2023/01/21 14:29:34 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1679548713 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.6 NVCC: Cuda compilation tools, release 11.6, V11.6.124 GCC: gcc (GCC) 5.4.0 PyTorch: 1.13.1 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.6 - 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.3.2 (built against CUDA 11.5) - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, 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, USE_ROCM=OFF, TorchVision: 0.14.1 OpenCV: 4.7.0 MMEngine: 0.4.0 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: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/01/21 14:29:36 - mmengine - INFO - Config: model = dict( type='Recognizer3D', backbone=dict( type='MViT', arch='small', drop_path_rate=0.1, dim_mul_in_attention=False, pretrained=None, pretrained_type='maskfeat', init_cfg=dict( type='Pretrained', checkpoint= '/mnt/petrelfs/fangyixiao/work_dirs/selfsup/maskfeat_mvit-small_16xb32-amp-coslr-300e_k400/20230117_traning/epoch_300.pth', prefix='backbone.')), data_preprocessor=dict( type='ActionDataPreprocessor', mean=[114.75, 114.75, 114.75], std=[57.375, 57.375, 57.375], format_shape='NCTHW', blending=dict( type='RandomBatchAugment', augments=[ dict(type='MixupBlending', alpha=0.8, num_classes=400), dict(type='CutmixBlending', alpha=1, num_classes=400) ])), cls_head=dict( type='MViTHead', in_channels=768, num_classes=400, label_smooth_eps=0.1, average_clips='prob', dropout_ratio=0.0, init_scale=0.001), _scope_='mmaction') default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook', _scope_='mmaction'), timer=dict(type='IterTimerHook', _scope_='mmaction'), logger=dict( type='LoggerHook', interval=100, ignore_last=False, _scope_='mmaction'), param_scheduler=dict(type='ParamSchedulerHook', _scope_='mmaction'), checkpoint=dict( type='CheckpointHook', interval=3, save_best='auto', _scope_='mmaction', max_keep_ckpts=20), sampler_seed=dict(type='DistSamplerSeedHook', _scope_='mmaction'), sync_buffers=dict(type='SyncBuffersHook', _scope_='mmaction')) 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, _scope_='mmaction') vis_backends = [dict(type='LocalVisBackend', _scope_='mmaction')] visualizer = dict( type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')], _scope_='mmaction') log_level = 'INFO' load_from = None resume = False dataset_type = 'VideoDataset' data_root = 's3://openmmlab/datasets/action/Kinetics400/videos_train' data_root_val = 's3://openmmlab/datasets/action/Kinetics400/videos_val' ann_file_train = 's3://openmmlab/datasets/action/Kinetics400/kinetics400_train_list_videos.txt' ann_file_val = 's3://openmmlab/datasets/action/Kinetics400/kinetics400_val_list_videos.txt' ann_file_test = 's3://openmmlab/datasets/action/Kinetics400/kinetics400_val_list_videos.txt' file_client_args = dict(io_backend='petrel') train_pipeline = [ dict(type='DecordInit', io_backend='petrel'), dict(type='SampleFrames', clip_len=16, frame_interval=4, num_clips=1), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='PytorchVideoWrapper', op='RandAugment', magnitude=7), dict(type='RandomResizedCrop'), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='Flip', flip_ratio=0.5), dict(type='RandomErasing', erase_prob=0.25, mode='rand'), dict(type='FormatShape', input_format='NCTHW'), dict(type='PackActionInputs') ] val_pipeline = [ dict(type='DecordInit', io_backend='petrel'), dict( type='SampleFrames', clip_len=16, frame_interval=4, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCTHW'), dict(type='PackActionInputs') ] test_pipeline = [ dict(type='DecordInit', io_backend='petrel'), dict( type='SampleFrames', clip_len=16, frame_interval=4, num_clips=10, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 224)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCTHW'), dict(type='PackActionInputs') ] repeat_sample = 2 train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), collate_fn=dict(type='repeat_pseudo_collate'), dataset=dict( type='RepeatAugDataset', num_repeats=2, ann_file= 's3://openmmlab/datasets/action/Kinetics400/kinetics400_train_list_videos.txt', data_prefix=dict( video='s3://openmmlab/datasets/action/Kinetics400/videos_train'), pipeline=[ dict(type='DecordInit', io_backend='petrel'), dict( type='SampleFrames', clip_len=16, frame_interval=4, num_clips=1), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='PytorchVideoWrapper', op='RandAugment', magnitude=7), dict(type='RandomResizedCrop'), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='Flip', flip_ratio=0.5), dict(type='RandomErasing', erase_prob=0.25, mode='rand'), dict(type='FormatShape', input_format='NCTHW'), 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= 's3://openmmlab/datasets/action/Kinetics400/kinetics400_val_list_videos.txt', data_prefix=dict( video='s3://openmmlab/datasets/action/Kinetics400/videos_val'), pipeline=[ dict(type='DecordInit', io_backend='petrel'), dict( type='SampleFrames', clip_len=16, frame_interval=4, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCTHW'), 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= 's3://openmmlab/datasets/action/Kinetics400/kinetics400_val_list_videos.txt', data_prefix=dict( video='s3://openmmlab/datasets/action/Kinetics400/videos_val'), pipeline=[ dict(type='DecordInit', io_backend='petrel'), dict( type='SampleFrames', clip_len=16, frame_interval=4, num_clips=10, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 224)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCTHW'), dict(type='PackActionInputs') ], test_mode=True)) val_evaluator = dict(type='AccMetric') test_evaluator = dict(type='AccMetric') train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') base_lr = 0.0096 optim_wrapper = dict( optimizer=dict( type='AdamW', lr=0.0096, betas=(0.9, 0.999), weight_decay=0.05), constructor='LearningRateDecayOptimizerConstructor', paramwise_cfg=dict( decay_rate=0.75, decay_type='layer_wise', num_layers=16), clip_grad=dict(max_norm=5, norm_type=2)) param_scheduler = [ dict( type='LinearLR', start_factor=0.0016666666666666668, by_epoch=True, begin=0, end=20, convert_to_iter_based=True), dict( type='CosineAnnealingLR', T_max=80, eta_min_ratio=0.0016666666666666668, by_epoch=True, begin=20, end=100, convert_to_iter_based=True) ] auto_scale_lr = dict(enable=True, base_batch_size=256) launcher = 'slurm' work_dir = '/mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2023/01/21 14:29:36 - mmengine - WARNING - The "visualizer" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:36 - mmengine - WARNING - The "vis_backend" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:38 - mmengine - WARNING - The "model" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:38 - mmengine - WARNING - The "hook" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:38 - 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 -------------------- 2023/01/21 14:29:40 - mmengine - WARNING - The "loop" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:40 - mmengine - WARNING - The "dataset" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:40 - mmengine - WARNING - The "transform" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:44 - mmengine - WARNING - The "data sampler" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:44 - mmengine - WARNING - The "optimizer wrapper constructor" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:44 - mmengine - INFO - self.paramwise_cfg is {'decay_rate': 0.75, 'decay_type': 'layer_wise', 'num_layers': 16} 2023/01/21 14:29:44 - mmengine - INFO - Build LearningRateDecayOptimizerConstructor layer_wise 0.75 - 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.cls_token as id 0 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.patch_embed.projection.weight as id 0 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.patch_embed.projection.bias as id 0 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.norm1.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.norm1.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.rel_pos_h as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.rel_pos_w as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.rel_pos_t as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.qkv.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.qkv.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.proj.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.proj.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.pool_q.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.norm_q.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.norm_q.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.pool_k.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.norm_k.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.norm_k.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.pool_v.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.norm_v.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.attn.norm_v.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.norm2.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.norm2.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.mlp.fc1.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.mlp.fc1.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.mlp.fc2.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.mlp.fc2.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.proj.weight as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.0.proj.bias as id 1 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.norm1.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.norm1.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.rel_pos_h as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.rel_pos_w as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.rel_pos_t as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.qkv.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.qkv.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.proj.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.proj.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.pool_q.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.norm_q.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.norm_q.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.pool_k.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.norm_k.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.norm_k.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.pool_v.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.norm_v.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.attn.norm_v.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.norm2.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.norm2.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.mlp.fc1.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.mlp.fc1.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.mlp.fc2.weight as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.1.mlp.fc2.bias as id 2 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.norm1.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.norm1.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.rel_pos_h as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.rel_pos_w as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.rel_pos_t as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.qkv.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.qkv.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.proj.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.proj.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.pool_q.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.norm_q.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.norm_q.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.pool_k.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.norm_k.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.norm_k.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.pool_v.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.norm_v.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.attn.norm_v.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.norm2.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.norm2.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.mlp.fc1.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.mlp.fc1.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.mlp.fc2.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.mlp.fc2.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.proj.weight as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.2.proj.bias as id 3 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.norm1.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.norm1.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.rel_pos_h as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.rel_pos_w as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.rel_pos_t as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.qkv.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.qkv.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.proj.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.proj.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.pool_q.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.norm_q.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.norm_q.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.pool_k.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.norm_k.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.norm_k.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.pool_v.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.norm_v.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.attn.norm_v.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.norm2.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.norm2.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.mlp.fc1.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.mlp.fc1.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.mlp.fc2.weight as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.3.mlp.fc2.bias as id 4 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.norm1.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.norm1.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.rel_pos_h as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.rel_pos_w as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.rel_pos_t as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.qkv.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.qkv.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.proj.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.proj.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.pool_q.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.norm_q.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.norm_q.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.pool_k.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.norm_k.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.norm_k.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.pool_v.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.norm_v.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.attn.norm_v.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.norm2.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.norm2.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.mlp.fc1.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.mlp.fc1.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.mlp.fc2.weight as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.4.mlp.fc2.bias as id 5 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.norm1.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.norm1.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.rel_pos_h as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.rel_pos_w as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.rel_pos_t as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.qkv.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.qkv.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.proj.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.proj.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.pool_q.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.norm_q.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.norm_q.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.pool_k.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.norm_k.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.norm_k.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.pool_v.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.norm_v.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.attn.norm_v.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.norm2.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.norm2.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.mlp.fc1.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.mlp.fc1.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.mlp.fc2.weight as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.5.mlp.fc2.bias as id 6 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.norm1.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.norm1.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.rel_pos_h as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.rel_pos_w as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.rel_pos_t as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.qkv.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.qkv.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.proj.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.proj.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.pool_q.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.norm_q.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.norm_q.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.pool_k.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.norm_k.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.norm_k.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.pool_v.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.norm_v.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.attn.norm_v.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.norm2.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.norm2.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.mlp.fc1.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.mlp.fc1.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.mlp.fc2.weight as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.6.mlp.fc2.bias as id 7 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.norm1.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.norm1.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.rel_pos_h as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.rel_pos_w as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.rel_pos_t as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.qkv.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.qkv.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.proj.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.proj.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.pool_q.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.norm_q.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.norm_q.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.pool_k.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.norm_k.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.norm_k.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.pool_v.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.norm_v.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.attn.norm_v.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.norm2.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.norm2.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.mlp.fc1.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.mlp.fc1.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.mlp.fc2.weight as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.7.mlp.fc2.bias as id 8 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.norm1.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.norm1.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.rel_pos_h as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.rel_pos_w as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.rel_pos_t as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.qkv.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.qkv.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.proj.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.proj.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.pool_q.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.norm_q.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.norm_q.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.pool_k.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.norm_k.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.norm_k.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.pool_v.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.norm_v.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.attn.norm_v.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.norm2.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.norm2.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.mlp.fc1.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.mlp.fc1.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.mlp.fc2.weight as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.8.mlp.fc2.bias as id 9 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.norm1.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.norm1.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.rel_pos_h as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.rel_pos_w as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.rel_pos_t as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.qkv.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.qkv.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.proj.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.proj.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.pool_q.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.norm_q.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.norm_q.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.pool_k.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.norm_k.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.norm_k.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.pool_v.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.norm_v.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.attn.norm_v.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.norm2.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.norm2.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.mlp.fc1.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.mlp.fc1.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.mlp.fc2.weight as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.9.mlp.fc2.bias as id 10 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.norm1.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.norm1.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.rel_pos_h as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.rel_pos_w as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.rel_pos_t as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.qkv.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.qkv.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.proj.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.proj.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.pool_q.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.norm_q.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.norm_q.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.pool_k.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.norm_k.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.norm_k.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.pool_v.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.norm_v.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.attn.norm_v.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.norm2.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.norm2.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.mlp.fc1.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.mlp.fc1.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.mlp.fc2.weight as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.10.mlp.fc2.bias as id 11 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.norm1.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.norm1.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.rel_pos_h as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.rel_pos_w as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.rel_pos_t as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.qkv.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.qkv.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.proj.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.proj.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.pool_q.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.norm_q.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.norm_q.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.pool_k.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.norm_k.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.norm_k.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.pool_v.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.norm_v.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.attn.norm_v.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.norm2.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.norm2.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.mlp.fc1.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.mlp.fc1.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.mlp.fc2.weight as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.11.mlp.fc2.bias as id 12 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.norm1.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.norm1.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.rel_pos_h as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.rel_pos_w as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.rel_pos_t as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.qkv.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.qkv.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.proj.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.proj.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.pool_q.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.norm_q.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.norm_q.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.pool_k.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.norm_k.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.norm_k.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.pool_v.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.norm_v.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.attn.norm_v.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.norm2.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.norm2.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.mlp.fc1.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.mlp.fc1.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.mlp.fc2.weight as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.12.mlp.fc2.bias as id 13 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.norm1.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.norm1.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.rel_pos_h as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.rel_pos_w as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.rel_pos_t as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.qkv.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.qkv.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.proj.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.proj.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.pool_q.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.norm_q.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.norm_q.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.pool_k.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.norm_k.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.norm_k.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.pool_v.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.norm_v.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.attn.norm_v.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.norm2.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.norm2.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.mlp.fc1.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.mlp.fc1.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.mlp.fc2.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.mlp.fc2.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.proj.weight as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.13.proj.bias as id 14 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.norm1.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.norm1.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.rel_pos_h as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.rel_pos_w as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.rel_pos_t as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.qkv.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.qkv.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.proj.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.proj.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.pool_q.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.norm_q.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.norm_q.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.pool_k.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.norm_k.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.norm_k.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.pool_v.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.norm_v.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.attn.norm_v.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.norm2.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.norm2.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.mlp.fc1.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.mlp.fc1.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.mlp.fc2.weight as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.14.mlp.fc2.bias as id 15 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.norm1.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.norm1.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.rel_pos_h as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.rel_pos_w as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.rel_pos_t as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.qkv.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.qkv.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.proj.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.proj.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.pool_q.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.norm_q.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.norm_q.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.pool_k.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.norm_k.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.norm_k.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.pool_v.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.norm_v.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.attn.norm_v.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.norm2.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.norm2.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.mlp.fc1.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.mlp.fc1.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.mlp.fc2.weight as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.blocks.15.mlp.fc2.bias as id 16 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.norm3.weight as id 17 2023/01/21 14:29:44 - mmengine - INFO - set param backbone.norm3.bias as id 17 2023/01/21 14:29:44 - mmengine - INFO - set param cls_head.fc_cls.weight as id 17 2023/01/21 14:29:44 - mmengine - INFO - set param cls_head.fc_cls.bias as id 17 2023/01/21 14:29:44 - mmengine - INFO - Param groups = { "layer_0_decay": { "param_names": [ "backbone.cls_token", "backbone.patch_embed.projection.weight" ], "lr_scale": 0.00751694681821391, "lr": 7.216268945485352e-05, "weight_decay": 0.05 }, "layer_0_no_decay": { "param_names": [ "backbone.patch_embed.projection.bias" ], "lr_scale": 0.00751694681821391, "lr": 7.216268945485352e-05, "weight_decay": 0.0 }, "layer_1_no_decay": { "param_names": [ "backbone.blocks.0.norm1.weight", "backbone.blocks.0.norm1.bias", "backbone.blocks.0.attn.qkv.bias", "backbone.blocks.0.attn.proj.bias", "backbone.blocks.0.attn.norm_q.weight", "backbone.blocks.0.attn.norm_q.bias", "backbone.blocks.0.attn.norm_k.weight", "backbone.blocks.0.attn.norm_k.bias", "backbone.blocks.0.attn.norm_v.weight", "backbone.blocks.0.attn.norm_v.bias", "backbone.blocks.0.norm2.weight", "backbone.blocks.0.norm2.bias", "backbone.blocks.0.mlp.fc1.bias", "backbone.blocks.0.mlp.fc2.bias", "backbone.blocks.0.proj.bias" ], "lr_scale": 0.010022595757618546, "lr": 9.621691927313804e-05, "weight_decay": 0.0 }, "layer_1_decay": { "param_names": [ "backbone.blocks.0.attn.rel_pos_h", "backbone.blocks.0.attn.rel_pos_w", "backbone.blocks.0.attn.rel_pos_t", "backbone.blocks.0.attn.qkv.weight", "backbone.blocks.0.attn.proj.weight", "backbone.blocks.0.attn.pool_q.weight", "backbone.blocks.0.attn.pool_k.weight", "backbone.blocks.0.attn.pool_v.weight", "backbone.blocks.0.mlp.fc1.weight", "backbone.blocks.0.mlp.fc2.weight", "backbone.blocks.0.proj.weight" ], "lr_scale": 0.010022595757618546, "lr": 9.621691927313804e-05, "weight_decay": 0.05 }, "layer_2_no_decay": { "param_names": [ "backbone.blocks.1.norm1.weight", "backbone.blocks.1.norm1.bias", "backbone.blocks.1.attn.qkv.bias", "backbone.blocks.1.attn.proj.bias", "backbone.blocks.1.attn.norm_q.weight", "backbone.blocks.1.attn.norm_q.bias", "backbone.blocks.1.attn.norm_k.weight", "backbone.blocks.1.attn.norm_k.bias", "backbone.blocks.1.attn.norm_v.weight", "backbone.blocks.1.attn.norm_v.bias", "backbone.blocks.1.norm2.weight", "backbone.blocks.1.norm2.bias", "backbone.blocks.1.mlp.fc1.bias", "backbone.blocks.1.mlp.fc2.bias" ], "lr_scale": 0.013363461010158062, "lr": 0.0001282892256975174, "weight_decay": 0.0 }, "layer_2_decay": { "param_names": [ "backbone.blocks.1.attn.rel_pos_h", "backbone.blocks.1.attn.rel_pos_w", "backbone.blocks.1.attn.rel_pos_t", "backbone.blocks.1.attn.qkv.weight", "backbone.blocks.1.attn.proj.weight", "backbone.blocks.1.attn.pool_q.weight", "backbone.blocks.1.attn.pool_k.weight", "backbone.blocks.1.attn.pool_v.weight", "backbone.blocks.1.mlp.fc1.weight", "backbone.blocks.1.mlp.fc2.weight" ], "lr_scale": 0.013363461010158062, "lr": 0.0001282892256975174, "weight_decay": 0.05 }, "layer_3_no_decay": { "param_names": [ "backbone.blocks.2.norm1.weight", "backbone.blocks.2.norm1.bias", "backbone.blocks.2.attn.qkv.bias", "backbone.blocks.2.attn.proj.bias", "backbone.blocks.2.attn.norm_q.weight", "backbone.blocks.2.attn.norm_q.bias", "backbone.blocks.2.attn.norm_k.weight", "backbone.blocks.2.attn.norm_k.bias", "backbone.blocks.2.attn.norm_v.weight", "backbone.blocks.2.attn.norm_v.bias", "backbone.blocks.2.norm2.weight", "backbone.blocks.2.norm2.bias", "backbone.blocks.2.mlp.fc1.bias", "backbone.blocks.2.mlp.fc2.bias", "backbone.blocks.2.proj.bias" ], "lr_scale": 0.017817948013544083, "lr": 0.00017105230093002318, "weight_decay": 0.0 }, "layer_3_decay": { "param_names": [ "backbone.blocks.2.attn.rel_pos_h", "backbone.blocks.2.attn.rel_pos_w", "backbone.blocks.2.attn.rel_pos_t", "backbone.blocks.2.attn.qkv.weight", "backbone.blocks.2.attn.proj.weight", "backbone.blocks.2.attn.pool_q.weight", "backbone.blocks.2.attn.pool_k.weight", "backbone.blocks.2.attn.pool_v.weight", "backbone.blocks.2.mlp.fc1.weight", "backbone.blocks.2.mlp.fc2.weight", "backbone.blocks.2.proj.weight" ], "lr_scale": 0.017817948013544083, "lr": 0.00017105230093002318, "weight_decay": 0.05 }, "layer_4_no_decay": { "param_names": [ "backbone.blocks.3.norm1.weight", "backbone.blocks.3.norm1.bias", "backbone.blocks.3.attn.qkv.bias", "backbone.blocks.3.attn.proj.bias", "backbone.blocks.3.attn.norm_q.weight", "backbone.blocks.3.attn.norm_q.bias", "backbone.blocks.3.attn.norm_k.weight", "backbone.blocks.3.attn.norm_k.bias", "backbone.blocks.3.attn.norm_v.weight", "backbone.blocks.3.attn.norm_v.bias", "backbone.blocks.3.norm2.weight", "backbone.blocks.3.norm2.bias", "backbone.blocks.3.mlp.fc1.bias", "backbone.blocks.3.mlp.fc2.bias" ], "lr_scale": 0.023757264018058777, "lr": 0.00022806973457336423, "weight_decay": 0.0 }, "layer_4_decay": { "param_names": [ "backbone.blocks.3.attn.rel_pos_h", "backbone.blocks.3.attn.rel_pos_w", "backbone.blocks.3.attn.rel_pos_t", "backbone.blocks.3.attn.qkv.weight", "backbone.blocks.3.attn.proj.weight", "backbone.blocks.3.attn.pool_q.weight", "backbone.blocks.3.attn.pool_k.weight", "backbone.blocks.3.attn.pool_v.weight", "backbone.blocks.3.mlp.fc1.weight", "backbone.blocks.3.mlp.fc2.weight" ], "lr_scale": 0.023757264018058777, "lr": 0.00022806973457336423, "weight_decay": 0.05 }, "layer_5_no_decay": { "param_names": [ "backbone.blocks.4.norm1.weight", "backbone.blocks.4.norm1.bias", "backbone.blocks.4.attn.qkv.bias", "backbone.blocks.4.attn.proj.bias", "backbone.blocks.4.attn.norm_q.weight", "backbone.blocks.4.attn.norm_q.bias", "backbone.blocks.4.attn.norm_k.weight", "backbone.blocks.4.attn.norm_k.bias", "backbone.blocks.4.attn.norm_v.weight", "backbone.blocks.4.attn.norm_v.bias", "backbone.blocks.4.norm2.weight", "backbone.blocks.4.norm2.bias", "backbone.blocks.4.mlp.fc1.bias", "backbone.blocks.4.mlp.fc2.bias" ], "lr_scale": 0.03167635202407837, "lr": 0.00030409297943115234, "weight_decay": 0.0 }, "layer_5_decay": { "param_names": [ "backbone.blocks.4.attn.rel_pos_h", "backbone.blocks.4.attn.rel_pos_w", "backbone.blocks.4.attn.rel_pos_t", "backbone.blocks.4.attn.qkv.weight", "backbone.blocks.4.attn.proj.weight", "backbone.blocks.4.attn.pool_q.weight", "backbone.blocks.4.attn.pool_k.weight", "backbone.blocks.4.attn.pool_v.weight", "backbone.blocks.4.mlp.fc1.weight", "backbone.blocks.4.mlp.fc2.weight" ], "lr_scale": 0.03167635202407837, "lr": 0.00030409297943115234, "weight_decay": 0.05 }, "layer_6_no_decay": { "param_names": [ "backbone.blocks.5.norm1.weight", "backbone.blocks.5.norm1.bias", "backbone.blocks.5.attn.qkv.bias", "backbone.blocks.5.attn.proj.bias", "backbone.blocks.5.attn.norm_q.weight", "backbone.blocks.5.attn.norm_q.bias", "backbone.blocks.5.attn.norm_k.weight", "backbone.blocks.5.attn.norm_k.bias", "backbone.blocks.5.attn.norm_v.weight", "backbone.blocks.5.attn.norm_v.bias", "backbone.blocks.5.norm2.weight", "backbone.blocks.5.norm2.bias", "backbone.blocks.5.mlp.fc1.bias", "backbone.blocks.5.mlp.fc2.bias" ], "lr_scale": 0.04223513603210449, "lr": 0.0004054573059082031, "weight_decay": 0.0 }, "layer_6_decay": { "param_names": [ "backbone.blocks.5.attn.rel_pos_h", "backbone.blocks.5.attn.rel_pos_w", "backbone.blocks.5.attn.rel_pos_t", "backbone.blocks.5.attn.qkv.weight", "backbone.blocks.5.attn.proj.weight", "backbone.blocks.5.attn.pool_q.weight", "backbone.blocks.5.attn.pool_k.weight", "backbone.blocks.5.attn.pool_v.weight", "backbone.blocks.5.mlp.fc1.weight", "backbone.blocks.5.mlp.fc2.weight" ], "lr_scale": 0.04223513603210449, "lr": 0.0004054573059082031, "weight_decay": 0.05 }, "layer_7_no_decay": { "param_names": [ "backbone.blocks.6.norm1.weight", "backbone.blocks.6.norm1.bias", "backbone.blocks.6.attn.qkv.bias", "backbone.blocks.6.attn.proj.bias", "backbone.blocks.6.attn.norm_q.weight", "backbone.blocks.6.attn.norm_q.bias", "backbone.blocks.6.attn.norm_k.weight", "backbone.blocks.6.attn.norm_k.bias", "backbone.blocks.6.attn.norm_v.weight", "backbone.blocks.6.attn.norm_v.bias", "backbone.blocks.6.norm2.weight", "backbone.blocks.6.norm2.bias", "backbone.blocks.6.mlp.fc1.bias", "backbone.blocks.6.mlp.fc2.bias" ], "lr_scale": 0.056313514709472656, "lr": 0.0005406097412109374, "weight_decay": 0.0 }, "layer_7_decay": { "param_names": [ "backbone.blocks.6.attn.rel_pos_h", "backbone.blocks.6.attn.rel_pos_w", "backbone.blocks.6.attn.rel_pos_t", "backbone.blocks.6.attn.qkv.weight", "backbone.blocks.6.attn.proj.weight", "backbone.blocks.6.attn.pool_q.weight", "backbone.blocks.6.attn.pool_k.weight", "backbone.blocks.6.attn.pool_v.weight", "backbone.blocks.6.mlp.fc1.weight", "backbone.blocks.6.mlp.fc2.weight" ], "lr_scale": 0.056313514709472656, "lr": 0.0005406097412109374, "weight_decay": 0.05 }, "layer_8_no_decay": { "param_names": [ "backbone.blocks.7.norm1.weight", "backbone.blocks.7.norm1.bias", "backbone.blocks.7.attn.qkv.bias", "backbone.blocks.7.attn.proj.bias", "backbone.blocks.7.attn.norm_q.weight", "backbone.blocks.7.attn.norm_q.bias", "backbone.blocks.7.attn.norm_k.weight", "backbone.blocks.7.attn.norm_k.bias", "backbone.blocks.7.attn.norm_v.weight", "backbone.blocks.7.attn.norm_v.bias", "backbone.blocks.7.norm2.weight", "backbone.blocks.7.norm2.bias", "backbone.blocks.7.mlp.fc1.bias", "backbone.blocks.7.mlp.fc2.bias" ], "lr_scale": 0.07508468627929688, "lr": 0.00072081298828125, "weight_decay": 0.0 }, "layer_8_decay": { "param_names": [ "backbone.blocks.7.attn.rel_pos_h", "backbone.blocks.7.attn.rel_pos_w", "backbone.blocks.7.attn.rel_pos_t", "backbone.blocks.7.attn.qkv.weight", "backbone.blocks.7.attn.proj.weight", "backbone.blocks.7.attn.pool_q.weight", "backbone.blocks.7.attn.pool_k.weight", "backbone.blocks.7.attn.pool_v.weight", "backbone.blocks.7.mlp.fc1.weight", "backbone.blocks.7.mlp.fc2.weight" ], "lr_scale": 0.07508468627929688, "lr": 0.00072081298828125, "weight_decay": 0.05 }, "layer_9_no_decay": { "param_names": [ "backbone.blocks.8.norm1.weight", "backbone.blocks.8.norm1.bias", "backbone.blocks.8.attn.qkv.bias", "backbone.blocks.8.attn.proj.bias", "backbone.blocks.8.attn.norm_q.weight", "backbone.blocks.8.attn.norm_q.bias", "backbone.blocks.8.attn.norm_k.weight", "backbone.blocks.8.attn.norm_k.bias", "backbone.blocks.8.attn.norm_v.weight", "backbone.blocks.8.attn.norm_v.bias", "backbone.blocks.8.norm2.weight", "backbone.blocks.8.norm2.bias", "backbone.blocks.8.mlp.fc1.bias", "backbone.blocks.8.mlp.fc2.bias" ], "lr_scale": 0.1001129150390625, "lr": 0.000961083984375, "weight_decay": 0.0 }, "layer_9_decay": { "param_names": [ "backbone.blocks.8.attn.rel_pos_h", "backbone.blocks.8.attn.rel_pos_w", "backbone.blocks.8.attn.rel_pos_t", "backbone.blocks.8.attn.qkv.weight", "backbone.blocks.8.attn.proj.weight", "backbone.blocks.8.attn.pool_q.weight", "backbone.blocks.8.attn.pool_k.weight", "backbone.blocks.8.attn.pool_v.weight", "backbone.blocks.8.mlp.fc1.weight", "backbone.blocks.8.mlp.fc2.weight" ], "lr_scale": 0.1001129150390625, "lr": 0.000961083984375, "weight_decay": 0.05 }, "layer_10_no_decay": { "param_names": [ "backbone.blocks.9.norm1.weight", "backbone.blocks.9.norm1.bias", "backbone.blocks.9.attn.qkv.bias", "backbone.blocks.9.attn.proj.bias", "backbone.blocks.9.attn.norm_q.weight", "backbone.blocks.9.attn.norm_q.bias", "backbone.blocks.9.attn.norm_k.weight", "backbone.blocks.9.attn.norm_k.bias", "backbone.blocks.9.attn.norm_v.weight", "backbone.blocks.9.attn.norm_v.bias", "backbone.blocks.9.norm2.weight", "backbone.blocks.9.norm2.bias", "backbone.blocks.9.mlp.fc1.bias", "backbone.blocks.9.mlp.fc2.bias" ], "lr_scale": 0.13348388671875, "lr": 0.0012814453124999998, "weight_decay": 0.0 }, "layer_10_decay": { "param_names": [ "backbone.blocks.9.attn.rel_pos_h", "backbone.blocks.9.attn.rel_pos_w", "backbone.blocks.9.attn.rel_pos_t", "backbone.blocks.9.attn.qkv.weight", "backbone.blocks.9.attn.proj.weight", "backbone.blocks.9.attn.pool_q.weight", "backbone.blocks.9.attn.pool_k.weight", "backbone.blocks.9.attn.pool_v.weight", "backbone.blocks.9.mlp.fc1.weight", "backbone.blocks.9.mlp.fc2.weight" ], "lr_scale": 0.13348388671875, "lr": 0.0012814453124999998, "weight_decay": 0.05 }, "layer_11_no_decay": { "param_names": [ "backbone.blocks.10.norm1.weight", "backbone.blocks.10.norm1.bias", "backbone.blocks.10.attn.qkv.bias", "backbone.blocks.10.attn.proj.bias", "backbone.blocks.10.attn.norm_q.weight", "backbone.blocks.10.attn.norm_q.bias", "backbone.blocks.10.attn.norm_k.weight", "backbone.blocks.10.attn.norm_k.bias", "backbone.blocks.10.attn.norm_v.weight", "backbone.blocks.10.attn.norm_v.bias", "backbone.blocks.10.norm2.weight", "backbone.blocks.10.norm2.bias", "backbone.blocks.10.mlp.fc1.bias", "backbone.blocks.10.mlp.fc2.bias" ], "lr_scale": 0.177978515625, "lr": 0.0017085937499999998, "weight_decay": 0.0 }, "layer_11_decay": { "param_names": [ "backbone.blocks.10.attn.rel_pos_h", "backbone.blocks.10.attn.rel_pos_w", "backbone.blocks.10.attn.rel_pos_t", "backbone.blocks.10.attn.qkv.weight", "backbone.blocks.10.attn.proj.weight", "backbone.blocks.10.attn.pool_q.weight", "backbone.blocks.10.attn.pool_k.weight", "backbone.blocks.10.attn.pool_v.weight", "backbone.blocks.10.mlp.fc1.weight", "backbone.blocks.10.mlp.fc2.weight" ], "lr_scale": 0.177978515625, "lr": 0.0017085937499999998, "weight_decay": 0.05 }, "layer_12_no_decay": { "param_names": [ "backbone.blocks.11.norm1.weight", "backbone.blocks.11.norm1.bias", "backbone.blocks.11.attn.qkv.bias", "backbone.blocks.11.attn.proj.bias", "backbone.blocks.11.attn.norm_q.weight", "backbone.blocks.11.attn.norm_q.bias", "backbone.blocks.11.attn.norm_k.weight", "backbone.blocks.11.attn.norm_k.bias", "backbone.blocks.11.attn.norm_v.weight", "backbone.blocks.11.attn.norm_v.bias", "backbone.blocks.11.norm2.weight", "backbone.blocks.11.norm2.bias", "backbone.blocks.11.mlp.fc1.bias", "backbone.blocks.11.mlp.fc2.bias" ], "lr_scale": 0.2373046875, "lr": 0.0022781249999999998, "weight_decay": 0.0 }, "layer_12_decay": { "param_names": [ "backbone.blocks.11.attn.rel_pos_h", "backbone.blocks.11.attn.rel_pos_w", "backbone.blocks.11.attn.rel_pos_t", "backbone.blocks.11.attn.qkv.weight", "backbone.blocks.11.attn.proj.weight", "backbone.blocks.11.attn.pool_q.weight", "backbone.blocks.11.attn.pool_k.weight", "backbone.blocks.11.attn.pool_v.weight", "backbone.blocks.11.mlp.fc1.weight", "backbone.blocks.11.mlp.fc2.weight" ], "lr_scale": 0.2373046875, "lr": 0.0022781249999999998, "weight_decay": 0.05 }, "layer_13_no_decay": { "param_names": [ "backbone.blocks.12.norm1.weight", "backbone.blocks.12.norm1.bias", "backbone.blocks.12.attn.qkv.bias", "backbone.blocks.12.attn.proj.bias", "backbone.blocks.12.attn.norm_q.weight", "backbone.blocks.12.attn.norm_q.bias", "backbone.blocks.12.attn.norm_k.weight", "backbone.blocks.12.attn.norm_k.bias", "backbone.blocks.12.attn.norm_v.weight", "backbone.blocks.12.attn.norm_v.bias", "backbone.blocks.12.norm2.weight", "backbone.blocks.12.norm2.bias", "backbone.blocks.12.mlp.fc1.bias", "backbone.blocks.12.mlp.fc2.bias" ], "lr_scale": 0.31640625, "lr": 0.0030375, "weight_decay": 0.0 }, "layer_13_decay": { "param_names": [ "backbone.blocks.12.attn.rel_pos_h", "backbone.blocks.12.attn.rel_pos_w", "backbone.blocks.12.attn.rel_pos_t", "backbone.blocks.12.attn.qkv.weight", "backbone.blocks.12.attn.proj.weight", "backbone.blocks.12.attn.pool_q.weight", "backbone.blocks.12.attn.pool_k.weight", "backbone.blocks.12.attn.pool_v.weight", "backbone.blocks.12.mlp.fc1.weight", "backbone.blocks.12.mlp.fc2.weight" ], "lr_scale": 0.31640625, "lr": 0.0030375, "weight_decay": 0.05 }, "layer_14_no_decay": { "param_names": [ "backbone.blocks.13.norm1.weight", "backbone.blocks.13.norm1.bias", "backbone.blocks.13.attn.qkv.bias", "backbone.blocks.13.attn.proj.bias", "backbone.blocks.13.attn.norm_q.weight", "backbone.blocks.13.attn.norm_q.bias", "backbone.blocks.13.attn.norm_k.weight", "backbone.blocks.13.attn.norm_k.bias", "backbone.blocks.13.attn.norm_v.weight", "backbone.blocks.13.attn.norm_v.bias", "backbone.blocks.13.norm2.weight", "backbone.blocks.13.norm2.bias", "backbone.blocks.13.mlp.fc1.bias", "backbone.blocks.13.mlp.fc2.bias", "backbone.blocks.13.proj.bias" ], "lr_scale": 0.421875, "lr": 0.00405, "weight_decay": 0.0 }, "layer_14_decay": { "param_names": [ "backbone.blocks.13.attn.rel_pos_h", "backbone.blocks.13.attn.rel_pos_w", "backbone.blocks.13.attn.rel_pos_t", "backbone.blocks.13.attn.qkv.weight", "backbone.blocks.13.attn.proj.weight", "backbone.blocks.13.attn.pool_q.weight", "backbone.blocks.13.attn.pool_k.weight", "backbone.blocks.13.attn.pool_v.weight", "backbone.blocks.13.mlp.fc1.weight", "backbone.blocks.13.mlp.fc2.weight", "backbone.blocks.13.proj.weight" ], "lr_scale": 0.421875, "lr": 0.00405, "weight_decay": 0.05 }, "layer_15_no_decay": { "param_names": [ "backbone.blocks.14.norm1.weight", "backbone.blocks.14.norm1.bias", "backbone.blocks.14.attn.qkv.bias", "backbone.blocks.14.attn.proj.bias", "backbone.blocks.14.attn.norm_q.weight", "backbone.blocks.14.attn.norm_q.bias", "backbone.blocks.14.attn.norm_k.weight", "backbone.blocks.14.attn.norm_k.bias", "backbone.blocks.14.attn.norm_v.weight", "backbone.blocks.14.attn.norm_v.bias", "backbone.blocks.14.norm2.weight", "backbone.blocks.14.norm2.bias", "backbone.blocks.14.mlp.fc1.bias", "backbone.blocks.14.mlp.fc2.bias" ], "lr_scale": 0.5625, "lr": 0.005399999999999999, "weight_decay": 0.0 }, "layer_15_decay": { "param_names": [ "backbone.blocks.14.attn.rel_pos_h", "backbone.blocks.14.attn.rel_pos_w", "backbone.blocks.14.attn.rel_pos_t", "backbone.blocks.14.attn.qkv.weight", "backbone.blocks.14.attn.proj.weight", "backbone.blocks.14.attn.pool_q.weight", "backbone.blocks.14.attn.pool_k.weight", "backbone.blocks.14.attn.pool_v.weight", "backbone.blocks.14.mlp.fc1.weight", "backbone.blocks.14.mlp.fc2.weight" ], "lr_scale": 0.5625, "lr": 0.005399999999999999, "weight_decay": 0.05 }, "layer_16_no_decay": { "param_names": [ "backbone.blocks.15.norm1.weight", "backbone.blocks.15.norm1.bias", "backbone.blocks.15.attn.qkv.bias", "backbone.blocks.15.attn.proj.bias", "backbone.blocks.15.attn.norm_q.weight", "backbone.blocks.15.attn.norm_q.bias", "backbone.blocks.15.attn.norm_k.weight", "backbone.blocks.15.attn.norm_k.bias", "backbone.blocks.15.attn.norm_v.weight", "backbone.blocks.15.attn.norm_v.bias", "backbone.blocks.15.norm2.weight", "backbone.blocks.15.norm2.bias", "backbone.blocks.15.mlp.fc1.bias", "backbone.blocks.15.mlp.fc2.bias" ], "lr_scale": 0.75, "lr": 0.0072, "weight_decay": 0.0 }, "layer_16_decay": { "param_names": [ "backbone.blocks.15.attn.rel_pos_h", "backbone.blocks.15.attn.rel_pos_w", "backbone.blocks.15.attn.rel_pos_t", "backbone.blocks.15.attn.qkv.weight", "backbone.blocks.15.attn.proj.weight", "backbone.blocks.15.attn.pool_q.weight", "backbone.blocks.15.attn.pool_k.weight", "backbone.blocks.15.attn.pool_v.weight", "backbone.blocks.15.mlp.fc1.weight", "backbone.blocks.15.mlp.fc2.weight" ], "lr_scale": 0.75, "lr": 0.0072, "weight_decay": 0.05 }, "layer_17_no_decay": { "param_names": [ "backbone.norm3.weight", "backbone.norm3.bias", "cls_head.fc_cls.bias" ], "lr_scale": 1.0, "lr": 0.0096, "weight_decay": 0.0 }, "layer_17_decay": { "param_names": [ "cls_head.fc_cls.weight" ], "lr_scale": 1.0, "lr": 0.0096, "weight_decay": 0.05 } } 2023/01/21 14:29:44 - mmengine - WARNING - The "optimizer" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:44 - mmengine - WARNING - The "optim_wrapper" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:44 - mmengine - INFO - LR is set based on batch size of 256 and the current batch size is 128. Scaling the original LR by 0.5. 2023/01/21 14:29:44 - mmengine - WARNING - The "parameter scheduler" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:44 - mmengine - WARNING - The "metric" registry in mmaction did not set import location. Fallback to call `mmaction.utils.register_all_modules` instead. 2023/01/21 14:29:46 - mmengine - INFO - load pretrained model from /mnt/petrelfs/fangyixiao/work_dirs/selfsup/maskfeat_mvit-small_16xb32-amp-coslr-300e_k400/20230117_traning/epoch_300.pth 2023/01/21 14:29:47 - mmengine - INFO - blocks.15.attn.rel_pos_h reshaped from (27, 96) to (13, 96) 2023/01/21 14:29:47 - mmengine - INFO - blocks.15.attn.rel_pos_w reshaped from (27, 96) to (13, 96) 2023/01/21 14:29:47 - mmengine - INFO - _IncompatibleKeys(missing_keys=['norm3.weight', 'norm3.bias'], unexpected_keys=['mask_token', 'norm2.weight', 'norm2.bias']) Name of parameter - Initialization information backbone.cls_token - torch.Size([1, 1, 96]): Initialized by user-defined `init_weights` in MViT backbone.patch_embed.projection.weight - torch.Size([96, 3, 3, 7, 7]): Initialized by user-defined `init_weights` in MViT backbone.patch_embed.projection.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.norm1.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.norm1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.rel_pos_h - torch.Size([111, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.rel_pos_w - torch.Size([111, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.qkv.weight - torch.Size([288, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.qkv.bias - torch.Size([288]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.proj.weight - torch.Size([96, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.proj.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.norm2.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.norm2.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.mlp.fc1.weight - torch.Size([384, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.mlp.fc1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.mlp.fc2.weight - torch.Size([192, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.mlp.fc2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.proj.weight - torch.Size([192, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.0.proj.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.norm1.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.norm1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.rel_pos_h - torch.Size([55, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.rel_pos_w - torch.Size([55, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.qkv.weight - torch.Size([576, 192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.qkv.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.proj.weight - torch.Size([192, 192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.proj.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.norm2.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.norm2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.mlp.fc1.weight - torch.Size([768, 192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.mlp.fc1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.mlp.fc2.weight - torch.Size([192, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.1.mlp.fc2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.norm1.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.norm1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.rel_pos_h - torch.Size([55, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.rel_pos_w - torch.Size([55, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.qkv.weight - torch.Size([576, 192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.qkv.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.proj.weight - torch.Size([192, 192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.proj.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.norm2.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.norm2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.mlp.fc1.weight - torch.Size([768, 192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.mlp.fc1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.mlp.fc2.weight - torch.Size([384, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.proj.weight - torch.Size([384, 192]): Initialized by user-defined `init_weights` in MViT backbone.blocks.2.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.3.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.4.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.5.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.6.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.7.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.8.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.9.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.10.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.11.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.mlp.fc2.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.12.mlp.fc2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.mlp.fc1.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.mlp.fc1.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.mlp.fc2.weight - torch.Size([768, 1536]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.mlp.fc2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.proj.weight - torch.Size([768, 384]): Initialized by user-defined `init_weights` in MViT backbone.blocks.13.proj.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.norm1.weight - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.norm1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.rel_pos_h - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.rel_pos_w - torch.Size([27, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.qkv.weight - torch.Size([2304, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.qkv.bias - torch.Size([2304]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.proj.weight - torch.Size([768, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.proj.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.norm2.weight - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.norm2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.mlp.fc1.weight - torch.Size([3072, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.mlp.fc1.bias - torch.Size([3072]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.mlp.fc2.weight - torch.Size([768, 3072]): Initialized by user-defined `init_weights` in MViT backbone.blocks.14.mlp.fc2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.norm1.weight - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.norm1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.rel_pos_h - torch.Size([13, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.rel_pos_w - torch.Size([13, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.rel_pos_t - torch.Size([15, 96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.qkv.weight - torch.Size([2304, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.qkv.bias - torch.Size([2304]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.proj.weight - torch.Size([768, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.proj.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.pool_q.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.norm_q.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.norm_q.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.pool_k.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.norm_k.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.norm_k.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.pool_v.weight - torch.Size([96, 1, 3, 3, 3]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.norm_v.