-04 eta: 0:34:25 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.3957 loss: 0.8486 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8486 2023/03/17 22:56:37 - mmengine - INFO - Epoch(train) [46][ 480/1320] lr: 2.0000e-04 eta: 0:34:18 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 7.3850 loss: 0.8869 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.8869 2023/03/17 22:56:44 - mmengine - INFO - Epoch(train) [46][ 500/1320] lr: 2.0000e-04 eta: 0:34:12 time: 0.3352 data_time: 0.0123 memory: 18752 grad_norm: 7.2838 loss: 0.8192 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8192 2023/03/17 22:56:50 - mmengine - INFO - Epoch(train) [46][ 520/1320] lr: 2.0000e-04 eta: 0:34:05 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 7.3535 loss: 0.7819 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7819 2023/03/17 22:56:57 - mmengine - INFO - Epoch(train) [46][ 540/1320] lr: 2.0000e-04 eta: 0:33:58 time: 0.3356 data_time: 0.0120 memory: 18752 grad_norm: 7.2536 loss: 0.7630 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7630 2023/03/17 22:57:04 - mmengine - INFO - Epoch(train) [46][ 560/1320] lr: 2.0000e-04 eta: 0:33:51 time: 0.3353 data_time: 0.0121 memory: 18752 grad_norm: 7.3237 loss: 0.7988 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7988 2023/03/17 22:57:11 - mmengine - INFO - Epoch(train) [46][ 580/1320] lr: 2.0000e-04 eta: 0:33:45 time: 0.3374 data_time: 0.0127 memory: 18752 grad_norm: 7.3498 loss: 0.9711 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9711 2023/03/17 22:57:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 22:57:17 - mmengine - INFO - Epoch(train) [46][ 600/1320] lr: 2.0000e-04 eta: 0:33:38 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 7.2260 loss: 0.7570 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7570 2023/03/17 22:57:24 - mmengine - INFO - Epoch(train) [46][ 620/1320] lr: 2.0000e-04 eta: 0:33:31 time: 0.3365 data_time: 0.0127 memory: 18752 grad_norm: 7.2320 loss: 0.8268 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8268 2023/03/17 22:57:31 - mmengine - INFO - Epoch(train) [46][ 640/1320] lr: 2.0000e-04 eta: 0:33:25 time: 0.3372 data_time: 0.0125 memory: 18752 grad_norm: 7.3324 loss: 0.9875 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9875 2023/03/17 22:57:38 - mmengine - INFO - Epoch(train) [46][ 660/1320] lr: 2.0000e-04 eta: 0:33:18 time: 0.3366 data_time: 0.0120 memory: 18752 grad_norm: 7.3768 loss: 0.9232 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9232 2023/03/17 22:57:44 - mmengine - INFO - Epoch(train) [46][ 680/1320] lr: 2.0000e-04 eta: 0:33:11 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 7.3151 loss: 0.7903 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7903 2023/03/17 22:57:51 - mmengine - INFO - Epoch(train) [46][ 700/1320] lr: 2.0000e-04 eta: 0:33:04 time: 0.3367 data_time: 0.0121 memory: 18752 grad_norm: 7.7306 loss: 0.7067 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7067 2023/03/17 22:57:58 - mmengine - INFO - Epoch(train) [46][ 720/1320] lr: 2.0000e-04 eta: 0:32:58 time: 0.3360 data_time: 0.0122 memory: 18752 grad_norm: 7.2574 loss: 0.9584 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9584 2023/03/17 22:58:04 - mmengine - INFO - Epoch(train) [46][ 740/1320] lr: 2.0000e-04 eta: 0:32:51 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 7.1897 loss: 0.8637 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8637 2023/03/17 22:58:11 - mmengine - INFO - Epoch(train) [46][ 760/1320] lr: 2.0000e-04 eta: 0:32:44 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.2911 loss: 0.7975 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7975 2023/03/17 22:58:18 - mmengine - INFO - Epoch(train) [46][ 780/1320] lr: 2.0000e-04 eta: 0:32:37 time: 0.3367 data_time: 0.0120 memory: 18752 grad_norm: 7.4201 loss: 0.8297 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8297 2023/03/17 22:58:25 - mmengine - INFO - Epoch(train) [46][ 800/1320] lr: 2.0000e-04 eta: 0:32:31 time: 0.3363 data_time: 0.0119 memory: 18752 grad_norm: 7.3901 loss: 0.8914 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8914 2023/03/17 22:58:31 - mmengine - INFO - Epoch(train) [46][ 820/1320] lr: 2.0000e-04 eta: 0:32:24 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 7.1255 loss: 0.7025 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7025 2023/03/17 22:58:38 - mmengine - INFO - Epoch(train) [46][ 840/1320] lr: 2.0000e-04 eta: 0:32:17 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 7.3232 loss: 0.8565 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8565 2023/03/17 22:58:45 - mmengine - INFO - Epoch(train) [46][ 860/1320] lr: 2.0000e-04 eta: 0:32:11 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 7.5341 loss: 0.8759 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8759 2023/03/17 22:58:52 - mmengine - INFO - Epoch(train) [46][ 880/1320] lr: 2.0000e-04 eta: 0:32:04 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 7.1808 loss: 0.8010 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8010 2023/03/17 22:58:58 - mmengine - INFO - Epoch(train) [46][ 900/1320] lr: 2.0000e-04 eta: 0:31:57 time: 0.3368 data_time: 0.0121 memory: 18752 grad_norm: 7.1912 loss: 0.9075 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9075 2023/03/17 22:59:05 - mmengine - INFO - Epoch(train) [46][ 920/1320] lr: 2.0000e-04 eta: 0:31:50 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 6.9700 loss: 0.7722 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7722 2023/03/17 22:59:12 - mmengine - INFO - Epoch(train) [46][ 940/1320] lr: 2.0000e-04 eta: 0:31:44 time: 0.3369 data_time: 0.0132 memory: 18752 grad_norm: 7.1425 loss: 0.7916 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7916 2023/03/17 22:59:19 - mmengine - INFO - Epoch(train) [46][ 960/1320] lr: 2.0000e-04 eta: 0:31:37 time: 0.3364 data_time: 0.0131 memory: 18752 grad_norm: 7.2225 loss: 0.8020 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8020 2023/03/17 22:59:25 - mmengine - INFO - Epoch(train) [46][ 980/1320] lr: 2.0000e-04 eta: 0:31:30 time: 0.3368 data_time: 0.0119 memory: 18752 grad_norm: 7.4412 loss: 0.7474 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7474 2023/03/17 22:59:32 - mmengine - INFO - Epoch(train) [46][1000/1320] lr: 2.0000e-04 eta: 0:31:23 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.4246 loss: 0.8130 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8130 2023/03/17 22:59:39 - mmengine - INFO - Epoch(train) [46][1020/1320] lr: 2.0000e-04 eta: 0:31:17 time: 0.3369 data_time: 0.0126 memory: 18752 grad_norm: 7.4845 loss: 0.8898 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8898 2023/03/17 22:59:45 - mmengine - INFO - Epoch(train) [46][1040/1320] lr: 2.0000e-04 eta: 0:31:10 time: 0.3361 data_time: 0.0123 memory: 18752 grad_norm: 7.2942 loss: 0.7452 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7452 2023/03/17 22:59:52 - mmengine - INFO - Epoch(train) [46][1060/1320] lr: 2.0000e-04 eta: 0:31:03 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 7.2441 loss: 0.9245 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9245 2023/03/17 22:59:59 - mmengine - INFO - Epoch(train) [46][1080/1320] lr: 2.0000e-04 eta: 0:30:57 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.6990 loss: 0.9117 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9117 2023/03/17 23:00:06 - mmengine - INFO - Epoch(train) [46][1100/1320] lr: 2.0000e-04 eta: 0:30:50 time: 0.3366 data_time: 0.0124 memory: 18752 grad_norm: 7.0287 loss: 0.7644 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7644 2023/03/17 23:00:12 - mmengine - INFO - Epoch(train) [46][1120/1320] lr: 2.0000e-04 eta: 0:30:43 time: 0.3368 data_time: 0.0132 memory: 18752 grad_norm: 7.1601 loss: 0.7124 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7124 2023/03/17 23:00:19 - mmengine - INFO - Epoch(train) [46][1140/1320] lr: 2.0000e-04 eta: 0:30:36 time: 0.3366 data_time: 0.0126 memory: 18752 grad_norm: 7.3560 loss: 0.8525 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8525 2023/03/17 23:00:26 - mmengine - INFO - Epoch(train) [46][1160/1320] lr: 2.0000e-04 eta: 0:30:30 time: 0.3367 data_time: 0.0129 memory: 18752 grad_norm: 7.6444 loss: 0.8623 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8623 2023/03/17 23:00:33 - mmengine - INFO - Epoch(train) [46][1180/1320] lr: 2.0000e-04 eta: 0:30:23 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.3539 loss: 0.9008 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9008 2023/03/17 23:00:39 - mmengine - INFO - Epoch(train) [46][1200/1320] lr: 2.0000e-04 eta: 0:30:16 time: 0.3362 data_time: 0.0127 memory: 18752 grad_norm: 7.1270 loss: 0.8136 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8136 2023/03/17 23:00:46 - mmengine - INFO - Epoch(train) [46][1220/1320] lr: 2.0000e-04 eta: 0:30:09 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 7.1900 loss: 0.8264 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8264 2023/03/17 23:00:53 - mmengine - INFO - Epoch(train) [46][1240/1320] lr: 2.0000e-04 eta: 0:30:03 time: 0.3367 data_time: 0.0121 memory: 18752 grad_norm: 7.1586 loss: 0.8607 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8607 2023/03/17 23:00:59 - mmengine - INFO - Epoch(train) [46][1260/1320] lr: 2.0000e-04 eta: 0:29:56 time: 0.3366 data_time: 0.0131 memory: 18752 grad_norm: 7.4118 loss: 0.8032 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8032 2023/03/17 23:01:06 - mmengine - INFO - Epoch(train) [46][1280/1320] lr: 2.0000e-04 eta: 0:29:49 time: 0.3370 data_time: 0.0124 memory: 18752 grad_norm: 7.6755 loss: 0.9180 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9180 2023/03/17 23:01:13 - mmengine - INFO - Epoch(train) [46][1300/1320] lr: 2.0000e-04 eta: 0:29:43 time: 0.3364 data_time: 0.0131 memory: 18752 grad_norm: 7.4250 loss: 0.8657 