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.attn.norm_v.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.norm2.weight - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.norm2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.mlp.fc1.weight - torch.Size([3072, 768]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.mlp.fc1.bias - torch.Size([3072]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.mlp.fc2.weight - torch.Size([768, 3072]): Initialized by user-defined `init_weights` in MViT backbone.blocks.15.mlp.fc2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in MViT backbone.norm3.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of Recognizer3D backbone.norm3.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of Recognizer3D cls_head.fc_cls.weight - torch.Size([400, 768]): Initialized by user-defined `init_weights` in MViTHead cls_head.fc_cls.bias - torch.Size([400]): Initialized by user-defined `init_weights` in MViTHead 2023/01/21 14:29:48 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400. 2023/01/21 14:33:36 - mmengine - INFO - Epoch(train) [1][ 100/1879] lr: 1.5503e-07 eta: 4 days, 23:17:37 time: 2.1503 data_time: 0.0386 memory: 48866 grad_norm: 2.1044 loss: 5.9516 loss_cls: 5.9516 2023/01/21 14:37:11 - mmengine - INFO - Epoch(train) [1][ 200/1879] lr: 2.5089e-07 eta: 4 days, 19:40:42 time: 2.1424 data_time: 0.0371 memory: 48866 grad_norm: 2.0566 loss: 5.9276 loss_cls: 5.9276 2023/01/21 14:40:45 - mmengine - INFO - Epoch(train) [1][ 300/1879] lr: 3.4674e-07 eta: 4 days, 18:16:32 time: 2.1383 data_time: 0.0375 memory: 48866 grad_norm: 2.5248 loss: 5.8445 loss_cls: 5.8445 2023/01/21 14:44:20 - mmengine - INFO - Epoch(train) [1][ 400/1879] lr: 4.4260e-07 eta: 4 days, 17:37:32 time: 2.1534 data_time: 0.0381 memory: 48866 grad_norm: 3.0386 loss: 5.7856 loss_cls: 5.7856 2023/01/21 14:47:55 - mmengine - INFO - Epoch(train) [1][ 500/1879] lr: 5.3845e-07 eta: 4 days, 17:13:21 time: 2.1327 data_time: 0.0367 memory: 48866 grad_norm: 4.1820 loss: 5.6456 loss_cls: 5.6456 2023/01/21 14:51:29 - mmengine - INFO - Epoch(train) [1][ 600/1879] lr: 6.3430e-07 eta: 4 days, 16:53:38 time: 2.1434 data_time: 0.0380 memory: 48866 grad_norm: 4.9178 loss: 5.5768 loss_cls: 5.5768 2023/01/21 14:55:05 - mmengine - INFO - Epoch(train) [1][ 700/1879] lr: 7.3016e-07 eta: 4 days, 16:41:20 time: 2.1545 data_time: 0.0384 memory: 48866 grad_norm: 4.3863 loss: 5.5144 loss_cls: 5.5144 2023/01/21 14:58:40 - mmengine - INFO - Epoch(train) [1][ 800/1879] lr: 8.2601e-07 eta: 4 days, 16:31:08 time: 2.1507 data_time: 0.0389 memory: 48866 grad_norm: 4.5639 loss: 5.4752 loss_cls: 5.4752 2023/01/21 15:02:14 - mmengine - INFO - Epoch(train) [1][ 900/1879] lr: 9.2187e-07 eta: 4 days, 16:22:02 time: 2.1537 data_time: 0.0376 memory: 48866 grad_norm: 7.4090 loss: 5.4164 loss_cls: 5.4164 2023/01/21 15:05:50 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 15:05:50 - mmengine - INFO - Epoch(train) [1][1000/1879] lr: 1.0177e-06 eta: 4 days, 16:14:33 time: 2.1501 data_time: 0.0386 memory: 48866 grad_norm: 6.2454 loss: 5.2934 loss_cls: 5.2934 2023/01/21 15:09:24 - mmengine - INFO - Epoch(train) [1][1100/1879] lr: 1.1136e-06 eta: 4 days, 16:06:18 time: 2.1366 data_time: 0.0377 memory: 48866 grad_norm: 6.2364 loss: 5.3780 loss_cls: 5.3780 2023/01/21 15:12:59 - mmengine - INFO - Epoch(train) [1][1200/1879] lr: 1.2094e-06 eta: 4 days, 15:59:03 time: 2.1478 data_time: 0.0383 memory: 48866 grad_norm: 6.9291 loss: 5.1746 loss_cls: 5.1746 2023/01/21 15:16:34 - mmengine - INFO - Epoch(train) [1][1300/1879] lr: 1.3053e-06 eta: 4 days, 15:53:16 time: 2.1483 data_time: 0.0374 memory: 48866 grad_norm: 6.2434 loss: 5.2795 loss_cls: 5.2795 2023/01/21 15:20:08 - mmengine - INFO - Epoch(train) [1][1400/1879] lr: 1.4011e-06 eta: 4 days, 15:46:22 time: 2.1447 data_time: 0.0384 memory: 48866 grad_norm: 6.9254 loss: 5.0934 loss_cls: 5.0934 2023/01/21 15:23:44 - mmengine - INFO - Epoch(train) [1][1500/1879] lr: 1.4970e-06 eta: 4 days, 15:42:21 time: 2.1520 data_time: 0.0378 memory: 48866 grad_norm: 8.3492 loss: 4.9820 loss_cls: 4.9820 2023/01/21 15:27:19 - mmengine - INFO - Epoch(train) [1][1600/1879] lr: 1.5929e-06 eta: 4 days, 15:37:55 time: 2.1524 data_time: 0.0383 memory: 48866 grad_norm: 6.6028 loss: 5.1919 loss_cls: 5.1919 2023/01/21 15:30:54 - mmengine - INFO - Epoch(train) [1][1700/1879] lr: 1.6887e-06 eta: 4 days, 15:32:36 time: 2.1444 data_time: 0.0396 memory: 48866 grad_norm: 7.5773 loss: 5.2034 loss_cls: 5.2034 2023/01/21 15:34:30 - mmengine - INFO - Epoch(train) [1][1800/1879] lr: 1.7846e-06 eta: 4 days, 15:29:42 time: 2.1562 data_time: 0.0396 memory: 48866 grad_norm: 7.6607 loss: 4.9554 loss_cls: 4.9554 2023/01/21 15:37:19 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 15:37:19 - mmengine - INFO - Epoch(train) [1][1879/1879] lr: 1.8603e-06 eta: 4 days, 15:24:45 time: 2.0921 data_time: 0.0422 memory: 48866 grad_norm: 8.0327 loss: 5.0190 loss_cls: 5.0190 2023/01/21 15:38:20 - mmengine - INFO - Epoch(val) [1][100/155] eta: 0:00:33 time: 0.6098 data_time: 0.2568 memory: 4950 2023/01/21 15:38:50 - mmengine - INFO - Epoch(val) [1][155/155] acc/top1: 0.1841 acc/top5: 0.4449 acc/mean1: 0.1840 2023/01/21 15:38:53 - mmengine - INFO - The best checkpoint with 0.1841 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2023/01/21 15:42:35 - mmengine - INFO - Epoch(train) [2][ 100/1879] lr: 1.9561e-06 eta: 4 days, 15:31:13 time: 2.1471 data_time: 0.0377 memory: 48866 grad_norm: 7.7814 loss: 4.9243 loss_cls: 4.9243 2023/01/21 15:43:20 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 15:46:10 - mmengine - INFO - Epoch(train) [2][ 200/1879] lr: 2.0520e-06 eta: 4 days, 15:26:28 time: 2.1551 data_time: 0.0380 memory: 48866 grad_norm: 7.3950 loss: 4.9198 loss_cls: 4.9198 2023/01/21 15:49:45 - mmengine - INFO - Epoch(train) [2][ 300/1879] lr: 2.1478e-06 eta: 4 days, 15:21:34 time: 2.1448 data_time: 0.0375 memory: 48866 grad_norm: 7.5916 loss: 4.9234 loss_cls: 4.9234 2023/01/21 15:53:20 - mmengine - INFO - Epoch(train) [2][ 400/1879] lr: 2.2437e-06 eta: 4 days, 15:17:03 time: 2.1447 data_time: 0.0372 memory: 48866 grad_norm: 8.0865 loss: 4.9817 loss_cls: 4.9817 2023/01/21 15:56:55 - mmengine - INFO - Epoch(train) [2][ 500/1879] lr: 2.3396e-06 eta: 4 days, 15:11:54 time: 2.1407 data_time: 0.0378 memory: 48866 grad_norm: 8.1995 loss: 4.9874 loss_cls: 4.9874 2023/01/21 16:00:31 - mmengine - INFO - Epoch(train) [2][ 600/1879] lr: 2.4354e-06 eta: 4 days, 15:08:24 time: 2.1633 data_time: 0.0388 memory: 48866 grad_norm: 8.5473 loss: 4.8299 loss_cls: 4.8299 2023/01/21 16:04:06 - mmengine - INFO - Epoch(train) [2][ 700/1879] lr: 2.5313e-06 eta: 4 days, 15:04:19 time: 2.1493 data_time: 0.0381 memory: 48866 grad_norm: 7.5386 loss: 4.9112 loss_cls: 4.9112 2023/01/21 16:07:42 - mmengine - INFO - Epoch(train) [2][ 800/1879] lr: 2.6271e-06 eta: 4 days, 15:00:27 time: 2.1596 data_time: 0.0376 memory: 48866 grad_norm: 8.4335 loss: 4.7191 loss_cls: 4.7191 2023/01/21 16:11:17 - mmengine - INFO - Epoch(train) [2][ 900/1879] lr: 2.7230e-06 eta: 4 days, 14:56:01 time: 2.1416 data_time: 0.0381 memory: 48866 grad_norm: 7.9941 loss: 4.8557 loss_cls: 4.8557 2023/01/21 16:14:52 - mmengine - INFO - Epoch(train) [2][1000/1879] lr: 2.8188e-06 eta: 4 days, 14:51:38 time: 2.1419 data_time: 0.0386 memory: 48866 grad_norm: 8.2410 loss: 4.7261 loss_cls: 4.7261 2023/01/21 16:18:27 - mmengine - INFO - Epoch(train) [2][1100/1879] lr: 2.9147e-06 eta: 4 days, 14:47:14 time: 2.1497 data_time: 0.0374 memory: 48866 grad_norm: 8.2393 loss: 4.6861 loss_cls: 4.6861 2023/01/21 16:19:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 16:22:02 - mmengine - INFO - Epoch(train) [2][1200/1879] lr: 3.0105e-06 eta: 4 days, 14:42:57 time: 2.1488 data_time: 0.0388 memory: 48866 grad_norm: 7.0743 loss: 4.6658 loss_cls: 4.6658 2023/01/21 16:25:37 - mmengine - INFO - Epoch(train) [2][1300/1879] lr: 3.1064e-06 eta: 4 days, 14:38:52 time: 2.1590 data_time: 0.0389 memory: 48866 grad_norm: 7.3781 loss: 4.7256 loss_cls: 4.7256 2023/01/21 16:29:12 - mmengine - INFO - Epoch(train) [2][1400/1879] lr: 3.2023e-06 eta: 4 days, 14:34:41 time: 2.1509 data_time: 0.0382 memory: 48866 grad_norm: 7.3341 loss: 4.4289 loss_cls: 4.4289 2023/01/21 16:32:47 - mmengine - INFO - Epoch(train) [2][1500/1879] lr: 3.2981e-06 eta: 4 days, 14:30:39 time: 2.1558 data_time: 0.0391 memory: 48866 grad_norm: 7.6957 loss: 4.6500 loss_cls: 4.6500 2023/01/21 16:36:22 - mmengine - INFO - Epoch(train) [2][1600/1879] lr: 3.3940e-06 eta: 4 days, 14:26:41 time: 2.1513 data_time: 0.0380 memory: 48866 grad_norm: 7.5780 loss: 4.5317 loss_cls: 4.5317 2023/01/21 16:39:57 - mmengine - INFO - Epoch(train) [2][1700/1879] lr: 3.4898e-06 eta: 4 days, 14:22:30 time: 2.1421 data_time: 0.0392 memory: 48866 grad_norm: 8.3678 loss: 4.6765 loss_cls: 4.6765 2023/01/21 16:43:32 - mmengine - INFO - Epoch(train) [2][1800/1879] lr: 3.5857e-06 eta: 4 days, 14:18:38 time: 2.1622 data_time: 0.0379 memory: 48866 grad_norm: 8.1558 loss: 4.6712 loss_cls: 4.6712 2023/01/21 16:46:21 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 16:46:21 - mmengine - INFO - Epoch(train) [2][1879/1879] lr: 3.6614e-06 eta: 4 days, 14:14:31 time: 2.1031 data_time: 0.0410 memory: 48866 grad_norm: 7.4137 loss: 4.6296 loss_cls: 4.6296 2023/01/21 16:47:16 - mmengine - INFO - Epoch(val) [2][100/155] eta: 0:00:30 time: 0.6122 data_time: 0.2699 memory: 4950 2023/01/21 16:47:47 - mmengine - INFO - Epoch(val) [2][155/155] acc/top1: 0.3622 acc/top5: 0.6447 acc/mean1: 0.3619 2023/01/21 16:47:47 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_1.pth is removed 2023/01/21 16:47:50 - mmengine - INFO - The best checkpoint with 0.3622 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2023/01/21 16:51:31 - mmengine - INFO - Epoch(train) [3][ 100/1879] lr: 3.7572e-06 eta: 4 days, 14:15:43 time: 2.1634 data_time: 0.0521 memory: 48866 grad_norm: 7.3639 loss: 4.6591 loss_cls: 4.6591 2023/01/21 16:55:06 - mmengine - INFO - Epoch(train) [3][ 200/1879] lr: 3.8531e-06 eta: 4 days, 14:11:44 time: 2.1610 data_time: 0.0379 memory: 48866 grad_norm: 8.2024 loss: 4.6420 loss_cls: 4.6420 2023/01/21 16:56:37 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 16:58:41 - mmengine - INFO - Epoch(train) [3][ 300/1879] lr: 3.9490e-06 eta: 4 days, 14:07:21 time: 2.1414 data_time: 0.0384 memory: 48866 grad_norm: 7.8596 loss: 4.5202 loss_cls: 4.5202 2023/01/21 17:02:16 - mmengine - INFO - Epoch(train) [3][ 400/1879] lr: 4.0448e-06 eta: 4 days, 14:03:31 time: 2.1571 data_time: 0.0375 memory: 48866 grad_norm: 7.9824 loss: 4.3745 loss_cls: 4.3745 2023/01/21 17:05:51 - mmengine - INFO - Epoch(train) [3][ 500/1879] lr: 4.1407e-06 eta: 4 days, 13:59:30 time: 2.1525 data_time: 0.0376 memory: 48866 grad_norm: 7.8407 loss: 4.4596 loss_cls: 4.4596 2023/01/21 17:09:26 - mmengine - INFO - Epoch(train) [3][ 600/1879] lr: 4.2365e-06 eta: 4 days, 13:55:14 time: 2.1451 data_time: 0.0377 memory: 48866 grad_norm: 8.8630 loss: 4.3937 loss_cls: 4.3937 2023/01/21 17:13:01 - mmengine - INFO - Epoch(train) [3][ 700/1879] lr: 4.3324e-06 eta: 4 days, 13:51:12 time: 2.1461 data_time: 0.0380 memory: 48866 grad_norm: 7.6727 loss: 4.4986 loss_cls: 4.4986 2023/01/21 17:16:37 - mmengine - INFO - Epoch(train) [3][ 800/1879] lr: 4.4282e-06 eta: 4 days, 13:47:37 time: 2.1550 data_time: 0.0374 memory: 48866 grad_norm: 6.8404 loss: 4.6665 loss_cls: 4.6665 2023/01/21 17:20:12 - mmengine - INFO - Epoch(train) [3][ 900/1879] lr: 4.5241e-06 eta: 4 days, 13:43:56 time: 2.1464 data_time: 0.0389 memory: 48866 grad_norm: 8.3667 loss: 4.5583 loss_cls: 4.5583 2023/01/21 17:23:47 - mmengine - INFO - Epoch(train) [3][1000/1879] lr: 4.6199e-06 eta: 4 days, 13:40:06 time: 2.1588 data_time: 0.0377 memory: 48866 grad_norm: 7.4371 loss: 4.5424 loss_cls: 4.5424 2023/01/21 17:27:23 - mmengine - INFO - Epoch(train) [3][1100/1879] lr: 4.7158e-06 eta: 4 days, 13:36:44 time: 2.1770 data_time: 0.0384 memory: 48866 grad_norm: 7.9359 loss: 4.4863 loss_cls: 4.4863 2023/01/21 17:30:58 - mmengine - INFO - Epoch(train) [3][1200/1879] lr: 4.8116e-06 eta: 4 days, 13:32:35 time: 2.1454 data_time: 0.0382 memory: 48866 grad_norm: 7.6866 loss: 4.5564 loss_cls: 4.5564 2023/01/21 17:32:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 17:34:33 - mmengine - INFO - Epoch(train) [3][1300/1879] lr: 4.9075e-06 eta: 4 days, 13:28:35 time: 2.1494 data_time: 0.0384 memory: 48866 grad_norm: 7.7576 loss: 4.4749 loss_cls: 4.4749 2023/01/21 17:38:07 - mmengine - INFO - Epoch(train) [3][1400/1879] lr: 5.0034e-06 eta: 4 days, 13:24:15 time: 2.1494 data_time: 0.0372 memory: 48866 grad_norm: 7.3415 loss: 4.5599 loss_cls: 4.5599 2023/01/21 17:41:42 - mmengine - INFO - Epoch(train) [3][1500/1879] lr: 5.0992e-06 eta: 4 days, 13:20:12 time: 2.1503 data_time: 0.0385 memory: 48866 grad_norm: 8.5539 loss: 4.2778 loss_cls: 4.2778 2023/01/21 17:45:16 - mmengine - INFO - Epoch(train) [3][1600/1879] lr: 5.1951e-06 eta: 4 days, 13:16:02 time: 2.1360 data_time: 0.0382 memory: 48866 grad_norm: 8.2835 loss: 4.4970 loss_cls: 4.4970 2023/01/21 17:48:51 - mmengine - INFO - Epoch(train) [3][1700/1879] lr: 5.2909e-06 eta: 4 days, 13:11:47 time: 2.1454 data_time: 0.0391 memory: 48866 grad_norm: 7.3616 loss: 4.3316 loss_cls: 4.3316 2023/01/21 17:52:26 - mmengine - INFO - Epoch(train) [3][1800/1879] lr: 5.3868e-06 eta: 4 days, 13:08:06 time: 2.1550 data_time: 0.0388 memory: 48866 grad_norm: 7.6572 loss: 4.5389 loss_cls: 4.5389 2023/01/21 17:55:15 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 17:55:15 - mmengine - INFO - Epoch(train) [3][1879/1879] lr: 5.4625e-06 eta: 4 days, 13:04:26 time: 2.0927 data_time: 0.0401 memory: 48866 grad_norm: 7.1518 loss: 4.5590 loss_cls: 4.5590 2023/01/21 17:55:15 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/01/21 17:56:14 - mmengine - INFO - Epoch(val) [3][100/155] eta: 0:00:30 time: 0.5423 data_time: 0.2017 memory: 4950 2023/01/21 17:56:43 - mmengine - INFO - Epoch(val) [3][155/155] acc/top1: 0.4191 acc/top5: 0.7066 acc/mean1: 0.4191 2023/01/21 17:56:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_2.pth is removed 2023/01/21 17:56:46 - mmengine - INFO - The best checkpoint with 0.4191 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2023/01/21 18:00:28 - mmengine - INFO - Epoch(train) [4][ 100/1879] lr: 5.5584e-06 eta: 4 days, 13:04:12 time: 2.1389 data_time: 0.0373 memory: 48866 grad_norm: 7.7888 loss: 4.3662 loss_cls: 4.3662 2023/01/21 18:04:03 - mmengine - INFO - Epoch(train) [4][ 200/1879] lr: 5.6542e-06 eta: 4 days, 13:00:21 time: 2.1424 data_time: 0.0375 memory: 48866 grad_norm: 7.8067 loss: 4.6007 loss_cls: 4.6007 2023/01/21 18:07:38 - mmengine - INFO - Epoch(train) [4][ 300/1879] lr: 5.7501e-06 eta: 4 days, 12:56:29 time: 2.1463 data_time: 0.0381 memory: 48866 grad_norm: 8.1285 loss: 4.2650 loss_cls: 4.2650 2023/01/21 18:09:53 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 18:11:12 - mmengine - INFO - Epoch(train) [4][ 400/1879] lr: 5.8459e-06 eta: 4 days, 12:52:21 time: 2.1440 data_time: 0.0377 memory: 48866 grad_norm: 7.2887 loss: 4.5076 loss_cls: 4.5076 2023/01/21 18:14:47 - mmengine - INFO - Epoch(train) [4][ 500/1879] lr: 5.9418e-06 eta: 4 days, 12:48:37 time: 2.1671 data_time: 0.0390 memory: 48866 grad_norm: 7.4249 loss: 4.2338 loss_cls: 4.2338 2023/01/21 18:18:22 - mmengine - INFO - Epoch(train) [4][ 600/1879] lr: 6.0376e-06 eta: 4 days, 12:44:43 time: 2.1562 data_time: 0.0380 memory: 48866 grad_norm: 7.0036 loss: 4.3739 loss_cls: 4.3739 2023/01/21 18:21:58 - mmengine - INFO - Epoch(train) [4][ 700/1879] lr: 6.1335e-06 eta: 4 days, 12:41:03 time: 2.1369 data_time: 0.0373 memory: 48866 grad_norm: 7.6548 loss: 4.3617 loss_cls: 4.3617 2023/01/21 18:25:32 - mmengine - INFO - Epoch(train) [4][ 800/1879] lr: 6.2293e-06 eta: 4 days, 12:37:10 time: 2.1496 data_time: 0.0383 memory: 48866 grad_norm: 8.1339 loss: 4.0700 loss_cls: 4.0700 2023/01/21 18:29:07 - mmengine - INFO - Epoch(train) [4][ 900/1879] lr: 6.3252e-06 eta: 4 days, 12:33:17 time: 2.1453 data_time: 0.0384 memory: 48866 grad_norm: 6.7285 loss: 4.5811 loss_cls: 4.5811 2023/01/21 18:32:42 - mmengine - INFO - Epoch(train) [4][1000/1879] lr: 6.4210e-06 eta: 4 days, 12:29:27 time: 2.1453 data_time: 0.0384 memory: 48866 grad_norm: 7.5481 loss: 4.3684 loss_cls: 4.3684 2023/01/21 18:36:17 - mmengine - INFO - Epoch(train) [4][1100/1879] lr: 6.5169e-06 eta: 4 days, 12:25:34 time: 2.1523 data_time: 0.0388 memory: 48866 grad_norm: 6.6273 loss: 4.5370 loss_cls: 4.5370 2023/01/21 18:39:52 - mmengine - INFO - Epoch(train) [4][1200/1879] lr: 6.6128e-06 eta: 4 days, 12:21:53 time: 2.1460 data_time: 0.0384 memory: 48866 grad_norm: 7.4386 loss: 4.2104 loss_cls: 4.2104 2023/01/21 18:43:27 - mmengine - INFO - Epoch(train) [4][1300/1879] lr: 6.7086e-06 eta: 4 days, 12:18:08 time: 2.1520 data_time: 0.0383 memory: 48866 grad_norm: 6.8244 loss: 4.1017 loss_cls: 4.1017 2023/01/21 18:45:43 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 18:47:02 - mmengine - INFO - Epoch(train) [4][1400/1879] lr: 6.8045e-06 eta: 4 days, 12:14:20 time: 2.1433 data_time: 0.0388 memory: 48866 grad_norm: 7.4759 loss: 4.2133 loss_cls: 4.2133 2023/01/21 18:50:37 - mmengine - INFO - Epoch(train) [4][1500/1879] lr: 6.9003e-06 eta: 4 days, 12:10:29 time: 2.1546 data_time: 0.0379 memory: 48866 grad_norm: 7.0400 loss: 4.2623 loss_cls: 4.2623 2023/01/21 18:54:13 - mmengine - INFO - Epoch(train) [4][1600/1879] lr: 6.9962e-06 eta: 4 days, 12:06:50 time: 2.1429 data_time: 0.0387 memory: 48866 grad_norm: 6.3426 loss: 4.4221 loss_cls: 4.4221 2023/01/21 18:57:47 - mmengine - INFO - Epoch(train) [4][1700/1879] lr: 7.0920e-06 eta: 4 days, 12:03:02 time: 2.1455 data_time: 0.0391 memory: 48866 grad_norm: 6.6260 loss: 4.3455 loss_cls: 4.3455 2023/01/21 19:01:22 - mmengine - INFO - Epoch(train) [4][1800/1879] lr: 7.1879e-06 eta: 4 days, 11:59:14 time: 2.1592 data_time: 0.0382 memory: 48866 grad_norm: 7.2212 loss: 4.2644 loss_cls: 4.2644 2023/01/21 19:04:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 19:04:12 - mmengine - INFO - Epoch(train) [4][1879/1879] lr: 7.2636e-06 eta: 4 days, 11:55:58 time: 2.1100 data_time: 0.0409 memory: 48866 grad_norm: 6.6358 loss: 4.1523 loss_cls: 4.1523 2023/01/21 19:05:06 - mmengine - INFO - Epoch(val) [4][100/155] eta: 0:00:29 time: 0.5599 data_time: 0.1992 memory: 4950 2023/01/21 19:05:36 - mmengine - INFO - Epoch(val) [4][155/155] acc/top1: 0.4659 acc/top5: 0.7342 acc/mean1: 0.4656 2023/01/21 19:05:36 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_3.pth is removed 2023/01/21 19:05:40 - mmengine - INFO - The best checkpoint with 0.4659 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2023/01/21 19:09:21 - mmengine - INFO - Epoch(train) [5][ 100/1879] lr: 7.3595e-06 eta: 4 days, 11:54:35 time: 2.1389 data_time: 0.0376 memory: 48866 grad_norm: 6.5419 loss: 4.2233 loss_cls: 4.2233 2023/01/21 19:12:55 - mmengine - INFO - Epoch(train) [5][ 200/1879] lr: 7.4553e-06 eta: 4 days, 11:50:31 time: 2.1496 data_time: 0.0386 memory: 48866 grad_norm: 7.3276 loss: 4.1818 loss_cls: 4.1818 2023/01/21 19:16:30 - mmengine - INFO - Epoch(train) [5][ 300/1879] lr: 7.5512e-06 eta: 4 days, 11:46:41 time: 2.1456 data_time: 0.0375 memory: 48866 grad_norm: 6.7576 loss: 4.2609 loss_cls: 4.2609 2023/01/21 19:20:05 - mmengine - INFO - Epoch(train) [5][ 400/1879] lr: 7.6470e-06 eta: 4 days, 11:43:10 time: 2.1646 data_time: 0.0389 memory: 48866 grad_norm: 6.9087 loss: 4.2454 loss_cls: 4.2454 2023/01/21 19:23:06 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 19:23:40 - mmengine - INFO - Epoch(train) [5][ 500/1879] lr: 7.7429e-06 eta: 4 days, 11:39:14 time: 2.1453 data_time: 0.0385 memory: 48866 grad_norm: 6.8096 loss: 4.4071 loss_cls: 4.4071 2023/01/21 19:27:15 - mmengine - INFO - Epoch(train) [5][ 600/1879] lr: 7.8387e-06 eta: 4 days, 11:35:28 time: 2.1449 data_time: 0.0380 memory: 48866 grad_norm: 7.0608 loss: 4.2780 loss_cls: 4.2780 2023/01/21 19:30:50 - mmengine - INFO - Epoch(train) [5][ 700/1879] lr: 7.9346e-06 eta: 4 days, 11:31:41 time: 2.1484 data_time: 0.0386 memory: 48866 grad_norm: 7.2574 loss: 4.4506 loss_cls: 4.4506 2023/01/21 19:34:25 - mmengine - INFO - Epoch(train) [5][ 800/1879] lr: 8.0304e-06 eta: 4 days, 11:27:57 time: 2.1360 data_time: 0.0392 memory: 48866 grad_norm: 6.3905 loss: 4.2706 loss_cls: 4.2706 2023/01/21 19:38:00 - mmengine - INFO - Epoch(train) [5][ 900/1879] lr: 8.1263e-06 eta: 4 days, 11:24:09 time: 2.1534 data_time: 0.0374 memory: 48866 grad_norm: 6.5215 loss: 4.4214 loss_cls: 4.4214 2023/01/21 19:41:35 - mmengine - INFO - Epoch(train) [5][1000/1879] lr: 8.2222e-06 eta: 4 days, 11:20:29 time: 2.1540 data_time: 0.0386 memory: 48866 grad_norm: 6.8420 loss: 4.1491 loss_cls: 4.1491 2023/01/21 19:45:10 - mmengine - INFO - Epoch(train) [5][1100/1879] lr: 8.3180e-06 eta: 4 days, 11:16:43 time: 2.1546 data_time: 0.0390 memory: 48866 grad_norm: 6.9019 loss: 4.2070 loss_cls: 4.2070 2023/01/21 19:48:45 - mmengine - INFO - Epoch(train) [5][1200/1879] lr: 8.4139e-06 eta: 4 days, 11:13:09 time: 2.1522 data_time: 0.0383 memory: 48866 grad_norm: 7.2579 loss: 4.2813 loss_cls: 4.2813 2023/01/21 19:52:20 - mmengine - INFO - Epoch(train) [5][1300/1879] lr: 8.5097e-06 eta: 4 days, 11:09:25 time: 2.1628 data_time: 0.0378 memory: 48866 grad_norm: 6.5382 loss: 4.1942 loss_cls: 4.1942 2023/01/21 19:55:55 - mmengine - INFO - Epoch(train) [5][1400/1879] lr: 8.6056e-06 eta: 4 days, 11:05:42 time: 2.1487 data_time: 0.0391 memory: 48866 grad_norm: 7.4206 loss: 4.2515 loss_cls: 4.2515 2023/01/21 19:58:56 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 19:59:31 - mmengine - INFO - Epoch(train) [5][1500/1879] lr: 8.7014e-06 eta: 4 days, 11:02:03 time: 2.1489 data_time: 0.0390 memory: 48866 grad_norm: 6.6800 loss: 4.1789 loss_cls: 4.1789 2023/01/21 20:03:05 - mmengine - INFO - Epoch(train) [5][1600/1879] lr: 8.7973e-06 eta: 4 days, 10:58:14 time: 2.1427 data_time: 0.0377 memory: 48866 grad_norm: 7.0906 loss: 4.1471 loss_cls: 4.1471 2023/01/21 20:06:40 - mmengine - INFO - Epoch(train) [5][1700/1879] lr: 8.8931e-06 eta: 4 days, 10:54:35 time: 2.1497 data_time: 0.0388 memory: 48866 grad_norm: 6.1134 loss: 4.3755 loss_cls: 4.3755 2023/01/21 20:10:15 - mmengine - INFO - Epoch(train) [5][1800/1879] lr: 8.9890e-06 eta: 4 days, 10:50:42 time: 2.1343 data_time: 0.0392 memory: 48866 grad_norm: 6.4825 loss: 4.3404 loss_cls: 4.3404 2023/01/21 20:13:03 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 20:13:03 - mmengine - INFO - Epoch(train) [5][1879/1879] lr: 9.0647e-06 eta: 4 days, 10:47:17 time: 2.0933 data_time: 0.0409 memory: 48866 grad_norm: 7.3176 loss: 4.2084 loss_cls: 4.2084 2023/01/21 20:13:58 - mmengine - INFO - Epoch(val) [5][100/155] eta: 0:00:30 time: 0.6181 data_time: 0.2825 memory: 4950 2023/01/21 20:14:28 - mmengine - INFO - Epoch(val) [5][155/155] acc/top1: 0.4825 acc/top5: 0.7512 acc/mean1: 0.4822 2023/01/21 20:14:28 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_4.pth is removed 2023/01/21 20:14:32 - mmengine - INFO - The best checkpoint with 0.4825 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2023/01/21 20:18:13 - mmengine - INFO - Epoch(train) [6][ 100/1879] lr: 9.1606e-06 eta: 4 days, 10:45:28 time: 2.1494 data_time: 0.0381 memory: 48866 grad_norm: 6.1690 loss: 4.1414 loss_cls: 4.1414 2023/01/21 20:21:47 - mmengine - INFO - Epoch(train) [6][ 200/1879] lr: 9.2564e-06 eta: 4 days, 10:41:35 time: 2.1556 data_time: 0.0383 memory: 48866 grad_norm: 6.8170 loss: 3.9789 loss_cls: 3.9789 2023/01/21 20:25:22 - mmengine - INFO - Epoch(train) [6][ 300/1879] lr: 9.3523e-06 eta: 4 days, 10:37:50 time: 2.1527 data_time: 0.0375 memory: 48866 grad_norm: 7.2524 loss: 4.0324 loss_cls: 4.0324 2023/01/21 20:28:57 - mmengine - INFO - Epoch(train) [6][ 400/1879] lr: 9.4481e-06 eta: 4 days, 10:34:05 time: 2.1493 data_time: 0.0393 memory: 48866 grad_norm: 6.3420 loss: 4.1628 loss_cls: 4.1628 2023/01/21 20:32:32 - mmengine - INFO - Epoch(train) [6][ 500/1879] lr: 9.5440e-06 eta: 4 days, 10:30:15 time: 2.1416 data_time: 0.0382 memory: 48866 grad_norm: 5.9927 loss: 4.2609 loss_cls: 4.2609 2023/01/21 20:36:06 - mmengine - INFO - Epoch(train) [6][ 600/1879] lr: 9.6398e-06 eta: 4 days, 10:26:27 time: 2.1520 data_time: 0.0383 memory: 48866 grad_norm: 6.3762 loss: 4.2682 loss_cls: 4.2682 2023/01/21 20:36:17 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 20:39:42 - mmengine - INFO - Epoch(train) [6][ 700/1879] lr: 9.7357e-06 eta: 4 days, 10:22:50 time: 2.1620 data_time: 0.0384 memory: 48866 grad_norm: 6.2211 loss: 4.0734 loss_cls: 4.0734 2023/01/21 20:43:17 - mmengine - INFO - Epoch(train) [6][ 800/1879] lr: 9.8316e-06 eta: 4 days, 10:19:09 time: 2.1438 data_time: 0.0388 memory: 48866 grad_norm: 6.6667 loss: 4.1174 loss_cls: 4.1174 2023/01/21 20:46:52 - mmengine - INFO - Epoch(train) [6][ 900/1879] lr: 9.9274e-06 eta: 4 days, 10:15:27 time: 2.1516 data_time: 0.0387 memory: 48866 grad_norm: 6.4447 loss: 4.0432 loss_cls: 4.0432 2023/01/21 20:50:26 - mmengine - INFO - Epoch(train) [6][1000/1879] lr: 1.0023e-05 eta: 4 days, 10:11:33 time: 2.1389 data_time: 0.0383 memory: 48866 grad_norm: 6.2762 loss: 4.2904 loss_cls: 4.2904 2023/01/21 20:54:00 - mmengine - INFO - Epoch(train) [6][1100/1879] lr: 1.0119e-05 eta: 4 days, 10:07:40 time: 2.1386 data_time: 0.0377 memory: 48866 grad_norm: 6.5573 loss: 4.2834 loss_cls: 4.2834 2023/01/21 20:57:35 - mmengine - INFO - Epoch(train) [6][1200/1879] lr: 1.0215e-05 eta: 4 days, 10:03:55 time: 2.1395 data_time: 0.0383 memory: 48866 grad_norm: 6.8131 loss: 4.1217 loss_cls: 4.1217 2023/01/21 21:01:09 - mmengine - INFO - Epoch(train) [6][1300/1879] lr: 1.0311e-05 eta: 4 days, 10:00:02 time: 2.1452 data_time: 0.0381 memory: 48866 grad_norm: 6.4323 loss: 3.9766 loss_cls: 3.9766 2023/01/21 21:04:45 - mmengine - INFO - Epoch(train) [6][1400/1879] lr: 1.0407e-05 eta: 4 days, 9:56:24 time: 2.1532 data_time: 0.0387 memory: 48866 grad_norm: 6.1261 loss: 4.2310 loss_cls: 4.2310 2023/01/21 21:08:19 - mmengine - INFO - Epoch(train) [6][1500/1879] lr: 1.0503e-05 eta: 4 days, 9:52:40 time: 2.1542 data_time: 0.0390 memory: 48866 grad_norm: 6.2642 loss: 4.1445 loss_cls: 4.1445 2023/01/21 21:11:54 - mmengine - INFO - Epoch(train) [6][1600/1879] lr: 1.0598e-05 eta: 4 days, 9:48:57 time: 2.1444 data_time: 0.0384 memory: 48866 grad_norm: 6.5013 loss: 4.3161 loss_cls: 4.3161 2023/01/21 21:12:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 21:15:29 - mmengine - INFO - Epoch(train) [6][1700/1879] lr: 1.0694e-05 eta: 4 days, 9:45:06 time: 2.1542 data_time: 0.0388 memory: 48866 grad_norm: 6.4750 loss: 4.0091 loss_cls: 4.0091 2023/01/21 21:19:04 - mmengine - INFO - Epoch(train) [6][1800/1879] lr: 1.0790e-05 eta: 4 days, 9:41:27 time: 2.1566 data_time: 0.0388 memory: 48866 grad_norm: 5.9801 loss: 4.1212 loss_cls: 4.1212 2023/01/21 21:21:52 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 21:21:52 - mmengine - INFO - Epoch(train) [6][1879/1879] lr: 1.0866e-05 eta: 4 days, 9:38:15 time: 2.0959 data_time: 0.0407 memory: 48866 grad_norm: 7.0261 loss: 3.8837 loss_cls: 3.8837 2023/01/21 21:21:52 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/01/21 21:22:53 - mmengine - INFO - Epoch(val) [6][100/155] eta: 0:00:30 time: 0.5850 data_time: 0.2470 memory: 4950 2023/01/21 21:23:20 - mmengine - INFO - Epoch(val) [6][155/155] acc/top1: 0.4972 acc/top5: 0.7586 acc/mean1: 0.4971 2023/01/21 21:23:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_5.pth is removed 2023/01/21 21:23:24 - mmengine - INFO - The best checkpoint with 0.4972 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2023/01/21 21:27:04 - mmengine - INFO - Epoch(train) [7][ 100/1879] lr: 1.0962e-05 eta: 4 days, 9:36:03 time: 2.1366 data_time: 0.0373 memory: 48866 grad_norm: 6.3213 loss: 4.2360 loss_cls: 4.2360 2023/01/21 21:30:39 - mmengine - INFO - Epoch(train) [7][ 200/1879] lr: 1.1058e-05 eta: 4 days, 9:32:17 time: 2.1471 data_time: 0.0377 memory: 48866 grad_norm: 5.9402 loss: 4.0575 loss_cls: 4.0575 2023/01/21 21:34:14 - mmengine - INFO - Epoch(train) [7][ 300/1879] lr: 1.1153e-05 eta: 4 days, 9:28:33 time: 2.1407 data_time: 0.0377 memory: 48866 grad_norm: 5.9490 loss: 4.1021 loss_cls: 4.1021 2023/01/21 21:37:49 - mmengine - INFO - Epoch(train) [7][ 400/1879] lr: 1.1249e-05 eta: 4 days, 9:24:52 time: 2.1549 data_time: 0.0383 memory: 48866 grad_norm: 5.8140 loss: 4.1500 loss_cls: 4.1500 2023/01/21 21:41:23 - mmengine - INFO - Epoch(train) [7][ 500/1879] lr: 1.1345e-05 eta: 4 days, 9:21:05 time: 2.1573 data_time: 0.0378 memory: 48866 grad_norm: 5.8527 loss: 4.1642 loss_cls: 4.1642 2023/01/21 21:44:58 - mmengine - INFO - Epoch(train) [7][ 600/1879] lr: 1.1441e-05 eta: 4 days, 9:17:21 time: 2.1420 data_time: 0.0383 memory: 48866 grad_norm: 6.1675 loss: 4.0820 loss_cls: 4.0820 2023/01/21 21:48:33 - mmengine - INFO - Epoch(train) [7][ 700/1879] lr: 1.1537e-05 eta: 4 days, 9:13:40 time: 2.1520 data_time: 0.0385 memory: 48866 grad_norm: 5.9626 loss: 4.1999 loss_cls: 4.1999 2023/01/21 21:49:29 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 21:52:08 - mmengine - INFO - Epoch(train) [7][ 800/1879] lr: 1.1633e-05 eta: 4 days, 9:09:56 time: 2.1388 data_time: 0.0374 memory: 48866 grad_norm: 6.0327 loss: 4.1953 loss_cls: 4.1953 2023/01/21 21:55:42 - mmengine - INFO - Epoch(train) [7][ 900/1879] lr: 1.1729e-05 eta: 4 days, 9:06:12 time: 2.1535 data_time: 0.0386 memory: 48866 grad_norm: 6.2494 loss: 4.1116 loss_cls: 4.1116 2023/01/21 21:59:17 - mmengine - INFO - Epoch(train) [7][1000/1879] lr: 1.1824e-05 eta: 4 days, 9:02:32 time: 2.1472 data_time: 0.0390 memory: 48866 grad_norm: 6.0017 loss: 4.1499 loss_cls: 4.1499 2023/01/21 22:02:53 - mmengine - INFO - Epoch(train) [7][1100/1879] lr: 1.1920e-05 eta: 4 days, 8:58:57 time: 2.1447 data_time: 0.0382 memory: 48866 grad_norm: 6.4733 loss: 4.0690 loss_cls: 4.0690 2023/01/21 22:06:27 - mmengine - INFO - Epoch(train) [7][1200/1879] lr: 1.2016e-05 eta: 4 days, 8:55:11 time: 2.1412 data_time: 0.0384 memory: 48866 grad_norm: 5.7341 loss: 4.2561 loss_cls: 4.2561 2023/01/21 22:10:03 - mmengine - INFO - Epoch(train) [7][1300/1879] lr: 1.2112e-05 eta: 4 days, 8:51:36 time: 2.1491 data_time: 0.0379 memory: 48866 grad_norm: 6.0669 loss: 4.0855 loss_cls: 4.0855 2023/01/21 22:13:37 - mmengine - INFO - Epoch(train) [7][1400/1879] lr: 1.2208e-05 eta: 4 days, 8:47:54 time: 2.1489 data_time: 0.0389 memory: 48866 grad_norm: 5.7137 loss: 4.2572 loss_cls: 4.2572 2023/01/21 22:17:12 - mmengine - INFO - Epoch(train) [7][1500/1879] lr: 1.2304e-05 eta: 4 days, 8:44:11 time: 2.1367 data_time: 0.0383 memory: 48866 grad_norm: 5.8616 loss: 4.0087 loss_cls: 4.0087 2023/01/21 22:20:47 - mmengine - INFO - Epoch(train) [7][1600/1879] lr: 1.2400e-05 eta: 4 days, 8:40:26 time: 2.1494 data_time: 0.0386 memory: 48866 grad_norm: 5.9934 loss: 4.1718 loss_cls: 4.1718 2023/01/21 22:24:21 - mmengine - INFO - Epoch(train) [7][1700/1879] lr: 1.2495e-05 eta: 4 days, 8:36:40 time: 2.1396 data_time: 0.0388 memory: 48866 grad_norm: 5.5819 loss: 3.9558 loss_cls: 3.9558 2023/01/21 22:25:17 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 22:27:56 - mmengine - INFO - Epoch(train) [7][1800/1879] lr: 1.2591e-05 eta: 4 days, 8:33:02 time: 2.1442 data_time: 0.0380 memory: 48866 grad_norm: 5.8807 loss: 3.9019 loss_cls: 3.9019 2023/01/21 22:30:45 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 22:30:45 - mmengine - INFO - Epoch(train) [7][1879/1879] lr: 1.2667e-05 eta: 4 days, 8:29:53 time: 2.1041 data_time: 0.0412 memory: 48866 grad_norm: 6.0787 loss: 4.0997 loss_cls: 4.0997 2023/01/21 22:31:40 - mmengine - INFO - Epoch(val) [7][100/155] eta: 0:00:30 time: 0.5830 data_time: 0.2207 memory: 4950 2023/01/21 22:32:11 - mmengine - INFO - Epoch(val) [7][155/155] acc/top1: 0.5125 acc/top5: 0.7776 acc/mean1: 0.5123 2023/01/21 22:32:11 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_6.pth is removed 2023/01/21 22:32:15 - mmengine - INFO - The best checkpoint with 0.5125 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2023/01/21 22:35:56 - mmengine - INFO - Epoch(train) [8][ 100/1879] lr: 1.2763e-05 eta: 4 days, 8:27:43 time: 2.1439 data_time: 0.0378 memory: 48866 grad_norm: 5.8327 loss: 4.1428 loss_cls: 4.1428 2023/01/21 22:39:32 - mmengine - INFO - Epoch(train) [8][ 200/1879] lr: 1.2859e-05 eta: 4 days, 8:24:08 time: 2.1474 data_time: 0.0374 memory: 48866 grad_norm: 5.7487 loss: 4.2269 loss_cls: 4.2269 2023/01/21 22:43:06 - mmengine - INFO - Epoch(train) [8][ 300/1879] lr: 1.2954e-05 eta: 4 days, 8:20:26 time: 2.1548 data_time: 0.0389 memory: 48866 grad_norm: 5.7028 loss: 4.1274 loss_cls: 4.1274 2023/01/21 22:46:41 - mmengine - INFO - Epoch(train) [8][ 400/1879] lr: 1.3050e-05 eta: 4 days, 8:16:43 time: 2.1437 data_time: 0.0369 memory: 48866 grad_norm: 5.4167 loss: 4.1658 loss_cls: 4.1658 2023/01/21 22:50:16 - mmengine - INFO - Epoch(train) [8][ 500/1879] lr: 1.3146e-05 eta: 4 days, 8:13:03 time: 2.1441 data_time: 0.0376 memory: 48866 grad_norm: 6.0339 loss: 3.9949 loss_cls: 3.9949 2023/01/21 22:53:51 - mmengine - INFO - Epoch(train) [8][ 600/1879] lr: 1.3242e-05 eta: 4 days, 8:09:28 time: 2.1561 data_time: 0.0386 memory: 48866 grad_norm: 5.5881 loss: 4.1667 loss_cls: 4.1667 2023/01/21 22:57:27 - mmengine - INFO - Epoch(train) [8][ 700/1879] lr: 1.3338e-05 eta: 4 days, 8:05:53 time: 2.1405 data_time: 0.0381 memory: 48866 grad_norm: 5.8954 loss: 4.0254 loss_cls: 4.0254 2023/01/21 23:01:02 - mmengine - INFO - Epoch(train) [8][ 800/1879] lr: 1.3434e-05 eta: 4 days, 8:02:16 time: 2.1471 data_time: 0.0381 memory: 48866 grad_norm: 6.2851 loss: 3.9442 loss_cls: 3.9442 2023/01/21 23:02:43 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 23:04:37 - mmengine - INFO - Epoch(train) [8][ 900/1879] lr: 1.3530e-05 eta: 4 days, 7:58:35 time: 2.1477 data_time: 0.0376 memory: 48866 grad_norm: 5.7067 loss: 4.1177 loss_cls: 4.1177 2023/01/21 23:08:12 - mmengine - INFO - Epoch(train) [8][1000/1879] lr: 1.3625e-05 eta: 4 days, 7:54:58 time: 2.1562 data_time: 0.0378 memory: 48866 grad_norm: 5.7610 loss: 3.9837 loss_cls: 3.9837 2023/01/21 23:11:48 - mmengine - INFO - Epoch(train) [8][1100/1879] lr: 1.3721e-05 eta: 4 days, 7:51:24 time: 2.1522 data_time: 0.0388 memory: 48866 grad_norm: 5.7578 loss: 4.2729 loss_cls: 4.2729 2023/01/21 23:15:23 - mmengine - INFO - Epoch(train) [8][1200/1879] lr: 1.3817e-05 eta: 4 days, 7:47:46 time: 2.1596 data_time: 0.0388 memory: 48866 grad_norm: 5.6669 loss: 4.1096 loss_cls: 4.1096 2023/01/21 23:18:58 - mmengine - INFO - Epoch(train) [8][1300/1879] lr: 1.3913e-05 eta: 4 days, 7:44:06 time: 2.1427 data_time: 0.0390 memory: 48866 grad_norm: 5.4696 loss: 4.0504 loss_cls: 4.0504 2023/01/21 23:22:33 - mmengine - INFO - Epoch(train) [8][1400/1879] lr: 1.4009e-05 eta: 4 days, 7:40:29 time: 2.1506 data_time: 0.0382 memory: 48866 grad_norm: 5.7221 loss: 3.9292 loss_cls: 3.9292 2023/01/21 23:26:07 - mmengine - INFO - Epoch(train) [8][1500/1879] lr: 1.4105e-05 eta: 4 days, 7:36:47 time: 2.1532 data_time: 0.0390 memory: 48866 grad_norm: 5.8368 loss: 4.1664 loss_cls: 4.1664 2023/01/21 23:29:42 - mmengine - INFO - Epoch(train) [8][1600/1879] lr: 1.4201e-05 eta: 4 days, 7:33:07 time: 2.1515 data_time: 0.0384 memory: 48866 grad_norm: 6.1121 loss: 4.0349 loss_cls: 4.0349 2023/01/21 23:33:18 - mmengine - INFO - Epoch(train) [8][1700/1879] lr: 1.4296e-05 eta: 4 days, 7:29:32 time: 2.1496 data_time: 0.0382 memory: 48866 grad_norm: 5.6575 loss: 4.2090 loss_cls: 4.2090 2023/01/21 23:36:53 - mmengine - INFO - Epoch(train) [8][1800/1879] lr: 1.4392e-05 eta: 4 days, 7:25:53 time: 2.1551 data_time: 0.0393 memory: 48866 grad_norm: 5.5148 loss: 4.0267 loss_cls: 4.0267 2023/01/21 23:38:34 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 23:39:42 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/21 23:39:42 - mmengine - INFO - Epoch(train) [8][1879/1879] lr: 1.4468e-05 eta: 4 days, 7:22:56 time: 2.0998 data_time: 0.0399 memory: 48866 grad_norm: 5.5508 loss: 4.1728 loss_cls: 4.1728 2023/01/21 23:40:37 - mmengine - INFO - Epoch(val) [8][100/155] eta: 0:00:30 time: 0.5982 data_time: 0.2644 memory: 4950 2023/01/21 23:41:08 - mmengine - INFO - Epoch(val) [8][155/155] acc/top1: 0.5256 acc/top5: 0.7841 acc/mean1: 0.5255 2023/01/21 23:41:08 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_7.pth is removed 2023/01/21 23:41:11 - mmengine - INFO - The best checkpoint with 0.5256 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2023/01/21 23:44:54 - mmengine - INFO - Epoch(train) [9][ 100/1879] lr: 1.4564e-05 eta: 4 days, 7:20:45 time: 2.1647 data_time: 0.0381 memory: 48866 grad_norm: 5.5401 loss: 4.0244 loss_cls: 4.0244 2023/01/21 23:48:28 - mmengine - INFO - Epoch(train) [9][ 200/1879] lr: 1.4660e-05 eta: 4 days, 7:16:57 time: 2.1408 data_time: 0.0378 memory: 48866 grad_norm: 5.4231 loss: 4.2058 loss_cls: 4.2058 2023/01/21 23:52:03 - mmengine - INFO - Epoch(train) [9][ 300/1879] lr: 1.4756e-05 eta: 4 days, 7:13:11 time: 2.1420 data_time: 0.0389 memory: 48866 grad_norm: 5.9226 loss: 3.8980 loss_cls: 3.8980 2023/01/21 23:55:37 - mmengine - INFO - Epoch(train) [9][ 400/1879] lr: 1.4851e-05 eta: 4 days, 7:09:30 time: 2.1461 data_time: 0.0385 memory: 48866 grad_norm: 5.4954 loss: 4.0535 loss_cls: 4.0535 2023/01/21 23:59:12 - mmengine - INFO - Epoch(train) [9][ 500/1879] lr: 1.4947e-05 eta: 4 days, 7:05:49 time: 2.1557 data_time: 0.0385 memory: 48866 grad_norm: 5.5714 loss: 4.1786 loss_cls: 4.1786 2023/01/22 00:02:47 - mmengine - INFO - Epoch(train) [9][ 600/1879] lr: 1.5043e-05 eta: 4 days, 7:02:12 time: 2.1518 data_time: 0.0379 memory: 48866 grad_norm: 5.4177 loss: 3.9756 loss_cls: 3.9756 2023/01/22 00:06:23 - mmengine - INFO - Epoch(train) [9][ 700/1879] lr: 1.5139e-05 eta: 4 days, 6:58:36 time: 2.1412 data_time: 0.0388 memory: 48866 grad_norm: 5.1984 loss: 4.1898 loss_cls: 4.1898 2023/01/22 00:09:57 - mmengine - INFO - Epoch(train) [9][ 800/1879] lr: 1.5235e-05 eta: 4 days, 6:54:52 time: 2.1398 data_time: 0.0391 memory: 48866 grad_norm: 5.2885 loss: 4.1097 loss_cls: 4.1097 2023/01/22 00:13:32 - mmengine - INFO - Epoch(train) [9][ 900/1879] lr: 1.5331e-05 eta: 4 days, 6:51:12 time: 2.1481 data_time: 0.0390 memory: 48866 grad_norm: 5.4837 loss: 4.0330 loss_cls: 4.0330 2023/01/22 00:15:58 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 00:17:07 - mmengine - INFO - Epoch(train) [9][1000/1879] lr: 1.5427e-05 eta: 4 days, 6:47:30 time: 2.1652 data_time: 0.0391 memory: 48866 grad_norm: 5.7579 loss: 4.0259 loss_cls: 4.0259 2023/01/22 00:20:41 - mmengine - INFO - Epoch(train) [9][1100/1879] lr: 1.5522e-05 eta: 4 days, 6:43:47 time: 2.1446 data_time: 0.0381 memory: 48866 grad_norm: 5.1648 loss: 4.1724 loss_cls: 4.1724 2023/01/22 00:24:17 - mmengine - INFO - Epoch(train) [9][1200/1879] lr: 1.5618e-05 eta: 4 days, 6:40:13 time: 2.1573 data_time: 0.0386 memory: 48866 grad_norm: 5.6196 loss: 3.8110 loss_cls: 3.8110 2023/01/22 00:27:52 - mmengine - INFO - Epoch(train) [9][1300/1879] lr: 1.5714e-05 eta: 4 days, 6:36:35 time: 2.1484 data_time: 0.0376 memory: 48866 grad_norm: 5.3220 loss: 3.9768 loss_cls: 3.9768 2023/01/22 00:31:27 - mmengine - INFO - Epoch(train) [9][1400/1879] lr: 1.5810e-05 eta: 4 days, 6:33:00 time: 2.1567 data_time: 0.0384 memory: 48866 grad_norm: 5.6093 loss: 3.9389 loss_cls: 3.9389 2023/01/22 00:35:02 - mmengine - INFO - Epoch(train) [9][1500/1879] lr: 1.5906e-05 eta: 4 days, 6:29:19 time: 2.1529 data_time: 0.0390 memory: 48866 grad_norm: 5.2724 loss: 4.0724 loss_cls: 4.0724 2023/01/22 00:38:37 - mmengine - INFO - Epoch(train) [9][1600/1879] lr: 1.6002e-05 eta: 4 days, 6:25:37 time: 2.1507 data_time: 0.0386 memory: 48866 grad_norm: 5.4056 loss: 4.0631 loss_cls: 4.0631 2023/01/22 00:42:12 - mmengine - INFO - Epoch(train) [9][1700/1879] lr: 1.6098e-05 eta: 4 days, 6:21:58 time: 2.1560 data_time: 0.0387 memory: 48866 grad_norm: 5.3080 loss: 4.0192 loss_cls: 4.0192 2023/01/22 00:45:47 - mmengine - INFO - Epoch(train) [9][1800/1879] lr: 1.6193e-05 eta: 4 days, 6:18:20 time: 2.1385 data_time: 0.0389 memory: 48866 grad_norm: 5.1703 loss: 4.2713 loss_cls: 4.2713 2023/01/22 00:48:35 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 00:48:35 - mmengine - INFO - Epoch(train) [9][1879/1879] lr: 1.6269e-05 eta: 4 days, 6:15:18 time: 2.0990 data_time: 0.0406 memory: 48866 grad_norm: 6.1133 loss: 4.0327 loss_cls: 4.0327 2023/01/22 00:48:35 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/01/22 00:49:36 - mmengine - INFO - Epoch(val) [9][100/155] eta: 0:00:30 time: 0.5783 data_time: 0.2242 memory: 4950 2023/01/22 00:50:04 - mmengine - INFO - Epoch(val) [9][155/155] acc/top1: 0.5425 acc/top5: 0.7983 acc/mean1: 0.5423 2023/01/22 00:50:04 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_8.pth is removed 2023/01/22 00:50:07 - mmengine - INFO - The best checkpoint with 0.5425 acc/top1 at 9 epoch is saved to best_acc/top1_epoch_9.pth. 2023/01/22 00:53:24 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 00:53:48 - mmengine - INFO - Epoch(train) [10][ 100/1879] lr: 1.6365e-05 eta: 4 days, 6:12:40 time: 2.1627 data_time: 0.0383 memory: 48866 grad_norm: 5.6000 loss: 4.1785 loss_cls: 4.1785 2023/01/22 00:57:22 - mmengine - INFO - Epoch(train) [10][ 200/1879] lr: 1.6461e-05 eta: 4 days, 6:08:56 time: 2.1496 data_time: 0.0382 memory: 48866 grad_norm: 5.7929 loss: 3.8195 loss_cls: 3.8195 2023/01/22 01:00:57 - mmengine - INFO - Epoch(train) [10][ 300/1879] lr: 1.6557e-05 eta: 4 days, 6:05:14 time: 2.1427 data_time: 0.0379 memory: 48866 grad_norm: 5.4486 loss: 3.9536 loss_cls: 3.9536 2023/01/22 01:04:32 - mmengine - INFO - Epoch(train) [10][ 400/1879] lr: 1.6653e-05 eta: 4 days, 6:01:34 time: 2.1546 data_time: 0.0403 memory: 48866 grad_norm: 5.7164 loss: 3.8001 loss_cls: 3.8001 2023/01/22 01:08:06 - mmengine - INFO - Epoch(train) [10][ 500/1879] lr: 1.6748e-05 eta: 4 days, 5:57:51 time: 2.1493 data_time: 0.0387 memory: 48866 grad_norm: 5.0867 loss: 4.1655 loss_cls: 4.1655 2023/01/22 01:11:41 - mmengine - INFO - Epoch(train) [10][ 600/1879] lr: 1.6844e-05 eta: 4 days, 5:54:12 time: 2.1375 data_time: 0.0383 memory: 48866 grad_norm: 5.4360 loss: 4.0410 loss_cls: 4.0410 2023/01/22 01:15:16 - mmengine - INFO - Epoch(train) [10][ 700/1879] lr: 1.6940e-05 eta: 4 days, 5:50:31 time: 2.1460 data_time: 0.0386 memory: 48866 grad_norm: 5.4934 loss: 4.0014 loss_cls: 4.0014 2023/01/22 01:18:52 - mmengine - INFO - Epoch(train) [10][ 800/1879] lr: 1.7036e-05 eta: 4 days, 5:46:58 time: 2.1695 data_time: 0.0386 memory: 48866 grad_norm: 5.6248 loss: 3.9270 loss_cls: 3.9270 2023/01/22 01:22:26 - mmengine - INFO - Epoch(train) [10][ 900/1879] lr: 1.7132e-05 eta: 4 days, 5:43:17 time: 2.1436 data_time: 0.0390 memory: 48866 grad_norm: 5.5335 loss: 3.9117 loss_cls: 3.9117 2023/01/22 01:26:00 - mmengine - INFO - Epoch(train) [10][1000/1879] lr: 1.7228e-05 eta: 4 days, 5:39:30 time: 2.1459 data_time: 0.0381 memory: 48866 grad_norm: 5.9150 loss: 3.8940 loss_cls: 3.8940 2023/01/22 01:29:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 01:29:35 - mmengine - INFO - Epoch(train) [10][1100/1879] lr: 1.7324e-05 eta: 4 days, 5:35:50 time: 2.1488 data_time: 0.0397 memory: 48866 grad_norm: 5.6262 loss: 4.1534 loss_cls: 4.1534 2023/01/22 01:33:10 - mmengine - INFO - Epoch(train) [10][1200/1879] lr: 1.7419e-05 eta: 4 days, 5:32:09 time: 2.1480 data_time: 0.0378 memory: 48866 grad_norm: 5.4480 loss: 3.7947 loss_cls: 3.7947 2023/01/22 01:36:45 - mmengine - INFO - Epoch(train) [10][1300/1879] lr: 1.7515e-05 eta: 4 days, 5:28:30 time: 2.1452 data_time: 0.0379 memory: 48866 grad_norm: 5.3863 loss: 4.0146 loss_cls: 4.0146 2023/01/22 01:40:20 - mmengine - INFO - Epoch(train) [10][1400/1879] lr: 1.7611e-05 eta: 4 days, 5:24:50 time: 2.1490 data_time: 0.0389 memory: 48866 grad_norm: 5.4526 loss: 3.8278 loss_cls: 3.8278 2023/01/22 01:43:55 - mmengine - INFO - Epoch(train) [10][1500/1879] lr: 1.7707e-05 eta: 4 days, 5:21:16 time: 2.1453 data_time: 0.0389 memory: 48866 grad_norm: 4.9758 loss: 4.1566 loss_cls: 4.1566 2023/01/22 01:47:30 - mmengine - INFO - Epoch(train) [10][1600/1879] lr: 1.7803e-05 eta: 4 days, 5:17:40 time: 2.1568 data_time: 0.0393 memory: 48866 grad_norm: 4.9900 loss: 4.1196 loss_cls: 4.1196 2023/01/22 01:51:05 - mmengine - INFO - Epoch(train) [10][1700/1879] lr: 1.7899e-05 eta: 4 days, 5:14:00 time: 2.1463 data_time: 0.0384 memory: 48866 grad_norm: 6.0306 loss: 3.9856 loss_cls: 3.9856 2023/01/22 01:54:40 - mmengine - INFO - Epoch(train) [10][1800/1879] lr: 1.7995e-05 eta: 4 days, 5:10:24 time: 2.1521 data_time: 0.0389 memory: 48866 grad_norm: 5.3817 loss: 3.9076 loss_cls: 3.9076 2023/01/22 01:57:29 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 01:57:29 - mmengine - INFO - Epoch(train) [10][1879/1879] lr: 1.8070e-05 eta: 4 days, 5:07:19 time: 2.1047 data_time: 0.0402 memory: 48866 grad_norm: 5.4895 loss: 3.8350 loss_cls: 3.8350 2023/01/22 01:58:22 - mmengine - INFO - Epoch(val) [10][100/155] eta: 0:00:29 time: 0.5470 data_time: 0.1768 memory: 4950 2023/01/22 01:58:53 - mmengine - INFO - Epoch(val) [10][155/155] acc/top1: 0.5475 acc/top5: 0.8025 acc/mean1: 0.5474 2023/01/22 01:58:53 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_9.pth is removed 2023/01/22 01:58:57 - mmengine - INFO - The best checkpoint with 0.5475 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2023/01/22 02:02:39 - mmengine - INFO - Epoch(train) [11][ 100/1879] lr: 1.8166e-05 eta: 4 days, 5:04:45 time: 2.1557 data_time: 0.0373 memory: 48866 grad_norm: 5.2074 loss: 3.9777 loss_cls: 3.9777 2023/01/22 02:06:14 - mmengine - INFO - Epoch(train) [11][ 200/1879] lr: 1.8262e-05 eta: 4 days, 5:01:11 time: 2.1516 data_time: 0.0381 memory: 48866 grad_norm: 5.2997 loss: 4.0543 loss_cls: 4.0543 2023/01/22 02:06:36 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 02:09:49 - mmengine - INFO - Epoch(train) [11][ 300/1879] lr: 1.8358e-05 eta: 4 days, 4:57:29 time: 2.1402 data_time: 0.0377 memory: 48866 grad_norm: 5.2216 loss: 4.1077 loss_cls: 4.1077 2023/01/22 02:13:23 - mmengine - INFO - Epoch(train) [11][ 400/1879] lr: 1.8454e-05 eta: 4 days, 4:53:49 time: 2.1502 data_time: 0.0383 memory: 48866 grad_norm: 5.4718 loss: 3.8027 loss_cls: 3.8027 2023/01/22 02:16:58 - mmengine - INFO - Epoch(train) [11][ 500/1879] lr: 1.8550e-05 eta: 4 days, 4:50:09 time: 2.1437 data_time: 0.0379 memory: 48866 grad_norm: 5.1999 loss: 3.8344 loss_cls: 3.8344 2023/01/22 02:20:33 - mmengine - INFO - Epoch(train) [11][ 600/1879] lr: 1.8645e-05 eta: 4 days, 4:46:31 time: 2.1475 data_time: 0.0385 memory: 48866 grad_norm: 5.3930 loss: 3.9878 loss_cls: 3.9878 2023/01/22 02:24:08 - mmengine - INFO - Epoch(train) [11][ 700/1879] lr: 1.8741e-05 eta: 4 days, 4:42:54 time: 2.1486 data_time: 0.0387 memory: 48866 grad_norm: 5.2426 loss: 3.9200 loss_cls: 3.9200 2023/01/22 02:27:44 - mmengine - INFO - Epoch(train) [11][ 800/1879] lr: 1.8837e-05 eta: 4 days, 4:39:21 time: 2.1568 data_time: 0.0376 memory: 48866 grad_norm: 5.7960 loss: 3.8643 loss_cls: 3.8643 2023/01/22 02:31:19 - mmengine - INFO - Epoch(train) [11][ 900/1879] lr: 1.8933e-05 eta: 4 days, 4:35:40 time: 2.1389 data_time: 0.0379 memory: 48866 grad_norm: 5.0711 loss: 4.0505 loss_cls: 4.0505 2023/01/22 02:34:54 - mmengine - INFO - Epoch(train) [11][1000/1879] lr: 1.9029e-05 eta: 4 days, 4:32:06 time: 2.1468 data_time: 0.0376 memory: 48866 grad_norm: 5.3867 loss: 4.0089 loss_cls: 4.0089 2023/01/22 02:38:29 - mmengine - INFO - Epoch(train) [11][1100/1879] lr: 1.9125e-05 eta: 4 days, 4:28:27 time: 2.1590 data_time: 0.0383 memory: 48866 grad_norm: 5.1204 loss: 3.7817 loss_cls: 3.7817 2023/01/22 02:42:03 - mmengine - INFO - Epoch(train) [11][1200/1879] lr: 1.9221e-05 eta: 4 days, 4:24:47 time: 2.1533 data_time: 0.0387 memory: 48866 grad_norm: 4.9768 loss: 3.6039 loss_cls: 3.6039 2023/01/22 02:42:25 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 02:45:39 - mmengine - INFO - Epoch(train) [11][1300/1879] lr: 1.9316e-05 eta: 4 days, 4:21:12 time: 2.1595 data_time: 0.0382 memory: 48866 grad_norm: 5.3072 loss: 3.9203 loss_cls: 3.9203 2023/01/22 02:49:14 - mmengine - INFO - Epoch(train) [11][1400/1879] lr: 1.9412e-05 eta: 4 days, 4:17:33 time: 2.1553 data_time: 0.0374 memory: 48866 grad_norm: 5.1774 loss: 3.9916 loss_cls: 3.9916 2023/01/22 02:52:48 - mmengine - INFO - Epoch(train) [11][1500/1879] lr: 1.9508e-05 eta: 4 days, 4:13:52 time: 2.1463 data_time: 0.0396 memory: 48866 grad_norm: 5.2201 loss: 3.9973 loss_cls: 3.9973 2023/01/22 02:56:23 - mmengine - INFO - Epoch(train) [11][1600/1879] lr: 1.9604e-05 eta: 4 days, 4:10:12 time: 2.1483 data_time: 0.0394 memory: 48866 grad_norm: 5.4967 loss: 3.6770 loss_cls: 3.6770 2023/01/22 02:59:58 - mmengine - INFO - Epoch(train) [11][1700/1879] lr: 1.9700e-05 eta: 4 days, 4:06:36 time: 2.1551 data_time: 0.0389 memory: 48866 grad_norm: 5.0622 loss: 4.0088 loss_cls: 4.0088 2023/01/22 03:03:34 - mmengine - INFO - Epoch(train) [11][1800/1879] lr: 1.9796e-05 eta: 4 days, 4:03:03 time: 2.1609 data_time: 0.0387 memory: 48866 grad_norm: 5.4230 loss: 3.7986 loss_cls: 3.7986 2023/01/22 03:06:23 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 03:06:23 - mmengine - INFO - Epoch(train) [11][1879/1879] lr: 1.9871e-05 eta: 4 days, 4:00:07 time: 2.1090 data_time: 0.0392 memory: 48866 grad_norm: 5.7231 loss: 3.8356 loss_cls: 3.8356 2023/01/22 03:07:17 - mmengine - INFO - Epoch(val) [11][100/155] eta: 0:00:29 time: 0.5484 data_time: 0.1956 memory: 4950 2023/01/22 03:07:48 - mmengine - INFO - Epoch(val) [11][155/155] acc/top1: 0.5498 acc/top5: 0.8082 acc/mean1: 0.5496 2023/01/22 03:07:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_10.pth is removed 2023/01/22 03:07:52 - mmengine - INFO - The best checkpoint with 0.5498 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2023/01/22 03:11:34 - mmengine - INFO - Epoch(train) [12][ 100/1879] lr: 1.9967e-05 eta: 4 days, 3:57:27 time: 2.1432 data_time: 0.0392 memory: 48866 grad_norm: 5.1818 loss: 3.8376 loss_cls: 3.8376 2023/01/22 03:15:08 - mmengine - INFO - Epoch(train) [12][ 200/1879] lr: 2.0063e-05 eta: 4 days, 3:53:45 time: 2.1395 data_time: 0.0376 memory: 48866 grad_norm: 5.1608 loss: 3.8338 loss_cls: 3.8338 2023/01/22 03:18:43 - mmengine - INFO - Epoch(train) [12][ 300/1879] lr: 2.0159e-05 eta: 4 days, 3:50:09 time: 2.1572 data_time: 0.0384 memory: 48866 grad_norm: 5.3562 loss: 3.9374 loss_cls: 3.9374 2023/01/22 03:19:50 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 03:22:19 - mmengine - INFO - Epoch(train) [12][ 400/1879] lr: 2.0255e-05 eta: 4 days, 3:46:35 time: 2.1537 data_time: 0.0376 memory: 48866 grad_norm: 5.1522 loss: 3.9024 loss_cls: 3.9024 2023/01/22 03:25:53 - mmengine - INFO - Epoch(train) [12][ 500/1879] lr: 2.0351e-05 eta: 4 days, 3:42:53 time: 2.1438 data_time: 0.0393 memory: 48866 grad_norm: 4.8402 loss: 4.1663 loss_cls: 4.1663 2023/01/22 03:29:28 - mmengine - INFO - Epoch(train) [12][ 600/1879] lr: 2.0446e-05 eta: 4 days, 3:39:17 time: 2.1475 data_time: 0.0376 memory: 48866 grad_norm: 4.9686 loss: 3.7957 loss_cls: 3.7957 2023/01/22 03:33:04 - mmengine - INFO - Epoch(train) [12][ 700/1879] lr: 2.0542e-05 eta: 4 days, 3:35:41 time: 2.1410 data_time: 0.0384 memory: 48866 grad_norm: 4.8721 loss: 3.9528 loss_cls: 3.9528 2023/01/22 03:36:39 - mmengine - INFO - Epoch(train) [12][ 800/1879] lr: 2.0638e-05 eta: 4 days, 3:32:04 time: 2.1656 data_time: 0.0389 memory: 48866 grad_norm: 5.2671 loss: 3.7632 loss_cls: 3.7632 2023/01/22 03:40:14 - mmengine - INFO - Epoch(train) [12][ 900/1879] lr: 2.0734e-05 eta: 4 days, 3:28:27 time: 2.1473 data_time: 0.0388 memory: 48866 grad_norm: 5.5561 loss: 3.9653 loss_cls: 3.9653 2023/01/22 03:43:49 - mmengine - INFO - Epoch(train) [12][1000/1879] lr: 2.0830e-05 eta: 4 days, 3:24:51 time: 2.1587 data_time: 0.0391 memory: 48866 grad_norm: 5.3608 loss: 3.8841 loss_cls: 3.8841 2023/01/22 03:47:24 - mmengine - INFO - Epoch(train) [12][1100/1879] lr: 2.0926e-05 eta: 4 days, 3:21:13 time: 2.1490 data_time: 0.0383 memory: 48866 grad_norm: 5.1303 loss: 3.9501 loss_cls: 3.9501 2023/01/22 03:50:59 - mmengine - INFO - Epoch(train) [12][1200/1879] lr: 2.1022e-05 eta: 4 days, 3:17:37 time: 2.1565 data_time: 0.0385 memory: 48866 grad_norm: 5.2068 loss: 3.8062 loss_cls: 3.8062 2023/01/22 03:54:34 - mmengine - INFO - Epoch(train) [12][1300/1879] lr: 2.1117e-05 eta: 4 days, 3:14:01 time: 2.1510 data_time: 0.0387 memory: 48866 grad_norm: 4.9863 loss: 4.0479 loss_cls: 4.0479 2023/01/22 03:55:41 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 03:58:09 - mmengine - INFO - Epoch(train) [12][1400/1879] lr: 2.1213e-05 eta: 4 days, 3:10:18 time: 2.1471 data_time: 0.0380 memory: 48866 grad_norm: 4.9958 loss: 3.9505 loss_cls: 3.9505 2023/01/22 04:01:44 - mmengine - INFO - Epoch(train) [12][1500/1879] lr: 2.1309e-05 eta: 4 days, 3:06:40 time: 2.1449 data_time: 0.0386 memory: 48866 grad_norm: 5.2593 loss: 3.8477 loss_cls: 3.8477 2023/01/22 04:05:19 - mmengine - INFO - Epoch(train) [12][1600/1879] lr: 2.1405e-05 eta: 4 days, 3:03:04 time: 2.1546 data_time: 0.0389 memory: 48866 grad_norm: 4.9718 loss: 3.9131 loss_cls: 3.9131 2023/01/22 04:08:54 - mmengine - INFO - Epoch(train) [12][1700/1879] lr: 2.1501e-05 eta: 4 days, 2:59:29 time: 2.1484 data_time: 0.0382 memory: 48866 grad_norm: 4.9416 loss: 4.0128 loss_cls: 4.0128 2023/01/22 04:12:29 - mmengine - INFO - Epoch(train) [12][1800/1879] lr: 2.1597e-05 eta: 4 days, 2:55:50 time: 2.1449 data_time: 0.0387 memory: 48866 grad_norm: 5.0928 loss: 3.7237 loss_cls: 3.7237 2023/01/22 04:15:18 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 04:15:18 - mmengine - INFO - Epoch(train) [12][1879/1879] lr: 2.1672e-05 eta: 4 days, 2:52:52 time: 2.0935 data_time: 0.0394 memory: 48866 grad_norm: 5.1424 loss: 3.8183 loss_cls: 3.8183 2023/01/22 04:15:18 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/01/22 04:16:19 - mmengine - INFO - Epoch(val) [12][100/155] eta: 0:00:31 time: 0.6008 data_time: 0.2533 memory: 4950 2023/01/22 04:16:46 - mmengine - INFO - Epoch(val) [12][155/155] acc/top1: 0.5524 acc/top5: 0.8067 acc/mean1: 0.5523 2023/01/22 04:16:46 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_11.pth is removed 2023/01/22 04:16:49 - mmengine - INFO - The best checkpoint with 0.5524 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2023/01/22 04:20:30 - mmengine - INFO - Epoch(train) [13][ 100/1879] lr: 2.1768e-05 eta: 4 days, 2:50:00 time: 2.1481 data_time: 0.0385 memory: 48866 grad_norm: 4.9951 loss: 3.8458 loss_cls: 3.8458 2023/01/22 04:24:05 - mmengine - INFO - Epoch(train) [13][ 200/1879] lr: 2.1864e-05 eta: 4 days, 2:46:22 time: 2.1490 data_time: 0.0385 memory: 48866 grad_norm: 5.1511 loss: 3.8935 loss_cls: 3.8935 2023/01/22 04:27:40 - mmengine - INFO - Epoch(train) [13][ 300/1879] lr: 2.1960e-05 eta: 4 days, 2:42:44 time: 2.1547 data_time: 0.0395 memory: 48866 grad_norm: 5.0462 loss: 3.7377 loss_cls: 3.7377 2023/01/22 04:31:15 - mmengine - INFO - Epoch(train) [13][ 400/1879] lr: 2.2056e-05 eta: 4 days, 2:39:06 time: 2.1395 data_time: 0.0381 memory: 48866 grad_norm: 5.4753 loss: 3.7251 loss_cls: 3.7251 2023/01/22 04:33:07 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 04:34:50 - mmengine - INFO - Epoch(train) [13][ 500/1879] lr: 2.2152e-05 eta: 4 days, 2:35:26 time: 2.1487 data_time: 0.0384 memory: 48866 grad_norm: 4.9649 loss: 3.9339 loss_cls: 3.9339 2023/01/22 04:38:25 - mmengine - INFO - Epoch(train) [13][ 600/1879] lr: 2.2248e-05 eta: 4 days, 2:31:49 time: 2.1623 data_time: 0.0392 memory: 48866 grad_norm: 5.1492 loss: 3.9065 loss_cls: 3.9065 2023/01/22 04:41:59 - mmengine - INFO - Epoch(train) [13][ 700/1879] lr: 2.2343e-05 eta: 4 days, 2:28:08 time: 2.1436 data_time: 0.0382 memory: 48866 grad_norm: 4.8742 loss: 3.8384 loss_cls: 3.8384 2023/01/22 04:45:34 - mmengine - INFO - Epoch(train) [13][ 800/1879] lr: 2.2439e-05 eta: 4 days, 2:24:28 time: 2.1450 data_time: 0.0391 memory: 48866 grad_norm: 5.0896 loss: 3.7802 loss_cls: 3.7802 2023/01/22 04:49:09 - mmengine - INFO - Epoch(train) [13][ 900/1879] lr: 2.2535e-05 eta: 4 days, 2:20:53 time: 2.1458 data_time: 0.0381 memory: 48866 grad_norm: 4.7852 loss: 4.0576 loss_cls: 4.0576 2023/01/22 04:52:44 - mmengine - INFO - Epoch(train) [13][1000/1879] lr: 2.2631e-05 eta: 4 days, 2:17:14 time: 2.1421 data_time: 0.0383 memory: 48866 grad_norm: 5.0636 loss: 3.9299 loss_cls: 3.9299 2023/01/22 04:56:18 - mmengine - INFO - Epoch(train) [13][1100/1879] lr: 2.2727e-05 eta: 4 days, 2:13:32 time: 2.1527 data_time: 0.0386 memory: 48866 grad_norm: 4.8821 loss: 3.8869 loss_cls: 3.8869 2023/01/22 04:59:54 - mmengine - INFO - Epoch(train) [13][1200/1879] lr: 2.2823e-05 eta: 4 days, 2:09:58 time: 2.1495 data_time: 0.0385 memory: 48866 grad_norm: 5.0139 loss: 3.8969 loss_cls: 3.8969 2023/01/22 05:03:29 - mmengine - INFO - Epoch(train) [13][1300/1879] lr: 2.2919e-05 eta: 4 days, 2:06:22 time: 2.1549 data_time: 0.0387 memory: 48866 grad_norm: 5.0950 loss: 3.7643 loss_cls: 3.7643 2023/01/22 05:07:04 - mmengine - INFO - Epoch(train) [13][1400/1879] lr: 2.3014e-05 eta: 4 days, 2:02:43 time: 2.1447 data_time: 0.0392 memory: 48866 grad_norm: 4.8066 loss: 3.9035 loss_cls: 3.9035 2023/01/22 05:08:56 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 05:10:39 - mmengine - INFO - Epoch(train) [13][1500/1879] lr: 2.3110e-05 eta: 4 days, 1:59:06 time: 2.1480 data_time: 0.0387 memory: 48866 grad_norm: 4.9005 loss: 3.7097 loss_cls: 3.7097 2023/01/22 05:14:14 - mmengine - INFO - Epoch(train) [13][1600/1879] lr: 2.3206e-05 eta: 4 days, 1:55:27 time: 2.1534 data_time: 0.0392 memory: 48866 grad_norm: 4.9959 loss: 3.8652 loss_cls: 3.8652 2023/01/22 05:17:49 - mmengine - INFO - Epoch(train) [13][1700/1879] lr: 2.3302e-05 eta: 4 days, 1:51:54 time: 2.1509 data_time: 0.0384 memory: 48866 grad_norm: 4.9870 loss: 3.7712 loss_cls: 3.7712 2023/01/22 05:21:24 - mmengine - INFO - Epoch(train) [13][1800/1879] lr: 2.3398e-05 eta: 4 days, 1:48:14 time: 2.1438 data_time: 0.0384 memory: 48866 grad_norm: 4.6863 loss: 3.8233 loss_cls: 3.8233 2023/01/22 05:24:13 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 05:24:13 - mmengine - INFO - Epoch(train) [13][1879/1879] lr: 2.3474e-05 eta: 4 days, 1:45:17 time: 2.0953 data_time: 0.0401 memory: 48866 grad_norm: 5.1812 loss: 3.9305 loss_cls: 3.9305 2023/01/22 05:25:08 - mmengine - INFO - Epoch(val) [13][100/155] eta: 0:00:30 time: 0.6123 data_time: 0.2733 memory: 4950 2023/01/22 05:25:37 - mmengine - INFO - Epoch(val) [13][155/155] acc/top1: 0.5560 acc/top5: 0.8094 acc/mean1: 0.5558 2023/01/22 05:25:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_12.pth is removed 2023/01/22 05:25:41 - mmengine - INFO - The best checkpoint with 0.5560 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2023/01/22 05:29:21 - mmengine - INFO - Epoch(train) [14][ 100/1879] lr: 2.3569e-05 eta: 4 days, 1:42:15 time: 2.1422 data_time: 0.0376 memory: 48866 grad_norm: 5.2777 loss: 3.6030 loss_cls: 3.6030 2023/01/22 05:32:56 - mmengine - INFO - Epoch(train) [14][ 200/1879] lr: 2.3665e-05 eta: 4 days, 1:38:34 time: 2.1557 data_time: 0.0384 memory: 48866 grad_norm: 4.8738 loss: 3.6945 loss_cls: 3.6945 2023/01/22 05:36:31 - mmengine - INFO - Epoch(train) [14][ 300/1879] lr: 2.3761e-05 eta: 4 days, 1:35:01 time: 2.1557 data_time: 0.0383 memory: 48866 grad_norm: 4.6149 loss: 3.7610 loss_cls: 3.7610 2023/01/22 05:40:06 - mmengine - INFO - Epoch(train) [14][ 400/1879] lr: 2.3857e-05 eta: 4 days, 1:31:20 time: 2.1414 data_time: 0.0389 memory: 48866 grad_norm: 4.8853 loss: 3.7058 loss_cls: 3.7058 2023/01/22 05:43:40 - mmengine - INFO - Epoch(train) [14][ 500/1879] lr: 2.3953e-05 eta: 4 days, 1:27:41 time: 2.1419 data_time: 0.0383 memory: 48866 grad_norm: 4.8431 loss: 3.7913 loss_cls: 3.7913 2023/01/22 05:46:18 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 05:47:16 - mmengine - INFO - Epoch(train) [14][ 600/1879] lr: 2.4049e-05 eta: 4 days, 1:24:05 time: 2.1491 data_time: 0.0391 memory: 48866 grad_norm: 5.0795 loss: 3.9056 loss_cls: 3.9056 2023/01/22 05:50:51 - mmengine - INFO - Epoch(train) [14][ 700/1879] lr: 2.4145e-05 eta: 4 days, 1:20:30 time: 2.1405 data_time: 0.0384 memory: 48866 grad_norm: 5.0613 loss: 3.7455 loss_cls: 3.7455 2023/01/22 05:54:26 - mmengine - INFO - Epoch(train) [14][ 800/1879] lr: 2.4240e-05 eta: 4 days, 1:16:51 time: 2.1516 data_time: 0.0384 memory: 48866 grad_norm: 4.6090 loss: 3.8320 loss_cls: 3.8320 2023/01/22 05:58:01 - mmengine - INFO - Epoch(train) [14][ 900/1879] lr: 2.4336e-05 eta: 4 days, 1:13:18 time: 2.1537 data_time: 0.0390 memory: 48866 grad_norm: 4.7891 loss: 3.9571 loss_cls: 3.9571 2023/01/22 06:01:37 - mmengine - INFO - Epoch(train) [14][1000/1879] lr: 2.4432e-05 eta: 4 days, 1:09:42 time: 2.1451 data_time: 0.0390 memory: 48866 grad_norm: 5.0577 loss: 3.8416 loss_cls: 3.8416 2023/01/22 06:05:11 - mmengine - INFO - Epoch(train) [14][1100/1879] lr: 2.4528e-05 eta: 4 days, 1:06:03 time: 2.1501 data_time: 0.0390 memory: 48866 grad_norm: 5.0958 loss: 3.9392 loss_cls: 3.9392 2023/01/22 06:08:46 - mmengine - INFO - Epoch(train) [14][1200/1879] lr: 2.4624e-05 eta: 4 days, 1:02:25 time: 2.1490 data_time: 0.0380 memory: 48866 grad_norm: 4.8040 loss: 3.8982 loss_cls: 3.8982 2023/01/22 06:12:21 - mmengine - INFO - Epoch(train) [14][1300/1879] lr: 2.4720e-05 eta: 4 days, 0:58:50 time: 2.1543 data_time: 0.0380 memory: 48866 grad_norm: 4.8552 loss: 3.9860 loss_cls: 3.9860 2023/01/22 06:15:56 - mmengine - INFO - Epoch(train) [14][1400/1879] lr: 2.4816e-05 eta: 4 days, 0:55:09 time: 2.1451 data_time: 0.0388 memory: 48866 grad_norm: 4.7526 loss: 3.7234 loss_cls: 3.7234 2023/01/22 06:19:31 - mmengine - INFO - Epoch(train) [14][1500/1879] lr: 2.4911e-05 eta: 4 days, 0:51:31 time: 2.1452 data_time: 0.0386 memory: 48866 grad_norm: 4.7791 loss: 3.6992 loss_cls: 3.6992 2023/01/22 06:22:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 06:23:07 - mmengine - INFO - Epoch(train) [14][1600/1879] lr: 2.5007e-05 eta: 4 days, 0:47:59 time: 2.1523 data_time: 0.0393 memory: 48866 grad_norm: 4.9341 loss: 3.9081 loss_cls: 3.9081 2023/01/22 06:26:42 - mmengine - INFO - Epoch(train) [14][1700/1879] lr: 2.5103e-05 eta: 4 days, 0:44:23 time: 2.1473 data_time: 0.0385 memory: 48866 grad_norm: 5.0069 loss: 3.6122 loss_cls: 3.6122 2023/01/22 06:30:17 - mmengine - INFO - Epoch(train) [14][1800/1879] lr: 2.5199e-05 eta: 4 days, 0:40:47 time: 2.1505 data_time: 0.0384 memory: 48866 grad_norm: 4.8543 loss: 3.8114 loss_cls: 3.8114 2023/01/22 06:33:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 06:33:05 - mmengine - INFO - Epoch(train) [14][1879/1879] lr: 2.5275e-05 eta: 4 days, 0:37:47 time: 2.0915 data_time: 0.0409 memory: 48866 grad_norm: 4.7990 loss: 3.7380 loss_cls: 3.7380 2023/01/22 06:33:59 - mmengine - INFO - Epoch(val) [14][100/155] eta: 0:00:29 time: 0.5886 data_time: 0.2350 memory: 4950 2023/01/22 06:34:30 - mmengine - INFO - Epoch(val) [14][155/155] acc/top1: 0.5617 acc/top5: 0.8125 acc/mean1: 0.5615 2023/01/22 06:34:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_13.pth is removed 2023/01/22 06:34:33 - mmengine - INFO - The best checkpoint with 0.5617 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2023/01/22 06:38:14 - mmengine - INFO - Epoch(train) [15][ 100/1879] lr: 2.5371e-05 eta: 4 days, 0:34:44 time: 2.1512 data_time: 0.0377 memory: 48866 grad_norm: 4.8597 loss: 3.9519 loss_cls: 3.9519 2023/01/22 06:41:49 - mmengine - INFO - Epoch(train) [15][ 200/1879] lr: 2.5466e-05 eta: 4 days, 0:31:07 time: 2.1575 data_time: 0.0547 memory: 48866 grad_norm: 5.0567 loss: 3.6970 loss_cls: 3.6970 2023/01/22 06:45:23 - mmengine - INFO - Epoch(train) [15][ 300/1879] lr: 2.5562e-05 eta: 4 days, 0:27:29 time: 2.1446 data_time: 0.0386 memory: 48866 grad_norm: 4.7104 loss: 3.7510 loss_cls: 3.7510 2023/01/22 06:48:58 - mmengine - INFO - Epoch(train) [15][ 400/1879] lr: 2.5658e-05 eta: 4 days, 0:23:47 time: 2.1365 data_time: 0.0381 memory: 48866 grad_norm: 4.8097 loss: 3.7214 loss_cls: 3.7214 2023/01/22 06:52:33 - mmengine - INFO - Epoch(train) [15][ 500/1879] lr: 2.5754e-05 eta: 4 days, 0:20:12 time: 2.1530 data_time: 0.0379 memory: 48866 grad_norm: 4.4869 loss: 3.8756 loss_cls: 3.8756 2023/01/22 06:56:08 - mmengine - INFO - Epoch(train) [15][ 600/1879] lr: 2.5850e-05 eta: 4 days, 0:16:33 time: 2.1520 data_time: 0.0380 memory: 48866 grad_norm: 4.8515 loss: 4.0153 loss_cls: 4.0153 2023/01/22 06:59:30 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 06:59:42 - mmengine - INFO - Epoch(train) [15][ 700/1879] lr: 2.5946e-05 eta: 4 days, 0:12:55 time: 2.1395 data_time: 0.0388 memory: 48866 grad_norm: 4.7547 loss: 3.9224 loss_cls: 3.9224 2023/01/22 07:03:18 - mmengine - INFO - Epoch(train) [15][ 800/1879] lr: 2.6042e-05 eta: 4 days, 0:09:19 time: 2.1458 data_time: 0.0385 memory: 48866 grad_norm: 4.6599 loss: 3.6998 loss_cls: 3.6998 2023/01/22 07:06:53 - mmengine - INFO - Epoch(train) [15][ 900/1879] lr: 2.6137e-05 eta: 4 days, 0:05:41 time: 2.1418 data_time: 0.0381 memory: 48866 grad_norm: 4.6779 loss: 4.0111 loss_cls: 4.0111 2023/01/22 07:10:27 - mmengine - INFO - Epoch(train) [15][1000/1879] lr: 2.6233e-05 eta: 4 days, 0:02:01 time: 2.1378 data_time: 0.0384 memory: 48866 grad_norm: 4.9394 loss: 3.6510 loss_cls: 3.6510 2023/01/22 07:14:02 - mmengine - INFO - Epoch(train) [15][1100/1879] lr: 2.6329e-05 eta: 3 days, 23:58:23 time: 2.1533 data_time: 0.0384 memory: 48866 grad_norm: 4.7675 loss: 3.9180 loss_cls: 3.9180 2023/01/22 07:17:37 - mmengine - INFO - Epoch(train) [15][1200/1879] lr: 2.6425e-05 eta: 3 days, 23:54:46 time: 2.1607 data_time: 0.0387 memory: 48866 grad_norm: 5.0023 loss: 3.8372 loss_cls: 3.8372 2023/01/22 07:21:12 - mmengine - INFO - Epoch(train) [15][1300/1879] lr: 2.6521e-05 eta: 3 days, 23:51:10 time: 2.1486 data_time: 0.0380 memory: 48866 grad_norm: 4.8367 loss: 3.8576 loss_cls: 3.8576 2023/01/22 07:24:47 - mmengine - INFO - Epoch(train) [15][1400/1879] lr: 2.6617e-05 eta: 3 days, 23:47:31 time: 2.1435 data_time: 0.0383 memory: 48866 grad_norm: 4.7291 loss: 3.7668 loss_cls: 3.7668 2023/01/22 07:28:22 - mmengine - INFO - Epoch(train) [15][1500/1879] lr: 2.6713e-05 eta: 3 days, 23:43:55 time: 2.1560 data_time: 0.0378 memory: 48866 grad_norm: 4.5025 loss: 3.8394 loss_cls: 3.8394 2023/01/22 07:31:57 - mmengine - INFO - Epoch(train) [15][1600/1879] lr: 2.6808e-05 eta: 3 days, 23:40:20 time: 2.1586 data_time: 0.0390 memory: 48866 grad_norm: 4.8820 loss: 3.8948 loss_cls: 3.8948 2023/01/22 07:35:19 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 07:35:32 - mmengine - INFO - Epoch(train) [15][1700/1879] lr: 2.6904e-05 eta: 3 days, 23:36:42 time: 2.1483 data_time: 0.0388 memory: 48866 grad_norm: 4.7741 loss: 3.9261 loss_cls: 3.9261 2023/01/22 07:39:06 - mmengine - INFO - Epoch(train) [15][1800/1879] lr: 2.7000e-05 eta: 3 days, 23:33:02 time: 2.1406 data_time: 0.0373 memory: 48866 grad_norm: 4.4887 loss: 4.0089 loss_cls: 4.0089 2023/01/22 07:41:55 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 07:41:55 - mmengine - INFO - Epoch(train) [15][1879/1879] lr: 2.7076e-05 eta: 3 days, 23:30:03 time: 2.0879 data_time: 0.0412 memory: 48866 grad_norm: 4.6282 loss: 3.8690 loss_cls: 3.8690 2023/01/22 07:41:55 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/01/22 07:42:55 - mmengine - INFO - Epoch(val) [15][100/155] eta: 0:00:30 time: 0.5512 data_time: 0.2100 memory: 4950 2023/01/22 07:43:22 - mmengine - INFO - Epoch(val) [15][155/155] acc/top1: 0.5793 acc/top5: 0.8247 acc/mean1: 0.5791 2023/01/22 07:43:22 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_14.pth is removed 2023/01/22 07:43:26 - mmengine - INFO - The best checkpoint with 0.5793 acc/top1 at 15 epoch is saved to best_acc/top1_epoch_15.pth. 2023/01/22 07:47:07 - mmengine - INFO - Epoch(train) [16][ 100/1879] lr: 2.7172e-05 eta: 3 days, 23:26:59 time: 2.1508 data_time: 0.0379 memory: 48866 grad_norm: 4.7652 loss: 3.8712 loss_cls: 3.8712 2023/01/22 07:50:41 - mmengine - INFO - Epoch(train) [16][ 200/1879] lr: 2.7268e-05 eta: 3 days, 23:23:20 time: 2.1462 data_time: 0.0379 memory: 48866 grad_norm: 4.6415 loss: 3.8770 loss_cls: 3.8770 2023/01/22 07:54:17 - mmengine - INFO - Epoch(train) [16][ 300/1879] lr: 2.7363e-05 eta: 3 days, 23:19:47 time: 2.1649 data_time: 0.0552 memory: 48866 grad_norm: 4.7291 loss: 3.9965 loss_cls: 3.9965 2023/01/22 07:57:52 - mmengine - INFO - Epoch(train) [16][ 400/1879] lr: 2.7459e-05 eta: 3 days, 23:16:11 time: 2.1416 data_time: 0.0386 memory: 48866 grad_norm: 4.5427 loss: 3.8807 loss_cls: 3.8807 2023/01/22 08:01:27 - mmengine - INFO - Epoch(train) [16][ 500/1879] lr: 2.7555e-05 eta: 3 days, 23:12:31 time: 2.1533 data_time: 0.0390 memory: 48866 grad_norm: 4.7509 loss: 3.6631 loss_cls: 3.6631 2023/01/22 08:05:03 - mmengine - INFO - Epoch(train) [16][ 600/1879] lr: 2.7651e-05 eta: 3 days, 23:09:00 time: 2.1467 data_time: 0.0389 memory: 48866 grad_norm: 4.8787 loss: 3.8445 loss_cls: 3.8445 2023/01/22 08:08:38 - mmengine - INFO - Epoch(train) [16][ 700/1879] lr: 2.7747e-05 eta: 3 days, 23:05:25 time: 2.1588 data_time: 0.0390 memory: 48866 grad_norm: 5.0448 loss: 3.8198 loss_cls: 3.8198 2023/01/22 08:12:13 - mmengine - INFO - Epoch(train) [16][ 800/1879] lr: 2.7843e-05 eta: 3 days, 23:01:50 time: 2.1531 data_time: 0.0387 memory: 48866 grad_norm: 4.4421 loss: 3.7287 loss_cls: 3.7287 2023/01/22 08:12:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 08:15:48 - mmengine - INFO - Epoch(train) [16][ 900/1879] lr: 2.7938e-05 eta: 3 days, 22:58:11 time: 2.1450 data_time: 0.0389 memory: 48866 grad_norm: 4.8870 loss: 3.8618 loss_cls: 3.8618 2023/01/22 08:19:23 - mmengine - INFO - Epoch(train) [16][1000/1879] lr: 2.8034e-05 eta: 3 days, 22:54:35 time: 2.1387 data_time: 0.0384 memory: 48866 grad_norm: 4.5710 loss: 3.6783 loss_cls: 3.6783 2023/01/22 08:22:58 - mmengine - INFO - Epoch(train) [16][1100/1879] lr: 2.8130e-05 eta: 3 days, 22:50:58 time: 2.1549 data_time: 0.0407 memory: 48866 grad_norm: 4.7692 loss: 3.8704 loss_cls: 3.8704 2023/01/22 08:26:33 - mmengine - INFO - Epoch(train) [16][1200/1879] lr: 2.8226e-05 eta: 3 days, 22:47:21 time: 2.1605 data_time: 0.0395 memory: 48866 grad_norm: 4.8076 loss: 3.9890 loss_cls: 3.9890 2023/01/22 08:30:08 - mmengine - INFO - Epoch(train) [16][1300/1879] lr: 2.8322e-05 eta: 3 days, 22:43:46 time: 2.1608 data_time: 0.0401 memory: 48866 grad_norm: 4.6626 loss: 3.8300 loss_cls: 3.8300 2023/01/22 08:33:43 - mmengine - INFO - Epoch(train) [16][1400/1879] lr: 2.8418e-05 eta: 3 days, 22:40:09 time: 2.1382 data_time: 0.0395 memory: 48866 grad_norm: 4.9913 loss: 3.8462 loss_cls: 3.8462 2023/01/22 08:37:19 - mmengine - INFO - Epoch(train) [16][1500/1879] lr: 2.8514e-05 eta: 3 days, 22:36:35 time: 2.1455 data_time: 0.0386 memory: 48866 grad_norm: 4.7347 loss: 3.8176 loss_cls: 3.8176 2023/01/22 08:40:54 - mmengine - INFO - Epoch(train) [16][1600/1879] lr: 2.8609e-05 eta: 3 days, 22:33:00 time: 2.1568 data_time: 0.0397 memory: 48866 grad_norm: 4.4464 loss: 4.0095 loss_cls: 4.0095 2023/01/22 08:44:29 - mmengine - INFO - Epoch(train) [16][1700/1879] lr: 2.8705e-05 eta: 3 days, 22:29:25 time: 2.1524 data_time: 0.0401 memory: 48866 grad_norm: 4.6812 loss: 3.8949 loss_cls: 3.8949 2023/01/22 08:48:05 - mmengine - INFO - Epoch(train) [16][1800/1879] lr: 2.8801e-05 eta: 3 days, 22:25:52 time: 2.1674 data_time: 0.0402 memory: 48866 grad_norm: 4.6957 loss: 3.7328 loss_cls: 3.7328 2023/01/22 08:48:38 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 08:50:54 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 08:50:54 - mmengine - INFO - Epoch(train) [16][1879/1879] lr: 2.8877e-05 eta: 3 days, 22:22:54 time: 2.0986 data_time: 0.0407 memory: 48866 grad_norm: 4.8822 loss: 3.6782 loss_cls: 3.6782 2023/01/22 08:51:47 - mmengine - INFO - Epoch(val) [16][100/155] eta: 0:00:29 time: 0.5609 data_time: 0.2011 memory: 4950 2023/01/22 08:52:18 - mmengine - INFO - Epoch(val) [16][155/155] acc/top1: 0.5699 acc/top5: 0.8169 acc/mean1: 0.5697 2023/01/22 08:56:02 - mmengine - INFO - Epoch(train) [17][ 100/1879] lr: 2.8973e-05 eta: 3 days, 22:20:03 time: 2.1484 data_time: 0.0390 memory: 48866 grad_norm: 4.6255 loss: 3.7678 loss_cls: 3.7678 2023/01/22 08:59:37 - mmengine - INFO - Epoch(train) [17][ 200/1879] lr: 2.9069e-05 eta: 3 days, 22:16:28 time: 2.1613 data_time: 0.0392 memory: 48866 grad_norm: 4.2417 loss: 4.1093 loss_cls: 4.1093 2023/01/22 09:03:11 - mmengine - INFO - Epoch(train) [17][ 300/1879] lr: 2.9164e-05 eta: 3 days, 22:12:48 time: 2.1535 data_time: 0.0379 memory: 48866 grad_norm: 4.4430 loss: 3.7976 loss_cls: 3.7976 2023/01/22 09:06:46 - mmengine - INFO - Epoch(train) [17][ 400/1879] lr: 2.9260e-05 eta: 3 days, 22:09:12 time: 2.1347 data_time: 0.0384 memory: 48866 grad_norm: 4.6382 loss: 3.8554 loss_cls: 3.8554 2023/01/22 09:10:22 - mmengine - INFO - Epoch(train) [17][ 500/1879] lr: 2.9356e-05 eta: 3 days, 22:05:37 time: 2.1548 data_time: 0.0393 memory: 48866 grad_norm: 4.8183 loss: 3.8880 loss_cls: 3.8880 2023/01/22 09:13:57 - mmengine - INFO - Epoch(train) [17][ 600/1879] lr: 2.9452e-05 eta: 3 days, 22:02:01 time: 2.1473 data_time: 0.0391 memory: 48866 grad_norm: 4.7384 loss: 3.5398 loss_cls: 3.5398 2023/01/22 09:17:33 - mmengine - INFO - Epoch(train) [17][ 700/1879] lr: 2.9548e-05 eta: 3 days, 21:58:30 time: 2.1581 data_time: 0.0389 memory: 48866 grad_norm: 4.6390 loss: 3.7701 loss_cls: 3.7701 2023/01/22 09:21:08 - mmengine - INFO - Epoch(train) [17][ 800/1879] lr: 2.9644e-05 eta: 3 days, 21:54:54 time: 2.1539 data_time: 0.0394 memory: 48866 grad_norm: 4.8065 loss: 3.8096 loss_cls: 3.8096 2023/01/22 09:24:43 - mmengine - INFO - Epoch(train) [17][ 900/1879] lr: 2.9740e-05 eta: 3 days, 21:51:17 time: 2.1476 data_time: 0.0396 memory: 48866 grad_norm: 4.5128 loss: 3.9411 loss_cls: 3.9411 2023/01/22 09:26:01 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 09:28:19 - mmengine - INFO - Epoch(train) [17][1000/1879] lr: 2.9835e-05 eta: 3 days, 21:47:43 time: 2.1631 data_time: 0.0382 memory: 48866 grad_norm: 4.4758 loss: 3.8666 loss_cls: 3.8666 2023/01/22 09:31:54 - mmengine - INFO - Epoch(train) [17][1100/1879] lr: 2.9931e-05 eta: 3 days, 21:44:07 time: 2.1435 data_time: 0.0388 memory: 48866 grad_norm: 4.6753 loss: 3.7778 loss_cls: 3.7778 2023/01/22 09:35:29 - mmengine - INFO - Epoch(train) [17][1200/1879] lr: 3.0027e-05 eta: 3 days, 21:40:32 time: 2.1514 data_time: 0.0396 memory: 48866 grad_norm: 4.8191 loss: 3.5981 loss_cls: 3.5981 2023/01/22 09:39:05 - mmengine - INFO - Epoch(train) [17][1300/1879] lr: 3.0123e-05 eta: 3 days, 21:36:56 time: 2.1706 data_time: 0.0394 memory: 48866 grad_norm: 4.8569 loss: 3.5583 loss_cls: 3.5583 2023/01/22 09:42:40 - mmengine - INFO - Epoch(train) [17][1400/1879] lr: 3.0219e-05 eta: 3 days, 21:33:20 time: 2.1466 data_time: 0.0386 memory: 48866 grad_norm: 4.7161 loss: 4.0237 loss_cls: 4.0237 2023/01/22 09:46:15 - mmengine - INFO - Epoch(train) [17][1500/1879] lr: 3.0315e-05 eta: 3 days, 21:29:44 time: 2.1533 data_time: 0.0392 memory: 48866 grad_norm: 4.6839 loss: 4.0702 loss_cls: 4.0702 2023/01/22 09:49:50 - mmengine - INFO - Epoch(train) [17][1600/1879] lr: 3.0411e-05 eta: 3 days, 21:26:09 time: 2.1500 data_time: 0.0393 memory: 48866 grad_norm: 4.8281 loss: 3.6984 loss_cls: 3.6984 2023/01/22 09:53:26 - mmengine - INFO - Epoch(train) [17][1700/1879] lr: 3.0506e-05 eta: 3 days, 21:22:34 time: 2.1521 data_time: 0.0392 memory: 48866 grad_norm: 4.5481 loss: 3.7914 loss_cls: 3.7914 2023/01/22 09:57:00 - mmengine - INFO - Epoch(train) [17][1800/1879] lr: 3.0602e-05 eta: 3 days, 21:18:56 time: 2.1440 data_time: 0.0385 memory: 48866 grad_norm: 4.2661 loss: 3.8101 loss_cls: 3.8101 2023/01/22 09:59:49 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 09:59:49 - mmengine - INFO - Epoch(train) [17][1879/1879] lr: 3.0678e-05 eta: 3 days, 21:16:00 time: 2.0911 data_time: 0.0414 memory: 48866 grad_norm: 4.6252 loss: 3.7168 loss_cls: 3.7168 2023/01/22 10:00:43 - mmengine - INFO - Epoch(val) [17][100/155] eta: 0:00:29 time: 0.5874 data_time: 0.2403 memory: 4950 2023/01/22 10:01:13 - mmengine - INFO - Epoch(val) [17][155/155] acc/top1: 0.5706 acc/top5: 0.8180 acc/mean1: 0.5704 2023/01/22 10:03:24 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 10:04:56 - mmengine - INFO - Epoch(train) [18][ 100/1879] lr: 3.0774e-05 eta: 3 days, 21:13:03 time: 2.1447 data_time: 0.0391 memory: 48866 grad_norm: 4.1873 loss: 3.7861 loss_cls: 3.7861 2023/01/22 10:08:31 - mmengine - INFO - Epoch(train) [18][ 200/1879] lr: 3.0870e-05 eta: 3 days, 21:09:26 time: 2.1603 data_time: 0.0380 memory: 48866 grad_norm: 4.5744 loss: 3.6645 loss_cls: 3.6645 2023/01/22 10:12:06 - mmengine - INFO - Epoch(train) [18][ 300/1879] lr: 3.0966e-05 eta: 3 days, 21:05:49 time: 2.1433 data_time: 0.0384 memory: 48866 grad_norm: 4.8617 loss: 3.5960 loss_cls: 3.5960 2023/01/22 10:15:42 - mmengine - INFO - Epoch(train) [18][ 400/1879] lr: 3.1061e-05 eta: 3 days, 21:02:16 time: 2.1581 data_time: 0.0388 memory: 48866 grad_norm: 4.5936 loss: 3.8172 loss_cls: 3.8172 2023/01/22 10:19:17 - mmengine - INFO - Epoch(train) [18][ 500/1879] lr: 3.1157e-05 eta: 3 days, 20:58:38 time: 2.1499 data_time: 0.0382 memory: 48866 grad_norm: 4.4604 loss: 3.8158 loss_cls: 3.8158 2023/01/22 10:22:52 - mmengine - INFO - Epoch(train) [18][ 600/1879] lr: 3.1253e-05 eta: 3 days, 20:55:01 time: 2.1434 data_time: 0.0388 memory: 48866 grad_norm: 4.8545 loss: 3.6702 loss_cls: 3.6702 2023/01/22 10:26:26 - mmengine - INFO - Epoch(train) [18][ 700/1879] lr: 3.1349e-05 eta: 3 days, 20:51:22 time: 2.1444 data_time: 0.0399 memory: 48866 grad_norm: 4.7409 loss: 3.7465 loss_cls: 3.7465 2023/01/22 10:30:02 - mmengine - INFO - Epoch(train) [18][ 800/1879] lr: 3.1445e-05 eta: 3 days, 20:47:46 time: 2.1485 data_time: 0.0387 memory: 48866 grad_norm: 4.4776 loss: 3.7770 loss_cls: 3.7770 2023/01/22 10:33:37 - mmengine - INFO - Epoch(train) [18][ 900/1879] lr: 3.1541e-05 eta: 3 days, 20:44:09 time: 2.1529 data_time: 0.0389 memory: 48866 grad_norm: 4.4945 loss: 3.8352 loss_cls: 3.8352 2023/01/22 10:37:11 - mmengine - INFO - Epoch(train) [18][1000/1879] lr: 3.1637e-05 eta: 3 days, 20:40:31 time: 2.1408 data_time: 0.0384 memory: 48866 grad_norm: 4.2246 loss: 3.7953 loss_cls: 3.7953 2023/01/22 10:39:14 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 10:40:46 - mmengine - INFO - Epoch(train) [18][1100/1879] lr: 3.1732e-05 eta: 3 days, 20:36:54 time: 2.1448 data_time: 0.0399 memory: 48866 grad_norm: 4.5737 loss: 3.6807 loss_cls: 3.6807 2023/01/22 10:44:21 - mmengine - INFO - Epoch(train) [18][1200/1879] lr: 3.1828e-05 eta: 3 days, 20:33:18 time: 2.1521 data_time: 0.0385 memory: 48866 grad_norm: 4.4009 loss: 3.9966 loss_cls: 3.9966 2023/01/22 10:47:57 - mmengine - INFO - Epoch(train) [18][1300/1879] lr: 3.1924e-05 eta: 3 days, 20:29:42 time: 2.1475 data_time: 0.0393 memory: 48866 grad_norm: 4.3786 loss: 3.8704 loss_cls: 3.8704 2023/01/22 10:51:32 - mmengine - INFO - Epoch(train) [18][1400/1879] lr: 3.2020e-05 eta: 3 days, 20:26:07 time: 2.1603 data_time: 0.0391 memory: 48866 grad_norm: 4.4238 loss: 3.8103 loss_cls: 3.8103 2023/01/22 10:55:07 - mmengine - INFO - Epoch(train) [18][1500/1879] lr: 3.2116e-05 eta: 3 days, 20:22:30 time: 2.1464 data_time: 0.0385 memory: 48866 grad_norm: 4.5533 loss: 3.5904 loss_cls: 3.5904 2023/01/22 10:58:42 - mmengine - INFO - Epoch(train) [18][1600/1879] lr: 3.2212e-05 eta: 3 days, 20:18:52 time: 2.1350 data_time: 0.0389 memory: 48866 grad_norm: 4.5319 loss: 3.9502 loss_cls: 3.9502 2023/01/22 11:02:17 - mmengine - INFO - Epoch(train) [18][1700/1879] lr: 3.2308e-05 eta: 3 days, 20:15:19 time: 2.1603 data_time: 0.0395 memory: 48866 grad_norm: 4.5462 loss: 3.7880 loss_cls: 3.7880 2023/01/22 11:05:52 - mmengine - INFO - Epoch(train) [18][1800/1879] lr: 3.2403e-05 eta: 3 days, 20:11:42 time: 2.1492 data_time: 0.0394 memory: 48866 grad_norm: 4.5995 loss: 3.7751 loss_cls: 3.7751 2023/01/22 11:08:42 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 11:08:42 - mmengine - INFO - Epoch(train) [18][1879/1879] lr: 3.2479e-05 eta: 3 days, 20:08:48 time: 2.1128 data_time: 0.0407 memory: 48866 grad_norm: 4.7189 loss: 3.7939 loss_cls: 3.7939 2023/01/22 11:08:42 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/01/22 11:09:43 - mmengine - INFO - Epoch(val) [18][100/155] eta: 0:00:31 time: 0.5860 data_time: 0.2325 memory: 4950 2023/01/22 11:10:10 - mmengine - INFO - Epoch(val) [18][155/155] acc/top1: 0.5725 acc/top5: 0.8223 acc/mean1: 0.5725 2023/01/22 11:13:52 - mmengine - INFO - Epoch(train) [19][ 100/1879] lr: 3.2575e-05 eta: 3 days, 20:05:46 time: 2.1438 data_time: 0.0380 memory: 48866 grad_norm: 4.7185 loss: 3.8833 loss_cls: 3.8833 2023/01/22 11:16:40 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 11:17:27 - mmengine - INFO - Epoch(train) [19][ 200/1879] lr: 3.2671e-05 eta: 3 days, 20:02:08 time: 2.1509 data_time: 0.0382 memory: 48866 grad_norm: 4.3457 loss: 3.7078 loss_cls: 3.7078 2023/01/22 11:21:02 - mmengine - INFO - Epoch(train) [19][ 300/1879] lr: 3.2767e-05 eta: 3 days, 19:58:31 time: 2.1531 data_time: 0.0385 memory: 48866 grad_norm: 4.3838 loss: 3.7366 loss_cls: 3.7366 2023/01/22 11:24:36 - mmengine - INFO - Epoch(train) [19][ 400/1879] lr: 3.2863e-05 eta: 3 days, 19:54:51 time: 2.1454 data_time: 0.0374 memory: 48866 grad_norm: 4.4404 loss: 3.7441 loss_cls: 3.7441 2023/01/22 11:28:11 - mmengine - INFO - Epoch(train) [19][ 500/1879] lr: 3.2958e-05 eta: 3 days, 19:51:15 time: 2.1589 data_time: 0.0403 memory: 48866 grad_norm: 4.3793 loss: 3.8269 loss_cls: 3.8269 2023/01/22 11:31:46 - mmengine - INFO - Epoch(train) [19][ 600/1879] lr: 3.3054e-05 eta: 3 days, 19:47:37 time: 2.1382 data_time: 0.0390 memory: 48866 grad_norm: 4.1978 loss: 4.1099 loss_cls: 4.1099 2023/01/22 11:35:21 - mmengine - INFO - Epoch(train) [19][ 700/1879] lr: 3.3150e-05 eta: 3 days, 19:44:01 time: 2.1434 data_time: 0.0388 memory: 48866 grad_norm: 4.3687 loss: 3.7819 loss_cls: 3.7819 2023/01/22 11:38:57 - mmengine - INFO - Epoch(train) [19][ 800/1879] lr: 3.3246e-05 eta: 3 days, 19:40:27 time: 2.1521 data_time: 0.0393 memory: 48866 grad_norm: 4.3713 loss: 3.7318 loss_cls: 3.7318 2023/01/22 11:42:31 - mmengine - INFO - Epoch(train) [19][ 900/1879] lr: 3.3342e-05 eta: 3 days, 19:36:47 time: 2.1447 data_time: 0.0387 memory: 48866 grad_norm: 4.5806 loss: 3.7577 loss_cls: 3.7577 2023/01/22 11:46:06 - mmengine - INFO - Epoch(train) [19][1000/1879] lr: 3.3438e-05 eta: 3 days, 19:33:11 time: 2.1503 data_time: 0.0388 memory: 48866 grad_norm: 4.3902 loss: 3.7289 loss_cls: 3.7289 2023/01/22 11:49:42 - mmengine - INFO - Epoch(train) [19][1100/1879] lr: 3.3534e-05 eta: 3 days, 19:29:36 time: 2.1670 data_time: 0.0382 memory: 48866 grad_norm: 4.4938 loss: 3.8237 loss_cls: 3.8237 2023/01/22 11:52:29 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 11:53:16 - mmengine - INFO - Epoch(train) [19][1200/1879] lr: 3.3629e-05 eta: 3 days, 19:25:58 time: 2.1467 data_time: 0.0392 memory: 48866 grad_norm: 4.6230 loss: 3.9194 loss_cls: 3.9194 2023/01/22 11:56:51 - mmengine - INFO - Epoch(train) [19][1300/1879] lr: 3.3725e-05 eta: 3 days, 19:22:19 time: 2.1409 data_time: 0.0387 memory: 48866 grad_norm: 4.5926 loss: 3.7200 loss_cls: 3.7200 2023/01/22 12:00:26 - mmengine - INFO - Epoch(train) [19][1400/1879] lr: 3.3821e-05 eta: 3 days, 19:18:44 time: 2.1606 data_time: 0.0390 memory: 48866 grad_norm: 4.2942 loss: 3.7447 loss_cls: 3.7447 2023/01/22 12:04:01 - mmengine - INFO - Epoch(train) [19][1500/1879] lr: 3.3917e-05 eta: 3 days, 19:15:08 time: 2.1502 data_time: 0.0392 memory: 48866 grad_norm: 4.5697 loss: 3.8063 loss_cls: 3.8063 2023/01/22 12:07:37 - mmengine - INFO - Epoch(train) [19][1600/1879] lr: 3.4013e-05 eta: 3 days, 19:11:33 time: 2.1637 data_time: 0.0386 memory: 48866 grad_norm: 4.1928 loss: 4.0120 loss_cls: 4.0120 2023/01/22 12:11:12 - mmengine - INFO - Epoch(train) [19][1700/1879] lr: 3.4109e-05 eta: 3 days, 19:07:57 time: 2.1442 data_time: 0.0384 memory: 48866 grad_norm: 4.2865 loss: 3.8980 loss_cls: 3.8980 2023/01/22 12:14:47 - mmengine - INFO - Epoch(train) [19][1800/1879] lr: 3.4205e-05 eta: 3 days, 19:04:21 time: 2.1405 data_time: 0.0392 memory: 48866 grad_norm: 4.3869 loss: 3.6855 loss_cls: 3.6855 2023/01/22 12:17:35 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 12:17:35 - mmengine - INFO - Epoch(train) [19][1879/1879] lr: 3.4280e-05 eta: 3 days, 19:01:23 time: 2.0878 data_time: 0.0399 memory: 48866 grad_norm: 4.6346 loss: 3.7884 loss_cls: 3.7884 2023/01/22 12:18:30 - mmengine - INFO - Epoch(val) [19][100/155] eta: 0:00:29 time: 0.5790 data_time: 0.2193 memory: 4950 2023/01/22 12:19:00 - mmengine - INFO - Epoch(val) [19][155/155] acc/top1: 0.5741 acc/top5: 0.8250 acc/mean1: 0.5741 2023/01/22 12:22:43 - mmengine - INFO - Epoch(train) [20][ 100/1879] lr: 3.4376e-05 eta: 3 days, 18:58:18 time: 2.1505 data_time: 0.0383 memory: 48866 grad_norm: 4.3597 loss: 3.8316 loss_cls: 3.8316 2023/01/22 12:26:17 - mmengine - INFO - Epoch(train) [20][ 200/1879] lr: 3.4472e-05 eta: 3 days, 18:54:40 time: 2.1323 data_time: 0.0383 memory: 48866 grad_norm: 4.6930 loss: 3.7180 loss_cls: 3.7180 2023/01/22 12:29:51 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 12:29:53 - mmengine - INFO - Epoch(train) [20][ 300/1879] lr: 3.4568e-05 eta: 3 days, 18:51:05 time: 2.1593 data_time: 0.0397 memory: 48866 grad_norm: 4.4091 loss: 3.8161 loss_cls: 3.8161 2023/01/22 12:33:29 - mmengine - INFO - Epoch(train) [20][ 400/1879] lr: 3.4664e-05 eta: 3 days, 18:47:31 time: 2.1639 data_time: 0.0374 memory: 48866 grad_norm: 4.3406 loss: 3.9319 loss_cls: 3.9319 2023/01/22 12:37:03 - mmengine - INFO - Epoch(train) [20][ 500/1879] lr: 3.4760e-05 eta: 3 days, 18:43:53 time: 2.1490 data_time: 0.0393 memory: 48866 grad_norm: 4.2951 loss: 3.7500 loss_cls: 3.7500 2023/01/22 12:40:39 - mmengine - INFO - Epoch(train) [20][ 600/1879] lr: 3.4855e-05 eta: 3 days, 18:40:18 time: 2.1639 data_time: 0.0390 memory: 48866 grad_norm: 4.5801 loss: 3.8055 loss_cls: 3.8055 2023/01/22 12:44:14 - mmengine - INFO - Epoch(train) [20][ 700/1879] lr: 3.4951e-05 eta: 3 days, 18:36:43 time: 2.1513 data_time: 0.0393 memory: 48866 grad_norm: 4.1107 loss: 3.9778 loss_cls: 3.9778 2023/01/22 12:47:49 - mmengine - INFO - Epoch(train) [20][ 800/1879] lr: 3.5047e-05 eta: 3 days, 18:33:07 time: 2.1496 data_time: 0.0391 memory: 48866 grad_norm: 4.4569 loss: 3.8020 loss_cls: 3.8020 2023/01/22 12:51:25 - mmengine - INFO - Epoch(train) [20][ 900/1879] lr: 3.5143e-05 eta: 3 days, 18:29:32 time: 2.1565 data_time: 0.0394 memory: 48866 grad_norm: 4.4974 loss: 3.7880 loss_cls: 3.7880 2023/01/22 12:55:00 - mmengine - INFO - Epoch(train) [20][1000/1879] lr: 3.5239e-05 eta: 3 days, 18:25:58 time: 2.1561 data_time: 0.0388 memory: 48866 grad_norm: 4.3677 loss: 3.8319 loss_cls: 3.8319 2023/01/22 12:58:36 - mmengine - INFO - Epoch(train) [20][1100/1879] lr: 3.5335e-05 eta: 3 days, 18:22:24 time: 2.1574 data_time: 0.0393 memory: 48866 grad_norm: 4.3926 loss: 3.7742 loss_cls: 3.7742 2023/01/22 13:02:11 - mmengine - INFO - Epoch(train) [20][1200/1879] lr: 3.5430e-05 eta: 3 days, 18:18:48 time: 2.1588 data_time: 0.0395 memory: 48866 grad_norm: 4.5130 loss: 3.7378 loss_cls: 3.7378 2023/01/22 13:05:44 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 13:05:46 - mmengine - INFO - Epoch(train) [20][1300/1879] lr: 3.5526e-05 eta: 3 days, 18:15:12 time: 2.1505 data_time: 0.0392 memory: 48866 grad_norm: 4.4048 loss: 3.6764 loss_cls: 3.6764 2023/01/22 13:09:22 - mmengine - INFO - Epoch(train) [20][1400/1879] lr: 3.5622e-05 eta: 3 days, 18:11:37 time: 2.1468 data_time: 0.0389 memory: 48866 grad_norm: 4.4766 loss: 3.6565 loss_cls: 3.6565 2023/01/22 13:12:57 - mmengine - INFO - Epoch(train) [20][1500/1879] lr: 3.5718e-05 eta: 3 days, 18:08:01 time: 2.1468 data_time: 0.0400 memory: 48866 grad_norm: 4.1185 loss: 4.0022 loss_cls: 4.0022 2023/01/22 13:16:32 - mmengine - INFO - Epoch(train) [20][1600/1879] lr: 3.5814e-05 eta: 3 days, 18:04:26 time: 2.1544 data_time: 0.0392 memory: 48866 grad_norm: 4.2716 loss: 3.7283 loss_cls: 3.7283 2023/01/22 13:20:07 - mmengine - INFO - Epoch(train) [20][1700/1879] lr: 3.5910e-05 eta: 3 days, 18:00:48 time: 2.1417 data_time: 0.0394 memory: 48866 grad_norm: 4.4528 loss: 3.8290 loss_cls: 3.8290 2023/01/22 13:23:42 - mmengine - INFO - Epoch(train) [20][1800/1879] lr: 3.6006e-05 eta: 3 days, 17:57:13 time: 2.1511 data_time: 0.0385 memory: 48866 grad_norm: 4.4414 loss: 3.6913 loss_cls: 3.6913 2023/01/22 13:26:31 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 13:26:31 - mmengine - INFO - Epoch(train) [20][1879/1879] lr: 3.6081e-05 eta: 3 days, 17:54:19 time: 2.0945 data_time: 0.0404 memory: 48866 grad_norm: 4.4950 loss: 3.8223 loss_cls: 3.8223 2023/01/22 13:27:26 - mmengine - INFO - Epoch(val) [20][100/155] eta: 0:00:29 time: 0.5694 data_time: 0.2134 memory: 4950 2023/01/22 13:27:57 - mmengine - INFO - Epoch(val) [20][155/155] acc/top1: 0.5778 acc/top5: 0.8233 acc/mean1: 0.5777 2023/01/22 13:31:39 - mmengine - INFO - Epoch(train) [21][ 100/1879] lr: 3.6081e-05 eta: 3 days, 17:51:12 time: 2.1502 data_time: 0.0394 memory: 48866 grad_norm: 4.3648 loss: 3.8214 loss_cls: 3.8214 2023/01/22 13:35:14 - mmengine - INFO - Epoch(train) [21][ 200/1879] lr: 3.6081e-05 eta: 3 days, 17:47:35 time: 2.1409 data_time: 0.0381 memory: 48866 grad_norm: 4.4106 loss: 3.6651 loss_cls: 3.6651 2023/01/22 13:38:50 - mmengine - INFO - Epoch(train) [21][ 300/1879] lr: 3.6081e-05 eta: 3 days, 17:44:00 time: 2.1555 data_time: 0.0385 memory: 48866 grad_norm: 4.2228 loss: 3.7648 loss_cls: 3.7648 2023/01/22 13:42:24 - mmengine - INFO - Epoch(train) [21][ 400/1879] lr: 3.6081e-05 eta: 3 days, 17:40:23 time: 2.1498 data_time: 0.0385 memory: 48866 grad_norm: 4.5086 loss: 3.7209 loss_cls: 3.7209 2023/01/22 13:43:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 13:46:00 - mmengine - INFO - Epoch(train) [21][ 500/1879] lr: 3.6080e-05 eta: 3 days, 17:36:48 time: 2.1409 data_time: 0.0384 memory: 48866 grad_norm: 4.2465 loss: 3.8207 loss_cls: 3.8207 2023/01/22 13:49:35 - mmengine - INFO - Epoch(train) [21][ 600/1879] lr: 3.6080e-05 eta: 3 days, 17:33:12 time: 2.1473 data_time: 0.0380 memory: 48866 grad_norm: 4.4874 loss: 3.8323 loss_cls: 3.8323 2023/01/22 13:53:11 - mmengine - INFO - Epoch(train) [21][ 700/1879] lr: 3.6079e-05 eta: 3 days, 17:29:37 time: 2.1504 data_time: 0.0388 memory: 48866 grad_norm: 4.3684 loss: 3.7395 loss_cls: 3.7395 2023/01/22 13:56:46 - mmengine - INFO - Epoch(train) [21][ 800/1879] lr: 3.6079e-05 eta: 3 days, 17:26:03 time: 2.1658 data_time: 0.0388 memory: 48866 grad_norm: 4.4203 loss: 3.7521 loss_cls: 3.7521 2023/01/22 14:00:21 - mmengine - INFO - Epoch(train) [21][ 900/1879] lr: 3.6078e-05 eta: 3 days, 17:22:24 time: 2.1500 data_time: 0.0394 memory: 48866 grad_norm: 4.3852 loss: 3.8752 loss_cls: 3.8752 2023/01/22 14:03:56 - mmengine - INFO - Epoch(train) [21][1000/1879] lr: 3.6077e-05 eta: 3 days, 17:18:48 time: 2.1467 data_time: 0.0390 memory: 48866 grad_norm: 4.3210 loss: 3.8470 loss_cls: 3.8470 2023/01/22 14:07:31 - mmengine - INFO - Epoch(train) [21][1100/1879] lr: 3.6077e-05 eta: 3 days, 17:15:13 time: 2.1579 data_time: 0.0403 memory: 48866 grad_norm: 4.5229 loss: 3.5871 loss_cls: 3.5871 2023/01/22 14:11:07 - mmengine - INFO - Epoch(train) [21][1200/1879] lr: 3.6076e-05 eta: 3 days, 17:11:39 time: 2.1479 data_time: 0.0387 memory: 48866 grad_norm: 4.2108 loss: 3.7444 loss_cls: 3.7444 2023/01/22 14:14:42 - mmengine - INFO - Epoch(train) [21][1300/1879] lr: 3.6075e-05 eta: 3 days, 17:08:04 time: 2.1633 data_time: 0.0393 memory: 48866 grad_norm: 4.0896 loss: 3.8114 loss_cls: 3.8114 2023/01/22 14:18:18 - mmengine - INFO - Epoch(train) [21][1400/1879] lr: 3.6074e-05 eta: 3 days, 17:04:30 time: 2.1481 data_time: 0.0384 memory: 48866 grad_norm: 4.1165 loss: 3.7914 loss_cls: 3.7914 2023/01/22 14:19:01 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 14:21:53 - mmengine - INFO - Epoch(train) [21][1500/1879] lr: 3.6073e-05 eta: 3 days, 17:00:54 time: 2.1463 data_time: 0.0387 memory: 48866 grad_norm: 4.4431 loss: 3.9465 loss_cls: 3.9465 2023/01/22 14:25:28 - mmengine - INFO - Epoch(train) [21][1600/1879] lr: 3.6071e-05 eta: 3 days, 16:57:18 time: 2.1504 data_time: 0.0392 memory: 48866 grad_norm: 4.4619 loss: 3.8067 loss_cls: 3.8067 2023/01/22 14:29:04 - mmengine - INFO - Epoch(train) [21][1700/1879] lr: 3.6070e-05 eta: 3 days, 16:53:45 time: 2.1434 data_time: 0.0388 memory: 48866 grad_norm: 4.1802 loss: 3.8612 loss_cls: 3.8612 2023/01/22 14:32:39 - mmengine - INFO - Epoch(train) [21][1800/1879] lr: 3.6069e-05 eta: 3 days, 16:50:09 time: 2.1488 data_time: 0.0387 memory: 48866 grad_norm: 4.4018 loss: 3.6160 loss_cls: 3.6160 2023/01/22 14:35:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 14:35:28 - mmengine - INFO - Epoch(train) [21][1879/1879] lr: 3.6067e-05 eta: 3 days, 16:47:15 time: 2.1079 data_time: 0.0381 memory: 48866 grad_norm: 4.4627 loss: 3.8521 loss_cls: 3.8521 2023/01/22 14:35:28 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/01/22 14:36:33 - mmengine - INFO - Epoch(val) [21][100/155] eta: 0:00:32 time: 0.6179 data_time: 0.2688 memory: 4950 2023/01/22 14:36:57 - mmengine - INFO - Epoch(val) [21][155/155] acc/top1: 0.5780 acc/top5: 0.8261 acc/mean1: 0.5780 2023/01/22 14:40:40 - mmengine - INFO - Epoch(train) [22][ 100/1879] lr: 3.6066e-05 eta: 3 days, 16:44:08 time: 2.1528 data_time: 0.0387 memory: 48866 grad_norm: 4.6766 loss: 3.6931 loss_cls: 3.6931 2023/01/22 14:44:16 - mmengine - INFO - Epoch(train) [22][ 200/1879] lr: 3.6064e-05 eta: 3 days, 16:40:33 time: 2.1674 data_time: 0.0381 memory: 48866 grad_norm: 4.5176 loss: 3.7588 loss_cls: 3.7588 2023/01/22 14:47:51 - mmengine - INFO - Epoch(train) [22][ 300/1879] lr: 3.6063e-05 eta: 3 days, 16:36:58 time: 2.1648 data_time: 0.0388 memory: 48866 grad_norm: 4.3636 loss: 3.5666 loss_cls: 3.5666 2023/01/22 14:51:26 - mmengine - INFO - Epoch(train) [22][ 400/1879] lr: 3.6061e-05 eta: 3 days, 16:33:22 time: 2.1483 data_time: 0.0376 memory: 48866 grad_norm: 4.1554 loss: 3.9303 loss_cls: 3.9303 2023/01/22 14:55:02 - mmengine - INFO - Epoch(train) [22][ 500/1879] lr: 3.6059e-05 eta: 3 days, 16:29:46 time: 2.1475 data_time: 0.0388 memory: 48866 grad_norm: 4.1583 loss: 3.8222 loss_cls: 3.8222 2023/01/22 14:56:30 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 14:58:37 - mmengine - INFO - Epoch(train) [22][ 600/1879] lr: 3.6057e-05 eta: 3 days, 16:26:10 time: 2.1602 data_time: 0.0382 memory: 48866 grad_norm: 4.3339 loss: 3.8264 loss_cls: 3.8264 2023/01/22 15:02:11 - mmengine - INFO - Epoch(train) [22][ 700/1879] lr: 3.6055e-05 eta: 3 days, 16:22:32 time: 2.1548 data_time: 0.0388 memory: 48866 grad_norm: 4.1885 loss: 4.0220 loss_cls: 4.0220 2023/01/22 15:05:47 - mmengine - INFO - Epoch(train) [22][ 800/1879] lr: 3.6053e-05 eta: 3 days, 16:18:59 time: 2.1625 data_time: 0.0394 memory: 48866 grad_norm: 4.4334 loss: 3.8323 loss_cls: 3.8323 2023/01/22 15:09:22 - mmengine - INFO - Epoch(train) [22][ 900/1879] lr: 3.6051e-05 eta: 3 days, 16:15:22 time: 2.1473 data_time: 0.0397 memory: 48866 grad_norm: 4.4519 loss: 3.9078 loss_cls: 3.9078 2023/01/22 15:12:57 - mmengine - INFO - Epoch(train) [22][1000/1879] lr: 3.6049e-05 eta: 3 days, 16:11:46 time: 2.1573 data_time: 0.0387 memory: 48866 grad_norm: 4.3190 loss: 3.8056 loss_cls: 3.8056 2023/01/22 15:16:33 - mmengine - INFO - Epoch(train) [22][1100/1879] lr: 3.6046e-05 eta: 3 days, 16:08:10 time: 2.1504 data_time: 0.0399 memory: 48866 grad_norm: 4.1820 loss: 3.6973 loss_cls: 3.6973 2023/01/22 15:20:08 - mmengine - INFO - Epoch(train) [22][1200/1879] lr: 3.6044e-05 eta: 3 days, 16:04:36 time: 2.1503 data_time: 0.0388 memory: 48866 grad_norm: 3.9185 loss: 3.7098 loss_cls: 3.7098 2023/01/22 15:23:43 - mmengine - INFO - Epoch(train) [22][1300/1879] lr: 3.6042e-05 eta: 3 days, 16:01:00 time: 2.1584 data_time: 0.0396 memory: 48866 grad_norm: 4.1730 loss: 3.7003 loss_cls: 3.7003 2023/01/22 15:27:19 - mmengine - INFO - Epoch(train) [22][1400/1879] lr: 3.6039e-05 eta: 3 days, 15:57:24 time: 2.1509 data_time: 0.0384 memory: 48866 grad_norm: 4.3031 loss: 3.6909 loss_cls: 3.6909 2023/01/22 15:30:54 - mmengine - INFO - Epoch(train) [22][1500/1879] lr: 3.6036e-05 eta: 3 days, 15:53:49 time: 2.1471 data_time: 0.0391 memory: 48866 grad_norm: 4.1460 loss: 3.6680 loss_cls: 3.6680 2023/01/22 15:32:22 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 15:34:30 - mmengine - INFO - Epoch(train) [22][1600/1879] lr: 3.6034e-05 eta: 3 days, 15:50:14 time: 2.1493 data_time: 0.0392 memory: 48866 grad_norm: 4.4104 loss: 3.6214 loss_cls: 3.6214 2023/01/22 15:38:05 - mmengine - INFO - Epoch(train) [22][1700/1879] lr: 3.6031e-05 eta: 3 days, 15:46:38 time: 2.1561 data_time: 0.0396 memory: 48866 grad_norm: 4.2988 loss: 3.9019 loss_cls: 3.9019 2023/01/22 15:41:41 - mmengine - INFO - Epoch(train) [22][1800/1879] lr: 3.6028e-05 eta: 3 days, 15:43:05 time: 2.1581 data_time: 0.0393 memory: 48866 grad_norm: 4.2633 loss: 3.6670 loss_cls: 3.6670 2023/01/22 15:44:30 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 15:44:30 - mmengine - INFO - Epoch(train) [22][1879/1879] lr: 3.6026e-05 eta: 3 days, 15:40:11 time: 2.0975 data_time: 0.0386 memory: 48866 grad_norm: 4.4765 loss: 3.6606 loss_cls: 3.6606 2023/01/22 15:45:25 - mmengine - INFO - Epoch(val) [22][100/155] eta: 0:00:30 time: 0.5909 data_time: 0.2303 memory: 4950 2023/01/22 15:45:55 - mmengine - INFO - Epoch(val) [22][155/155] acc/top1: 0.5715 acc/top5: 0.8216 acc/mean1: 0.5715 2023/01/22 15:49:38 - mmengine - INFO - Epoch(train) [23][ 100/1879] lr: 3.6023e-05 eta: 3 days, 15:37:01 time: 2.1493 data_time: 0.0386 memory: 48866 grad_norm: 4.4228 loss: 3.7546 loss_cls: 3.7546 2023/01/22 15:53:13 - mmengine - INFO - Epoch(train) [23][ 200/1879] lr: 3.6020e-05 eta: 3 days, 15:33:26 time: 2.1487 data_time: 0.0381 memory: 48866 grad_norm: 4.3107 loss: 3.6828 loss_cls: 3.6828 2023/01/22 15:56:48 - mmengine - INFO - Epoch(train) [23][ 300/1879] lr: 3.6017e-05 eta: 3 days, 15:29:49 time: 2.1409 data_time: 0.0395 memory: 48866 grad_norm: 4.4862 loss: 3.8321 loss_cls: 3.8321 2023/01/22 16:00:24 - mmengine - INFO - Epoch(train) [23][ 400/1879] lr: 3.6013e-05 eta: 3 days, 15:26:16 time: 2.1489 data_time: 0.0390 memory: 48866 grad_norm: 4.4846 loss: 3.7464 loss_cls: 3.7464 2023/01/22 16:03:59 - mmengine - INFO - Epoch(train) [23][ 500/1879] lr: 3.6010e-05 eta: 3 days, 15:22:41 time: 2.1547 data_time: 0.0382 memory: 48866 grad_norm: 4.5998 loss: 3.7463 loss_cls: 3.7463 2023/01/22 16:07:34 - mmengine - INFO - Epoch(train) [23][ 600/1879] lr: 3.6007e-05 eta: 3 days, 15:19:05 time: 2.1556 data_time: 0.0386 memory: 48866 grad_norm: 3.9481 loss: 4.0177 loss_cls: 4.0177 2023/01/22 16:09:48 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 16:11:10 - mmengine - INFO - Epoch(train) [23][ 700/1879] lr: 3.6003e-05 eta: 3 days, 15:15:31 time: 2.1606 data_time: 0.0391 memory: 48866 grad_norm: 4.5518 loss: 3.7127 loss_cls: 3.7127 2023/01/22 16:14:45 - mmengine - INFO - Epoch(train) [23][ 800/1879] lr: 3.6000e-05 eta: 3 days, 15:11:54 time: 2.1548 data_time: 0.0395 memory: 48866 grad_norm: 4.4312 loss: 3.7148 loss_cls: 3.7148 2023/01/22 16:18:20 - mmengine - INFO - Epoch(train) [23][ 900/1879] lr: 3.5996e-05 eta: 3 days, 15:08:16 time: 2.1512 data_time: 0.0388 memory: 48866 grad_norm: 4.2437 loss: 3.6122 loss_cls: 3.6122 2023/01/22 16:21:55 - mmengine - INFO - Epoch(train) [23][1000/1879] lr: 3.5992e-05 eta: 3 days, 15:04:40 time: 2.1555 data_time: 0.0388 memory: 48866 grad_norm: 4.5456 loss: 3.5641 loss_cls: 3.5641 2023/01/22 16:25:31 - mmengine - INFO - Epoch(train) [23][1100/1879] lr: 3.5989e-05 eta: 3 days, 15:01:05 time: 2.1594 data_time: 0.0393 memory: 48866 grad_norm: 4.4219 loss: 3.5993 loss_cls: 3.5993 2023/01/22 16:29:05 - mmengine - INFO - Epoch(train) [23][1200/1879] lr: 3.5985e-05 eta: 3 days, 14:57:28 time: 2.1489 data_time: 0.0388 memory: 48866 grad_norm: 4.3855 loss: 3.7764 loss_cls: 3.7764 2023/01/22 16:32:41 - mmengine - INFO - Epoch(train) [23][1300/1879] lr: 3.5981e-05 eta: 3 days, 14:53:53 time: 2.1554 data_time: 0.0396 memory: 48866 grad_norm: 4.2642 loss: 3.5866 loss_cls: 3.5866 2023/01/22 16:36:16 - mmengine - INFO - Epoch(train) [23][1400/1879] lr: 3.5977e-05 eta: 3 days, 14:50:18 time: 2.1499 data_time: 0.0386 memory: 48866 grad_norm: 4.3241 loss: 3.8700 loss_cls: 3.8700 2023/01/22 16:39:52 - mmengine - INFO - Epoch(train) [23][1500/1879] lr: 3.5973e-05 eta: 3 days, 14:46:43 time: 2.1449 data_time: 0.0387 memory: 48866 grad_norm: 4.1623 loss: 3.7859 loss_cls: 3.7859 2023/01/22 16:43:27 - mmengine - INFO - Epoch(train) [23][1600/1879] lr: 3.5969e-05 eta: 3 days, 14:43:08 time: 2.1538 data_time: 0.0389 memory: 48866 grad_norm: 4.3690 loss: 3.7266 loss_cls: 3.7266 2023/01/22 16:45:41 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 16:47:03 - mmengine - INFO - Epoch(train) [23][1700/1879] lr: 3.5964e-05 eta: 3 days, 14:39:33 time: 2.1469 data_time: 0.0396 memory: 48866 grad_norm: 4.4293 loss: 3.7891 loss_cls: 3.7891 2023/01/22 16:50:38 - mmengine - INFO - Epoch(train) [23][1800/1879] lr: 3.5960e-05 eta: 3 days, 14:35:57 time: 2.1567 data_time: 0.0384 memory: 48866 grad_norm: 4.2189 loss: 3.7071 loss_cls: 3.7071 2023/01/22 16:53:27 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 16:53:27 - mmengine - INFO - Epoch(train) [23][1879/1879] lr: 3.5957e-05 eta: 3 days, 14:33:02 time: 2.0940 data_time: 0.0406 memory: 48866 grad_norm: 4.5422 loss: 3.7450 loss_cls: 3.7450 2023/01/22 16:54:20 - mmengine - INFO - Epoch(val) [23][100/155] eta: 0:00:29 time: 0.5686 data_time: 0.2036 memory: 4950 2023/01/22 16:54:51 - mmengine - INFO - Epoch(val) [23][155/155] acc/top1: 0.5750 acc/top5: 0.8268 acc/mean1: 0.5748 2023/01/22 16:58:34 - mmengine - INFO - Epoch(train) [24][ 100/1879] lr: 3.5952e-05 eta: 3 days, 14:29:52 time: 2.1546 data_time: 0.0389 memory: 48866 grad_norm: 4.2653 loss: 3.5248 loss_cls: 3.5248 2023/01/22 17:02:09 - mmengine - INFO - Epoch(train) [24][ 200/1879] lr: 3.5948e-05 eta: 3 days, 14:26:16 time: 2.1504 data_time: 0.0375 memory: 48866 grad_norm: 4.3576 loss: 3.8280 loss_cls: 3.8280 2023/01/22 17:05:45 - mmengine - INFO - Epoch(train) [24][ 300/1879] lr: 3.5943e-05 eta: 3 days, 14:22:42 time: 2.1620 data_time: 0.0390 memory: 48866 grad_norm: 4.3914 loss: 3.4703 loss_cls: 3.4703 2023/01/22 17:09:21 - mmengine - INFO - Epoch(train) [24][ 400/1879] lr: 3.5938e-05 eta: 3 days, 14:19:07 time: 2.1542 data_time: 0.0392 memory: 48866 grad_norm: 4.7042 loss: 3.4586 loss_cls: 3.4586 2023/01/22 17:12:56 - mmengine - INFO - Epoch(train) [24][ 500/1879] lr: 3.5933e-05 eta: 3 days, 14:15:32 time: 2.1551 data_time: 0.0394 memory: 48866 grad_norm: 4.1999 loss: 3.8123 loss_cls: 3.8123 2023/01/22 17:16:32 - mmengine - INFO - Epoch(train) [24][ 600/1879] lr: 3.5929e-05 eta: 3 days, 14:11:57 time: 2.1522 data_time: 0.0392 memory: 48866 grad_norm: 4.3916 loss: 3.5307 loss_cls: 3.5307 2023/01/22 17:20:07 - mmengine - INFO - Epoch(train) [24][ 700/1879] lr: 3.5924e-05 eta: 3 days, 14:08:20 time: 2.1527 data_time: 0.0390 memory: 48866 grad_norm: 4.2143 loss: 3.7024 loss_cls: 3.7024 2023/01/22 17:23:06 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 17:23:42 - mmengine - INFO - Epoch(train) [24][ 800/1879] lr: 3.5919e-05 eta: 3 days, 14:04:46 time: 2.1567 data_time: 0.0394 memory: 48866 grad_norm: 4.2037 loss: 3.7042 loss_cls: 3.7042 2023/01/22 17:27:18 - mmengine - INFO - Epoch(train) [24][ 900/1879] lr: 3.5914e-05 eta: 3 days, 14:01:10 time: 2.1536 data_time: 0.0392 memory: 48866 grad_norm: 4.1966 loss: 3.5893 loss_cls: 3.5893 2023/01/22 17:30:52 - mmengine - INFO - Epoch(train) [24][1000/1879] lr: 3.5908e-05 eta: 3 days, 13:57:33 time: 2.1499 data_time: 0.0383 memory: 48866 grad_norm: 4.5890 loss: 3.8263 loss_cls: 3.8263 2023/01/22 17:34:27 - mmengine - INFO - Epoch(train) [24][1100/1879] lr: 3.5903e-05 eta: 3 days, 13:53:57 time: 2.1454 data_time: 0.0393 memory: 48866 grad_norm: 4.3403 loss: 3.7338 loss_cls: 3.7338 2023/01/22 17:38:03 - mmengine - INFO - Epoch(train) [24][1200/1879] lr: 3.5898e-05 eta: 3 days, 13:50:22 time: 2.1497 data_time: 0.0401 memory: 48866 grad_norm: 4.3970 loss: 3.6899 loss_cls: 3.6899 2023/01/22 17:41:38 - mmengine - INFO - Epoch(train) [24][1300/1879] lr: 3.5892e-05 eta: 3 days, 13:46:47 time: 2.1587 data_time: 0.0390 memory: 48866 grad_norm: 4.2819 loss: 3.6234 loss_cls: 3.6234 2023/01/22 17:45:14 - mmengine - INFO - Epoch(train) [24][1400/1879] lr: 3.5887e-05 eta: 3 days, 13:43:11 time: 2.1589 data_time: 0.0403 memory: 48866 grad_norm: 4.2467 loss: 3.5629 loss_cls: 3.5629 2023/01/22 17:48:49 - mmengine - INFO - Epoch(train) [24][1500/1879] lr: 3.5881e-05 eta: 3 days, 13:39:35 time: 2.1558 data_time: 0.0407 memory: 48866 grad_norm: 4.2465 loss: 3.6745 loss_cls: 3.6745 2023/01/22 17:52:24 - mmengine - INFO - Epoch(train) [24][1600/1879] lr: 3.5876e-05 eta: 3 days, 13:35:59 time: 2.1437 data_time: 0.0388 memory: 48866 grad_norm: 4.1527 loss: 3.7931 loss_cls: 3.7931 2023/01/22 17:56:00 - mmengine - INFO - Epoch(train) [24][1700/1879] lr: 3.5870e-05 eta: 3 days, 13:32:24 time: 2.1502 data_time: 0.0393 memory: 48866 grad_norm: 4.2019 loss: 3.7952 loss_cls: 3.7952 2023/01/22 17:58:58 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 17:59:35 - mmengine - INFO - Epoch(train) [24][1800/1879] lr: 3.5864e-05 eta: 3 days, 13:28:49 time: 2.1578 data_time: 0.0390 memory: 48866 grad_norm: 4.3512 loss: 3.8204 loss_cls: 3.8204 2023/01/22 18:02:24 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 18:02:24 - mmengine - INFO - Epoch(train) [24][1879/1879] lr: 3.5860e-05 eta: 3 days, 13:25:56 time: 2.0965 data_time: 0.0412 memory: 48866 grad_norm: 4.4513 loss: 3.7140 loss_cls: 3.7140 2023/01/22 18:02:24 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/01/22 18:03:25 - mmengine - INFO - Epoch(val) [24][100/155] eta: 0:00:30 time: 0.5525 data_time: 0.2193 memory: 4950 2023/01/22 18:03:52 - mmengine - INFO - Epoch(val) [24][155/155] acc/top1: 0.6006 acc/top5: 0.8366 acc/mean1: 0.6005 2023/01/22 18:03:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_15.pth is removed 2023/01/22 18:03:56 - mmengine - INFO - The best checkpoint with 0.6006 acc/top1 at 24 epoch is saved to best_acc/top1_epoch_24.pth. 2023/01/22 18:07:38 - mmengine - INFO - Epoch(train) [25][ 100/1879] lr: 3.5854e-05 eta: 3 days, 13:22:41 time: 2.1419 data_time: 0.0379 memory: 48866 grad_norm: 4.3524 loss: 3.6825 loss_cls: 3.6825 2023/01/22 18:11:13 - mmengine - INFO - Epoch(train) [25][ 200/1879] lr: 3.5848e-05 eta: 3 days, 13:19:05 time: 2.1484 data_time: 0.0387 memory: 48866 grad_norm: 4.5468 loss: 3.5787 loss_cls: 3.5787 2023/01/22 18:14:48 - mmengine - INFO - Epoch(train) [25][ 300/1879] lr: 3.5842e-05 eta: 3 days, 13:15:30 time: 2.1589 data_time: 0.0389 memory: 48866 grad_norm: 4.2320 loss: 3.6848 loss_cls: 3.6848 2023/01/22 18:18:23 - mmengine - INFO - Epoch(train) [25][ 400/1879] lr: 3.5835e-05 eta: 3 days, 13:11:51 time: 2.1564 data_time: 0.0387 memory: 48866 grad_norm: 4.3570 loss: 3.5379 loss_cls: 3.5379 2023/01/22 18:21:58 - mmengine - INFO - Epoch(train) [25][ 500/1879] lr: 3.5829e-05 eta: 3 days, 13:08:16 time: 2.1588 data_time: 0.0391 memory: 48866 grad_norm: 4.5591 loss: 3.6443 loss_cls: 3.6443 2023/01/22 18:25:33 - mmengine - INFO - Epoch(train) [25][ 600/1879] lr: 3.5823e-05 eta: 3 days, 13:04:41 time: 2.1576 data_time: 0.0385 memory: 48866 grad_norm: 4.3065 loss: 3.5771 loss_cls: 3.5771 2023/01/22 18:29:09 - mmengine - INFO - Epoch(train) [25][ 700/1879] lr: 3.5817e-05 eta: 3 days, 13:01:06 time: 2.1565 data_time: 0.0397 memory: 48866 grad_norm: 4.5294 loss: 3.4030 loss_cls: 3.4030 2023/01/22 18:32:45 - mmengine - INFO - Epoch(train) [25][ 800/1879] lr: 3.5810e-05 eta: 3 days, 12:57:32 time: 2.1677 data_time: 0.0386 memory: 48866 grad_norm: 4.4349 loss: 3.7900 loss_cls: 3.7900 2023/01/22 18:36:20 - mmengine - INFO - Epoch(train) [25][ 900/1879] lr: 3.5804e-05 eta: 3 days, 12:53:57 time: 2.1414 data_time: 0.0394 memory: 48866 grad_norm: 4.4505 loss: 3.6933 loss_cls: 3.6933 2023/01/22 18:36:29 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 18:39:56 - mmengine - INFO - Epoch(train) [25][1000/1879] lr: 3.5797e-05 eta: 3 days, 12:50:21 time: 2.1538 data_time: 0.0382 memory: 48866 grad_norm: 4.3922 loss: 3.4253 loss_cls: 3.4253 2023/01/22 18:43:31 - mmengine - INFO - Epoch(train) [25][1100/1879] lr: 3.5790e-05 eta: 3 days, 12:46:45 time: 2.1631 data_time: 0.0404 memory: 48866 grad_norm: 4.3995 loss: 3.5847 loss_cls: 3.5847 2023/01/22 18:47:06 - mmengine - INFO - Epoch(train) [25][1200/1879] lr: 3.5783e-05 eta: 3 days, 12:43:09 time: 2.1551 data_time: 0.0404 memory: 48866 grad_norm: 4.0366 loss: 3.7663 loss_cls: 3.7663 2023/01/22 18:50:41 - mmengine - INFO - Epoch(train) [25][1300/1879] lr: 3.5777e-05 eta: 3 days, 12:39:33 time: 2.1481 data_time: 0.0387 memory: 48866 grad_norm: 4.3633 loss: 3.6960 loss_cls: 3.6960 2023/01/22 18:54:16 - mmengine - INFO - Epoch(train) [25][1400/1879] lr: 3.5770e-05 eta: 3 days, 12:35:57 time: 2.1529 data_time: 0.0394 memory: 48866 grad_norm: 4.3016 loss: 3.7019 loss_cls: 3.7019 2023/01/22 18:57:52 - mmengine - INFO - Epoch(train) [25][1500/1879] lr: 3.5763e-05 eta: 3 days, 12:32:22 time: 2.1446 data_time: 0.0395 memory: 48866 grad_norm: 4.2683 loss: 3.7837 loss_cls: 3.7837 2023/01/22 19:01:27 - mmengine - INFO - Epoch(train) [25][1600/1879] lr: 3.5756e-05 eta: 3 days, 12:28:45 time: 2.1403 data_time: 0.0385 memory: 48866 grad_norm: 4.2717 loss: 3.6121 loss_cls: 3.6121 2023/01/22 19:05:02 - mmengine - INFO - Epoch(train) [25][1700/1879] lr: 3.5748e-05 eta: 3 days, 12:25:10 time: 2.1530 data_time: 0.0393 memory: 48866 grad_norm: 4.3819 loss: 3.6639 loss_cls: 3.6639 2023/01/22 19:08:38 - mmengine - INFO - Epoch(train) [25][1800/1879] lr: 3.5741e-05 eta: 3 days, 12:21:34 time: 2.1573 data_time: 0.0390 memory: 48866 grad_norm: 4.3759 loss: 3.6602 loss_cls: 3.6602 2023/01/22 19:11:27 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 19:11:27 - mmengine - INFO - Epoch(train) [25][1879/1879] lr: 3.5735e-05 eta: 3 days, 12:18:41 time: 2.1028 data_time: 0.0420 memory: 48866 grad_norm: 4.4991 loss: 3.7321 loss_cls: 3.7321 2023/01/22 19:12:21 - mmengine - INFO - Epoch(val) [25][100/155] eta: 0:00:29 time: 0.6023 data_time: 0.2365 memory: 4950 2023/01/22 19:12:51 - mmengine - INFO - Epoch(val) [25][155/155] acc/top1: 0.6049 acc/top5: 0.8402 acc/mean1: 0.6047 2023/01/22 19:12:51 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_24.pth is removed 2023/01/22 19:12:54 - mmengine - INFO - The best checkpoint with 0.6049 acc/top1 at 25 epoch is saved to best_acc/top1_epoch_25.pth. 2023/01/22 19:13:54 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 19:16:36 - mmengine - INFO - Epoch(train) [26][ 100/1879] lr: 3.5728e-05 eta: 3 days, 12:15:23 time: 2.1449 data_time: 0.0397 memory: 48866 grad_norm: 4.2747 loss: 3.7345 loss_cls: 3.7345 2023/01/22 19:20:11 - mmengine - INFO - Epoch(train) [26][ 200/1879] lr: 3.5721e-05 eta: 3 days, 12:11:49 time: 2.1598 data_time: 0.0382 memory: 48866 grad_norm: 4.3996 loss: 3.7752 loss_cls: 3.7752 2023/01/22 19:23:47 - mmengine - INFO - Epoch(train) [26][ 300/1879] lr: 3.5713e-05 eta: 3 days, 12:08:14 time: 2.1548 data_time: 0.0388 memory: 48866 grad_norm: 4.5230 loss: 3.4804 loss_cls: 3.4804 2023/01/22 19:27:22 - mmengine - INFO - Epoch(train) [26][ 400/1879] lr: 3.5705e-05 eta: 3 days, 12:04:37 time: 2.1504 data_time: 0.0383 memory: 48866 grad_norm: 4.3866 loss: 3.7343 loss_cls: 3.7343 2023/01/22 19:30:57 - mmengine - INFO - Epoch(train) [26][ 500/1879] lr: 3.5698e-05 eta: 3 days, 12:01:02 time: 2.1482 data_time: 0.0396 memory: 48866 grad_norm: 4.4025 loss: 3.5138 loss_cls: 3.5138 2023/01/22 19:34:33 - mmengine - INFO - Epoch(train) [26][ 600/1879] lr: 3.5690e-05 eta: 3 days, 11:57:28 time: 2.1730 data_time: 0.0393 memory: 48866 grad_norm: 4.1730 loss: 3.5323 loss_cls: 3.5323 2023/01/22 19:38:09 - mmengine - INFO - Epoch(train) [26][ 700/1879] lr: 3.5682e-05 eta: 3 days, 11:53:53 time: 2.1631 data_time: 0.0393 memory: 48866 grad_norm: 4.3146 loss: 3.6705 loss_cls: 3.6705 2023/01/22 19:41:44 - mmengine - INFO - Epoch(train) [26][ 800/1879] lr: 3.5674e-05 eta: 3 days, 11:50:17 time: 2.1599 data_time: 0.0394 memory: 48866 grad_norm: 4.3145 loss: 3.7191 loss_cls: 3.7191 2023/01/22 19:45:20 - mmengine - INFO - Epoch(train) [26][ 900/1879] lr: 3.5666e-05 eta: 3 days, 11:46:42 time: 2.1470 data_time: 0.0389 memory: 48866 grad_norm: 4.1321 loss: 3.5495 loss_cls: 3.5495 2023/01/22 19:48:56 - mmengine - INFO - Epoch(train) [26][1000/1879] lr: 3.5658e-05 eta: 3 days, 11:43:09 time: 2.1694 data_time: 0.0390 memory: 48866 grad_norm: 4.4723 loss: 3.7211 loss_cls: 3.7211 2023/01/22 19:49:49 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 19:52:31 - mmengine - INFO - Epoch(train) [26][1100/1879] lr: 3.5650e-05 eta: 3 days, 11:39:33 time: 2.1510 data_time: 0.0392 memory: 48866 grad_norm: 4.3465 loss: 3.7032 loss_cls: 3.7032 2023/01/22 19:56:06 - mmengine - INFO - Epoch(train) [26][1200/1879] lr: 3.5642e-05 eta: 3 days, 11:35:57 time: 2.1461 data_time: 0.0388 memory: 48866 grad_norm: 4.2157 loss: 3.7304 loss_cls: 3.7304 2023/01/22 19:59:41 - mmengine - INFO - Epoch(train) [26][1300/1879] lr: 3.5633e-05 eta: 3 days, 11:32:22 time: 2.1590 data_time: 0.0392 memory: 48866 grad_norm: 4.1597 loss: 3.7196 loss_cls: 3.7196 2023/01/22 20:03:16 - mmengine - INFO - Epoch(train) [26][1400/1879] lr: 3.5625e-05 eta: 3 days, 11:28:45 time: 2.1390 data_time: 0.0396 memory: 48866 grad_norm: 4.3324 loss: 3.6015 loss_cls: 3.6015 2023/01/22 20:06:51 - mmengine - INFO - Epoch(train) [26][1500/1879] lr: 3.5617e-05 eta: 3 days, 11:25:08 time: 2.1457 data_time: 0.0395 memory: 48866 grad_norm: 4.4064 loss: 3.6867 loss_cls: 3.6867 2023/01/22 20:10:26 - mmengine - INFO - Epoch(train) [26][1600/1879] lr: 3.5608e-05 eta: 3 days, 11:21:31 time: 2.1564 data_time: 0.0394 memory: 48866 grad_norm: 4.1951 loss: 3.7173 loss_cls: 3.7173 2023/01/22 20:14:01 - mmengine - INFO - Epoch(train) [26][1700/1879] lr: 3.5599e-05 eta: 3 days, 11:17:55 time: 2.1353 data_time: 0.0388 memory: 48866 grad_norm: 4.2442 loss: 3.8486 loss_cls: 3.8486 2023/01/22 20:17:37 - mmengine - INFO - Epoch(train) [26][1800/1879] lr: 3.5591e-05 eta: 3 days, 11:14:20 time: 2.1510 data_time: 0.0390 memory: 48866 grad_norm: 4.4020 loss: 3.7137 loss_cls: 3.7137 2023/01/22 20:20:26 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 20:20:26 - mmengine - INFO - Epoch(train) [26][1879/1879] lr: 3.5584e-05 eta: 3 days, 11:11:27 time: 2.0891 data_time: 0.0413 memory: 48866 grad_norm: 4.5112 loss: 3.5182 loss_cls: 3.5182 2023/01/22 20:21:19 - mmengine - INFO - Epoch(val) [26][100/155] eta: 0:00:29 time: 0.5574 data_time: 0.2070 memory: 4950 2023/01/22 20:21:50 - mmengine - INFO - Epoch(val) [26][155/155] acc/top1: 0.6073 acc/top5: 0.8430 acc/mean1: 0.6074 2023/01/22 20:21:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_25.pth is removed 2023/01/22 20:21:54 - mmengine - INFO - The best checkpoint with 0.6073 acc/top1 at 26 epoch is saved to best_acc/top1_epoch_26.pth. 2023/01/22 20:25:36 - mmengine - INFO - Epoch(train) [27][ 100/1879] lr: 3.5575e-05 eta: 3 days, 11:08:12 time: 2.1483 data_time: 0.0384 memory: 48866 grad_norm: 4.3413 loss: 3.6984 loss_cls: 3.6984 2023/01/22 20:27:15 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 20:29:11 - mmengine - INFO - Epoch(train) [27][ 200/1879] lr: 3.5566e-05 eta: 3 days, 11:04:35 time: 2.1373 data_time: 0.0381 memory: 48866 grad_norm: 4.1930 loss: 3.6045 loss_cls: 3.6045 2023/01/22 20:32:46 - mmengine - INFO - Epoch(train) [27][ 300/1879] lr: 3.5557e-05 eta: 3 days, 11:00:59 time: 2.1514 data_time: 0.0391 memory: 48866 grad_norm: 4.2670 loss: 3.6310 loss_cls: 3.6310 2023/01/22 20:36:21 - mmengine - INFO - Epoch(train) [27][ 400/1879] lr: 3.5548e-05 eta: 3 days, 10:57:22 time: 2.1596 data_time: 0.0385 memory: 48866 grad_norm: 4.1770 loss: 3.7316 loss_cls: 3.7316 2023/01/22 20:39:57 - mmengine - INFO - Epoch(train) [27][ 500/1879] lr: 3.5539e-05 eta: 3 days, 10:53:46 time: 2.1502 data_time: 0.0375 memory: 48866 grad_norm: 4.3812 loss: 3.6044 loss_cls: 3.6044 2023/01/22 20:43:31 - mmengine - INFO - Epoch(train) [27][ 600/1879] lr: 3.5530e-05 eta: 3 days, 10:50:10 time: 2.1527 data_time: 0.0389 memory: 48866 grad_norm: 4.4215 loss: 3.6152 loss_cls: 3.6152 2023/01/22 20:47:06 - mmengine - INFO - Epoch(train) [27][ 700/1879] lr: 3.5520e-05 eta: 3 days, 10:46:32 time: 2.1365 data_time: 0.0396 memory: 48866 grad_norm: 4.4801 loss: 3.7077 loss_cls: 3.7077 2023/01/22 20:50:41 - mmengine - INFO - Epoch(train) [27][ 800/1879] lr: 3.5511e-05 eta: 3 days, 10:42:56 time: 2.1591 data_time: 0.0378 memory: 48866 grad_norm: 4.4056 loss: 3.6089 loss_cls: 3.6089 2023/01/22 20:54:16 - mmengine - INFO - Epoch(train) [27][ 900/1879] lr: 3.5502e-05 eta: 3 days, 10:39:19 time: 2.1386 data_time: 0.0385 memory: 48866 grad_norm: 4.1481 loss: 3.8098 loss_cls: 3.8098 2023/01/22 20:57:51 - mmengine - INFO - Epoch(train) [27][1000/1879] lr: 3.5492e-05 eta: 3 days, 10:35:43 time: 2.1565 data_time: 0.0391 memory: 48866 grad_norm: 4.2984 loss: 3.7114 loss_cls: 3.7114 2023/01/22 21:01:27 - mmengine - INFO - Epoch(train) [27][1100/1879] lr: 3.5483e-05 eta: 3 days, 10:32:07 time: 2.1602 data_time: 0.0398 memory: 48866 grad_norm: 4.2948 loss: 3.7998 loss_cls: 3.7998 2023/01/22 21:03:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 21:05:02 - mmengine - INFO - Epoch(train) [27][1200/1879] lr: 3.5473e-05 eta: 3 days, 10:28:31 time: 2.1523 data_time: 0.0389 memory: 48866 grad_norm: 4.3269 loss: 3.7485 loss_cls: 3.7485 2023/01/22 21:08:36 - mmengine - INFO - Epoch(train) [27][1300/1879] lr: 3.5463e-05 eta: 3 days, 10:24:54 time: 2.1365 data_time: 0.0396 memory: 48866 grad_norm: 4.2694 loss: 3.5586 loss_cls: 3.5586 2023/01/22 21:12:11 - mmengine - INFO - Epoch(train) [27][1400/1879] lr: 3.5453e-05 eta: 3 days, 10:21:18 time: 2.1594 data_time: 0.0377 memory: 48866 grad_norm: 4.3586 loss: 3.5645 loss_cls: 3.5645 2023/01/22 21:15:46 - mmengine - INFO - Epoch(train) [27][1500/1879] lr: 3.5443e-05 eta: 3 days, 10:17:40 time: 2.1498 data_time: 0.0399 memory: 48866 grad_norm: 4.2366 loss: 3.6705 loss_cls: 3.6705 2023/01/22 21:19:21 - mmengine - INFO - Epoch(train) [27][1600/1879] lr: 3.5433e-05 eta: 3 days, 10:14:03 time: 2.1427 data_time: 0.0387 memory: 48866 grad_norm: 4.2240 loss: 3.7291 loss_cls: 3.7291 2023/01/22 21:22:56 - mmengine - INFO - Epoch(train) [27][1700/1879] lr: 3.5423e-05 eta: 3 days, 10:10:27 time: 2.1489 data_time: 0.0391 memory: 48866 grad_norm: 4.3583 loss: 3.7544 loss_cls: 3.7544 2023/01/22 21:26:31 - mmengine - INFO - Epoch(train) [27][1800/1879] lr: 3.5413e-05 eta: 3 days, 10:06:52 time: 2.1589 data_time: 0.0389 memory: 48866 grad_norm: 4.3033 loss: 3.6824 loss_cls: 3.6824 2023/01/22 21:29:20 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 21:29:20 - mmengine - INFO - Epoch(train) [27][1879/1879] lr: 3.5405e-05 eta: 3 days, 10:03:58 time: 2.0898 data_time: 0.0412 memory: 48866 grad_norm: 4.3198 loss: 3.7550 loss_cls: 3.7550 2023/01/22 21:29:20 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/01/22 21:30:21 - mmengine - INFO - Epoch(val) [27][100/155] eta: 0:00:30 time: 0.5875 data_time: 0.2532 memory: 4950 2023/01/22 21:30:49 - mmengine - INFO - Epoch(val) [27][155/155] acc/top1: 0.6097 acc/top5: 0.8485 acc/mean1: 0.6097 2023/01/22 21:30:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_26.pth is removed 2023/01/22 21:30:53 - mmengine - INFO - The best checkpoint with 0.6097 acc/top1 at 27 epoch is saved to best_acc/top1_epoch_27.pth. 2023/01/22 21:34:34 - mmengine - INFO - Epoch(train) [28][ 100/1879] lr: 3.5395e-05 eta: 3 days, 10:00:38 time: 2.1449 data_time: 0.0381 memory: 48866 grad_norm: 4.3433 loss: 3.5364 loss_cls: 