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8657 2023/03/17 23:01:20 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:01:20 - mmengine - INFO - Epoch(train) [46][1320/1320] lr: 2.0000e-04 eta: 0:29:36 time: 0.3318 data_time: 0.0132 memory: 18752 grad_norm: 6.9464 loss: 0.7722 top1_acc: 0.7273 top5_acc: 0.9091 loss_cls: 0.7722 2023/03/17 23:01:22 - mmengine - INFO - Epoch(val) [46][ 20/194] eta: 0:00:22 time: 0.1272 data_time: 0.0407 memory: 2112 2023/03/17 23:01:24 - mmengine - INFO - Epoch(val) [46][ 40/194] eta: 0:00:17 time: 0.0967 data_time: 0.0107 memory: 2112 2023/03/17 23:01:26 - mmengine - INFO - Epoch(val) [46][ 60/194] eta: 0:00:14 time: 0.0964 data_time: 0.0106 memory: 2112 2023/03/17 23:01:28 - mmengine - INFO - Epoch(val) [46][ 80/194] eta: 0:00:11 time: 0.0969 data_time: 0.0108 memory: 2112 2023/03/17 23:01:30 - mmengine - INFO - Epoch(val) [46][100/194] eta: 0:00:09 time: 0.0964 data_time: 0.0104 memory: 2112 2023/03/17 23:01:32 - mmengine - INFO - Epoch(val) [46][120/194] eta: 0:00:07 time: 0.0964 data_time: 0.0107 memory: 2112 2023/03/17 23:01:34 - mmengine - INFO - Epoch(val) [46][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 23:01:36 - mmengine - INFO - Epoch(val) [46][160/194] eta: 0:00:03 time: 0.0980 data_time: 0.0118 memory: 2112 2023/03/17 23:01:38 - mmengine - INFO - Epoch(val) [46][180/194] eta: 0:00:01 time: 0.0975 data_time: 0.0111 memory: 2112 2023/03/17 23:01:41 - mmengine - INFO - Epoch(val) [46][194/194] acc/top1: 0.6111 acc/top5: 0.8667 acc/mean1: 0.5519 2023/03/17 23:01:41 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_32.pth is removed 2023/03/17 23:01:42 - mmengine - INFO - The best checkpoint with 0.6111 acc/top1 at 46 epoch is saved to best_acc/top1_epoch_46.pth. 2023/03/17 23:01:50 - mmengine - INFO - Epoch(train) [47][ 20/1320] lr: 2.0000e-04 eta: 0:29:29 time: 0.3689 data_time: 0.0372 memory: 18752 grad_norm: 7.2380 loss: 0.7239 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7239 2023/03/17 23:01:57 - mmengine - INFO - Epoch(train) [47][ 40/1320] lr: 2.0000e-04 eta: 0:29:22 time: 0.3375 data_time: 0.0126 memory: 18752 grad_norm: 7.3419 loss: 0.7789 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7789 2023/03/17 23:02:03 - mmengine - INFO - Epoch(train) [47][ 60/1320] lr: 2.0000e-04 eta: 0:29:16 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 7.3273 loss: 0.8009 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8009 2023/03/17 23:02:10 - mmengine - INFO - Epoch(train) [47][ 80/1320] lr: 2.0000e-04 eta: 0:29:09 time: 0.3361 data_time: 0.0130 memory: 18752 grad_norm: 7.6899 loss: 0.8498 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8498 2023/03/17 23:02:17 - mmengine - INFO - Epoch(train) [47][ 100/1320] lr: 2.0000e-04 eta: 0:29:02 time: 0.3370 data_time: 0.0122 memory: 18752 grad_norm: 7.3923 loss: 1.0514 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0514 2023/03/17 23:02:23 - mmengine - INFO - Epoch(train) [47][ 120/1320] lr: 2.0000e-04 eta: 0:28:55 time: 0.3356 data_time: 0.0131 memory: 18752 grad_norm: 7.2392 loss: 0.7736 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7736 2023/03/17 23:02:30 - mmengine - INFO - Epoch(train) [47][ 140/1320] lr: 2.0000e-04 eta: 0:28:49 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.4722 loss: 0.8633 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8633 2023/03/17 23:02:37 - mmengine - INFO - Epoch(train) [47][ 160/1320] lr: 2.0000e-04 eta: 0:28:42 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 7.4064 loss: 0.7643 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7643 2023/03/17 23:02:44 - mmengine - INFO - Epoch(train) [47][ 180/1320] lr: 2.0000e-04 eta: 0:28:35 time: 0.3355 data_time: 0.0124 memory: 18752 grad_norm: 7.0855 loss: 0.7874 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7874 2023/03/17 23:02:50 - mmengine - INFO - Epoch(train) [47][ 200/1320] lr: 2.0000e-04 eta: 0:28:29 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 7.3849 loss: 0.8612 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8612 2023/03/17 23:02:57 - mmengine - INFO - Epoch(train) [47][ 220/1320] lr: 2.0000e-04 eta: 0:28:22 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.1492 loss: 0.8157 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8157 2023/03/17 23:03:04 - mmengine - INFO - Epoch(train) [47][ 240/1320] lr: 2.0000e-04 eta: 0:28:15 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.3323 loss: 0.8382 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8382 2023/03/17 23:03:10 - mmengine - INFO - Epoch(train) [47][ 260/1320] lr: 2.0000e-04 eta: 0:28:08 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.4365 loss: 0.8635 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8635 2023/03/17 23:03:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:03:17 - mmengine - INFO - Epoch(train) [47][ 280/1320] lr: 2.0000e-04 eta: 0:28:02 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 7.4013 loss: 0.8255 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8255 2023/03/17 23:03:24 - mmengine - INFO - Epoch(train) [47][ 300/1320] lr: 2.0000e-04 eta: 0:27:55 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 7.0416 loss: 0.8191 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8191 2023/03/17 23:03:31 - mmengine - INFO - Epoch(train) [47][ 320/1320] lr: 2.0000e-04 eta: 0:27:48 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.2119 loss: 0.6866 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6866 2023/03/17 23:03:37 - mmengine - INFO - Epoch(train) [47][ 340/1320] lr: 2.0000e-04 eta: 0:27:41 time: 0.3362 data_time: 0.0126 memory: 18752 grad_norm: 7.6835 loss: 0.8388 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8388 2023/03/17 23:03:44 - mmengine - INFO - Epoch(train) [47][ 360/1320] lr: 2.0000e-04 eta: 0:27:35 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 7.4311 loss: 0.9696 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9696 2023/03/17 23:03:51 - mmengine - INFO - Epoch(train) [47][ 380/1320] lr: 2.0000e-04 eta: 0:27:28 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.2940 loss: 0.7870 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7870 2023/03/17 23:03:58 - mmengine - INFO - Epoch(train) [47][ 400/1320] lr: 2.0000e-04 eta: 0:27:21 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.4335 loss: 0.8009 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8009 2023/03/17 23:04:04 - mmengine - INFO - Epoch(train) [47][ 420/1320] lr: 2.0000e-04 eta: 0:27:15 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.3970 loss: 0.8319 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8319 2023/03/17 23:04:11 - mmengine - INFO - Epoch(train) [47][ 440/1320] lr: 2.0000e-04 eta: 0:27:08 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 7.4167 loss: 0.9399 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9399 2023/03/17 23:04:18 - mmengine - INFO - Epoch(train) [47][ 460/1320] lr: 2.0000e-04 eta: 0:27:01 time: 0.3360 data_time: 0.0125 memory: 18752 grad_norm: 7.7456 loss: 0.8495 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8495 2023/03/17 23:04:24 - mmengine - INFO - Epoch(train) [47][ 480/1320] lr: 2.0000e-04 eta: 0:26:54 time: 0.3362 data_time: 0.0124 memory: 18752 grad_norm: 7.5926 loss: 0.8057 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8057 2023/03/17 23:04:31 - mmengine - INFO - Epoch(train) [47][ 500/1320] lr: 2.0000e-04 eta: 0:26:48 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 7.4223 loss: 0.8027 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8027 2023/03/17 23:04:38 - mmengine - INFO - Epoch(train) [47][ 520/1320] lr: 2.0000e-04 eta: 0:26:41 time: 0.3360 data_time: 0.0124 memory: 18752 grad_norm: 7.1729 loss: 0.6968 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6968 2023/03/17 23:04:45 - mmengine - INFO - Epoch(train) [47][ 540/1320] lr: 2.0000e-04 eta: 0:26:34 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 7.4120 loss: 0.8366 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8366 2023/03/17 23:04:51 - mmengine - INFO - Epoch(train) [47][ 560/1320] lr: 2.0000e-04 eta: 0:26:27 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.2919 loss: 0.7723 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7723 2023/03/17 23:04:58 - mmengine - INFO - Epoch(train) [47][ 580/1320] lr: 2.0000e-04 eta: 0:26:21 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.3198 loss: 0.8879 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8879 2023/03/17 23:05:05 - mmengine - INFO - Epoch(train) [47][ 600/1320] lr: 2.0000e-04 eta: 0:26:14 time: 0.3366 data_time: 0.0124 memory: 18752 grad_norm: 7.5968 loss: 0.7830 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7830 2023/03/17 23:05:11 - mmengine - INFO - Epoch(train) [47][ 620/1320] lr: 2.0000e-04 eta: 0:26:07 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.2053 loss: 0.7660 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7660 2023/03/17 23:05:18 - mmengine - INFO - Epoch(train) [47][ 640/1320] lr: 2.0000e-04 eta: 0:26:01 time: 0.3356 data_time: 0.0123 memory: 18752 grad_norm: 7.3356 loss: 0.8060 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8060 2023/03/17 23:05:25 - mmengine - INFO - Epoch(train) [47][ 660/1320] lr: 2.0000e-04 eta: 0:25:54 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 7.4774 loss: 0.8395 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8395 2023/03/17 23:05:32 - mmengine - INFO - Epoch(train) [47][ 680/1320] lr: 2.0000e-04 eta: 0:25:47 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.2281 loss: 0.6965 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6965 2023/03/17 23:05:38 - mmengine - INFO - Epoch(train) [47][ 700/1320] lr: 2.0000e-04 eta: 0:25:40 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 7.2877 loss: 0.9600 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9600 2023/03/17 