3.5364 2023/01/22 21:38:09 - mmengine - INFO - Epoch(train) [28][ 200/1879] lr: 3.5385e-05 eta: 3 days, 9:57:02 time: 2.1499 data_time: 0.0395 memory: 48866 grad_norm: 4.2263 loss: 3.7194 loss_cls: 3.7194 2023/01/22 21:40:33 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 21:41:44 - mmengine - INFO - Epoch(train) [28][ 300/1879] lr: 3.5374e-05 eta: 3 days, 9:53:26 time: 2.1476 data_time: 0.0398 memory: 48866 grad_norm: 4.2673 loss: 3.6324 loss_cls: 3.6324 2023/01/22 21:45:19 - mmengine - INFO - Epoch(train) [28][ 400/1879] lr: 3.5364e-05 eta: 3 days, 9:49:51 time: 2.1473 data_time: 0.0389 memory: 48866 grad_norm: 4.1737 loss: 3.8266 loss_cls: 3.8266 2023/01/22 21:48:55 - mmengine - INFO - Epoch(train) [28][ 500/1879] lr: 3.5353e-05 eta: 3 days, 9:46:17 time: 2.1727 data_time: 0.0402 memory: 48866 grad_norm: 4.3999 loss: 3.5792 loss_cls: 3.5792 2023/01/22 21:52:30 - mmengine - INFO - Epoch(train) [28][ 600/1879] lr: 3.5343e-05 eta: 3 days, 9:42:41 time: 2.1620 data_time: 0.0401 memory: 48866 grad_norm: 4.4442 loss: 3.7177 loss_cls: 3.7177 2023/01/22 21:56:06 - mmengine - INFO - Epoch(train) [28][ 700/1879] lr: 3.5332e-05 eta: 3 days, 9:39:04 time: 2.1601 data_time: 0.0400 memory: 48866 grad_norm: 4.3373 loss: 3.5933 loss_cls: 3.5933 2023/01/22 21:59:41 - mmengine - INFO - Epoch(train) [28][ 800/1879] lr: 3.5321e-05 eta: 3 days, 9:35:30 time: 2.1559 data_time: 0.0398 memory: 48866 grad_norm: 4.2714 loss: 3.6517 loss_cls: 3.6517 2023/01/22 22:03:17 - mmengine - INFO - Epoch(train) [28][ 900/1879] lr: 3.5310e-05 eta: 3 days, 9:31:55 time: 2.1616 data_time: 0.0395 memory: 48866 grad_norm: 4.3137 loss: 3.6304 loss_cls: 3.6304 2023/01/22 22:06:52 - mmengine - INFO - Epoch(train) [28][1000/1879] lr: 3.5299e-05 eta: 3 days, 9:28:20 time: 2.1478 data_time: 0.0393 memory: 48866 grad_norm: 4.1386 loss: 3.5884 loss_cls: 3.5884 2023/01/22 22:10:28 - mmengine - INFO - Epoch(train) [28][1100/1879] lr: 3.5288e-05 eta: 3 days, 9:24:45 time: 2.1541 data_time: 0.0394 memory: 48866 grad_norm: 4.3178 loss: 3.4611 loss_cls: 3.4611 2023/01/22 22:14:05 - mmengine - INFO - Epoch(train) [28][1200/1879] lr: 3.5277e-05 eta: 3 days, 9:21:13 time: 2.1655 data_time: 0.0384 memory: 48866 grad_norm: 4.5910 loss: 3.5311 loss_cls: 3.5311 2023/01/22 22:16:29 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 22:17:40 - mmengine - INFO - Epoch(train) [28][1300/1879] lr: 3.5266e-05 eta: 3 days, 9:17:37 time: 2.1507 data_time: 0.0393 memory: 48866 grad_norm: 4.3276 loss: 3.5501 loss_cls: 3.5501 2023/01/22 22:21:15 - mmengine - INFO - Epoch(train) [28][1400/1879] lr: 3.5255e-05 eta: 3 days, 9:14:01 time: 2.1565 data_time: 0.0382 memory: 48866 grad_norm: 4.4182 loss: 3.5736 loss_cls: 3.5736 2023/01/22 22:24:50 - mmengine - INFO - Epoch(train) [28][1500/1879] lr: 3.5244e-05 eta: 3 days, 9:10:25 time: 2.1514 data_time: 0.0384 memory: 48866 grad_norm: 4.4282 loss: 3.6132 loss_cls: 3.6132 2023/01/22 22:28:25 - mmengine - INFO - Epoch(train) [28][1600/1879] lr: 3.5232e-05 eta: 3 days, 9:06:48 time: 2.1548 data_time: 0.0388 memory: 48866 grad_norm: 4.2755 loss: 3.3929 loss_cls: 3.3929 2023/01/22 22:32:01 - mmengine - INFO - Epoch(train) [28][1700/1879] lr: 3.5221e-05 eta: 3 days, 9:03:13 time: 2.1480 data_time: 0.0389 memory: 48866 grad_norm: 4.1588 loss: 3.5265 loss_cls: 3.5265 2023/01/22 22:35:36 - mmengine - INFO - Epoch(train) [28][1800/1879] lr: 3.5209e-05 eta: 3 days, 8:59:38 time: 2.1514 data_time: 0.0394 memory: 48866 grad_norm: 4.3493 loss: 3.4254 loss_cls: 3.4254 2023/01/22 22:38:25 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 22:38:25 - mmengine - INFO - Epoch(train) [28][1879/1879] lr: 3.5200e-05 eta: 3 days, 8:56:44 time: 2.0786 data_time: 0.0388 memory: 48866 grad_norm: 4.4562 loss: 3.5689 loss_cls: 3.5689 2023/01/22 22:39:18 - mmengine - INFO - Epoch(val) [28][100/155] eta: 0:00:29 time: 0.5519 data_time: 0.2230 memory: 4950 2023/01/22 22:39:49 - mmengine - INFO - Epoch(val) [28][155/155] acc/top1: 0.6125 acc/top5: 0.8455 acc/mean1: 0.6125 2023/01/22 22:39:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_27.pth is removed 2023/01/22 22:39:52 - mmengine - INFO - The best checkpoint with 0.6125 acc/top1 at 28 epoch is saved to best_acc/top1_epoch_28.pth. 2023/01/22 22:43:33 - mmengine - INFO - Epoch(train) [29][ 100/1879] lr: 3.5188e-05 eta: 3 days, 8:53:23 time: 2.1472 data_time: 0.0381 memory: 48866 grad_norm: 4.6607 loss: 3.4381 loss_cls: 3.4381 2023/01/22 22:47:09 - mmengine - INFO - Epoch(train) [29][ 200/1879] lr: 3.5177e-05 eta: 3 days, 8:49:48 time: 2.1512 data_time: 0.0388 memory: 48866 grad_norm: 4.2791 loss: 3.6956 loss_cls: 3.6956 2023/01/22 22:50:44 - mmengine - INFO - Epoch(train) [29][ 300/1879] lr: 3.5165e-05 eta: 3 days, 8:46:11 time: 2.1429 data_time: 0.0387 memory: 48866 grad_norm: 4.6204 loss: 3.6889 loss_cls: 3.6889 2023/01/22 22:53:53 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 22:54:19 - mmengine - INFO - Epoch(train) [29][ 400/1879] lr: 3.5153e-05 eta: 3 days, 8:42:36 time: 2.1574 data_time: 0.0389 memory: 48866 grad_norm: 4.2658 loss: 3.5911 loss_cls: 3.5911 2023/01/22 22:57:55 - mmengine - INFO - Epoch(train) [29][ 500/1879] lr: 3.5141e-05 eta: 3 days, 8:39:01 time: 2.1567 data_time: 0.0392 memory: 48866 grad_norm: 4.2023 loss: 3.4600 loss_cls: 3.4600 2023/01/22 23:01:30 - mmengine - INFO - Epoch(train) [29][ 600/1879] lr: 3.5129e-05 eta: 3 days, 8:35:25 time: 2.1336 data_time: 0.0391 memory: 48866 grad_norm: 4.2336 loss: 3.7076 loss_cls: 3.7076 2023/01/22 23:05:05 - mmengine - INFO - Epoch(train) [29][ 700/1879] lr: 3.5117e-05 eta: 3 days, 8:31:49 time: 2.1487 data_time: 0.0389 memory: 48866 grad_norm: 4.3158 loss: 3.5195 loss_cls: 3.5195 2023/01/22 23:08:41 - mmengine - INFO - Epoch(train) [29][ 800/1879] lr: 3.5105e-05 eta: 3 days, 8:28:14 time: 2.1537 data_time: 0.0396 memory: 48866 grad_norm: 4.3176 loss: 3.6182 loss_cls: 3.6182 2023/01/22 23:12:16 - mmengine - INFO - Epoch(train) [29][ 900/1879] lr: 3.5092e-05 eta: 3 days, 8:24:39 time: 2.1519 data_time: 0.0406 memory: 48866 grad_norm: 4.3138 loss: 3.8009 loss_cls: 3.8009 2023/01/22 23:15:52 - mmengine - INFO - Epoch(train) [29][1000/1879] lr: 3.5080e-05 eta: 3 days, 8:21:03 time: 2.1588 data_time: 0.0380 memory: 48866 grad_norm: 4.2080 loss: 3.5214 loss_cls: 3.5214 2023/01/22 23:19:26 - mmengine - INFO - Epoch(train) [29][1100/1879] lr: 3.5068e-05 eta: 3 days, 8:17:26 time: 2.1382 data_time: 0.0396 memory: 48866 grad_norm: 4.4004 loss: 3.4559 loss_cls: 3.4559 2023/01/22 23:23:02 - mmengine - INFO - Epoch(train) [29][1200/1879] lr: 3.5055e-05 eta: 3 days, 8:13:52 time: 2.1455 data_time: 0.0393 memory: 48866 grad_norm: 4.3442 loss: 3.5146 loss_cls: 3.5146 2023/01/22 23:26:37 - mmengine - INFO - Epoch(train) [29][1300/1879] lr: 3.5042e-05 eta: 3 days, 8:10:16 time: 2.1429 data_time: 0.0377 memory: 48866 grad_norm: 4.3643 loss: 3.6428 loss_cls: 3.6428 2023/01/22 23:29:47 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 23:30:13 - mmengine - INFO - Epoch(train) [29][1400/1879] lr: 3.5030e-05 eta: 3 days, 8:06:40 time: 2.1463 data_time: 0.0396 memory: 48866 grad_norm: 4.3664 loss: 3.4790 loss_cls: 3.4790 2023/01/22 23:33:48 - mmengine - INFO - Epoch(train) [29][1500/1879] lr: 3.5017e-05 eta: 3 days, 8:03:05 time: 2.1527 data_time: 0.0385 memory: 48866 grad_norm: 4.3843 loss: 3.5669 loss_cls: 3.5669 2023/01/22 23:37:23 - mmengine - INFO - Epoch(train) [29][1600/1879] lr: 3.5004e-05 eta: 3 days, 7:59:29 time: 2.1454 data_time: 0.0388 memory: 48866 grad_norm: 4.3405 loss: 3.3946 loss_cls: 3.3946 2023/01/22 23:40:59 - mmengine - INFO - Epoch(train) [29][1700/1879] lr: 3.4991e-05 eta: 3 days, 7:55:53 time: 2.1417 data_time: 0.0382 memory: 48866 grad_norm: 4.2079 loss: 3.7351 loss_cls: 3.7351 2023/01/22 23:44:34 - mmengine - INFO - Epoch(train) [29][1800/1879] lr: 3.4979e-05 eta: 3 days, 7:52:19 time: 2.1554 data_time: 0.0390 memory: 48866 grad_norm: 4.3990 loss: 3.5534 loss_cls: 3.5534 2023/01/22 23:47:23 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/22 23:47:23 - mmengine - INFO - Epoch(train) [29][1879/1879] lr: 3.4968e-05 eta: 3 days, 7:49:25 time: 2.0889 data_time: 0.0394 memory: 48866 grad_norm: 4.3241 loss: 3.6117 loss_cls: 3.6117 2023/01/22 23:48:18 - mmengine - INFO - Epoch(val) [29][100/155] eta: 0:00:30 time: 0.5890 data_time: 0.2337 memory: 4950 2023/01/22 23:48:48 - mmengine - INFO - Epoch(val) [29][155/155] acc/top1: 0.6082 acc/top5: 0.8444 acc/mean1: 0.6081 2023/01/22 23:52:31 - mmengine - INFO - Epoch(train) [30][ 100/1879] lr: 3.4955e-05 eta: 3 days, 7:46:08 time: 2.1643 data_time: 0.0386 memory: 48866 grad_norm: 4.0611 loss: 3.6347 loss_cls: 3.6347 2023/01/22 23:56:06 - mmengine - INFO - Epoch(train) [30][ 200/1879] lr: 3.4942e-05 eta: 3 days, 7:42:33 time: 2.1572 data_time: 0.0387 memory: 48866 grad_norm: 4.5749 loss: 3.6929 loss_cls: 3.6929 2023/01/22 23:59:41 - mmengine - INFO - Epoch(train) [30][ 300/1879] lr: 3.4929e-05 eta: 3 days, 7:38:57 time: 2.1459 data_time: 0.0391 memory: 48866 grad_norm: 4.5591 loss: 3.5243 loss_cls: 3.5243 2023/01/23 00:03:17 - mmengine - INFO - Epoch(train) [30][ 400/1879] lr: 3.4916e-05 eta: 3 days, 7:35:22 time: 2.1636 data_time: 0.0397 memory: 48866 grad_norm: 4.2538 loss: 3.7236 loss_cls: 3.7236 2023/01/23 00:06:52 - mmengine - INFO - Epoch(train) [30][ 500/1879] lr: 3.4902e-05 eta: 3 days, 7:31:46 time: 2.1517 data_time: 0.0393 memory: 48866 grad_norm: 4.1806 loss: 3.6013 loss_cls: 3.6013 2023/01/23 00:07:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 00:10:27 - mmengine - INFO - Epoch(train) [30][ 600/1879] lr: 3.4889e-05 eta: 3 days, 7:28:10 time: 2.1534 data_time: 0.0389 memory: 48866 grad_norm: 4.4208 loss: 3.5861 loss_cls: 3.5861 2023/01/23 00:14:03 - mmengine - INFO - Epoch(train) [30][ 700/1879] lr: 3.4875e-05 eta: 3 days, 7:24:35 time: 2.1497 data_time: 0.0389 memory: 48866 grad_norm: 4.2865 loss: 3.5112 loss_cls: 3.5112 2023/01/23 00:17:38 - mmengine - INFO - Epoch(train) [30][ 800/1879] lr: 3.4862e-05 eta: 3 days, 7:21:00 time: 2.1682 data_time: 0.0397 memory: 48866 grad_norm: 4.3049 loss: 3.6031 loss_cls: 3.6031 2023/01/23 00:21:13 - mmengine - INFO - Epoch(train) [30][ 900/1879] lr: 3.4848e-05 eta: 3 days, 7:17:23 time: 2.1576 data_time: 0.0389 memory: 48866 grad_norm: 4.5021 loss: 3.4717 loss_cls: 3.4717 2023/01/23 00:24:49 - mmengine - INFO - Epoch(train) [30][1000/1879] lr: 3.4834e-05 eta: 3 days, 7:13:48 time: 2.1497 data_time: 0.0379 memory: 48866 grad_norm: 4.4771 loss: 3.2757 loss_cls: 3.2757 2023/01/23 00:28:24 - mmengine - INFO - Epoch(train) [30][1100/1879] lr: 3.4821e-05 eta: 3 days, 7:10:13 time: 2.1654 data_time: 0.0387 memory: 48866 grad_norm: 4.3939 loss: 3.5414 loss_cls: 3.5414 2023/01/23 00:32:00 - mmengine - INFO - Epoch(train) [30][1200/1879] lr: 3.4807e-05 eta: 3 days, 7:06:37 time: 2.1582 data_time: 0.0384 memory: 48866 grad_norm: 4.2937 loss: 3.5430 loss_cls: 3.5430 2023/01/23 00:35:35 - mmengine - INFO - Epoch(train) [30][1300/1879] lr: 3.4793e-05 eta: 3 days, 7:03:02 time: 2.1449 data_time: 0.0383 memory: 48866 grad_norm: 4.4417 loss: 3.6028 loss_cls: 3.6028 2023/01/23 00:39:10 - mmengine - INFO - Epoch(train) [30][1400/1879] lr: 3.4779e-05 eta: 3 days, 6:59:25 time: 2.1537 data_time: 0.0393 memory: 48866 grad_norm: 4.4697 loss: 3.3072 loss_cls: 3.3072 2023/01/23 00:42:46 - mmengine - INFO - Epoch(train) [30][1500/1879] lr: 3.4765e-05 eta: 3 days, 6:55:50 time: 2.1565 data_time: 0.0389 memory: 48866 grad_norm: 4.2190 loss: 3.6973 loss_cls: 3.6973 2023/01/23 00:43:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 00:46:21 - mmengine - INFO - Epoch(train) [30][1600/1879] lr: 3.4750e-05 eta: 3 days, 6:52:15 time: 2.1446 data_time: 0.0388 memory: 48866 grad_norm: 4.3198 loss: 3.4657 loss_cls: 3.4657 2023/01/23 00:49:56 - mmengine - INFO - Epoch(train) [30][1700/1879] lr: 3.4736e-05 eta: 3 days, 6:48:38 time: 2.1549 data_time: 0.0394 memory: 48866 grad_norm: 4.5635 loss: 3.4572 loss_cls: 3.4572 2023/01/23 00:53:32 - mmengine - INFO - Epoch(train) [30][1800/1879] lr: 3.4722e-05 eta: 3 days, 6:45:03 time: 2.1500 data_time: 0.0380 memory: 48866 grad_norm: 4.3523 loss: 3.7429 loss_cls: 3.7429 2023/01/23 00:56:21 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 00:56:21 - mmengine - INFO - Epoch(train) [30][1879/1879] lr: 3.4711e-05 eta: 3 days, 6:42:12 time: 2.1087 data_time: 0.0401 memory: 48866 grad_norm: 4.3199 loss: 3.5127 loss_cls: 3.5127 2023/01/23 00:56:21 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/01/23 00:57:20 - mmengine - INFO - Epoch(val) [30][100/155] eta: 0:00:30 time: 0.5476 data_time: 0.1928 memory: 4950 2023/01/23 00:57:49 - mmengine - INFO - Epoch(val) [30][155/155] acc/top1: 0.6234 acc/top5: 0.8534 acc/mean1: 0.6232 2023/01/23 00:57:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_28.pth is removed 2023/01/23 00:57:53 - mmengine - INFO - The best checkpoint with 0.6234 acc/top1 at 30 epoch is saved to best_acc/top1_epoch_30.pth. 2023/01/23 01:01:35 - mmengine - INFO - Epoch(train) [31][ 100/1879] lr: 3.4696e-05 eta: 3 days, 6:38:52 time: 2.1419 data_time: 0.0388 memory: 48866 grad_norm: 4.1009 loss: 3.5628 loss_cls: 3.5628 2023/01/23 01:05:10 - mmengine - INFO - Epoch(train) [31][ 200/1879] lr: 3.4682e-05 eta: 3 days, 6:35:15 time: 2.1541 data_time: 0.0387 memory: 48866 grad_norm: 4.1611 loss: 3.4339 loss_cls: 3.4339 2023/01/23 01:08:45 - mmengine - INFO - Epoch(train) [31][ 300/1879] lr: 3.4667e-05 eta: 3 days, 6:31:39 time: 2.1630 data_time: 0.0385 memory: 48866 grad_norm: 4.4302 loss: 3.5218 loss_cls: 3.5218 2023/01/23 01:12:20 - mmengine - INFO - Epoch(train) [31][ 400/1879] lr: 3.4652e-05 eta: 3 days, 6:28:03 time: 2.1357 data_time: 0.0388 memory: 48866 grad_norm: 4.5899 loss: 3.5665 loss_cls: 3.5665 2023/01/23 01:15:56 - mmengine - INFO - Epoch(train) [31][ 500/1879] lr: 3.4638e-05 eta: 3 days, 6:24:28 time: 2.1632 data_time: 0.0394 memory: 48866 grad_norm: 4.1609 loss: 3.6369 loss_cls: 3.6369 2023/01/23 01:19:30 - mmengine - INFO - Epoch(train) [31][ 600/1879] lr: 3.4623e-05 eta: 3 days, 6:20:51 time: 2.1467 data_time: 0.0388 memory: 48866 grad_norm: 4.1491 loss: 3.7411 loss_cls: 3.7411 2023/01/23 01:20:35 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 01:23:05 - mmengine - INFO - Epoch(train) [31][ 700/1879] lr: 3.4608e-05 eta: 3 days, 6:17:15 time: 2.1562 data_time: 0.0386 memory: 48866 grad_norm: 4.3272 loss: 3.5445 loss_cls: 3.5445 2023/01/23 01:26:41 - mmengine - INFO - Epoch(train) [31][ 800/1879] lr: 3.4593e-05 eta: 3 days, 6:13:39 time: 2.1512 data_time: 0.0385 memory: 48866 grad_norm: 4.3005 loss: 3.5948 loss_cls: 3.5948 2023/01/23 01:30:16 - mmengine - INFO - Epoch(train) [31][ 900/1879] lr: 3.4578e-05 eta: 3 days, 6:10:04 time: 2.1576 data_time: 0.0386 memory: 48866 grad_norm: 4.1604 loss: 3.8262 loss_cls: 3.8262 2023/01/23 01:33:51 - mmengine - INFO - Epoch(train) [31][1000/1879] lr: 3.4563e-05 eta: 3 days, 6:06:28 time: 2.1411 data_time: 0.0384 memory: 48866 grad_norm: 4.4094 loss: 3.4824 loss_cls: 3.4824 2023/01/23 01:37:26 - mmengine - INFO - Epoch(train) [31][1100/1879] lr: 3.4548e-05 eta: 3 days, 6:02:51 time: 2.1493 data_time: 0.0393 memory: 48866 grad_norm: 4.4987 loss: 3.5036 loss_cls: 3.5036 2023/01/23 01:41:01 - mmengine - INFO - Epoch(train) [31][1200/1879] lr: 3.4532e-05 eta: 3 days, 5:59:14 time: 2.1454 data_time: 0.0387 memory: 48866 grad_norm: 4.1747 loss: 3.6012 loss_cls: 3.6012 2023/01/23 01:44:36 - mmengine - INFO - Epoch(train) [31][1300/1879] lr: 3.4517e-05 eta: 3 days, 5:55:38 time: 2.1550 data_time: 0.0399 memory: 48866 grad_norm: 4.3731 loss: 3.3099 loss_cls: 3.3099 2023/01/23 01:48:11 - mmengine - INFO - Epoch(train) [31][1400/1879] lr: 3.4502e-05 eta: 3 days, 5:52:02 time: 2.1638 data_time: 0.0396 memory: 48866 grad_norm: 4.3253 loss: 3.4437 loss_cls: 3.4437 2023/01/23 01:51:46 - mmengine - INFO - Epoch(train) [31][1500/1879] lr: 3.4486e-05 eta: 3 days, 5:48:25 time: 2.1504 data_time: 0.0389 memory: 48866 grad_norm: 4.4150 loss: 3.5915 loss_cls: 3.5915 2023/01/23 01:55:22 - mmengine - INFO - Epoch(train) [31][1600/1879] lr: 3.4471e-05 eta: 3 days, 5:44:50 time: 2.1500 data_time: 0.0388 memory: 48866 grad_norm: 4.2364 loss: 3.5989 loss_cls: 3.5989 2023/01/23 01:56:26 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 01:58:57 - mmengine - INFO - Epoch(train) [31][1700/1879] lr: 3.4455e-05 eta: 3 days, 5:41:15 time: 2.1487 data_time: 0.0391 memory: 48866 grad_norm: 4.5129 loss: 3.8025 loss_cls: 3.8025 2023/01/23 02:02:33 - mmengine - INFO - Epoch(train) [31][1800/1879] lr: 3.4440e-05 eta: 3 days, 5:37:40 time: 2.1488 data_time: 0.0403 memory: 48866 grad_norm: 4.2683 loss: 3.5558 loss_cls: 3.5558 2023/01/23 02:05:22 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 02:05:22 - mmengine - INFO - Epoch(train) [31][1879/1879] lr: 3.4427e-05 eta: 3 days, 5:34:48 time: 2.1058 data_time: 0.0401 memory: 48866 grad_norm: 4.5165 loss: 3.7428 loss_cls: 3.7428 2023/01/23 02:06:16 - mmengine - INFO - Epoch(val) [31][100/155] eta: 0:00:29 time: 0.5518 data_time: 0.2100 memory: 4950 2023/01/23 02:06:46 - mmengine - INFO - Epoch(val) [31][155/155] acc/top1: 0.6221 acc/top5: 0.8487 acc/mean1: 0.6220 2023/01/23 02:10:30 - mmengine - INFO - Epoch(train) [32][ 100/1879] lr: 3.4411e-05 eta: 3 days, 5:31:30 time: 2.1557 data_time: 0.0388 memory: 48866 grad_norm: 4.2704 loss: 3.5727 loss_cls: 3.5727 2023/01/23 02:14:05 - mmengine - INFO - Epoch(train) [32][ 200/1879] lr: 3.4395e-05 eta: 3 days, 5:27:54 time: 2.1467 data_time: 0.0381 memory: 48866 grad_norm: 4.3500 loss: 3.3870 loss_cls: 3.3870 2023/01/23 02:17:40 - mmengine - INFO - Epoch(train) [32][ 300/1879] lr: 3.4380e-05 eta: 3 days, 5:24:19 time: 2.1414 data_time: 0.0388 memory: 48866 grad_norm: 4.3211 loss: 3.5927 loss_cls: 3.5927 2023/01/23 02:21:16 - mmengine - INFO - Epoch(train) [32][ 400/1879] lr: 3.4364e-05 eta: 3 days, 5:20:44 time: 2.1479 data_time: 0.0379 memory: 48866 grad_norm: 4.2505 loss: 3.6157 loss_cls: 3.6157 2023/01/23 02:24:51 - mmengine - INFO - Epoch(train) [32][ 500/1879] lr: 3.4347e-05 eta: 3 days, 5:17:07 time: 2.1529 data_time: 0.0390 memory: 48866 grad_norm: 4.4059 loss: 3.4535 loss_cls: 3.4535 2023/01/23 02:28:26 - mmengine - INFO - Epoch(train) [32][ 600/1879] lr: 3.4331e-05 eta: 3 days, 5:13:30 time: 2.1483 data_time: 0.0384 memory: 48866 grad_norm: 4.6281 loss: 3.1816 loss_cls: 3.1816 2023/01/23 02:32:01 - mmengine - INFO - Epoch(train) [32][ 700/1879] lr: 3.4315e-05 eta: 3 days, 5:09:55 time: 2.1467 data_time: 0.0382 memory: 48866 grad_norm: 4.4209 loss: 3.5820 loss_cls: 3.5820 2023/01/23 02:33:51 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 02:35:37 - mmengine - INFO - Epoch(train) [32][ 800/1879] lr: 3.4299e-05 eta: 3 days, 5:06:20 time: 2.1515 data_time: 0.0389 memory: 48866 grad_norm: 4.4397 loss: 3.3178 loss_cls: 3.3178 2023/01/23 02:39:12 - mmengine - INFO - Epoch(train) [32][ 900/1879] lr: 3.4282e-05 eta: 3 days, 5:02:44 time: 2.1443 data_time: 0.0389 memory: 48866 grad_norm: 4.4327 loss: 3.5096 loss_cls: 3.5096 2023/01/23 02:42:48 - mmengine - INFO - Epoch(train) [32][1000/1879] lr: 3.4266e-05 eta: 3 days, 4:59:10 time: 2.1525 data_time: 0.0390 memory: 48866 grad_norm: 4.3356 loss: 3.4631 loss_cls: 3.4631 2023/01/23 02:46:23 - mmengine - INFO - Epoch(train) [32][1100/1879] lr: 3.4249e-05 eta: 3 days, 4:55:33 time: 2.1348 data_time: 0.0389 memory: 48866 grad_norm: 4.2671 loss: 3.5050 loss_cls: 3.5050 2023/01/23 02:49:58 - mmengine - INFO - Epoch(train) [32][1200/1879] lr: 3.4233e-05 eta: 3 days, 4:51:58 time: 2.1568 data_time: 0.0384 memory: 48866 grad_norm: 4.4292 loss: 3.3867 loss_cls: 3.3867 2023/01/23 02:53:34 - mmengine - INFO - Epoch(train) [32][1300/1879] lr: 3.4216e-05 eta: 3 days, 4:48:22 time: 2.1494 data_time: 0.0396 memory: 48866 grad_norm: 4.5806 loss: 3.7308 loss_cls: 3.7308 2023/01/23 02:57:09 - mmengine - INFO - Epoch(train) [32][1400/1879] lr: 3.4200e-05 eta: 3 days, 4:44:46 time: 2.1414 data_time: 0.0390 memory: 48866 grad_norm: 4.0720 loss: 3.5256 loss_cls: 3.5256 2023/01/23 03:00:44 - mmengine - INFO - Epoch(train) [32][1500/1879] lr: 3.4183e-05 eta: 3 days, 4:41:11 time: 2.1448 data_time: 0.0395 memory: 48866 grad_norm: 4.2791 loss: 3.4885 loss_cls: 3.4885 2023/01/23 03:04:19 - mmengine - INFO - Epoch(train) [32][1600/1879] lr: 3.4166e-05 eta: 3 days, 4:37:35 time: 2.1488 data_time: 0.0391 memory: 48866 grad_norm: 4.4810 loss: 3.4510 loss_cls: 3.4510 2023/01/23 03:07:55 - mmengine - INFO - Epoch(train) [32][1700/1879] lr: 3.4149e-05 eta: 3 days, 4:33:59 time: 2.1491 data_time: 0.0397 memory: 48866 grad_norm: 4.5077 loss: 3.6393 loss_cls: 3.6393 2023/01/23 03:09:45 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 03:11:30 - mmengine - INFO - Epoch(train) [32][1800/1879] lr: 3.4132e-05 eta: 3 days, 4:30:24 time: 2.1603 data_time: 0.0387 memory: 48866 grad_norm: 4.1501 loss: 3.3711 loss_cls: 3.3711 2023/01/23 03:14:20 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 03:14:20 - mmengine - INFO - Epoch(train) [32][1879/1879] lr: 3.4118e-05 eta: 3 days, 4:27:32 time: 2.1129 data_time: 0.0407 memory: 48866 grad_norm: 4.2501 loss: 3.6044 loss_cls: 3.6044 2023/01/23 03:15:14 - mmengine - INFO - Epoch(val) [32][100/155] eta: 0:00:29 time: 0.6081 data_time: 0.2694 memory: 4950 2023/01/23 03:15:44 - mmengine - INFO - Epoch(val) [32][155/155] acc/top1: 0.6274 acc/top5: 0.8514 acc/mean1: 0.6273 2023/01/23 03:15:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_30.pth is removed 2023/01/23 03:15:47 - mmengine - INFO - The best checkpoint with 0.6274 acc/top1 at 32 epoch is saved to best_acc/top1_epoch_32.pth. 2023/01/23 03:19:29 - mmengine - INFO - Epoch(train) [33][ 100/1879] lr: 3.4101e-05 eta: 3 days, 4:24:10 time: 2.1506 data_time: 0.0388 memory: 48866 grad_norm: 4.2858 loss: 3.4392 loss_cls: 3.4392 2023/01/23 03:23:04 - mmengine - INFO - Epoch(train) [33][ 200/1879] lr: 3.4084e-05 eta: 3 days, 4:20:33 time: 2.1480 data_time: 0.0380 memory: 48866 grad_norm: 4.4874 loss: 3.4033 loss_cls: 3.4033 2023/01/23 03:26:39 - mmengine - INFO - Epoch(train) [33][ 300/1879] lr: 3.4067e-05 eta: 3 days, 4:16:56 time: 2.1536 data_time: 0.0382 memory: 48866 grad_norm: 4.3202 loss: 3.5109 loss_cls: 3.5109 2023/01/23 03:30:15 - mmengine - INFO - Epoch(train) [33][ 400/1879] lr: 3.4050e-05 eta: 3 days, 4:13:22 time: 2.1547 data_time: 0.0383 memory: 48866 grad_norm: 4.2765 loss: 3.2360 loss_cls: 3.2360 2023/01/23 03:33:50 - mmengine - INFO - Epoch(train) [33][ 500/1879] lr: 3.4032e-05 eta: 3 days, 4:09:46 time: 2.1477 data_time: 0.0394 memory: 48866 grad_norm: 4.6087 loss: 3.5696 loss_cls: 3.5696 2023/01/23 03:37:25 - mmengine - INFO - Epoch(train) [33][ 600/1879] lr: 3.4015e-05 eta: 3 days, 4:06:10 time: 2.1477 data_time: 0.0386 memory: 48866 grad_norm: 4.2718 loss: 3.5471 loss_cls: 3.5471 2023/01/23 03:41:01 - mmengine - INFO - Epoch(train) [33][ 700/1879] lr: 3.3997e-05 eta: 3 days, 4:02:35 time: 2.1566 data_time: 0.0397 memory: 48866 grad_norm: 4.6671 loss: 3.4507 loss_cls: 3.4507 2023/01/23 03:44:36 - mmengine - INFO - Epoch(train) [33][ 800/1879] lr: 3.3980e-05 eta: 3 days, 3:58:59 time: 2.1431 data_time: 0.0387 memory: 48866 grad_norm: 4.2742 loss: 3.5213 loss_cls: 3.5213 2023/01/23 03:47:11 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 03:48:11 - mmengine - INFO - Epoch(train) [33][ 900/1879] lr: 3.3962e-05 eta: 3 days, 3:55:23 time: 2.1428 data_time: 0.0387 memory: 48866 grad_norm: 4.4452 loss: 3.4796 loss_cls: 3.4796 2023/01/23 03:51:46 - mmengine - INFO - Epoch(train) [33][1000/1879] lr: 3.3944e-05 eta: 3 days, 3:51:48 time: 2.1569 data_time: 0.0388 memory: 48866 grad_norm: 4.6222 loss: 3.6155 loss_cls: 3.6155 2023/01/23 03:55:22 - mmengine - INFO - Epoch(train) [33][1100/1879] lr: 3.3926e-05 eta: 3 days, 3:48:12 time: 2.1551 data_time: 0.0396 memory: 48866 grad_norm: 4.4705 loss: 3.5685 loss_cls: 3.5685 2023/01/23 03:58:57 - mmengine - INFO - Epoch(train) [33][1200/1879] lr: 3.3908e-05 eta: 3 days, 3:44:36 time: 2.1447 data_time: 0.0383 memory: 48866 grad_norm: 4.1901 loss: 3.5222 loss_cls: 3.5222 2023/01/23 04:02:32 - mmengine - INFO - Epoch(train) [33][1300/1879] lr: 3.3890e-05 eta: 3 days, 3:41:00 time: 2.1627 data_time: 0.0397 memory: 48866 grad_norm: 4.4147 loss: 3.3725 loss_cls: 3.3725 2023/01/23 04:06:08 - mmengine - INFO - Epoch(train) [33][1400/1879] lr: 3.3872e-05 eta: 3 days, 3:37:25 time: 2.1491 data_time: 0.0398 memory: 48866 grad_norm: 4.2833 loss: 3.6009 loss_cls: 3.6009 2023/01/23 04:09:42 - mmengine - INFO - Epoch(train) [33][1500/1879] lr: 3.3854e-05 eta: 3 days, 3:33:48 time: 2.1433 data_time: 0.0394 memory: 48866 grad_norm: 4.3493 loss: 3.5964 loss_cls: 3.5964 2023/01/23 04:13:18 - mmengine - INFO - Epoch(train) [33][1600/1879] lr: 3.3836e-05 eta: 3 days, 3:30:13 time: 2.1523 data_time: 0.0390 memory: 48866 grad_norm: 4.1095 loss: 3.5975 loss_cls: 3.5975 2023/01/23 04:16:54 - mmengine - INFO - Epoch(train) [33][1700/1879] lr: 3.3818e-05 eta: 3 days, 3:26:38 time: 2.1507 data_time: 0.0394 memory: 48866 grad_norm: 4.3932 loss: 3.5882 loss_cls: 3.5882 2023/01/23 04:20:29 - mmengine - INFO - Epoch(train) [33][1800/1879] lr: 3.3800e-05 eta: 3 days, 3:23:03 time: 2.1593 data_time: 0.0396 memory: 48866 grad_norm: 4.4846 loss: 3.4446 loss_cls: 3.4446 2023/01/23 04:23:04 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 04:23:18 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 04:23:18 - mmengine - INFO - Epoch(train) [33][1879/1879] lr: 3.3785e-05 eta: 3 days, 3:20:10 time: 2.1047 data_time: 0.0404 memory: 48866 grad_norm: 4.4736 loss: 3.5308 loss_cls: 3.5308 2023/01/23 04:23:18 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/01/23 04:24:19 - mmengine - INFO - Epoch(val) [33][100/155] eta: 0:00:30 time: 0.6046 data_time: 0.2468 memory: 4950 2023/01/23 04:24:46 - mmengine - INFO - Epoch(val) [33][155/155] acc/top1: 0.6272 acc/top5: 0.8519 acc/mean1: 0.6272 2023/01/23 04:28:29 - mmengine - INFO - Epoch(train) [34][ 100/1879] lr: 3.3767e-05 eta: 3 days, 3:16:51 time: 2.1405 data_time: 0.0388 memory: 48866 grad_norm: 4.1732 loss: 3.3895 loss_cls: 3.3895 2023/01/23 04:32:05 - mmengine - INFO - Epoch(train) [34][ 200/1879] lr: 3.3748e-05 eta: 3 days, 3:13:15 time: 2.1558 data_time: 0.0384 memory: 48866 grad_norm: 4.4213 loss: 3.4882 loss_cls: 3.4882 2023/01/23 04:35:40 - mmengine - INFO - Epoch(train) [34][ 300/1879] lr: 3.3730e-05 eta: 3 days, 3:09:39 time: 2.1545 data_time: 0.0389 memory: 48866 grad_norm: 4.5955 loss: 3.7436 loss_cls: 3.7436 2023/01/23 04:39:15 - mmengine - INFO - Epoch(train) [34][ 400/1879] lr: 3.3711e-05 eta: 3 days, 3:06:03 time: 2.1559 data_time: 0.0391 memory: 48866 grad_norm: 4.5077 loss: 3.5661 loss_cls: 3.5661 2023/01/23 04:42:50 - mmengine - INFO - Epoch(train) [34][ 500/1879] lr: 3.3692e-05 eta: 3 days, 3:02:27 time: 2.1478 data_time: 0.0387 memory: 48866 grad_norm: 4.4509 loss: 3.5919 loss_cls: 3.5919 2023/01/23 04:46:25 - mmengine - INFO - Epoch(train) [34][ 600/1879] lr: 3.3674e-05 eta: 3 days, 2:58:51 time: 2.1565 data_time: 0.0388 memory: 48866 grad_norm: 4.6760 loss: 3.5126 loss_cls: 3.5126 2023/01/23 04:50:01 - mmengine - INFO - Epoch(train) [34][ 700/1879] lr: 3.3655e-05 eta: 3 days, 2:55:15 time: 2.1571 data_time: 0.0395 memory: 48866 grad_norm: 4.4565 loss: 3.6294 loss_cls: 3.6294 2023/01/23 04:53:36 - mmengine - INFO - Epoch(train) [34][ 800/1879] lr: 3.3636e-05 eta: 3 days, 2:51:39 time: 2.1427 data_time: 0.0393 memory: 48866 grad_norm: 4.4017 loss: 3.5019 loss_cls: 3.5019 2023/01/23 04:57:11 - mmengine - INFO - Epoch(train) [34][ 900/1879] lr: 3.3617e-05 eta: 3 days, 2:48:03 time: 2.1509 data_time: 0.0381 memory: 48866 grad_norm: 4.1811 loss: 3.5766 loss_cls: 3.5766 2023/01/23 05:00:31 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 05:00:46 - mmengine - INFO - Epoch(train) [34][1000/1879] lr: 3.3598e-05 eta: 3 days, 2:44:27 time: 2.1515 data_time: 0.0385 memory: 48866 grad_norm: 4.3456 loss: 3.4340 loss_cls: 3.4340 2023/01/23 05:04:22 - mmengine - INFO - Epoch(train) [34][1100/1879] lr: 3.3579e-05 eta: 3 days, 2:40:52 time: 2.1523 data_time: 0.0396 memory: 48866 grad_norm: 4.4683 loss: 3.3689 loss_cls: 3.3689 2023/01/23 05:07:57 - mmengine - INFO - Epoch(train) [34][1200/1879] lr: 3.3559e-05 eta: 3 days, 2:37:16 time: 2.1562 data_time: 0.0393 memory: 48866 grad_norm: 4.6765 loss: 3.5933 loss_cls: 3.5933 2023/01/23 05:11:33 - mmengine - INFO - Epoch(train) [34][1300/1879] lr: 3.3540e-05 eta: 3 days, 2:33:41 time: 2.1608 data_time: 0.0390 memory: 48866 grad_norm: 4.3896 loss: 3.5159 loss_cls: 3.5159 2023/01/23 05:15:08 - mmengine - INFO - Epoch(train) [34][1400/1879] lr: 3.3521e-05 eta: 3 days, 2:30:05 time: 2.1680 data_time: 0.0388 memory: 48866 grad_norm: 4.4923 loss: 3.5342 loss_cls: 3.5342 2023/01/23 05:18:44 - mmengine - INFO - Epoch(train) [34][1500/1879] lr: 3.3502e-05 eta: 3 days, 2:26:31 time: 2.1626 data_time: 0.0403 memory: 48866 grad_norm: 4.4047 loss: 3.2708 loss_cls: 3.2708 2023/01/23 05:22:20 - mmengine - INFO - Epoch(train) [34][1600/1879] lr: 3.3482e-05 eta: 3 days, 2:22:56 time: 2.1504 data_time: 0.0397 memory: 48866 grad_norm: 4.3976 loss: 3.3180 loss_cls: 3.3180 2023/01/23 05:25:55 - mmengine - INFO - Epoch(train) [34][1700/1879] lr: 3.3463e-05 eta: 3 days, 2:19:21 time: 2.1519 data_time: 0.0385 memory: 48866 grad_norm: 4.2896 loss: 3.6908 loss_cls: 3.6908 2023/01/23 05:29:31 - mmengine - INFO - Epoch(train) [34][1800/1879] lr: 3.3443e-05 eta: 3 days, 2:15:46 time: 2.1541 data_time: 0.0403 memory: 48866 grad_norm: 4.2023 loss: 3.6615 loss_cls: 3.6615 2023/01/23 05:32:20 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 05:32:20 - mmengine - INFO - Epoch(train) [34][1879/1879] lr: 3.3428e-05 eta: 3 days, 2:12:54 time: 2.1076 data_time: 0.0380 memory: 48866 grad_norm: 4.6678 loss: 3.3361 loss_cls: 3.3361 2023/01/23 05:33:14 - mmengine - INFO - Epoch(val) [34][100/155] eta: 0:00:29 time: 0.5829 data_time: 0.2354 memory: 4950 2023/01/23 05:33:45 - mmengine - INFO - Epoch(val) [34][155/155] acc/top1: 0.6281 acc/top5: 0.8572 acc/mean1: 0.6280 2023/01/23 05:33:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_32.pth is removed 2023/01/23 05:33:48 - mmengine - INFO - The best checkpoint with 0.6281 acc/top1 at 34 epoch is saved to best_acc/top1_epoch_34.pth. 2023/01/23 05:37:29 - mmengine - INFO - Epoch(train) [35][ 100/1879] lr: 3.3408e-05 eta: 3 days, 2:09:29 time: 2.1491 data_time: 0.0376 memory: 48866 grad_norm: 4.3998 loss: 3.3059 loss_cls: 3.3059 2023/01/23 05:37:59 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 05:41:05 - mmengine - INFO - Epoch(train) [35][ 200/1879] lr: 3.3388e-05 eta: 3 days, 2:05:53 time: 2.1613 data_time: 0.0384 memory: 48866 grad_norm: 4.2746 loss: 3.4543 loss_cls: 3.4543 2023/01/23 05:44:40 - mmengine - INFO - Epoch(train) [35][ 300/1879] lr: 3.3368e-05 eta: 3 days, 2:02:17 time: 2.1539 data_time: 0.0383 memory: 48866 grad_norm: 4.3368 loss: 3.6301 loss_cls: 3.6301 2023/01/23 05:48:15 - mmengine - INFO - Epoch(train) [35][ 400/1879] lr: 3.3348e-05 eta: 3 days, 1:58:42 time: 2.1652 data_time: 0.0390 memory: 48866 grad_norm: 4.4696 loss: 3.4377 loss_cls: 3.4377 2023/01/23 05:51:50 - mmengine - INFO - Epoch(train) [35][ 500/1879] lr: 3.3328e-05 eta: 3 days, 1:55:06 time: 2.1399 data_time: 0.0391 memory: 48866 grad_norm: 4.3807 loss: 3.4421 loss_cls: 3.4421 2023/01/23 05:55:25 - mmengine - INFO - Epoch(train) [35][ 600/1879] lr: 3.3308e-05 eta: 3 days, 1:51:29 time: 2.1504 data_time: 0.0386 memory: 48866 grad_norm: 4.4229 loss: 3.3861 loss_cls: 3.3861 2023/01/23 05:59:00 - mmengine - INFO - Epoch(train) [35][ 700/1879] lr: 3.3288e-05 eta: 3 days, 1:47:53 time: 2.1472 data_time: 0.0381 memory: 48866 grad_norm: 4.5368 loss: 3.4075 loss_cls: 3.4075 2023/01/23 06:02:36 - mmengine - INFO - Epoch(train) [35][ 800/1879] lr: 3.3268e-05 eta: 3 days, 1:44:17 time: 2.1518 data_time: 0.0381 memory: 48866 grad_norm: 4.5051 loss: 3.6557 loss_cls: 3.6557 2023/01/23 06:06:11 - mmengine - INFO - Epoch(train) [35][ 900/1879] lr: 3.3248e-05 eta: 3 days, 1:40:41 time: 2.1525 data_time: 0.0383 memory: 48866 grad_norm: 4.2513 loss: 3.4135 loss_cls: 3.4135 2023/01/23 06:09:47 - mmengine - INFO - Epoch(train) [35][1000/1879] lr: 3.3227e-05 eta: 3 days, 1:37:07 time: 2.1722 data_time: 0.0379 memory: 48866 grad_norm: 4.4015 loss: 3.3133 loss_cls: 3.3133 2023/01/23 06:13:22 - mmengine - INFO - Epoch(train) [35][1100/1879] lr: 3.3207e-05 eta: 3 days, 1:33:31 time: 2.1488 data_time: 0.0391 memory: 48866 grad_norm: 4.5427 loss: 3.5402 loss_cls: 3.5402 2023/01/23 06:13:52 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 06:16:57 - mmengine - INFO - Epoch(train) [35][1200/1879] lr: 3.3187e-05 eta: 3 days, 1:29:55 time: 2.1469 data_time: 0.0380 memory: 48866 grad_norm: 4.6850 loss: 3.3483 loss_cls: 3.3483 2023/01/23 06:20:32 - mmengine - INFO - Epoch(train) [35][1300/1879] lr: 3.3166e-05 eta: 3 days, 1:26:18 time: 2.1527 data_time: 0.0396 memory: 48866 grad_norm: 4.3842 loss: 3.3447 loss_cls: 3.3447 2023/01/23 06:24:07 - mmengine - INFO - Epoch(train) [35][1400/1879] lr: 3.3146e-05 eta: 3 days, 1:22:43 time: 2.1488 data_time: 0.0387 memory: 48866 grad_norm: 4.4205 loss: 3.5018 loss_cls: 3.5018 2023/01/23 06:27:42 - mmengine - INFO - Epoch(train) [35][1500/1879] lr: 3.3125e-05 eta: 3 days, 1:19:07 time: 2.1519 data_time: 0.0386 memory: 48866 grad_norm: 4.3566 loss: 3.1774 loss_cls: 3.1774 2023/01/23 06:31:18 - mmengine - INFO - Epoch(train) [35][1600/1879] lr: 3.3104e-05 eta: 3 days, 1:15:31 time: 2.1460 data_time: 0.0392 memory: 48866 grad_norm: 4.4158 loss: 3.3619 loss_cls: 3.3619 2023/01/23 06:34:53 - mmengine - INFO - Epoch(train) [35][1700/1879] lr: 3.3084e-05 eta: 3 days, 1:11:55 time: 2.1569 data_time: 0.0402 memory: 48866 grad_norm: 4.3170 loss: 3.7077 loss_cls: 3.7077 2023/01/23 06:38:28 - mmengine - INFO - Epoch(train) [35][1800/1879] lr: 3.3063e-05 eta: 3 days, 1:08:19 time: 2.1574 data_time: 0.0396 memory: 48866 grad_norm: 4.3399 loss: 3.6284 loss_cls: 3.6284 2023/01/23 06:41:17 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 06:41:17 - mmengine - INFO - Epoch(train) [35][1879/1879] lr: 3.3046e-05 eta: 3 days, 1:05:26 time: 2.1033 data_time: 0.0414 memory: 48866 grad_norm: 4.5958 loss: 3.5336 loss_cls: 3.5336 2023/01/23 06:42:11 - mmengine - INFO - Epoch(val) [35][100/155] eta: 0:00:29 time: 0.6169 data_time: 0.2532 memory: 4950 2023/01/23 06:42:41 - mmengine - INFO - Epoch(val) [35][155/155] acc/top1: 0.6276 acc/top5: 0.8578 acc/mean1: 0.6276 2023/01/23 06:46:25 - mmengine - INFO - Epoch(train) [36][ 100/1879] lr: 3.3025e-05 eta: 3 days, 1:02:05 time: 2.1468 data_time: 0.0387 memory: 48866 grad_norm: 4.2524 loss: 3.6637 loss_cls: 3.6637 2023/01/23 06:50:00 - mmengine - INFO - Epoch(train) [36][ 200/1879] lr: 3.3004e-05 eta: 3 days, 0:58:29 time: 2.1543 data_time: 0.0382 memory: 48866 grad_norm: 4.4331 loss: 3.3667 loss_cls: 3.3667 2023/01/23 06:51:15 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 06:53:35 - mmengine - INFO - Epoch(train) [36][ 300/1879] lr: 3.2983e-05 eta: 3 days, 0:54:54 time: 2.1588 data_time: 0.0386 memory: 48866 grad_norm: 4.3261 loss: 3.5989 loss_cls: 3.5989 2023/01/23 06:57:11 - mmengine - INFO - Epoch(train) [36][ 400/1879] lr: 3.2962e-05 eta: 3 days, 0:51:18 time: 2.1556 data_time: 0.0388 memory: 48866 grad_norm: 4.3931 loss: 3.5335 loss_cls: 3.5335 2023/01/23 07:00:46 - mmengine - INFO - Epoch(train) [36][ 500/1879] lr: 3.2941e-05 eta: 3 days, 0:47:42 time: 2.1512 data_time: 0.0394 memory: 48866 grad_norm: 4.4857 loss: 3.3835 loss_cls: 3.3835 2023/01/23 07:04:21 - mmengine - INFO - Epoch(train) [36][ 600/1879] lr: 3.2920e-05 eta: 3 days, 0:44:07 time: 2.1577 data_time: 0.0390 memory: 48866 grad_norm: 4.3182 loss: 3.4526 loss_cls: 3.4526 2023/01/23 07:07:57 - mmengine - INFO - Epoch(train) [36][ 700/1879] lr: 3.2898e-05 eta: 3 days, 0:40:31 time: 2.1590 data_time: 0.0388 memory: 48866 grad_norm: 4.2317 loss: 3.3119 loss_cls: 3.3119 2023/01/23 07:11:32 - mmengine - INFO - Epoch(train) [36][ 800/1879] lr: 3.2877e-05 eta: 3 days, 0:36:55 time: 2.1550 data_time: 0.0382 memory: 48866 grad_norm: 4.4439 loss: 3.4855 loss_cls: 3.4855 2023/01/23 07:15:07 - mmengine - INFO - Epoch(train) [36][ 900/1879] lr: 3.2855e-05 eta: 3 days, 0:33:18 time: 2.1519 data_time: 0.0400 memory: 48866 grad_norm: 4.2658 loss: 3.3881 loss_cls: 3.3881 2023/01/23 07:18:42 - mmengine - INFO - Epoch(train) [36][1000/1879] lr: 3.2834e-05 eta: 3 days, 0:29:42 time: 2.1477 data_time: 0.0394 memory: 48866 grad_norm: 4.3632 loss: 3.3380 loss_cls: 3.3380 2023/01/23 07:22:17 - mmengine - INFO - Epoch(train) [36][1100/1879] lr: 3.2812e-05 eta: 3 days, 0:26:07 time: 2.1516 data_time: 0.0389 memory: 48866 grad_norm: 4.3681 loss: 3.4453 loss_cls: 3.4453 2023/01/23 07:25:53 - mmengine - INFO - Epoch(train) [36][1200/1879] lr: 3.2791e-05 eta: 3 days, 0:22:31 time: 2.1631 data_time: 0.0389 memory: 48866 grad_norm: 4.3586 loss: 3.3084 loss_cls: 3.3084 2023/01/23 07:27:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 07:29:28 - mmengine - INFO - Epoch(train) [36][1300/1879] lr: 3.2769e-05 eta: 3 days, 0:18:55 time: 2.1547 data_time: 0.0389 memory: 48866 grad_norm: 4.2600 loss: 3.6476 loss_cls: 3.6476 2023/01/23 07:33:03 - mmengine - INFO - Epoch(train) [36][1400/1879] lr: 3.2747e-05 eta: 3 days, 0:15:20 time: 2.1475 data_time: 0.0388 memory: 48866 grad_norm: 4.2902 loss: 3.5036 loss_cls: 3.5036 2023/01/23 07:36:39 - mmengine - INFO - Epoch(train) [36][1500/1879] lr: 3.2725e-05 eta: 3 days, 0:11:44 time: 2.1626 data_time: 0.0398 memory: 48866 grad_norm: 4.4784 loss: 3.4930 loss_cls: 3.4930 2023/01/23 07:40:14 - mmengine - INFO - Epoch(train) [36][1600/1879] lr: 3.2703e-05 eta: 3 days, 0:08:09 time: 2.1541 data_time: 0.0390 memory: 48866 grad_norm: 4.5819 loss: 3.4403 loss_cls: 3.4403 2023/01/23 07:43:50 - mmengine - INFO - Epoch(train) [36][1700/1879] lr: 3.2681e-05 eta: 3 days, 0:04:33 time: 2.1605 data_time: 0.0387 memory: 48866 grad_norm: 4.4819 loss: 3.4439 loss_cls: 3.4439 2023/01/23 07:47:25 - mmengine - INFO - Epoch(train) [36][1800/1879] lr: 3.2659e-05 eta: 3 days, 0:00:58 time: 2.1533 data_time: 0.0380 memory: 48866 grad_norm: 4.3248 loss: 3.4280 loss_cls: 3.4280 2023/01/23 07:50:14 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 07:50:14 - mmengine - INFO - Epoch(train) [36][1879/1879] lr: 3.2642e-05 eta: 2 days, 23:58:05 time: 2.0947 data_time: 0.0403 memory: 48866 grad_norm: 4.3221 loss: 3.5426 loss_cls: 3.5426 2023/01/23 07:50:14 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/01/23 07:51:14 - mmengine - INFO - Epoch(val) [36][100/155] eta: 0:00:30 time: 0.5636 data_time: 0.2174 memory: 4950 2023/01/23 07:51:42 - mmengine - INFO - Epoch(val) [36][155/155] acc/top1: 0.6453 acc/top5: 0.8642 acc/mean1: 0.6452 2023/01/23 07:51:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_34.pth is removed 2023/01/23 07:51:46 - mmengine - INFO - The best checkpoint with 0.6453 acc/top1 at 36 epoch is saved to best_acc/top1_epoch_36.pth. 2023/01/23 07:55:27 - mmengine - INFO - Epoch(train) [37][ 100/1879] lr: 3.2620e-05 eta: 2 days, 23:54:39 time: 2.1522 data_time: 0.0390 memory: 48866 grad_norm: 4.3912 loss: 3.5935 loss_cls: 3.5935 2023/01/23 07:59:01 - mmengine - INFO - Epoch(train) [37][ 200/1879] lr: 3.2597e-05 eta: 2 days, 23:51:03 time: 2.1485 data_time: 0.0381 memory: 48866 grad_norm: 4.3986 loss: 3.4807 loss_cls: 3.4807 2023/01/23 08:02:37 - mmengine - INFO - Epoch(train) [37][ 300/1879] lr: 3.2575e-05 eta: 2 days, 23:47:27 time: 2.1514 data_time: 0.0384 memory: 48866 grad_norm: 4.5993 loss: 3.4076 loss_cls: 3.4076 2023/01/23 08:04:38 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 08:06:12 - mmengine - INFO - Epoch(train) [37][ 400/1879] lr: 3.2553e-05 eta: 2 days, 23:43:51 time: 2.1517 data_time: 0.0385 memory: 48866 grad_norm: 4.5356 loss: 3.5547 loss_cls: 3.5547 2023/01/23 08:09:47 - mmengine - INFO - Epoch(train) [37][ 500/1879] lr: 3.2530e-05 eta: 2 days, 23:40:14 time: 2.1386 data_time: 0.0382 memory: 48866 grad_norm: 4.4043 loss: 3.4702 loss_cls: 3.4702 2023/01/23 08:13:22 - mmengine - INFO - Epoch(train) [37][ 600/1879] lr: 3.2508e-05 eta: 2 days, 23:36:39 time: 2.1648 data_time: 0.0390 memory: 48866 grad_norm: 4.4496 loss: 3.3974 loss_cls: 3.3974 2023/01/23 08:16:57 - mmengine - INFO - Epoch(train) [37][ 700/1879] lr: 3.2485e-05 eta: 2 days, 23:33:03 time: 2.1526 data_time: 0.0392 memory: 48866 grad_norm: 4.3624 loss: 3.4487 loss_cls: 3.4487 2023/01/23 08:20:32 - mmengine - INFO - Epoch(train) [37][ 800/1879] lr: 3.2463e-05 eta: 2 days, 23:29:26 time: 2.1560 data_time: 0.0394 memory: 48866 grad_norm: 4.1315 loss: 3.5878 loss_cls: 3.5878 2023/01/23 08:24:08 - mmengine - INFO - Epoch(train) [37][ 900/1879] lr: 3.2440e-05 eta: 2 days, 23:25:51 time: 2.1469 data_time: 0.0386 memory: 48866 grad_norm: 4.4547 loss: 3.4360 loss_cls: 3.4360 2023/01/23 08:27:43 - mmengine - INFO - Epoch(train) [37][1000/1879] lr: 3.2417e-05 eta: 2 days, 23:22:15 time: 2.1524 data_time: 0.0384 memory: 48866 grad_norm: 4.4169 loss: 3.4302 loss_cls: 3.4302 2023/01/23 08:31:18 - mmengine - INFO - Epoch(train) [37][1100/1879] lr: 3.2395e-05 eta: 2 days, 23:18:39 time: 2.1480 data_time: 0.0395 memory: 48866 grad_norm: 4.3631 loss: 3.3765 loss_cls: 3.3765 2023/01/23 08:34:53 - mmengine - INFO - Epoch(train) [37][1200/1879] lr: 3.2372e-05 eta: 2 days, 23:15:02 time: 2.1554 data_time: 0.0405 memory: 48866 grad_norm: 4.6739 loss: 3.5184 loss_cls: 3.5184 2023/01/23 08:38:28 - mmengine - INFO - Epoch(train) [37][1300/1879] lr: 3.2349e-05 eta: 2 days, 23:11:26 time: 2.1438 data_time: 0.0395 memory: 48866 grad_norm: 4.3753 loss: 3.5335 loss_cls: 3.5335 2023/01/23 08:40:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 08:42:03 - mmengine - INFO - Epoch(train) [37][1400/1879] lr: 3.2326e-05 eta: 2 days, 23:07:50 time: 2.1710 data_time: 0.0414 memory: 48866 grad_norm: 4.3275 loss: 3.4687 loss_cls: 3.4687 2023/01/23 08:45:39 - mmengine - INFO - Epoch(train) [37][1500/1879] lr: 3.2303e-05 eta: 2 days, 23:04:15 time: 2.1464 data_time: 0.0391 memory: 48866 grad_norm: 4.4629 loss: 3.4202 loss_cls: 3.4202 2023/01/23 08:49:14 - mmengine - INFO - Epoch(train) [37][1600/1879] lr: 3.2280e-05 eta: 2 days, 23:00:40 time: 2.1683 data_time: 0.0383 memory: 48866 grad_norm: 4.3964 loss: 3.2949 loss_cls: 3.2949 2023/01/23 08:52:50 - mmengine - INFO - Epoch(train) [37][1700/1879] lr: 3.2257e-05 eta: 2 days, 22:57:05 time: 2.1465 data_time: 0.0389 memory: 48866 grad_norm: 4.4412 loss: 3.4198 loss_cls: 3.4198 2023/01/23 08:56:25 - mmengine - INFO - Epoch(train) [37][1800/1879] lr: 3.2233e-05 eta: 2 days, 22:53:29 time: 2.1542 data_time: 0.0392 memory: 48866 grad_norm: 4.3504 loss: 3.3854 loss_cls: 3.3854 2023/01/23 08:59:15 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 08:59:15 - mmengine - INFO - Epoch(train) [37][1879/1879] lr: 3.2215e-05 eta: 2 days, 22:50:38 time: 2.0987 data_time: 0.0406 memory: 48866 grad_norm: 4.5147 loss: 3.5209 loss_cls: 3.5209 2023/01/23 09:00:08 - mmengine - INFO - Epoch(val) [37][100/155] eta: 0:00:29 time: 0.5250 data_time: 0.1731 memory: 4950 2023/01/23 09:00:39 - mmengine - INFO - Epoch(val) [37][155/155] acc/top1: 0.6410 acc/top5: 0.8606 acc/mean1: 0.6410 2023/01/23 09:04:21 - mmengine - INFO - Epoch(train) [38][ 100/1879] lr: 3.2192e-05 eta: 2 days, 22:47:14 time: 2.1620 data_time: 0.0387 memory: 48866 grad_norm: 4.2025 loss: 3.5576 loss_cls: 3.5576 2023/01/23 09:07:56 - mmengine - INFO - Epoch(train) [38][ 200/1879] lr: 3.2168e-05 eta: 2 days, 22:43:38 time: 2.1430 data_time: 0.0385 memory: 48866 grad_norm: 4.3905 loss: 3.4917 loss_cls: 3.4917 2023/01/23 09:11:31 - mmengine - INFO - Epoch(train) [38][ 300/1879] lr: 3.2145e-05 eta: 2 days, 22:40:01 time: 2.1527 data_time: 0.0386 memory: 48866 grad_norm: 4.5847 loss: 3.4832 loss_cls: 3.4832 2023/01/23 09:15:07 - mmengine - INFO - Epoch(train) [38][ 400/1879] lr: 3.2121e-05 eta: 2 days, 22:36:26 time: 2.1517 data_time: 0.0395 memory: 48866 grad_norm: 4.4400 loss: 3.3291 loss_cls: 3.3291 2023/01/23 09:17:53 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 09:18:42 - mmengine - INFO - Epoch(train) [38][ 500/1879] lr: 3.2098e-05 eta: 2 days, 22:32:51 time: 2.1596 data_time: 0.0387 memory: 48866 grad_norm: 4.5966 loss: 3.5471 loss_cls: 3.5471 2023/01/23 09:22:17 - mmengine - INFO - Epoch(train) [38][ 600/1879] lr: 3.2074e-05 eta: 2 days, 22:29:14 time: 2.1573 data_time: 0.0386 memory: 48866 grad_norm: 4.5490 loss: 3.4927 loss_cls: 3.4927 2023/01/23 09:25:53 - mmengine - INFO - Epoch(train) [38][ 700/1879] lr: 3.2050e-05 eta: 2 days, 22:25:39 time: 2.1516 data_time: 0.0400 memory: 48866 grad_norm: 4.6923 loss: 3.1232 loss_cls: 3.1232 2023/01/23 09:29:29 - mmengine - INFO - Epoch(train) [38][ 800/1879] lr: 3.2027e-05 eta: 2 days, 22:22:04 time: 2.1564 data_time: 0.0399 memory: 48866 grad_norm: 4.4958 loss: 3.4297 loss_cls: 3.4297 2023/01/23 09:33:04 - mmengine - INFO - Epoch(train) [38][ 900/1879] lr: 3.2003e-05 eta: 2 days, 22:18:29 time: 2.1601 data_time: 0.0394 memory: 48866 grad_norm: 4.4983 loss: 3.4342 loss_cls: 3.4342 2023/01/23 09:36:40 - mmengine - INFO - Epoch(train) [38][1000/1879] lr: 3.1979e-05 eta: 2 days, 22:14:54 time: 2.1618 data_time: 0.0389 memory: 48866 grad_norm: 4.2857 loss: 3.5025 loss_cls: 3.5025 2023/01/23 09:40:15 - mmengine - INFO - Epoch(train) [38][1100/1879] lr: 3.1955e-05 eta: 2 days, 22:11:17 time: 2.1408 data_time: 0.0407 memory: 48866 grad_norm: 4.3328 loss: 3.5257 loss_cls: 3.5257 2023/01/23 09:43:50 - mmengine - INFO - Epoch(train) [38][1200/1879] lr: 3.1931e-05 eta: 2 days, 22:07:42 time: 2.1495 data_time: 0.0395 memory: 48866 grad_norm: 4.4413 loss: 3.4655 loss_cls: 3.4655 2023/01/23 09:47:26 - mmengine - INFO - Epoch(train) [38][1300/1879] lr: 3.1907e-05 eta: 2 days, 22:04:06 time: 2.1430 data_time: 0.0388 memory: 48866 grad_norm: 4.3135 loss: 3.3736 loss_cls: 3.3736 2023/01/23 09:51:01 - mmengine - INFO - Epoch(train) [38][1400/1879] lr: 3.1883e-05 eta: 2 days, 22:00:31 time: 2.1621 data_time: 0.0393 memory: 48866 grad_norm: 4.3660 loss: 3.6024 loss_cls: 3.6024 2023/01/23 09:53:47 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 09:54:37 - mmengine - INFO - Epoch(train) [38][1500/1879] lr: 3.1859e-05 eta: 2 days, 21:56:56 time: 2.1426 data_time: 0.0395 memory: 48866 grad_norm: 4.5051 loss: 3.4388 loss_cls: 3.4388 2023/01/23 09:58:12 - mmengine - INFO - Epoch(train) [38][1600/1879] lr: 3.1834e-05 eta: 2 days, 21:53:21 time: 2.1563 data_time: 0.0383 memory: 48866 grad_norm: 4.4894 loss: 3.6584 loss_cls: 3.6584 2023/01/23 10:01:48 - mmengine - INFO - Epoch(train) [38][1700/1879] lr: 3.1810e-05 eta: 2 days, 21:49:45 time: 2.1575 data_time: 0.0387 memory: 48866 grad_norm: 4.5483 loss: 3.5197 loss_cls: 3.5197 2023/01/23 10:05:23 - mmengine - INFO - Epoch(train) [38][1800/1879] lr: 3.1786e-05 eta: 2 days, 21:46:09 time: 2.1463 data_time: 0.0387 memory: 48866 grad_norm: 4.4792 loss: 3.3039 loss_cls: 3.3039 2023/01/23 10:08:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 10:08:12 - mmengine - INFO - Epoch(train) [38][1879/1879] lr: 3.1766e-05 eta: 2 days, 21:43:17 time: 2.0969 data_time: 0.0393 memory: 48866 grad_norm: 4.6137 loss: 3.4857 loss_cls: 3.4857 2023/01/23 10:09:05 - mmengine - INFO - Epoch(val) [38][100/155] eta: 0:00:29 time: 0.5625 data_time: 0.2146 memory: 4950 2023/01/23 10:09:36 - mmengine - INFO - Epoch(val) [38][155/155] acc/top1: 0.6475 acc/top5: 0.8639 acc/mean1: 0.6475 2023/01/23 10:09:36 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_36.pth is removed 2023/01/23 10:09:40 - mmengine - INFO - The best checkpoint with 0.6475 acc/top1 at 38 epoch is saved to best_acc/top1_epoch_38.pth. 2023/01/23 10:13:22 - mmengine - INFO - Epoch(train) [39][ 100/1879] lr: 3.1742e-05 eta: 2 days, 21:39:52 time: 2.1442 data_time: 0.0380 memory: 48866 grad_norm: 4.4280 loss: 3.5129 loss_cls: 3.5129 2023/01/23 10:16:57 - mmengine - INFO - Epoch(train) [39][ 200/1879] lr: 3.1717e-05 eta: 2 days, 21:36:16 time: 2.1453 data_time: 0.0379 memory: 48866 grad_norm: 4.3913 loss: 3.2989 loss_cls: 3.2989 2023/01/23 10:20:33 - mmengine - INFO - Epoch(train) [39][ 300/1879] lr: 3.1693e-05 eta: 2 days, 21:32:41 time: 2.1481 data_time: 0.0385 memory: 48866 grad_norm: 4.7491 loss: 3.4666 loss_cls: 3.4666 2023/01/23 10:24:08 - mmengine - INFO - Epoch(train) [39][ 400/1879] lr: 3.1668e-05 eta: 2 days, 21:29:05 time: 2.1516 data_time: 0.0386 memory: 48866 grad_norm: 4.5306 loss: 3.2755 loss_cls: 3.2755 2023/01/23 10:27:43 - mmengine - INFO - Epoch(train) [39][ 500/1879] lr: 3.1643e-05 eta: 2 days, 21:25:29 time: 2.1443 data_time: 0.0387 memory: 48866 grad_norm: 4.5737 loss: 3.4854 loss_cls: 3.4854 2023/01/23 10:31:14 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 10:31:18 - mmengine - INFO - Epoch(train) [39][ 600/1879] lr: 3.1619e-05 eta: 2 days, 21:21:52 time: 2.1486 data_time: 0.0389 memory: 48866 grad_norm: 4.9072 loss: 3.4723 loss_cls: 3.4723 2023/01/23 10:34:53 - mmengine - INFO - Epoch(train) [39][ 700/1879] lr: 3.1594e-05 eta: 2 days, 21:18:16 time: 2.1440 data_time: 0.0398 memory: 48866 grad_norm: 4.4191 loss: 3.5781 loss_cls: 3.5781 2023/01/23 10:38:27 - mmengine - INFO - Epoch(train) [39][ 800/1879] lr: 3.1569e-05 eta: 2 days, 21:14:39 time: 2.1466 data_time: 0.0390 memory: 48866 grad_norm: 4.6502 loss: 3.3627 loss_cls: 3.3627 2023/01/23 10:42:03 - mmengine - INFO - Epoch(train) [39][ 900/1879] lr: 3.1544e-05 eta: 2 days, 21:11:03 time: 2.1498 data_time: 0.0389 memory: 48866 grad_norm: 4.5542 loss: 3.5257 loss_cls: 3.5257 2023/01/23 10:45:38 - mmengine - INFO - Epoch(train) [39][1000/1879] lr: 3.1519e-05 eta: 2 days, 21:07:28 time: 2.1537 data_time: 0.0385 memory: 48866 grad_norm: 4.4920 loss: 3.4614 loss_cls: 3.4614 2023/01/23 10:49:13 - mmengine - INFO - Epoch(train) [39][1100/1879] lr: 3.1494e-05 eta: 2 days, 21:03:51 time: 2.1494 data_time: 0.0390 memory: 48866 grad_norm: 4.5844 loss: 3.4112 loss_cls: 3.4112 2023/01/23 10:52:48 - mmengine - INFO - Epoch(train) [39][1200/1879] lr: 3.1469e-05 eta: 2 days, 21:00:16 time: 2.1596 data_time: 0.0383 memory: 48866 grad_norm: 4.5196 loss: 3.3289 loss_cls: 3.3289 2023/01/23 10:56:24 - mmengine - INFO - Epoch(train) [39][1300/1879] lr: 3.1444e-05 eta: 2 days, 20:56:40 time: 2.1602 data_time: 0.0380 memory: 48866 grad_norm: 4.4575 loss: 3.2557 loss_cls: 3.2557 2023/01/23 10:59:59 - mmengine - INFO - Epoch(train) [39][1400/1879] lr: 3.1418e-05 eta: 2 days, 20:53:04 time: 2.1582 data_time: 0.0389 memory: 48866 grad_norm: 4.6570 loss: 3.4698 loss_cls: 3.4698 2023/01/23 11:03:35 - mmengine - INFO - Epoch(train) [39][1500/1879] lr: 3.1393e-05 eta: 2 days, 20:49:30 time: 2.1533 data_time: 0.0388 memory: 48866 grad_norm: 4.2395 loss: 3.4762 loss_cls: 3.4762 2023/01/23 11:07:06 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 11:07:10 - mmengine - INFO - Epoch(train) [39][1600/1879] lr: 3.1368e-05 eta: 2 days, 20:45:54 time: 2.1383 data_time: 0.0383 memory: 48866 grad_norm: 4.5298 loss: 3.4392 loss_cls: 3.4392 2023/01/23 11:10:46 - mmengine - INFO - Epoch(train) [39][1700/1879] lr: 3.1342e-05 eta: 2 days, 20:42:19 time: 2.1550 data_time: 0.0384 memory: 48866 grad_norm: 4.3950 loss: 3.5762 loss_cls: 3.5762 2023/01/23 11:14:21 - mmengine - INFO - Epoch(train) [39][1800/1879] lr: 3.1317e-05 eta: 2 days, 20:38:43 time: 2.1469 data_time: 0.0386 memory: 48866 grad_norm: 4.4602 loss: 3.4211 loss_cls: 3.4211 2023/01/23 11:17:10 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 11:17:10 - mmengine - INFO - Epoch(train) [39][1879/1879] lr: 3.1297e-05 eta: 2 days, 20:35:51 time: 2.1057 data_time: 0.0406 memory: 48866 grad_norm: 4.6993 loss: 3.3246 loss_cls: 3.3246 2023/01/23 11:17:10 - mmengine - INFO - Saving checkpoint at 39 epochs 2023/01/23 11:18:10 - mmengine - INFO - Epoch(val) [39][100/155] eta: 0:00:30 time: 0.5638 data_time: 0.2098 memory: 4950 2023/01/23 11:18:38 - mmengine - INFO - Epoch(val) [39][155/155] acc/top1: 0.6457 acc/top5: 0.8651 acc/mean1: 0.6457 2023/01/23 11:22:20 - mmengine - INFO - Epoch(train) [40][ 100/1879] lr: 3.1271e-05 eta: 2 days, 20:32:26 time: 2.1567 data_time: 0.0407 memory: 48866 grad_norm: 4.5979 loss: 3.2483 loss_cls: 3.2483 2023/01/23 11:25:56 - mmengine - INFO - Epoch(train) [40][ 200/1879] lr: 3.1245e-05 eta: 2 days, 20:28:51 time: 2.1529 data_time: 0.0388 memory: 48866 grad_norm: 4.5158 loss: 3.1841 loss_cls: 3.1841 2023/01/23 11:29:30 - mmengine - INFO - Epoch(train) [40][ 300/1879] lr: 3.1220e-05 eta: 2 days, 20:25:14 time: 2.1423 data_time: 0.0395 memory: 48866 grad_norm: 4.3898 loss: 3.4622 loss_cls: 3.4622 2023/01/23 11:33:07 - mmengine - INFO - Epoch(train) [40][ 400/1879] lr: 3.1194e-05 eta: 2 days, 20:21:40 time: 2.1500 data_time: 0.0393 memory: 48866 grad_norm: 4.4304 loss: 3.5127 loss_cls: 3.5127 2023/01/23 11:36:42 - mmengine - INFO - Epoch(train) [40][ 500/1879] lr: 3.1168e-05 eta: 2 days, 20:18:04 time: 2.1450 data_time: 0.0385 memory: 48866 grad_norm: 4.4918 loss: 3.5799 loss_cls: 3.5799 2023/01/23 11:40:17 - mmengine - INFO - Epoch(train) [40][ 600/1879] lr: 3.1142e-05 eta: 2 days, 20:14:28 time: 2.1505 data_time: 0.0387 memory: 48866 grad_norm: 4.4651 loss: 3.5845 loss_cls: 3.5845 2023/01/23 11:43:53 - mmengine - INFO - Epoch(train) [40][ 700/1879] lr: 3.1116e-05 eta: 2 days, 20:10:53 time: 2.1561 data_time: 0.0390 memory: 48866 grad_norm: 4.4209 loss: 3.4559 loss_cls: 3.4559 2023/01/23 11:44:34 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 11:47:28 - mmengine - INFO - Epoch(train) [40][ 800/1879] lr: 3.1090e-05 eta: 2 days, 20:07:18 time: 2.1538 data_time: 0.0384 memory: 48866 grad_norm: 4.3297 loss: 3.2623 loss_cls: 3.2623 2023/01/23 11:51:03 - mmengine - INFO - Epoch(train) [40][ 900/1879] lr: 3.1064e-05 eta: 2 days, 20:03:41 time: 2.1529 data_time: 0.0389 memory: 48866 grad_norm: 4.5794 loss: 3.3980 loss_cls: 3.3980 2023/01/23 11:54:39 - mmengine - INFO - Epoch(train) [40][1000/1879] lr: 3.1038e-05 eta: 2 days, 20:00:07 time: 2.1738 data_time: 0.0382 memory: 48866 grad_norm: 4.6020 loss: 3.3767 loss_cls: 3.3767 2023/01/23 11:58:14 - mmengine - INFO - Epoch(train) [40][1100/1879] lr: 3.1012e-05 eta: 2 days, 19:56:31 time: 2.1527 data_time: 0.0394 memory: 48866 grad_norm: 4.4237 loss: 3.3404 loss_cls: 3.3404 2023/01/23 12:01:50 - mmengine - INFO - Epoch(train) [40][1200/1879] lr: 3.0986e-05 eta: 2 days, 19:52:55 time: 2.1542 data_time: 0.0391 memory: 48866 grad_norm: 4.5298 loss: 3.1393 loss_cls: 3.1393 2023/01/23 12:05:25 - mmengine - INFO - Epoch(train) [40][1300/1879] lr: 3.0960e-05 eta: 2 days, 19:49:19 time: 2.1419 data_time: 0.0388 memory: 48866 grad_norm: 4.5830 loss: 3.2267 loss_cls: 3.2267 2023/01/23 12:09:01 - mmengine - INFO - Epoch(train) [40][1400/1879] lr: 3.0933e-05 eta: 2 days, 19:45:44 time: 2.1472 data_time: 0.0394 memory: 48866 grad_norm: 4.4828 loss: 3.3434 loss_cls: 3.3434 2023/01/23 12:12:36 - mmengine - INFO - Epoch(train) [40][1500/1879] lr: 3.0907e-05 eta: 2 days, 19:42:08 time: 2.1487 data_time: 0.0391 memory: 48866 grad_norm: 4.2631 loss: 3.5021 loss_cls: 3.5021 2023/01/23 12:16:12 - mmengine - INFO - Epoch(train) [40][1600/1879] lr: 3.0880e-05 eta: 2 days, 19:38:33 time: 2.1508 data_time: 0.0388 memory: 48866 grad_norm: 4.5116 loss: 3.3356 loss_cls: 3.3356 2023/01/23 12:19:48 - mmengine - INFO - Epoch(train) [40][1700/1879] lr: 3.0854e-05 eta: 2 days, 19:34:58 time: 2.1535 data_time: 0.0399 memory: 48866 grad_norm: 4.5821 loss: 3.6098 loss_cls: 3.6098 2023/01/23 12:20:29 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 12:23:24 - mmengine - INFO - Epoch(train) [40][1800/1879] lr: 3.0827e-05 eta: 2 days, 19:31:24 time: 2.1503 data_time: 0.0383 memory: 48866 grad_norm: 4.6307 loss: 3.3104 loss_cls: 3.3104 2023/01/23 12:26:13 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 12:26:13 - mmengine - INFO - Epoch(train) [40][1879/1879] lr: 3.0806e-05 eta: 2 days, 19:28:32 time: 2.0977 data_time: 0.0405 memory: 48866 grad_norm: 4.6126 loss: 3.3045 loss_cls: 3.3045 2023/01/23 12:27:05 - mmengine - INFO - Epoch(val) [40][100/155] eta: 0:00:28 time: 0.5418 data_time: 0.1944 memory: 4950 2023/01/23 12:27:37 - mmengine - INFO - Epoch(val) [40][155/155] acc/top1: 0.6470 acc/top5: 0.8680 acc/mean1: 0.6471 2023/01/23 12:31:20 - mmengine - INFO - Epoch(train) [41][ 100/1879] lr: 3.0780e-05 eta: 2 days, 19:25:08 time: 2.1579 data_time: 0.0382 memory: 48866 grad_norm: 4.6030 loss: 3.2273 loss_cls: 3.2273 2023/01/23 12:34:54 - mmengine - INFO - Epoch(train) [41][ 200/1879] lr: 3.0753e-05 eta: 2 days, 19:21:31 time: 2.1427 data_time: 0.0384 memory: 48866 grad_norm: 4.4239 loss: 3.3257 loss_cls: 3.3257 2023/01/23 12:38:30 - mmengine - INFO - Epoch(train) [41][ 300/1879] lr: 3.0726e-05 eta: 2 days, 19:17:55 time: 2.1562 data_time: 0.0378 memory: 48866 grad_norm: 4.4799 loss: 3.5033 loss_cls: 3.5033 2023/01/23 12:42:04 - mmengine - INFO - Epoch(train) [41][ 400/1879] lr: 3.0700e-05 eta: 2 days, 19:14:19 time: 2.1469 data_time: 0.0380 memory: 48866 grad_norm: 4.3790 loss: 3.1865 loss_cls: 3.1865 2023/01/23 12:45:40 - mmengine - INFO - Epoch(train) [41][ 500/1879] lr: 3.0673e-05 eta: 2 days, 19:10:43 time: 2.1421 data_time: 0.0390 memory: 48866 grad_norm: 4.7026 loss: 3.4281 loss_cls: 3.4281 2023/01/23 12:49:15 - mmengine - INFO - Epoch(train) [41][ 600/1879] lr: 3.0646e-05 eta: 2 days, 19:07:08 time: 2.1560 data_time: 0.0389 memory: 48866 grad_norm: 4.6225 loss: 3.4305 loss_cls: 3.4305 2023/01/23 12:52:50 - mmengine - INFO - Epoch(train) [41][ 700/1879] lr: 3.0619e-05 eta: 2 days, 19:03:31 time: 2.1511 data_time: 0.0397 memory: 48866 grad_norm: 4.6806 loss: 3.5766 loss_cls: 3.5766 2023/01/23 12:56:25 - mmengine - INFO - Epoch(train) [41][ 800/1879] lr: 3.0592e-05 eta: 2 days, 18:59:55 time: 2.1503 data_time: 0.0382 memory: 48866 grad_norm: 4.7580 loss: 3.3642 loss_cls: 3.3642 2023/01/23 12:57:51 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 13:00:00 - mmengine - INFO - Epoch(train) [41][ 900/1879] lr: 3.0565e-05 eta: 2 days, 18:56:19 time: 2.1546 data_time: 0.0392 memory: 48866 grad_norm: 4.6677 loss: 3.6055 loss_cls: 3.6055 2023/01/23 13:03:35 - mmengine - INFO - Epoch(train) [41][1000/1879] lr: 3.0538e-05 eta: 2 days, 18:52:43 time: 2.1528 data_time: 0.0379 memory: 48866 grad_norm: 4.5219 loss: 3.3905 loss_cls: 3.3905 2023/01/23 13:07:11 - mmengine - INFO - Epoch(train) [41][1100/1879] lr: 3.0510e-05 eta: 2 days, 18:49:07 time: 2.1589 data_time: 0.0394 memory: 48866 grad_norm: 4.5881 loss: 3.2969 loss_cls: 3.2969 2023/01/23 13:10:46 - mmengine - INFO - Epoch(train) [41][1200/1879] lr: 3.0483e-05 eta: 2 days, 18:45:32 time: 2.1520 data_time: 0.0388 memory: 48866 grad_norm: 4.4473 loss: 3.4545 loss_cls: 3.4545 2023/01/23 13:14:22 - mmengine - INFO - Epoch(train) [41][1300/1879] lr: 3.0456e-05 eta: 2 days, 18:41:57 time: 2.1534 data_time: 0.0395 memory: 48866 grad_norm: 4.4517 loss: 3.2655 loss_cls: 3.2655 2023/01/23 13:17:57 - mmengine - INFO - Epoch(train) [41][1400/1879] lr: 3.0428e-05 eta: 2 days, 18:38:22 time: 2.1571 data_time: 0.0384 memory: 48866 grad_norm: 4.5532 loss: 3.3625 loss_cls: 3.3625 2023/01/23 13:21:33 - mmengine - INFO - Epoch(train) [41][1500/1879] lr: 3.0401e-05 eta: 2 days, 18:34:47 time: 2.1537 data_time: 0.0388 memory: 48866 grad_norm: 4.5684 loss: 3.2122 loss_cls: 3.2122 2023/01/23 13:25:09 - mmengine - INFO - Epoch(train) [41][1600/1879] lr: 3.0374e-05 eta: 2 days, 18:31:11 time: 2.1543 data_time: 0.0388 memory: 48866 grad_norm: 4.4394 loss: 3.3901 loss_cls: 3.3901 2023/01/23 13:28:44 - mmengine - INFO - Epoch(train) [41][1700/1879] lr: 3.0346e-05 eta: 2 days, 18:27:35 time: 2.1437 data_time: 0.0393 memory: 48866 grad_norm: 4.4107 loss: 3.4430 loss_cls: 3.4430 2023/01/23 13:32:19 - mmengine - INFO - Epoch(train) [41][1800/1879] lr: 3.0318e-05 eta: 2 days, 18:24:00 time: 2.1463 data_time: 0.0386 memory: 48866 grad_norm: 4.5172 loss: 3.3987 loss_cls: 3.3987 2023/01/23 13:33:45 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 13:35:09 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 13:35:09 - mmengine - INFO - Epoch(train) [41][1879/1879] lr: 3.0297e-05 eta: 2 days, 18:21:08 time: 2.1129 data_time: 0.0406 memory: 48866 grad_norm: 4.6440 loss: 3.3734 loss_cls: 3.3734 2023/01/23 13:36:02 - mmengine - INFO - Epoch(val) [41][100/155] eta: 0:00:29 time: 0.5684 data_time: 0.2103 memory: 4950 2023/01/23 13:36:34 - mmengine - INFO - Epoch(val) [41][155/155] acc/top1: 0.6515 acc/top5: 0.8685 acc/mean1: 0.6514 2023/01/23 13:36:34 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_38.pth is removed 2023/01/23 13:36:37 - mmengine - INFO - The best checkpoint with 0.6515 acc/top1 at 41 epoch is saved to best_acc/top1_epoch_41.pth. 2023/01/23 13:40:19 - mmengine - INFO - Epoch(train) [42][ 100/1879] lr: 3.0269e-05 eta: 2 days, 18:17:41 time: 2.1473 data_time: 0.0378 memory: 48866 grad_norm: 4.5509 loss: 3.3465 loss_cls: 3.3465 2023/01/23 13:43:54 - mmengine - INFO - Epoch(train) [42][ 200/1879] lr: 3.0241e-05 eta: 2 days, 18:14:05 time: 2.1449 data_time: 0.0388 memory: 48866 grad_norm: 4.3524 loss: 3.3698 loss_cls: 3.3698 2023/01/23 13:47:29 - mmengine - INFO - Epoch(train) [42][ 300/1879] lr: 3.0213e-05 eta: 2 days, 18:10:29 time: 2.1434 data_time: 0.0384 memory: 48866 grad_norm: 4.5860 loss: 3.2905 loss_cls: 3.2905 2023/01/23 13:51:04 - mmengine - INFO - Epoch(train) [42][ 400/1879] lr: 3.0186e-05 eta: 2 days, 18:06:53 time: 2.1554 data_time: 0.0384 memory: 48866 grad_norm: 4.4305 loss: 3.4835 loss_cls: 3.4835 2023/01/23 13:54:39 - mmengine - INFO - Epoch(train) [42][ 500/1879] lr: 3.0158e-05 eta: 2 days, 18:03:17 time: 2.1418 data_time: 0.0394 memory: 48866 grad_norm: 4.6505 loss: 3.3414 loss_cls: 3.3414 2023/01/23 13:58:15 - mmengine - INFO - Epoch(train) [42][ 600/1879] lr: 3.0130e-05 eta: 2 days, 17:59:42 time: 2.1535 data_time: 0.0384 memory: 48866 grad_norm: 4.3574 loss: 3.4638 loss_cls: 3.4638 2023/01/23 14:01:51 - mmengine - INFO - Epoch(train) [42][ 700/1879] lr: 3.0102e-05 eta: 2 days, 17:56:07 time: 2.1703 data_time: 0.0393 memory: 48866 grad_norm: 4.4759 loss: 3.4768 loss_cls: 3.4768 2023/01/23 14:05:26 - mmengine - INFO - Epoch(train) [42][ 800/1879] lr: 3.0074e-05 eta: 2 days, 17:52:32 time: 2.1540 data_time: 0.0395 memory: 48866 grad_norm: 4.6261 loss: 3.3064 loss_cls: 3.3064 2023/01/23 14:09:02 - mmengine - INFO - Epoch(train) [42][ 900/1879] lr: 3.0046e-05 eta: 2 days, 17:48:57 time: 2.1565 data_time: 0.0393 memory: 48866 grad_norm: 4.8175 loss: 3.5277 loss_cls: 3.5277 2023/01/23 14:11:13 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 14:12:37 - mmengine - INFO - Epoch(train) [42][1000/1879] lr: 3.0018e-05 eta: 2 days, 17:45:21 time: 2.1531 data_time: 0.0386 memory: 48866 grad_norm: 4.5456 loss: 3.3720 loss_cls: 3.3720 2023/01/23 14:16:13 - mmengine - INFO - Epoch(train) [42][1100/1879] lr: 2.9989e-05 eta: 2 days, 17:41:46 time: 2.1461 data_time: 0.0393 memory: 48866 grad_norm: 4.7361 loss: 3.5365 loss_cls: 3.5365 2023/01/23 14:19:49 - mmengine - INFO - Epoch(train) [42][1200/1879] lr: 2.9961e-05 eta: 2 days, 17:38:10 time: 2.1652 data_time: 0.0388 memory: 48866 grad_norm: 4.6750 loss: 3.2520 loss_cls: 3.2520 2023/01/23 14:23:24 - mmengine - INFO - Epoch(train) [42][1300/1879] lr: 2.9933e-05 eta: 2 days, 17:34:34 time: 2.1531 data_time: 0.0396 memory: 48866 grad_norm: 4.6834 loss: 3.5383 loss_cls: 3.5383 2023/01/23 14:26:59 - mmengine - INFO - Epoch(train) [42][1400/1879] lr: 2.9904e-05 eta: 2 days, 17:30:58 time: 2.1609 data_time: 0.0386 memory: 48866 grad_norm: 4.5321 loss: 3.6030 loss_cls: 3.6030 2023/01/23 14:30:34 - mmengine - INFO - Epoch(train) [42][1500/1879] lr: 2.9876e-05 eta: 2 days, 17:27:22 time: 2.1449 data_time: 0.0393 memory: 48866 grad_norm: 4.4841 loss: 3.3713 loss_cls: 3.3713 2023/01/23 14:34:10 - mmengine - INFO - Epoch(train) [42][1600/1879] lr: 2.9848e-05 eta: 2 days, 17:23:47 time: 2.1458 data_time: 0.0389 memory: 48866 grad_norm: 4.4144 loss: 3.4077 loss_cls: 3.4077 2023/01/23 14:37:45 - mmengine - INFO - Epoch(train) [42][1700/1879] lr: 2.9819e-05 eta: 2 days, 17:20:11 time: 2.1489 data_time: 0.0397 memory: 48866 grad_norm: 4.6631 loss: 2.9785 loss_cls: 2.9785 2023/01/23 14:41:20 - mmengine - INFO - Epoch(train) [42][1800/1879] lr: 2.9791e-05 eta: 2 days, 17:16:36 time: 2.1562 data_time: 0.0386 memory: 48866 grad_norm: 4.6129 loss: 3.2271 loss_cls: 3.2271 2023/01/23 14:44:10 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 14:44:10 - mmengine - INFO - Epoch(train) [42][1879/1879] lr: 2.9768e-05 eta: 2 days, 17:13:44 time: 2.0886 data_time: 0.0394 memory: 48866 grad_norm: 4.9425 loss: 3.3082 loss_cls: 3.3082 2023/01/23 14:44:10 - mmengine - INFO - Saving checkpoint at 42 epochs 2023/01/23 14:45:09 - mmengine - INFO - Epoch(val) [42][100/155] eta: 0:00:30 time: 0.5720 data_time: 0.2183 memory: 4950 2023/01/23 14:45:37 - mmengine - INFO - Epoch(val) [42][155/155] acc/top1: 0.6531 acc/top5: 0.8681 acc/mean1: 0.6530 2023/01/23 14:45:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_41.pth is removed 2023/01/23 14:45:40 - mmengine - INFO - The best checkpoint with 0.6531 acc/top1 at 42 epoch is saved to best_acc/top1_epoch_42.pth. 2023/01/23 14:48:44 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 14:49:22 - mmengine - INFO - Epoch(train) [43][ 100/1879] lr: 2.9739e-05 eta: 2 days, 17:10:18 time: 2.1346 data_time: 0.0366 memory: 48866 grad_norm: 4.5782 loss: 3.2386 loss_cls: 3.2386 2023/01/23 14:52:57 - mmengine - INFO - Epoch(train) [43][ 200/1879] lr: 2.9711e-05 eta: 2 days, 17:06:41 time: 2.1570 data_time: 0.0388 memory: 48866 grad_norm: 4.5764 loss: 3.3708 loss_cls: 3.3708 2023/01/23 14:56:32 - mmengine - INFO - Epoch(train) [43][ 300/1879] lr: 2.9682e-05 eta: 2 days, 17:03:05 time: 2.1513 data_time: 0.0392 memory: 48866 grad_norm: 4.6212 loss: 3.4045 loss_cls: 3.4045 2023/01/23 15:00:08 - mmengine - INFO - Epoch(train) [43][ 400/1879] lr: 2.9653e-05 eta: 2 days, 16:59:30 time: 2.1573 data_time: 0.0376 memory: 48866 grad_norm: 4.2948 loss: 3.4356 loss_cls: 3.4356 2023/01/23 15:03:42 - mmengine - INFO - Epoch(train) [43][ 500/1879] lr: 2.9624e-05 eta: 2 days, 16:55:53 time: 2.1502 data_time: 0.0397 memory: 48866 grad_norm: 4.6761 loss: 3.3564 loss_cls: 3.3564 2023/01/23 15:07:17 - mmengine - INFO - Epoch(train) [43][ 600/1879] lr: 2.9595e-05 eta: 2 days, 16:52:17 time: 2.1495 data_time: 0.0378 memory: 48866 grad_norm: 4.7098 loss: 3.2834 loss_cls: 3.2834 2023/01/23 15:10:53 - mmengine - INFO - Epoch(train) [43][ 700/1879] lr: 2.9566e-05 eta: 2 days, 16:48:41 time: 2.1541 data_time: 0.0383 memory: 48866 grad_norm: 4.4526 loss: 3.3291 loss_cls: 3.3291 2023/01/23 15:14:28 - mmengine - INFO - Epoch(train) [43][ 800/1879] lr: 2.9537e-05 eta: 2 days, 16:45:05 time: 2.1475 data_time: 0.0380 memory: 48866 grad_norm: 4.6423 loss: 3.3924 loss_cls: 3.3924 2023/01/23 15:18:03 - mmengine - INFO - Epoch(train) [43][ 900/1879] lr: 2.9508e-05 eta: 2 days, 16:41:29 time: 2.1532 data_time: 0.0386 memory: 48866 grad_norm: 4.7303 loss: 3.2653 loss_cls: 3.2653 2023/01/23 15:21:38 - mmengine - INFO - Epoch(train) [43][1000/1879] lr: 2.9479e-05 eta: 2 days, 16:37:53 time: 2.1434 data_time: 0.0386 memory: 48866 grad_norm: 4.3631 loss: 3.5843 loss_cls: 3.5843 2023/01/23 15:24:35 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 15:25:13 - mmengine - INFO - Epoch(train) [43][1100/1879] lr: 2.9450e-05 eta: 2 days, 16:34:17 time: 2.1362 data_time: 0.0392 memory: 48866 grad_norm: 4.5038 loss: 3.4474 loss_cls: 3.4474 2023/01/23 15:28:48 - mmengine - INFO - Epoch(train) [43][1200/1879] lr: 2.9421e-05 eta: 2 days, 16:30:42 time: 2.1452 data_time: 0.0382 memory: 48866 grad_norm: 4.5614 loss: 3.2880 loss_cls: 3.2880 2023/01/23 15:32:24 - mmengine - INFO - Epoch(train) [43][1300/1879] lr: 2.9392e-05 eta: 2 days, 16:27:06 time: 2.1503 data_time: 0.0387 memory: 48866 grad_norm: 4.6060 loss: 3.4746 loss_cls: 3.4746 2023/01/23 15:35:59 - mmengine - INFO - Epoch(train) [43][1400/1879] lr: 2.9362e-05 eta: 2 days, 16:23:30 time: 2.1580 data_time: 0.0378 memory: 48866 grad_norm: 4.6384 loss: 3.2222 loss_cls: 3.2222 2023/01/23 15:39:34 - mmengine - INFO - Epoch(train) [43][1500/1879] lr: 2.9333e-05 eta: 2 days, 16:19:54 time: 2.1499 data_time: 0.0387 memory: 48866 grad_norm: 4.4896 loss: 3.5013 loss_cls: 3.5013 2023/01/23 15:43:09 - mmengine - INFO - Epoch(train) [43][1600/1879] lr: 2.9304e-05 eta: 2 days, 16:16:18 time: 2.1503 data_time: 0.0382 memory: 48866 grad_norm: 4.5868 loss: 3.3084 loss_cls: 3.3084 2023/01/23 15:46:44 - mmengine - INFO - Epoch(train) [43][1700/1879] lr: 2.9274e-05 eta: 2 days, 16:12:42 time: 2.1575 data_time: 0.0398 memory: 48866 grad_norm: 4.5965 loss: 3.2809 loss_cls: 3.2809 2023/01/23 15:50:20 - mmengine - INFO - Epoch(train) [43][1800/1879] lr: 2.9245e-05 eta: 2 days, 16:09:07 time: 2.1495 data_time: 0.0394 memory: 48866 grad_norm: 4.4886 loss: 3.4202 loss_cls: 3.4202 2023/01/23 15:53:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 15:53:08 - mmengine - INFO - Epoch(train) [43][1879/1879] lr: 2.9221e-05 eta: 2 days, 16:06:14 time: 2.0882 data_time: 0.0421 memory: 48866 grad_norm: 4.4417 loss: 3.2114 loss_cls: 3.2114 2023/01/23 15:54:03 - mmengine - INFO - Epoch(val) [43][100/155] eta: 0:00:29 time: 0.5798 data_time: 0.2285 memory: 4950 2023/01/23 15:54:33 - mmengine - INFO - Epoch(val) [43][155/155] acc/top1: 0.6603 acc/top5: 0.8723 acc/mean1: 0.6602 2023/01/23 15:54:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_42.pth is removed 2023/01/23 15:54:37 - mmengine - INFO - The best checkpoint with 0.6603 acc/top1 at 43 epoch is saved to best_acc/top1_epoch_43.pth. 2023/01/23 15:58:18 - mmengine - INFO - Epoch(train) [44][ 100/1879] lr: 2.9192e-05 eta: 2 days, 16:02:46 time: 2.1518 data_time: 0.0385 memory: 48866 grad_norm: 4.4412 loss: 3.3129 loss_cls: 3.3129 2023/01/23 16:01:54 - mmengine - INFO - Epoch(train) [44][ 200/1879] lr: 2.9162e-05 eta: 2 days, 15:59:11 time: 2.1556 data_time: 0.0387 memory: 48866 grad_norm: 4.6416 loss: 3.4153 loss_cls: 3.4153 2023/01/23 16:02:01 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 16:05:30 - mmengine - INFO - Epoch(train) [44][ 300/1879] lr: 2.9132e-05 eta: 2 days, 15:55:36 time: 2.1550 data_time: 0.0388 memory: 48866 grad_norm: 4.5255 loss: 3.4062 loss_cls: 3.4062 2023/01/23 16:09:05 - mmengine - INFO - Epoch(train) [44][ 400/1879] lr: 2.9103e-05 eta: 2 days, 15:52:00 time: 2.1461 data_time: 0.0382 memory: 48866 grad_norm: 4.5006 loss: 3.3686 loss_cls: 3.3686 2023/01/23 16:12:41 - mmengine - INFO - Epoch(train) [44][ 500/1879] lr: 2.9073e-05 eta: 2 days, 15:48:25 time: 2.1563 data_time: 0.0392 memory: 48866 grad_norm: 4.6933 loss: 3.3594 loss_cls: 3.3594 2023/01/23 16:16:15 - mmengine - INFO - Epoch(train) [44][ 600/1879] lr: 2.9043e-05 eta: 2 days, 15:44:49 time: 2.1473 data_time: 0.0378 memory: 48866 grad_norm: 4.5302 loss: 3.3921 loss_cls: 3.3921 2023/01/23 16:19:50 - mmengine - INFO - Epoch(train) [44][ 700/1879] lr: 2.9013e-05 eta: 2 days, 15:41:12 time: 2.1412 data_time: 0.0388 memory: 48866 grad_norm: 4.5366 loss: 3.4874 loss_cls: 3.4874 2023/01/23 16:23:25 - mmengine - INFO - Epoch(train) [44][ 800/1879] lr: 2.8983e-05 eta: 2 days, 15:37:36 time: 2.1510 data_time: 0.0382 memory: 48866 grad_norm: 4.7494 loss: 3.4065 loss_cls: 3.4065 2023/01/23 16:27:00 - mmengine - INFO - Epoch(train) [44][ 900/1879] lr: 2.8953e-05 eta: 2 days, 15:34:00 time: 2.1526 data_time: 0.0388 memory: 48866 grad_norm: 4.4725 loss: 3.3931 loss_cls: 3.3931 2023/01/23 16:30:35 - mmengine - INFO - Epoch(train) [44][1000/1879] lr: 2.8923e-05 eta: 2 days, 15:30:24 time: 2.1555 data_time: 0.0390 memory: 48866 grad_norm: 4.5968 loss: 3.4359 loss_cls: 3.4359 2023/01/23 16:34:11 - mmengine - INFO - Epoch(train) [44][1100/1879] lr: 2.8893e-05 eta: 2 days, 15:26:49 time: 2.1629 data_time: 0.0389 memory: 48866 grad_norm: 4.6841 loss: 3.5321 loss_cls: 3.5321 2023/01/23 16:37:46 - mmengine - INFO - Epoch(train) [44][1200/1879] lr: 2.8863e-05 eta: 2 days, 15:23:13 time: 2.1472 data_time: 0.0389 memory: 48866 grad_norm: 4.4979 loss: 3.6240 loss_cls: 3.6240 2023/01/23 16:37:53 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 16:41:21 - mmengine - INFO - Epoch(train) [44][1300/1879] lr: 2.8833e-05 eta: 2 days, 15:19:37 time: 2.1543 data_time: 0.0390 memory: 48866 grad_norm: 4.3527 loss: 3.2967 loss_cls: 3.2967 2023/01/23 16:44:57 - mmengine - INFO - Epoch(train) [44][1400/1879] lr: 2.8803e-05 eta: 2 days, 15:16:01 time: 2.1474 data_time: 0.0390 memory: 48866 grad_norm: 4.6131 loss: 3.2738 loss_cls: 3.2738 2023/01/23 16:48:32 - mmengine - INFO - Epoch(train) [44][1500/1879] lr: 2.8772e-05 eta: 2 days, 15:12:25 time: 2.1442 data_time: 0.0387 memory: 48866 grad_norm: 4.4675 loss: 3.4666 loss_cls: 3.4666 2023/01/23 16:52:07 - mmengine - INFO - Epoch(train) [44][1600/1879] lr: 2.8742e-05 eta: 2 days, 15:08:50 time: 2.1565 data_time: 0.0388 memory: 48866 grad_norm: 4.8946 loss: 3.1972 loss_cls: 3.1972 2023/01/23 16:55:43 - mmengine - INFO - Epoch(train) [44][1700/1879] lr: 2.8712e-05 eta: 2 days, 15:05:14 time: 2.1583 data_time: 0.0395 memory: 48866 grad_norm: 4.4706 loss: 3.4365 loss_cls: 3.4365 2023/01/23 16:59:18 - mmengine - INFO - Epoch(train) [44][1800/1879] lr: 2.8681e-05 eta: 2 days, 15:01:39 time: 2.1547 data_time: 0.0386 memory: 48866 grad_norm: 4.4499 loss: 3.2105 loss_cls: 3.2105 2023/01/23 17:02:07 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 17:02:07 - mmengine - INFO - Epoch(train) [44][1879/1879] lr: 2.8657e-05 eta: 2 days, 14:58:47 time: 2.0931 data_time: 0.0397 memory: 48866 grad_norm: 4.7426 loss: 3.3102 loss_cls: 3.3102 2023/01/23 17:03:01 - mmengine - INFO - Epoch(val) [44][100/155] eta: 0:00:29 time: 0.5423 data_time: 0.2150 memory: 4950 2023/01/23 17:03:32 - mmengine - INFO - Epoch(val) [44][155/155] acc/top1: 0.6566 acc/top5: 0.8703 acc/mean1: 0.6565 2023/01/23 17:07:16 - mmengine - INFO - Epoch(train) [45][ 100/1879] lr: 2.8627e-05 eta: 2 days, 14:55:22 time: 2.1508 data_time: 0.0382 memory: 48866 grad_norm: 4.6522 loss: 3.2872 loss_cls: 3.2872 2023/01/23 17:10:51 - mmengine - INFO - Epoch(train) [45][ 200/1879] lr: 2.8596e-05 eta: 2 days, 14:51:46 time: 2.1478 data_time: 0.0388 memory: 48866 grad_norm: 4.6457 loss: 3.2848 loss_cls: 3.2848 2023/01/23 17:14:26 - mmengine - INFO - Epoch(train) [45][ 300/1879] lr: 2.8566e-05 eta: 2 days, 14:48:10 time: 2.1568 data_time: 0.0388 memory: 48866 grad_norm: 4.9487 loss: 3.4663 loss_cls: 3.4663 2023/01/23 17:15:18 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 17:18:02 - mmengine - INFO - Epoch(train) [45][ 400/1879] lr: 2.8535e-05 eta: 2 days, 14:44:35 time: 2.1522 data_time: 0.0386 memory: 48866 grad_norm: 4.3918 loss: 3.1802 loss_cls: 3.1802 2023/01/23 17:21:37 - mmengine - INFO - Epoch(train) [45][ 500/1879] lr: 2.8505e-05 eta: 2 days, 14:40:59 time: 2.1455 data_time: 0.0381 memory: 48866 grad_norm: 4.5883 loss: 3.3470 loss_cls: 3.3470 2023/01/23 17:25:13 - mmengine - INFO - Epoch(train) [45][ 600/1879] lr: 2.8474e-05 eta: 2 days, 14:37:24 time: 2.1654 data_time: 0.0386 memory: 48866 grad_norm: 4.9525 loss: 3.4419 loss_cls: 3.4419 2023/01/23 17:28:48 - mmengine - INFO - Epoch(train) [45][ 700/1879] lr: 2.8443e-05 eta: 2 days, 14:33:48 time: 2.1407 data_time: 0.0391 memory: 48866 grad_norm: 4.3616 loss: 3.4125 loss_cls: 3.4125 2023/01/23 17:32:24 - mmengine - INFO - Epoch(train) [45][ 800/1879] lr: 2.8412e-05 eta: 2 days, 14:30:13 time: 2.1465 data_time: 0.0387 memory: 48866 grad_norm: 4.6056 loss: 3.3471 loss_cls: 3.3471 2023/01/23 17:35:59 - mmengine - INFO - Epoch(train) [45][ 900/1879] lr: 2.8381e-05 eta: 2 days, 14:26:37 time: 2.1483 data_time: 0.0378 memory: 48866 grad_norm: 4.5168 loss: 3.2373 loss_cls: 3.2373 2023/01/23 17:39:34 - mmengine - INFO - Epoch(train) [45][1000/1879] lr: 2.8351e-05 eta: 2 days, 14:23:01 time: 2.1451 data_time: 0.0391 memory: 48866 grad_norm: 4.7044 loss: 3.4028 loss_cls: 3.4028 2023/01/23 17:43:09 - mmengine - INFO - Epoch(train) [45][1100/1879] lr: 2.8320e-05 eta: 2 days, 14:19:25 time: 2.1543 data_time: 0.0391 memory: 48866 grad_norm: 4.5360 loss: 3.4445 loss_cls: 3.4445 2023/01/23 17:46:45 - mmengine - INFO - Epoch(train) [45][1200/1879] lr: 2.8289e-05 eta: 2 days, 14:15:50 time: 2.1519 data_time: 0.0388 memory: 48866 grad_norm: 4.8506 loss: 3.3790 loss_cls: 3.3790 2023/01/23 17:50:20 - mmengine - INFO - Epoch(train) [45][1300/1879] lr: 2.8258e-05 eta: 2 days, 14:12:14 time: 2.1402 data_time: 0.0389 memory: 48866 grad_norm: 4.5676 loss: 3.4160 loss_cls: 3.4160 2023/01/23 17:51:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 17:53:56 - mmengine - INFO - Epoch(train) [45][1400/1879] lr: 2.8227e-05 eta: 2 days, 14:08:39 time: 2.1551 data_time: 0.0383 memory: 48866 grad_norm: 4.6703 loss: 3.3745 loss_cls: 3.3745 2023/01/23 17:57:30 - mmengine - INFO - Epoch(train) [45][1500/1879] lr: 2.8196e-05 eta: 2 days, 14:05:02 time: 2.1526 data_time: 0.0404 memory: 48866 grad_norm: 4.6883 loss: 3.5274 loss_cls: 3.5274 2023/01/23 18:01:06 - mmengine - INFO - Epoch(train) [45][1600/1879] lr: 2.8164e-05 eta: 2 days, 14:01:26 time: 2.1504 data_time: 0.0389 memory: 48866 grad_norm: 4.9461 loss: 3.3018 loss_cls: 3.3018 2023/01/23 18:04:41 - mmengine - INFO - Epoch(train) [45][1700/1879] lr: 2.8133e-05 eta: 2 days, 13:57:51 time: 2.1492 data_time: 0.0389 memory: 48866 grad_norm: 4.7650 loss: 3.2430 loss_cls: 3.2430 2023/01/23 18:08:16 - mmengine - INFO - Epoch(train) [45][1800/1879] lr: 2.8102e-05 eta: 2 days, 13:54:15 time: 2.1546 data_time: 0.0391 memory: 48866 grad_norm: 4.5852 loss: 3.2602 loss_cls: 3.2602 2023/01/23 18:11:06 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 18:11:06 - mmengine - INFO - Epoch(train) [45][1879/1879] lr: 2.8077e-05 eta: 2 days, 13:51:24 time: 2.0996 data_time: 0.0389 memory: 48866 grad_norm: 4.5009 loss: 3.1360 loss_cls: 3.1360 2023/01/23 18:11:06 - mmengine - INFO - Saving checkpoint at 45 epochs 2023/01/23 18:12:06 - mmengine - INFO - Epoch(val) [45][100/155] eta: 0:00:31 time: 0.5883 data_time: 0.2514 memory: 4950 2023/01/23 18:12:33 - mmengine - INFO - Epoch(val) [45][155/155] acc/top1: 0.6621 acc/top5: 0.8768 acc/mean1: 0.6621 2023/01/23 18:12:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_43.pth is removed 2023/01/23 18:12:37 - mmengine - INFO - The best checkpoint with 0.6621 acc/top1 at 45 epoch is saved to best_acc/top1_epoch_45.pth. 2023/01/23 18:16:19 - mmengine - INFO - Epoch(train) [46][ 100/1879] lr: 2.8046e-05 eta: 2 days, 13:47:56 time: 2.1519 data_time: 0.0393 memory: 48866 grad_norm: 4.6810 loss: 3.4663 loss_cls: 3.4663 2023/01/23 18:19:53 - mmengine - INFO - Epoch(train) [46][ 200/1879] lr: 2.8015e-05 eta: 2 days, 13:44:20 time: 2.1538 data_time: 0.0384 memory: 48866 grad_norm: 4.6355 loss: 3.1468 loss_cls: 3.1468 2023/01/23 18:23:29 - mmengine - INFO - Epoch(train) [46][ 300/1879] lr: 2.7983e-05 eta: 2 days, 13:40:44 time: 2.1468 data_time: 0.0384 memory: 48866 grad_norm: 4.7387 loss: 3.5925 loss_cls: 3.5925 2023/01/23 18:27:04 - mmengine - INFO - Epoch(train) [46][ 400/1879] lr: 2.7952e-05 eta: 2 days, 13:37:08 time: 2.1469 data_time: 0.0395 memory: 48866 grad_norm: 4.7945 loss: 3.2105 loss_cls: 3.2105 2023/01/23 18:28:41 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 18:30:39 - mmengine - INFO - Epoch(train) [46][ 500/1879] lr: 2.7920e-05 eta: 2 days, 13:33:32 time: 2.1546 data_time: 0.0385 memory: 48866 grad_norm: 4.9183 loss: 3.2297 loss_cls: 3.2297 2023/01/23 18:34:15 - mmengine - INFO - Epoch(train) [46][ 600/1879] lr: 2.7889e-05 eta: 2 days, 13:29:57 time: 2.1479 data_time: 0.0391 memory: 48866 grad_norm: 4.5121 loss: 3.3227 loss_cls: 3.3227 2023/01/23 18:37:49 - mmengine - INFO - Epoch(train) [46][ 700/1879] lr: 2.7857e-05 eta: 2 days, 13:26:20 time: 2.1477 data_time: 0.0395 memory: 48866 grad_norm: 4.5902 loss: 3.3500 loss_cls: 3.3500 2023/01/23 18:41:24 - mmengine - INFO - Epoch(train) [46][ 800/1879] lr: 2.7825e-05 eta: 2 days, 13:22:44 time: 2.1482 data_time: 0.0384 memory: 48866 grad_norm: 4.7201 loss: 3.3859 loss_cls: 3.3859 2023/01/23 18:44:59 - mmengine - INFO - Epoch(train) [46][ 900/1879] lr: 2.7794e-05 eta: 2 days, 13:19:08 time: 2.1546 data_time: 0.0398 memory: 48866 grad_norm: 4.6912 loss: 3.2383 loss_cls: 3.2383 2023/01/23 18:48:35 - mmengine - INFO - Epoch(train) [46][1000/1879] lr: 2.7762e-05 eta: 2 days, 13:15:33 time: 2.1541 data_time: 0.0393 memory: 48866 grad_norm: 4.8144 loss: 3.2845 loss_cls: 3.2845 2023/01/23 18:52:10 - mmengine - INFO - Epoch(train) [46][1100/1879] lr: 2.7730e-05 eta: 2 days, 13:11:57 time: 2.1530 data_time: 0.0393 memory: 48866 grad_norm: 4.8077 loss: 3.3423 loss_cls: 3.3423 2023/01/23 18:55:46 - mmengine - INFO - Epoch(train) [46][1200/1879] lr: 2.7699e-05 eta: 2 days, 13:08:21 time: 2.1588 data_time: 0.0397 memory: 48866 grad_norm: 4.8532 loss: 3.2017 loss_cls: 3.2017 2023/01/23 18:59:21 - mmengine - INFO - Epoch(train) [46][1300/1879] lr: 2.7667e-05 eta: 2 days, 13:04:46 time: 2.1526 data_time: 0.0389 memory: 48866 grad_norm: 4.6419 loss: 3.3739 loss_cls: 3.3739 2023/01/23 19:02:56 - mmengine - INFO - Epoch(train) [46][1400/1879] lr: 2.7635e-05 eta: 2 days, 13:01:09 time: 2.1614 data_time: 0.0399 memory: 48866 grad_norm: 4.5548 loss: 3.4104 loss_cls: 3.4104 2023/01/23 19:04:32 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 19:06:31 - mmengine - INFO - Epoch(train) [46][1500/1879] lr: 2.7603e-05 eta: 2 days, 12:57:33 time: 2.1561 data_time: 0.0388 memory: 48866 grad_norm: 4.8707 loss: 3.3446 loss_cls: 3.3446 2023/01/23 19:10:06 - mmengine - INFO - Epoch(train) [46][1600/1879] lr: 2.7571e-05 eta: 2 days, 12:53:57 time: 2.1491 data_time: 0.0395 memory: 48866 grad_norm: 4.7091 loss: 3.3058 loss_cls: 3.3058 2023/01/23 19:13:42 - mmengine - INFO - Epoch(train) [46][1700/1879] lr: 2.7539e-05 eta: 2 days, 12:50:22 time: 2.1546 data_time: 0.0389 memory: 48866 grad_norm: 4.4725 loss: 3.2087 loss_cls: 3.2087 2023/01/23 19:17:17 - mmengine - INFO - Epoch(train) [46][1800/1879] lr: 2.7507e-05 eta: 2 days, 12:46:47 time: 2.1617 data_time: 0.0396 memory: 48866 grad_norm: 4.4685 loss: 3.2364 loss_cls: 3.2364 2023/01/23 19:20:07 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 19:20:07 - mmengine - INFO - Epoch(train) [46][1879/1879] lr: 2.7482e-05 eta: 2 days, 12:43:56 time: 2.0994 data_time: 0.0394 memory: 48866 grad_norm: 4.8171 loss: 3.3084 loss_cls: 3.3084 2023/01/23 19:21:01 - mmengine - INFO - Epoch(val) [46][100/155] eta: 0:00:29 time: 0.5819 data_time: 0.2107 memory: 4950 2023/01/23 19:21:31 - mmengine - INFO - Epoch(val) [46][155/155] acc/top1: 0.6670 acc/top5: 0.8769 acc/mean1: 0.6671 2023/01/23 19:21:31 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_45.pth is removed 2023/01/23 19:21:35 - mmengine - INFO - The best checkpoint with 0.6670 acc/top1 at 46 epoch is saved to best_acc/top1_epoch_46.pth. 2023/01/23 19:25:18 - mmengine - INFO - Epoch(train) [47][ 100/1879] lr: 2.7449e-05 eta: 2 days, 12:40:29 time: 2.1558 data_time: 0.0385 memory: 48866 grad_norm: 4.3707 loss: 3.3676 loss_cls: 3.3676 2023/01/23 19:28:53 - mmengine - INFO - Epoch(train) [47][ 200/1879] lr: 2.7417e-05 eta: 2 days, 12:36:53 time: 2.1561 data_time: 0.0378 memory: 48866 grad_norm: 4.6852 loss: 3.3882 loss_cls: 3.3882 2023/01/23 19:32:28 - mmengine - INFO - Epoch(train) [47][ 300/1879] lr: 2.7385e-05 eta: 2 days, 12:33:17 time: 2.1618 data_time: 0.0392 memory: 48866 grad_norm: 4.8187 loss: 3.3954 loss_cls: 3.3954 2023/01/23 19:36:04 - mmengine - INFO - Epoch(train) [47][ 400/1879] lr: 2.7353e-05 eta: 2 days, 12:29:41 time: 2.1504 data_time: 0.0383 memory: 48866 grad_norm: 4.7016 loss: 3.3348 loss_cls: 3.3348 2023/01/23 19:39:39 - mmengine - INFO - Epoch(train) [47][ 500/1879] lr: 2.7321e-05 eta: 2 days, 12:26:06 time: 2.1624 data_time: 0.0396 memory: 48866 grad_norm: 4.6017 loss: 3.1984 loss_cls: 3.1984 2023/01/23 19:42:01 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 19:43:14 - mmengine - INFO - Epoch(train) [47][ 600/1879] lr: 2.7288e-05 eta: 2 days, 12:22:30 time: 2.1461 data_time: 0.0383 memory: 48866 grad_norm: 4.5504 loss: 3.1177 loss_cls: 3.1177 2023/01/23 19:46:50 - mmengine - INFO - Epoch(train) [47][ 700/1879] lr: 2.7256e-05 eta: 2 days, 12:18:55 time: 2.1632 data_time: 0.0397 memory: 48866 grad_norm: 4.4592 loss: 3.3022 loss_cls: 3.3022 2023/01/23 19:50:26 - mmengine - INFO - Epoch(train) [47][ 800/1879] lr: 2.7224e-05 eta: 2 days, 12:15:20 time: 2.1481 data_time: 0.0388 memory: 48866 grad_norm: 4.8443 loss: 3.2779 loss_cls: 3.2779 2023/01/23 19:54:01 - mmengine - INFO - Epoch(train) [47][ 900/1879] lr: 2.7191e-05 eta: 2 days, 12:11:44 time: 2.1570 data_time: 0.0396 memory: 48866 grad_norm: 4.5979 loss: 3.3384 loss_cls: 3.3384 2023/01/23 19:57:36 - mmengine - INFO - Epoch(train) [47][1000/1879] lr: 2.7159e-05 eta: 2 days, 12:08:08 time: 2.1501 data_time: 0.0396 memory: 48866 grad_norm: 4.6381 loss: 3.3130 loss_cls: 3.3130 2023/01/23 20:01:12 - mmengine - INFO - Epoch(train) [47][1100/1879] lr: 2.7126e-05 eta: 2 days, 12:04:32 time: 2.1668 data_time: 0.0391 memory: 48866 grad_norm: 4.6671 loss: 3.3396 loss_cls: 3.3396 2023/01/23 20:04:48 - mmengine - INFO - Epoch(train) [47][1200/1879] lr: 2.7094e-05 eta: 2 days, 12:00:57 time: 2.1599 data_time: 0.0396 memory: 48866 grad_norm: 4.7610 loss: 3.2295 loss_cls: 3.2295 2023/01/23 20:08:23 - mmengine - INFO - Epoch(train) [47][1300/1879] lr: 2.7061e-05 eta: 2 days, 11:57:22 time: 2.1584 data_time: 0.0399 memory: 48866 grad_norm: 4.5444 loss: 2.9964 loss_cls: 2.9964 2023/01/23 20:11:59 - mmengine - INFO - Epoch(train) [47][1400/1879] lr: 2.7028e-05 eta: 2 days, 11:53:46 time: 2.1546 data_time: 0.0381 memory: 48866 grad_norm: 4.7108 loss: 3.4410 loss_cls: 3.4410 2023/01/23 20:15:34 - mmengine - INFO - Epoch(train) [47][1500/1879] lr: 2.6996e-05 eta: 2 days, 11:50:11 time: 2.1569 data_time: 0.0397 memory: 48866 grad_norm: 4.8460 loss: 3.2402 loss_cls: 3.2402 2023/01/23 20:17:56 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 20:19:09 - mmengine - INFO - Epoch(train) [47][1600/1879] lr: 2.6963e-05 eta: 2 days, 11:46:35 time: 2.1573 data_time: 0.0392 memory: 48866 grad_norm: 4.6317 loss: 3.3843 loss_cls: 3.3843 2023/01/23 20:22:45 - mmengine - INFO - Epoch(train) [47][1700/1879] lr: 2.6930e-05 eta: 2 days, 11:42:59 time: 2.1472 data_time: 0.0398 memory: 48866 grad_norm: 4.8308 loss: 3.5051 loss_cls: 3.5051 2023/01/23 20:26:20 - mmengine - INFO - Epoch(train) [47][1800/1879] lr: 2.6897e-05 eta: 2 days, 11:39:24 time: 2.1607 data_time: 0.0386 memory: 48866 grad_norm: 4.6777 loss: 3.1576 loss_cls: 3.1576 2023/01/23 20:29:09 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 20:29:09 - mmengine - INFO - Epoch(train) [47][1879/1879] lr: 2.6871e-05 eta: 2 days, 11:36:32 time: 2.1048 data_time: 0.0406 memory: 48866 grad_norm: 4.7608 loss: 3.3012 loss_cls: 3.3012 2023/01/23 20:30:03 - mmengine - INFO - Epoch(val) [47][100/155] eta: 0:00:29 time: 0.5770 data_time: 0.2146 memory: 4950 2023/01/23 20:30:33 - mmengine - INFO - Epoch(val) [47][155/155] acc/top1: 0.6695 acc/top5: 0.8795 acc/mean1: 0.6693 2023/01/23 20:30:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_46.pth is removed 2023/01/23 20:30:37 - mmengine - INFO - The best checkpoint with 0.6695 acc/top1 at 47 epoch is saved to best_acc/top1_epoch_47.pth. 2023/01/23 20:34:19 - mmengine - INFO - Epoch(train) [48][ 100/1879] lr: 2.6839e-05 eta: 2 days, 11:33:05 time: 2.1519 data_time: 0.0382 memory: 48866 grad_norm: 4.5171 loss: 3.4328 loss_cls: 3.4328 2023/01/23 20:37:54 - mmengine - INFO - Epoch(train) [48][ 200/1879] lr: 2.6806e-05 eta: 2 days, 11:29:29 time: 2.1384 data_time: 0.0385 memory: 48866 grad_norm: 4.8660 loss: 3.0783 loss_cls: 3.0783 2023/01/23 20:41:30 - mmengine - INFO - Epoch(train) [48][ 300/1879] lr: 2.6773e-05 eta: 2 days, 11:25:53 time: 2.1525 data_time: 0.0384 memory: 48866 grad_norm: 4.8284 loss: 3.2282 loss_cls: 3.2282 2023/01/23 20:45:05 - mmengine - INFO - Epoch(train) [48][ 400/1879] lr: 2.6740e-05 eta: 2 days, 11:22:17 time: 2.1565 data_time: 0.0387 memory: 48866 grad_norm: 4.9187 loss: 3.2282 loss_cls: 3.2282 2023/01/23 20:48:40 - mmengine - INFO - Epoch(train) [48][ 500/1879] lr: 2.6707e-05 eta: 2 days, 11:18:41 time: 2.1502 data_time: 0.0391 memory: 48866 grad_norm: 4.5787 loss: 3.2432 loss_cls: 3.2432 2023/01/23 20:52:16 - mmengine - INFO - Epoch(train) [48][ 600/1879] lr: 2.6674e-05 eta: 2 days, 11:15:06 time: 2.1681 data_time: 0.0391 memory: 48866 grad_norm: 4.9072 loss: 3.2419 loss_cls: 3.2419 2023/01/23 20:55:23 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 20:55:51 - mmengine - INFO - Epoch(train) [48][ 700/1879] lr: 2.6641e-05 eta: 2 days, 11:11:30 time: 2.1505 data_time: 0.0387 memory: 48866 grad_norm: 4.5693 loss: 3.0768 loss_cls: 3.0768 2023/01/23 20:59:26 - mmengine - INFO - Epoch(train) [48][ 800/1879] lr: 2.6607e-05 eta: 2 days, 11:07:55 time: 2.1502 data_time: 0.0399 memory: 48866 grad_norm: 4.7501 loss: 3.2361 loss_cls: 3.2361 2023/01/23 21:03:02 - mmengine - INFO - Epoch(train) [48][ 900/1879] lr: 2.6574e-05 eta: 2 days, 11:04:19 time: 2.1703 data_time: 0.0403 memory: 48866 grad_norm: 4.6387 loss: 3.5193 loss_cls: 3.5193 2023/01/23 21:06:37 - mmengine - INFO - Epoch(train) [48][1000/1879] lr: 2.6541e-05 eta: 2 days, 11:00:43 time: 2.1454 data_time: 0.0391 memory: 48866 grad_norm: 4.7267 loss: 3.3664 loss_cls: 3.3664 2023/01/23 21:10:12 - mmengine - INFO - Epoch(train) [48][1100/1879] lr: 2.6508e-05 eta: 2 days, 10:57:07 time: 2.1625 data_time: 0.0400 memory: 48866 grad_norm: 4.9559 loss: 3.4218 loss_cls: 3.4218 2023/01/23 21:13:47 - mmengine - INFO - Epoch(train) [48][1200/1879] lr: 2.6475e-05 eta: 2 days, 10:53:32 time: 2.1542 data_time: 0.0398 memory: 48866 grad_norm: 4.7464 loss: 3.1498 loss_cls: 3.1498 2023/01/23 21:17:22 - mmengine - INFO - Epoch(train) [48][1300/1879] lr: 2.6441e-05 eta: 2 days, 10:49:56 time: 2.1571 data_time: 0.0397 memory: 48866 grad_norm: 4.8354 loss: 3.1321 loss_cls: 3.1321 2023/01/23 21:20:58 - mmengine - INFO - Epoch(train) [48][1400/1879] lr: 2.6408e-05 eta: 2 days, 10:46:20 time: 2.1513 data_time: 0.0389 memory: 48866 grad_norm: 4.8844 loss: 3.3703 loss_cls: 3.3703 2023/01/23 21:24:33 - mmengine - INFO - Epoch(train) [48][1500/1879] lr: 2.6375e-05 eta: 2 days, 10:42:45 time: 2.1383 data_time: 0.0396 memory: 48866 grad_norm: 4.8243 loss: 3.2065 loss_cls: 3.2065 2023/01/23 21:28:09 - mmengine - INFO - Epoch(train) [48][1600/1879] lr: 2.6341e-05 eta: 2 days, 10:39:10 time: 2.1654 data_time: 0.0390 memory: 48866 grad_norm: 4.7012 loss: 3.3326 loss_cls: 3.3326 2023/01/23 21:31:16 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 21:31:44 - mmengine - INFO - Epoch(train) [48][1700/1879] lr: 2.6308e-05 eta: 2 days, 10:35:33 time: 2.1516 data_time: 0.0400 memory: 48866 grad_norm: 4.8244 loss: 3.4168 loss_cls: 3.4168 2023/01/23 21:35:19 - mmengine - INFO - Epoch(train) [48][1800/1879] lr: 2.6274e-05 eta: 2 days, 10:31:57 time: 2.1531 data_time: 0.0391 memory: 48866 grad_norm: 4.8543 loss: 3.0477 loss_cls: 3.0477 2023/01/23 21:38:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 21:38:08 - mmengine - INFO - Epoch(train) [48][1879/1879] lr: 2.6248e-05 eta: 2 days, 10:29:06 time: 2.0882 data_time: 0.0394 memory: 48866 grad_norm: 4.7531 loss: 3.1999 loss_cls: 3.1999 2023/01/23 21:38:08 - mmengine - INFO - Saving checkpoint at 48 epochs 2023/01/23 21:39:08 - mmengine - INFO - Epoch(val) [48][100/155] eta: 0:00:30 time: 0.5863 data_time: 0.2349 memory: 4950 2023/01/23 21:39:35 - mmengine - INFO - Epoch(val) [48][155/155] acc/top1: 0.6711 acc/top5: 0.8792 acc/mean1: 0.6712 2023/01/23 21:39:35 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_47.pth is removed 2023/01/23 21:39:39 - mmengine - INFO - The best checkpoint with 0.6711 acc/top1 at 48 epoch is saved to best_acc/top1_epoch_48.pth. 2023/01/23 21:43:21 - mmengine - INFO - Epoch(train) [49][ 100/1879] lr: 2.6214e-05 eta: 2 days, 10:25:38 time: 2.1529 data_time: 0.0391 memory: 48866 grad_norm: 4.7134 loss: 3.4515 loss_cls: 3.4515 2023/01/23 21:46:56 - mmengine - INFO - Epoch(train) [49][ 200/1879] lr: 2.6181e-05 eta: 2 days, 10:22:02 time: 2.1509 data_time: 0.0387 memory: 48866 grad_norm: 4.9125 loss: 3.2397 loss_cls: 3.2397 2023/01/23 21:50:32 - mmengine - INFO - Epoch(train) [49][ 300/1879] lr: 2.6147e-05 eta: 2 days, 10:18:26 time: 2.1550 data_time: 0.0380 memory: 48866 grad_norm: 4.8148 loss: 3.0875 loss_cls: 3.0875 2023/01/23 21:54:07 - mmengine - INFO - Epoch(train) [49][ 400/1879] lr: 2.6113e-05 eta: 2 days, 10:14:50 time: 2.1434 data_time: 0.0384 memory: 48866 grad_norm: 4.8668 loss: 3.4558 loss_cls: 3.4558 2023/01/23 21:57:42 - mmengine - INFO - Epoch(train) [49][ 500/1879] lr: 2.6080e-05 eta: 2 days, 10:11:14 time: 2.1430 data_time: 0.0381 memory: 48866 grad_norm: 4.6766 loss: 3.3287 loss_cls: 3.3287 2023/01/23 22:01:18 - mmengine - INFO - Epoch(train) [49][ 600/1879] lr: 2.6046e-05 eta: 2 days, 10:07:39 time: 2.1643 data_time: 0.0394 memory: 48866 grad_norm: 4.5765 loss: 3.4639 loss_cls: 3.4639 2023/01/23 22:04:53 - mmengine - INFO - Epoch(train) [49][ 700/1879] lr: 2.6012e-05 eta: 2 days, 10:04:04 time: 2.1535 data_time: 0.0383 memory: 48866 grad_norm: 4.6808 loss: 3.4504 loss_cls: 3.4504 2023/01/23 22:08:29 - mmengine - INFO - Epoch(train) [49][ 800/1879] lr: 2.5978e-05 eta: 2 days, 10:00:28 time: 2.1559 data_time: 0.0383 memory: 48866 grad_norm: 4.8088 loss: 3.2988 loss_cls: 3.2988 2023/01/23 22:08:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 22:12:04 - mmengine - INFO - Epoch(train) [49][ 900/1879] lr: 2.5944e-05 eta: 2 days, 9:56:53 time: 2.1518 data_time: 0.0391 memory: 48866 grad_norm: 4.9247 loss: 3.3487 loss_cls: 3.3487 2023/01/23 22:15:40 - mmengine - INFO - Epoch(train) [49][1000/1879] lr: 2.5911e-05 eta: 2 days, 9:53:17 time: 2.1531 data_time: 0.0387 memory: 48866 grad_norm: 4.7725 loss: 3.3064 loss_cls: 3.3064 2023/01/23 22:19:15 - mmengine - INFO - Epoch(train) [49][1100/1879] lr: 2.5877e-05 eta: 2 days, 9:49:42 time: 2.1500 data_time: 0.0389 memory: 48866 grad_norm: 4.7340 loss: 3.3529 loss_cls: 3.3529 2023/01/23 22:22:51 - mmengine - INFO - Epoch(train) [49][1200/1879] lr: 2.5843e-05 eta: 2 days, 9:46:06 time: 2.1680 data_time: 0.0394 memory: 48866 grad_norm: 4.8010 loss: 3.2612 loss_cls: 3.2612 2023/01/23 22:26:26 - mmengine - INFO - Epoch(train) [49][1300/1879] lr: 2.5809e-05 eta: 2 days, 9:42:31 time: 2.1495 data_time: 0.0391 memory: 48866 grad_norm: 4.7308 loss: 3.1056 loss_cls: 3.1056 2023/01/23 22:30:01 - mmengine - INFO - Epoch(train) [49][1400/1879] lr: 2.5775e-05 eta: 2 days, 9:38:55 time: 2.1419 data_time: 0.0387 memory: 48866 grad_norm: 4.7109 loss: 3.3957 loss_cls: 3.3957 2023/01/23 22:33:37 - mmengine - INFO - Epoch(train) [49][1500/1879] lr: 2.5741e-05 eta: 2 days, 9:35:19 time: 2.1532 data_time: 0.0390 memory: 48866 grad_norm: 4.9192 loss: 3.2552 loss_cls: 3.2552 2023/01/23 22:37:12 - mmengine - INFO - Epoch(train) [49][1600/1879] lr: 2.5707e-05 eta: 2 days, 9:31:44 time: 2.1520 data_time: 0.0397 memory: 48866 grad_norm: 4.9122 loss: 2.9946 loss_cls: 2.9946 2023/01/23 22:40:47 - mmengine - INFO - Epoch(train) [49][1700/1879] lr: 2.5673e-05 eta: 2 days, 9:28:08 time: 2.1443 data_time: 0.0393 memory: 48866 grad_norm: 4.6874 loss: 3.2048 loss_cls: 3.2048 2023/01/23 22:44:23 - mmengine - INFO - Epoch(train) [49][1800/1879] lr: 2.5638e-05 eta: 2 days, 9:24:33 time: 2.1639 data_time: 0.0393 memory: 48866 grad_norm: 4.6480 loss: 3.2028 loss_cls: 3.2028 2023/01/23 22:44:40 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 22:47:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 22:47:12 - mmengine - INFO - Epoch(train) [49][1879/1879] lr: 2.5611e-05 eta: 2 days, 9:21:41 time: 2.0919 data_time: 0.0405 memory: 48866 grad_norm: 4.7763 loss: 3.2770 loss_cls: 3.2770 2023/01/23 22:48:07 - mmengine - INFO - Epoch(val) [49][100/155] eta: 0:00:30 time: 0.6145 data_time: 0.2627 memory: 4950 2023/01/23 22:48:37 - mmengine - INFO - Epoch(val) [49][155/155] acc/top1: 0.6759 acc/top5: 0.8839 acc/mean1: 0.6759 2023/01/23 22:48:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_48.pth is removed 2023/01/23 22:48:40 - mmengine - INFO - The best checkpoint with 0.6759 acc/top1 at 49 epoch is saved to best_acc/top1_epoch_49.pth. 2023/01/23 22:52:22 - mmengine - INFO - Epoch(train) [50][ 100/1879] lr: 2.5577e-05 eta: 2 days, 9:18:12 time: 2.1470 data_time: 0.0384 memory: 48866 grad_norm: 4.7391 loss: 3.2395 loss_cls: 3.2395 2023/01/23 22:55:57 - mmengine - INFO - Epoch(train) [50][ 200/1879] lr: 2.5543e-05 eta: 2 days, 9:14:36 time: 2.1597 data_time: 0.0383 memory: 48866 grad_norm: 4.8446 loss: 3.1496 loss_cls: 3.1496 2023/01/23 22:59:31 - mmengine - INFO - Epoch(train) [50][ 300/1879] lr: 2.5509e-05 eta: 2 days, 9:11:00 time: 2.1502 data_time: 0.0379 memory: 48866 grad_norm: 4.7737 loss: 3.1517 loss_cls: 3.1517 2023/01/23 23:03:07 - mmengine - INFO - Epoch(train) [50][ 400/1879] lr: 2.5474e-05 eta: 2 days, 9:07:24 time: 2.1590 data_time: 0.0396 memory: 48866 grad_norm: 4.9501 loss: 3.2255 loss_cls: 3.2255 2023/01/23 23:06:42 - mmengine - INFO - Epoch(train) [50][ 500/1879] lr: 2.5440e-05 eta: 2 days, 9:03:48 time: 2.1506 data_time: 0.0385 memory: 48866 grad_norm: 4.8266 loss: 3.2079 loss_cls: 3.2079 2023/01/23 23:10:17 - mmengine - INFO - Epoch(train) [50][ 600/1879] lr: 2.5406e-05 eta: 2 days, 9:00:12 time: 2.1463 data_time: 0.0389 memory: 48866 grad_norm: 4.9509 loss: 3.2620 loss_cls: 3.2620 2023/01/23 23:13:53 - mmengine - INFO - Epoch(train) [50][ 700/1879] lr: 2.5371e-05 eta: 2 days, 8:56:37 time: 2.1450 data_time: 0.0389 memory: 48866 grad_norm: 4.9579 loss: 3.4495 loss_cls: 3.4495 2023/01/23 23:17:28 - mmengine - INFO - Epoch(train) [50][ 800/1879] lr: 2.5337e-05 eta: 2 days, 8:53:01 time: 2.1604 data_time: 0.0386 memory: 48866 grad_norm: 4.6569 loss: 3.3248 loss_cls: 3.3248 2023/01/23 23:21:04 - mmengine - INFO - Epoch(train) [50][ 900/1879] lr: 2.5302e-05 eta: 2 days, 8:49:26 time: 2.1537 data_time: 0.0393 memory: 48866 grad_norm: 4.7928 loss: 3.2174 loss_cls: 3.2174 2023/01/23 23:22:06 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 23:24:39 - mmengine - INFO - Epoch(train) [50][1000/1879] lr: 2.5268e-05 eta: 2 days, 8:45:51 time: 2.1434 data_time: 0.0387 memory: 48866 grad_norm: 4.5487 loss: 3.4785 loss_cls: 3.4785 2023/01/23 23:28:14 - mmengine - INFO - Epoch(train) [50][1100/1879] lr: 2.5233e-05 eta: 2 days, 8:42:15 time: 2.1545 data_time: 0.0395 memory: 48866 grad_norm: 4.6436 loss: 3.2974 loss_cls: 3.2974 2023/01/23 23:31:50 - mmengine - INFO - Epoch(train) [50][1200/1879] lr: 2.5199e-05 eta: 2 days, 8:38:39 time: 2.1583 data_time: 0.0398 memory: 48866 grad_norm: 4.8570 loss: 3.3368 loss_cls: 3.3368 2023/01/23 23:35:25 - mmengine - INFO - Epoch(train) [50][1300/1879] lr: 2.5164e-05 eta: 2 days, 8:35:04 time: 2.1449 data_time: 0.0391 memory: 48866 grad_norm: 4.6549 loss: 3.1731 loss_cls: 3.1731 2023/01/23 23:39:01 - mmengine - INFO - Epoch(train) [50][1400/1879] lr: 2.5130e-05 eta: 2 days, 8:31:28 time: 2.1479 data_time: 0.0388 memory: 48866 grad_norm: 4.7909 loss: 3.2088 loss_cls: 3.2088 2023/01/23 23:42:36 - mmengine - INFO - Epoch(train) [50][1500/1879] lr: 2.5095e-05 eta: 2 days, 8:27:53 time: 2.1474 data_time: 0.0398 memory: 48866 grad_norm: 4.6527 loss: 3.2171 loss_cls: 3.2171 2023/01/23 23:46:12 - mmengine - INFO - Epoch(train) [50][1600/1879] lr: 2.5060e-05 eta: 2 days, 8:24:17 time: 2.1455 data_time: 0.0397 memory: 48866 grad_norm: 4.8044 loss: 3.2479 loss_cls: 3.2479 2023/01/23 23:49:47 - mmengine - INFO - Epoch(train) [50][1700/1879] lr: 2.5026e-05 eta: 2 days, 8:20:41 time: 2.1436 data_time: 0.0400 memory: 48866 grad_norm: 4.9681 loss: 3.4007 loss_cls: 3.4007 2023/01/23 23:53:22 - mmengine - INFO - Epoch(train) [50][1800/1879] lr: 2.4991e-05 eta: 2 days, 8:17:06 time: 2.1631 data_time: 0.0403 memory: 48866 grad_norm: 4.9608 loss: 3.2780 loss_cls: 3.2780 2023/01/23 23:56:11 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/23 23:56:11 - mmengine - INFO - Epoch(train) [50][1879/1879] lr: 2.4963e-05 eta: 2 days, 8:14:14 time: 2.0898 data_time: 0.0403 memory: 48866 grad_norm: 4.9349 loss: 3.2300 loss_cls: 3.2300 2023/01/23 23:57:05 - mmengine - INFO - Epoch(val) [50][100/155] eta: 0:00:29 time: 0.5673 data_time: 0.2041 memory: 4950 2023/01/23 23:57:36 - mmengine - INFO - Epoch(val) [50][155/155] acc/top1: 0.6744 acc/top5: 0.8797 acc/mean1: 0.6742 2023/01/23 23:59:31 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 00:01:18 - mmengine - INFO - Epoch(train) [51][ 100/1879] lr: 2.4929e-05 eta: 2 days, 8:10:46 time: 2.1542 data_time: 0.0395 memory: 48866 grad_norm: 4.9441 loss: 3.0469 loss_cls: 3.0469 2023/01/24 00:04:54 - mmengine - INFO - Epoch(train) [51][ 200/1879] lr: 2.4894e-05 eta: 2 days, 8:07:10 time: 2.1493 data_time: 0.0385 memory: 48866 grad_norm: 4.9157 loss: 3.4667 loss_cls: 3.4667 2023/01/24 00:08:28 - mmengine - INFO - Epoch(train) [51][ 300/1879] lr: 2.4859e-05 eta: 2 days, 8:03:34 time: 2.1488 data_time: 0.0525 memory: 48866 grad_norm: 5.0630 loss: 2.9423 loss_cls: 2.9423 2023/01/24 00:12:03 - mmengine - INFO - Epoch(train) [51][ 400/1879] lr: 2.4824e-05 eta: 2 days, 7:59:58 time: 2.1553 data_time: 0.0386 memory: 48866 grad_norm: 4.9218 loss: 3.0613 loss_cls: 3.0613 2023/01/24 00:15:39 - mmengine - INFO - Epoch(train) [51][ 500/1879] lr: 2.4789e-05 eta: 2 days, 7:56:22 time: 2.1492 data_time: 0.0393 memory: 48866 grad_norm: 4.9969 loss: 2.9267 loss_cls: 2.9267 2023/01/24 00:19:14 - mmengine - INFO - Epoch(train) [51][ 600/1879] lr: 2.4754e-05 eta: 2 days, 7:52:47 time: 2.1592 data_time: 0.0391 memory: 48866 grad_norm: 4.7152 loss: 3.3716 loss_cls: 3.3716 2023/01/24 00:22:49 - mmengine - INFO - Epoch(train) [51][ 700/1879] lr: 2.4719e-05 eta: 2 days, 7:49:11 time: 2.1476 data_time: 0.0391 memory: 48866 grad_norm: 4.7664 loss: 3.1757 loss_cls: 3.1757 2023/01/24 00:26:25 - mmengine - INFO - Epoch(train) [51][ 800/1879] lr: 2.4684e-05 eta: 2 days, 7:45:35 time: 2.1493 data_time: 0.0383 memory: 48866 grad_norm: 5.0573 loss: 3.3213 loss_cls: 3.3213 2023/01/24 00:30:00 - mmengine - INFO - Epoch(train) [51][ 900/1879] lr: 2.4649e-05 eta: 2 days, 7:41:59 time: 2.1562 data_time: 0.0400 memory: 48866 grad_norm: 4.9003 loss: 3.3440 loss_cls: 3.3440 2023/01/24 00:33:35 - mmengine - INFO - Epoch(train) [51][1000/1879] lr: 2.4614e-05 eta: 2 days, 7:38:23 time: 2.1394 data_time: 0.0391 memory: 48866 grad_norm: 4.8399 loss: 3.2244 loss_cls: 3.2244 2023/01/24 00:35:23 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 00:37:11 - mmengine - INFO - Epoch(train) [51][1100/1879] lr: 2.4579e-05 eta: 2 days, 7:34:48 time: 2.1613 data_time: 0.0398 memory: 48866 grad_norm: 4.8852 loss: 3.3745 loss_cls: 3.3745 2023/01/24 00:40:46 - mmengine - INFO - Epoch(train) [51][1200/1879] lr: 2.4544e-05 eta: 2 days, 7:31:12 time: 2.1560 data_time: 0.0395 memory: 48866 grad_norm: 5.0701 loss: 3.3578 loss_cls: 3.3578 2023/01/24 00:44:21 - mmengine - INFO - Epoch(train) [51][1300/1879] lr: 2.4509e-05 eta: 2 days, 7:27:37 time: 2.1417 data_time: 0.0390 memory: 48866 grad_norm: 4.7861 loss: 3.1735 loss_cls: 3.1735 2023/01/24 00:47:57 - mmengine - INFO - Epoch(train) [51][1400/1879] lr: 2.4474e-05 eta: 2 days, 7:24:01 time: 2.1511 data_time: 0.0382 memory: 48866 grad_norm: 4.8441 loss: 3.3450 loss_cls: 3.3450 2023/01/24 00:51:31 - mmengine - INFO - Epoch(train) [51][1500/1879] lr: 2.4439e-05 eta: 2 days, 7:20:25 time: 2.1421 data_time: 0.0384 memory: 48866 grad_norm: 4.6552 loss: 3.0470 loss_cls: 3.0470 2023/01/24 00:55:06 - mmengine - INFO - Epoch(train) [51][1600/1879] lr: 2.4403e-05 eta: 2 days, 7:16:48 time: 2.1563 data_time: 0.0395 memory: 48866 grad_norm: 4.7388 loss: 3.2529 loss_cls: 3.2529 2023/01/24 00:58:41 - mmengine - INFO - Epoch(train) [51][1700/1879] lr: 2.4368e-05 eta: 2 days, 7:13:13 time: 2.1565 data_time: 0.0409 memory: 48866 grad_norm: 4.5903 loss: 3.4181 loss_cls: 3.4181 2023/01/24 01:02:16 - mmengine - INFO - Epoch(train) [51][1800/1879] lr: 2.4333e-05 eta: 2 days, 7:09:36 time: 2.1568 data_time: 0.0400 memory: 48866 grad_norm: 4.7650 loss: 3.3574 loss_cls: 3.3574 2023/01/24 01:05:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 01:05:05 - mmengine - INFO - Epoch(train) [51][1879/1879] lr: 2.4305e-05 eta: 2 days, 7:06:45 time: 2.0903 data_time: 0.0407 memory: 48866 grad_norm: 4.8158 loss: 3.1477 loss_cls: 3.1477 2023/01/24 01:05:05 - mmengine - INFO - Saving checkpoint at 51 epochs 2023/01/24 01:06:04 - mmengine - INFO - Epoch(val) [51][100/155] eta: 0:00:30 time: 0.5198 data_time: 0.1798 memory: 4950 2023/01/24 01:06:32 - mmengine - INFO - Epoch(val) [51][155/155] acc/top1: 0.6847 acc/top5: 0.8864 acc/mean1: 0.6847 2023/01/24 01:06:32 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_49.pth is removed 2023/01/24 01:06:36 - mmengine - INFO - The best checkpoint with 0.6847 acc/top1 at 51 epoch is saved to best_acc/top1_epoch_51.pth. 2023/01/24 01:10:17 - mmengine - INFO - Epoch(train) [52][ 100/1879] lr: 2.4270e-05 eta: 2 days, 7:03:15 time: 2.1463 data_time: 0.0407 memory: 48866 grad_norm: 4.6928 loss: 3.1709 loss_cls: 3.1709 2023/01/24 01:12:50 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 01:13:52 - mmengine - INFO - Epoch(train) [52][ 200/1879] lr: 2.4234e-05 eta: 2 days, 6:59:39 time: 2.1571 data_time: 0.0387 memory: 48866 grad_norm: 4.9219 loss: 3.1884 loss_cls: 3.1884 2023/01/24 01:17:27 - mmengine - INFO - Epoch(train) [52][ 300/1879] lr: 2.4199e-05 eta: 2 days, 6:56:03 time: 2.1518 data_time: 0.0393 memory: 48866 grad_norm: 4.7074 loss: 3.3315 loss_cls: 3.3315 2023/01/24 01:21:03 - mmengine - INFO - Epoch(train) [52][ 400/1879] lr: 2.4163e-05 eta: 2 days, 6:52:28 time: 2.1571 data_time: 0.0401 memory: 48866 grad_norm: 5.0108 loss: 3.3248 loss_cls: 3.3248 2023/01/24 01:24:38 - mmengine - INFO - Epoch(train) [52][ 500/1879] lr: 2.4128e-05 eta: 2 days, 6:48:52 time: 2.1763 data_time: 0.0408 memory: 48866 grad_norm: 4.5895 loss: 3.3289 loss_cls: 3.3289 2023/01/24 01:28:14 - mmengine - INFO - Epoch(train) [52][ 600/1879] lr: 2.4093e-05 eta: 2 days, 6:45:17 time: 2.1558 data_time: 0.0403 memory: 48866 grad_norm: 4.8769 loss: 3.0203 loss_cls: 3.0203 2023/01/24 01:31:49 - mmengine - INFO - Epoch(train) [52][ 700/1879] lr: 2.4057e-05 eta: 2 days, 6:41:41 time: 2.1541 data_time: 0.0399 memory: 48866 grad_norm: 4.7132 loss: 3.2879 loss_cls: 3.2879 2023/01/24 01:35:25 - mmengine - INFO - Epoch(train) [52][ 800/1879] lr: 2.4021e-05 eta: 2 days, 6:38:06 time: 2.1647 data_time: 0.0399 memory: 48866 grad_norm: 4.9709 loss: 3.0510 loss_cls: 3.0510 2023/01/24 01:39:00 - mmengine - INFO - Epoch(train) [52][ 900/1879] lr: 2.3986e-05 eta: 2 days, 6:34:30 time: 2.1522 data_time: 0.0402 memory: 48866 grad_norm: 5.0663 loss: 3.0515 loss_cls: 3.0515 2023/01/24 01:42:36 - mmengine - INFO - Epoch(train) [52][1000/1879] lr: 2.3950e-05 eta: 2 days, 6:30:54 time: 2.1602 data_time: 0.0404 memory: 48866 grad_norm: 5.0615 loss: 3.3293 loss_cls: 3.3293 2023/01/24 01:46:11 - mmengine - INFO - Epoch(train) [52][1100/1879] lr: 2.3915e-05 eta: 2 days, 6:27:19 time: 2.1598 data_time: 0.0388 memory: 48866 grad_norm: 4.6874 loss: 3.1203 loss_cls: 3.1203 2023/01/24 01:48:44 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 01:49:47 - mmengine - INFO - Epoch(train) [52][1200/1879] lr: 2.3879e-05 eta: 2 days, 6:23:43 time: 2.1573 data_time: 0.0400 memory: 48866 grad_norm: 4.9359 loss: 3.0797 loss_cls: 3.0797 2023/01/24 01:53:22 - mmengine - INFO - Epoch(train) [52][1300/1879] lr: 2.3844e-05 eta: 2 days, 6:20:08 time: 2.1645 data_time: 0.0400 memory: 48866 grad_norm: 4.8362 loss: 3.3605 loss_cls: 3.3605 2023/01/24 01:56:58 - mmengine - INFO - Epoch(train) [52][1400/1879] lr: 2.3808e-05 eta: 2 days, 6:16:33 time: 2.1640 data_time: 0.0397 memory: 48866 grad_norm: 4.6105 loss: 3.3235 loss_cls: 3.3235 2023/01/24 02:00:34 - mmengine - INFO - Epoch(train) [52][1500/1879] lr: 2.3772e-05 eta: 2 days, 6:12:58 time: 2.1613 data_time: 0.0400 memory: 48866 grad_norm: 4.9239 loss: 3.1497 loss_cls: 3.1497 2023/01/24 02:04:10 - mmengine - INFO - Epoch(train) [52][1600/1879] lr: 2.3736e-05 eta: 2 days, 6:09:22 time: 2.1567 data_time: 0.0396 memory: 48866 grad_norm: 4.8076 loss: 3.2678 loss_cls: 3.2678 2023/01/24 02:07:45 - mmengine - INFO - Epoch(train) [52][1700/1879] lr: 2.3701e-05 eta: 2 days, 6:05:47 time: 2.1494 data_time: 0.0399 memory: 48866 grad_norm: 4.6816 loss: 3.1284 loss_cls: 3.1284 2023/01/24 02:11:21 - mmengine - INFO - Epoch(train) [52][1800/1879] lr: 2.3665e-05 eta: 2 days, 6:02:12 time: 2.1598 data_time: 0.0402 memory: 48866 grad_norm: 4.9512 loss: 3.2161 loss_cls: 3.2161 2023/01/24 02:14:10 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 02:14:10 - mmengine - INFO - Epoch(train) [52][1879/1879] lr: 2.3637e-05 eta: 2 days, 5:59:21 time: 2.1151 data_time: 0.0413 memory: 48866 grad_norm: 4.7626 loss: 3.0565 loss_cls: 3.0565 2023/01/24 02:15:05 - mmengine - INFO - Epoch(val) [52][100/155] eta: 0:00:30 time: 0.5982 data_time: 0.2461 memory: 4950 2023/01/24 02:15:35 - mmengine - INFO - Epoch(val) [52][155/155] acc/top1: 0.6859 acc/top5: 0.8857 acc/mean1: 0.6858 2023/01/24 02:15:35 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_51.pth is removed 2023/01/24 02:15:38 - mmengine - INFO - The best checkpoint with 0.6859 acc/top1 at 52 epoch is saved to best_acc/top1_epoch_52.pth. 2023/01/24 02:19:21 - mmengine - INFO - Epoch(train) [53][ 100/1879] lr: 2.3601e-05 eta: 2 days, 5:55:52 time: 2.1508 data_time: 0.0389 memory: 48866 grad_norm: 5.1476 loss: 3.3939 loss_cls: 3.3939 2023/01/24 02:22:56 - mmengine - INFO - Epoch(train) [53][ 200/1879] lr: 2.3565e-05 eta: 2 days, 5:52:16 time: 2.1594 data_time: 0.0401 memory: 48866 grad_norm: 4.9034 loss: 3.2038 loss_cls: 3.2038 2023/01/24 02:26:14 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 02:26:32 - mmengine - INFO - Epoch(train) [53][ 300/1879] lr: 2.3529e-05 eta: 2 days, 5:48:41 time: 2.1629 data_time: 0.0405 memory: 48866 grad_norm: 5.0129 loss: 3.3367 loss_cls: 3.3367 2023/01/24 02:30:07 - mmengine - INFO - Epoch(train) [53][ 400/1879] lr: 2.3493e-05 eta: 2 days, 5:45:05 time: 2.1469 data_time: 0.0395 memory: 48866 grad_norm: 4.8419 loss: 3.3519 loss_cls: 3.3519 2023/01/24 02:33:42 - mmengine - INFO - Epoch(train) [53][ 500/1879] lr: 2.3457e-05 eta: 2 days, 5:41:29 time: 2.1584 data_time: 0.0400 memory: 48866 grad_norm: 4.6633 loss: 3.3256 loss_cls: 3.3256 2023/01/24 02:37:17 - mmengine - INFO - Epoch(train) [53][ 600/1879] lr: 2.3421e-05 eta: 2 days, 5:37:53 time: 2.1522 data_time: 0.0392 memory: 48866 grad_norm: 4.7697 loss: 3.2947 loss_cls: 3.2947 2023/01/24 02:40:53 - mmengine - INFO - Epoch(train) [53][ 700/1879] lr: 2.3386e-05 eta: 2 days, 5:34:18 time: 2.1518 data_time: 0.0407 memory: 48866 grad_norm: 4.9205 loss: 3.3494 loss_cls: 3.3494 2023/01/24 02:44:29 - mmengine - INFO - Epoch(train) [53][ 800/1879] lr: 2.3350e-05 eta: 2 days, 5:30:42 time: 2.1536 data_time: 0.0395 memory: 48866 grad_norm: 5.1405 loss: 3.1547 loss_cls: 3.1547 2023/01/24 02:48:04 - mmengine - INFO - Epoch(train) [53][ 900/1879] lr: 2.3314e-05 eta: 2 days, 5:27:07 time: 2.1522 data_time: 0.0407 memory: 48866 grad_norm: 4.8937 loss: 3.1332 loss_cls: 3.1332 2023/01/24 02:51:40 - mmengine - INFO - Epoch(train) [53][1000/1879] lr: 2.3278e-05 eta: 2 days, 5:23:32 time: 2.1561 data_time: 0.0396 memory: 48866 grad_norm: 4.7596 loss: 3.2361 loss_cls: 3.2361 2023/01/24 02:55:16 - mmengine - INFO - Epoch(train) [53][1100/1879] lr: 2.3241e-05 eta: 2 days, 5:19:56 time: 2.1693 data_time: 0.0404 memory: 48866 grad_norm: 4.9040 loss: 3.2466 loss_cls: 3.2466 2023/01/24 02:58:51 - mmengine - INFO - Epoch(train) [53][1200/1879] lr: 2.3205e-05 eta: 2 days, 5:16:21 time: 2.1350 data_time: 0.0387 memory: 48866 grad_norm: 5.2548 loss: 2.9981 loss_cls: 2.9981 2023/01/24 03:02:09 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 03:02:26 - mmengine - INFO - Epoch(train) [53][1300/1879] lr: 2.3169e-05 eta: 2 days, 5:12:45 time: 2.1475 data_time: 0.0390 memory: 48866 grad_norm: 4.9832 loss: 3.3662 loss_cls: 3.3662 2023/01/24 03:06:02 - mmengine - INFO - Epoch(train) [53][1400/1879] lr: 2.3133e-05 eta: 2 days, 5:09:10 time: 2.1578 data_time: 0.0405 memory: 48866 grad_norm: 4.8801 loss: 3.1806 loss_cls: 3.1806 2023/01/24 03:09:38 - mmengine - INFO - Epoch(train) [53][1500/1879] lr: 2.3097e-05 eta: 2 days, 5:05:34 time: 2.1507 data_time: 0.0392 memory: 48866 grad_norm: 5.1380 loss: 3.1981 loss_cls: 3.1981 2023/01/24 03:13:13 - mmengine - INFO - Epoch(train) [53][1600/1879] lr: 2.3061e-05 eta: 2 days, 5:01:59 time: 2.1584 data_time: 0.0398 memory: 48866 grad_norm: 4.9865 loss: 3.1850 loss_cls: 3.1850 2023/01/24 03:16:49 - mmengine - INFO - Epoch(train) [53][1700/1879] lr: 2.3025e-05 eta: 2 days, 4:58:23 time: 2.1591 data_time: 0.0398 memory: 48866 grad_norm: 4.8342 loss: 3.2129 loss_cls: 3.2129 2023/01/24 03:20:25 - mmengine - INFO - Epoch(train) [53][1800/1879] lr: 2.2989e-05 eta: 2 days, 4:54:48 time: 2.1685 data_time: 0.0397 memory: 48866 grad_norm: 4.7869 loss: 3.2494 loss_cls: 3.2494 2023/01/24 03:23:14 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 03:23:14 - mmengine - INFO - Epoch(train) [53][1879/1879] lr: 2.2960e-05 eta: 2 days, 4:51:57 time: 2.0930 data_time: 0.0413 memory: 48866 grad_norm: 4.9991 loss: 3.2656 loss_cls: 3.2656 2023/01/24 03:24:07 - mmengine - INFO - Epoch(val) [53][100/155] eta: 0:00:29 time: 0.5521 data_time: 0.2042 memory: 4950 2023/01/24 03:24:38 - mmengine - INFO - Epoch(val) [53][155/155] acc/top1: 0.6848 acc/top5: 0.8834 acc/mean1: 0.6846 2023/01/24 03:28:22 - mmengine - INFO - Epoch(train) [54][ 100/1879] lr: 2.2924e-05 eta: 2 days, 4:48:28 time: 2.1673 data_time: 0.0399 memory: 48866 grad_norm: 5.2079 loss: 3.0727 loss_cls: 3.0727 2023/01/24 03:31:57 - mmengine - INFO - Epoch(train) [54][ 200/1879] lr: 2.2887e-05 eta: 2 days, 4:44:53 time: 2.1564 data_time: 0.0393 memory: 48866 grad_norm: 5.1373 loss: 3.3824 loss_cls: 3.3824 2023/01/24 03:35:33 - mmengine - INFO - Epoch(train) [54][ 300/1879] lr: 2.2851e-05 eta: 2 days, 4:41:18 time: 2.1481 data_time: 0.0389 memory: 48866 grad_norm: 5.1664 loss: 3.1506 loss_cls: 3.1506 2023/01/24 03:39:09 - mmengine - INFO - Epoch(train) [54][ 400/1879] lr: 2.2815e-05 eta: 2 days, 4:37:43 time: 2.1606 data_time: 0.0401 memory: 48866 grad_norm: 4.9132 loss: 2.9545 loss_cls: 2.9545 2023/01/24 03:39:37 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 03:42:45 - mmengine - INFO - Epoch(train) [54][ 500/1879] lr: 2.2779e-05 eta: 2 days, 4:34:08 time: 2.1600 data_time: 0.0395 memory: 48866 grad_norm: 4.9200 loss: 3.0883 loss_cls: 3.0883 2023/01/24 03:46:21 - mmengine - INFO - Epoch(train) [54][ 600/1879] lr: 2.2742e-05 eta: 2 days, 4:30:32 time: 2.1585 data_time: 0.0407 memory: 48866 grad_norm: 4.9918 loss: 3.2529 loss_cls: 3.2529 2023/01/24 03:49:57 - mmengine - INFO - Epoch(train) [54][ 700/1879] lr: 2.2706e-05 eta: 2 days, 4:26:57 time: 2.1693 data_time: 0.0401 memory: 48866 grad_norm: 4.9498 loss: 3.0795 loss_cls: 3.0795 2023/01/24 03:53:32 - mmengine - INFO - Epoch(train) [54][ 800/1879] lr: 2.2669e-05 eta: 2 days, 4:23:21 time: 2.1591 data_time: 0.0391 memory: 48866 grad_norm: 4.9210 loss: 3.1835 loss_cls: 3.1835 2023/01/24 03:57:07 - mmengine - INFO - Epoch(train) [54][ 900/1879] lr: 2.2633e-05 eta: 2 days, 4:19:46 time: 2.1501 data_time: 0.0393 memory: 48866 grad_norm: 4.7516 loss: 3.1565 loss_cls: 3.1565 2023/01/24 04:00:43 - mmengine - INFO - Epoch(train) [54][1000/1879] lr: 2.2597e-05 eta: 2 days, 4:16:10 time: 2.1619 data_time: 0.0409 memory: 48866 grad_norm: 4.7727 loss: 3.2111 loss_cls: 3.2111 2023/01/24 04:04:19 - mmengine - INFO - Epoch(train) [54][1100/1879] lr: 2.2560e-05 eta: 2 days, 4:12:35 time: 2.1580 data_time: 0.0406 memory: 48866 grad_norm: 4.9809 loss: 3.1523 loss_cls: 3.1523 2023/01/24 04:07:55 - mmengine - INFO - Epoch(train) [54][1200/1879] lr: 2.2524e-05 eta: 2 days, 4:09:00 time: 2.1558 data_time: 0.0408 memory: 48866 grad_norm: 4.8582 loss: 3.0262 loss_cls: 3.0262 2023/01/24 04:11:31 - mmengine - INFO - Epoch(train) [54][1300/1879] lr: 2.2487e-05 eta: 2 days, 4:05:25 time: 2.1683 data_time: 0.0390 memory: 48866 grad_norm: 5.1165 loss: 3.1817 loss_cls: 3.1817 2023/01/24 04:15:07 - mmengine - INFO - Epoch(train) [54][1400/1879] lr: 2.2451e-05 eta: 2 days, 4:01:49 time: 2.1615 data_time: 0.0396 memory: 48866 grad_norm: 4.9993 loss: 3.2780 loss_cls: 3.2780 2023/01/24 04:15:35 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 04:18:42 - mmengine - INFO - Epoch(train) [54][1500/1879] lr: 2.2414e-05 eta: 2 days, 3:58:14 time: 2.1631 data_time: 0.0410 memory: 48866 grad_norm: 4.9893 loss: 3.2294 loss_cls: 3.2294 2023/01/24 04:22:18 - mmengine - INFO - Epoch(train) [54][1600/1879] lr: 2.2378e-05 eta: 2 days, 3:54:38 time: 2.1611 data_time: 0.0403 memory: 48866 grad_norm: 4.8338 loss: 3.3478 loss_cls: 3.3478 2023/01/24 04:25:53 - mmengine - INFO - Epoch(train) [54][1700/1879] lr: 2.2341e-05 eta: 2 days, 3:51:03 time: 2.1511 data_time: 0.0402 memory: 48866 grad_norm: 4.8623 loss: 3.1200 loss_cls: 3.1200 2023/01/24 04:29:30 - mmengine - INFO - Epoch(train) [54][1800/1879] lr: 2.2305e-05 eta: 2 days, 3:47:28 time: 2.1568 data_time: 0.0406 memory: 48866 grad_norm: 5.2221 loss: 3.2402 loss_cls: 3.2402 2023/01/24 04:32:19 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 04:32:19 - mmengine - INFO - Epoch(train) [54][1879/1879] lr: 2.2276e-05 eta: 2 days, 3:44:37 time: 2.0926 data_time: 0.0408 memory: 48866 grad_norm: 5.1904 loss: 3.2438 loss_cls: 3.2438 2023/01/24 04:32:19 - mmengine - INFO - Saving checkpoint at 54 epochs 2023/01/24 04:33:20 - mmengine - INFO - Epoch(val) [54][100/155] eta: 0:00:30 time: 0.5754 data_time: 0.2237 memory: 4950 2023/01/24 04:33:47 - mmengine - INFO - Epoch(val) [54][155/155] acc/top1: 0.6867 acc/top5: 0.8875 acc/mean1: 0.6866 2023/01/24 04:33:47 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_52.pth is removed 2023/01/24 04:33:51 - mmengine - INFO - The best checkpoint with 0.6867 acc/top1 at 54 epoch is saved to best_acc/top1_epoch_54.pth. 2023/01/24 04:37:33 - mmengine - INFO - Epoch(train) [55][ 100/1879] lr: 2.2239e-05 eta: 2 days, 3:41:08 time: 2.1522 data_time: 0.0388 memory: 48866 grad_norm: 4.8583 loss: 3.2426 loss_cls: 3.2426 2023/01/24 04:41:08 - mmengine - INFO - Epoch(train) [55][ 200/1879] lr: 2.2202e-05 eta: 2 days, 3:37:32 time: 2.1438 data_time: 0.0395 memory: 48866 grad_norm: 4.9849 loss: 3.3174 loss_cls: 3.3174 2023/01/24 04:44:44 - mmengine - INFO - Epoch(train) [55][ 300/1879] lr: 2.2166e-05 eta: 2 days, 3:33:57 time: 2.1587 data_time: 0.0401 memory: 48866 grad_norm: 4.8661 loss: 3.2114 loss_cls: 3.2114 2023/01/24 04:48:20 - mmengine - INFO - Epoch(train) [55][ 400/1879] lr: 2.2129e-05 eta: 2 days, 3:30:21 time: 2.1399 data_time: 0.0397 memory: 48866 grad_norm: 5.0412 loss: 3.0325 loss_cls: 3.0325 2023/01/24 04:51:55 - mmengine - INFO - Epoch(train) [55][ 500/1879] lr: 2.2092e-05 eta: 2 days, 3:26:45 time: 2.1429 data_time: 0.0381 memory: 48866 grad_norm: 5.0636 loss: 3.1037 loss_cls: 3.1037 2023/01/24 04:53:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 04:55:31 - mmengine - INFO - Epoch(train) [55][ 600/1879] lr: 2.2056e-05 eta: 2 days, 3:23:10 time: 2.1552 data_time: 0.0396 memory: 48866 grad_norm: 4.9851 loss: 3.1250 loss_cls: 3.1250 2023/01/24 04:59:07 - mmengine - INFO - Epoch(train) [55][ 700/1879] lr: 2.2019e-05 eta: 2 days, 3:19:35 time: 2.1595 data_time: 0.0391 memory: 48866 grad_norm: 5.1406 loss: 3.2840 loss_cls: 3.2840 2023/01/24 05:02:43 - mmengine - INFO - Epoch(train) [55][ 800/1879] lr: 2.1982e-05 eta: 2 days, 3:16:00 