23:05:45 - mmengine - INFO - Epoch(train) [47][ 720/1320] lr: 2.0000e-04 eta: 0:25:34 time: 0.3361 data_time: 0.0129 memory: 18752 grad_norm: 7.4391 loss: 0.8262 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8262 2023/03/17 23:05:52 - mmengine - INFO - Epoch(train) [47][ 740/1320] lr: 2.0000e-04 eta: 0:25:27 time: 0.3367 data_time: 0.0125 memory: 18752 grad_norm: 7.5020 loss: 0.9107 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9107 2023/03/17 23:05:59 - mmengine - INFO - Epoch(train) [47][ 760/1320] lr: 2.0000e-04 eta: 0:25:20 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 7.2545 loss: 0.6872 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6872 2023/03/17 23:06:05 - mmengine - INFO - Epoch(train) [47][ 780/1320] lr: 2.0000e-04 eta: 0:25:13 time: 0.3360 data_time: 0.0119 memory: 18752 grad_norm: 7.3047 loss: 0.8211 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8211 2023/03/17 23:06:12 - mmengine - INFO - Epoch(train) [47][ 800/1320] lr: 2.0000e-04 eta: 0:25:07 time: 0.3367 data_time: 0.0122 memory: 18752 grad_norm: 7.3969 loss: 0.8211 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8211 2023/03/17 23:06:19 - mmengine - INFO - Epoch(train) [47][ 820/1320] lr: 2.0000e-04 eta: 0:25:00 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 7.2494 loss: 0.8066 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8066 2023/03/17 23:06:25 - mmengine - INFO - Epoch(train) [47][ 840/1320] lr: 2.0000e-04 eta: 0:24:53 time: 0.3365 data_time: 0.0125 memory: 18752 grad_norm: 7.5381 loss: 0.9110 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9110 2023/03/17 23:06:32 - mmengine - INFO - Epoch(train) [47][ 860/1320] lr: 2.0000e-04 eta: 0:24:46 time: 0.3367 data_time: 0.0120 memory: 18752 grad_norm: 7.0181 loss: 0.7401 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7401 2023/03/17 23:06:39 - mmengine - INFO - Epoch(train) [47][ 880/1320] lr: 2.0000e-04 eta: 0:24:40 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 7.6040 loss: 0.7642 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7642 2023/03/17 23:06:46 - mmengine - INFO - Epoch(train) [47][ 900/1320] lr: 2.0000e-04 eta: 0:24:33 time: 0.3363 data_time: 0.0115 memory: 18752 grad_norm: 7.3057 loss: 0.7891 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7891 2023/03/17 23:06:52 - mmengine - INFO - Epoch(train) [47][ 920/1320] lr: 2.0000e-04 eta: 0:24:26 time: 0.3367 data_time: 0.0125 memory: 18752 grad_norm: 7.3268 loss: 0.7675 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7675 2023/03/17 23:06:59 - mmengine - INFO - Epoch(train) [47][ 940/1320] lr: 2.0000e-04 eta: 0:24:20 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 7.1620 loss: 0.8616 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8616 2023/03/17 23:07:06 - mmengine - INFO - Epoch(train) [47][ 960/1320] lr: 2.0000e-04 eta: 0:24:13 time: 0.3366 data_time: 0.0123 memory: 18752 grad_norm: 7.6745 loss: 0.7051 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7051 2023/03/17 23:07:13 - mmengine - INFO - Epoch(train) [47][ 980/1320] lr: 2.0000e-04 eta: 0:24:06 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 7.5137 loss: 0.7637 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7637 2023/03/17 23:07:19 - mmengine - INFO - Epoch(train) [47][1000/1320] lr: 2.0000e-04 eta: 0:23:59 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.3892 loss: 0.8145 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8145 2023/03/17 23:07:26 - mmengine - INFO - Epoch(train) [47][1020/1320] lr: 2.0000e-04 eta: 0:23:53 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 7.4669 loss: 0.6640 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6640 2023/03/17 23:07:33 - mmengine - INFO - Epoch(train) [47][1040/1320] lr: 2.0000e-04 eta: 0:23:46 time: 0.3366 data_time: 0.0118 memory: 18752 grad_norm: 7.5283 loss: 0.8629 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8629 2023/03/17 23:07:39 - mmengine - INFO - Epoch(train) [47][1060/1320] lr: 2.0000e-04 eta: 0:23:39 time: 0.3359 data_time: 0.0119 memory: 18752 grad_norm: 7.3496 loss: 0.7105 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7105 2023/03/17 23:07:46 - mmengine - INFO - Epoch(train) [47][1080/1320] lr: 2.0000e-04 eta: 0:23:32 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 7.5979 loss: 0.7838 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7838 2023/03/17 23:07:53 - mmengine - INFO - Epoch(train) [47][1100/1320] lr: 2.0000e-04 eta: 0:23:26 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.2838 loss: 0.8076 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8076 2023/03/17 23:08:00 - mmengine - INFO - Epoch(train) [47][1120/1320] lr: 2.0000e-04 eta: 0:23:19 time: 0.3357 data_time: 0.0125 memory: 18752 grad_norm: 7.5422 loss: 0.8519 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8519 2023/03/17 23:08:06 - mmengine - INFO - Epoch(train) [47][1140/1320] lr: 2.0000e-04 eta: 0:23:12 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.4918 loss: 0.7640 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7640 2023/03/17 23:08:13 - mmengine - INFO - Epoch(train) [47][1160/1320] lr: 2.0000e-04 eta: 0:23:06 time: 0.3366 data_time: 0.0122 memory: 18752 grad_norm: 7.3696 loss: 0.9025 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9025 2023/03/17 23:08:20 - mmengine - INFO - Epoch(train) [47][1180/1320] lr: 2.0000e-04 eta: 0:22:59 time: 0.3368 data_time: 0.0120 memory: 18752 grad_norm: 7.5481 loss: 0.9287 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9287 2023/03/17 23:08:27 - mmengine - INFO - Epoch(train) [47][1200/1320] lr: 2.0000e-04 eta: 0:22:52 time: 0.3364 data_time: 0.0121 memory: 18752 grad_norm: 7.3785 loss: 0.8372 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8372 2023/03/17 23:08:33 - mmengine - INFO - Epoch(train) [47][1220/1320] lr: 2.0000e-04 eta: 0:22:45 time: 0.3365 data_time: 0.0121 memory: 18752 grad_norm: 7.4650 loss: 0.9548 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 0.9548 2023/03/17 23:08:40 - mmengine - INFO - Epoch(train) [47][1240/1320] lr: 2.0000e-04 eta: 0:22:39 time: 0.3367 data_time: 0.0126 memory: 18752 grad_norm: 7.4449 loss: 0.7180 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7180 2023/03/17 23:08:47 - mmengine - INFO - Epoch(train) [47][1260/1320] lr: 2.0000e-04 eta: 0:22:32 time: 0.3369 data_time: 0.0115 memory: 18752 grad_norm: 7.3585 loss: 0.9486 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9486 2023/03/17 23:08:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:08:53 - mmengine - INFO - Epoch(train) [47][1280/1320] lr: 2.0000e-04 eta: 0:22:25 time: 0.3361 data_time: 0.0116 memory: 18752 grad_norm: 7.5235 loss: 0.9598 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9598 2023/03/17 23:09:00 - mmengine - INFO - Epoch(train) [47][1300/1320] lr: 2.0000e-04 eta: 0:22:18 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 7.3692 loss: 0.8107 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8107 2023/03/17 23:09:07 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:09:07 - mmengine - INFO - Epoch(train) [47][1320/1320] lr: 2.0000e-04 eta: 0:22:12 time: 0.3311 data_time: 0.0120 memory: 18752 grad_norm: 7.3329 loss: 0.8687 top1_acc: 0.7273 top5_acc: 0.9091 loss_cls: 0.8687 2023/03/17 23:09:09 - mmengine - INFO - Epoch(val) [47][ 20/194] eta: 0:00:22 time: 0.1301 data_time: 0.0438 memory: 2112 2023/03/17 23:09:11 - mmengine - INFO - Epoch(val) [47][ 40/194] eta: 0:00:17 time: 0.0950 data_time: 0.0092 memory: 2112 2023/03/17 23:09:13 - mmengine - INFO - Epoch(val) [47][ 60/194] eta: 0:00:14 time: 0.0968 data_time: 0.0108 memory: 2112 2023/03/17 23:09:15 - mmengine - INFO - Epoch(val) [47][ 80/194] eta: 0:00:11 time: 0.0971 data_time: 0.0109 memory: 2112 2023/03/17 23:09:17 - mmengine - INFO - Epoch(val) [47][100/194] eta: 0:00:09 time: 0.0967 data_time: 0.0107 memory: 2112 2023/03/17 23:09:19 - mmengine - INFO - Epoch(val) [47][120/194] eta: 0:00:07 time: 0.0988 data_time: 0.0118 memory: 2112 2023/03/17 23:09:21 - mmengine - INFO - Epoch(val) [47][140/194] eta: 0:00:05 time: 0.0975 data_time: 0.0110 memory: 2112 2023/03/17 23:09:23 - mmengine - INFO - Epoch(val) [47][160/194] eta: 0:00:03 time: 0.0977 data_time: 0.0116 memory: 2112 2023/03/17 23:09:25 - mmengine - INFO - Epoch(val) [47][180/194] eta: 0:00:01 time: 0.0978 data_time: 0.0116 memory: 2112 2023/03/17 23:09:29 - mmengine - INFO - Epoch(val) [47][194/194] acc/top1: 0.6115 acc/top5: 0.8662 acc/mean1: 0.5522 2023/03/17 23:09:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_46.pth is removed 2023/03/17 23:09:30 - mmengine - INFO - The best checkpoint with 0.6115 acc/top1 at 47 epoch is saved to best_acc/top1_epoch_47.pth. 2023/03/17 23:09:38 - mmengine - INFO - Epoch(train) [48][ 20/1320] lr: 2.0000e-04 eta: 0:22:05 time: 0.3683 data_time: 0.0375 memory: 18752 grad_norm: 7.5393 loss: 0.8350 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8350 2023/03/17 23:09:45 - mmengine - INFO - Epoch(train) [48][ 40/1320] lr: 2.0000e-04 eta: 0:21:58 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 7.1053 loss: 0.7572 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7572 2023/03/17 23:09:51 - mmengine - INFO - Epoch(train) [48][ 60/1320] lr: 2.0000e-04 eta: 0:21:52 time: 0.3370 data_time: 0.0128 memory: 18752 grad_norm: 7.2251 loss: 0.8496 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.8496 2023/03/17 23:09:58 - mmengine - INFO - Epoch(train) [48][ 80/1320] lr: 2.0000e-04 eta: 0:21:45 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 7.3126 loss: 0.7449 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7449 2023/03/17 23:10:05 - mmengine - INFO - Epoch(train) [48][ 100/1320] lr: 2.0000e-04 eta: 0:21:38 time: 0.3365 data_time: 0.0125 memory: 18752 grad_norm: 7.2589 loss: 0.9165 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9165 