time: 2.1520 data_time: 0.0396 memory: 48866 grad_norm: 4.9750 loss: 3.0644 loss_cls: 3.0644 2023/01/24 05:06:18 - mmengine - INFO - Epoch(train) [55][ 900/1879] lr: 2.1945e-05 eta: 2 days, 3:12:24 time: 2.1484 data_time: 0.0401 memory: 48866 grad_norm: 4.9978 loss: 2.9126 loss_cls: 2.9126 2023/01/24 05:09:53 - mmengine - INFO - Epoch(train) [55][1000/1879] lr: 2.1909e-05 eta: 2 days, 3:08:48 time: 2.1433 data_time: 0.0396 memory: 48866 grad_norm: 5.1070 loss: 3.1050 loss_cls: 3.1050 2023/01/24 05:13:29 - mmengine - INFO - Epoch(train) [55][1100/1879] lr: 2.1872e-05 eta: 2 days, 3:05:13 time: 2.1754 data_time: 0.0398 memory: 48866 grad_norm: 4.9082 loss: 3.1606 loss_cls: 3.1606 2023/01/24 05:17:05 - mmengine - INFO - Epoch(train) [55][1200/1879] lr: 2.1835e-05 eta: 2 days, 3:01:38 time: 2.1501 data_time: 0.0404 memory: 48866 grad_norm: 4.8996 loss: 3.2708 loss_cls: 3.2708 2023/01/24 05:20:42 - mmengine - INFO - Epoch(train) [55][1300/1879] lr: 2.1798e-05 eta: 2 days, 2:58:03 time: 2.1609 data_time: 0.0390 memory: 48866 grad_norm: 5.0583 loss: 3.1164 loss_cls: 3.1164 2023/01/24 05:24:18 - mmengine - INFO - Epoch(train) [55][1400/1879] lr: 2.1761e-05 eta: 2 days, 2:54:28 time: 2.1504 data_time: 0.0403 memory: 48866 grad_norm: 5.1828 loss: 3.1758 loss_cls: 3.1758 2023/01/24 05:27:53 - mmengine - INFO - Epoch(train) [55][1500/1879] lr: 2.1725e-05 eta: 2 days, 2:50:53 time: 2.1512 data_time: 0.0400 memory: 48866 grad_norm: 4.8386 loss: 3.0875 loss_cls: 3.0875 2023/01/24 05:29:06 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 05:31:29 - mmengine - INFO - Epoch(train) [55][1600/1879] lr: 2.1688e-05 eta: 2 days, 2:47:17 time: 2.1568 data_time: 0.0405 memory: 48866 grad_norm: 4.9409 loss: 2.9995 loss_cls: 2.9995 2023/01/24 05:35:05 - mmengine - INFO - Epoch(train) [55][1700/1879] lr: 2.1651e-05 eta: 2 days, 2:43:42 time: 2.1558 data_time: 0.0405 memory: 48866 grad_norm: 4.9427 loss: 3.3132 loss_cls: 3.3132 2023/01/24 05:38:41 - mmengine - INFO - Epoch(train) [55][1800/1879] lr: 2.1614e-05 eta: 2 days, 2:40:07 time: 2.1500 data_time: 0.0396 memory: 48866 grad_norm: 4.8162 loss: 3.2951 loss_cls: 3.2951 2023/01/24 05:41:31 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 05:41:31 - mmengine - INFO - Epoch(train) [55][1879/1879] lr: 2.1585e-05 eta: 2 days, 2:37:16 time: 2.0939 data_time: 0.0417 memory: 48866 grad_norm: 5.0639 loss: 3.2521 loss_cls: 3.2521 2023/01/24 05:42:24 - mmengine - INFO - Epoch(val) [55][100/155] eta: 0:00:29 time: 0.5578 data_time: 0.2003 memory: 4950 2023/01/24 05:42:55 - mmengine - INFO - Epoch(val) [55][155/155] acc/top1: 0.6930 acc/top5: 0.8914 acc/mean1: 0.6929 2023/01/24 05:42:55 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_54.pth is removed 2023/01/24 05:42:58 - mmengine - INFO - The best checkpoint with 0.6930 acc/top1 at 55 epoch is saved to best_acc/top1_epoch_55.pth. 2023/01/24 05:46:41 - mmengine - INFO - Epoch(train) [56][ 100/1879] lr: 2.1548e-05 eta: 2 days, 2:33:46 time: 2.1505 data_time: 0.0407 memory: 48866 grad_norm: 4.8881 loss: 3.2047 loss_cls: 3.2047 2023/01/24 05:50:17 - mmengine - INFO - Epoch(train) [56][ 200/1879] lr: 2.1511e-05 eta: 2 days, 2:30:11 time: 2.1533 data_time: 0.0403 memory: 48866 grad_norm: 5.1251 loss: 3.1134 loss_cls: 3.1134 2023/01/24 05:53:53 - mmengine - INFO - Epoch(train) [56][ 300/1879] lr: 2.1474e-05 eta: 2 days, 2:26:36 time: 2.1655 data_time: 0.0411 memory: 48866 grad_norm: 5.0102 loss: 3.1747 loss_cls: 3.1747 2023/01/24 05:57:28 - mmengine - INFO - Epoch(train) [56][ 400/1879] lr: 2.1437e-05 eta: 2 days, 2:23:00 time: 2.1685 data_time: 0.0397 memory: 48866 grad_norm: 5.0898 loss: 3.1600 loss_cls: 3.1600 2023/01/24 06:01:04 - mmengine - INFO - Epoch(train) [56][ 500/1879] lr: 2.1400e-05 eta: 2 days, 2:19:25 time: 2.1531 data_time: 0.0411 memory: 48866 grad_norm: 5.3342 loss: 3.0022 loss_cls: 3.0022 2023/01/24 06:04:40 - mmengine - INFO - Epoch(train) [56][ 600/1879] lr: 2.1363e-05 eta: 2 days, 2:15:50 time: 2.1580 data_time: 0.0403 memory: 48866 grad_norm: 4.9547 loss: 3.0947 loss_cls: 3.0947 2023/01/24 06:06:39 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 06:08:16 - mmengine - INFO - Epoch(train) [56][ 700/1879] lr: 2.1326e-05 eta: 2 days, 2:12:15 time: 2.1610 data_time: 0.0415 memory: 48866 grad_norm: 5.1534 loss: 3.1093 loss_cls: 3.1093 2023/01/24 06:11:52 - mmengine - INFO - Epoch(train) [56][ 800/1879] lr: 2.1289e-05 eta: 2 days, 2:08:40 time: 2.1646 data_time: 0.0394 memory: 48866 grad_norm: 4.9075 loss: 3.3734 loss_cls: 3.3734 2023/01/24 06:15:28 - mmengine - INFO - Epoch(train) [56][ 900/1879] lr: 2.1252e-05 eta: 2 days, 2:05:05 time: 2.1613 data_time: 0.0413 memory: 48866 grad_norm: 4.9529 loss: 3.1197 loss_cls: 3.1197 2023/01/24 06:19:04 - mmengine - INFO - Epoch(train) [56][1000/1879] lr: 2.1215e-05 eta: 2 days, 2:01:29 time: 2.1579 data_time: 0.0408 memory: 48866 grad_norm: 4.8957 loss: 3.2537 loss_cls: 3.2537 2023/01/24 06:22:40 - mmengine - INFO - Epoch(train) [56][1100/1879] lr: 2.1178e-05 eta: 2 days, 1:57:54 time: 2.1525 data_time: 0.0406 memory: 48866 grad_norm: 4.8249 loss: 3.3065 loss_cls: 3.3065 2023/01/24 06:26:16 - mmengine - INFO - Epoch(train) [56][1200/1879] lr: 2.1141e-05 eta: 2 days, 1:54:19 time: 2.1567 data_time: 0.0400 memory: 48866 grad_norm: 4.9813 loss: 3.1345 loss_cls: 3.1345 2023/01/24 06:29:52 - mmengine - INFO - Epoch(train) [56][1300/1879] lr: 2.1104e-05 eta: 2 days, 1:50:43 time: 2.1619 data_time: 0.0402 memory: 48866 grad_norm: 5.3791 loss: 3.2637 loss_cls: 3.2637 2023/01/24 06:33:27 - mmengine - INFO - Epoch(train) [56][1400/1879] lr: 2.1067e-05 eta: 2 days, 1:47:08 time: 2.1646 data_time: 0.0401 memory: 48866 grad_norm: 5.0087 loss: 3.1149 loss_cls: 3.1149 2023/01/24 06:37:03 - mmengine - INFO - Epoch(train) [56][1500/1879] lr: 2.1029e-05 eta: 2 days, 1:43:33 time: 2.1596 data_time: 0.0401 memory: 48866 grad_norm: 4.8100 loss: 3.2279 loss_cls: 3.2279 2023/01/24 06:40:39 - mmengine - INFO - Epoch(train) [56][1600/1879] lr: 2.0992e-05 eta: 2 days, 1:39:57 time: 2.1506 data_time: 0.0405 memory: 48866 grad_norm: 4.9378 loss: 3.1797 loss_cls: 3.1797 2023/01/24 06:42:37 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 06:44:14 - mmengine - INFO - Epoch(train) [56][1700/1879] lr: 2.0955e-05 eta: 2 days, 1:36:22 time: 2.1614 data_time: 0.0403 memory: 48866 grad_norm: 4.8928 loss: 3.2352 loss_cls: 3.2352 2023/01/24 06:47:50 - mmengine - INFO - Epoch(train) [56][1800/1879] lr: 2.0918e-05 eta: 2 days, 1:32:46 time: 2.1610 data_time: 0.0410 memory: 48866 grad_norm: 4.8071 loss: 3.2343 loss_cls: 3.2343 2023/01/24 06:50:40 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 06:50:40 - mmengine - INFO - Epoch(train) [56][1879/1879] lr: 2.0889e-05 eta: 2 days, 1:29:56 time: 2.1023 data_time: 0.0408 memory: 48866 grad_norm: 5.1812 loss: 3.0873 loss_cls: 3.0873 2023/01/24 06:51:34 - mmengine - INFO - Epoch(val) [56][100/155] eta: 0:00:29 time: 0.5734 data_time: 0.2291 memory: 4950 2023/01/24 06:52:05 - mmengine - INFO - Epoch(val) [56][155/155] acc/top1: 0.6911 acc/top5: 0.8897 acc/mean1: 0.6910 2023/01/24 06:55:48 - mmengine - INFO - Epoch(train) [57][ 100/1879] lr: 2.0851e-05 eta: 2 days, 1:26:27 time: 2.1494 data_time: 0.0400 memory: 48866 grad_norm: 4.9094 loss: 3.0363 loss_cls: 3.0363 2023/01/24 06:59:24 - mmengine - INFO - Epoch(train) [57][ 200/1879] lr: 2.0814e-05 eta: 2 days, 1:22:51 time: 2.1628 data_time: 0.0403 memory: 48866 grad_norm: 5.0582 loss: 3.0635 loss_cls: 3.0635 2023/01/24 07:03:00 - mmengine - INFO - Epoch(train) [57][ 300/1879] lr: 2.0777e-05 eta: 2 days, 1:19:16 time: 2.1615 data_time: 0.0401 memory: 48866 grad_norm: 5.0277 loss: 3.2464 loss_cls: 3.2464 2023/01/24 07:06:36 - mmengine - INFO - Epoch(train) [57][ 400/1879] lr: 2.0740e-05 eta: 2 days, 1:15:41 time: 2.1652 data_time: 0.0395 memory: 48866 grad_norm: 4.9789 loss: 3.1712 loss_cls: 3.1712 2023/01/24 07:10:12 - mmengine - INFO - Epoch(train) [57][ 500/1879] lr: 2.0703e-05 eta: 2 days, 1:12:06 time: 2.1624 data_time: 0.0406 memory: 48866 grad_norm: 4.9980 loss: 3.0580 loss_cls: 3.0580 2023/01/24 07:13:48 - mmengine - INFO - Epoch(train) [57][ 600/1879] lr: 2.0665e-05 eta: 2 days, 1:08:30 time: 2.1527 data_time: 0.0402 memory: 48866 grad_norm: 5.0128 loss: 3.1524 loss_cls: 3.1524 2023/01/24 07:17:24 - mmengine - INFO - Epoch(train) [57][ 700/1879] lr: 2.0628e-05 eta: 2 days, 1:04:56 time: 2.1705 data_time: 0.0404 memory: 48866 grad_norm: 5.3830 loss: 3.1930 loss_cls: 3.1930 2023/01/24 07:20:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 07:21:00 - mmengine - INFO - Epoch(train) [57][ 800/1879] lr: 2.0591e-05 eta: 2 days, 1:01:20 time: 2.1650 data_time: 0.0411 memory: 48866 grad_norm: 5.0311 loss: 3.3073 loss_cls: 3.3073 2023/01/24 07:24:36 - mmengine - INFO - Epoch(train) [57][ 900/1879] lr: 2.0554e-05 eta: 2 days, 0:57:45 time: 2.1636 data_time: 0.0411 memory: 48866 grad_norm: 4.8935 loss: 3.0316 loss_cls: 3.0316 2023/01/24 07:28:12 - mmengine - INFO - Epoch(train) [57][1000/1879] lr: 2.0516e-05 eta: 2 days, 0:54:09 time: 2.1614 data_time: 0.0401 memory: 48866 grad_norm: 4.8745 loss: 3.1484 loss_cls: 3.1484 2023/01/24 07:31:48 - mmengine - INFO - Epoch(train) [57][1100/1879] lr: 2.0479e-05 eta: 2 days, 0:50:34 time: 2.1598 data_time: 0.0411 memory: 48866 grad_norm: 5.0892 loss: 2.9856 loss_cls: 2.9856 2023/01/24 07:35:24 - mmengine - INFO - Epoch(train) [57][1200/1879] lr: 2.0442e-05 eta: 2 days, 0:46:59 time: 2.1614 data_time: 0.0404 memory: 48866 grad_norm: 5.2366 loss: 3.1367 loss_cls: 3.1367 2023/01/24 07:39:00 - mmengine - INFO - Epoch(train) [57][1300/1879] lr: 2.0404e-05 eta: 2 days, 0:43:24 time: 2.1528 data_time: 0.0392 memory: 48866 grad_norm: 5.1819 loss: 3.3793 loss_cls: 3.3793 2023/01/24 07:42:36 - mmengine - INFO - Epoch(train) [57][1400/1879] lr: 2.0367e-05 eta: 2 days, 0:39:49 time: 2.1801 data_time: 0.0406 memory: 48866 grad_norm: 5.1300 loss: 3.1769 loss_cls: 3.1769 2023/01/24 07:46:12 - mmengine - INFO - Epoch(train) [57][1500/1879] lr: 2.0330e-05 eta: 2 days, 0:36:14 time: 2.1446 data_time: 0.0400 memory: 48866 grad_norm: 4.9937 loss: 3.2543 loss_cls: 3.2543 2023/01/24 07:49:49 - mmengine - INFO - Epoch(train) [57][1600/1879] lr: 2.0292e-05 eta: 2 days, 0:32:39 time: 2.1612 data_time: 0.0400 memory: 48866 grad_norm: 5.0263 loss: 3.0009 loss_cls: 3.0009 2023/01/24 07:53:25 - mmengine - INFO - Epoch(train) [57][1700/1879] lr: 2.0255e-05 eta: 2 days, 0:29:04 time: 2.1587 data_time: 0.0397 memory: 48866 grad_norm: 4.8466 loss: 3.0667 loss_cls: 3.0667 2023/01/24 07:56:09 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 07:57:01 - mmengine - INFO - Epoch(train) [57][1800/1879] lr: 2.0218e-05 eta: 2 days, 0:25:29 time: 2.1554 data_time: 0.0398 memory: 48866 grad_norm: 4.7316 loss: 3.2954 loss_cls: 3.2954 2023/01/24 07:59:51 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 07:59:51 - mmengine - INFO - Epoch(train) [57][1879/1879] lr: 2.0188e-05 eta: 2 days, 0:22:38 time: 2.1018 data_time: 0.0425 memory: 48866 grad_norm: 5.1127 loss: 3.1813 loss_cls: 3.1813 2023/01/24 07:59:51 - mmengine - INFO - Saving checkpoint at 57 epochs 2023/01/24 08:00:49 - mmengine - INFO - Epoch(val) [57][100/155] eta: 0:00:30 time: 0.5410 data_time: 0.1877 memory: 4950 2023/01/24 08:01:18 - mmengine - INFO - Epoch(val) [57][155/155] acc/top1: 0.7016 acc/top5: 0.8938 acc/mean1: 0.7015 2023/01/24 08:01:18 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_55.pth is removed 2023/01/24 08:01:21 - mmengine - INFO - The best checkpoint with 0.7016 acc/top1 at 57 epoch is saved to best_acc/top1_epoch_57.pth. 2023/01/24 08:05:05 - mmengine - INFO - Epoch(train) [58][ 100/1879] lr: 2.0151e-05 eta: 2 days, 0:19:09 time: 2.1656 data_time: 0.0404 memory: 48866 grad_norm: 5.1949 loss: 2.7525 loss_cls: 2.7525 2023/01/24 08:08:41 - mmengine - INFO - Epoch(train) [58][ 200/1879] lr: 2.0113e-05 eta: 2 days, 0:15:33 time: 2.1604 data_time: 0.0410 memory: 48866 grad_norm: 4.6117 loss: 3.2457 loss_cls: 3.2457 2023/01/24 08:12:17 - mmengine - INFO - Epoch(train) [58][ 300/1879] lr: 2.0076e-05 eta: 2 days, 0:11:58 time: 2.1596 data_time: 0.0402 memory: 48866 grad_norm: 5.0800 loss: 3.1711 loss_cls: 3.1711 2023/01/24 08:15:53 - mmengine - INFO - Epoch(train) [58][ 400/1879] lr: 2.0038e-05 eta: 2 days, 0:08:22 time: 2.1527 data_time: 0.0402 memory: 48866 grad_norm: 5.3443 loss: 3.1189 loss_cls: 3.1189 2023/01/24 08:19:29 - mmengine - INFO - Epoch(train) [58][ 500/1879] lr: 2.0001e-05 eta: 2 days, 0:04:47 time: 2.1684 data_time: 0.0398 memory: 48866 grad_norm: 5.0966 loss: 3.2307 loss_cls: 3.2307 2023/01/24 08:23:04 - mmengine - INFO - Epoch(train) [58][ 600/1879] lr: 1.9964e-05 eta: 2 days, 0:01:12 time: 2.1575 data_time: 0.0414 memory: 48866 grad_norm: 4.9157 loss: 3.0244 loss_cls: 3.0244 2023/01/24 08:26:40 - mmengine - INFO - Epoch(train) [58][ 700/1879] lr: 1.9926e-05 eta: 1 day, 23:57:36 time: 2.1641 data_time: 0.0401 memory: 48866 grad_norm: 5.0717 loss: 3.2317 loss_cls: 3.2317 2023/01/24 08:30:16 - mmengine - INFO - Epoch(train) [58][ 800/1879] lr: 1.9889e-05 eta: 1 day, 23:54:01 time: 2.1498 data_time: 0.0397 memory: 48866 grad_norm: 5.0146 loss: 2.9710 loss_cls: 2.9710 2023/01/24 08:33:45 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 08:33:51 - mmengine - INFO - Epoch(train) [58][ 900/1879] lr: 1.9851e-05 eta: 1 day, 23:50:25 time: 2.1648 data_time: 0.0399 memory: 48866 grad_norm: 4.8382 loss: 3.1800 loss_cls: 3.1800 2023/01/24 08:37:27 - mmengine - INFO - Epoch(train) [58][1000/1879] lr: 1.9814e-05 eta: 1 day, 23:46:50 time: 2.1672 data_time: 0.0406 memory: 48866 grad_norm: 4.9076 loss: 3.1360 loss_cls: 3.1360 2023/01/24 08:41:04 - mmengine - INFO - Epoch(train) [58][1100/1879] lr: 1.9776e-05 eta: 1 day, 23:43:15 time: 2.1695 data_time: 0.0409 memory: 48866 grad_norm: 5.1623 loss: 3.1377 loss_cls: 3.1377 2023/01/24 08:44:40 - mmengine - INFO - Epoch(train) [58][1200/1879] lr: 1.9739e-05 eta: 1 day, 23:39:40 time: 2.1689 data_time: 0.0399 memory: 48866 grad_norm: 5.1435 loss: 3.0584 loss_cls: 3.0584 2023/01/24 08:48:16 - mmengine - INFO - Epoch(train) [58][1300/1879] lr: 1.9701e-05 eta: 1 day, 23:36:05 time: 2.1573 data_time: 0.0402 memory: 48866 grad_norm: 5.1694 loss: 2.9736 loss_cls: 2.9736 2023/01/24 08:51:52 - mmengine - INFO - Epoch(train) [58][1400/1879] lr: 1.9664e-05 eta: 1 day, 23:32:29 time: 2.1608 data_time: 0.0410 memory: 48866 grad_norm: 5.0768 loss: 3.1934 loss_cls: 3.1934 2023/01/24 08:55:28 - mmengine - INFO - Epoch(train) [58][1500/1879] lr: 1.9626e-05 eta: 1 day, 23:28:54 time: 2.1699 data_time: 0.0405 memory: 48866 grad_norm: 4.9700 loss: 3.0705 loss_cls: 3.0705 2023/01/24 08:59:03 - mmengine - INFO - Epoch(train) [58][1600/1879] lr: 1.9589e-05 eta: 1 day, 23:25:18 time: 2.1479 data_time: 0.0399 memory: 48866 grad_norm: 5.1341 loss: 3.2181 loss_cls: 3.2181 2023/01/24 09:02:39 - mmengine - INFO - Epoch(train) [58][1700/1879] lr: 1.9551e-05 eta: 1 day, 23:21:43 time: 2.1628 data_time: 0.0407 memory: 48866 grad_norm: 5.1328 loss: 3.0181 loss_cls: 3.0181 2023/01/24 09:06:15 - mmengine - INFO - Epoch(train) [58][1800/1879] lr: 1.9514e-05 eta: 1 day, 23:18:08 time: 2.1756 data_time: 0.0402 memory: 48866 grad_norm: 4.9993 loss: 3.3356 loss_cls: 3.3356 2023/01/24 09:09:04 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 09:09:04 - mmengine - INFO - Epoch(train) [58][1879/1879] lr: 1.9484e-05 eta: 1 day, 23:15:17 time: 2.1027 data_time: 0.0396 memory: 48866 grad_norm: 4.8902 loss: 3.2616 loss_cls: 3.2616 2023/01/24 09:09:57 - mmengine - INFO - Epoch(val) [58][100/155] eta: 0:00:29 time: 0.5374 data_time: 0.1961 memory: 4950 2023/01/24 09:10:28 - mmengine - INFO - Epoch(val) [58][155/155] acc/top1: 0.7014 acc/top5: 0.8976 acc/mean1: 0.7012 2023/01/24 09:11:14 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 09:14:12 - mmengine - INFO - Epoch(train) [59][ 100/1879] lr: 1.9447e-05 eta: 1 day, 23:11:47 time: 2.1506 data_time: 0.0556 memory: 48866 grad_norm: 4.9506 loss: 3.1278 loss_cls: 3.1278 2023/01/24 09:17:48 - mmengine - INFO - Epoch(train) [59][ 200/1879] lr: 1.9409e-05 eta: 1 day, 23:08:11 time: 2.1739 data_time: 0.0397 memory: 48866 grad_norm: 4.9679 loss: 3.2384 loss_cls: 3.2384 2023/01/24 09:21:24 - mmengine - INFO - Epoch(train) [59][ 300/1879] lr: 1.9372e-05 eta: 1 day, 23:04:36 time: 2.1570 data_time: 0.0406 memory: 48866 grad_norm: 4.8615 loss: 3.2584 loss_cls: 3.2584 2023/01/24 09:24:59 - mmengine - INFO - Epoch(train) [59][ 400/1879] lr: 1.9334e-05 eta: 1 day, 23:01:01 time: 2.1612 data_time: 0.0398 memory: 48866 grad_norm: 5.1407 loss: 2.9795 loss_cls: 2.9795 2023/01/24 09:28:35 - mmengine - INFO - Epoch(train) [59][ 500/1879] lr: 1.9297e-05 eta: 1 day, 22:57:25 time: 2.1562 data_time: 0.0403 memory: 48866 grad_norm: 5.0195 loss: 3.0007 loss_cls: 3.0007 2023/01/24 09:32:12 - mmengine - INFO - Epoch(train) [59][ 600/1879] lr: 1.9259e-05 eta: 1 day, 22:53:50 time: 2.1878 data_time: 0.0407 memory: 48866 grad_norm: 5.2591 loss: 3.0887 loss_cls: 3.0887 2023/01/24 09:35:48 - mmengine - INFO - Epoch(train) [59][ 700/1879] lr: 1.9221e-05 eta: 1 day, 22:50:15 time: 2.1623 data_time: 0.0386 memory: 48866 grad_norm: 5.0138 loss: 3.0112 loss_cls: 3.0112 2023/01/24 09:39:24 - mmengine - INFO - Epoch(train) [59][ 800/1879] lr: 1.9184e-05 eta: 1 day, 22:46:40 time: 2.1733 data_time: 0.0401 memory: 48866 grad_norm: 4.9975 loss: 3.0663 loss_cls: 3.0663 2023/01/24 09:43:01 - mmengine - INFO - Epoch(train) [59][ 900/1879] lr: 1.9146e-05 eta: 1 day, 22:43:05 time: 2.1684 data_time: 0.0408 memory: 48866 grad_norm: 5.0272 loss: 2.8673 loss_cls: 2.8673 2023/01/24 09:46:37 - mmengine - INFO - Epoch(train) [59][1000/1879] lr: 1.9109e-05 eta: 1 day, 22:39:30 time: 2.1565 data_time: 0.0408 memory: 48866 grad_norm: 5.4277 loss: 2.9084 loss_cls: 2.9084 2023/01/24 09:47:16 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 09:50:13 - mmengine - INFO - Epoch(train) [59][1100/1879] lr: 1.9071e-05 eta: 1 day, 22:35:55 time: 2.1596 data_time: 0.0404 memory: 48866 grad_norm: 5.1850 loss: 3.3315 loss_cls: 3.3315 2023/01/24 09:53:49 - mmengine - INFO - Epoch(train) [59][1200/1879] lr: 1.9034e-05 eta: 1 day, 22:32:19 time: 2.1677 data_time: 0.0394 memory: 48866 grad_norm: 5.1895 loss: 3.2294 loss_cls: 3.2294 2023/01/24 09:57:25 - mmengine - INFO - Epoch(train) [59][1300/1879] lr: 1.8996e-05 eta: 1 day, 22:28:44 time: 2.1469 data_time: 0.0404 memory: 48866 grad_norm: 5.2339 loss: 3.1394 loss_cls: 3.1394 2023/01/24 10:01:01 - mmengine - INFO - Epoch(train) [59][1400/1879] lr: 1.8958e-05 eta: 1 day, 22:25:09 time: 2.1698 data_time: 0.0397 memory: 48866 grad_norm: 5.0477 loss: 3.2185 loss_cls: 3.2185 2023/01/24 10:04:37 - mmengine - INFO - Epoch(train) [59][1500/1879] lr: 1.8921e-05 eta: 1 day, 22:21:34 time: 2.1592 data_time: 0.0399 memory: 48866 grad_norm: 5.2211 loss: 3.2693 loss_cls: 3.2693 2023/01/24 10:08:13 - mmengine - INFO - Epoch(train) [59][1600/1879] lr: 1.8883e-05 eta: 1 day, 22:17:58 time: 2.1566 data_time: 0.0407 memory: 48866 grad_norm: 5.0508 loss: 3.1906 loss_cls: 3.1906 2023/01/24 10:11:48 - mmengine - INFO - Epoch(train) [59][1700/1879] lr: 1.8846e-05 eta: 1 day, 22:14:23 time: 2.1637 data_time: 0.0402 memory: 48866 grad_norm: 5.4482 loss: 3.3413 loss_cls: 3.3413 2023/01/24 10:15:24 - mmengine - INFO - Epoch(train) [59][1800/1879] lr: 1.8808e-05 eta: 1 day, 22:10:47 time: 2.1602 data_time: 0.0403 memory: 48866 grad_norm: 5.0637 loss: 3.0514 loss_cls: 3.0514 2023/01/24 10:18:14 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 10:18:14 - mmengine - INFO - Epoch(train) [59][1879/1879] lr: 1.8778e-05 eta: 1 day, 22:07:56 time: 2.0957 data_time: 0.0395 memory: 48866 grad_norm: 4.9407 loss: 3.0917 loss_cls: 3.0917 2023/01/24 10:19:07 - mmengine - INFO - Epoch(val) [59][100/155] eta: 0:00:29 time: 0.5432 data_time: 0.1872 memory: 4950 2023/01/24 10:19:37 - mmengine - INFO - Epoch(val) [59][155/155] acc/top1: 0.7001 acc/top5: 0.8915 acc/mean1: 0.7000 2023/01/24 10:23:21 - mmengine - INFO - Epoch(train) [60][ 100/1879] lr: 1.8741e-05 eta: 1 day, 22:04:26 time: 2.1584 data_time: 0.0401 memory: 48866 grad_norm: 5.0648 loss: 3.0346 loss_cls: 3.0346 2023/01/24 10:24:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 10:26:58 - mmengine - INFO - Epoch(train) [60][ 200/1879] lr: 1.8703e-05 eta: 1 day, 22:00:51 time: 2.1557 data_time: 0.0400 memory: 48866 grad_norm: 5.3982 loss: 3.0783 loss_cls: 3.0783 2023/01/24 10:30:34 - mmengine - INFO - Epoch(train) [60][ 300/1879] lr: 1.8665e-05 eta: 1 day, 21:57:16 time: 2.1542 data_time: 0.0405 memory: 48866 grad_norm: 4.9899 loss: 3.1592 loss_cls: 3.1592 2023/01/24 10:34:09 - mmengine - INFO - Epoch(train) [60][ 400/1879] lr: 1.8628e-05 eta: 1 day, 21:53:40 time: 2.1485 data_time: 0.0403 memory: 48866 grad_norm: 5.0021 loss: 3.1443 loss_cls: 3.1443 2023/01/24 10:37:45 - mmengine - INFO - Epoch(train) [60][ 500/1879] lr: 1.8590e-05 eta: 1 day, 21:50:05 time: 2.1555 data_time: 0.0411 memory: 48866 grad_norm: 5.3885 loss: 3.0885 loss_cls: 3.0885 2023/01/24 10:41:21 - mmengine - INFO - Epoch(train) [60][ 600/1879] lr: 1.8552e-05 eta: 1 day, 21:46:29 time: 2.1444 data_time: 0.0404 memory: 48866 grad_norm: 4.9107 loss: 3.0562 loss_cls: 3.0562 2023/01/24 10:44:57 - mmengine - INFO - Epoch(train) [60][ 700/1879] lr: 1.8515e-05 eta: 1 day, 21:42:54 time: 2.1547 data_time: 0.0411 memory: 48866 grad_norm: 5.3764 loss: 3.2549 loss_cls: 3.2549 2023/01/24 10:48:32 - mmengine - INFO - Epoch(train) [60][ 800/1879] lr: 1.8477e-05 eta: 1 day, 21:39:19 time: 2.1524 data_time: 0.0400 memory: 48866 grad_norm: 5.0386 loss: 3.0471 loss_cls: 3.0471 2023/01/24 10:52:09 - mmengine - INFO - Epoch(train) [60][ 900/1879] lr: 1.8440e-05 eta: 1 day, 21:35:44 time: 2.1662 data_time: 0.0406 memory: 48866 grad_norm: 5.1991 loss: 3.2409 loss_cls: 3.2409 2023/01/24 10:55:45 - mmengine - INFO - Epoch(train) [60][1000/1879] lr: 1.8402e-05 eta: 1 day, 21:32:08 time: 2.1548 data_time: 0.0404 memory: 48866 grad_norm: 5.4078 loss: 3.0395 loss_cls: 3.0395 2023/01/24 10:59:21 - mmengine - INFO - Epoch(train) [60][1100/1879] lr: 1.8364e-05 eta: 1 day, 21:28:33 time: 2.1620 data_time: 0.0408 memory: 48866 grad_norm: 5.1706 loss: 2.9462 loss_cls: 2.9462 2023/01/24 11:00:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 11:02:57 - mmengine - INFO - Epoch(train) [60][1200/1879] lr: 1.8327e-05 eta: 1 day, 21:24:58 time: 2.1504 data_time: 0.0405 memory: 48866 grad_norm: 5.3073 loss: 3.0605 loss_cls: 3.0605 2023/01/24 11:06:33 - mmengine - INFO - Epoch(train) [60][1300/1879] lr: 1.8289e-05 eta: 1 day, 21:21:23 time: 2.1517 data_time: 0.0403 memory: 48866 grad_norm: 4.9249 loss: 3.1486 loss_cls: 3.1486 2023/01/24 11:10:09 - mmengine - INFO - Epoch(train) [60][1400/1879] lr: 1.8251e-05 eta: 1 day, 21:17:47 time: 2.1523 data_time: 0.0394 memory: 48866 grad_norm: 5.3682 loss: 3.1992 loss_cls: 3.1992 2023/01/24 11:13:45 - mmengine - INFO - Epoch(train) [60][1500/1879] lr: 1.8214e-05 eta: 1 day, 21:14:12 time: 2.1612 data_time: 0.0405 memory: 48866 grad_norm: 5.1246 loss: 3.0567 loss_cls: 3.0567 2023/01/24 11:17:22 - mmengine - INFO - Epoch(train) [60][1600/1879] lr: 1.8176e-05 eta: 1 day, 21:10:37 time: 2.1597 data_time: 0.0399 memory: 48866 grad_norm: 4.8374 loss: 3.1135 loss_cls: 3.1135 2023/01/24 11:20:57 - mmengine - INFO - Epoch(train) [60][1700/1879] lr: 1.8138e-05 eta: 1 day, 21:07:01 time: 2.1585 data_time: 0.0396 memory: 48866 grad_norm: 5.0443 loss: 3.2971 loss_cls: 3.2971 2023/01/24 11:24:34 - mmengine - INFO - Epoch(train) [60][1800/1879] lr: 1.8101e-05 eta: 1 day, 21:03:26 time: 2.1536 data_time: 0.0403 memory: 48866 grad_norm: 5.1109 loss: 3.3039 loss_cls: 3.3039 2023/01/24 11:27:24 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 11:27:24 - mmengine - INFO - Epoch(train) [60][1879/1879] lr: 1.8071e-05 eta: 1 day, 21:00:36 time: 2.1005 data_time: 0.0394 memory: 48866 grad_norm: 5.3987 loss: 3.0202 loss_cls: 3.0202 2023/01/24 11:27:24 - mmengine - INFO - Saving checkpoint at 60 epochs 2023/01/24 11:28:23 - mmengine - INFO - Epoch(val) [60][100/155] eta: 0:00:30 time: 0.5452 data_time: 0.1925 memory: 4950 2023/01/24 11:28:50 - mmengine - INFO - Epoch(val) [60][155/155] acc/top1: 0.7019 acc/top5: 0.8961 acc/mean1: 0.7019 2023/01/24 11:28:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_57.pth is removed 2023/01/24 11:28:54 - mmengine - INFO - The best checkpoint with 0.7019 acc/top1 at 60 epoch is saved to best_acc/top1_epoch_60.pth. 2023/01/24 11:32:35 - mmengine - INFO - Epoch(train) [61][ 100/1879] lr: 1.8033e-05 eta: 1 day, 20:57:04 time: 2.1555 data_time: 0.0402 memory: 48866 grad_norm: 5.1699 loss: 3.1813 loss_cls: 3.1813 2023/01/24 11:36:11 - mmengine - INFO - Epoch(train) [61][ 200/1879] lr: 1.7996e-05 eta: 1 day, 20:53:28 time: 2.1443 data_time: 0.0401 memory: 48866 grad_norm: 5.3880 loss: 3.1124 loss_cls: 3.1124 2023/01/24 11:38:21 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 11:39:47 - mmengine - INFO - Epoch(train) [61][ 300/1879] lr: 1.7958e-05 eta: 1 day, 20:49:53 time: 2.1457 data_time: 0.0406 memory: 48866 grad_norm: 5.2806 loss: 2.9982 loss_cls: 2.9982 2023/01/24 11:43:22 - mmengine - INFO - Epoch(train) [61][ 400/1879] lr: 1.7921e-05 eta: 1 day, 20:46:18 time: 2.1456 data_time: 0.0388 memory: 48866 grad_norm: 5.0007 loss: 2.9589 loss_cls: 2.9589 2023/01/24 11:46:59 - mmengine - INFO - Epoch(train) [61][ 500/1879] lr: 1.7883e-05 eta: 1 day, 20:42:43 time: 2.1704 data_time: 0.0408 memory: 48866 grad_norm: 5.2779 loss: 3.1884 loss_cls: 3.1884 2023/01/24 11:50:36 - mmengine - INFO - Epoch(train) [61][ 600/1879] lr: 1.7845e-05 eta: 1 day, 20:39:08 time: 2.1655 data_time: 0.0398 memory: 48866 grad_norm: 5.0473 loss: 3.3282 loss_cls: 3.3282 2023/01/24 11:54:11 - mmengine - INFO - Epoch(train) [61][ 700/1879] lr: 1.7808e-05 eta: 1 day, 20:35:32 time: 2.1534 data_time: 0.0410 memory: 48866 grad_norm: 5.1566 loss: 3.1217 loss_cls: 3.1217 2023/01/24 11:57:47 - mmengine - INFO - Epoch(train) [61][ 800/1879] lr: 1.7770e-05 eta: 1 day, 20:31:57 time: 2.1575 data_time: 0.0397 memory: 48866 grad_norm: 5.1621 loss: 3.0068 loss_cls: 3.0068 2023/01/24 12:01:24 - mmengine - INFO - Epoch(train) [61][ 900/1879] lr: 1.7732e-05 eta: 1 day, 20:28:22 time: 2.1593 data_time: 0.0395 memory: 48866 grad_norm: 5.0958 loss: 3.1860 loss_cls: 3.1860 2023/01/24 12:04:59 - mmengine - INFO - Epoch(train) [61][1000/1879] lr: 1.7695e-05 eta: 1 day, 20:24:46 time: 2.1574 data_time: 0.0402 memory: 48866 grad_norm: 5.2220 loss: 3.0433 loss_cls: 3.0433 2023/01/24 12:08:35 - mmengine - INFO - Epoch(train) [61][1100/1879] lr: 1.7657e-05 eta: 1 day, 20:21:11 time: 2.1523 data_time: 0.0410 memory: 48866 grad_norm: 5.2890 loss: 3.0066 loss_cls: 3.0066 2023/01/24 12:12:11 - mmengine - INFO - Epoch(train) [61][1200/1879] lr: 1.7619e-05 eta: 1 day, 20:17:35 time: 2.1675 data_time: 0.0413 memory: 48866 grad_norm: 5.3795 loss: 2.9959 loss_cls: 2.9959 2023/01/24 12:14:21 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 12:15:48 - mmengine - INFO - Epoch(train) [61][1300/1879] lr: 1.7582e-05 eta: 1 day, 20:14:00 time: 2.1533 data_time: 0.0400 memory: 48866 grad_norm: 5.3570 loss: 3.1360 loss_cls: 3.1360 2023/01/24 12:19:24 - mmengine - INFO - Epoch(train) [61][1400/1879] lr: 1.7544e-05 eta: 1 day, 20:10:25 time: 2.1597 data_time: 0.0406 memory: 48866 grad_norm: 5.4576 loss: 3.1664 loss_cls: 3.1664 2023/01/24 12:23:01 - mmengine - INFO - Epoch(train) [61][1500/1879] lr: 1.7507e-05 eta: 1 day, 20:06:50 time: 2.1581 data_time: 0.0408 memory: 48866 grad_norm: 5.1348 loss: 3.1979 loss_cls: 3.1979 2023/01/24 12:26:37 - mmengine - INFO - Epoch(train) [61][1600/1879] lr: 1.7469e-05 eta: 1 day, 20:03:15 time: 2.1582 data_time: 0.0411 memory: 48866 grad_norm: 4.8806 loss: 3.1092 loss_cls: 3.1092 2023/01/24 12:30:13 - mmengine - INFO - Epoch(train) [61][1700/1879] lr: 1.7431e-05 eta: 1 day, 19:59:40 time: 2.1600 data_time: 0.0412 memory: 48866 grad_norm: 5.1677 loss: 3.2429 loss_cls: 3.2429 2023/01/24 12:33:50 - mmengine - INFO - Epoch(train) [61][1800/1879] lr: 1.7394e-05 eta: 1 day, 19:56:05 time: 2.1624 data_time: 0.0404 memory: 48866 grad_norm: 5.2539 loss: 3.0116 loss_cls: 3.0116 2023/01/24 12:36:39 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 12:36:39 - mmengine - INFO - Epoch(train) [61][1879/1879] lr: 1.7364e-05 eta: 1 day, 19:53:14 time: 2.1007 data_time: 0.0418 memory: 48866 grad_norm: 5.5271 loss: 3.0465 loss_cls: 3.0465 2023/01/24 12:37:32 - mmengine - INFO - Epoch(val) [61][100/155] eta: 0:00:29 time: 0.5440 data_time: 0.1830 memory: 4950 2023/01/24 12:38:04 - mmengine - INFO - Epoch(val) [61][155/155] acc/top1: 0.7082 acc/top5: 0.8983 acc/mean1: 0.7081 2023/01/24 12:38:04 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_60.pth is removed 2023/01/24 12:38:07 - mmengine - INFO - The best checkpoint with 0.7082 acc/top1 at 61 epoch is saved to best_acc/top1_epoch_61.pth. 2023/01/24 12:41:50 - mmengine - INFO - Epoch(train) [62][ 100/1879] lr: 1.7326e-05 eta: 1 day, 19:49:43 time: 2.1704 data_time: 0.0545 memory: 48866 grad_norm: 5.2468 loss: 3.0736 loss_cls: 3.0736 2023/01/24 12:45:26 - mmengine - INFO - Epoch(train) [62][ 200/1879] lr: 1.7289e-05 eta: 1 day, 19:46:07 time: 2.1588 data_time: 0.0405 memory: 48866 grad_norm: 5.4249 loss: 2.9164 loss_cls: 2.9164 2023/01/24 12:49:02 - mmengine - INFO - Epoch(train) [62][ 300/1879] lr: 1.7251e-05 eta: 1 day, 19:42:32 time: 2.1590 data_time: 0.0400 memory: 48866 grad_norm: 5.0961 loss: 2.9051 loss_cls: 2.9051 2023/01/24 12:51:57 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 12:52:38 - mmengine - INFO - Epoch(train) [62][ 400/1879] lr: 1.7214e-05 eta: 1 day, 19:38:57 time: 2.1808 data_time: 0.0409 memory: 48866 grad_norm: 5.3001 loss: 3.1358 loss_cls: 3.1358 2023/01/24 12:56:15 - mmengine - INFO - Epoch(train) [62][ 500/1879] lr: 1.7176e-05 eta: 1 day, 19:35:22 time: 2.1636 data_time: 0.0411 memory: 48866 grad_norm: 5.4867 loss: 3.0094 loss_cls: 3.0094 2023/01/24 12:59:51 - mmengine - INFO - Epoch(train) [62][ 600/1879] lr: 1.7138e-05 eta: 1 day, 19:31:46 time: 2.1587 data_time: 0.0395 memory: 48866 grad_norm: 5.0216 loss: 2.8441 loss_cls: 2.8441 2023/01/24 13:03:27 - mmengine - INFO - Epoch(train) [62][ 700/1879] lr: 1.7101e-05 eta: 1 day, 19:28:11 time: 2.1611 data_time: 0.0410 memory: 48866 grad_norm: 5.2779 loss: 3.1494 loss_cls: 3.1494 2023/01/24 13:07:03 - mmengine - INFO - Epoch(train) [62][ 800/1879] lr: 1.7063e-05 eta: 1 day, 19:24:36 time: 2.1705 data_time: 0.0408 memory: 48866 grad_norm: 5.2683 loss: 3.0626 loss_cls: 3.0626 2023/01/24 13:10:39 - mmengine - INFO - Epoch(train) [62][ 900/1879] lr: 1.7026e-05 eta: 1 day, 19:21:01 time: 2.1659 data_time: 0.0410 memory: 48866 grad_norm: 5.3210 loss: 3.1247 loss_cls: 3.1247 2023/01/24 13:14:15 - mmengine - INFO - Epoch(train) [62][1000/1879] lr: 1.6988e-05 eta: 1 day, 19:17:25 time: 2.1625 data_time: 0.0407 memory: 48866 grad_norm: 5.3245 loss: 3.0286 loss_cls: 3.0286 2023/01/24 13:17:51 - mmengine - INFO - Epoch(train) [62][1100/1879] lr: 1.6951e-05 eta: 1 day, 19:13:50 time: 2.1657 data_time: 0.0411 memory: 48866 grad_norm: 5.4740 loss: 3.1315 loss_cls: 3.1315 2023/01/24 13:21:28 - mmengine - INFO - Epoch(train) [62][1200/1879] lr: 1.6913e-05 eta: 1 day, 19:10:15 time: 2.1571 data_time: 0.0406 memory: 48866 grad_norm: 5.1853 loss: 3.1084 loss_cls: 3.1084 2023/01/24 13:25:03 - mmengine - INFO - Epoch(train) [62][1300/1879] lr: 1.6875e-05 eta: 1 day, 19:06:39 time: 2.1531 data_time: 0.0407 memory: 48866 grad_norm: 5.2499 loss: 3.1179 loss_cls: 3.1179 2023/01/24 13:27:58 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 13:28:39 - mmengine - INFO - Epoch(train) [62][1400/1879] lr: 1.6838e-05 eta: 1 day, 19:03:03 time: 2.1568 data_time: 0.0408 memory: 48866 grad_norm: 5.1886 loss: 3.1134 loss_cls: 3.1134 2023/01/24 13:32:15 - mmengine - INFO - Epoch(train) [62][1500/1879] lr: 1.6800e-05 eta: 1 day, 18:59:28 time: 2.1533 data_time: 0.0406 memory: 48866 grad_norm: 5.3008 loss: 3.1437 loss_cls: 3.1437 2023/01/24 13:35:51 - mmengine - INFO - Epoch(train) [62][1600/1879] lr: 1.6763e-05 eta: 1 day, 18:55:53 time: 2.1761 data_time: 0.0400 memory: 48866 grad_norm: 5.6007 loss: 3.0031 loss_cls: 3.0031 2023/01/24 13:39:27 - mmengine - INFO - Epoch(train) [62][1700/1879] lr: 1.6725e-05 eta: 1 day, 18:52:17 time: 2.1556 data_time: 0.0410 memory: 48866 grad_norm: 5.2170 loss: 2.9548 loss_cls: 2.9548 2023/01/24 13:43:03 - mmengine - INFO - Epoch(train) [62][1800/1879] lr: 1.6688e-05 eta: 1 day, 18:48:42 time: 2.1544 data_time: 0.0397 memory: 48866 grad_norm: 5.3258 loss: 3.1645 loss_cls: 3.1645 2023/01/24 13:45:53 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 13:45:53 - mmengine - INFO - Epoch(train) [62][1879/1879] lr: 1.6658e-05 eta: 1 day, 18:45:51 time: 2.1062 data_time: 0.0413 memory: 48866 grad_norm: 5.5865 loss: 3.0212 loss_cls: 3.0212 2023/01/24 13:46:46 - mmengine - INFO - Epoch(val) [62][100/155] eta: 0:00:29 time: 0.5471 data_time: 0.2103 memory: 4950 2023/01/24 13:47:17 - mmengine - INFO - Epoch(val) [62][155/155] acc/top1: 0.7114 acc/top5: 0.8968 acc/mean1: 0.7113 2023/01/24 13:47:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_61.pth is removed 2023/01/24 13:47:21 - mmengine - INFO - The best checkpoint with 0.7114 acc/top1 at 62 epoch is saved to best_acc/top1_epoch_62.pth. 2023/01/24 13:51:02 - mmengine - INFO - Epoch(train) [63][ 100/1879] lr: 1.6620e-05 eta: 1 day, 18:42:19 time: 2.1536 data_time: 0.0399 memory: 48866 grad_norm: 5.1027 loss: 2.9860 loss_cls: 2.9860 2023/01/24 13:54:38 - mmengine - INFO - Epoch(train) [63][ 200/1879] lr: 1.6583e-05 eta: 1 day, 18:38:43 time: 2.1477 data_time: 0.0411 memory: 48866 grad_norm: 5.1347 loss: 3.0091 loss_cls: 3.0091 2023/01/24 13:58:13 - mmengine - INFO - Epoch(train) [63][ 300/1879] lr: 1.6545e-05 eta: 1 day, 18:35:08 time: 2.1482 data_time: 0.0403 memory: 48866 grad_norm: 5.3291 loss: 2.9803 loss_cls: 2.9803 2023/01/24 14:01:49 - mmengine - INFO - Epoch(train) [63][ 400/1879] lr: 1.6508e-05 eta: 1 day, 18:31:32 time: 2.1558 data_time: 0.0393 memory: 48866 grad_norm: 5.2325 loss: 2.8385 loss_cls: 2.8385 2023/01/24 14:05:25 - mmengine - INFO - Epoch(train) [63][ 500/1879] lr: 1.6470e-05 eta: 1 day, 18:27:57 time: 2.1529 data_time: 0.0406 memory: 48866 grad_norm: 5.2140 loss: 3.0251 loss_cls: 3.0251 2023/01/24 14:05:29 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 14:09:01 - mmengine - INFO - Epoch(train) [63][ 600/1879] lr: 1.6433e-05 eta: 1 day, 18:24:21 time: 2.1551 data_time: 0.0399 memory: 48866 grad_norm: 5.4239 loss: 2.9098 loss_cls: 2.9098 2023/01/24 14:12:36 - mmengine - INFO - Epoch(train) [63][ 700/1879] lr: 1.6396e-05 eta: 1 day, 18:20:46 time: 2.1509 data_time: 0.0397 memory: 48866 grad_norm: 5.1821 loss: 2.9130 loss_cls: 2.9130 2023/01/24 14:16:13 - mmengine - INFO - Epoch(train) [63][ 800/1879] lr: 1.6358e-05 eta: 1 day, 18:17:11 time: 2.1789 data_time: 0.0404 memory: 48866 grad_norm: 5.1693 loss: 3.1337 loss_cls: 3.1337 2023/01/24 14:19:49 - mmengine - INFO - Epoch(train) [63][ 900/1879] lr: 1.6321e-05 eta: 1 day, 18:13:35 time: 2.1724 data_time: 0.0401 memory: 48866 grad_norm: 5.1761 loss: 3.1264 loss_cls: 3.1264 2023/01/24 14:23:25 - mmengine - INFO - Epoch(train) [63][1000/1879] lr: 1.6283e-05 eta: 1 day, 18:10:00 time: 2.1540 data_time: 0.0412 memory: 48866 grad_norm: 5.4771 loss: 3.1096 loss_cls: 3.1096 2023/01/24 14:27:01 - mmengine - INFO - Epoch(train) [63][1100/1879] lr: 1.6246e-05 eta: 1 day, 18:06:24 time: 2.1611 data_time: 0.0395 memory: 48866 grad_norm: 5.6024 loss: 2.9429 loss_cls: 2.9429 2023/01/24 14:30:37 - mmengine - INFO - Epoch(train) [63][1200/1879] lr: 1.6208e-05 eta: 1 day, 18:02:49 time: 2.1476 data_time: 0.0409 memory: 48866 grad_norm: 5.2848 loss: 3.1615 loss_cls: 3.1615 2023/01/24 14:34:13 - mmengine - INFO - Epoch(train) [63][1300/1879] lr: 1.6171e-05 eta: 1 day, 17:59:14 time: 2.1489 data_time: 0.0409 memory: 48866 grad_norm: 5.2353 loss: 3.0678 loss_cls: 3.0678 2023/01/24 14:37:49 - mmengine - INFO - Epoch(train) [63][1400/1879] lr: 1.6133e-05 eta: 1 day, 17:55:38 time: 2.1604 data_time: 0.0400 memory: 48866 grad_norm: 5.1295 loss: 2.9755 loss_cls: 2.9755 2023/01/24 14:41:25 - mmengine - INFO - Epoch(train) [63][1500/1879] lr: 1.6096e-05 eta: 1 day, 17:52:03 time: 2.1562 data_time: 0.0396 memory: 48866 grad_norm: 5.2847 loss: 2.9433 loss_cls: 2.9433 2023/01/24 14:41:30 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 14:45:01 - mmengine - INFO - Epoch(train) [63][1600/1879] lr: 1.6059e-05 eta: 1 day, 17:48:28 time: 2.1519 data_time: 0.0397 memory: 48866 grad_norm: 5.0241 loss: 2.7519 loss_cls: 2.7519 2023/01/24 14:48:37 - mmengine - INFO - Epoch(train) [63][1700/1879] lr: 1.6021e-05 eta: 1 day, 17:44:52 time: 2.1524 data_time: 0.0408 memory: 48866 grad_norm: 4.9259 loss: 3.2649 loss_cls: 3.2649 2023/01/24 14:52:12 - mmengine - INFO - Epoch(train) [63][1800/1879] lr: 1.5984e-05 eta: 1 day, 17:41:16 time: 2.1453 data_time: 0.0409 memory: 48866 grad_norm: 5.5489 loss: 2.9759 loss_cls: 2.9759 2023/01/24 14:55:02 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 14:55:02 - mmengine - INFO - Epoch(train) [63][1879/1879] lr: 1.5954e-05 eta: 1 day, 17:38:25 time: 2.0980 data_time: 0.0415 memory: 48866 grad_norm: 5.3674 loss: 3.0211 loss_cls: 3.0211 2023/01/24 14:55:02 - mmengine - INFO - Saving checkpoint at 63 epochs 2023/01/24 14:56:01 - mmengine - INFO - Epoch(val) [63][100/155] eta: 0:00:30 time: 0.5390 data_time: 0.1805 memory: 4950 2023/01/24 14:56:29 - mmengine - INFO - Epoch(val) [63][155/155] acc/top1: 0.7103 acc/top5: 0.9004 acc/mean1: 0.7104 2023/01/24 15:00:11 - mmengine - INFO - Epoch(train) [64][ 100/1879] lr: 1.5917e-05 eta: 1 day, 17:34:54 time: 2.1559 data_time: 0.0399 memory: 48866 grad_norm: 5.3730 loss: 2.9881 loss_cls: 2.9881 2023/01/24 15:03:48 - mmengine - INFO - Epoch(train) [64][ 200/1879] lr: 1.5879e-05 eta: 1 day, 17:31:19 time: 2.1581 data_time: 0.0409 memory: 48866 grad_norm: 5.3620 loss: 2.9261 loss_cls: 2.9261 2023/01/24 15:07:24 - mmengine - INFO - Epoch(train) [64][ 300/1879] lr: 1.5842e-05 eta: 1 day, 17:27:43 time: 2.1626 data_time: 0.0395 memory: 48866 grad_norm: 5.0883 loss: 2.8583 loss_cls: 2.8583 2023/01/24 15:11:00 - mmengine - INFO - Epoch(train) [64][ 400/1879] lr: 1.5805e-05 eta: 1 day, 17:24:08 time: 2.1549 data_time: 0.0398 memory: 48866 grad_norm: 5.1906 loss: 2.9709 loss_cls: 2.9709 2023/01/24 15:14:36 - mmengine - INFO - Epoch(train) [64][ 500/1879] lr: 1.5767e-05 eta: 1 day, 17:20:32 time: 2.1519 data_time: 0.0406 memory: 48866 grad_norm: 5.2488 loss: 3.1472 loss_cls: 3.1472 2023/01/24 15:18:12 - mmengine - INFO - Epoch(train) [64][ 600/1879] lr: 1.5730e-05 eta: 1 day, 17:16:57 time: 2.1719 data_time: 0.0389 memory: 48866 grad_norm: 5.1554 loss: 3.1848 loss_cls: 3.1848 2023/01/24 15:19:02 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 15:21:48 - mmengine - INFO - Epoch(train) [64][ 700/1879] lr: 1.5693e-05 eta: 1 day, 17:13:22 time: 2.1528 data_time: 0.0416 memory: 48866 grad_norm: 5.6788 loss: 2.8601 loss_cls: 2.8601 2023/01/24 15:25:25 - mmengine - INFO - Epoch(train) [64][ 800/1879] lr: 1.5655e-05 eta: 1 day, 17:09:47 time: 2.1697 data_time: 0.0396 memory: 48866 grad_norm: 5.1388 loss: 3.0127 loss_cls: 3.0127 2023/01/24 15:29:01 - mmengine - INFO - Epoch(train) [64][ 900/1879] lr: 1.5618e-05 eta: 1 day, 17:06:11 time: 2.1642 data_time: 0.0413 memory: 48866 grad_norm: 5.3035 loss: 3.2403 loss_cls: 3.2403 2023/01/24 15:32:37 - mmengine - INFO - Epoch(train) [64][1000/1879] lr: 1.5581e-05 eta: 1 day, 17:02:36 time: 2.1670 data_time: 0.0394 memory: 48866 grad_norm: 5.4403 loss: 2.8456 loss_cls: 2.8456 2023/01/24 15:36:13 - mmengine - INFO - Epoch(train) [64][1100/1879] lr: 1.5544e-05 eta: 1 day, 16:59:01 time: 2.1625 data_time: 0.0395 memory: 48866 grad_norm: 5.3080 loss: 3.1686 loss_cls: 3.1686 2023/01/24 15:39:49 - mmengine - INFO - Epoch(train) [64][1200/1879] lr: 1.5506e-05 eta: 1 day, 16:55:25 time: 2.1706 data_time: 0.0399 memory: 48866 grad_norm: 5.4477 loss: 3.0402 loss_cls: 3.0402 2023/01/24 15:43:25 - mmengine - INFO - Epoch(train) [64][1300/1879] lr: 1.5469e-05 eta: 1 day, 16:51:50 time: 2.1569 data_time: 0.0408 memory: 48866 grad_norm: 5.3569 loss: 2.9151 loss_cls: 2.9151 2023/01/24 15:47:01 - mmengine - INFO - Epoch(train) [64][1400/1879] lr: 1.5432e-05 eta: 1 day, 16:48:14 time: 2.1416 data_time: 0.0401 memory: 48866 grad_norm: 5.5848 loss: 2.9315 loss_cls: 2.9315 2023/01/24 15:50:37 - mmengine - INFO - Epoch(train) [64][1500/1879] lr: 1.5395e-05 eta: 1 day, 16:44:39 time: 2.1753 data_time: 0.0401 memory: 48866 grad_norm: 5.1076 loss: 2.9732 loss_cls: 2.9732 2023/01/24 15:54:14 - mmengine - INFO - Epoch(train) [64][1600/1879] lr: 1.5357e-05 eta: 1 day, 16:41:04 time: 2.1553 data_time: 0.0399 memory: 48866 grad_norm: 5.4220 loss: 3.0706 loss_cls: 3.0706 2023/01/24 15:55:03 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 15:57:50 - mmengine - INFO - Epoch(train) [64][1700/1879] lr: 1.5320e-05 eta: 1 day, 16:37:28 time: 2.1689 data_time: 0.0411 memory: 48866 grad_norm: 5.1760 loss: 2.9535 loss_cls: 2.9535 2023/01/24 16:01:26 - mmengine - INFO - Epoch(train) [64][1800/1879] lr: 1.5283e-05 eta: 1 day, 16:33:53 time: 2.1569 data_time: 0.0399 memory: 48866 grad_norm: 5.5310 loss: 3.1771 loss_cls: 3.1771 2023/01/24 16:04:15 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 16:04:15 - mmengine - INFO - Epoch(train) [64][1879/1879] lr: 1.5254e-05 eta: 1 day, 16:31:02 time: 2.0973 data_time: 0.0407 memory: 48866 grad_norm: 5.2674 loss: 3.0244 loss_cls: 3.0244 2023/01/24 16:05:08 - mmengine - INFO - Epoch(val) [64][100/155] eta: 0:00:29 time: 0.5245 data_time: 0.2006 memory: 4950 2023/01/24 16:05:39 - mmengine - INFO - Epoch(val) [64][155/155] acc/top1: 0.7141 acc/top5: 0.9013 acc/mean1: 0.7140 2023/01/24 16:05:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_62.pth is removed 2023/01/24 16:05:42 - mmengine - INFO - The best checkpoint with 0.7141 acc/top1 at 64 epoch is saved to best_acc/top1_epoch_64.pth. 2023/01/24 16:09:26 - mmengine - INFO - Epoch(train) [65][ 100/1879] lr: 1.5216e-05 eta: 1 day, 16:27:31 time: 2.1560 data_time: 0.0402 memory: 48866 grad_norm: 5.2365 loss: 2.9351 loss_cls: 2.9351 2023/01/24 16:13:02 - mmengine - INFO - Epoch(train) [65][ 200/1879] lr: 1.5179e-05 eta: 1 day, 16:23:55 time: 2.1695 data_time: 0.0401 memory: 48866 grad_norm: 5.6029 loss: 3.1089 loss_cls: 3.1089 2023/01/24 16:16:37 - mmengine - INFO - Epoch(train) [65][ 300/1879] lr: 1.5142e-05 eta: 1 day, 16:20:20 time: 2.1492 data_time: 0.0393 memory: 48866 grad_norm: 5.3055 loss: 2.9732 loss_cls: 2.9732 2023/01/24 16:20:13 - mmengine - INFO - Epoch(train) [65][ 400/1879] lr: 1.5105e-05 eta: 1 day, 16:16:44 time: 2.1548 data_time: 0.0401 memory: 48866 grad_norm: 5.5141 loss: 3.0659 loss_cls: 3.0659 2023/01/24 16:23:48 - mmengine - INFO - Epoch(train) [65][ 500/1879] lr: 1.5068e-05 eta: 1 day, 16:13:08 time: 2.1599 data_time: 0.0399 memory: 48866 grad_norm: 5.2529 loss: 2.9737 loss_cls: 2.9737 2023/01/24 16:27:24 - mmengine - INFO - Epoch(train) [65][ 600/1879] lr: 1.5031e-05 eta: 1 day, 16:09:33 time: 2.1538 data_time: 0.0403 memory: 48866 grad_norm: 5.2950 loss: 3.0850 loss_cls: 3.0850 2023/01/24 16:31:00 - mmengine - INFO - Epoch(train) [65][ 700/1879] lr: 1.4994e-05 eta: 1 day, 16:05:57 time: 2.1757 data_time: 0.0401 memory: 48866 grad_norm: 5.3565 loss: 3.0780 loss_cls: 3.0780 2023/01/24 16:32:35 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 16:34:37 - mmengine - INFO - Epoch(train) [65][ 800/1879] lr: 1.4957e-05 eta: 1 day, 16:02:22 time: 2.1676 data_time: 0.0398 memory: 48866 grad_norm: 5.3400 loss: 3.0457 loss_cls: 3.0457 2023/01/24 16:38:13 - mmengine - INFO - Epoch(train) [65][ 900/1879] lr: 1.4920e-05 eta: 1 day, 15:58:47 time: 2.1709 data_time: 0.0407 memory: 48866 grad_norm: 5.2341 loss: 3.1489 loss_cls: 3.1489 2023/01/24 16:41:49 - mmengine - INFO - Epoch(train) [65][1000/1879] lr: 1.4882e-05 eta: 1 day, 15:55:11 time: 2.1659 data_time: 0.0391 memory: 48866 grad_norm: 5.4566 loss: 2.9134 loss_cls: 2.9134 2023/01/24 16:45:25 - mmengine - INFO - Epoch(train) [65][1100/1879] lr: 1.4845e-05 eta: 1 day, 15:51:36 time: 2.1614 data_time: 0.0403 memory: 48866 grad_norm: 5.3816 loss: 3.0224 loss_cls: 3.0224 2023/01/24 16:49:01 - mmengine - INFO - Epoch(train) [65][1200/1879] lr: 1.4808e-05 eta: 1 day, 15:48:01 time: 2.1680 data_time: 0.0396 memory: 48866 grad_norm: 5.4786 loss: 3.1497 loss_cls: 3.1497 2023/01/24 16:52:38 - mmengine - INFO - Epoch(train) [65][1300/1879] lr: 1.4771e-05 eta: 1 day, 15:44:25 time: 2.1677 data_time: 0.0406 memory: 48866 grad_norm: 5.2764 loss: 3.1151 loss_cls: 3.1151 2023/01/24 16:56:14 - mmengine - INFO - Epoch(train) [65][1400/1879] lr: 1.4734e-05 eta: 1 day, 15:40:50 time: 2.1511 data_time: 0.0404 memory: 48866 grad_norm: 5.2115 loss: 2.9721 loss_cls: 2.9721 2023/01/24 16:59:50 - mmengine - INFO - Epoch(train) [65][1500/1879] lr: 1.4697e-05 eta: 1 day, 15:37:15 time: 2.1733 data_time: 0.0405 memory: 48866 grad_norm: 5.4631 loss: 2.9610 loss_cls: 2.9610 2023/01/24 17:03:26 - mmengine - INFO - Epoch(train) [65][1600/1879] lr: 1.4660e-05 eta: 1 day, 15:33:39 time: 2.1779 data_time: 0.0412 memory: 48866 grad_norm: 5.3923 loss: 2.9584 loss_cls: 2.9584 2023/01/24 17:07:02 - mmengine - INFO - Epoch(train) [65][1700/1879] lr: 1.4624e-05 eta: 1 day, 15:30:04 time: 2.1488 data_time: 0.0399 memory: 48866 grad_norm: 5.0006 loss: 2.9253 loss_cls: 2.9253 2023/01/24 17:08:37 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 17:10:38 - mmengine - INFO - Epoch(train) [65][1800/1879] lr: 1.4587e-05 eta: 1 day, 15:26:28 time: 2.1556 data_time: 0.0391 memory: 48866 grad_norm: 5.3974 loss: 3.0769 loss_cls: 3.0769 2023/01/24 17:13:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 17:13:28 - mmengine - INFO - Epoch(train) [65][1879/1879] lr: 1.4557e-05 eta: 1 day, 15:23:37 time: 2.1034 data_time: 0.0402 memory: 48866 grad_norm: 5.5513 loss: 2.9426 loss_cls: 2.9426 2023/01/24 17:14:22 - mmengine - INFO - Epoch(val) [65][100/155] eta: 0:00:29 time: 0.5434 data_time: 0.1875 memory: 4950 2023/01/24 17:14:53 - mmengine - INFO - Epoch(val) [65][155/155] acc/top1: 0.7185 acc/top5: 0.9039 acc/mean1: 0.7185 2023/01/24 17:14:53 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_64.pth is removed 2023/01/24 17:14:56 - mmengine - INFO - The best checkpoint with 0.7185 acc/top1 at 65 epoch is saved to best_acc/top1_epoch_65.pth. 2023/01/24 17:18:39 - mmengine - INFO - Epoch(train) [66][ 100/1879] lr: 1.4521e-05 eta: 1 day, 15:20:06 time: 2.1654 data_time: 0.0404 memory: 48866 grad_norm: 5.3496 loss: 2.9944 loss_cls: 2.9944 2023/01/24 17:22:15 - mmengine - INFO - Epoch(train) [66][ 200/1879] lr: 1.4484e-05 eta: 1 day, 15:16:30 time: 2.1609 data_time: 0.0397 memory: 48866 grad_norm: 5.5265 loss: 3.0935 loss_cls: 3.0935 2023/01/24 17:25:51 - mmengine - INFO - Epoch(train) [66][ 300/1879] lr: 1.4447e-05 eta: 1 day, 15:12:55 time: 2.1531 data_time: 0.0402 memory: 48866 grad_norm: 5.4532 loss: 2.8952 loss_cls: 2.8952 2023/01/24 17:29:28 - mmengine - INFO - Epoch(train) [66][ 400/1879] lr: 1.4410e-05 eta: 1 day, 15:09:19 time: 2.1654 data_time: 0.0398 memory: 48866 grad_norm: 5.5019 loss: 2.9923 loss_cls: 2.9923 2023/01/24 17:33:04 - mmengine - INFO - Epoch(train) [66][ 500/1879] lr: 1.4373e-05 eta: 1 day, 15:05:44 time: 2.1557 data_time: 0.0403 memory: 48866 grad_norm: 5.5018 loss: 3.0068 loss_cls: 3.0068 2023/01/24 17:36:39 - mmengine - INFO - Epoch(train) [66][ 600/1879] lr: 1.4336e-05 eta: 1 day, 15:02:08 time: 2.1478 data_time: 0.0406 memory: 48866 grad_norm: 5.4823 loss: 2.9631 loss_cls: 2.9631 2023/01/24 17:40:15 - mmengine - INFO - Epoch(train) [66][ 700/1879] lr: 1.4299e-05 eta: 1 day, 14:58:33 time: 2.1570 data_time: 0.0402 memory: 48866 grad_norm: 5.6407 loss: 2.9312 loss_cls: 2.9312 2023/01/24 17:43:51 - mmengine - INFO - Epoch(train) [66][ 800/1879] lr: 1.4263e-05 eta: 1 day, 14:54:57 time: 2.1600 data_time: 0.0407 memory: 48866 grad_norm: 5.2279 loss: 3.1040 loss_cls: 3.1040 2023/01/24 17:46:11 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 17:47:27 - mmengine - INFO - Epoch(train) [66][ 900/1879] lr: 1.4226e-05 eta: 1 day, 14:51:22 time: 2.1714 data_time: 0.0404 memory: 48866 grad_norm: 5.3894 loss: 2.8965 loss_cls: 2.8965 2023/01/24 17:51:03 - mmengine - INFO - Epoch(train) [66][1000/1879] lr: 1.4189e-05 eta: 1 day, 14:47:46 time: 2.1704 data_time: 0.0403 memory: 48866 grad_norm: 5.3633 loss: 2.9830 loss_cls: 2.9830 2023/01/24 17:54:39 - mmengine - INFO - Epoch(train) [66][1100/1879] lr: 1.4152e-05 eta: 1 day, 14:44:11 time: 2.1561 data_time: 0.0401 memory: 48866 grad_norm: 5.2543 loss: 2.9379 loss_cls: 2.9379 2023/01/24 17:58:15 - mmengine - INFO - Epoch(train) [66][1200/1879] lr: 1.4116e-05 eta: 1 day, 14:40:35 time: 2.1604 data_time: 0.0398 memory: 48866 grad_norm: 5.3743 loss: 2.8987 loss_cls: 2.8987 2023/01/24 18:01:51 - mmengine - INFO - Epoch(train) [66][1300/1879] lr: 1.4079e-05 eta: 1 day, 14:37:00 time: 2.1558 data_time: 0.0403 memory: 48866 grad_norm: 5.2125 loss: 3.0319 loss_cls: 3.0319 2023/01/24 18:05:27 - mmengine - INFO - Epoch(train) [66][1400/1879] lr: 1.4042e-05 eta: 1 day, 14:33:24 time: 2.1537 data_time: 0.0414 memory: 48866 grad_norm: 5.2023 loss: 3.0030 loss_cls: 3.0030 2023/01/24 18:09:03 - mmengine - INFO - Epoch(train) [66][1500/1879] lr: 1.4005e-05 eta: 1 day, 14:29:49 time: 2.1665 data_time: 0.0409 memory: 48866 grad_norm: 5.4878 loss: 2.9823 loss_cls: 2.9823 2023/01/24 18:12:38 - mmengine - INFO - Epoch(train) [66][1600/1879] lr: 1.3969e-05 eta: 1 day, 14:26:13 time: 2.1659 data_time: 0.0406 memory: 48866 grad_norm: 5.6212 loss: 2.8992 loss_cls: 2.8992 2023/01/24 18:16:15 - mmengine - INFO - Epoch(train) [66][1700/1879] lr: 1.3932e-05 eta: 1 day, 14:22:38 time: 2.1489 data_time: 0.0408 memory: 48866 grad_norm: 5.5966 loss: 2.8802 loss_cls: 2.8802 2023/01/24 18:19:50 - mmengine - INFO - Epoch(train) [66][1800/1879] lr: 1.3896e-05 eta: 1 day, 14:19:02 time: 2.1532 data_time: 0.0411 memory: 48866 grad_norm: 5.4126 loss: 2.9624 loss_cls: 2.9624 2023/01/24 18:22:10 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 18:22:39 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 18:22:39 - mmengine - INFO - Epoch(train) [66][1879/1879] lr: 1.3867e-05 eta: 1 day, 14:16:11 time: 2.1004 data_time: 0.0405 memory: 48866 grad_norm: 5.7593 loss: 3.0414 loss_cls: 3.0414 2023/01/24 18:22:39 - mmengine - INFO - Saving checkpoint at 66 epochs 2023/01/24 18:23:40 - mmengine - INFO - Epoch(val) [66][100/155] eta: 0:00:30 time: 0.5720 data_time: 0.2164 memory: 4950 2023/01/24 18:24:07 - mmengine - INFO - Epoch(val) [66][155/155] acc/top1: 0.7187 acc/top5: 0.9036 acc/mean1: 0.7186 2023/01/24 18:24:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_65.pth is removed 2023/01/24 18:24:10 - mmengine - INFO - The best checkpoint with 0.7187 acc/top1 at 66 epoch is saved to best_acc/top1_epoch_66.pth. 2023/01/24 18:27:52 - mmengine - INFO - Epoch(train) [67][ 100/1879] lr: 1.3830e-05 eta: 1 day, 14:12:39 time: 2.1564 data_time: 0.0402 memory: 48866 grad_norm: 5.1880 loss: 3.1519 loss_cls: 3.1519 2023/01/24 18:31:28 - mmengine - INFO - Epoch(train) [67][ 200/1879] lr: 1.3793e-05 eta: 1 day, 14:09:03 time: 2.1544 data_time: 0.0402 memory: 48866 grad_norm: 5.4460 loss: 3.2529 loss_cls: 3.2529 2023/01/24 18:35:04 - mmengine - INFO - Epoch(train) [67][ 300/1879] lr: 1.3757e-05 eta: 1 day, 14:05:28 time: 2.1663 data_time: 0.0392 memory: 48866 grad_norm: 5.5359 loss: 2.8203 loss_cls: 2.8203 2023/01/24 18:38:40 - mmengine - INFO - Epoch(train) [67][ 400/1879] lr: 1.3720e-05 eta: 1 day, 14:01:52 time: 2.1560 data_time: 0.0400 memory: 48866 grad_norm: 5.5111 loss: 2.9416 loss_cls: 2.9416 2023/01/24 18:42:16 - mmengine - INFO - Epoch(train) [67][ 500/1879] lr: 1.3684e-05 eta: 1 day, 13:58:17 time: 2.1478 data_time: 0.0402 memory: 48866 grad_norm: 5.3480 loss: 2.9948 loss_cls: 2.9948 2023/01/24 18:45:52 - mmengine - INFO - Epoch(train) [67][ 600/1879] lr: 1.3647e-05 eta: 1 day, 13:54:41 time: 2.1542 data_time: 0.0393 memory: 48866 grad_norm: 5.6342 loss: 2.9463 loss_cls: 2.9463 2023/01/24 18:49:28 - mmengine - INFO - Epoch(train) [67][ 700/1879] lr: 1.3611e-05 eta: 1 day, 13:51:06 time: 2.1580 data_time: 0.0402 memory: 48866 grad_norm: 5.4908 loss: 3.0750 loss_cls: 3.0750 2023/01/24 18:53:04 - mmengine - INFO - Epoch(train) [67][ 800/1879] lr: 1.3574e-05 eta: 1 day, 13:47:30 time: 2.1628 data_time: 0.0403 memory: 48866 grad_norm: 5.3527 loss: 3.0838 loss_cls: 3.0838 2023/01/24 18:56:40 - mmengine - INFO - Epoch(train) [67][ 900/1879] lr: 1.3538e-05 eta: 1 day, 13:43:54 time: 2.1716 data_time: 0.0393 memory: 48866 grad_norm: 5.4476 loss: 2.9431 loss_cls: 2.9431 2023/01/24 18:59:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 19:00:16 - mmengine - INFO - Epoch(train) [67][1000/1879] lr: 1.3502e-05 eta: 1 day, 13:40:19 time: 2.1663 data_time: 0.0397 memory: 48866 grad_norm: 5.6600 loss: 3.0380 loss_cls: 3.0380 2023/01/24 19:03:52 - mmengine - INFO - Epoch(train) [67][1100/1879] lr: 1.3465e-05 eta: 1 day, 13:36:43 time: 2.1641 data_time: 0.0400 memory: 48866 grad_norm: 5.4366 loss: 2.9280 loss_cls: 2.9280 2023/01/24 19:07:28 - mmengine - INFO - Epoch(train) [67][1200/1879] lr: 1.3429e-05 eta: 1 day, 13:33:08 time: 2.1558 data_time: 0.0404 memory: 48866 grad_norm: 5.3866 loss: 3.0969 loss_cls: 3.0969 2023/01/24 19:11:03 - mmengine - INFO - Epoch(train) [67][1300/1879] lr: 1.3392e-05 eta: 1 day, 13:29:32 time: 2.1509 data_time: 0.0397 memory: 48866 grad_norm: 5.4855 loss: 3.1442 loss_cls: 3.1442 2023/01/24 19:14:39 - mmengine - INFO - Epoch(train) [67][1400/1879] lr: 1.3356e-05 eta: 1 day, 13:25:57 time: 2.1591 data_time: 0.0407 memory: 48866 grad_norm: 5.3523 loss: 2.7916 loss_cls: 2.7916 2023/01/24 19:18:15 - mmengine - INFO - Epoch(train) [67][1500/1879] lr: 1.3320e-05 eta: 1 day, 13:22:21 time: 2.1596 data_time: 0.0407 memory: 48866 grad_norm: 5.4006 loss: 2.9708 loss_cls: 2.9708 2023/01/24 19:21:51 - mmengine - INFO - Epoch(train) [67][1600/1879] lr: 1.3283e-05 eta: 1 day, 13:18:46 time: 2.1576 data_time: 0.0405 memory: 48866 grad_norm: 5.5918 loss: 3.0320 loss_cls: 3.0320 2023/01/24 19:25:27 - mmengine - INFO - Epoch(train) [67][1700/1879] lr: 1.3247e-05 eta: 1 day, 13:15:10 time: 2.1706 data_time: 0.0406 memory: 48866 grad_norm: 5.4349 loss: 2.8798 loss_cls: 2.8798 2023/01/24 19:29:04 - mmengine - INFO - Epoch(train) [67][1800/1879] lr: 1.3211e-05 eta: 1 day, 13:11:35 time: 2.1545 data_time: 0.0416 memory: 48866 grad_norm: 5.5993 loss: 2.8376 loss_cls: 2.8376 2023/01/24 19:31:53 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 19:31:53 - mmengine - INFO - Epoch(train) [67][1879/1879] lr: 1.3182e-05 eta: 1 day, 13:08:44 time: 2.0989 data_time: 0.0418 memory: 48866 grad_norm: 5.3702 loss: 2.7021 loss_cls: 2.7021 2023/01/24 19:32:46 - mmengine - INFO - Epoch(val) [67][100/155] eta: 0:00:29 time: 0.5365 data_time: 0.1979 memory: 4950 2023/01/24 19:33:17 - mmengine - INFO - Epoch(val) [67][155/155] acc/top1: 0.7205 acc/top5: 0.9038 acc/mean1: 0.7205 2023/01/24 19:33:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_66.pth is removed 2023/01/24 19:33:21 - mmengine - INFO - The best checkpoint with 0.7205 acc/top1 at 67 epoch is saved to best_acc/top1_epoch_67.pth. 2023/01/24 19:37:03 - mmengine - INFO - Epoch(train) [68][ 100/1879] lr: 1.3146e-05 eta: 1 day, 13:05:12 time: 2.1618 data_time: 0.0404 memory: 48866 grad_norm: 5.7461 loss: 2.9929 loss_cls: 2.9929 2023/01/24 19:37:18 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 19:40:39 - mmengine - INFO - Epoch(train) [68][ 200/1879] lr: 1.3110e-05 eta: 1 day, 13:01:36 time: 2.1561 data_time: 0.0406 memory: 48866 grad_norm: 5.4993 loss: 2.8467 loss_cls: 2.8467 2023/01/24 19:44:16 - mmengine - INFO - Epoch(train) [68][ 300/1879] lr: 1.3074e-05 eta: 1 day, 12:58:01 time: 2.1574 data_time: 0.0406 memory: 48866 grad_norm: 5.5366 loss: 2.9305 loss_cls: 2.9305 2023/01/24 19:47:51 - mmengine - INFO - Epoch(train) [68][ 400/1879] lr: 1.3038e-05 eta: 1 day, 12:54:25 time: 2.1590 data_time: 0.0410 memory: 48866 grad_norm: 5.4448 loss: 2.8955 loss_cls: 2.8955 2023/01/24 19:51:27 - mmengine - INFO - Epoch(train) [68][ 500/1879] lr: 1.3001e-05 eta: 1 day, 12:50:50 time: 2.1609 data_time: 0.0391 memory: 48866 grad_norm: 5.7387 loss: 2.9014 loss_cls: 2.9014 2023/01/24 19:55:03 - mmengine - INFO - Epoch(train) [68][ 600/1879] lr: 1.2965e-05 eta: 1 day, 12:47:14 time: 2.1599 data_time: 0.0404 memory: 48866 grad_norm: 5.7270 loss: 2.7627 loss_cls: 2.7627 2023/01/24 19:58:39 - mmengine - INFO - Epoch(train) [68][ 700/1879] lr: 1.2929e-05 eta: 1 day, 12:43:39 time: 2.1656 data_time: 0.0412 memory: 48866 grad_norm: 5.2964 loss: 2.9862 loss_cls: 2.9862 2023/01/24 20:02:15 - mmengine - INFO - Epoch(train) [68][ 800/1879] lr: 1.2893e-05 eta: 1 day, 12:40:03 time: 2.1493 data_time: 0.0394 memory: 48866 grad_norm: 5.6375 loss: 2.9485 loss_cls: 2.9485 2023/01/24 20:05:52 - mmengine - INFO - Epoch(train) [68][ 900/1879] lr: 1.2857e-05 eta: 1 day, 12:36:28 time: 2.1568 data_time: 0.0413 memory: 48866 grad_norm: 5.4517 loss: 2.9025 loss_cls: 2.9025 2023/01/24 20:09:27 - mmengine - INFO - Epoch(train) [68][1000/1879] lr: 1.2821e-05 eta: 1 day, 12:32:52 time: 2.1634 data_time: 0.0405 memory: 48866 grad_norm: 5.3725 loss: 3.1501 loss_cls: 3.1501 2023/01/24 20:13:04 - mmengine - INFO - Epoch(train) [68][1100/1879] lr: 1.2785e-05 eta: 1 day, 12:29:17 time: 2.1556 data_time: 0.0409 memory: 48866 grad_norm: 5.4190 loss: 2.9922 loss_cls: 2.9922 2023/01/24 20:13:19 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 20:16:40 - mmengine - INFO - Epoch(train) [68][1200/1879] lr: 1.2749e-05 eta: 1 day, 12:25:41 time: 2.1530 data_time: 0.0403 memory: 48866 grad_norm: 5.6285 loss: 2.9535 loss_cls: 2.9535 2023/01/24 20:20:16 - mmengine - INFO - Epoch(train) [68][1300/1879] lr: 1.2713e-05 eta: 1 day, 12:22:06 time: 2.1747 data_time: 0.0400 memory: 48866 grad_norm: 5.3384 loss: 3.0445 loss_cls: 3.0445 2023/01/24 20:23:52 - mmengine - INFO - Epoch(train) [68][1400/1879] lr: 1.2677e-05 eta: 1 day, 12:18:30 time: 2.1557 data_time: 0.0402 memory: 48866 grad_norm: 5.2749 loss: 2.9955 loss_cls: 2.9955 2023/01/24 20:27:28 - mmengine - INFO - Epoch(train) [68][1500/1879] lr: 1.2641e-05 eta: 1 day, 12:14:55 time: 2.1636 data_time: 0.0411 memory: 48866 grad_norm: 5.7810 loss: 3.0363 loss_cls: 3.0363 2023/01/24 20:31:04 - mmengine - INFO - Epoch(train) [68][1600/1879] lr: 1.2605e-05 eta: 1 day, 12:11:19 time: 2.1615 data_time: 0.0411 memory: 48866 grad_norm: 5.5463 loss: 2.9051 loss_cls: 2.9051 2023/01/24 20:34:40 - mmengine - INFO - Epoch(train) [68][1700/1879] lr: 1.2570e-05 eta: 1 day, 12:07:44 time: 2.1542 data_time: 0.0403 memory: 48866 grad_norm: 5.4699 loss: 2.8320 loss_cls: 2.8320 2023/01/24 20:38:16 - mmengine - INFO - Epoch(train) [68][1800/1879] lr: 1.2534e-05 eta: 1 day, 12:04:08 time: 2.1516 data_time: 0.0399 memory: 48866 grad_norm: 5.6275 loss: 3.0334 loss_cls: 3.0334 2023/01/24 20:41:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 20:41:05 - mmengine - INFO - Epoch(train) [68][1879/1879] lr: 1.2506e-05 eta: 1 day, 12:01:17 time: 2.0981 data_time: 0.0408 memory: 48866 grad_norm: 5.7949 loss: 2.8500 loss_cls: 2.8500 2023/01/24 20:41:59 - mmengine - INFO - Epoch(val) [68][100/155] eta: 0:00:29 time: 0.5631 data_time: 0.2108 memory: 4950 2023/01/24 20:42:30 - mmengine - INFO - Epoch(val) [68][155/155] acc/top1: 0.7239 acc/top5: 0.9063 acc/mean1: 0.7238 2023/01/24 20:42:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_67.pth is removed 2023/01/24 20:42:33 - mmengine - INFO - The best checkpoint with 0.7239 acc/top1 at 68 epoch is saved to best_acc/top1_epoch_68.pth. 2023/01/24 20:46:16 - mmengine - INFO - Epoch(train) [69][ 100/1879] lr: 1.2470e-05 eta: 1 day, 11:57:45 time: 2.1595 data_time: 0.0399 memory: 48866 grad_norm: 5.6477 loss: 2.8964 loss_cls: 2.8964 2023/01/24 20:49:51 - mmengine - INFO - Epoch(train) [69][ 200/1879] lr: 1.2434e-05 eta: 1 day, 11:54:09 time: 2.1565 data_time: 0.0400 memory: 48866 grad_norm: 5.4285 loss: 2.9726 loss_cls: 2.9726 2023/01/24 20:50:51 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 20:53:27 - mmengine - INFO - Epoch(train) [69][ 300/1879] lr: 1.2398e-05 eta: 1 day, 11:50:33 time: 2.1679 data_time: 0.0399 memory: 48866 grad_norm: 5.5182 loss: 3.0135 loss_cls: 3.0135 2023/01/24 20:57:03 - mmengine - INFO - Epoch(train) [69][ 400/1879] lr: 1.2363e-05 eta: 1 day, 11:46:58 time: 2.1537 data_time: 0.0393 memory: 48866 grad_norm: 5.6820 loss: 2.9715 loss_cls: 2.9715 2023/01/24 21:00:38 - mmengine - INFO - Epoch(train) [69][ 500/1879] lr: 1.2327e-05 eta: 1 day, 11:43:22 time: 2.1510 data_time: 0.0397 memory: 48866 grad_norm: 5.8356 loss: 2.8350 loss_cls: 2.8350 2023/01/24 21:04:14 - mmengine - INFO - Epoch(train) [69][ 600/1879] lr: 1.2291e-05 eta: 1 day, 11:39:47 time: 2.1588 data_time: 0.0404 memory: 48866 grad_norm: 5.5098 loss: 3.0089 loss_cls: 3.0089 2023/01/24 21:07:50 - mmengine - INFO - Epoch(train) [69][ 700/1879] lr: 1.2256e-05 eta: 1 day, 11:36:11 time: 2.1549 data_time: 0.0399 memory: 48866 grad_norm: 5.6422 loss: 2.9529 loss_cls: 2.9529 2023/01/24 21:11:27 - mmengine - INFO - Epoch(train) [69][ 800/1879] lr: 1.2220e-05 eta: 1 day, 11:32:36 time: 2.1615 data_time: 0.0410 memory: 48866 grad_norm: 5.5206 loss: 3.2127 loss_cls: 3.2127 2023/01/24 21:15:03 - mmengine - INFO - Epoch(train) [69][ 900/1879] lr: 1.2184e-05 eta: 1 day, 11:29:00 time: 2.1669 data_time: 0.0410 memory: 48866 grad_norm: 5.8843 loss: 2.9208 loss_cls: 2.9208 2023/01/24 21:18:39 - mmengine - INFO - Epoch(train) [69][1000/1879] lr: 1.2149e-05 eta: 1 day, 11:25:25 time: 2.1605 data_time: 0.0400 memory: 48866 grad_norm: 5.5931 loss: 3.0120 loss_cls: 3.0120 2023/01/24 21:22:15 - mmengine - INFO - Epoch(train) [69][1100/1879] lr: 1.2113e-05 eta: 1 day, 11:21:49 time: 2.1700 data_time: 0.0410 memory: 48866 grad_norm: 5.3373 loss: 3.0109 loss_cls: 3.0109 2023/01/24 21:25:51 - mmengine - INFO - Epoch(train) [69][1200/1879] lr: 1.2078e-05 eta: 1 day, 11:18:14 time: 2.1505 data_time: 0.0400 memory: 48866 grad_norm: 5.4092 loss: 2.8687 loss_cls: 2.8687 2023/01/24 21:26:51 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 21:29:28 - mmengine - INFO - Epoch(train) [69][1300/1879] lr: 1.2042e-05 eta: 1 day, 11:14:38 time: 2.1683 data_time: 0.0417 memory: 48866 grad_norm: 5.4664 loss: 3.1145 loss_cls: 3.1145 2023/01/24 21:33:03 - mmengine - INFO - Epoch(train) [69][1400/1879] lr: 1.2007e-05 eta: 1 day, 11:11:03 time: 2.1538 data_time: 0.0399 memory: 48866 grad_norm: 5.5914 loss: 2.9383 loss_cls: 2.9383 2023/01/24 21:36:40 - mmengine - INFO - Epoch(train) [69][1500/1879] lr: 1.1971e-05 eta: 1 day, 11:07:27 time: 2.1753 data_time: 0.0404 memory: 48866 grad_norm: 5.9444 loss: 2.8782 loss_cls: 2.8782 2023/01/24 21:40:16 - mmengine - INFO - Epoch(train) [69][1600/1879] lr: 1.1936e-05 eta: 1 day, 11:03:52 time: 2.1667 data_time: 0.0414 memory: 48866 grad_norm: 5.6152 loss: 3.1555 loss_cls: 3.1555 2023/01/24 21:43:53 - mmengine - INFO - Epoch(train) [69][1700/1879] lr: 1.1901e-05 eta: 1 day, 11:00:17 time: 2.1568 data_time: 0.0408 memory: 48866 grad_norm: 5.5528 loss: 2.8729 loss_cls: 2.8729 2023/01/24 21:47:30 - mmengine - INFO - Epoch(train) [69][1800/1879] lr: 1.1865e-05 eta: 1 day, 10:56:42 time: 2.1628 data_time: 0.0408 memory: 48866 grad_norm: 5.6880 loss: 2.9141 loss_cls: 2.9141 2023/01/24 21:50:20 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 21:50:20 - mmengine - INFO - Epoch(train) [69][1879/1879] lr: 1.1837e-05 eta: 1 day, 10:53:51 time: 2.1204 data_time: 0.0416 memory: 48866 grad_norm: 5.8451 loss: 2.7570 loss_cls: 2.7570 2023/01/24 21:50:20 - mmengine - INFO - Saving checkpoint at 69 epochs 2023/01/24 21:51:20 - mmengine - INFO - Epoch(val) [69][100/155] eta: 0:00:30 time: 0.5567 data_time: 0.2252 memory: 4950 2023/01/24 21:51:47 - mmengine - INFO - Epoch(val) [69][155/155] acc/top1: 0.7279 acc/top5: 0.9076 acc/mean1: 0.7279 2023/01/24 21:51:47 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_68.pth is removed 2023/01/24 21:51:51 - mmengine - INFO - The best checkpoint with 0.7279 acc/top1 at 69 epoch is saved to best_acc/top1_epoch_69.pth. 2023/01/24 21:55:33 - mmengine - INFO - Epoch(train) [70][ 100/1879] lr: 1.1802e-05 eta: 1 day, 10:50:18 time: 2.1539 data_time: 0.0396 memory: 48866 grad_norm: 5.7184 loss: 2.9422 loss_cls: 2.9422 2023/01/24 21:59:09 - mmengine - INFO - Epoch(train) [70][ 200/1879] lr: 1.1767e-05 eta: 1 day, 10:46:43 time: 2.1583 data_time: 0.0399 memory: 48866 grad_norm: 5.3893 loss: 2.8941 loss_cls: 2.8941 2023/01/24 22:02:44 - mmengine - INFO - Epoch(train) [70][ 300/1879] lr: 1.1731e-05 eta: 1 day, 10:43:07 time: 2.1609 data_time: 0.0399 memory: 48866 grad_norm: 5.6783 loss: 3.0179 loss_cls: 3.0179 2023/01/24 22:04:30 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 22:06:20 - mmengine - INFO - Epoch(train) [70][ 400/1879] lr: 1.1696e-05 eta: 1 day, 10:39:31 time: 2.1533 data_time: 0.0410 memory: 48866 grad_norm: 5.6977 loss: 2.9650 loss_cls: 2.9650 2023/01/24 22:09:56 - mmengine - INFO - Epoch(train) [70][ 500/1879] lr: 1.1661e-05 eta: 1 day, 10:35:55 time: 2.1561 data_time: 0.0405 memory: 48866 grad_norm: 5.7551 loss: 2.8826 loss_cls: 2.8826 2023/01/24 22:13:32 - mmengine - INFO - Epoch(train) [70][ 600/1879] lr: 1.1626e-05 eta: 1 day, 10:32:20 time: 2.1565 data_time: 0.0405 memory: 48866 grad_norm: 5.5793 loss: 2.9170 loss_cls: 2.9170 2023/01/24 22:17:08 - mmengine - INFO - Epoch(train) [70][ 700/1879] lr: 1.1591e-05 eta: 1 day, 10:28:44 time: 2.1584 data_time: 0.0403 memory: 48866 grad_norm: 5.7271 loss: 2.9459 loss_cls: 2.9459 2023/01/24 22:20:43 - mmengine - INFO - Epoch(train) [70][ 800/1879] lr: 1.1556e-05 eta: 1 day, 10:25:09 time: 2.1521 data_time: 0.0397 memory: 48866 grad_norm: 5.6104 loss: 2.9980 loss_cls: 2.9980 2023/01/24 22:24:19 - mmengine - INFO - Epoch(train) [70][ 900/1879] lr: 1.1521e-05 eta: 1 day, 10:21:33 time: 2.1699 data_time: 0.0412 memory: 48866 grad_norm: 5.6426 loss: 2.7746 loss_cls: 2.7746 2023/01/24 22:27:55 - mmengine - INFO - Epoch(train) [70][1000/1879] lr: 1.1486e-05 eta: 1 day, 10:17:58 time: 2.1590 data_time: 0.0413 memory: 48866 grad_norm: 5.8468 loss: 2.6827 loss_cls: 2.6827 2023/01/24 22:31:31 - mmengine - INFO - Epoch(train) [70][1100/1879] lr: 1.1451e-05 eta: 1 day, 10:14:22 time: 2.1577 data_time: 0.0412 memory: 48866 grad_norm: 5.7925 loss: 2.8812 loss_cls: 2.8812 2023/01/24 22:35:07 - mmengine - INFO - Epoch(train) [70][1200/1879] lr: 1.1416e-05 eta: 1 day, 10:10:46 time: 2.1514 data_time: 0.0401 memory: 48866 grad_norm: 5.6380 loss: 3.0703 loss_cls: 3.0703 2023/01/24 22:38:43 - mmengine - INFO - Epoch(train) [70][1300/1879] lr: 1.1381e-05 eta: 1 day, 10:07:11 time: 2.1630 data_time: 0.0405 memory: 48866 grad_norm: 5.3604 loss: 3.0341 loss_cls: 3.0341 2023/01/24 22:40:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 22:42:19 - mmengine - INFO - Epoch(train) [70][1400/1879] lr: 1.1346e-05 eta: 1 day, 10:03:35 time: 2.1457 data_time: 0.0405 memory: 48866 grad_norm: 5.5533 loss: 2.6940 loss_cls: 2.6940 2023/01/24 22:45:55 - mmengine - INFO - Epoch(train) [70][1500/1879] lr: 1.1311e-05 eta: 1 day, 10:00:00 time: 2.1577 data_time: 0.0400 memory: 48866 grad_norm: 5.5609 loss: 2.9508 loss_cls: 2.9508 2023/01/24 22:49:31 - mmengine - INFO - Epoch(train) [70][1600/1879] lr: 1.1276e-05 eta: 1 day, 9:56:24 time: 2.1551 data_time: 0.0404 memory: 48866 grad_norm: 5.5718 loss: 2.9545 loss_cls: 2.9545 2023/01/24 22:53:07 - mmengine - INFO - Epoch(train) [70][1700/1879] lr: 1.1241e-05 eta: 1 day, 9:52:49 time: 2.1633 data_time: 0.0415 memory: 48866 grad_norm: 5.4127 loss: 2.7719 loss_cls: 2.7719 2023/01/24 22:56:43 - mmengine - INFO - Epoch(train) [70][1800/1879] lr: 1.1206e-05 eta: 1 day, 9:49:13 time: 2.1645 data_time: 0.0401 memory: 48866 grad_norm: 5.3119 loss: 2.9363 loss_cls: 2.9363 2023/01/24 22:59:33 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 22:59:33 - mmengine - INFO - Epoch(train) [70][1879/1879] lr: 1.1179e-05 eta: 1 day, 9:46:22 time: 2.1120 data_time: 0.0420 memory: 48866 grad_norm: 6.0891 loss: 2.8994 loss_cls: 2.8994 2023/01/24 23:00:26 - mmengine - INFO - Epoch(val) [70][100/155] eta: 0:00:29 time: 0.5604 data_time: 0.2227 memory: 4950 2023/01/24 23:00:57 - mmengine - INFO - Epoch(val) [70][155/155] acc/top1: 0.7345 acc/top5: 0.9081 acc/mean1: 0.7344 2023/01/24 23:00:57 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_69.pth is removed 2023/01/24 23:01:01 - mmengine - INFO - The best checkpoint with 0.7345 acc/top1 at 70 epoch is saved to best_acc/top1_epoch_70.pth. 2023/01/24 23:04:44 - mmengine - INFO - Epoch(train) [71][ 100/1879] lr: 1.1144e-05 eta: 1 day, 9:42:50 time: 2.1578 data_time: 0.0393 memory: 48866 grad_norm: 5.6071 loss: 2.9030 loss_cls: 2.9030 2023/01/24 23:08:20 - mmengine - INFO - Epoch(train) [71][ 200/1879] lr: 1.1109e-05 eta: 1 day, 9:39:14 time: 2.1475 data_time: 0.0398 memory: 48866 grad_norm: 5.4849 loss: 3.1020 loss_cls: 3.1020 2023/01/24 23:11:56 - mmengine - INFO - Epoch(train) [71][ 300/1879] lr: 1.1075e-05 eta: 1 day, 9:35:39 time: 2.1658 data_time: 0.0411 memory: 48866 grad_norm: 5.7846 loss: 3.0802 loss_cls: 3.0802 2023/01/24 23:15:32 - mmengine - INFO - Epoch(train) [71][ 400/1879] lr: 1.1040e-05 eta: 1 day, 9:32:03 time: 2.1590 data_time: 0.0405 memory: 48866 grad_norm: 5.9405 loss: 2.7953 loss_cls: 2.7953 2023/01/24 23:18:03 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 23:19:08 - mmengine - INFO - Epoch(train) [71][ 500/1879] lr: 1.1005e-05 eta: 1 day, 9:28:27 time: 2.1613 data_time: 0.0398 memory: 48866 grad_norm: 5.4331 loss: 2.9084 loss_cls: 2.9084 2023/01/24 23:22:44 - mmengine - INFO - Epoch(train) [71][ 600/1879] lr: 1.0971e-05 eta: 1 day, 9:24:52 time: 2.1693 data_time: 0.0400 memory: 48866 grad_norm: 5.5339 loss: 3.1437 loss_cls: 3.1437 2023/01/24 23:26:21 - mmengine - INFO - Epoch(train) [71][ 700/1879] lr: 1.0936e-05 eta: 1 day, 9:21:17 time: 2.1707 data_time: 0.0398 memory: 48866 grad_norm: 5.7901 loss: 2.9509 loss_cls: 2.9509 2023/01/24 23:29:56 - mmengine - INFO - Epoch(train) [71][ 800/1879] lr: 1.0901e-05 eta: 1 day, 9:17:41 time: 2.1615 data_time: 0.0401 memory: 48866 grad_norm: 5.7146 loss: 2.9812 loss_cls: 2.9812 2023/01/24 23:33:32 - mmengine - INFO - Epoch(train) [71][ 900/1879] lr: 1.0867e-05 eta: 1 day, 9:14:05 time: 2.1430 data_time: 0.0399 memory: 48866 grad_norm: 5.5895 loss: 2.7766 loss_cls: 2.7766 2023/01/24 23:37:09 - mmengine - INFO - Epoch(train) [71][1000/1879] lr: 1.0832e-05 eta: 1 day, 9:10:30 time: 2.1766 data_time: 0.0402 memory: 48866 grad_norm: 5.8110 loss: 2.7508 loss_cls: 2.7508 2023/01/24 23:40:44 - mmengine - INFO - Epoch(train) [71][1100/1879] lr: 1.0798e-05 eta: 1 day, 9:06:54 time: 2.1530 data_time: 0.0404 memory: 48866 grad_norm: 5.6336 loss: 2.9154 loss_cls: 2.9154 2023/01/24 23:44:20 - mmengine - INFO - Epoch(train) [71][1200/1879] lr: 1.0764e-05 eta: 1 day, 9:03:18 time: 2.1605 data_time: 0.0414 memory: 48866 grad_norm: 5.5079 loss: 3.0467 loss_cls: 3.0467 2023/01/24 23:47:57 - mmengine - INFO - Epoch(train) [71][1300/1879] lr: 1.0729e-05 eta: 1 day, 8:59:43 time: 2.1620 data_time: 0.0412 memory: 48866 grad_norm: 5.5055 loss: 2.8444 loss_cls: 2.8444 2023/01/24 23:51:33 - mmengine - INFO - Epoch(train) [71][1400/1879] lr: 1.0695e-05 eta: 1 day, 8:56:08 time: 2.1714 data_time: 0.0401 memory: 48866 grad_norm: 5.8532 loss: 2.9356 loss_cls: 2.9356 2023/01/24 23:54:04 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/24 23:55:09 - mmengine - INFO - Epoch(train) [71][1500/1879] lr: 1.0661e-05 eta: 1 day, 8:52:32 time: 2.1624 data_time: 0.0421 memory: 48866 grad_norm: 5.3424 loss: 2.9481 loss_cls: 2.9481 2023/01/24 23:58:45 - mmengine - INFO - Epoch(train) [71][1600/1879] lr: 1.0626e-05 eta: 1 day, 8:48:56 time: 2.1593 data_time: 0.0409 memory: 48866 grad_norm: 5.6630 loss: 2.9468 loss_cls: 2.9468 2023/01/25 00:02:21 - mmengine - INFO - Epoch(train) [71][1700/1879] lr: 1.0592e-05 eta: 1 day, 8:45:21 time: 2.1550 data_time: 0.0412 memory: 48866 grad_norm: 5.9654 loss: 2.7979 loss_cls: 2.7979 2023/01/25 00:05:57 - mmengine - INFO - Epoch(train) [71][1800/1879] lr: 1.0558e-05 eta: 1 day, 8:41:45 time: 2.1612 data_time: 0.0404 memory: 48866 grad_norm: 5.6183 loss: 3.0022 loss_cls: 3.0022 2023/01/25 00:08:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 00:08:46 - mmengine - INFO - Epoch(train) [71][1879/1879] lr: 1.0531e-05 eta: 1 day, 8:38:55 time: 2.0973 data_time: 0.0423 memory: 48866 grad_norm: 5.7577 loss: 2.9815 loss_cls: 2.9815 2023/01/25 00:09:39 - mmengine - INFO - Epoch(val) [71][100/155] eta: 0:00:29 time: 0.5518 data_time: 0.2126 memory: 4950 2023/01/25 00:10:10 - mmengine - INFO - Epoch(val) [71][155/155] acc/top1: 0.7355 acc/top5: 0.9106 acc/mean1: 0.7354 2023/01/25 00:10:10 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_70.pth is removed 2023/01/25 00:10:14 - mmengine - INFO - The best checkpoint with 0.7355 acc/top1 at 71 epoch is saved to best_acc/top1_epoch_71.pth. 2023/01/25 00:13:58 - mmengine - INFO - Epoch(train) [72][ 100/1879] lr: 1.0497e-05 eta: 1 day, 8:35:22 time: 2.1569 data_time: 0.0408 memory: 48866 grad_norm: 5.5940 loss: 2.8567 loss_cls: 2.8567 2023/01/25 00:17:33 - mmengine - INFO - Epoch(train) [72][ 200/1879] lr: 1.0462e-05 eta: 1 day, 8:31:46 time: 2.1802 data_time: 0.0414 memory: 48866 grad_norm: 6.1595 loss: 3.0111 loss_cls: 3.0111 2023/01/25 00:21:09 - mmengine - INFO - Epoch(train) [72][ 300/1879] lr: 1.0428e-05 eta: 1 day, 8:28:11 time: 2.1524 data_time: 0.0407 memory: 48866 grad_norm: 5.3227 loss: 3.0145 loss_cls: 3.0145 2023/01/25 00:24:45 - mmengine - INFO - Epoch(train) [72][ 400/1879] lr: 1.0394e-05 eta: 1 day, 8:24:35 time: 2.1611 data_time: 0.0416 memory: 48866 grad_norm: 5.8808 loss: 2.8879 loss_cls: 2.8879 2023/01/25 00:28:21 - mmengine - INFO - Epoch(train) [72][ 500/1879] lr: 1.0360e-05 eta: 1 day, 8:21:00 time: 2.1603 data_time: 0.0402 memory: 48866 grad_norm: 5.6211 loss: 3.0187 loss_cls: 3.0187 2023/01/25 00:31:38 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 00:31:58 - mmengine - INFO - Epoch(train) [72][ 600/1879] lr: 1.0326e-05 eta: 1 day, 8:17:24 time: 2.1614 data_time: 0.0408 memory: 48866 grad_norm: 5.8949 loss: 2.8422 loss_cls: 2.8422 2023/01/25 00:35:34 - mmengine - INFO - Epoch(train) [72][ 700/1879] lr: 1.0292e-05 eta: 1 day, 8:13:49 time: 2.1628 data_time: 0.0406 memory: 48866 grad_norm: 5.5991 loss: 2.8229 loss_cls: 2.8229 2023/01/25 00:39:09 - mmengine - INFO - Epoch(train) [72][ 800/1879] lr: 1.0258e-05 eta: 1 day, 8:10:13 time: 2.1534 data_time: 0.0397 memory: 48866 grad_norm: 5.6618 loss: 2.9391 loss_cls: 2.9391 2023/01/25 00:42:46 - mmengine - INFO - Epoch(train) [72][ 900/1879] lr: 1.0224e-05 eta: 1 day, 8:06:38 time: 2.1600 data_time: 0.0410 memory: 48866 grad_norm: 5.5706 loss: 3.0634 loss_cls: 3.0634 2023/01/25 00:46:22 - mmengine - INFO - Epoch(train) [72][1000/1879] lr: 1.0191e-05 eta: 1 day, 8:03:02 time: 2.1568 data_time: 0.0393 memory: 48866 grad_norm: 5.5816 loss: 2.9821 loss_cls: 2.9821 2023/01/25 00:49:58 - mmengine - INFO - Epoch(train) [72][1100/1879] lr: 1.0157e-05 eta: 1 day, 7:59:26 time: 2.1679 data_time: 0.0401 memory: 48866 grad_norm: 5.6509 loss: 2.6617 loss_cls: 2.6617 2023/01/25 00:53:34 - mmengine - INFO - Epoch(train) [72][1200/1879] lr: 1.0123e-05 eta: 1 day, 7:55:51 time: 2.1685 data_time: 0.0410 memory: 48866 grad_norm: 5.8035 loss: 2.9408 loss_cls: 2.9408 2023/01/25 00:57:10 - mmengine - INFO - Epoch(train) [72][1300/1879] lr: 1.0089e-05 eta: 1 day, 7:52:15 time: 2.1603 data_time: 0.0409 memory: 48866 grad_norm: 5.7363 loss: 2.9989 loss_cls: 2.9989 2023/01/25 01:00:46 - mmengine - INFO - Epoch(train) [72][1400/1879] lr: 1.0055e-05 eta: 1 day, 7:48:40 time: 2.1637 data_time: 0.0414 memory: 48866 grad_norm: 5.7766 loss: 2.7819 loss_cls: 2.7819 2023/01/25 01:04:22 - mmengine - INFO - Epoch(train) [72][1500/1879] lr: 1.0022e-05 eta: 1 day, 7:45:04 time: 2.1588 data_time: 0.0407 memory: 48866 grad_norm: 5.7429 loss: 2.7039 loss_cls: 2.7039 2023/01/25 01:07:39 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 01:07:58 - mmengine - INFO - Epoch(train) [72][1600/1879] lr: 9.9881e-06 eta: 1 day, 7:41:28 time: 2.1635 data_time: 0.0406 memory: 48866 grad_norm: 5.6037 loss: 2.9239 loss_cls: 2.9239 2023/01/25 01:11:34 - mmengine - INFO - Epoch(train) [72][1700/1879] lr: 9.9545e-06 eta: 1 day, 7:37:53 time: 2.1519 data_time: 0.0413 memory: 48866 grad_norm: 5.6176 loss: 2.9999 loss_cls: 2.9999 2023/01/25 01:15:10 - mmengine - INFO - Epoch(train) [72][1800/1879] lr: 9.9209e-06 eta: 1 day, 7:34:17 time: 2.1640 data_time: 0.0394 memory: 48866 grad_norm: 5.6954 loss: 2.9572 loss_cls: 2.9572 2023/01/25 01:18:00 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 01:18:00 - mmengine - INFO - Epoch(train) [72][1879/1879] lr: 9.8944e-06 eta: 1 day, 7:31:27 time: 2.1310 data_time: 0.0414 memory: 48866 grad_norm: 5.8765 loss: 2.8915 loss_cls: 2.8915 2023/01/25 01:18:00 - mmengine - INFO - Saving checkpoint at 72 epochs 2023/01/25 01:18:59 - mmengine - INFO - Epoch(val) [72][100/155] eta: 0:00:30 time: 0.5520 data_time: 0.1968 memory: 4950 2023/01/25 01:19:27 - mmengine - INFO - Epoch(val) [72][155/155] acc/top1: 0.7351 acc/top5: 0.9097 acc/mean1: 0.7351 2023/01/25 01:23:10 - mmengine - INFO - Epoch(train) [73][ 100/1879] lr: 9.8609e-06 eta: 1 day, 7:27:54 time: 2.1497 data_time: 0.0405 memory: 48866 grad_norm: 5.7952 loss: 2.9470 loss_cls: 2.9470 2023/01/25 01:26:46 - mmengine - INFO - Epoch(train) [73][ 200/1879] lr: 9.8274e-06 eta: 1 day, 7:24:18 time: 2.1517 data_time: 0.0395 memory: 48866 grad_norm: 5.8189 loss: 2.7850 loss_cls: 2.7850 2023/01/25 01:30:22 - mmengine - INFO - Epoch(train) [73][ 300/1879] lr: 9.7940e-06 eta: 1 day, 7:20:43 time: 2.1545 data_time: 0.0403 memory: 48866 grad_norm: 5.9967 loss: 2.7652 loss_cls: 2.7652 2023/01/25 01:33:58 - mmengine - INFO - Epoch(train) [73][ 400/1879] lr: 9.7606e-06 eta: 1 day, 7:17:07 time: 2.1575 data_time: 0.0403 memory: 48866 grad_norm: 5.8801 loss: 2.8043 loss_cls: 2.8043 2023/01/25 01:37:34 - mmengine - INFO - Epoch(train) [73][ 500/1879] lr: 9.7272e-06 eta: 1 day, 7:13:31 time: 2.1547 data_time: 0.0405 memory: 48866 grad_norm: 5.4106 loss: 2.8002 loss_cls: 2.8002 2023/01/25 01:41:10 - mmengine - INFO - Epoch(train) [73][ 600/1879] lr: 9.6938e-06 eta: 1 day, 7:09:56 time: 2.1647 data_time: 0.0411 memory: 48866 grad_norm: 5.5092 loss: 2.9406 loss_cls: 2.9406 2023/01/25 01:44:46 - mmengine - INFO - Epoch(train) [73][ 700/1879] lr: 9.6605e-06 eta: 1 day, 7:06:20 time: 2.1600 data_time: 0.0407 memory: 48866 grad_norm: 5.5948 loss: 2.8679 loss_cls: 2.8679 2023/01/25 01:45:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 01:48:22 - mmengine - INFO - Epoch(train) [73][ 800/1879] lr: 9.6273e-06 eta: 1 day, 7:02:45 time: 2.1590 data_time: 0.0415 memory: 48866 grad_norm: 5.5654 loss: 2.8199 loss_cls: 2.8199 2023/01/25 01:51:59 - mmengine - INFO - Epoch(train) [73][ 900/1879] lr: 9.5940e-06 eta: 1 day, 6:59:09 time: 2.1628 data_time: 0.0410 memory: 48866 grad_norm: 5.8358 loss: 2.8657 loss_cls: 2.8657 2023/01/25 01:55:34 - mmengine - INFO - Epoch(train) [73][1000/1879] lr: 9.5609e-06 eta: 1 day, 6:55:33 time: 2.1615 data_time: 0.0413 memory: 48866 grad_norm: 5.6170 loss: 2.8126 loss_cls: 2.8126 2023/01/25 01:59:10 - mmengine - INFO - Epoch(train) [73][1100/1879] lr: 9.5277e-06 eta: 1 day, 6:51:58 time: 2.1561 data_time: 0.0416 memory: 48866 grad_norm: 5.9402 loss: 2.7226 loss_cls: 2.7226 2023/01/25 02:02:47 - mmengine - INFO - Epoch(train) [73][1200/1879] lr: 9.4946e-06 eta: 1 day, 6:48:22 time: 2.1592 data_time: 0.0402 memory: 48866 grad_norm: 5.5996 loss: 3.1138 loss_cls: 3.1138 2023/01/25 02:06:23 - mmengine - INFO - Epoch(train) [73][1300/1879] lr: 9.4615e-06 eta: 1 day, 6:44:47 time: 2.1640 data_time: 0.0407 memory: 48866 grad_norm: 5.4621 loss: 2.8011 loss_cls: 2.8011 2023/01/25 02:09:59 - mmengine - INFO - Epoch(train) [73][1400/1879] lr: 9.4285e-06 eta: 1 day, 6:41:11 time: 2.1545 data_time: 0.0409 memory: 48866 grad_norm: 6.2345 loss: 2.8104 loss_cls: 2.8104 2023/01/25 02:13:35 - mmengine - INFO - Epoch(train) [73][1500/1879] lr: 9.3955e-06 eta: 1 day, 6:37:36 time: 2.1660 data_time: 0.0408 memory: 48866 grad_norm: 5.9659 loss: 2.8049 loss_cls: 2.8049 2023/01/25 02:17:11 - mmengine - INFO - Epoch(train) [73][1600/1879] lr: 9.3625e-06 eta: 1 day, 6:34:00 time: 2.1582 data_time: 0.0398 memory: 48866 grad_norm: 6.0519 loss: 2.8221 loss_cls: 2.8221 2023/01/25 02:20:47 - mmengine - INFO - Epoch(train) [73][1700/1879] lr: 9.3296e-06 eta: 1 day, 6:30:24 time: 2.1706 data_time: 0.0409 memory: 48866 grad_norm: 5.7146 loss: 2.9146 loss_cls: 2.9146 2023/01/25 02:21:13 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 02:24:23 - mmengine - INFO - Epoch(train) [73][1800/1879] lr: 9.2967e-06 eta: 1 day, 6:26:49 time: 2.1618 data_time: 0.0399 memory: 48866 grad_norm: 5.9334 loss: 2.7654 loss_cls: 2.7654 2023/01/25 02:27:13 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 02:27:13 - mmengine - INFO - Epoch(train) [73][1879/1879] lr: 9.2707e-06 eta: 1 day, 6:23:58 time: 2.1004 data_time: 0.0407 memory: 48866 grad_norm: 6.2080 loss: 2.7749 loss_cls: 2.7749 2023/01/25 02:28:07 - mmengine - INFO - Epoch(val) [73][100/155] eta: 0:00:29 time: 0.5951 data_time: 0.2446 memory: 4950 2023/01/25 02:28:37 - mmengine - INFO - Epoch(val) [73][155/155] acc/top1: 0.7371 acc/top5: 0.9136 acc/mean1: 0.7371 2023/01/25 02:28:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_71.pth is removed 2023/01/25 02:28:41 - mmengine - INFO - The best checkpoint with 0.7371 acc/top1 at 73 epoch is saved to best_acc/top1_epoch_73.pth. 2023/01/25 02:32:24 - mmengine - INFO - Epoch(train) [74][ 100/1879] lr: 9.2379e-06 eta: 1 day, 6:20:25 time: 2.1719 data_time: 0.0396 memory: 48866 grad_norm: 5.6848 loss: 2.9734 loss_cls: 2.9734 2023/01/25 02:36:00 - mmengine - INFO - Epoch(train) [74][ 200/1879] lr: 9.2051e-06 eta: 1 day, 6:16:50 time: 2.1539 data_time: 0.0410 memory: 48866 grad_norm: 5.7217 loss: 2.8981 loss_cls: 2.8981 2023/01/25 02:39:36 - mmengine - INFO - Epoch(train) [74][ 300/1879] lr: 9.1724e-06 eta: 1 day, 6:13:14 time: 2.1796 data_time: 0.0571 memory: 48866 grad_norm: 5.8199 loss: 2.7363 loss_cls: 2.7363 2023/01/25 02:43:12 - mmengine - INFO - Epoch(train) [74][ 400/1879] lr: 9.1396e-06 eta: 1 day, 6:09:39 time: 2.1685 data_time: 0.0405 memory: 48866 grad_norm: 5.8270 loss: 2.8114 loss_cls: 2.8114 2023/01/25 02:46:48 - mmengine - INFO - Epoch(train) [74][ 500/1879] lr: 9.1070e-06 eta: 1 day, 6:06:03 time: 2.1616 data_time: 0.0405 memory: 48866 grad_norm: 6.0629 loss: 2.6183 loss_cls: 2.6183 2023/01/25 02:50:24 - mmengine - INFO - Epoch(train) [74][ 600/1879] lr: 9.0743e-06 eta: 1 day, 6:02:27 time: 2.1643 data_time: 0.0408 memory: 48866 grad_norm: 5.8390 loss: 2.8137 loss_cls: 2.8137 2023/01/25 02:54:00 - mmengine - INFO - Epoch(train) [74][ 700/1879] lr: 9.0418e-06 eta: 1 day, 5:58:52 time: 2.1626 data_time: 0.0404 memory: 48866 grad_norm: 5.9587 loss: 2.8135 loss_cls: 2.8135 2023/01/25 02:57:37 - mmengine - INFO - Epoch(train) [74][ 800/1879] lr: 9.0092e-06 eta: 1 day, 5:55:16 time: 2.1580 data_time: 0.0401 memory: 48866 grad_norm: 5.7597 loss: 3.0156 loss_cls: 3.0156 2023/01/25 02:58:48 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 03:01:13 - mmengine - INFO - Epoch(train) [74][ 900/1879] lr: 8.9767e-06 eta: 1 day, 5:51:41 time: 2.1761 data_time: 0.0404 memory: 48866 grad_norm: 5.7248 loss: 3.0438 loss_cls: 3.0438 2023/01/25 03:04:49 - mmengine - INFO - Epoch(train) [74][1000/1879] lr: 8.9442e-06 eta: 1 day, 5:48:05 time: 2.1577 data_time: 0.0409 memory: 48866 grad_norm: 5.7993 loss: 2.9045 loss_cls: 2.9045 2023/01/25 03:08:25 - mmengine - INFO - Epoch(train) [74][1100/1879] lr: 8.9118e-06 eta: 1 day, 5:44:30 time: 2.1513 data_time: 0.0409 memory: 48866 grad_norm: 5.5454 loss: 2.9675 loss_cls: 2.9675 2023/01/25 03:12:02 - mmengine - INFO - Epoch(train) [74][1200/1879] lr: 8.8794e-06 eta: 1 day, 5:40:54 time: 2.1571 data_time: 0.0411 memory: 48866 grad_norm: 5.7756 loss: 2.7925 loss_cls: 2.7925 2023/01/25 03:15:37 - mmengine - INFO - Epoch(train) [74][1300/1879] lr: 8.8471e-06 eta: 1 day, 5:37:18 time: 2.1654 data_time: 0.0405 memory: 48866 grad_norm: 5.6565 loss: 2.6756 loss_cls: 2.6756 2023/01/25 03:19:14 - mmengine - INFO - Epoch(train) [74][1400/1879] lr: 8.8147e-06 eta: 1 day, 5:33:43 time: 2.1608 data_time: 0.0408 memory: 48866 grad_norm: 5.7897 loss: 2.8343 loss_cls: 2.8343 2023/01/25 03:22:49 - mmengine - INFO - Epoch(train) [74][1500/1879] lr: 8.7825e-06 eta: 1 day, 5:30:07 time: 2.1545 data_time: 0.0410 memory: 48866 grad_norm: 5.5739 loss: 2.8300 loss_cls: 2.8300 2023/01/25 03:26:25 - mmengine - INFO - Epoch(train) [74][1600/1879] lr: 8.7502e-06 eta: 1 day, 5:26:32 time: 2.1563 data_time: 0.0405 memory: 48866 grad_norm: 5.8474 loss: 2.8307 loss_cls: 2.8307 2023/01/25 03:30:02 - mmengine - INFO - Epoch(train) [74][1700/1879] lr: 8.7181e-06 eta: 1 day, 5:22:56 time: 2.1639 data_time: 0.0401 memory: 48866 grad_norm: 5.7198 loss: 2.9715 loss_cls: 2.9715 2023/01/25 03:33:38 - mmengine - INFO - Epoch(train) [74][1800/1879] lr: 8.6859e-06 eta: 1 day, 5:19:20 time: 2.1637 data_time: 0.0416 memory: 48866 grad_norm: 5.7572 loss: 3.0369 loss_cls: 3.0369 2023/01/25 03:34:49 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 03:36:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 03:36:28 - mmengine - INFO - Epoch(train) [74][1879/1879] lr: 8.6605e-06 eta: 1 day, 5:16:30 time: 2.1035 data_time: 0.0408 memory: 48866 grad_norm: 5.9246 loss: 2.7808 loss_cls: 2.7808 2023/01/25 03:37:20 - mmengine - INFO - Epoch(val) [74][100/155] eta: 0:00:29 time: 0.5209 data_time: 0.1728 memory: 4950 2023/01/25 03:37:52 - mmengine - INFO - Epoch(val) [74][155/155] acc/top1: 0.7384 acc/top5: 0.9132 acc/mean1: 0.7384 2023/01/25 03:37:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_73.pth is removed 2023/01/25 03:37:55 - mmengine - INFO - The best checkpoint with 0.7384 acc/top1 at 74 epoch is saved to best_acc/top1_epoch_74.pth. 2023/01/25 03:41:39 - mmengine - INFO - Epoch(train) [75][ 100/1879] lr: 8.6285e-06 eta: 1 day, 5:12:57 time: 2.1657 data_time: 0.0389 memory: 48866 grad_norm: 5.7425 loss: 2.7007 loss_cls: 2.7007 2023/01/25 03:45:14 - mmengine - INFO - Epoch(train) [75][ 200/1879] lr: 8.5964e-06 eta: 1 day, 5:09:21 time: 2.1633 data_time: 0.0390 memory: 48866 grad_norm: 5.8386 loss: 2.9484 loss_cls: 2.9484 2023/01/25 03:48:51 - mmengine - INFO - Epoch(train) [75][ 300/1879] lr: 8.5644e-06 eta: 1 day, 5:05:45 time: 2.1568 data_time: 0.0392 memory: 48866 grad_norm: 6.0221 loss: 2.6829 loss_cls: 2.6829 2023/01/25 03:52:28 - mmengine - INFO - Epoch(train) [75][ 400/1879] lr: 8.5325e-06 eta: 1 day, 5:02:10 time: 2.1583 data_time: 0.0399 memory: 48866 grad_norm: 5.7373 loss: 3.0449 loss_cls: 3.0449 2023/01/25 03:56:04 - mmengine - INFO - Epoch(train) [75][ 500/1879] lr: 8.5006e-06 eta: 1 day, 4:58:35 time: 2.1618 data_time: 0.0408 memory: 48866 grad_norm: 5.7696 loss: 2.9181 loss_cls: 2.9181 2023/01/25 03:59:40 - mmengine - INFO - Epoch(train) [75][ 600/1879] lr: 8.4687e-06 eta: 1 day, 4:54:59 time: 2.1603 data_time: 0.0403 memory: 48866 grad_norm: 5.4951 loss: 2.8365 loss_cls: 2.8365 2023/01/25 04:03:16 - mmengine - INFO - Epoch(train) [75][ 700/1879] lr: 8.4369e-06 eta: 1 day, 4:51:23 time: 2.1599 data_time: 0.0401 memory: 48866 grad_norm: 5.7055 loss: 2.8604 loss_cls: 2.8604 2023/01/25 04:06:52 - mmengine - INFO - Epoch(train) [75][ 800/1879] lr: 8.4051e-06 eta: 1 day, 4:47:48 time: 2.1541 data_time: 0.0401 memory: 48866 grad_norm: 5.8912 loss: 2.7230 loss_cls: 2.7230 2023/01/25 04:10:28 - mmengine - INFO - Epoch(train) [75][ 900/1879] lr: 8.3734e-06 eta: 1 day, 4:44:12 time: 2.1611 data_time: 0.0399 memory: 48866 grad_norm: 5.6345 loss: 2.8580 loss_cls: 2.8580 2023/01/25 04:12:25 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 04:14:04 - mmengine - INFO - Epoch(train) [75][1000/1879] lr: 8.3417e-06 eta: 1 day, 4:40:36 time: 2.1586 data_time: 0.0408 memory: 48866 grad_norm: 5.9642 loss: 2.6276 loss_cls: 2.6276 2023/01/25 04:17:41 - mmengine - INFO - Epoch(train) [75][1100/1879] lr: 8.3100e-06 eta: 1 day, 4:37:01 time: 2.1622 data_time: 0.0406 memory: 48866 grad_norm: 5.8079 loss: 2.8629 loss_cls: 2.8629 2023/01/25 04:21:17 - mmengine - INFO - Epoch(train) [75][1200/1879] lr: 8.2784e-06 eta: 1 day, 4:33:25 time: 2.1643 data_time: 0.0421 memory: 48866 grad_norm: 5.6296 loss: 2.8285 loss_cls: 2.8285 2023/01/25 04:24:53 - mmengine - INFO - Epoch(train) [75][1300/1879] lr: 8.2468e-06 eta: 1 day, 4:29:50 time: 2.1674 data_time: 0.0411 memory: 48866 grad_norm: 5.6645 loss: 2.6761 loss_cls: 2.6761 2023/01/25 04:28:30 - mmengine - INFO - Epoch(train) [75][1400/1879] lr: 8.2153e-06 eta: 1 day, 4:26:15 time: 2.1855 data_time: 0.0417 memory: 48866 grad_norm: 5.5704 loss: 2.9732 loss_cls: 2.9732 2023/01/25 04:32:06 - mmengine - INFO - Epoch(train) [75][1500/1879] lr: 8.1838e-06 eta: 1 day, 4:22:39 time: 2.1521 data_time: 0.0410 memory: 48866 grad_norm: 5.6169 loss: 2.8940 loss_cls: 2.8940 2023/01/25 04:35:43 - mmengine - INFO - Epoch(train) [75][1600/1879] lr: 8.1524e-06 eta: 1 day, 4:19:04 time: 2.1591 data_time: 0.0410 memory: 48866 grad_norm: 5.6700 loss: 2.6932 loss_cls: 2.6932 2023/01/25 04:39:19 - mmengine - INFO - Epoch(train) [75][1700/1879] lr: 8.1210e-06 eta: 1 day, 4:15:28 time: 2.1567 data_time: 0.0399 memory: 48866 grad_norm: 5.9463 loss: 2.9217 loss_cls: 2.9217 2023/01/25 04:42:55 - mmengine - INFO - Epoch(train) [75][1800/1879] lr: 8.0896e-06 eta: 1 day, 4:11:52 time: 2.1530 data_time: 0.0410 memory: 48866 grad_norm: 5.7216 loss: 2.7535 loss_cls: 2.7535 2023/01/25 04:45:45 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 04:45:45 - mmengine - INFO - Epoch(train) [75][1879/1879] lr: 8.0649e-06 eta: 1 day, 4:09:02 time: 2.1019 data_time: 0.0403 memory: 48866 grad_norm: 6.2079 loss: 2.9105 loss_cls: 2.9105 2023/01/25 04:45:45 - mmengine - INFO - Saving checkpoint at 75 epochs 2023/01/25 04:46:44 - mmengine - INFO - Epoch(val) [75][100/155] eta: 0:00:30 time: 0.5613 data_time: 0.2055 memory: 4950 2023/01/25 04:47:12 - mmengine - INFO - Epoch(val) [75][155/155] acc/top1: 0.7435 acc/top5: 0.9147 acc/mean1: 0.7434 2023/01/25 04:47:12 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_74.pth is removed 2023/01/25 04:47:15 - mmengine - INFO - The best checkpoint with 0.7435 acc/top1 at 75 epoch is saved to best_acc/top1_epoch_75.pth. 2023/01/25 04:50:04 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 04:50:58 - mmengine - INFO - Epoch(train) [76][ 100/1879] lr: 8.0336e-06 eta: 1 day, 4:05:28 time: 2.1536 data_time: 0.0411 memory: 48866 grad_norm: 5.9378 loss: 2.7541 loss_cls: 2.7541 2023/01/25 04:54:34 - mmengine - INFO - Epoch(train) [76][ 200/1879] lr: 8.0024e-06 eta: 1 day, 4:01:52 time: 2.1496 data_time: 0.0405 memory: 48866 grad_norm: 5.9699 loss: 2.7263 loss_cls: 2.7263 2023/01/25 04:58:10 - mmengine - INFO - Epoch(train) [76][ 300/1879] lr: 7.9712e-06 eta: 1 day, 3:58:17 time: 2.1669 data_time: 0.0409 memory: 48866 grad_norm: 5.8051 loss: 2.7883 loss_cls: 2.7883 2023/01/25 05:01:45 - mmengine - INFO - Epoch(train) [76][ 400/1879] lr: 7.9401e-06 eta: 1 day, 3:54:41 time: 2.1518 data_time: 0.0409 memory: 48866 grad_norm: 5.6546 loss: 2.8723 loss_cls: 2.8723 2023/01/25 05:05:22 - mmengine - INFO - Epoch(train) [76][ 500/1879] lr: 7.9090e-06 eta: 1 day, 3:51:05 time: 2.1602 data_time: 0.0409 memory: 48866 grad_norm: 5.8334 loss: 2.8593 loss_cls: 2.8593 2023/01/25 05:08:58 - mmengine - INFO - Epoch(train) [76][ 600/1879] lr: 7.8779e-06 eta: 1 day, 3:47:30 time: 2.1483 data_time: 0.0404 memory: 48866 grad_norm: 5.8334 loss: 2.6489 loss_cls: 2.6489 2023/01/25 05:12:34 - mmengine - INFO - Epoch(train) [76][ 700/1879] lr: 7.8469e-06 eta: 1 day, 3:43:54 time: 2.1570 data_time: 0.0404 memory: 48866 grad_norm: 6.0127 loss: 2.8637 loss_cls: 2.8637 2023/01/25 05:16:10 - mmengine - INFO - Epoch(train) [76][ 800/1879] lr: 7.8159e-06 eta: 1 day, 3:40:18 time: 2.1552 data_time: 0.0407 memory: 48866 grad_norm: 5.8957 loss: 2.7943 loss_cls: 2.7943 2023/01/25 05:19:46 - mmengine - INFO - Epoch(train) [76][ 900/1879] lr: 7.7850e-06 eta: 1 day, 3:36:43 time: 2.1659 data_time: 0.0410 memory: 48866 grad_norm: 5.9290 loss: 2.8590 loss_cls: 2.8590 2023/01/25 05:23:22 - mmengine - INFO - Epoch(train) [76][1000/1879] lr: 7.7541e-06 eta: 1 day, 3:33:07 time: 2.1620 data_time: 0.0396 memory: 48866 grad_norm: 5.8870 loss: 2.5711 loss_cls: 2.5711 2023/01/25 05:26:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 05:26:58 - mmengine - INFO - Epoch(train) [76][1100/1879] lr: 7.7233e-06 eta: 1 day, 3:29:32 time: 2.1588 data_time: 0.0412 memory: 48866 grad_norm: 5.7931 loss: 2.8773 loss_cls: 2.8773 2023/01/25 05:30:35 - mmengine - INFO - Epoch(train) [76][1200/1879] lr: 7.6925e-06 eta: 1 day, 3:25:56 time: 2.1706 data_time: 0.0399 memory: 48866 grad_norm: 5.9388 loss: 2.5111 loss_cls: 2.5111 2023/01/25 05:34:11 - mmengine - INFO - Epoch(train) [76][1300/1879] lr: 7.6618e-06 eta: 1 day, 3:22:20 time: 2.1509 data_time: 0.0401 memory: 48866 grad_norm: 5.6824 loss: 2.8960 loss_cls: 2.8960 2023/01/25 05:37:47 - mmengine - INFO - Epoch(train) [76][1400/1879] lr: 7.6311e-06 eta: 1 day, 3:18:45 time: 2.1488 data_time: 0.0403 memory: 48866 grad_norm: 5.8615 loss: 2.7414 loss_cls: 2.7414 2023/01/25 05:41:23 - mmengine - INFO - Epoch(train) [76][1500/1879] lr: 7.6004e-06 eta: 1 day, 3:15:09 time: 2.1741 data_time: 0.0409 memory: 48866 grad_norm: 6.0544 loss: 2.7922 loss_cls: 2.7922 2023/01/25 05:45:00 - mmengine - INFO - Epoch(train) [76][1600/1879] lr: 7.5698e-06 eta: 1 day, 3:11:34 time: 2.1694 data_time: 0.0396 memory: 48866 grad_norm: 5.5664 loss: 2.7908 loss_cls: 2.7908 2023/01/25 05:48:36 - mmengine - INFO - Epoch(train) [76][1700/1879] lr: 7.5393e-06 eta: 1 day, 3:07:58 time: 2.1635 data_time: 0.0412 memory: 48866 grad_norm: 6.0238 loss: 2.7355 loss_cls: 2.7355 2023/01/25 05:52:12 - mmengine - INFO - Epoch(train) [76][1800/1879] lr: 7.5087e-06 eta: 1 day, 3:04:22 time: 2.1562 data_time: 0.0408 memory: 48866 grad_norm: 6.0785 loss: 2.8192 loss_cls: 2.8192 2023/01/25 05:55:02 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 05:55:02 - mmengine - INFO - Epoch(train) [76][1879/1879] lr: 7.4847e-06 eta: 1 day, 3:01:32 time: 2.1088 data_time: 0.0418 memory: 48866 grad_norm: 6.0706 loss: 2.9170 loss_cls: 2.9170 2023/01/25 05:55:56 - mmengine - INFO - Epoch(val) [76][100/155] eta: 0:00:29 time: 0.5718 data_time: 0.2147 memory: 4950 2023/01/25 05:56:27 - mmengine - INFO - Epoch(val) [76][155/155] acc/top1: 0.7459 acc/top5: 0.9149 acc/mean1: 0.7458 2023/01/25 05:56:27 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_75.pth is removed 2023/01/25 05:56:30 - mmengine - INFO - The best checkpoint with 0.7459 acc/top1 at 76 epoch is saved to best_acc/top1_epoch_76.pth. 2023/01/25 06:00:13 - mmengine - INFO - Epoch(train) [77][ 100/1879] lr: 7.4542e-06 eta: 1 day, 2:57:58 time: 2.1573 data_time: 0.0392 memory: 48866 grad_norm: 5.6822 loss: 2.7870 loss_cls: 2.7870 2023/01/25 06:03:40 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 06:03:49 - mmengine - INFO - Epoch(train) [77][ 200/1879] lr: 7.4239e-06 eta: 1 day, 2:54:23 time: 2.1514 data_time: 0.0401 memory: 48866 grad_norm: 5.9564 loss: 2.6485 loss_cls: 2.6485 2023/01/25 06:07:25 - mmengine - INFO - Epoch(train) [77][ 300/1879] lr: 7.3935e-06 eta: 1 day, 2:50:47 time: 2.1566 data_time: 0.0399 memory: 48866 grad_norm: 5.7424 loss: 2.7329 loss_cls: 2.7329 2023/01/25 06:11:01 - mmengine - INFO - Epoch(train) [77][ 400/1879] lr: 7.3632e-06 eta: 1 day, 2:47:11 time: 2.1593 data_time: 0.0396 memory: 48866 grad_norm: 5.6842 loss: 2.7155 loss_cls: 2.7155 2023/01/25 06:14:36 - mmengine - INFO - Epoch(train) [77][ 500/1879] lr: 7.3330e-06 eta: 1 day, 2:43:36 time: 2.1666 data_time: 0.0406 memory: 48866 grad_norm: 5.8654 loss: 2.8424 loss_cls: 2.8424 2023/01/25 06:18:13 - mmengine - INFO - Epoch(train) [77][ 600/1879] lr: 7.3028e-06 eta: 1 day, 2:40:00 time: 2.1584 data_time: 0.0397 memory: 48866 grad_norm: 5.8685 loss: 2.6565 loss_cls: 2.6565 2023/01/25 06:21:48 - mmengine - INFO - Epoch(train) [77][ 700/1879] lr: 7.2726e-06 eta: 1 day, 2:36:24 time: 2.1577 data_time: 0.0402 memory: 48866 grad_norm: 5.9193 loss: 2.5816 loss_cls: 2.5816 2023/01/25 06:25:24 - mmengine - INFO - Epoch(train) [77][ 800/1879] lr: 7.2425e-06 eta: 1 day, 2:32:48 time: 2.1438 data_time: 0.0409 memory: 48866 grad_norm: 5.8428 loss: 2.6398 loss_cls: 2.6398 2023/01/25 06:29:00 - mmengine - INFO - Epoch(train) [77][ 900/1879] lr: 7.2125e-06 eta: 1 day, 2:29:13 time: 2.1531 data_time: 0.0402 memory: 48866 grad_norm: 5.9932 loss: 2.9547 loss_cls: 2.9547 2023/01/25 06:32:35 - mmengine - INFO - Epoch(train) [77][1000/1879] lr: 7.1825e-06 eta: 1 day, 2:25:37 time: 2.1654 data_time: 0.0408 memory: 48866 grad_norm: 5.9146 loss: 2.6586 loss_cls: 2.6586 2023/01/25 06:36:11 - mmengine - INFO - Epoch(train) [77][1100/1879] lr: 7.1525e-06 eta: 1 day, 2:22:01 time: 2.1584 data_time: 0.0403 memory: 48866 grad_norm: 5.8820 loss: 2.7532 loss_cls: 2.7532 2023/01/25 06:39:39 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 06:39:47 - mmengine - INFO - Epoch(train) [77][1200/1879] lr: 7.1226e-06 eta: 1 day, 2:18:25 time: 2.1588 data_time: 0.0394 memory: 48866 grad_norm: 5.9182 loss: 2.8732 loss_cls: 2.8732 2023/01/25 06:43:23 - mmengine - INFO - Epoch(train) [77][1300/1879] lr: 7.0927e-06 eta: 1 day, 2:14:50 time: 2.1585 data_time: 0.0400 memory: 48866 grad_norm: 5.7189 loss: 2.8560 loss_cls: 2.8560 2023/01/25 06:46:59 - mmengine - INFO - Epoch(train) [77][1400/1879] lr: 7.0629e-06 eta: 1 day, 2:11:14 time: 2.1583 data_time: 0.0407 memory: 48866 grad_norm: 6.1193 loss: 2.8171 loss_cls: 2.8171 2023/01/25 06:50:35 - mmengine - INFO - Epoch(train) [77][1500/1879] lr: 7.0332e-06 eta: 1 day, 2:07:38 time: 2.1643 data_time: 0.0405 memory: 48866 grad_norm: 6.0220 loss: 2.7141 loss_cls: 2.7141 2023/01/25 06:54:11 - mmengine - INFO - Epoch(train) [77][1600/1879] lr: 7.0034e-06 eta: 1 day, 2:04:03 time: 2.1511 data_time: 0.0401 memory: 48866 grad_norm: 5.9564 loss: 2.8033 loss_cls: 2.8033 2023/01/25 06:57:47 - mmengine - INFO - Epoch(train) [77][1700/1879] lr: 6.9738e-06 eta: 1 day, 2:00:27 time: 2.1612 data_time: 0.0413 memory: 48866 grad_norm: 6.3028 loss: 2.6004 loss_cls: 2.6004 2023/01/25 07:01:23 - mmengine - INFO - Epoch(train) [77][1800/1879] lr: 6.9441e-06 eta: 1 day, 1:56:51 time: 2.1632 data_time: 0.0406 memory: 48866 grad_norm: 6.0722 loss: 2.8169 loss_cls: 2.8169 2023/01/25 07:04:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 07:04:12 - mmengine - INFO - Epoch(train) [77][1879/1879] lr: 6.9208e-06 eta: 1 day, 1:54:01 time: 2.0992 data_time: 0.0408 memory: 48866 grad_norm: 5.9804 loss: 2.5161 loss_cls: 2.5161 2023/01/25 07:05:07 - mmengine - INFO - Epoch(val) [77][100/155] eta: 0:00:29 time: 0.5884 data_time: 0.2360 memory: 4950 2023/01/25 07:05:37 - mmengine - INFO - Epoch(val) [77][155/155] acc/top1: 0.7444 acc/top5: 0.9156 acc/mean1: 0.7443 2023/01/25 07:09:21 - mmengine - INFO - Epoch(train) [78][ 100/1879] lr: 6.8912e-06 eta: 1 day, 1:50:27 time: 2.1619 data_time: 0.0402 memory: 48866 grad_norm: 6.1046 loss: 2.9314 loss_cls: 2.9314 2023/01/25 07:12:57 - mmengine - INFO - Epoch(train) [78][ 200/1879] lr: 6.8618e-06 eta: 1 day, 1:46:52 time: 2.1633 data_time: 0.0396 memory: 48866 grad_norm: 5.8152 loss: 2.6399 loss_cls: 2.6399 2023/01/25 07:16:33 - mmengine - INFO - Epoch(train) [78][ 300/1879] lr: 6.8323e-06 eta: 1 day, 1:43:16 time: 2.1706 data_time: 0.0557 memory: 48866 grad_norm: 5.7298 loss: 2.7305 loss_cls: 2.7305 2023/01/25 07:17:09 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 07:20:08 - mmengine - INFO - Epoch(train) [78][ 400/1879] lr: 6.8029e-06 eta: 1 day, 1:39:40 time: 2.1588 data_time: 0.0399 memory: 48866 grad_norm: 6.0626 loss: 2.8646 loss_cls: 2.8646 2023/01/25 07:23:45 - mmengine - INFO - Epoch(train) [78][ 500/1879] lr: 6.7736e-06 eta: 1 day, 1:36:05 time: 2.1628 data_time: 0.0399 memory: 48866 grad_norm: 5.9600 loss: 2.5763 loss_cls: 2.5763 2023/01/25 07:27:21 - mmengine - INFO - Epoch(train) [78][ 600/1879] lr: 6.7443e-06 eta: 1 day, 1:32:29 time: 2.1703 data_time: 0.0400 memory: 48866 grad_norm: 5.9052 loss: 2.9156 loss_cls: 2.9156 2023/01/25 07:30:57 - mmengine - INFO - Epoch(train) [78][ 700/1879] lr: 6.7151e-06 eta: 1 day, 1:28:53 time: 2.1435 data_time: 0.0394 memory: 48866 grad_norm: 6.0254 loss: 2.8175 loss_cls: 2.8175 2023/01/25 07:34:33 - mmengine - INFO - Epoch(train) [78][ 800/1879] lr: 6.6859e-06 eta: 1 day, 1:25:18 time: 2.1719 data_time: 0.0415 memory: 48866 grad_norm: 6.2089 loss: 2.8311 loss_cls: 2.8311 2023/01/25 07:38:09 - mmengine - INFO - Epoch(train) [78][ 900/1879] lr: 6.6567e-06 eta: 1 day, 1:21:42 time: 2.1635 data_time: 0.0416 memory: 48866 grad_norm: 5.9375 loss: 2.8105 loss_cls: 2.8105 2023/01/25 07:41:46 - mmengine - INFO - Epoch(train) [78][1000/1879] lr: 6.6276e-06 eta: 1 day, 1:18:07 time: 2.1807 data_time: 0.0412 memory: 48866 grad_norm: 6.0558 loss: 2.7767 loss_cls: 2.7767 2023/01/25 07:45:22 - mmengine - INFO - Epoch(train) [78][1100/1879] lr: 6.5986e-06 eta: 1 day, 1:14:31 time: 2.1557 data_time: 0.0416 memory: 48866 grad_norm: 6.0012 loss: 2.7818 loss_cls: 2.7818 2023/01/25 07:48:58 - mmengine - INFO - Epoch(train) [78][1200/1879] lr: 6.5696e-06 eta: 1 day, 1:10:55 time: 2.1635 data_time: 0.0397 memory: 48866 grad_norm: 5.8669 loss: 2.5850 loss_cls: 2.5850 2023/01/25 07:52:34 - mmengine - INFO - Epoch(train) [78][1300/1879] lr: 6.5407e-06 eta: 1 day, 1:07:19 time: 2.1726 data_time: 0.0404 memory: 48866 grad_norm: 5.8080 loss: 2.6979 loss_cls: 2.6979 2023/01/25 07:53:11 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 07:56:11 - mmengine - INFO - Epoch(train) [78][1400/1879] lr: 6.5118e-06 eta: 1 day, 1:03:44 time: 2.1690 data_time: 0.0407 memory: 48866 grad_norm: 6.0166 loss: 2.8642 loss_cls: 2.8642 2023/01/25 07:59:47 - mmengine - INFO - Epoch(train) [78][1500/1879] lr: 6.4829e-06 eta: 1 day, 1:00:08 time: 2.1572 data_time: 0.0414 memory: 48866 grad_norm: 5.8711 loss: 2.5562 loss_cls: 2.5562 2023/01/25 08:03:23 - mmengine - INFO - Epoch(train) [78][1600/1879] lr: 6.4541e-06 eta: 1 day, 0:56:32 time: 2.1641 data_time: 0.0402 memory: 48866 grad_norm: 6.0143 loss: 2.6892 loss_cls: 2.6892 2023/01/25 08:06:59 - mmengine - INFO - Epoch(train) [78][1700/1879] lr: 6.4254e-06 eta: 1 day, 0:52:57 time: 2.1620 data_time: 0.0411 memory: 48866 grad_norm: 6.3122 loss: 2.7002 loss_cls: 2.7002 2023/01/25 08:10:35 - mmengine - INFO - Epoch(train) [78][1800/1879] lr: 6.3967e-06 eta: 1 day, 0:49:21 time: 2.1706 data_time: 0.0403 memory: 48866 grad_norm: 5.9889 loss: 2.6827 loss_cls: 2.6827 2023/01/25 08:13:24 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 08:13:24 - mmengine - INFO - Epoch(train) [78][1879/1879] lr: 6.3741e-06 eta: 1 day, 0:46:30 time: 2.0983 data_time: 0.0401 memory: 48866 grad_norm: 6.1179 loss: 2.6356 loss_cls: 2.6356 2023/01/25 08:13:24 - mmengine - INFO - Saving checkpoint at 78 epochs 2023/01/25 08:14:25 - mmengine - INFO - Epoch(val) [78][100/155] eta: 0:00:30 time: 0.5814 data_time: 0.2411 memory: 4950 2023/01/25 08:14:52 - mmengine - INFO - Epoch(val) [78][155/155] acc/top1: 0.7472 acc/top5: 0.9155 acc/mean1: 0.7472 2023/01/25 08:14:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_76.pth is removed 2023/01/25 08:14:55 - mmengine - INFO - The best checkpoint with 0.7472 acc/top1 at 78 epoch is saved to best_acc/top1_epoch_78.pth. 2023/01/25 08:18:37 - mmengine - INFO - Epoch(train) [79][ 100/1879] lr: 6.3455e-06 eta: 1 day, 0:42:56 time: 2.1446 data_time: 0.0398 memory: 48866 grad_norm: 5.9952 loss: 2.9563 loss_cls: 2.9563 2023/01/25 08:22:13 - mmengine - INFO - Epoch(train) [79][ 200/1879] lr: 6.3169e-06 eta: 1 day, 0:39:20 time: 2.1578 data_time: 0.0400 memory: 48866 grad_norm: 6.4703 loss: 2.7024 loss_cls: 2.7024 2023/01/25 08:25:49 - mmengine - INFO - Epoch(train) [79][ 300/1879] lr: 6.2884e-06 eta: 1 day, 0:35:45 time: 2.1654 data_time: 0.0415 memory: 48866 grad_norm: 6.0964 loss: 2.7720 loss_cls: 2.7720 2023/01/25 08:29:24 - mmengine - INFO - Epoch(train) [79][ 400/1879] lr: 6.2600e-06 eta: 1 day, 0:32:09 time: 2.1474 data_time: 0.0394 memory: 48866 grad_norm: 6.0015 loss: 2.6894 loss_cls: 2.6894 2023/01/25 08:30:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 08:33:00 - mmengine - INFO - Epoch(train) [79][ 500/1879] lr: 6.2316e-06 eta: 1 day, 0:28:33 time: 2.1489 data_time: 0.0404 memory: 48866 grad_norm: 5.9119 loss: 2.8319 loss_cls: 2.8319 2023/01/25 08:36:36 - mmengine - INFO - Epoch(train) [79][ 600/1879] lr: 6.2033e-06 eta: 1 day, 0:24:58 time: 2.1573 data_time: 0.0403 memory: 48866 grad_norm: 6.0579 loss: 2.7165 loss_cls: 2.7165 2023/01/25 08:40:12 - mmengine - INFO - Epoch(train) [79][ 700/1879] lr: 6.1750e-06 eta: 1 day, 0:21:22 time: 2.1459 data_time: 0.0408 memory: 48866 grad_norm: 6.0622 loss: 2.8403 loss_cls: 2.8403 2023/01/25 08:43:48 - mmengine - INFO - Epoch(train) [79][ 800/1879] lr: 6.1467e-06 eta: 1 day, 0:17:46 time: 2.1524 data_time: 0.0399 memory: 48866 grad_norm: 6.0453 loss: 2.8997 loss_cls: 2.8997 2023/01/25 08:47:24 - mmengine - INFO - Epoch(train) [79][ 900/1879] lr: 6.1186e-06 eta: 1 day, 0:14:10 time: 2.1856 data_time: 0.0410 memory: 48866 grad_norm: 6.1308 loss: 2.9312 loss_cls: 2.9312 2023/01/25 08:51:01 - mmengine - INFO - Epoch(train) [79][1000/1879] lr: 6.0904e-06 eta: 1 day, 0:10:35 time: 2.1549 data_time: 0.0402 memory: 48866 grad_norm: 5.9797 loss: 2.6483 loss_cls: 2.6483 2023/01/25 08:54:36 - mmengine - INFO - Epoch(train) [79][1100/1879] lr: 6.0623e-06 eta: 1 day, 0:06:59 time: 2.1603 data_time: 0.0392 memory: 48866 grad_norm: 5.7266 loss: 2.7203 loss_cls: 2.7203 2023/01/25 08:58:12 - mmengine - INFO - Epoch(train) [79][1200/1879] lr: 6.0343e-06 eta: 1 day, 0:03:23 time: 2.1553 data_time: 0.0404 memory: 48866 grad_norm: 6.0408 loss: 2.6364 loss_cls: 2.6364 2023/01/25 09:01:48 - mmengine - INFO - Epoch(train) [79][1300/1879] lr: 6.0063e-06 eta: 23:59:47 time: 2.1568 data_time: 0.0408 memory: 48866 grad_norm: 6.1437 loss: 2.6959 loss_cls: 2.6959 2023/01/25 09:05:24 - mmengine - INFO - Epoch(train) [79][1400/1879] lr: 5.9784e-06 eta: 23:56:12 time: 2.1571 data_time: 0.0405 memory: 48866 grad_norm: 5.9528 loss: 2.8018 loss_cls: 2.8018 2023/01/25 09:06:45 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 09:08:59 - mmengine - INFO - Epoch(train) [79][1500/1879] lr: 5.9505e-06 eta: 23:52:36 time: 2.1662 data_time: 0.0406 memory: 48866 grad_norm: 5.7525 loss: 2.8737 loss_cls: 2.8737 2023/01/25 09:12:35 - mmengine - INFO - Epoch(train) [79][1600/1879] lr: 5.9227e-06 eta: 23:49:00 time: 2.1559 data_time: 0.0402 memory: 48866 grad_norm: 5.9223 loss: 2.7555 loss_cls: 2.7555 2023/01/25 09:16:11 - mmengine - INFO - Epoch(train) [79][1700/1879] lr: 5.8950e-06 eta: 23:45:24 time: 2.1543 data_time: 0.0405 memory: 48866 grad_norm: 6.0534 loss: 2.8921 loss_cls: 2.8921 2023/01/25 09:19:47 - mmengine - INFO - Epoch(train) [79][1800/1879] lr: 5.8673e-06 eta: 23:41:49 time: 2.1528 data_time: 0.0405 memory: 48866 grad_norm: 6.1485 loss: 2.8977 loss_cls: 2.8977 2023/01/25 09:22:36 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 09:22:36 - mmengine - INFO - Epoch(train) [79][1879/1879] lr: 5.8454e-06 eta: 23:38:58 time: 2.0984 data_time: 0.0413 memory: 48866 grad_norm: 6.5160 loss: 2.8657 loss_cls: 2.8657 2023/01/25 09:23:30 - mmengine - INFO - Epoch(val) [79][100/155] eta: 0:00:29 time: 0.5594 data_time: 0.2186 memory: 4950 2023/01/25 09:24:00 - mmengine - INFO - Epoch(val) [79][155/155] acc/top1: 0.7491 acc/top5: 0.9177 acc/mean1: 0.7491 2023/01/25 09:24:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_78.pth is removed 2023/01/25 09:24:03 - mmengine - INFO - The best checkpoint with 0.7491 acc/top1 at 79 epoch is saved to best_acc/top1_epoch_79.pth. 2023/01/25 09:27:46 - mmengine - INFO - Epoch(train) [80][ 100/1879] lr: 5.8178e-06 eta: 23:35:24 time: 2.1555 data_time: 0.0407 memory: 48866 grad_norm: 6.1326 loss: 2.6109 loss_cls: 2.6109 2023/01/25 09:31:22 - mmengine - INFO - Epoch(train) [80][ 200/1879] lr: 5.7902e-06 eta: 23:31:48 time: 2.1535 data_time: 0.0393 memory: 48866 grad_norm: 5.9980 loss: 2.6663 loss_cls: 2.6663 2023/01/25 09:34:58 - mmengine - INFO - Epoch(train) [80][ 300/1879] lr: 5.7627e-06 eta: 23:28:12 time: 2.1562 data_time: 0.0405 memory: 48866 grad_norm: 6.2866 loss: 2.7566 loss_cls: 2.7566 2023/01/25 09:38:34 - mmengine - INFO - Epoch(train) [80][ 400/1879] lr: 5.7353e-06 eta: 23:24:37 time: 2.1576 data_time: 0.0397 memory: 48866 grad_norm: 6.2109 loss: 2.6434 loss_cls: 2.6434 2023/01/25 09:42:10 - mmengine - INFO - Epoch(train) [80][ 500/1879] lr: 5.7079e-06 eta: 23:21:01 time: 2.1633 data_time: 0.0404 memory: 48866 grad_norm: 6.2228 loss: 2.7797 loss_cls: 2.7797 2023/01/25 09:44:17 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 09:45:45 - mmengine - INFO - Epoch(train) [80][ 600/1879] lr: 5.6805e-06 eta: 23:17:25 time: 2.1646 data_time: 0.0401 memory: 48866 grad_norm: 6.1391 loss: 2.9731 loss_cls: 2.9731 2023/01/25 09:49:21 - mmengine - INFO - Epoch(train) [80][ 700/1879] lr: 5.6532e-06 eta: 23:13:49 time: 2.1484 data_time: 0.0417 memory: 48866 grad_norm: 6.1770 loss: 2.6666 loss_cls: 2.6666 2023/01/25 09:52:58 - mmengine - INFO - Epoch(train) [80][ 800/1879] lr: 5.6260e-06 eta: 23:10:14 time: 2.1638 data_time: 0.0412 memory: 48866 grad_norm: 5.7625 loss: 2.6760 loss_cls: 2.6760 2023/01/25 09:56:33 - mmengine - INFO - Epoch(train) [80][ 900/1879] lr: 5.5988e-06 eta: 23:06:38 time: 2.1545 data_time: 0.0406 memory: 48866 grad_norm: 6.2959 loss: 2.7481 loss_cls: 2.7481 2023/01/25 10:00:10 - mmengine - INFO - Epoch(train) [80][1000/1879] lr: 5.5717e-06 eta: 23:03:02 time: 2.1571 data_time: 0.0409 memory: 48866 grad_norm: 6.0292 loss: 2.8025 loss_cls: 2.8025 2023/01/25 10:03:46 - mmengine - INFO - Epoch(train) [80][1100/1879] lr: 5.5446e-06 eta: 22:59:27 time: 2.1642 data_time: 0.0415 memory: 48866 grad_norm: 5.9589 loss: 2.5797 loss_cls: 2.5797 2023/01/25 10:07:21 - mmengine - INFO - Epoch(train) [80][1200/1879] lr: 5.5176e-06 eta: 22:55:51 time: 2.1468 data_time: 0.0412 memory: 48866 grad_norm: 5.7358 loss: 2.7094 loss_cls: 2.7094 2023/01/25 10:10:57 - mmengine - INFO - Epoch(train) [80][1300/1879] lr: 5.4906e-06 eta: 22:52:15 time: 2.1544 data_time: 0.0407 memory: 48866 grad_norm: 6.3298 loss: 2.8167 loss_cls: 2.8167 2023/01/25 10:14:33 - mmengine - INFO - Epoch(train) [80][1400/1879] lr: 5.4637e-06 eta: 22:48:39 time: 2.1760 data_time: 0.0406 memory: 48866 grad_norm: 6.0724 loss: 2.7010 loss_cls: 2.7010 2023/01/25 10:18:10 - mmengine - INFO - Epoch(train) [80][1500/1879] lr: 5.4369e-06 eta: 22:45:04 time: 2.1664 data_time: 0.0398 memory: 48866 grad_norm: 5.7936 loss: 2.7742 loss_cls: 2.7742 2023/01/25 10:20:17 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 10:21:45 - mmengine - INFO - Epoch(train) [80][1600/1879] lr: 5.4101e-06 eta: 22:41:28 time: 2.1562 data_time: 0.0395 memory: 48866 grad_norm: 5.8698 loss: 2.7954 loss_cls: 2.7954 2023/01/25 10:25:22 - mmengine - INFO - Epoch(train) [80][1700/1879] lr: 5.3833e-06 eta: 22:37:52 time: 2.1602 data_time: 0.0398 memory: 48866 grad_norm: 6.1459 loss: 2.5979 loss_cls: 2.5979 2023/01/25 10:28:57 - mmengine - INFO - Epoch(train) [80][1800/1879] lr: 5.3566e-06 eta: 22:34:17 time: 2.1644 data_time: 0.0393 memory: 48866 grad_norm: 6.0897 loss: 2.9104 loss_cls: 2.9104 2023/01/25 10:31:47 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 10:31:47 - mmengine - INFO - Epoch(train) [80][1879/1879] lr: 5.3356e-06 eta: 22:31:26 time: 2.1018 data_time: 0.0405 memory: 48866 grad_norm: 6.2736 loss: 2.7200 loss_cls: 2.7200 2023/01/25 10:32:41 - mmengine - INFO - Epoch(val) [80][100/155] eta: 0:00:29 time: 0.5699 data_time: 0.2170 memory: 4950 2023/01/25 10:33:12 - mmengine - INFO - Epoch(val) [80][155/155] acc/top1: 0.7499 acc/top5: 0.9173 acc/mean1: 0.7498 2023/01/25 10:33:12 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_79.pth is removed 2023/01/25 10:33:15 - mmengine - INFO - The best checkpoint with 0.7499 acc/top1 at 80 epoch is saved to best_acc/top1_epoch_80.pth. 2023/01/25 10:36:57 - mmengine - INFO - Epoch(train) [81][ 100/1879] lr: 5.3090e-06 eta: 22:27:52 time: 2.1593 data_time: 0.0398 memory: 48866 grad_norm: 6.0551 loss: 2.8304 loss_cls: 2.8304 2023/01/25 10:40:34 - mmengine - INFO - Epoch(train) [81][ 200/1879] lr: 5.2825e-06 eta: 22:24:16 time: 2.1598 data_time: 0.0405 memory: 48866 grad_norm: 6.2405 loss: 2.5828 loss_cls: 2.5828 2023/01/25 10:44:09 - mmengine - INFO - Epoch(train) [81][ 300/1879] lr: 5.2560e-06 eta: 22:20:40 time: 2.1497 data_time: 0.0403 memory: 48866 grad_norm: 6.0029 loss: 2.6547 loss_cls: 2.6547 2023/01/25 10:47:46 - mmengine - INFO - Epoch(train) [81][ 400/1879] lr: 5.2296e-06 eta: 22:17:05 time: 2.1647 data_time: 0.0399 memory: 48866 grad_norm: 5.8224 loss: 2.7282 loss_cls: 2.7282 2023/01/25 10:51:22 - mmengine - INFO - Epoch(train) [81][ 500/1879] lr: 5.2032e-06 eta: 22:13:29 time: 2.1679 data_time: 0.0412 memory: 48866 grad_norm: 6.1443 loss: 2.7114 loss_cls: 2.7114 2023/01/25 10:54:57 - mmengine - INFO - Epoch(train) [81][ 600/1879] lr: 5.1769e-06 eta: 22:09:53 time: 2.1670 data_time: 0.0401 memory: 48866 grad_norm: 6.6135 loss: 2.7488 loss_cls: 2.7488 2023/01/25 10:57:50 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 10:58:33 - mmengine - INFO - Epoch(train) [81][ 700/1879] lr: 5.1506e-06 eta: 22:06:18 time: 2.1558 data_time: 0.0418 memory: 48866 grad_norm: 5.7588 loss: 2.5182 loss_cls: 2.5182 2023/01/25 11:02:09 - mmengine - INFO - Epoch(train) [81][ 800/1879] lr: 5.1244e-06 eta: 22:02:42 time: 2.1589 data_time: 0.0401 memory: 48866 grad_norm: 5.9384 loss: 2.5669 loss_cls: 2.5669 2023/01/25 11:05:46 - mmengine - INFO - Epoch(train) [81][ 900/1879] lr: 5.0983e-06 eta: 21:59:06 time: 2.1619 data_time: 0.0411 memory: 48866 grad_norm: 6.1062 loss: 2.6488 loss_cls: 2.6488 2023/01/25 11:09:23 - mmengine - INFO - Epoch(train) [81][1000/1879] lr: 5.0722e-06 eta: 21:55:31 time: 2.1596 data_time: 0.0404 memory: 48866 grad_norm: 5.9593 loss: 2.7594 loss_cls: 2.7594 2023/01/25 11:12:58 - mmengine - INFO - Epoch(train) [81][1100/1879] lr: 5.0462e-06 eta: 21:51:55 time: 2.1626 data_time: 0.0415 memory: 48866 grad_norm: 5.9909 loss: 2.7348 loss_cls: 2.7348 2023/01/25 11:16:34 - mmengine - INFO - Epoch(train) [81][1200/1879] lr: 5.0202e-06 eta: 21:48:19 time: 2.1438 data_time: 0.0407 memory: 48866 grad_norm: 6.1948 loss: 2.9443 loss_cls: 2.9443 2023/01/25 11:20:10 - mmengine - INFO - Epoch(train) [81][1300/1879] lr: 4.9943e-06 eta: 21:44:43 time: 2.1623 data_time: 0.0409 memory: 48866 grad_norm: 5.9199 loss: 2.6672 loss_cls: 2.6672 2023/01/25 11:23:46 - mmengine - INFO - Epoch(train) [81][1400/1879] lr: 4.9685e-06 eta: 21:41:08 time: 2.1549 data_time: 0.0414 memory: 48866 grad_norm: 5.8935 loss: 2.8095 loss_cls: 2.8095 2023/01/25 11:27:22 - mmengine - INFO - Epoch(train) [81][1500/1879] lr: 4.9427e-06 eta: 21:37:32 time: 2.1589 data_time: 0.0410 memory: 48866 grad_norm: 5.8445 loss: 2.7632 loss_cls: 2.7632 2023/01/25 11:30:58 - mmengine - INFO - Epoch(train) [81][1600/1879] lr: 4.9169e-06 eta: 21:33:56 time: 2.1661 data_time: 0.0417 memory: 48866 grad_norm: 6.0748 loss: 2.7141 loss_cls: 2.7141 2023/01/25 11:33:51 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 11:34:34 - mmengine - INFO - Epoch(train) [81][1700/1879] lr: 4.8912e-06 eta: 21:30:20 time: 2.1493 data_time: 0.0408 memory: 48866 grad_norm: 6.3504 loss: 2.5112 loss_cls: 2.5112 2023/01/25 11:38:10 - mmengine - INFO - Epoch(train) [81][1800/1879] lr: 4.8656e-06 eta: 21:26:45 time: 2.1494 data_time: 0.0416 memory: 48866 grad_norm: 6.1965 loss: 2.6618 loss_cls: 2.6618 2023/01/25 11:41:00 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 11:41:00 - mmengine - INFO - Epoch(train) [81][1879/1879] lr: 4.8454e-06 eta: 21:23:54 time: 2.1233 data_time: 0.0428 memory: 48866 grad_norm: 6.1725 loss: 2.7461 loss_cls: 2.7461 2023/01/25 11:41:00 - mmengine - INFO - Saving checkpoint at 81 epochs 2023/01/25 11:41:59 - mmengine - INFO - Epoch(val) [81][100/155] eta: 0:00:30 time: 0.5612 data_time: 0.2104 memory: 4950 2023/01/25 11:42:27 - mmengine - INFO - Epoch(val) [81][155/155] acc/top1: 0.7550 acc/top5: 0.9180 acc/mean1: 0.7549 2023/01/25 11:42:27 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_80.pth is removed 2023/01/25 11:42:31 - mmengine - INFO - The best checkpoint with 0.7550 acc/top1 at 81 epoch is saved to best_acc/top1_epoch_81.pth. 2023/01/25 11:46:13 - mmengine - INFO - Epoch(train) [82][ 100/1879] lr: 4.8199e-06 eta: 21:20:20 time: 2.1439 data_time: 0.0405 memory: 48866 grad_norm: 5.9753 loss: 2.6785 loss_cls: 2.6785 2023/01/25 11:49:49 - mmengine - INFO - Epoch(train) [82][ 200/1879] lr: 4.7944e-06 eta: 21:16:44 time: 2.1557 data_time: 0.0400 memory: 48866 grad_norm: 6.0285 loss: 2.7638 loss_cls: 2.7638 2023/01/25 11:53:25 - mmengine - INFO - Epoch(train) [82][ 300/1879] lr: 4.7690e-06 eta: 21:13:08 time: 2.1701 data_time: 0.0411 memory: 48866 grad_norm: 6.1570 loss: 2.7426 loss_cls: 2.7426 2023/01/25 11:57:02 - mmengine - INFO - Epoch(train) [82][ 400/1879] lr: 4.7437e-06 eta: 21:09:33 time: 2.1624 data_time: 0.0409 memory: 48866 grad_norm: 6.2870 loss: 2.6291 loss_cls: 2.6291 2023/01/25 12:00:38 - mmengine - INFO - Epoch(train) [82][ 500/1879] lr: 4.7184e-06 eta: 21:05:57 time: 2.1507 data_time: 0.0415 memory: 48866 grad_norm: 6.2077 loss: 2.6682 loss_cls: 2.6682 2023/01/25 12:04:14 - mmengine - INFO - Epoch(train) [82][ 600/1879] lr: 4.6931e-06 eta: 21:02:21 time: 2.1575 data_time: 0.0414 memory: 48866 grad_norm: 5.9684 loss: 2.7482 loss_cls: 2.7482 2023/01/25 12:07:50 - mmengine - INFO - Epoch(train) [82][ 700/1879] lr: 4.6680e-06 eta: 20:58:46 time: 2.1545 data_time: 0.0414 memory: 48866 grad_norm: 6.0855 loss: 2.7606 loss_cls: 2.7606 2023/01/25 12:11:26 - mmengine - INFO - Epoch(train) [82][ 800/1879] lr: 4.6429e-06 eta: 20:55:10 time: 2.1606 data_time: 0.0414 memory: 48866 grad_norm: 5.8436 loss: 2.6450 loss_cls: 2.6450 2023/01/25 12:11:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 12:15:02 - mmengine - INFO - Epoch(train) [82][ 900/1879] lr: 4.6178e-06 eta: 20:51:34 time: 2.1654 data_time: 0.0419 memory: 48866 grad_norm: 6.0978 loss: 2.7633 loss_cls: 2.7633 2023/01/25 12:18:38 - mmengine - INFO - Epoch(train) [82][1000/1879] lr: 4.5928e-06 eta: 20:47:59 time: 2.1563 data_time: 0.0406 memory: 48866 grad_norm: 6.1472 loss: 2.6740 loss_cls: 2.6740 2023/01/25 12:22:14 - mmengine - INFO - Epoch(train) [82][1100/1879] lr: 4.5679e-06 eta: 20:44:23 time: 2.1574 data_time: 0.0406 memory: 48866 grad_norm: 6.0954 loss: 2.6776 loss_cls: 2.6776 2023/01/25 12:25:50 - mmengine - INFO - Epoch(train) [82][1200/1879] lr: 4.5430e-06 eta: 20:40:47 time: 2.1508 data_time: 0.0406 memory: 48866 grad_norm: 6.0826 loss: 2.8204 loss_cls: 2.8204 2023/01/25 12:29:26 - mmengine - INFO - Epoch(train) [82][1300/1879] lr: 4.5182e-06 eta: 20:37:11 time: 2.1641 data_time: 0.0412 memory: 48866 grad_norm: 5.9683 loss: 2.5786 loss_cls: 2.5786 2023/01/25 12:33:02 - mmengine - INFO - Epoch(train) [82][1400/1879] lr: 4.4934e-06 eta: 20:33:36 time: 2.1689 data_time: 0.0417 memory: 48866 grad_norm: 6.0421 loss: 2.8416 loss_cls: 2.8416 