2023/03/17 23:10:12 - mmengine - INFO - Epoch(train) [48][ 120/1320] lr: 2.0000e-04 eta: 0:21:31 time: 0.3361 data_time: 0.0122 memory: 18752 grad_norm: 7.4502 loss: 0.8656 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8656 2023/03/17 23:10:18 - mmengine - INFO - Epoch(train) [48][ 140/1320] lr: 2.0000e-04 eta: 0:21:25 time: 0.3366 data_time: 0.0120 memory: 18752 grad_norm: 7.4313 loss: 0.7300 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7300 2023/03/17 23:10:25 - mmengine - INFO - Epoch(train) [48][ 160/1320] lr: 2.0000e-04 eta: 0:21:18 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 7.3816 loss: 0.9043 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9043 2023/03/17 23:10:32 - mmengine - INFO - Epoch(train) [48][ 180/1320] lr: 2.0000e-04 eta: 0:21:11 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 7.3183 loss: 0.6551 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6551 2023/03/17 23:10:38 - mmengine - INFO - Epoch(train) [48][ 200/1320] lr: 2.0000e-04 eta: 0:21:04 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 7.2320 loss: 0.8487 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8487 2023/03/17 23:10:45 - mmengine - INFO - Epoch(train) [48][ 220/1320] lr: 2.0000e-04 eta: 0:20:58 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 7.2623 loss: 0.7352 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7352 2023/03/17 23:10:52 - mmengine - INFO - Epoch(train) [48][ 240/1320] lr: 2.0000e-04 eta: 0:20:51 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.5583 loss: 0.9216 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9216 2023/03/17 23:10:59 - mmengine - INFO - Epoch(train) [48][ 260/1320] lr: 2.0000e-04 eta: 0:20:44 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.4143 loss: 0.7477 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7477 2023/03/17 23:11:05 - mmengine - INFO - Epoch(train) [48][ 280/1320] lr: 2.0000e-04 eta: 0:20:38 time: 0.3358 data_time: 0.0122 memory: 18752 grad_norm: 7.2211 loss: 0.7776 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7776 2023/03/17 23:11:12 - mmengine - INFO - Epoch(train) [48][ 300/1320] lr: 2.0000e-04 eta: 0:20:31 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.2828 loss: 0.8299 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8299 2023/03/17 23:11:19 - mmengine - INFO - Epoch(train) [48][ 320/1320] lr: 2.0000e-04 eta: 0:20:24 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.4961 loss: 0.8299 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8299 2023/03/17 23:11:25 - mmengine - INFO - Epoch(train) [48][ 340/1320] lr: 2.0000e-04 eta: 0:20:17 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 7.6755 loss: 0.8488 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8488 2023/03/17 23:11:32 - mmengine - INFO - Epoch(train) [48][ 360/1320] lr: 2.0000e-04 eta: 0:20:11 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.5623 loss: 0.7591 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7591 2023/03/17 23:11:39 - mmengine - INFO - Epoch(train) [48][ 380/1320] lr: 2.0000e-04 eta: 0:20:04 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 7.1548 loss: 0.9060 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9060 2023/03/17 23:11:46 - mmengine - INFO - Epoch(train) [48][ 400/1320] lr: 2.0000e-04 eta: 0:19:57 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 7.7482 loss: 0.9228 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9228 2023/03/17 23:11:52 - mmengine - INFO - Epoch(train) [48][ 420/1320] lr: 2.0000e-04 eta: 0:19:50 time: 0.3367 data_time: 0.0119 memory: 18752 grad_norm: 7.3893 loss: 0.6336 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6336 2023/03/17 23:11:59 - mmengine - INFO - Epoch(train) [48][ 440/1320] lr: 2.0000e-04 eta: 0:19:44 time: 0.3367 data_time: 0.0120 memory: 18752 grad_norm: 7.4381 loss: 0.9119 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9119 2023/03/17 23:12:06 - mmengine - INFO - Epoch(train) [48][ 460/1320] lr: 2.0000e-04 eta: 0:19:37 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 7.3229 loss: 0.8772 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8772 2023/03/17 23:12:13 - mmengine - INFO - Epoch(train) [48][ 480/1320] lr: 2.0000e-04 eta: 0:19:30 time: 0.3357 data_time: 0.0120 memory: 18752 grad_norm: 7.2315 loss: 0.7579 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7579 2023/03/17 23:12:19 - mmengine - INFO - Epoch(train) [48][ 500/1320] lr: 2.0000e-04 eta: 0:19:24 time: 0.3362 data_time: 0.0123 memory: 18752 grad_norm: 7.7120 loss: 0.7866 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7866 2023/03/17 23:12:26 - mmengine - INFO - Epoch(train) [48][ 520/1320] lr: 2.0000e-04 eta: 0:19:17 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.3716 loss: 0.7564 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7564 2023/03/17 23:12:33 - mmengine - INFO - Epoch(train) [48][ 540/1320] lr: 2.0000e-04 eta: 0:19:10 time: 0.3366 data_time: 0.0122 memory: 18752 grad_norm: 7.4130 loss: 0.9125 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9125 2023/03/17 23:12:39 - mmengine - INFO - Epoch(train) [48][ 560/1320] lr: 2.0000e-04 eta: 0:19:03 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.6028 loss: 0.8346 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8346 2023/03/17 23:12:46 - mmengine - INFO - Epoch(train) [48][ 580/1320] lr: 2.0000e-04 eta: 0:18:57 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.3740 loss: 0.7853 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7853 2023/03/17 23:12:53 - mmengine - INFO - Epoch(train) [48][ 600/1320] lr: 2.0000e-04 eta: 0:18:50 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 7.3501 loss: 0.8525 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8525 2023/03/17 23:13:00 - mmengine - INFO - Epoch(train) [48][ 620/1320] lr: 2.0000e-04 eta: 0:18:43 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 7.6046 loss: 0.7646 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7646 2023/03/17 23:13:06 - mmengine - INFO - Epoch(train) [48][ 640/1320] lr: 2.0000e-04 eta: 0:18:36 time: 0.3355 data_time: 0.0118 memory: 18752 grad_norm: 7.4311 loss: 0.7597 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7597 2023/03/17 23:13:13 - mmengine - INFO - Epoch(train) [48][ 660/1320] lr: 2.0000e-04 eta: 0:18:30 time: 0.3368 data_time: 0.0123 memory: 18752 grad_norm: 7.2221 loss: 0.8992 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.8992 2023/03/17 23:13:20 - mmengine - INFO - Epoch(train) [48][ 680/1320] lr: 2.0000e-04 eta: 0:18:23 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 7.5348 loss: 0.7126 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7126 2023/03/17 23:13:27 - mmengine - INFO - Epoch(train) [48][ 700/1320] lr: 2.0000e-04 eta: 0:18:16 time: 0.3366 data_time: 0.0118 memory: 18752 grad_norm: 7.3417 loss: 0.7924 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7924 2023/03/17 23:13:33 - mmengine - INFO - Epoch(train) [48][ 720/1320] lr: 2.0000e-04 eta: 0:18:10 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 7.4763 loss: 0.7401 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7401 2023/03/17 23:13:40 - mmengine - INFO - Epoch(train) [48][ 740/1320] lr: 2.0000e-04 eta: 0:18:03 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 7.5868 loss: 0.8664 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8664 2023/03/17 23:13:47 - mmengine - INFO - Epoch(train) [48][ 760/1320] lr: 2.0000e-04 eta: 0:17:56 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.5875 loss: 0.8749 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8749 2023/03/17 23:13:53 - mmengine - INFO - Epoch(train) [48][ 780/1320] lr: 2.0000e-04 eta: 0:17:49 time: 0.3369 data_time: 0.0124 memory: 18752 grad_norm: 7.7315 loss: 0.8050 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8050 2023/03/17 23:14:00 - mmengine - INFO - Epoch(train) [48][ 800/1320] lr: 2.0000e-04 eta: 0:17:43 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 7.5028 loss: 0.7828 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7828 2023/03/17 23:14:07 - mmengine - INFO - Epoch(train) [48][ 820/1320] lr: 2.0000e-04 eta: 0:17:36 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.3780 loss: 0.7917 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7917 2023/03/17 23:14:14 - mmengine - INFO - Epoch(train) [48][ 840/1320] lr: 2.0000e-04 eta: 0:17:29 time: 0.3365 data_time: 0.0121 memory: 18752 grad_norm: 7.5906 loss: 0.7047 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7047 2023/03/17 23:14:20 - mmengine - INFO - Epoch(train) [48][ 860/1320] lr: 2.0000e-04 eta: 0:17:22 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 7.4380 loss: 0.6984 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6984 2023/03/17 23:14:27 - mmengine - INFO - Epoch(train) [48][ 880/1320] lr: 2.0000e-04 eta: 0:17:16 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.3549 loss: 0.7919 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7919 2023/03/17 23:14:34 - mmengine - INFO - Epoch(train) [48][ 900/1320] lr: 2.0000e-04 eta: 0:17:09 time: 0.3365 data_time: 0.0117 memory: 18752 grad_norm: 7.3568 loss: 0.7418 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7418 2023/03/17 23:14:41 - mmengine - INFO - Epoch(train) [48][ 920/1320] lr: 2.0000e-04 eta: 0:17:02 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 7.3725 loss: 0.7186 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7186 2023/03/17 23:14:47 - mmengine - INFO - Epoch(train) [48][ 940/1320] lr: 2.0000e-04 eta: 0:16:56 time: 0.3364 data_time: 0.0118 memory: 18752 grad_norm: 7.6801 loss: 0.8169 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8169 2023/03/17 23:14:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:14:54 - mmengine - INFO - Epoch(train) [48][ 960/1320] lr: 2.0000e-04 eta: 0:16:49 time: 0.3361 data_time: 0.0117 memory: 18752 grad_norm: 7.3063 loss: 0.7286 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7286 2023/03/17 23:15:01 - mmengine - INFO - Epoch(train) [48][ 980/1320] lr: 2.0000e-04 eta: 0:16:42 time: 0.3382 data_time: 0.0117 memory: 18752 grad_norm: 7.6101 loss: 0.7710 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7710 2023/03/17 23:15:07 - mmengine - INFO - Epoch(train) [48][1000/1320] lr: 2.0000e-04 eta: 0:16:35 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 7.5910 loss: 0.8323 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8323 2023/03/17 23:15:14 - mmengine - INFO - Epoch(train) [48][1020/1320] lr: 2.0000e-04 eta: 0:16:29 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 7.4421 loss: 0.8849 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8849 2023/03/17 23:15:21 - mmengine - INFO - Epoch(train) [48][1040/1320] lr: 2.0000e-04 eta: 0:16:22 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.4887 loss: 0.8976 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8976 2023/03/17 23:15:28 - mmengine - INFO - Epoch(train) [48][1060/1320] lr: 2.0000e-04 eta: 0:16:15 time: 0.3365 data_time: 0.0120 memory: 18752 grad_norm: 7.6646 loss: 0.7955 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7955 2023/03/17 23:15:34 - mmengine - INFO - Epoch(train) [48][1080/1320] lr: 2.0000e-04 eta: 0:16:08 time: 0.3354 data_time: 0.0123 memory: 18752 grad_norm: 7.2730 loss: 0.7551 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7551 2023/03/17 23:15:41 - mmengine - INFO - Epoch(train) [48][1100/1320] lr: 2.0000e-04 eta: 0:16:02 time: 0.3361 data_time: 0.0119 memory: 18752 grad_norm: 7.4203 loss: 0.6974 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6974 2023/03/17 23:15:48 - mmengine - INFO - Epoch(train) [48][1120/1320] lr: 2.0000e-04 eta: 0:15:55 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.4061 loss: 0.7812 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7812 2023/03/17 23:15:55 - mmengine - INFO - Epoch(train) [48][1140/1320] lr: 2.0000e-04 eta: 0:15:48 time: 0.3359 data_time: 0.0116 memory: 18752 grad_norm: 7.3061 loss: 0.7876 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7876 2023/03/17 23:16:01 - mmengine - INFO - Epoch(train) [48][1160/1320] lr: 2.0000e-04 eta: 0:15:41 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 7.3035 loss: 0.6953 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6953 2023/03/17 23:16:08 - mmengine - INFO - Epoch(train) [48][1180/1320] lr: 2.0000e-04 eta: 0:15:35 time: 0.3359 data_time: 0.0120 memory: 18752 grad_norm: 7.7311 loss: 0.7724 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7724 2023/03/17 23:16:15 - mmengine - INFO - Epoch(train) [48][1200/1320] lr: 2.0000e-04 eta: 0:15:28 time: 0.3357 data_time: 0.0118 memory: 18752 grad_norm: 7.3559 loss: 0.7932 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7932 2023/03/17 23:16:21 - mmengine - INFO - Epoch(train) [48][1220/1320] lr: 2.0000e-04 eta: 0:15:21 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 7.4935 loss: 0.8824 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8824 2023/03/17 23:16:28 - mmengine - INFO - Epoch(train) [48][1240/1320] lr: 2.0000e-04 eta: 0:15:15 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 7.5647 loss: 0.7976 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7976 2023/03/17 23:16:35 - mmengine - INFO - Epoch(train) [48][1260/1320] lr: 2.0000e-04 eta: 0:15:08 time: 0.3366 data_time: 0.0126 memory: 18752 grad_norm: 7.5420 loss: 0.7761 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7761 2023/03/17 23:16:42 - mmengine - INFO - Epoch(train) [48][1280/1320] lr: 2.0000e-04 eta: 0:15:01 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 7.4162 loss: 0.7451 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7451 2023/03/17 23:16:48 - mmengine - INFO - Epoch(train) [48][1300/1320] lr: 2.0000e-04 eta: 0:14:54 time: 0.3363 data_time: 0.0121 memory: 18752 grad_norm: 7.7702 loss: 0.8735 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8735 2023/03/17 23:16:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:16:55 - mmengine - INFO - Epoch(train) [48][1320/1320] lr: 2.0000e-04 eta: 0:14:48 time: 0.3319 data_time: 0.0124 memory: 18752 grad_norm: 7.4338 loss: 0.8195 top1_acc: 0.5455 top5_acc: 0.7273 loss_cls: 0.8195 2023/03/17 23:16:55 - mmengine - INFO - Saving checkpoint at 48 epochs 2023/03/17 23:17:01 - mmengine - INFO - Epoch(val) [48][ 20/194] eta: 0:00:22 time: 0.1276 data_time: 0.0407 memory: 2112 2023/03/17 23:17:02 - mmengine - INFO - Epoch(val) [48][ 40/194] eta: 0:00:17 time: 0.0967 data_time: 0.0110 memory: 2112 2023/03/17 23:17:04 - mmengine - INFO - Epoch(val) [48][ 60/194] eta: 0:00:14 time: 0.0966 data_time: 0.0105 memory: 2112 2023/03/17 23:17:06 - mmengine - INFO - Epoch(val) [48][ 80/194] eta: 0:00:11 time: 0.0964 data_time: 0.0105 memory: 2112 2023/03/17 23:17:08 - mmengine - INFO - Epoch(val) [48][100/194] eta: 0:00:09 time: 0.0966 data_time: 0.0105 memory: 2112 2023/03/17 23:17:10 - mmengine - INFO - Epoch(val) [48][120/194] eta: 0:00:07 time: 0.0980 data_time: 0.0118 memory: 2112 2023/03/17 23:17:12 - mmengine - INFO - Epoch(val) [48][140/194] eta: 0:00:05 time: 0.0965 data_time: 0.0107 memory: 2112 2023/03/17 23:17:14 - mmengine - INFO - Epoch(val) [48][160/194] eta: 0:00:03 time: 0.0957 data_time: 0.0100 memory: 2112 2023/03/17 23:17:16 - mmengine - INFO - Epoch(val) [48][180/194] eta: 0:00:01 time: 0.0969 data_time: 0.0104 memory: 2112 2023/03/17 23:17:18 - mmengine - INFO - Epoch(val) [48][194/194] acc/top1: 0.6116 acc/top5: 0.8671 acc/mean1: 0.5521 2023/03/17 23:17:19 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_47.pth is removed 2023/03/17 23:17:20 - mmengine - INFO - The best checkpoint with 0.6116 acc/top1 at 48 epoch is saved to best_acc/top1_epoch_48.pth. 2023/03/17 23:17:27 - mmengine - INFO - Epoch(train) [49][ 20/1320] lr: 2.0000e-04 eta: 0:14:41 time: 0.3631 data_time: 0.0344 memory: 18752 grad_norm: 7.4381 loss: 0.7838 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7838 2023/03/17 23:17:34 - mmengine - INFO - Epoch(train) [49][ 40/1320] lr: 2.0000e-04 eta: 0:14:34 time: 0.3366 data_time: 0.0118 memory: 18752 grad_norm: 7.5444 loss: 0.7036 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7036 2023/03/17 23:17:41 - mmengine - INFO - Epoch(train) [49][ 60/1320] lr: 2.0000e-04 eta: 0:14:27 time: 0.3354 data_time: 0.0117 memory: 18752 grad_norm: 7.6631 loss: 0.8046 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8046 2023/03/17 23:17:48 - mmengine - INFO - Epoch(train) [49][ 80/1320] lr: 2.0000e-04 eta: 0:14:21 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 7.4452 loss: 0.8440 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8440 2023/03/17 23:17:54 - mmengine - INFO - Epoch(train) [49][ 100/1320] lr: 2.0000e-04 eta: 0:14:14 time: 0.3358 data_time: 0.0121 memory: 18752 grad_norm: 7.5016 loss: 0.7002 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7002 2023/03/17 23:18:01 - mmengine - INFO - Epoch(train) [49][ 120/1320] lr: 2.0000e-04 eta: 0:14:07 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 7.4481 loss: 0.7734 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7734 2023/03/17 23:18:08 - mmengine - INFO - Epoch(train) [49][ 140/1320] lr: 2.0000e-04 eta: 0:14:01 time: 0.3358 data_time: 0.0120 memory: 18752 grad_norm: 7.4851 loss: 0.7600 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7600 2023/03/17 23:18:14 - mmengine - INFO - Epoch(train) [49][ 160/1320] lr: 2.0000e-04 eta: 0:13:54 time: 0.3352 data_time: 0.0119 memory: 18752 grad_norm: 7.5598 loss: 0.7477 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7477 2023/03/17 23:18:21 - mmengine - INFO - Epoch(train) [49][ 180/1320] lr: 2.0000e-04 eta: 0:13:47 time: 0.3362 data_time: 0.0119 memory: 18752 grad_norm: 7.5222 loss: 0.8436 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8436 2023/03/17 23:18:28 - mmengine - INFO - Epoch(train) [49][ 200/1320] lr: 2.0000e-04 eta: 0:13:40 time: 0.3361 data_time: 0.0131 memory: 18752 grad_norm: 7.2137 loss: 0.7412 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7412 2023/03/17 23:18:35 - mmengine - INFO - Epoch(train) [49][ 220/1320] lr: 2.0000e-04 eta: 0:13:34 time: 0.3360 data_time: 0.0120 memory: 18752 grad_norm: 7.3955 loss: 0.7123 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7123 2023/03/17 23:18:41 - mmengine - INFO - Epoch(train) [49][ 240/1320] lr: 2.0000e-04 eta: 0:13:27 time: 0.3372 data_time: 0.0122 memory: 18752 grad_norm: 7.3765 loss: 0.8012 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8012 2023/03/17 23:18:48 - mmengine - INFO - Epoch(train) [49][ 260/1320] lr: 2.0000e-04 eta: 0:13:20 time: 0.3355 data_time: 0.0120 memory: 18752 grad_norm: 7.7145 loss: 0.7956 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7956 2023/03/17 23:18:55 - mmengine - INFO - Epoch(train) [49][ 280/1320] lr: 2.0000e-04 eta: 0:13:13 time: 0.3357 data_time: 0.0121 memory: 18752 grad_norm: 7.1715 loss: 0.8331 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8331 2023/03/17 23:19:01 - mmengine - INFO - Epoch(train) [49][ 300/1320] lr: 2.0000e-04 eta: 0:13:07 time: 0.3364 data_time: 0.0129 memory: 18752 grad_norm: 7.2882 loss: 0.7892 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7892 2023/03/17 23:19:08 - mmengine - INFO - Epoch(train) [49][ 320/1320] lr: 2.0000e-04 eta: 0:13:00 time: 0.3362 data_time: 0.0129 memory: 18752 grad_norm: 7.5051 loss: 0.7792 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7792 2023/03/17 23:19:15 - mmengine - INFO - Epoch(train) [49][ 340/1320] lr: 2.0000e-04 eta: 0:12:53 time: 0.3359 data_time: 0.0123 memory: 18752 grad_norm: 7.6563 loss: 0.6021 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6021 2023/03/17 23:19:22 - mmengine - INFO - Epoch(train) [49][ 360/1320] lr: 2.0000e-04 eta: 0:12:47 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 7.7862 loss: 0.7603 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7603 2023/03/17 23:19:28 - mmengine - INFO - Epoch(train) [49][ 380/1320] lr: 2.0000e-04 eta: 0:12:40 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.5633 loss: 0.6802 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6802 2023/03/17 23:19:35 - mmengine - INFO - Epoch(train) [49][ 400/1320] lr: 2.0000e-04 eta: 0:12:33 time: 0.3355 data_time: 0.0131 memory: 18752 grad_norm: 7.4698 loss: 0.8471 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8471 2023/03/17 23:19:42 - mmengine - INFO - Epoch(train) [49][ 420/1320] lr: 2.0000e-04 eta: 0:12:26 time: 0.3354 data_time: 0.0124 memory: 18752 grad_norm: 7.5636 loss: 0.8881 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8881 2023/03/17 23:19:49 - mmengine - INFO - Epoch(train) [49][ 440/1320] lr: 2.0000e-04 eta: 0:12:20 time: 0.3362 data_time: 0.0126 memory: 18752 grad_norm: 7.5490 loss: 0.8863 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8863 2023/03/17 23:19:55 - mmengine - INFO - Epoch(train) [49][ 460/1320] lr: 2.0000e-04 eta: 0:12:13 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 7.7230 loss: 0.7756 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7756 2023/03/17 23:20:02 - mmengine - INFO - Epoch(train) [49][ 480/1320] lr: 2.0000e-04 eta: 0:12:06 time: 0.3361 data_time: 0.0131 memory: 18752 grad_norm: 7.6887 loss: 0.8280 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8280 2023/03/17 23:20:09 - mmengine - INFO - Epoch(train) [49][ 500/1320] lr: 2.0000e-04 eta: 0:11:59 time: 0.3359 data_time: 0.0121 memory: 18752 grad_norm: 7.5816 loss: 0.8679 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8679 2023/03/17 23:20:15 - mmengine - INFO - Epoch(train) [49][ 520/1320] lr: 2.0000e-04 eta: 0:11:53 time: 0.3360 data_time: 0.0123 memory: 18752 grad_norm: 7.6516 loss: 0.7782 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7782 2023/03/17 23:20:22 - mmengine - INFO - Epoch(train) [49][ 540/1320] lr: 2.0000e-04 eta: 0:11:46 time: 0.3361 data_time: 0.0126 memory: 18752 grad_norm: 7.4858 loss: 0.7540 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7540 2023/03/17 23:20:29 - mmengine - INFO - Epoch(train) [49][ 560/1320] lr: 2.0000e-04 eta: 0:11:39 time: 0.3359 data_time: 0.0124 memory: 18752 grad_norm: 7.6529 loss: 0.7952 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.7952 2023/03/17 23:20:36 - mmengine - INFO - Epoch(train) [49][ 580/1320] lr: 2.0000e-04 eta: 0:11:33 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 7.6540 loss: 0.7029 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7029 2023/03/17 23:20:42 - mmengine - INFO - Epoch(train) [49][ 600/1320] lr: 2.0000e-04 eta: 0:11:26 time: 0.3371 data_time: 0.0121 memory: 18752 grad_norm: 7.7038 loss: 0.8690 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8690 2023/03/17 23:20:49 - mmengine - INFO - Epoch(train) [49][ 620/1320] lr: 2.0000e-04 eta: 0:11:19 time: 0.3370 data_time: 0.0123 memory: 18752 grad_norm: 7.3350 loss: 0.7612 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7612 2023/03/17 23:20:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:20:56 - mmengine - INFO - Epoch(train) [49][ 640/1320] lr: 2.0000e-04 eta: 0:11:12 time: 0.3368 data_time: 0.0127 memory: 18752 grad_norm: 7.4058 loss: 0.8150 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8150 2023/03/17 23:21:03 - mmengine - INFO - Epoch(train) [49][ 660/1320] lr: 2.0000e-04 eta: 0:11:06 time: 0.3364 data_time: 0.0124 memory: 18752 grad_norm: 7.5734 loss: 0.7841 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7841 2023/03/17 23:21:09 - mmengine - INFO - Epoch(train) [49][ 680/1320] lr: 2.0000e-04 eta: 0:10:59 time: 0.3367 data_time: 0.0126 memory: 18752 grad_norm: 7.3467 loss: 0.7781 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7781 2023/03/17 23:21:16 - mmengine - INFO - Epoch(train) [49][ 700/1320] lr: 2.0000e-04 eta: 0:10:52 time: 0.3368 data_time: 0.0123 memory: 18752 grad_norm: 7.5216 loss: 0.7660 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7660 2023/03/17 23:21:23 - mmengine - INFO - Epoch(train) [49][ 720/1320] lr: 2.0000e-04 eta: 0:10:45 time: 0.3355 data_time: 0.0121 memory: 18752 grad_norm: 7.5425 loss: 0.8512 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8512 2023/03/17 23:21:29 - mmengine - INFO - Epoch(train) [49][ 740/1320] lr: 2.0000e-04 eta: 0:10:39 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.3414 loss: 0.8611 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8611 2023/03/17 23:21:36 - mmengine - INFO - Epoch(train) [49][ 760/1320] lr: 2.0000e-04 eta: 0:10:32 time: 0.3365 data_time: 0.0134 memory: 18752 grad_norm: 7.6353 loss: 0.7833 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7833 2023/03/17 23:21:43 - mmengine - INFO - Epoch(train) [49][ 780/1320] lr: 2.0000e-04 eta: 0:10:25 time: 0.3361 data_time: 0.0124 memory: 18752 grad_norm: 7.3596 loss: 0.7341 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7341 2023/03/17 23:21:50 - mmengine - INFO - Epoch(train) [49][ 800/1320] lr: 2.0000e-04 eta: 0:10:19 time: 0.3363 data_time: 0.0128 memory: 18752 grad_norm: 7.5248 loss: 0.8363 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8363 2023/03/17 23:21:56 - mmengine - INFO - Epoch(train) [49][ 820/1320] lr: 2.0000e-04 eta: 0:10:12 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 7.5616 loss: 0.8023 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8023 2023/03/17 23:22:03 - mmengine - INFO - Epoch(train) [49][ 840/1320] lr: 2.0000e-04 eta: 0:10:05 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.6737 loss: 0.9030 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9030 2023/03/17 23:22:10 - mmengine - INFO - Epoch(train) [49][ 860/1320] lr: 2.0000e-04 eta: 0:09:58 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 7.4710 loss: 0.7698 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7698 2023/03/17 23:22:17 - mmengine - INFO - Epoch(train) [49][ 880/1320] lr: 2.0000e-04 eta: 0:09:52 time: 0.3359 data_time: 0.0122 memory: 18752 grad_norm: 7.3973 loss: 0.7665 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7665 2023/03/17 23:22:23 - mmengine - INFO - Epoch(train) [49][ 900/1320] lr: 2.0000e-04 eta: 0:09:45 time: 0.3358 data_time: 0.0118 memory: 18752 grad_norm: 7.5646 loss: 0.8500 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8500 2023/03/17 23:22:30 - mmengine - INFO - Epoch(train) [49][ 920/1320] lr: 2.0000e-04 eta: 0:09:38 time: 0.3355 data_time: 0.0119 memory: 18752 grad_norm: 7.6463 loss: 0.7388 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7388 2023/03/17 23:22:37 - mmengine - INFO - Epoch(train) [49][ 940/1320] lr: 2.0000e-04 eta: 0:09:31 time: 0.3354 data_time: 0.0118 memory: 18752 grad_norm: 7.4490 loss: 0.7775 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7775 2023/03/17 23:22:43 - mmengine - INFO - Epoch(train) [49][ 960/1320] lr: 2.0000e-04 eta: 0:09:25 time: 0.3356 data_time: 0.0121 memory: 18752 grad_norm: 7.4883 loss: 0.6316 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6316 2023/03/17 23:22:50 - mmengine - INFO - Epoch(train) [49][ 980/1320] lr: 2.0000e-04 eta: 0:09:18 time: 0.3367 data_time: 0.0125 memory: 18752 grad_norm: 7.7874 loss: 0.8644 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8644 2023/03/17 23:22:57 - mmengine - INFO - Epoch(train) [49][1000/1320] lr: 2.0000e-04 eta: 0:09:11 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.3675 loss: 0.7036 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7036 2023/03/17 23:23:04 - mmengine - INFO - Epoch(train) [49][1020/1320] lr: 2.0000e-04 eta: 0:09:05 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 7.1749 loss: 0.7457 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7457 2023/03/17 23:23:10 - mmengine - INFO - Epoch(train) [49][1040/1320] lr: 2.0000e-04 eta: 0:08:58 time: 0.3362 data_time: 0.0121 memory: 18752 grad_norm: 7.5693 loss: 0.7684 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7684 2023/03/17 23:23:17 - mmengine - INFO - Epoch(train) [49][1060/1320] lr: 2.0000e-04 eta: 0:08:51 time: 0.3365 data_time: 0.0119 memory: 18752 grad_norm: 7.6001 loss: 0.6612 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.6612 2023/03/17 23:23:24 - mmengine - INFO - Epoch(train) [49][1080/1320] lr: 2.0000e-04 eta: 0:08:44 time: 0.3364 data_time: 0.0126 memory: 18752 grad_norm: 7.7309 loss: 0.7395 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7395 2023/03/17 23:23:30 - mmengine - INFO - Epoch(train) [49][1100/1320] lr: 2.0000e-04 eta: 0:08:38 time: 0.3360 data_time: 0.0121 memory: 18752 grad_norm: 7.5345 loss: 0.9945 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 0.9945 2023/03/17 23:23:37 - mmengine - INFO - Epoch(train) [49][1120/1320] lr: 2.0000e-04 eta: 0:08:31 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 7.8272 loss: 0.7659 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7659 2023/03/17 23:23:44 - mmengine - INFO - Epoch(train) [49][1140/1320] lr: 2.0000e-04 eta: 0:08:24 time: 0.3365 data_time: 0.0122 memory: 18752 grad_norm: 7.6497 loss: 0.8089 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8089 2023/03/17 23:23:51 - mmengine - INFO - Epoch(train) [49][1160/1320] lr: 2.0000e-04 eta: 0:08:17 time: 0.3363 data_time: 0.0125 memory: 18752 grad_norm: 7.4105 loss: 0.7963 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7963 2023/03/17 23:23:57 - mmengine - INFO - Epoch(train) [49][1180/1320] lr: 2.0000e-04 eta: 0:08:11 time: 0.3367 data_time: 0.0123 memory: 18752 grad_norm: 7.6236 loss: 0.6958 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.6958 2023/03/17 23:24:04 - mmengine - INFO - Epoch(train) [49][1200/1320] lr: 2.0000e-04 eta: 0:08:04 time: 0.3357 data_time: 0.0123 memory: 18752 grad_norm: 7.2992 loss: 0.8668 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8668 2023/03/17 23:24:11 - mmengine - INFO - Epoch(train) [49][1220/1320] lr: 2.0000e-04 eta: 0:07:57 time: 0.3364 data_time: 0.0125 memory: 18752 grad_norm: 7.4448 loss: 0.8062 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8062 2023/03/17 23:24:18 - mmengine - INFO - Epoch(train) [49][1240/1320] lr: 2.0000e-04 eta: 0:07:50 time: 0.3359 data_time: 0.0128 memory: 18752 grad_norm: 7.3458 loss: 0.7577 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7577 2023/03/17 23:24:24 - mmengine - INFO - Epoch(train) [49][1260/1320] lr: 2.0000e-04 eta: 0:07:44 time: 0.3362 data_time: 0.0125 memory: 18752 grad_norm: 7.4247 loss: 0.6664 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6664 2023/03/17 23:24:31 - mmengine - INFO - Epoch(train) [49][1280/1320] lr: 2.0000e-04 eta: 0:07:37 time: 0.3368 data_time: 0.0126 memory: 18752 grad_norm: 7.4566 loss: 0.8517 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8517 2023/03/17 23:24:38 - mmengine - INFO - Epoch(train) [49][1300/1320] lr: 2.0000e-04 eta: 0:07:30 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.6079 loss: 0.7900 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7900 2023/03/17 23:24:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:24:44 - mmengine - INFO - Epoch(train) [49][1320/1320] lr: 2.0000e-04 eta: 0:07:24 time: 0.3306 data_time: 0.0120 memory: 18752 grad_norm: 7.5054 loss: 0.7935 top1_acc: 0.5455 top5_acc: 0.8182 loss_cls: 0.7935 2023/03/17 23:24:47 - mmengine - INFO - Epoch(val) [49][ 20/194] eta: 0:00:22 time: 0.1291 data_time: 0.0423 memory: 2112 2023/03/17 23:24:49 - mmengine - INFO - Epoch(val) [49][ 40/194] eta: 0:00:17 time: 0.0955 data_time: 0.0099 memory: 2112 2023/03/17 23:24:51 - mmengine - INFO - Epoch(val) [49][ 60/194] eta: 0:00:14 time: 0.0968 data_time: 0.0109 memory: 2112 2023/03/17 23:24:53 - mmengine - INFO - Epoch(val) [49][ 80/194] eta: 0:00:11 time: 0.0969 data_time: 0.0106 memory: 2112 2023/03/17 23:24:55 - mmengine - INFO - Epoch(val) [49][100/194] eta: 0:00:09 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 23:24:57 - mmengine - INFO - Epoch(val) [49][120/194] eta: 0:00:07 time: 0.0973 data_time: 0.0115 memory: 2112 2023/03/17 23:24:59 - mmengine - INFO - Epoch(val) [49][140/194] eta: 0:00:05 time: 0.0972 data_time: 0.0112 memory: 2112 2023/03/17 23:25:01 - mmengine - INFO - Epoch(val) [49][160/194] eta: 0:00:03 time: 0.0966 data_time: 0.0106 memory: 2112 2023/03/17 23:25:02 - mmengine - INFO - Epoch(val) [49][180/194] eta: 0:00:01 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 23:25:06 - mmengine - INFO - Epoch(val) [49][194/194] acc/top1: 0.6115 acc/top5: 0.8677 acc/mean1: 0.5520 2023/03/17 23:25:13 - mmengine - INFO - Epoch(train) [50][ 20/1320] lr: 2.0000e-04 eta: 0:07:17 time: 0.3723 data_time: 0.0412 memory: 18752 grad_norm: 7.4343 loss: 0.7575 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7575 2023/03/17 23:25:20 - mmengine - INFO - Epoch(train) [50][ 40/1320] lr: 2.0000e-04 eta: 0:07:10 time: 0.3374 data_time: 0.0119 memory: 18752 grad_norm: 7.5248 loss: 0.7895 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7895 2023/03/17 23:25:27 - mmengine - INFO - Epoch(train) [50][ 60/1320] lr: 2.0000e-04 eta: 0:07:03 time: 0.3367 data_time: 0.0120 memory: 18752 grad_norm: 7.6388 loss: 0.7704 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7704 2023/03/17 23:25:33 - mmengine - INFO - Epoch(train) [50][ 80/1320] lr: 2.0000e-04 eta: 0:06:57 time: 0.3369 data_time: 0.0117 memory: 18752 grad_norm: 7.5461 loss: 0.8352 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8352 2023/03/17 23:25:40 - mmengine - INFO - Epoch(train) [50][ 100/1320] lr: 2.0000e-04 eta: 0:06:50 time: 0.3367 data_time: 0.0118 memory: 18752 grad_norm: 7.5980 loss: 0.8571 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8571 2023/03/17 23:25:47 - mmengine - INFO - Epoch(train) [50][ 120/1320] lr: 2.0000e-04 eta: 0:06:43 time: 0.3369 data_time: 0.0124 memory: 18752 grad_norm: 7.5221 loss: 0.6587 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6587 2023/03/17 23:25:54 - mmengine - INFO - Epoch(train) [50][ 140/1320] lr: 2.0000e-04 eta: 0:06:36 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 7.3667 loss: 0.7653 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7653 2023/03/17 23:26:00 - mmengine - INFO - Epoch(train) [50][ 160/1320] lr: 2.0000e-04 eta: 0:06:30 time: 0.3363 data_time: 0.0122 memory: 18752 grad_norm: 7.4796 loss: 0.7090 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7090 2023/03/17 23:26:07 - mmengine - INFO - Epoch(train) [50][ 180/1320] lr: 2.0000e-04 eta: 0:06:23 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 7.8465 loss: 0.8275 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8275 2023/03/17 23:26:14 - mmengine - INFO - Epoch(train) [50][ 200/1320] lr: 2.0000e-04 eta: 0:06:16 time: 0.3366 data_time: 0.0121 memory: 18752 grad_norm: 7.6648 loss: 0.8268 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8268 2023/03/17 23:26:21 - mmengine - INFO - Epoch(train) [50][ 220/1320] lr: 2.0000e-04 eta: 0:06:10 time: 0.3376 data_time: 0.0117 memory: 18752 grad_norm: 7.4767 loss: 0.6951 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6951 2023/03/17 23:26:27 - mmengine - INFO - Epoch(train) [50][ 240/1320] lr: 2.0000e-04 eta: 0:06:03 time: 0.3364 data_time: 0.0119 memory: 18752 grad_norm: 7.6446 loss: 0.8052 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8052 2023/03/17 23:26:34 - mmengine - INFO - Epoch(train) [50][ 260/1320] lr: 2.0000e-04 eta: 0:05:56 time: 0.3366 data_time: 0.0117 memory: 18752 grad_norm: 7.5005 loss: 0.7542 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.7542 2023/03/17 23:26:41 - mmengine - INFO - Epoch(train) [50][ 280/1320] lr: 2.0000e-04 eta: 0:05:49 time: 0.3362 data_time: 0.0118 memory: 18752 grad_norm: 7.3859 loss: 0.5973 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.5973 2023/03/17 23:26:48 - mmengine - INFO - Epoch(train) [50][ 300/1320] lr: 2.0000e-04 eta: 0:05:43 time: 0.3368 data_time: 0.0122 memory: 18752 grad_norm: 7.6847 loss: 0.6956 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6956 2023/03/17 23:26:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:26:54 - mmengine - INFO - Epoch(train) [50][ 320/1320] lr: 2.0000e-04 eta: 0:05:36 time: 0.3366 data_time: 0.0117 memory: 18752 grad_norm: 7.7721 loss: 0.7713 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7713 2023/03/17 23:27:01 - mmengine - INFO - Epoch(train) [50][ 340/1320] lr: 2.0000e-04 eta: 0:05:29 time: 0.3373 data_time: 0.0119 memory: 18752 grad_norm: 7.6401 loss: 0.8387 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8387 2023/03/17 23:27:08 - mmengine - INFO - Epoch(train) [50][ 360/1320] lr: 2.0000e-04 eta: 0:05:22 time: 0.3368 data_time: 0.0117 memory: 18752 grad_norm: 7.6409 loss: 0.8987 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8987 2023/03/17 23:27:15 - mmengine - INFO - Epoch(train) [50][ 380/1320] lr: 2.0000e-04 eta: 0:05:16 time: 0.3374 data_time: 0.0120 memory: 18752 grad_norm: 7.3873 loss: 0.7739 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7739 2023/03/17 23:27:21 - mmengine - INFO - Epoch(train) [50][ 400/1320] lr: 2.0000e-04 eta: 0:05:09 time: 0.3372 data_time: 0.0122 memory: 18752 grad_norm: 7.7156 loss: 0.6463 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6463 2023/03/17 23:27:28 - mmengine - INFO - Epoch(train) [50][ 420/1320] lr: 2.0000e-04 eta: 0:05:02 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 7.6283 loss: 0.8383 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8383 2023/03/17 23:27:35 - mmengine - INFO - Epoch(train) [50][ 440/1320] lr: 2.0000e-04 eta: 0:04:56 time: 0.3364 data_time: 0.0123 memory: 18752 grad_norm: 7.5821 loss: 0.7634 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7634 2023/03/17 23:27:41 - mmengine - INFO - Epoch(train) [50][ 460/1320] lr: 2.0000e-04 eta: 0:04:49 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 7.5061 loss: 0.8539 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8539 2023/03/17 23:27:48 - mmengine - INFO - Epoch(train) [50][ 480/1320] lr: 2.0000e-04 eta: 0:04:42 time: 0.3365 data_time: 0.0127 memory: 18752 grad_norm: 7.5462 loss: 0.7766 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7766 2023/03/17 23:27:55 - mmengine - INFO - Epoch(train) [50][ 500/1320] lr: 2.0000e-04 eta: 0:04:35 time: 0.3371 data_time: 0.0121 memory: 18752 grad_norm: 7.5683 loss: 0.7851 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7851 2023/03/17 23:28:02 - mmengine - INFO - Epoch(train) [50][ 520/1320] lr: 2.0000e-04 eta: 0:04:29 time: 0.3371 data_time: 0.0124 memory: 18752 grad_norm: 7.5283 loss: 0.7281 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7281 2023/03/17 23:28:08 - mmengine - INFO - Epoch(train) [50][ 540/1320] lr: 2.0000e-04 eta: 0:04:22 time: 0.3366 data_time: 0.0120 memory: 18752 grad_norm: 7.4777 loss: 0.7340 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7340 2023/03/17 23:28:15 - mmengine - INFO - Epoch(train) [50][ 560/1320] lr: 2.0000e-04 eta: 0:04:15 time: 0.3364 data_time: 0.0119 memory: 18752 grad_norm: 7.4266 loss: 0.7609 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7609 2023/03/17 23:28:22 - mmengine - INFO - Epoch(train) [50][ 580/1320] lr: 2.0000e-04 eta: 0:04:08 time: 0.3368 data_time: 0.0118 memory: 18752 grad_norm: 7.7583 loss: 0.8376 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8376 2023/03/17 23:28:29 - mmengine - INFO - Epoch(train) [50][ 600/1320] lr: 2.0000e-04 eta: 0:04:02 time: 0.3361 data_time: 0.0118 memory: 18752 grad_norm: 7.5659 loss: 0.8603 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8603 2023/03/17 23:28:35 - mmengine - INFO - Epoch(train) [50][ 620/1320] lr: 2.0000e-04 eta: 0:03:55 time: 0.3367 data_time: 0.0119 memory: 18752 grad_norm: 7.7185 loss: 0.7781 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7781 2023/03/17 23:28:42 - mmengine - INFO - Epoch(train) [50][ 640/1320] lr: 2.0000e-04 eta: 0:03:48 time: 0.3372 data_time: 0.0120 memory: 18752 grad_norm: 7.5006 loss: 0.7461 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7461 2023/03/17 23:28:49 - mmengine - INFO - Epoch(train) [50][ 660/1320] lr: 2.0000e-04 eta: 0:03:42 time: 0.3368 data_time: 0.0117 memory: 18752 grad_norm: 7.8761 loss: 0.7690 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7690 2023/03/17 23:28:56 - mmengine - INFO - Epoch(train) [50][ 680/1320] lr: 2.0000e-04 eta: 0:03:35 time: 0.3364 data_time: 0.0129 memory: 18752 grad_norm: 7.6092 loss: 0.7307 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7307 2023/03/17 23:29:02 - mmengine - INFO - Epoch(train) [50][ 700/1320] lr: 2.0000e-04 eta: 0:03:28 time: 0.3370 data_time: 0.0119 memory: 18752 grad_norm: 7.4219 loss: 0.7007 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7007 2023/03/17 23:29:09 - mmengine - INFO - Epoch(train) [50][ 720/1320] lr: 2.0000e-04 eta: 0:03:21 time: 0.3373 data_time: 0.0127 memory: 18752 grad_norm: 7.5056 loss: 0.6632 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.6632 2023/03/17 23:29:16 - mmengine - INFO - Epoch(train) [50][ 740/1320] lr: 2.0000e-04 eta: 0:03:15 time: 0.3364 data_time: 0.0120 memory: 18752 grad_norm: 7.4447 loss: 0.7541 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.7541 2023/03/17 23:29:22 - mmengine - INFO - Epoch(train) [50][ 760/1320] lr: 2.0000e-04 eta: 0:03:08 time: 0.3363 data_time: 0.0123 memory: 18752 grad_norm: 7.6556 loss: 0.7460 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7460 2023/03/17 23:29:29 - mmengine - INFO - Epoch(train) [50][ 780/1320] lr: 2.0000e-04 eta: 0:03:01 time: 0.3376 data_time: 0.0122 memory: 18752 grad_norm: 7.5850 loss: 0.7638 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7638 2023/03/17 23:29:36 - mmengine - INFO - Epoch(train) [50][ 800/1320] lr: 2.0000e-04 eta: 0:02:54 time: 0.3366 data_time: 0.0127 memory: 18752 grad_norm: 7.7396 loss: 0.7920 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7920 2023/03/17 23:29:43 - mmengine - INFO - Epoch(train) [50][ 820/1320] lr: 2.0000e-04 eta: 0:02:48 time: 0.3369 data_time: 0.0123 memory: 18752 grad_norm: 7.6461 loss: 0.8426 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8426 2023/03/17 23:29:49 - mmengine - INFO - Epoch(train) [50][ 840/1320] lr: 2.0000e-04 eta: 0:02:41 time: 0.3375 data_time: 0.0120 memory: 18752 grad_norm: 7.4918 loss: 0.5587 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5587 2023/03/17 23:29:56 - mmengine - INFO - Epoch(train) [50][ 860/1320] lr: 2.0000e-04 eta: 0:02:34 time: 0.3370 data_time: 0.0120 memory: 18752 grad_norm: 7.7176 loss: 0.7206 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7206 2023/03/17 23:30:03 - mmengine - INFO - Epoch(train) [50][ 880/1320] lr: 2.0000e-04 eta: 0:02:28 time: 0.3363 data_time: 0.0117 memory: 18752 grad_norm: 7.3464 loss: 0.7830 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7830 2023/03/17 23:30:10 - mmengine - INFO - Epoch(train) [50][ 900/1320] lr: 2.0000e-04 eta: 0:02:21 time: 0.3369 data_time: 0.0119 memory: 18752 grad_norm: 7.5375 loss: 0.7261 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7261 2023/03/17 23:30:16 - mmengine - INFO - Epoch(train) [50][ 920/1320] lr: 2.0000e-04 eta: 0:02:14 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 7.6715 loss: 0.8382 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8382 2023/03/17 23:30:23 - mmengine - INFO - Epoch(train) [50][ 940/1320] lr: 2.0000e-04 eta: 0:02:07 time: 0.3367 data_time: 0.0124 memory: 18752 grad_norm: 8.1070 loss: 0.8299 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8299 2023/03/17 23:30:30 - mmengine - INFO - Epoch(train) [50][ 960/1320] lr: 2.0000e-04 eta: 0:02:01 time: 0.3365 data_time: 0.0123 memory: 18752 grad_norm: 7.7797 loss: 0.8396 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8396 2023/03/17 23:30:37 - mmengine - INFO - Epoch(train) [50][ 980/1320] lr: 2.0000e-04 eta: 0:01:54 time: 0.3367 data_time: 0.0120 memory: 18752 grad_norm: 7.6041 loss: 0.7951 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7951 2023/03/17 23:30:43 - mmengine - INFO - Epoch(train) [50][1000/1320] lr: 2.0000e-04 eta: 0:01:47 time: 0.3369 data_time: 0.0123 memory: 18752 grad_norm: 7.4561 loss: 0.7335 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7335 2023/03/17 23:30:50 - mmengine - INFO - Epoch(train) [50][1020/1320] lr: 2.0000e-04 eta: 0:01:40 time: 0.3363 data_time: 0.0118 memory: 18752 grad_norm: 7.8209 loss: 0.8833 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8833 2023/03/17 23:30:57 - mmengine - INFO - Epoch(train) [50][1040/1320] lr: 2.0000e-04 eta: 0:01:34 time: 0.3369 data_time: 0.0120 memory: 18752 grad_norm: 7.5522 loss: 0.8025 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8025 2023/03/17 23:31:04 - mmengine - INFO - Epoch(train) [50][1060/1320] lr: 2.0000e-04 eta: 0:01:27 time: 0.3367 data_time: 0.0119 memory: 18752 grad_norm: 7.7282 loss: 0.8374 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8374 2023/03/17 23:31:10 - mmengine - INFO - Epoch(train) [50][1080/1320] lr: 2.0000e-04 eta: 0:01:20 time: 0.3361 data_time: 0.0121 memory: 18752 grad_norm: 7.5175 loss: 0.7300 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7300 2023/03/17 23:31:17 - mmengine - INFO - Epoch(train) [50][1100/1320] lr: 2.0000e-04 eta: 0:01:14 time: 0.3366 data_time: 0.0125 memory: 18752 grad_norm: 7.6872 loss: 0.8927 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.8927 2023/03/17 23:31:24 - mmengine - INFO - Epoch(train) [50][1120/1320] lr: 2.0000e-04 eta: 0:01:07 time: 0.3363 data_time: 0.0126 memory: 18752 grad_norm: 7.4600 loss: 0.6751 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6751 2023/03/17 23:31:30 - mmengine - INFO - Epoch(train) [50][1140/1320] lr: 2.0000e-04 eta: 0:01:00 time: 0.3364 data_time: 0.0122 memory: 18752 grad_norm: 7.6179 loss: 0.8368 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8368 2023/03/17 23:31:37 - mmengine - INFO - Epoch(train) [50][1160/1320] lr: 2.0000e-04 eta: 0:00:53 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.4912 loss: 0.6782 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6782 2023/03/17 23:31:44 - mmengine - INFO - Epoch(train) [50][1180/1320] lr: 2.0000e-04 eta: 0:00:47 time: 0.3362 data_time: 0.0122 memory: 18752 grad_norm: 7.4027 loss: 0.6447 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6447 2023/03/17 23:31:51 - mmengine - INFO - Epoch(train) [50][1200/1320] lr: 2.0000e-04 eta: 0:00:40 time: 0.3361 data_time: 0.0120 memory: 18752 grad_norm: 7.4327 loss: 0.8100 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8100 2023/03/17 23:31:57 - mmengine - INFO - Epoch(train) [50][1220/1320] lr: 2.0000e-04 eta: 0:00:33 time: 0.3362 data_time: 0.0120 memory: 18752 grad_norm: 7.3970 loss: 0.6308 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6308 2023/03/17 23:32:04 - mmengine - INFO - Epoch(train) [50][1240/1320] lr: 2.0000e-04 eta: 0:00:26 time: 0.3361 data_time: 0.0128 memory: 18752 grad_norm: 7.8118 loss: 0.8586 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8586 2023/03/17 23:32:11 - mmengine - INFO - Epoch(train) [50][1260/1320] lr: 2.0000e-04 eta: 0:00:20 time: 0.3359 data_time: 0.0125 memory: 18752 grad_norm: 7.9252 loss: 0.7775 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7775 2023/03/17 23:32:18 - mmengine - INFO - Epoch(train) [50][1280/1320] lr: 2.0000e-04 eta: 0:00:13 time: 0.3356 data_time: 0.0125 memory: 18752 grad_norm: 7.5263 loss: 0.8102 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8102 2023/03/17 23:32:24 - mmengine - INFO - Epoch(train) [50][1300/1320] lr: 2.0000e-04 eta: 0:00:06 time: 0.3364 data_time: 0.0127 memory: 18752 grad_norm: 7.6824 loss: 0.7101 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7101 2023/03/17 23:32:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_20230317_170118 2023/03/17 23:32:31 - mmengine - INFO - Epoch(train) [50][1320/1320] lr: 2.0000e-04 eta: 0:00:00 time: 0.3306 data_time: 0.0125 memory: 18752 grad_norm: 7.7692 loss: 0.7912 top1_acc: 0.8182 top5_acc: 0.9091 loss_cls: 0.7912 2023/03/17 23:32:31 - mmengine - INFO - Saving checkpoint at 50 epochs 2023/03/17 23:32:36 - mmengine - INFO - Epoch(val) [50][ 20/194] eta: 0:00:22 time: 0.1276 data_time: 0.0408 memory: 2112 2023/03/17 23:32:38 - mmengine - INFO - Epoch(val) [50][ 40/194] eta: 0:00:17 time: 0.0957 data_time: 0.0099 memory: 2112 2023/03/17 23:32:40 - mmengine - INFO - Epoch(val) [50][ 60/194] eta: 0:00:14 time: 0.0970 data_time: 0.0111 memory: 2112 2023/03/17 23:32:42 - mmengine - INFO - Epoch(val) [50][ 80/194] eta: 0:00:11 time: 0.0976 data_time: 0.0113 memory: 2112 2023/03/17 23:32:44 - mmengine - INFO - Epoch(val) [50][100/194] eta: 0:00:09 time: 0.0969 data_time: 0.0108 memory: 2112 2023/03/17 23:32:46 - mmengine - INFO - Epoch(val) [50][120/194] eta: 0:00:07 time: 0.0965 data_time: 0.0106 memory: 2112 2023/03/17 23:32:48 - mmengine - INFO - Epoch(val) [50][140/194] eta: 0:00:05 time: 0.0970 data_time: 0.0110 memory: 2112 2023/03/17 23:32:50 - mmengine - INFO - Epoch(val) [50][160/194] eta: 0:00:03 time: 0.0969 data_time: 0.0109 memory: 2112 2023/03/17 23:32:51 - mmengine - INFO - Epoch(val) [50][180/194] eta: 0:00:01 time: 0.0967 data_time: 0.0106 memory: 2112 2023/03/17 23:32:54 - mmengine - INFO - Epoch(val) [50][194/194] acc/top1: 0.6118 acc/top5: 0.8679 acc/mean1: 0.5520 2023/03/17 23:32:54 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/fix_flip/tsm_imagenet-pretrained-r101_8xb16-1x1x8-50e_sthv2-rgb_torchvision_pretrain/best_acc/top1_epoch_48.pth is removed 2023/03/17 23:32:55 - mmengine - INFO - The best checkpoint with 0.6118 acc/top1 at 50 epoch is saved to best_acc/top1_epoch_50.pth.