2023/01/25 12:36:38 - mmengine - INFO - Epoch(train) [82][1500/1879] lr: 4.4687e-06 eta: 20:30:00 time: 2.1553 data_time: 0.0418 memory: 48866 grad_norm: 6.3570 loss: 2.6535 loss_cls: 2.6535 2023/01/25 12:40:14 - mmengine - INFO - Epoch(train) [82][1600/1879] lr: 4.4441e-06 eta: 20:26:24 time: 2.1525 data_time: 0.0425 memory: 48866 grad_norm: 6.0130 loss: 2.7156 loss_cls: 2.7156 2023/01/25 12:43:50 - mmengine - INFO - Epoch(train) [82][1700/1879] lr: 4.4195e-06 eta: 20:22:48 time: 2.1662 data_time: 0.0419 memory: 48866 grad_norm: 5.9751 loss: 2.6438 loss_cls: 2.6438 2023/01/25 12:47:26 - mmengine - INFO - Epoch(train) [82][1800/1879] lr: 4.3949e-06 eta: 20:19:13 time: 2.1608 data_time: 0.0412 memory: 48866 grad_norm: 5.7894 loss: 2.6335 loss_cls: 2.6335 2023/01/25 12:47:28 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 12:50:15 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 12:50:15 - mmengine - INFO - Epoch(train) [82][1879/1879] lr: 4.3756e-06 eta: 20:16:22 time: 2.0898 data_time: 0.0417 memory: 48866 grad_norm: 6.0980 loss: 2.8310 loss_cls: 2.8310 2023/01/25 12:51:08 - mmengine - INFO - Epoch(val) [82][100/155] eta: 0:00:29 time: 0.5131 data_time: 0.1605 memory: 4950 2023/01/25 12:51:39 - mmengine - INFO - Epoch(val) [82][155/155] acc/top1: 0.7520 acc/top5: 0.9171 acc/mean1: 0.7520 2023/01/25 12:55:23 - mmengine - INFO - Epoch(train) [83][ 100/1879] lr: 4.3512e-06 eta: 20:12:48 time: 2.1705 data_time: 0.0408 memory: 48866 grad_norm: 6.2902 loss: 2.7886 loss_cls: 2.7886 2023/01/25 12:58:58 - mmengine - INFO - Epoch(train) [83][ 200/1879] lr: 4.3268e-06 eta: 20:09:12 time: 2.1578 data_time: 0.0411 memory: 48866 grad_norm: 6.2498 loss: 2.8225 loss_cls: 2.8225 2023/01/25 13:02:34 - mmengine - INFO - Epoch(train) [83][ 300/1879] lr: 4.3025e-06 eta: 20:05:36 time: 2.1714 data_time: 0.0400 memory: 48866 grad_norm: 6.1388 loss: 2.7625 loss_cls: 2.7625 2023/01/25 13:06:10 - mmengine - INFO - Epoch(train) [83][ 400/1879] lr: 4.2783e-06 eta: 20:02:00 time: 2.1603 data_time: 0.0416 memory: 48866 grad_norm: 6.5549 loss: 2.7910 loss_cls: 2.7910 2023/01/25 13:09:46 - mmengine - INFO - Epoch(train) [83][ 500/1879] lr: 4.2541e-06 eta: 19:58:25 time: 2.1599 data_time: 0.0417 memory: 48866 grad_norm: 6.3097 loss: 2.5627 loss_cls: 2.5627 2023/01/25 13:13:22 - mmengine - INFO - Epoch(train) [83][ 600/1879] lr: 4.2300e-06 eta: 19:54:49 time: 2.1601 data_time: 0.0386 memory: 48866 grad_norm: 6.3759 loss: 2.5842 loss_cls: 2.5842 2023/01/25 13:16:59 - mmengine - INFO - Epoch(train) [83][ 700/1879] lr: 4.2060e-06 eta: 19:51:13 time: 2.1663 data_time: 0.0405 memory: 48866 grad_norm: 6.0743 loss: 2.5301 loss_cls: 2.5301 2023/01/25 13:20:35 - mmengine - INFO - Epoch(train) [83][ 800/1879] lr: 4.1820e-06 eta: 19:47:38 time: 2.1729 data_time: 0.0409 memory: 48866 grad_norm: 6.0939 loss: 2.6284 loss_cls: 2.6284 2023/01/25 13:24:11 - mmengine - INFO - Epoch(train) [83][ 900/1879] lr: 4.1580e-06 eta: 19:44:02 time: 2.1500 data_time: 0.0410 memory: 48866 grad_norm: 6.0532 loss: 2.7525 loss_cls: 2.7525 2023/01/25 13:24:58 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 13:27:47 - mmengine - INFO - Epoch(train) [83][1000/1879] lr: 4.1342e-06 eta: 19:40:26 time: 2.1637 data_time: 0.0403 memory: 48866 grad_norm: 6.3871 loss: 2.5216 loss_cls: 2.5216 2023/01/25 13:31:23 - mmengine - INFO - Epoch(train) [83][1100/1879] lr: 4.1103e-06 eta: 19:36:50 time: 2.1689 data_time: 0.0412 memory: 48866 grad_norm: 6.1525 loss: 2.8322 loss_cls: 2.8322 2023/01/25 13:34:59 - mmengine - INFO - Epoch(train) [83][1200/1879] lr: 4.0866e-06 eta: 19:33:15 time: 2.1625 data_time: 0.0407 memory: 48866 grad_norm: 5.8789 loss: 2.7628 loss_cls: 2.7628 2023/01/25 13:38:36 - mmengine - INFO - Epoch(train) [83][1300/1879] lr: 4.0629e-06 eta: 19:29:39 time: 2.1598 data_time: 0.0403 memory: 48866 grad_norm: 6.4104 loss: 2.9285 loss_cls: 2.9285 2023/01/25 13:42:12 - mmengine - INFO - Epoch(train) [83][1400/1879] lr: 4.0393e-06 eta: 19:26:03 time: 2.1588 data_time: 0.0413 memory: 48866 grad_norm: 5.8991 loss: 2.6385 loss_cls: 2.6385 2023/01/25 13:45:48 - mmengine - INFO - Epoch(train) [83][1500/1879] lr: 4.0157e-06 eta: 19:22:27 time: 2.1637 data_time: 0.0403 memory: 48866 grad_norm: 6.4635 loss: 2.7973 loss_cls: 2.7973 2023/01/25 13:49:24 - mmengine - INFO - Epoch(train) [83][1600/1879] lr: 3.9922e-06 eta: 19:18:52 time: 2.1592 data_time: 0.0411 memory: 48866 grad_norm: 6.1822 loss: 2.6727 loss_cls: 2.6727 2023/01/25 13:53:00 - mmengine - INFO - Epoch(train) [83][1700/1879] lr: 3.9688e-06 eta: 19:15:16 time: 2.1669 data_time: 0.0423 memory: 48866 grad_norm: 6.2078 loss: 2.6710 loss_cls: 2.6710 2023/01/25 13:56:36 - mmengine - INFO - Epoch(train) [83][1800/1879] lr: 3.9454e-06 eta: 19:11:40 time: 2.1746 data_time: 0.0420 memory: 48866 grad_norm: 6.4598 loss: 2.6391 loss_cls: 2.6391 2023/01/25 13:59:26 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 13:59:26 - mmengine - INFO - Epoch(train) [83][1879/1879] lr: 3.9269e-06 eta: 19:08:50 time: 2.1104 data_time: 0.0424 memory: 48866 grad_norm: 6.0296 loss: 2.5097 loss_cls: 2.5097 2023/01/25 14:00:19 - mmengine - INFO - Epoch(val) [83][100/155] eta: 0:00:29 time: 0.5523 data_time: 0.1993 memory: 4950 2023/01/25 14:00:50 - mmengine - INFO - Epoch(val) [83][155/155] acc/top1: 0.7564 acc/top5: 0.9182 acc/mean1: 0.7564 2023/01/25 14:00:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_81.pth is removed 2023/01/25 14:00:54 - mmengine - INFO - The best checkpoint with 0.7564 acc/top1 at 83 epoch is saved to best_acc/top1_epoch_83.pth. 2023/01/25 14:02:33 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 14:04:37 - mmengine - INFO - Epoch(train) [84][ 100/1879] lr: 3.9037e-06 eta: 19:05:15 time: 2.1609 data_time: 0.0404 memory: 48866 grad_norm: 6.1581 loss: 2.7274 loss_cls: 2.7274 2023/01/25 14:08:13 - mmengine - INFO - Epoch(train) [84][ 200/1879] lr: 3.8805e-06 eta: 19:01:40 time: 2.1685 data_time: 0.0413 memory: 48866 grad_norm: 6.4245 loss: 2.6686 loss_cls: 2.6686 2023/01/25 14:11:49 - mmengine - INFO - Epoch(train) [84][ 300/1879] lr: 3.8573e-06 eta: 18:58:04 time: 2.1706 data_time: 0.0417 memory: 48866 grad_norm: 6.4232 loss: 2.6126 loss_cls: 2.6126 2023/01/25 14:15:25 - mmengine - INFO - Epoch(train) [84][ 400/1879] lr: 3.8342e-06 eta: 18:54:28 time: 2.1578 data_time: 0.0403 memory: 48866 grad_norm: 6.1405 loss: 2.6729 loss_cls: 2.6729 2023/01/25 14:19:00 - mmengine - INFO - Epoch(train) [84][ 500/1879] lr: 3.8112e-06 eta: 18:50:52 time: 2.1613 data_time: 0.0414 memory: 48866 grad_norm: 6.1511 loss: 2.7957 loss_cls: 2.7957 2023/01/25 14:22:36 - mmengine - INFO - Epoch(train) [84][ 600/1879] lr: 3.7882e-06 eta: 18:47:16 time: 2.1621 data_time: 0.0415 memory: 48866 grad_norm: 6.3364 loss: 2.6592 loss_cls: 2.6592 2023/01/25 14:26:12 - mmengine - INFO - Epoch(train) [84][ 700/1879] lr: 3.7653e-06 eta: 18:43:41 time: 2.1532 data_time: 0.0412 memory: 48866 grad_norm: 6.1685 loss: 2.5095 loss_cls: 2.5095 2023/01/25 14:29:49 - mmengine - INFO - Epoch(train) [84][ 800/1879] lr: 3.7425e-06 eta: 18:40:05 time: 2.1656 data_time: 0.0417 memory: 48866 grad_norm: 5.9660 loss: 2.6784 loss_cls: 2.6784 2023/01/25 14:33:25 - mmengine - INFO - Epoch(train) [84][ 900/1879] lr: 3.7197e-06 eta: 18:36:29 time: 2.1530 data_time: 0.0415 memory: 48866 grad_norm: 6.2871 loss: 2.5408 loss_cls: 2.5408 2023/01/25 14:37:01 - mmengine - INFO - Epoch(train) [84][1000/1879] lr: 3.6970e-06 eta: 18:32:54 time: 2.1623 data_time: 0.0413 memory: 48866 grad_norm: 5.9292 loss: 2.7023 loss_cls: 2.7023 2023/01/25 14:38:34 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 14:40:37 - mmengine - INFO - Epoch(train) [84][1100/1879] lr: 3.6744e-06 eta: 18:29:18 time: 2.1647 data_time: 0.0418 memory: 48866 grad_norm: 6.1594 loss: 2.7390 loss_cls: 2.7390 2023/01/25 14:44:14 - mmengine - INFO - Epoch(train) [84][1200/1879] lr: 3.6518e-06 eta: 18:25:42 time: 2.1634 data_time: 0.0409 memory: 48866 grad_norm: 6.5066 loss: 2.7114 loss_cls: 2.7114 2023/01/25 14:47:49 - mmengine - INFO - Epoch(train) [84][1300/1879] lr: 3.6292e-06 eta: 18:22:06 time: 2.1675 data_time: 0.0395 memory: 48866 grad_norm: 6.4160 loss: 2.7664 loss_cls: 2.7664 2023/01/25 14:51:25 - mmengine - INFO - Epoch(train) [84][1400/1879] lr: 3.6068e-06 eta: 18:18:31 time: 2.1589 data_time: 0.0418 memory: 48866 grad_norm: 6.3291 loss: 2.5230 loss_cls: 2.5230 2023/01/25 14:55:01 - mmengine - INFO - Epoch(train) [84][1500/1879] lr: 3.5844e-06 eta: 18:14:55 time: 2.1596 data_time: 0.0410 memory: 48866 grad_norm: 6.5368 loss: 2.6972 loss_cls: 2.6972 2023/01/25 14:58:37 - mmengine - INFO - Epoch(train) [84][1600/1879] lr: 3.5621e-06 eta: 18:11:19 time: 2.1568 data_time: 0.0409 memory: 48866 grad_norm: 6.4296 loss: 2.7862 loss_cls: 2.7862 2023/01/25 15:02:13 - mmengine - INFO - Epoch(train) [84][1700/1879] lr: 3.5398e-06 eta: 18:07:43 time: 2.1659 data_time: 0.0409 memory: 48866 grad_norm: 6.1331 loss: 2.5719 loss_cls: 2.5719 2023/01/25 15:05:49 - mmengine - INFO - Epoch(train) [84][1800/1879] lr: 3.5176e-06 eta: 18:04:08 time: 2.1473 data_time: 0.0400 memory: 48866 grad_norm: 6.5154 loss: 2.7257 loss_cls: 2.7257 2023/01/25 15:08:39 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 15:08:39 - mmengine - INFO - Epoch(train) [84][1879/1879] lr: 3.5001e-06 eta: 18:01:17 time: 2.0980 data_time: 0.0416 memory: 48866 grad_norm: 6.6994 loss: 2.9373 loss_cls: 2.9373 2023/01/25 15:08:39 - mmengine - INFO - Saving checkpoint at 84 epochs 2023/01/25 15:09:40 - mmengine - INFO - Epoch(val) [84][100/155] eta: 0:00:31 time: 0.6313 data_time: 0.2774 memory: 4950 2023/01/25 15:10:06 - mmengine - INFO - Epoch(val) [84][155/155] acc/top1: 0.7565 acc/top5: 0.9206 acc/mean1: 0.7565 2023/01/25 15:10:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_83.pth is removed 2023/01/25 15:10:10 - mmengine - INFO - The best checkpoint with 0.7565 acc/top1 at 84 epoch is saved to best_acc/top1_epoch_84.pth. 2023/01/25 15:13:53 - mmengine - INFO - Epoch(train) [85][ 100/1879] lr: 3.4780e-06 eta: 17:57:42 time: 2.1542 data_time: 0.0403 memory: 48866 grad_norm: 5.9393 loss: 2.5910 loss_cls: 2.5910 2023/01/25 15:16:11 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 15:17:29 - mmengine - INFO - Epoch(train) [85][ 200/1879] lr: 3.4560e-06 eta: 17:54:07 time: 2.1582 data_time: 0.0409 memory: 48866 grad_norm: 6.4009 loss: 2.7306 loss_cls: 2.7306 2023/01/25 15:21:04 - mmengine - INFO - Epoch(train) [85][ 300/1879] lr: 3.4340e-06 eta: 17:50:31 time: 2.1509 data_time: 0.0399 memory: 48866 grad_norm: 5.9063 loss: 2.5388 loss_cls: 2.5388 2023/01/25 15:24:41 - mmengine - INFO - Epoch(train) [85][ 400/1879] lr: 3.4121e-06 eta: 17:46:55 time: 2.1615 data_time: 0.0398 memory: 48866 grad_norm: 6.4384 loss: 2.6315 loss_cls: 2.6315 2023/01/25 15:28:17 - mmengine - INFO - Epoch(train) [85][ 500/1879] lr: 3.3902e-06 eta: 17:43:19 time: 2.1585 data_time: 0.0418 memory: 48866 grad_norm: 6.6563 loss: 2.6463 loss_cls: 2.6463 2023/01/25 15:31:53 - mmengine - INFO - Epoch(train) [85][ 600/1879] lr: 3.3685e-06 eta: 17:39:44 time: 2.1629 data_time: 0.0396 memory: 48866 grad_norm: 6.4514 loss: 2.8090 loss_cls: 2.8090 2023/01/25 15:35:30 - mmengine - INFO - Epoch(train) [85][ 700/1879] lr: 3.3468e-06 eta: 17:36:08 time: 2.1590 data_time: 0.0395 memory: 48866 grad_norm: 6.3617 loss: 2.7046 loss_cls: 2.7046 2023/01/25 15:39:06 - mmengine - INFO - Epoch(train) [85][ 800/1879] lr: 3.3251e-06 eta: 17:32:32 time: 2.1622 data_time: 0.0402 memory: 48866 grad_norm: 6.3771 loss: 2.6651 loss_cls: 2.6651 2023/01/25 15:42:42 - mmengine - INFO - Epoch(train) [85][ 900/1879] lr: 3.3035e-06 eta: 17:28:56 time: 2.1611 data_time: 0.0403 memory: 48866 grad_norm: 6.6836 loss: 2.5114 loss_cls: 2.5114 2023/01/25 15:46:18 - mmengine - INFO - Epoch(train) [85][1000/1879] lr: 3.2820e-06 eta: 17:25:21 time: 2.1729 data_time: 0.0418 memory: 48866 grad_norm: 6.0672 loss: 2.5789 loss_cls: 2.5789 2023/01/25 15:49:54 - mmengine - INFO - Epoch(train) [85][1100/1879] lr: 3.2606e-06 eta: 17:21:45 time: 2.1608 data_time: 0.0406 memory: 48866 grad_norm: 6.2806 loss: 2.6978 loss_cls: 2.6978 2023/01/25 15:52:12 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 15:53:30 - mmengine - INFO - Epoch(train) [85][1200/1879] lr: 3.2392e-06 eta: 17:18:09 time: 2.1656 data_time: 0.0404 memory: 48866 grad_norm: 6.4782 loss: 2.7049 loss_cls: 2.7049 2023/01/25 15:57:07 - mmengine - INFO - Epoch(train) [85][1300/1879] lr: 3.2179e-06 eta: 17:14:34 time: 2.1732 data_time: 0.0411 memory: 48866 grad_norm: 6.3624 loss: 2.7570 loss_cls: 2.7570 2023/01/25 16:00:43 - mmengine - INFO - Epoch(train) [85][1400/1879] lr: 3.1966e-06 eta: 17:10:58 time: 2.1692 data_time: 0.0405 memory: 48866 grad_norm: 6.4021 loss: 2.7770 loss_cls: 2.7770 2023/01/25 16:04:19 - mmengine - INFO - Epoch(train) [85][1500/1879] lr: 3.1754e-06 eta: 17:07:22 time: 2.1523 data_time: 0.0409 memory: 48866 grad_norm: 6.1365 loss: 2.6375 loss_cls: 2.6375 2023/01/25 16:07:55 - mmengine - INFO - Epoch(train) [85][1600/1879] lr: 3.1543e-06 eta: 17:03:46 time: 2.1594 data_time: 0.0414 memory: 48866 grad_norm: 6.5490 loss: 2.5724 loss_cls: 2.5724 2023/01/25 16:11:31 - mmengine - INFO - Epoch(train) [85][1700/1879] lr: 3.1332e-06 eta: 17:00:10 time: 2.1552 data_time: 0.0417 memory: 48866 grad_norm: 6.2763 loss: 2.6665 loss_cls: 2.6665 2023/01/25 16:15:07 - mmengine - INFO - Epoch(train) [85][1800/1879] lr: 3.1122e-06 eta: 16:56:35 time: 2.1679 data_time: 0.0412 memory: 48866 grad_norm: 6.3346 loss: 2.4280 loss_cls: 2.4280 2023/01/25 16:17:57 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 16:17:57 - mmengine - INFO - Epoch(train) [85][1879/1879] lr: 3.0957e-06 eta: 16:53:44 time: 2.1009 data_time: 0.0421 memory: 48866 grad_norm: 6.3644 loss: 2.6486 loss_cls: 2.6486 2023/01/25 16:18:51 - mmengine - INFO - Epoch(val) [85][100/155] eta: 0:00:29 time: 0.6069 data_time: 0.2679 memory: 4950 2023/01/25 16:19:21 - mmengine - INFO - Epoch(val) [85][155/155] acc/top1: 0.7572 acc/top5: 0.9202 acc/mean1: 0.7571 2023/01/25 16:19:21 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_84.pth is removed 2023/01/25 16:19:25 - mmengine - INFO - The best checkpoint with 0.7572 acc/top1 at 85 epoch is saved to best_acc/top1_epoch_85.pth. 2023/01/25 16:23:06 - mmengine - INFO - Epoch(train) [86][ 100/1879] lr: 3.0748e-06 eta: 16:50:09 time: 2.1637 data_time: 0.0402 memory: 48866 grad_norm: 6.2201 loss: 2.6458 loss_cls: 2.6458 2023/01/25 16:26:42 - mmengine - INFO - Epoch(train) [86][ 200/1879] lr: 3.0540e-06 eta: 16:46:34 time: 2.1625 data_time: 0.0412 memory: 48866 grad_norm: 6.1372 loss: 2.6090 loss_cls: 2.6090 2023/01/25 16:29:46 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 16:30:19 - mmengine - INFO - Epoch(train) [86][ 300/1879] lr: 3.0332e-06 eta: 16:42:58 time: 2.1663 data_time: 0.0401 memory: 48866 grad_norm: 6.2161 loss: 2.4833 loss_cls: 2.4833 2023/01/25 16:33:55 - mmengine - INFO - Epoch(train) [86][ 400/1879] lr: 3.0126e-06 eta: 16:39:22 time: 2.1535 data_time: 0.0404 memory: 48866 grad_norm: 6.2809 loss: 2.4493 loss_cls: 2.4493 2023/01/25 16:37:31 - mmengine - INFO - Epoch(train) [86][ 500/1879] lr: 2.9919e-06 eta: 16:35:46 time: 2.1678 data_time: 0.0407 memory: 48866 grad_norm: 6.4176 loss: 2.7486 loss_cls: 2.7486 2023/01/25 16:41:07 - mmengine - INFO - Epoch(train) [86][ 600/1879] lr: 2.9714e-06 eta: 16:32:11 time: 2.1542 data_time: 0.0408 memory: 48866 grad_norm: 6.3123 loss: 2.5007 loss_cls: 2.5007 2023/01/25 16:44:43 - mmengine - INFO - Epoch(train) [86][ 700/1879] lr: 2.9509e-06 eta: 16:28:35 time: 2.1668 data_time: 0.0421 memory: 48866 grad_norm: 6.5470 loss: 2.7496 loss_cls: 2.7496 2023/01/25 16:48:19 - mmengine - INFO - Epoch(train) [86][ 800/1879] lr: 2.9305e-06 eta: 16:24:59 time: 2.1586 data_time: 0.0405 memory: 48866 grad_norm: 6.4768 loss: 2.6523 loss_cls: 2.6523 2023/01/25 16:51:55 - mmengine - INFO - Epoch(train) [86][ 900/1879] lr: 2.9101e-06 eta: 16:21:23 time: 2.1600 data_time: 0.0415 memory: 48866 grad_norm: 6.1812 loss: 2.7002 loss_cls: 2.7002 2023/01/25 16:55:30 - mmengine - INFO - Epoch(train) [86][1000/1879] lr: 2.8898e-06 eta: 16:17:47 time: 2.1668 data_time: 0.0423 memory: 48866 grad_norm: 6.3486 loss: 2.6624 loss_cls: 2.6624 2023/01/25 16:59:06 - mmengine - INFO - Epoch(train) [86][1100/1879] lr: 2.8696e-06 eta: 16:14:12 time: 2.1504 data_time: 0.0403 memory: 48866 grad_norm: 6.0090 loss: 2.7106 loss_cls: 2.7106 2023/01/25 17:02:42 - mmengine - INFO - Epoch(train) [86][1200/1879] lr: 2.8495e-06 eta: 16:10:36 time: 2.1468 data_time: 0.0410 memory: 48866 grad_norm: 6.2793 loss: 2.7968 loss_cls: 2.7968 2023/01/25 17:05:45 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 17:06:17 - mmengine - INFO - Epoch(train) [86][1300/1879] lr: 2.8294e-06 eta: 16:07:00 time: 2.1562 data_time: 0.0406 memory: 48866 grad_norm: 6.2888 loss: 2.6420 loss_cls: 2.6420 2023/01/25 17:09:53 - mmengine - INFO - Epoch(train) [86][1400/1879] lr: 2.8093e-06 eta: 16:03:24 time: 2.1585 data_time: 0.0411 memory: 48866 grad_norm: 6.9514 loss: 2.5902 loss_cls: 2.5902 2023/01/25 17:13:29 - mmengine - INFO - Epoch(train) [86][1500/1879] lr: 2.7894e-06 eta: 15:59:48 time: 2.1619 data_time: 0.0426 memory: 48866 grad_norm: 6.2456 loss: 2.5892 loss_cls: 2.5892 2023/01/25 17:17:06 - mmengine - INFO - Epoch(train) [86][1600/1879] lr: 2.7695e-06 eta: 15:56:13 time: 2.1647 data_time: 0.0413 memory: 48866 grad_norm: 6.2782 loss: 2.6294 loss_cls: 2.6294 2023/01/25 17:20:41 - mmengine - INFO - Epoch(train) [86][1700/1879] lr: 2.7497e-06 eta: 15:52:37 time: 2.1462 data_time: 0.0406 memory: 48866 grad_norm: 6.5444 loss: 2.6226 loss_cls: 2.6226 2023/01/25 17:24:18 - mmengine - INFO - Epoch(train) [86][1800/1879] lr: 2.7299e-06 eta: 15:49:01 time: 2.1633 data_time: 0.0412 memory: 48866 grad_norm: 6.4286 loss: 2.6385 loss_cls: 2.6385 2023/01/25 17:27:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 17:27:08 - mmengine - INFO - Epoch(train) [86][1879/1879] lr: 2.7144e-06 eta: 15:46:10 time: 2.1228 data_time: 0.0437 memory: 48866 grad_norm: 6.2533 loss: 2.5727 loss_cls: 2.5727 2023/01/25 17:28:02 - mmengine - INFO - Epoch(val) [86][100/155] eta: 0:00:29 time: 0.5658 data_time: 0.2170 memory: 4950 2023/01/25 17:28:33 - mmengine - INFO - Epoch(val) [86][155/155] acc/top1: 0.7620 acc/top5: 0.9201 acc/mean1: 0.7619 2023/01/25 17:28:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_85.pth is removed 2023/01/25 17:28:36 - mmengine - INFO - The best checkpoint with 0.7620 acc/top1 at 86 epoch is saved to best_acc/top1_epoch_86.pth. 2023/01/25 17:32:20 - mmengine - INFO - Epoch(train) [87][ 100/1879] lr: 2.6947e-06 eta: 15:42:36 time: 2.1623 data_time: 0.0410 memory: 48866 grad_norm: 6.4704 loss: 2.5130 loss_cls: 2.5130 2023/01/25 17:35:56 - mmengine - INFO - Epoch(train) [87][ 200/1879] lr: 2.6752e-06 eta: 15:39:00 time: 2.1675 data_time: 0.0420 memory: 48866 grad_norm: 6.6574 loss: 2.5326 loss_cls: 2.5326 2023/01/25 17:39:32 - mmengine - INFO - Epoch(train) [87][ 300/1879] lr: 2.6557e-06 eta: 15:35:24 time: 2.1519 data_time: 0.0415 memory: 48866 grad_norm: 6.2107 loss: 2.4695 loss_cls: 2.4695 2023/01/25 17:43:09 - mmengine - INFO - Epoch(train) [87][ 400/1879] lr: 2.6362e-06 eta: 15:31:49 time: 2.1677 data_time: 0.0419 memory: 48866 grad_norm: 6.7360 loss: 2.8619 loss_cls: 2.8619 2023/01/25 17:43:22 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 17:46:45 - mmengine - INFO - Epoch(train) [87][ 500/1879] lr: 2.6169e-06 eta: 15:28:13 time: 2.1549 data_time: 0.0406 memory: 48866 grad_norm: 6.2920 loss: 2.5268 loss_cls: 2.5268 2023/01/25 17:50:21 - mmengine - INFO - Epoch(train) [87][ 600/1879] lr: 2.5976e-06 eta: 15:24:37 time: 2.1507 data_time: 0.0412 memory: 48866 grad_norm: 6.2699 loss: 2.6008 loss_cls: 2.6008 2023/01/25 17:53:57 - mmengine - INFO - Epoch(train) [87][ 700/1879] lr: 2.5783e-06 eta: 15:21:01 time: 2.1642 data_time: 0.0400 memory: 48866 grad_norm: 6.6401 loss: 2.7191 loss_cls: 2.7191 2023/01/25 17:57:33 - mmengine - INFO - Epoch(train) [87][ 800/1879] lr: 2.5592e-06 eta: 15:17:26 time: 2.1562 data_time: 0.0406 memory: 48866 grad_norm: 6.4145 loss: 2.4691 loss_cls: 2.4691 2023/01/25 18:01:10 - mmengine - INFO - Epoch(train) [87][ 900/1879] lr: 2.5401e-06 eta: 15:13:50 time: 2.1530 data_time: 0.0409 memory: 48866 grad_norm: 6.1617 loss: 2.6398 loss_cls: 2.6398 2023/01/25 18:04:46 - mmengine - INFO - Epoch(train) [87][1000/1879] lr: 2.5211e-06 eta: 15:10:14 time: 2.1503 data_time: 0.0409 memory: 48866 grad_norm: 6.4445 loss: 2.6321 loss_cls: 2.6321 2023/01/25 18:08:22 - mmengine - INFO - Epoch(train) [87][1100/1879] lr: 2.5021e-06 eta: 15:06:38 time: 2.1483 data_time: 0.0406 memory: 48866 grad_norm: 6.4437 loss: 2.6719 loss_cls: 2.6719 2023/01/25 18:11:59 - mmengine - INFO - Epoch(train) [87][1200/1879] lr: 2.4832e-06 eta: 15:03:03 time: 2.1671 data_time: 0.0406 memory: 48866 grad_norm: 6.3723 loss: 2.6734 loss_cls: 2.6734 2023/01/25 18:15:35 - mmengine - INFO - Epoch(train) [87][1300/1879] lr: 2.4644e-06 eta: 14:59:27 time: 2.1604 data_time: 0.0417 memory: 48866 grad_norm: 6.4612 loss: 2.6346 loss_cls: 2.6346 2023/01/25 18:19:11 - mmengine - INFO - Epoch(train) [87][1400/1879] lr: 2.4456e-06 eta: 14:55:51 time: 2.1441 data_time: 0.0402 memory: 48866 grad_norm: 6.3081 loss: 2.7341 loss_cls: 2.7341 2023/01/25 18:19:24 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 18:22:47 - mmengine - INFO - Epoch(train) [87][1500/1879] lr: 2.4269e-06 eta: 14:52:15 time: 2.1582 data_time: 0.0415 memory: 48866 grad_norm: 6.3697 loss: 2.6727 loss_cls: 2.6727 2023/01/25 18:26:23 - mmengine - INFO - Epoch(train) [87][1600/1879] lr: 2.4083e-06 eta: 14:48:40 time: 2.1611 data_time: 0.0410 memory: 48866 grad_norm: 6.3411 loss: 2.5338 loss_cls: 2.5338 2023/01/25 18:29:59 - mmengine - INFO - Epoch(train) [87][1700/1879] lr: 2.3898e-06 eta: 14:45:04 time: 2.1585 data_time: 0.0423 memory: 48866 grad_norm: 6.2731 loss: 2.6488 loss_cls: 2.6488 2023/01/25 18:33:35 - mmengine - INFO - Epoch(train) [87][1800/1879] lr: 2.3713e-06 eta: 14:41:28 time: 2.1561 data_time: 0.0419 memory: 48866 grad_norm: 6.3810 loss: 2.5729 loss_cls: 2.5729 2023/01/25 18:36:25 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 18:36:25 - mmengine - INFO - Epoch(train) [87][1879/1879] lr: 2.3567e-06 eta: 14:38:37 time: 2.0928 data_time: 0.0433 memory: 48866 grad_norm: 6.4233 loss: 2.5428 loss_cls: 2.5428 2023/01/25 18:36:25 - mmengine - INFO - Saving checkpoint at 87 epochs 2023/01/25 18:37:25 - mmengine - INFO - Epoch(val) [87][100/155] eta: 0:00:30 time: 0.5859 data_time: 0.2466 memory: 4950 2023/01/25 18:37:51 - mmengine - INFO - Epoch(val) [87][155/155] acc/top1: 0.7583 acc/top5: 0.9208 acc/mean1: 0.7582 2023/01/25 18:41:35 - mmengine - INFO - Epoch(train) [88][ 100/1879] lr: 2.3384e-06 eta: 14:35:03 time: 2.1525 data_time: 0.0412 memory: 48866 grad_norm: 5.8301 loss: 2.5673 loss_cls: 2.5673 2023/01/25 18:45:11 - mmengine - INFO - Epoch(train) [88][ 200/1879] lr: 2.3201e-06 eta: 14:31:27 time: 2.1596 data_time: 0.0400 memory: 48866 grad_norm: 6.6047 loss: 2.4518 loss_cls: 2.4518 2023/01/25 18:48:47 - mmengine - INFO - Epoch(train) [88][ 300/1879] lr: 2.3019e-06 eta: 14:27:51 time: 2.1616 data_time: 0.0407 memory: 48866 grad_norm: 7.0331 loss: 2.5161 loss_cls: 2.5161 2023/01/25 18:52:23 - mmengine - INFO - Epoch(train) [88][ 400/1879] lr: 2.2837e-06 eta: 14:24:15 time: 2.1646 data_time: 0.0405 memory: 48866 grad_norm: 6.1800 loss: 2.6064 loss_cls: 2.6064 2023/01/25 18:55:59 - mmengine - INFO - Epoch(train) [88][ 500/1879] lr: 2.2656e-06 eta: 14:20:39 time: 2.1676 data_time: 0.0403 memory: 48866 grad_norm: 6.4988 loss: 2.6617 loss_cls: 2.6617 2023/01/25 18:56:57 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 18:59:34 - mmengine - INFO - Epoch(train) [88][ 600/1879] lr: 2.2476e-06 eta: 14:17:04 time: 2.1620 data_time: 0.0405 memory: 48866 grad_norm: 6.3479 loss: 2.4501 loss_cls: 2.4501 2023/01/25 19:03:10 - mmengine - INFO - Epoch(train) [88][ 700/1879] lr: 2.2297e-06 eta: 14:13:28 time: 2.1532 data_time: 0.0405 memory: 48866 grad_norm: 6.0734 loss: 2.6377 loss_cls: 2.6377 2023/01/25 19:06:46 - mmengine - INFO - Epoch(train) [88][ 800/1879] lr: 2.2118e-06 eta: 14:09:52 time: 2.1562 data_time: 0.0399 memory: 48866 grad_norm: 6.3035 loss: 2.5864 loss_cls: 2.5864 2023/01/25 19:10:21 - mmengine - INFO - Epoch(train) [88][ 900/1879] lr: 2.1940e-06 eta: 14:06:16 time: 2.1612 data_time: 0.0400 memory: 48866 grad_norm: 7.0816 loss: 2.5875 loss_cls: 2.5875 2023/01/25 19:13:57 - mmengine - INFO - Epoch(train) [88][1000/1879] lr: 2.1763e-06 eta: 14:02:40 time: 2.1518 data_time: 0.0414 memory: 48866 grad_norm: 6.1182 loss: 2.4418 loss_cls: 2.4418 2023/01/25 19:17:33 - mmengine - INFO - Epoch(train) [88][1100/1879] lr: 2.1586e-06 eta: 13:59:04 time: 2.1511 data_time: 0.0407 memory: 48866 grad_norm: 6.4504 loss: 2.5962 loss_cls: 2.5962 2023/01/25 19:21:09 - mmengine - INFO - Epoch(train) [88][1200/1879] lr: 2.1410e-06 eta: 13:55:29 time: 2.1708 data_time: 0.0412 memory: 48866 grad_norm: 6.2643 loss: 2.4877 loss_cls: 2.4877 2023/01/25 19:24:45 - mmengine - INFO - Epoch(train) [88][1300/1879] lr: 2.1235e-06 eta: 13:51:53 time: 2.1634 data_time: 0.0404 memory: 48866 grad_norm: 6.4042 loss: 2.7190 loss_cls: 2.7190 2023/01/25 19:28:21 - mmengine - INFO - Epoch(train) [88][1400/1879] lr: 2.1060e-06 eta: 13:48:17 time: 2.1620 data_time: 0.0411 memory: 48866 grad_norm: 6.3006 loss: 2.6243 loss_cls: 2.6243 2023/01/25 19:31:57 - mmengine - INFO - Epoch(train) [88][1500/1879] lr: 2.0886e-06 eta: 13:44:41 time: 2.1534 data_time: 0.0402 memory: 48866 grad_norm: 6.3942 loss: 2.5535 loss_cls: 2.5535 2023/01/25 19:32:55 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 19:35:32 - mmengine - INFO - Epoch(train) [88][1600/1879] lr: 2.0713e-06 eta: 13:41:05 time: 2.1462 data_time: 0.0405 memory: 48866 grad_norm: 6.6580 loss: 2.6586 loss_cls: 2.6586 2023/01/25 19:39:08 - mmengine - INFO - Epoch(train) [88][1700/1879] lr: 2.0540e-06 eta: 13:37:29 time: 2.1668 data_time: 0.0402 memory: 48866 grad_norm: 6.3765 loss: 2.5244 loss_cls: 2.5244 2023/01/25 19:42:44 - mmengine - INFO - Epoch(train) [88][1800/1879] lr: 2.0369e-06 eta: 13:33:54 time: 2.1718 data_time: 0.0419 memory: 48866 grad_norm: 6.3639 loss: 2.7682 loss_cls: 2.7682 2023/01/25 19:45:34 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 19:45:34 - mmengine - INFO - Epoch(train) [88][1879/1879] lr: 2.0233e-06 eta: 13:31:03 time: 2.1013 data_time: 0.0418 memory: 48866 grad_norm: 6.2344 loss: 2.6025 loss_cls: 2.6025 2023/01/25 19:46:27 - mmengine - INFO - Epoch(val) [88][100/155] eta: 0:00:28 time: 0.5201 data_time: 0.1830 memory: 4950 2023/01/25 19:46:58 - mmengine - INFO - Epoch(val) [88][155/155] acc/top1: 0.7607 acc/top5: 0.9212 acc/mean1: 0.7606 2023/01/25 19:50:41 - mmengine - INFO - Epoch(train) [89][ 100/1879] lr: 2.0063e-06 eta: 13:27:28 time: 2.1395 data_time: 0.0392 memory: 48866 grad_norm: 6.0695 loss: 2.4715 loss_cls: 2.4715 2023/01/25 19:54:18 - mmengine - INFO - Epoch(train) [89][ 200/1879] lr: 1.9893e-06 eta: 13:23:52 time: 2.1670 data_time: 0.0396 memory: 48866 grad_norm: 6.5370 loss: 2.6985 loss_cls: 2.6985 2023/01/25 19:57:54 - mmengine - INFO - Epoch(train) [89][ 300/1879] lr: 1.9724e-06 eta: 13:20:17 time: 2.1638 data_time: 0.0397 memory: 48866 grad_norm: 6.3767 loss: 2.6026 loss_cls: 2.6026 2023/01/25 20:01:30 - mmengine - INFO - Epoch(train) [89][ 400/1879] lr: 1.9555e-06 eta: 13:16:41 time: 2.1606 data_time: 0.0397 memory: 48866 grad_norm: 6.1893 loss: 2.3840 loss_cls: 2.3840 2023/01/25 20:05:06 - mmengine - INFO - Epoch(train) [89][ 500/1879] lr: 1.9388e-06 eta: 13:13:05 time: 2.1547 data_time: 0.0397 memory: 48866 grad_norm: 6.7255 loss: 2.7854 loss_cls: 2.7854 2023/01/25 20:08:41 - mmengine - INFO - Epoch(train) [89][ 600/1879] lr: 1.9221e-06 eta: 13:09:29 time: 2.1526 data_time: 0.0393 memory: 48866 grad_norm: 6.0056 loss: 2.5640 loss_cls: 2.5640 2023/01/25 20:10:26 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 20:12:18 - mmengine - INFO - Epoch(train) [89][ 700/1879] lr: 1.9054e-06 eta: 13:05:53 time: 2.1498 data_time: 0.0408 memory: 48866 grad_norm: 6.0134 loss: 2.5203 loss_cls: 2.5203 2023/01/25 20:15:54 - mmengine - INFO - Epoch(train) [89][ 800/1879] lr: 1.8889e-06 eta: 13:02:18 time: 2.1611 data_time: 0.0398 memory: 48866 grad_norm: 6.4346 loss: 2.7046 loss_cls: 2.7046 2023/01/25 20:19:30 - mmengine - INFO - Epoch(train) [89][ 900/1879] lr: 1.8724e-06 eta: 12:58:42 time: 2.1540 data_time: 0.0405 memory: 48866 grad_norm: 6.2374 loss: 2.4761 loss_cls: 2.4761 2023/01/25 20:23:06 - mmengine - INFO - Epoch(train) [89][1000/1879] lr: 1.8560e-06 eta: 12:55:06 time: 2.1511 data_time: 0.0406 memory: 48866 grad_norm: 6.1765 loss: 2.5453 loss_cls: 2.5453 2023/01/25 20:26:42 - mmengine - INFO - Epoch(train) [89][1100/1879] lr: 1.8396e-06 eta: 12:51:30 time: 2.1637 data_time: 0.0398 memory: 48866 grad_norm: 6.2028 loss: 2.6208 loss_cls: 2.6208 2023/01/25 20:30:18 - mmengine - INFO - Epoch(train) [89][1200/1879] lr: 1.8233e-06 eta: 12:47:54 time: 2.1617 data_time: 0.0397 memory: 48866 grad_norm: 6.1787 loss: 2.4301 loss_cls: 2.4301 2023/01/25 20:33:54 - mmengine - INFO - Epoch(train) [89][1300/1879] lr: 1.8071e-06 eta: 12:44:19 time: 2.1585 data_time: 0.0402 memory: 48866 grad_norm: 6.6166 loss: 2.4190 loss_cls: 2.4190 2023/01/25 20:37:30 - mmengine - INFO - Epoch(train) [89][1400/1879] lr: 1.7910e-06 eta: 12:40:43 time: 2.1554 data_time: 0.0404 memory: 48866 grad_norm: 6.4768 loss: 2.5415 loss_cls: 2.5415 2023/01/25 20:41:07 - mmengine - INFO - Epoch(train) [89][1500/1879] lr: 1.7749e-06 eta: 12:37:07 time: 2.1575 data_time: 0.0406 memory: 48866 grad_norm: 6.4645 loss: 2.7540 loss_cls: 2.7540 2023/01/25 20:44:43 - mmengine - INFO - Epoch(train) [89][1600/1879] lr: 1.7589e-06 eta: 12:33:31 time: 2.1449 data_time: 0.0406 memory: 48866 grad_norm: 6.5120 loss: 2.6626 loss_cls: 2.6626 2023/01/25 20:46:26 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 20:48:18 - mmengine - INFO - Epoch(train) [89][1700/1879] lr: 1.7430e-06 eta: 12:29:55 time: 2.1563 data_time: 0.0413 memory: 48866 grad_norm: 6.5603 loss: 2.6006 loss_cls: 2.6006 2023/01/25 20:51:54 - mmengine - INFO - Epoch(train) [89][1800/1879] lr: 1.7272e-06 eta: 12:26:20 time: 2.1555 data_time: 0.0397 memory: 48866 grad_norm: 6.3803 loss: 2.3867 loss_cls: 2.3867 2023/01/25 20:54:44 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 20:54:44 - mmengine - INFO - Epoch(train) [89][1879/1879] lr: 1.7147e-06 eta: 12:23:29 time: 2.1045 data_time: 0.0404 memory: 48866 grad_norm: 6.5273 loss: 2.5921 loss_cls: 2.5921 2023/01/25 20:55:36 - mmengine - INFO - Epoch(val) [89][100/155] eta: 0:00:28 time: 0.5374 data_time: 0.2064 memory: 4950 2023/01/25 20:56:08 - mmengine - INFO - Epoch(val) [89][155/155] acc/top1: 0.7639 acc/top5: 0.9226 acc/mean1: 0.7639 2023/01/25 20:56:08 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_86.pth is removed 2023/01/25 20:56:12 - mmengine - INFO - The best checkpoint with 0.7639 acc/top1 at 89 epoch is saved to best_acc/top1_epoch_89.pth. 2023/01/25 20:59:55 - mmengine - INFO - Epoch(train) [90][ 100/1879] lr: 1.6990e-06 eta: 12:19:54 time: 2.1791 data_time: 0.0529 memory: 48866 grad_norm: 6.3803 loss: 2.5324 loss_cls: 2.5324 2023/01/25 21:03:30 - mmengine - INFO - Epoch(train) [90][ 200/1879] lr: 1.6833e-06 eta: 12:16:18 time: 2.1450 data_time: 0.0389 memory: 48866 grad_norm: 6.2053 loss: 2.6133 loss_cls: 2.6133 2023/01/25 21:07:06 - mmengine - INFO - Epoch(train) [90][ 300/1879] lr: 1.6677e-06 eta: 12:12:42 time: 2.1722 data_time: 0.0405 memory: 48866 grad_norm: 6.5539 loss: 2.6657 loss_cls: 2.6657 2023/01/25 21:10:43 - mmengine - INFO - Epoch(train) [90][ 400/1879] lr: 1.6522e-06 eta: 12:09:07 time: 2.1655 data_time: 0.0400 memory: 48866 grad_norm: 6.5569 loss: 2.5408 loss_cls: 2.5408 2023/01/25 21:14:19 - mmengine - INFO - Epoch(train) [90][ 500/1879] lr: 1.6368e-06 eta: 12:05:31 time: 2.1603 data_time: 0.0405 memory: 48866 grad_norm: 7.0598 loss: 2.4997 loss_cls: 2.4997 2023/01/25 21:17:54 - mmengine - INFO - Epoch(train) [90][ 600/1879] lr: 1.6214e-06 eta: 12:01:55 time: 2.1675 data_time: 0.0388 memory: 48866 grad_norm: 6.4081 loss: 2.6358 loss_cls: 2.6358 2023/01/25 21:21:30 - mmengine - INFO - Epoch(train) [90][ 700/1879] lr: 1.6061e-06 eta: 11:58:19 time: 2.1600 data_time: 0.0405 memory: 48866 grad_norm: 6.8414 loss: 2.5365 loss_cls: 2.5365 2023/01/25 21:23:59 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 21:25:06 - mmengine - INFO - Epoch(train) [90][ 800/1879] lr: 1.5909e-06 eta: 11:54:43 time: 2.1591 data_time: 0.0391 memory: 48866 grad_norm: 6.6050 loss: 2.3993 loss_cls: 2.3993 2023/01/25 21:28:42 - mmengine - INFO - Epoch(train) [90][ 900/1879] lr: 1.5758e-06 eta: 11:51:07 time: 2.1561 data_time: 0.0404 memory: 48866 grad_norm: 6.3794 loss: 2.6800 loss_cls: 2.6800 2023/01/25 21:32:18 - mmengine - INFO - Epoch(train) [90][1000/1879] lr: 1.5607e-06 eta: 11:47:32 time: 2.1629 data_time: 0.0416 memory: 48866 grad_norm: 6.4372 loss: 2.5523 loss_cls: 2.5523 2023/01/25 21:35:54 - mmengine - INFO - Epoch(train) [90][1100/1879] lr: 1.5457e-06 eta: 11:43:56 time: 2.1583 data_time: 0.0393 memory: 48866 grad_norm: 6.4830 loss: 2.6523 loss_cls: 2.6523 2023/01/25 21:39:30 - mmengine - INFO - Epoch(train) [90][1200/1879] lr: 1.5307e-06 eta: 11:40:20 time: 2.1619 data_time: 0.0401 memory: 48866 grad_norm: 6.2976 loss: 2.6520 loss_cls: 2.6520 2023/01/25 21:43:07 - mmengine - INFO - Epoch(train) [90][1300/1879] lr: 1.5159e-06 eta: 11:36:44 time: 2.1721 data_time: 0.0404 memory: 48866 grad_norm: 6.3727 loss: 2.6656 loss_cls: 2.6656 2023/01/25 21:46:42 - mmengine - INFO - Epoch(train) [90][1400/1879] lr: 1.5011e-06 eta: 11:33:08 time: 2.1528 data_time: 0.0407 memory: 48866 grad_norm: 6.2177 loss: 2.6540 loss_cls: 2.6540 2023/01/25 21:50:18 - mmengine - INFO - Epoch(train) [90][1500/1879] lr: 1.4864e-06 eta: 11:29:32 time: 2.1503 data_time: 0.0403 memory: 48866 grad_norm: 6.2707 loss: 2.6396 loss_cls: 2.6396 2023/01/25 21:53:54 - mmengine - INFO - Epoch(train) [90][1600/1879] lr: 1.4717e-06 eta: 11:25:57 time: 2.1722 data_time: 0.0404 memory: 48866 grad_norm: 6.4075 loss: 2.6107 loss_cls: 2.6107 2023/01/25 21:57:30 - mmengine - INFO - Epoch(train) [90][1700/1879] lr: 1.4572e-06 eta: 11:22:21 time: 2.1509 data_time: 0.0400 memory: 48866 grad_norm: 6.4652 loss: 2.5195 loss_cls: 2.5195 2023/01/25 21:59:59 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 22:01:06 - mmengine - INFO - Epoch(train) [90][1800/1879] lr: 1.4427e-06 eta: 11:18:45 time: 2.1613 data_time: 0.0399 memory: 48866 grad_norm: 6.3475 loss: 2.4281 loss_cls: 2.4281 2023/01/25 22:03:55 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 22:03:55 - mmengine - INFO - Epoch(train) [90][1879/1879] lr: 1.4313e-06 eta: 11:15:54 time: 2.1024 data_time: 0.0413 memory: 48866 grad_norm: 6.8247 loss: 2.4705 loss_cls: 2.4705 2023/01/25 22:03:55 - mmengine - INFO - Saving checkpoint at 90 epochs 2023/01/25 22:04:55 - mmengine - INFO - Epoch(val) [90][100/155] eta: 0:00:30 time: 0.5728 data_time: 0.2264 memory: 4950 2023/01/25 22:05:22 - mmengine - INFO - Epoch(val) [90][155/155] acc/top1: 0.7651 acc/top5: 0.9218 acc/mean1: 0.7651 2023/01/25 22:05:23 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_89.pth is removed 2023/01/25 22:05:26 - mmengine - INFO - The best checkpoint with 0.7651 acc/top1 at 90 epoch is saved to best_acc/top1_epoch_90.pth. 2023/01/25 22:09:08 - mmengine - INFO - Epoch(train) [91][ 100/1879] lr: 1.4169e-06 eta: 11:12:19 time: 2.1548 data_time: 0.0403 memory: 48866 grad_norm: 6.3489 loss: 2.5757 loss_cls: 2.5757 2023/01/25 22:12:45 - mmengine - INFO - Epoch(train) [91][ 200/1879] lr: 1.4026e-06 eta: 11:08:43 time: 2.1630 data_time: 0.0387 memory: 48866 grad_norm: 6.3599 loss: 2.5040 loss_cls: 2.5040 2023/01/25 22:16:21 - mmengine - INFO - Epoch(train) [91][ 300/1879] lr: 1.3884e-06 eta: 11:05:08 time: 2.1549 data_time: 0.0402 memory: 48866 grad_norm: 6.0412 loss: 2.5555 loss_cls: 2.5555 2023/01/25 22:19:57 - mmengine - INFO - Epoch(train) [91][ 400/1879] lr: 1.3742e-06 eta: 11:01:32 time: 2.1637 data_time: 0.0407 memory: 48866 grad_norm: 6.3314 loss: 2.6825 loss_cls: 2.6825 2023/01/25 22:23:32 - mmengine - INFO - Epoch(train) [91][ 500/1879] lr: 1.3601e-06 eta: 10:57:56 time: 2.1579 data_time: 0.0393 memory: 48866 grad_norm: 6.2983 loss: 2.8050 loss_cls: 2.8050 2023/01/25 22:27:09 - mmengine - INFO - Epoch(train) [91][ 600/1879] lr: 1.3461e-06 eta: 10:54:20 time: 2.1511 data_time: 0.0390 memory: 48866 grad_norm: 6.3233 loss: 2.6184 loss_cls: 2.6184 2023/01/25 22:30:45 - mmengine - INFO - Epoch(train) [91][ 700/1879] lr: 1.3322e-06 eta: 10:50:44 time: 2.1556 data_time: 0.0405 memory: 48866 grad_norm: 6.4214 loss: 2.3945 loss_cls: 2.3945 2023/01/25 22:34:21 - mmengine - INFO - Epoch(train) [91][ 800/1879] lr: 1.3183e-06 eta: 10:47:09 time: 2.1548 data_time: 0.0397 memory: 48866 grad_norm: 6.3761 loss: 2.6197 loss_cls: 2.6197 2023/01/25 22:37:35 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 22:37:57 - mmengine - INFO - Epoch(train) [91][ 900/1879] lr: 1.3046e-06 eta: 10:43:33 time: 2.1498 data_time: 0.0406 memory: 48866 grad_norm: 6.3263 loss: 2.5569 loss_cls: 2.5569 2023/01/25 22:41:33 - mmengine - INFO - Epoch(train) [91][1000/1879] lr: 1.2908e-06 eta: 10:39:57 time: 2.1611 data_time: 0.0405 memory: 48866 grad_norm: 6.2292 loss: 2.6449 loss_cls: 2.6449 2023/01/25 22:45:08 - mmengine - INFO - Epoch(train) [91][1100/1879] lr: 1.2772e-06 eta: 10:36:21 time: 2.1609 data_time: 0.0401 memory: 48866 grad_norm: 6.3246 loss: 2.4840 loss_cls: 2.4840 2023/01/25 22:48:45 - mmengine - INFO - Epoch(train) [91][1200/1879] lr: 1.2636e-06 eta: 10:32:45 time: 2.1720 data_time: 0.0408 memory: 48866 grad_norm: 6.5745 loss: 2.6177 loss_cls: 2.6177 2023/01/25 22:52:20 - mmengine - INFO - Epoch(train) [91][1300/1879] lr: 1.2502e-06 eta: 10:29:09 time: 2.1597 data_time: 0.0403 memory: 48866 grad_norm: 6.3648 loss: 2.4555 loss_cls: 2.4555 2023/01/25 22:55:56 - mmengine - INFO - Epoch(train) [91][1400/1879] lr: 1.2367e-06 eta: 10:25:34 time: 2.1501 data_time: 0.0407 memory: 48866 grad_norm: 6.3251 loss: 2.5703 loss_cls: 2.5703 2023/01/25 22:59:32 - mmengine - INFO - Epoch(train) [91][1500/1879] lr: 1.2234e-06 eta: 10:21:58 time: 2.1677 data_time: 0.0402 memory: 48866 grad_norm: 6.3783 loss: 2.4392 loss_cls: 2.4392 2023/01/25 23:03:08 - mmengine - INFO - Epoch(train) [91][1600/1879] lr: 1.2101e-06 eta: 10:18:22 time: 2.1523 data_time: 0.0396 memory: 48866 grad_norm: 6.3323 loss: 2.4097 loss_cls: 2.4097 2023/01/25 23:06:44 - mmengine - INFO - Epoch(train) [91][1700/1879] lr: 1.1969e-06 eta: 10:14:46 time: 2.1663 data_time: 0.0409 memory: 48866 grad_norm: 6.4619 loss: 2.6372 loss_cls: 2.6372 2023/01/25 23:10:20 - mmengine - INFO - Epoch(train) [91][1800/1879] lr: 1.1838e-06 eta: 10:11:10 time: 2.1561 data_time: 0.0397 memory: 48866 grad_norm: 6.1925 loss: 2.3559 loss_cls: 2.3559 2023/01/25 23:13:10 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 23:13:10 - mmengine - INFO - Epoch(train) [91][1879/1879] lr: 1.1735e-06 eta: 10:08:20 time: 2.1080 data_time: 0.0418 memory: 48866 grad_norm: 6.3867 loss: 2.4604 loss_cls: 2.4604 2023/01/25 23:14:04 - mmengine - INFO - Epoch(val) [91][100/155] eta: 0:00:29 time: 0.5316 data_time: 0.1878 memory: 4950 2023/01/25 23:14:34 - mmengine - INFO - Epoch(val) [91][155/155] acc/top1: 0.7638 acc/top5: 0.9222 acc/mean1: 0.7637 2023/01/25 23:15:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 23:18:18 - mmengine - INFO - Epoch(train) [92][ 100/1879] lr: 1.1605e-06 eta: 10:04:45 time: 2.1699 data_time: 0.0395 memory: 48866 grad_norm: 6.7263 loss: 2.5524 loss_cls: 2.5524 2023/01/25 23:21:53 - mmengine - INFO - Epoch(train) [92][ 200/1879] lr: 1.1476e-06 eta: 10:01:09 time: 2.1559 data_time: 0.0395 memory: 48866 grad_norm: 6.5472 loss: 2.5611 loss_cls: 2.5611 2023/01/25 23:25:29 - mmengine - INFO - Epoch(train) [92][ 300/1879] lr: 1.1347e-06 eta: 9:57:33 time: 2.1565 data_time: 0.0398 memory: 48866 grad_norm: 6.7147 loss: 2.6376 loss_cls: 2.6376 2023/01/25 23:29:05 - mmengine - INFO - Epoch(train) [92][ 400/1879] lr: 1.1220e-06 eta: 9:53:57 time: 2.1615 data_time: 0.0395 memory: 48866 grad_norm: 6.3268 loss: 2.5281 loss_cls: 2.5281 2023/01/25 23:32:41 - mmengine - INFO - Epoch(train) [92][ 500/1879] lr: 1.1093e-06 eta: 9:50:21 time: 2.1674 data_time: 0.0404 memory: 48866 grad_norm: 6.3339 loss: 2.3960 loss_cls: 2.3960 2023/01/25 23:36:17 - mmengine - INFO - Epoch(train) [92][ 600/1879] lr: 1.0966e-06 eta: 9:46:45 time: 2.1566 data_time: 0.0398 memory: 48866 grad_norm: 6.2500 loss: 2.6250 loss_cls: 2.6250 2023/01/25 23:39:52 - mmengine - INFO - Epoch(train) [92][ 700/1879] lr: 1.0841e-06 eta: 9:43:09 time: 2.1688 data_time: 0.0402 memory: 48866 grad_norm: 6.1441 loss: 2.4728 loss_cls: 2.4728 2023/01/25 23:43:29 - mmengine - INFO - Epoch(train) [92][ 800/1879] lr: 1.0716e-06 eta: 9:39:34 time: 2.1600 data_time: 0.0395 memory: 48866 grad_norm: 6.2414 loss: 2.4247 loss_cls: 2.4247 2023/01/25 23:47:05 - mmengine - INFO - Epoch(train) [92][ 900/1879] lr: 1.0592e-06 eta: 9:35:58 time: 2.1559 data_time: 0.0395 memory: 48866 grad_norm: 6.4422 loss: 2.6556 loss_cls: 2.6556 2023/01/25 23:50:41 - mmengine - INFO - Epoch(train) [92][1000/1879] lr: 1.0469e-06 eta: 9:32:22 time: 2.1591 data_time: 0.0404 memory: 48866 grad_norm: 6.0026 loss: 2.6904 loss_cls: 2.6904 2023/01/25 23:51:05 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/25 23:54:17 - mmengine - INFO - Epoch(train) [92][1100/1879] lr: 1.0346e-06 eta: 9:28:46 time: 2.1680 data_time: 0.0393 memory: 48866 grad_norm: 6.0632 loss: 2.5070 loss_cls: 2.5070 2023/01/25 23:57:53 - mmengine - INFO - Epoch(train) [92][1200/1879] lr: 1.0225e-06 eta: 9:25:10 time: 2.1585 data_time: 0.0407 memory: 48866 grad_norm: 6.1416 loss: 2.5477 loss_cls: 2.5477 2023/01/26 00:01:29 - mmengine - INFO - Epoch(train) [92][1300/1879] lr: 1.0104e-06 eta: 9:21:35 time: 2.1610 data_time: 0.0407 memory: 48866 grad_norm: 6.3033 loss: 2.4419 loss_cls: 2.4419 2023/01/26 00:05:05 - mmengine - INFO - Epoch(train) [92][1400/1879] lr: 9.9833e-07 eta: 9:17:59 time: 2.1568 data_time: 0.0386 memory: 48866 grad_norm: 6.6695 loss: 2.6582 loss_cls: 2.6582 2023/01/26 00:08:41 - mmengine - INFO - Epoch(train) [92][1500/1879] lr: 9.8638e-07 eta: 9:14:23 time: 2.1693 data_time: 0.0400 memory: 48866 grad_norm: 6.4239 loss: 2.5916 loss_cls: 2.5916 2023/01/26 00:12:17 - mmengine - INFO - Epoch(train) [92][1600/1879] lr: 9.7450e-07 eta: 9:10:47 time: 2.1685 data_time: 0.0396 memory: 48866 grad_norm: 6.6691 loss: 2.5000 loss_cls: 2.5000 2023/01/26 00:15:53 - mmengine - INFO - Epoch(train) [92][1700/1879] lr: 9.6270e-07 eta: 9:07:11 time: 2.1676 data_time: 0.0407 memory: 48866 grad_norm: 6.7483 loss: 2.5554 loss_cls: 2.5554 2023/01/26 00:19:29 - mmengine - INFO - Epoch(train) [92][1800/1879] lr: 9.5097e-07 eta: 9:03:35 time: 2.1575 data_time: 0.0401 memory: 48866 grad_norm: 6.1429 loss: 2.5758 loss_cls: 2.5758 2023/01/26 00:22:18 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 00:22:18 - mmengine - INFO - Epoch(train) [92][1879/1879] lr: 9.4175e-07 eta: 9:00:45 time: 2.0986 data_time: 0.0402 memory: 48866 grad_norm: 6.7370 loss: 2.5941 loss_cls: 2.5941 2023/01/26 00:23:11 - mmengine - INFO - Epoch(val) [92][100/155] eta: 0:00:29 time: 0.5542 data_time: 0.1927 memory: 4950 2023/01/26 00:23:42 - mmengine - INFO - Epoch(val) [92][155/155] acc/top1: 0.7666 acc/top5: 0.9213 acc/mean1: 0.7665 2023/01/26 00:23:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_90.pth is removed 2023/01/26 00:23:45 - mmengine - INFO - The best checkpoint with 0.7666 acc/top1 at 92 epoch is saved to best_acc/top1_epoch_92.pth. 2023/01/26 00:27:28 - mmengine - INFO - Epoch(train) [93][ 100/1879] lr: 9.3016e-07 eta: 8:57:09 time: 2.1468 data_time: 0.0390 memory: 48866 grad_norm: 6.5533 loss: 2.6752 loss_cls: 2.6752 2023/01/26 00:28:37 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 00:31:03 - mmengine - INFO - Epoch(train) [93][ 200/1879] lr: 9.1864e-07 eta: 8:53:34 time: 2.1549 data_time: 0.0397 memory: 48866 grad_norm: 6.2496 loss: 2.5389 loss_cls: 2.5389 2023/01/26 00:34:39 - mmengine - INFO - Epoch(train) [93][ 300/1879] lr: 9.0719e-07 eta: 8:49:58 time: 2.1535 data_time: 0.0403 memory: 48866 grad_norm: 6.0390 loss: 2.4714 loss_cls: 2.4714 2023/01/26 00:38:15 - mmengine - INFO - Epoch(train) [93][ 400/1879] lr: 8.9582e-07 eta: 8:46:22 time: 2.1521 data_time: 0.0401 memory: 48866 grad_norm: 6.5942 loss: 2.7372 loss_cls: 2.7372 2023/01/26 00:41:50 - mmengine - INFO - Epoch(train) [93][ 500/1879] lr: 8.8453e-07 eta: 8:42:46 time: 2.1557 data_time: 0.0402 memory: 48866 grad_norm: 6.4622 loss: 2.4928 loss_cls: 2.4928 2023/01/26 00:45:27 - mmengine - INFO - Epoch(train) [93][ 600/1879] lr: 8.7331e-07 eta: 8:39:10 time: 2.1523 data_time: 0.0397 memory: 48866 grad_norm: 6.5323 loss: 2.6998 loss_cls: 2.6998 2023/01/26 00:49:03 - mmengine - INFO - Epoch(train) [93][ 700/1879] lr: 8.6216e-07 eta: 8:35:34 time: 2.1627 data_time: 0.0401 memory: 48866 grad_norm: 6.3410 loss: 2.5682 loss_cls: 2.5682 2023/01/26 00:52:38 - mmengine - INFO - Epoch(train) [93][ 800/1879] lr: 8.5109e-07 eta: 8:31:59 time: 2.1574 data_time: 0.0403 memory: 48866 grad_norm: 6.4645 loss: 2.5403 loss_cls: 2.5403 2023/01/26 00:56:14 - mmengine - INFO - Epoch(train) [93][ 900/1879] lr: 8.4010e-07 eta: 8:28:23 time: 2.1704 data_time: 0.0408 memory: 48866 grad_norm: 6.5965 loss: 2.5129 loss_cls: 2.5129 2023/01/26 00:59:50 - mmengine - INFO - Epoch(train) [93][1000/1879] lr: 8.2918e-07 eta: 8:24:47 time: 2.1576 data_time: 0.0387 memory: 48866 grad_norm: 6.3385 loss: 2.6077 loss_cls: 2.6077 2023/01/26 01:03:27 - mmengine - INFO - Epoch(train) [93][1100/1879] lr: 8.1833e-07 eta: 8:21:11 time: 2.1631 data_time: 0.0413 memory: 48866 grad_norm: 6.3776 loss: 2.5650 loss_cls: 2.5650 2023/01/26 01:04:36 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 01:07:03 - mmengine - INFO - Epoch(train) [93][1200/1879] lr: 8.0757e-07 eta: 8:17:35 time: 2.1592 data_time: 0.0402 memory: 48866 grad_norm: 6.3858 loss: 2.5545 loss_cls: 2.5545 2023/01/26 01:10:39 - mmengine - INFO - Epoch(train) [93][1300/1879] lr: 7.9687e-07 eta: 8:13:59 time: 2.1754 data_time: 0.0405 memory: 48866 grad_norm: 6.4583 loss: 2.7803 loss_cls: 2.7803 2023/01/26 01:14:16 - mmengine - INFO - Epoch(train) [93][1400/1879] lr: 7.8625e-07 eta: 8:10:24 time: 2.1708 data_time: 0.0403 memory: 48866 grad_norm: 6.3866 loss: 2.5499 loss_cls: 2.5499 2023/01/26 01:17:52 - mmengine - INFO - Epoch(train) [93][1500/1879] lr: 7.7571e-07 eta: 8:06:48 time: 2.1651 data_time: 0.0400 memory: 48866 grad_norm: 6.7742 loss: 2.6027 loss_cls: 2.6027 2023/01/26 01:21:28 - mmengine - INFO - Epoch(train) [93][1600/1879] lr: 7.6524e-07 eta: 8:03:12 time: 2.1620 data_time: 0.0404 memory: 48866 grad_norm: 6.6743 loss: 2.4816 loss_cls: 2.4816 2023/01/26 01:25:04 - mmengine - INFO - Epoch(train) [93][1700/1879] lr: 7.5485e-07 eta: 7:59:36 time: 2.1615 data_time: 0.0394 memory: 48866 grad_norm: 6.3874 loss: 2.3870 loss_cls: 2.3870 2023/01/26 01:28:40 - mmengine - INFO - Epoch(train) [93][1800/1879] lr: 7.4454e-07 eta: 7:56:00 time: 2.1629 data_time: 0.0400 memory: 48866 grad_norm: 6.3761 loss: 2.5855 loss_cls: 2.5855 2023/01/26 01:31:30 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 01:31:30 - mmengine - INFO - Epoch(train) [93][1879/1879] lr: 7.3644e-07 eta: 7:53:10 time: 2.0968 data_time: 0.0406 memory: 48866 grad_norm: 6.5971 loss: 2.4252 loss_cls: 2.4252 2023/01/26 01:31:30 - mmengine - INFO - Saving checkpoint at 93 epochs 2023/01/26 01:32:29 - mmengine - INFO - Epoch(val) [93][100/155] eta: 0:00:30 time: 0.5929 data_time: 0.2420 memory: 4950 2023/01/26 01:32:56 - mmengine - INFO - Epoch(val) [93][155/155] acc/top1: 0.7666 acc/top5: 0.9228 acc/mean1: 0.7665 2023/01/26 01:36:40 - mmengine - INFO - Epoch(train) [94][ 100/1879] lr: 7.2626e-07 eta: 7:49:34 time: 2.1596 data_time: 0.0537 memory: 48866 grad_norm: 6.3277 loss: 2.6069 loss_cls: 2.6069 2023/01/26 01:40:16 - mmengine - INFO - Epoch(train) [94][ 200/1879] lr: 7.1616e-07 eta: 7:45:59 time: 2.1732 data_time: 0.0399 memory: 48866 grad_norm: 6.2707 loss: 2.3347 loss_cls: 2.3347 2023/01/26 01:42:10 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 01:43:52 - mmengine - INFO - Epoch(train) [94][ 300/1879] lr: 7.0613e-07 eta: 7:42:23 time: 2.1588 data_time: 0.0390 memory: 48866 grad_norm: 6.1518 loss: 2.3800 loss_cls: 2.3800 2023/01/26 01:47:28 - mmengine - INFO - Epoch(train) [94][ 400/1879] lr: 6.9618e-07 eta: 7:38:47 time: 2.1458 data_time: 0.0394 memory: 48866 grad_norm: 6.3166 loss: 2.5723 loss_cls: 2.5723 2023/01/26 01:51:04 - mmengine - INFO - Epoch(train) [94][ 500/1879] lr: 6.8630e-07 eta: 7:35:11 time: 2.1634 data_time: 0.0398 memory: 48866 grad_norm: 6.4379 loss: 2.7118 loss_cls: 2.7118 2023/01/26 01:54:39 - mmengine - INFO - Epoch(train) [94][ 600/1879] lr: 6.7650e-07 eta: 7:31:35 time: 2.1468 data_time: 0.0396 memory: 48866 grad_norm: 6.8760 loss: 2.4465 loss_cls: 2.4465 2023/01/26 01:58:16 - mmengine - INFO - Epoch(train) [94][ 700/1879] lr: 6.6677e-07 eta: 7:27:59 time: 2.1585 data_time: 0.0408 memory: 48866 grad_norm: 6.3468 loss: 2.5400 loss_cls: 2.5400 2023/01/26 02:01:52 - mmengine - INFO - Epoch(train) [94][ 800/1879] lr: 6.5712e-07 eta: 7:24:24 time: 2.1711 data_time: 0.0410 memory: 48866 grad_norm: 6.4541 loss: 2.8436 loss_cls: 2.8436 2023/01/26 02:05:28 - mmengine - INFO - Epoch(train) [94][ 900/1879] lr: 6.4755e-07 eta: 7:20:48 time: 2.1561 data_time: 0.0395 memory: 48866 grad_norm: 6.3658 loss: 2.4189 loss_cls: 2.4189 2023/01/26 02:09:04 - mmengine - INFO - Epoch(train) [94][1000/1879] lr: 6.3805e-07 eta: 7:17:12 time: 2.1494 data_time: 0.0400 memory: 48866 grad_norm: 6.5185 loss: 2.4893 loss_cls: 2.4893 2023/01/26 02:12:40 - mmengine - INFO - Epoch(train) [94][1100/1879] lr: 6.2863e-07 eta: 7:13:36 time: 2.1660 data_time: 0.0410 memory: 48866 grad_norm: 6.5403 loss: 2.6318 loss_cls: 2.6318 2023/01/26 02:16:16 - mmengine - INFO - Epoch(train) [94][1200/1879] lr: 6.1929e-07 eta: 7:10:00 time: 2.1541 data_time: 0.0391 memory: 48866 grad_norm: 6.5839 loss: 2.7898 loss_cls: 2.7898 2023/01/26 02:18:10 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 02:19:51 - mmengine - INFO - Epoch(train) [94][1300/1879] lr: 6.1002e-07 eta: 7:06:24 time: 2.1500 data_time: 0.0404 memory: 48866 grad_norm: 6.5304 loss: 2.6163 loss_cls: 2.6163 2023/01/26 02:23:27 - mmengine - INFO - Epoch(train) [94][1400/1879] lr: 6.0083e-07 eta: 7:02:48 time: 2.1586 data_time: 0.0402 memory: 48866 grad_norm: 6.3917 loss: 2.6352 loss_cls: 2.6352 2023/01/26 02:27:03 - mmengine - INFO - Epoch(train) [94][1500/1879] lr: 5.9171e-07 eta: 6:59:13 time: 2.1635 data_time: 0.0410 memory: 48866 grad_norm: 6.5495 loss: 2.6047 loss_cls: 2.6047 2023/01/26 02:30:39 - mmengine - INFO - Epoch(train) [94][1600/1879] lr: 5.8267e-07 eta: 6:55:37 time: 2.1639 data_time: 0.0408 memory: 48866 grad_norm: 6.1362 loss: 2.6437 loss_cls: 2.6437 2023/01/26 02:34:15 - mmengine - INFO - Epoch(train) [94][1700/1879] lr: 5.7371e-07 eta: 6:52:01 time: 2.1605 data_time: 0.0398 memory: 48866 grad_norm: 6.5051 loss: 2.5868 loss_cls: 2.5868 2023/01/26 02:37:52 - mmengine - INFO - Epoch(train) [94][1800/1879] lr: 5.6482e-07 eta: 6:48:25 time: 2.1686 data_time: 0.0398 memory: 48866 grad_norm: 6.4911 loss: 2.6003 loss_cls: 2.6003 2023/01/26 02:40:41 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 02:40:41 - mmengine - INFO - Epoch(train) [94][1879/1879] lr: 5.5786e-07 eta: 6:45:35 time: 2.1057 data_time: 0.0397 memory: 48866 grad_norm: 6.5394 loss: 2.3766 loss_cls: 2.3766 2023/01/26 02:41:35 - mmengine - INFO - Epoch(val) [94][100/155] eta: 0:00:29 time: 0.5803 data_time: 0.2110 memory: 4950 2023/01/26 02:42:05 - mmengine - INFO - Epoch(val) [94][155/155] acc/top1: 0.7665 acc/top5: 0.9224 acc/mean1: 0.7664 2023/01/26 02:45:49 - mmengine - INFO - Epoch(train) [95][ 100/1879] lr: 5.4911e-07 eta: 6:41:59 time: 2.1483 data_time: 0.0402 memory: 48866 grad_norm: 6.4981 loss: 2.6872 loss_cls: 2.6872 2023/01/26 02:49:24 - mmengine - INFO - Epoch(train) [95][ 200/1879] lr: 5.4044e-07 eta: 6:38:23 time: 2.1511 data_time: 0.0399 memory: 48866 grad_norm: 5.7378 loss: 2.4883 loss_cls: 2.4883 2023/01/26 02:53:00 - mmengine - INFO - Epoch(train) [95][ 300/1879] lr: 5.3184e-07 eta: 6:34:47 time: 2.1607 data_time: 0.0386 memory: 48866 grad_norm: 6.4069 loss: 2.5189 loss_cls: 2.5189 2023/01/26 02:55:39 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 02:56:36 - mmengine - INFO - Epoch(train) [95][ 400/1879] lr: 5.2332e-07 eta: 6:31:12 time: 2.1619 data_time: 0.0399 memory: 48866 grad_norm: 6.7231 loss: 2.6656 loss_cls: 2.6656 2023/01/26 03:00:11 - mmengine - INFO - Epoch(train) [95][ 500/1879] lr: 5.1488e-07 eta: 6:27:36 time: 2.1540 data_time: 0.0396 memory: 48866 grad_norm: 6.5783 loss: 2.6166 loss_cls: 2.6166 2023/01/26 03:03:47 - mmengine - INFO - Epoch(train) [95][ 600/1879] lr: 5.0651e-07 eta: 6:24:00 time: 2.1652 data_time: 0.0403 memory: 48866 grad_norm: 6.6038 loss: 2.4419 loss_cls: 2.4419 2023/01/26 03:07:23 - mmengine - INFO - Epoch(train) [95][ 700/1879] lr: 4.9822e-07 eta: 6:20:24 time: 2.1482 data_time: 0.0405 memory: 48866 grad_norm: 6.6671 loss: 2.6507 loss_cls: 2.6507 2023/01/26 03:10:59 - mmengine - INFO - Epoch(train) [95][ 800/1879] lr: 4.9001e-07 eta: 6:16:48 time: 2.1531 data_time: 0.0405 memory: 48866 grad_norm: 6.6811 loss: 2.5047 loss_cls: 2.5047 2023/01/26 03:14:35 - mmengine - INFO - Epoch(train) [95][ 900/1879] lr: 4.8187e-07 eta: 6:13:12 time: 2.1668 data_time: 0.0401 memory: 48866 grad_norm: 6.1822 loss: 2.5093 loss_cls: 2.5093 2023/01/26 03:18:11 - mmengine - INFO - Epoch(train) [95][1000/1879] lr: 4.7381e-07 eta: 6:09:36 time: 2.1521 data_time: 0.0385 memory: 48866 grad_norm: 6.5689 loss: 2.2624 loss_cls: 2.2624 2023/01/26 03:21:46 - mmengine - INFO - Epoch(train) [95][1100/1879] lr: 4.6583e-07 eta: 6:06:01 time: 2.1534 data_time: 0.0410 memory: 48866 grad_norm: 6.2507 loss: 2.5658 loss_cls: 2.5658 2023/01/26 03:25:22 - mmengine - INFO - Epoch(train) [95][1200/1879] lr: 4.5792e-07 eta: 6:02:25 time: 2.1526 data_time: 0.0408 memory: 48866 grad_norm: 6.4741 loss: 2.7408 loss_cls: 2.7408 2023/01/26 03:28:58 - mmengine - INFO - Epoch(train) [95][1300/1879] lr: 4.5009e-07 eta: 5:58:49 time: 2.1567 data_time: 0.0398 memory: 48866 grad_norm: 6.7234 loss: 2.6118 loss_cls: 2.6118 2023/01/26 03:31:38 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 03:32:34 - mmengine - INFO - Epoch(train) [95][1400/1879] lr: 4.4234e-07 eta: 5:55:13 time: 2.1606 data_time: 0.0396 memory: 48866 grad_norm: 6.6522 loss: 2.5371 loss_cls: 2.5371 2023/01/26 03:36:10 - mmengine - INFO - Epoch(train) [95][1500/1879] lr: 4.3467e-07 eta: 5:51:37 time: 2.1649 data_time: 0.0403 memory: 48866 grad_norm: 6.5192 loss: 2.7291 loss_cls: 2.7291 2023/01/26 03:39:46 - mmengine - INFO - Epoch(train) [95][1600/1879] lr: 4.2707e-07 eta: 5:48:01 time: 2.1595 data_time: 0.0401 memory: 48866 grad_norm: 6.4699 loss: 2.4606 loss_cls: 2.4606 2023/01/26 03:43:22 - mmengine - INFO - Epoch(train) [95][1700/1879] lr: 4.1955e-07 eta: 5:44:25 time: 2.1718 data_time: 0.0415 memory: 48866 grad_norm: 6.3590 loss: 2.6190 loss_cls: 2.6190 2023/01/26 03:46:58 - mmengine - INFO - Epoch(train) [95][1800/1879] lr: 4.1210e-07 eta: 5:40:50 time: 2.1640 data_time: 0.0400 memory: 48866 grad_norm: 6.6869 loss: 2.4907 loss_cls: 2.4907 2023/01/26 03:49:47 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 03:49:47 - mmengine - INFO - Epoch(train) [95][1879/1879] lr: 4.0628e-07 eta: 5:37:59 time: 2.0893 data_time: 0.0422 memory: 48866 grad_norm: 6.6828 loss: 2.5402 loss_cls: 2.5402 2023/01/26 03:50:40 - mmengine - INFO - Epoch(val) [95][100/155] eta: 0:00:29 time: 0.5892 data_time: 0.2308 memory: 4950 2023/01/26 03:51:11 - mmengine - INFO - Epoch(val) [95][155/155] acc/top1: 0.7665 acc/top5: 0.9225 acc/mean1: 0.7664 2023/01/26 03:54:54 - mmengine - INFO - Epoch(train) [96][ 100/1879] lr: 3.9897e-07 eta: 5:34:24 time: 2.1605 data_time: 0.0393 memory: 48866 grad_norm: 6.2889 loss: 2.5979 loss_cls: 2.5979 2023/01/26 03:58:30 - mmengine - INFO - Epoch(train) [96][ 200/1879] lr: 3.9174e-07 eta: 5:30:48 time: 2.1691 data_time: 0.0389 memory: 48866 grad_norm: 6.2836 loss: 2.5976 loss_cls: 2.5976 2023/01/26 04:02:06 - mmengine - INFO - Epoch(train) [96][ 300/1879] lr: 3.8459e-07 eta: 5:27:12 time: 2.1773 data_time: 0.0407 memory: 48866 grad_norm: 6.1812 loss: 2.5484 loss_cls: 2.5484 2023/01/26 04:05:43 - mmengine - INFO - Epoch(train) [96][ 400/1879] lr: 3.7752e-07 eta: 5:23:36 time: 2.1747 data_time: 0.0394 memory: 48866 grad_norm: 6.3592 loss: 2.4588 loss_cls: 2.4588 2023/01/26 04:09:08 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 04:09:19 - mmengine - INFO - Epoch(train) [96][ 500/1879] lr: 3.7052e-07 eta: 5:20:00 time: 2.1689 data_time: 0.0397 memory: 48866 grad_norm: 6.6727 loss: 2.5258 loss_cls: 2.5258 2023/01/26 04:12:55 - mmengine - INFO - Epoch(train) [96][ 600/1879] lr: 3.6360e-07 eta: 5:16:24 time: 2.1570 data_time: 0.0393 memory: 48866 grad_norm: 6.7448 loss: 2.6390 loss_cls: 2.6390 2023/01/26 04:16:31 - mmengine - INFO - Epoch(train) [96][ 700/1879] lr: 3.5676e-07 eta: 5:12:48 time: 2.1596 data_time: 0.0410 memory: 48866 grad_norm: 6.4506 loss: 2.5310 loss_cls: 2.5310 2023/01/26 04:20:07 - mmengine - INFO - Epoch(train) [96][ 800/1879] lr: 3.5000e-07 eta: 5:09:13 time: 2.1580 data_time: 0.0405 memory: 48866 grad_norm: 6.4566 loss: 2.2971 loss_cls: 2.2971 2023/01/26 04:23:43 - mmengine - INFO - Epoch(train) [96][ 900/1879] lr: 3.4331e-07 eta: 5:05:37 time: 2.1658 data_time: 0.0407 memory: 48866 grad_norm: 6.5343 loss: 2.4508 loss_cls: 2.4508 2023/01/26 04:27:19 - mmengine - INFO - Epoch(train) [96][1000/1879] lr: 3.3670e-07 eta: 5:02:01 time: 2.1502 data_time: 0.0408 memory: 48866 grad_norm: 6.6753 loss: 2.5195 loss_cls: 2.5195 2023/01/26 04:30:55 - mmengine - INFO - Epoch(train) [96][1100/1879] lr: 3.3017e-07 eta: 4:58:25 time: 2.1510 data_time: 0.0392 memory: 48866 grad_norm: 6.2953 loss: 2.6157 loss_cls: 2.6157 2023/01/26 04:34:31 - mmengine - INFO - Epoch(train) [96][1200/1879] lr: 3.2371e-07 eta: 4:54:49 time: 2.1571 data_time: 0.0397 memory: 48866 grad_norm: 7.0405 loss: 2.4040 loss_cls: 2.4040 2023/01/26 04:38:07 - mmengine - INFO - Epoch(train) [96][1300/1879] lr: 3.1733e-07 eta: 4:51:13 time: 2.1568 data_time: 0.0408 memory: 48866 grad_norm: 6.2716 loss: 2.5123 loss_cls: 2.5123 2023/01/26 04:41:43 - mmengine - INFO - Epoch(train) [96][1400/1879] lr: 3.1103e-07 eta: 4:47:37 time: 2.1555 data_time: 0.0400 memory: 48866 grad_norm: 6.3837 loss: 2.4689 loss_cls: 2.4689 2023/01/26 04:45:09 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 04:45:19 - mmengine - INFO - Epoch(train) [96][1500/1879] lr: 3.0481e-07 eta: 4:44:02 time: 2.1569 data_time: 0.0405 memory: 48866 grad_norm: 6.4639 loss: 2.4451 loss_cls: 2.4451 2023/01/26 04:48:55 - mmengine - INFO - Epoch(train) [96][1600/1879] lr: 2.9867e-07 eta: 4:40:26 time: 2.1579 data_time: 0.0397 memory: 48866 grad_norm: 6.7156 loss: 2.5275 loss_cls: 2.5275 2023/01/26 04:52:32 - mmengine - INFO - Epoch(train) [96][1700/1879] lr: 2.9260e-07 eta: 4:36:50 time: 2.1719 data_time: 0.0403 memory: 48866 grad_norm: 6.5536 loss: 2.5568 loss_cls: 2.5568 2023/01/26 04:56:08 - mmengine - INFO - Epoch(train) [96][1800/1879] lr: 2.8661e-07 eta: 4:33:14 time: 2.1520 data_time: 0.0404 memory: 48866 grad_norm: 6.3775 loss: 2.7108 loss_cls: 2.7108 2023/01/26 04:58:57 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 04:58:57 - mmengine - INFO - Epoch(train) [96][1879/1879] lr: 2.8193e-07 eta: 4:30:23 time: 2.0942 data_time: 0.0422 memory: 48866 grad_norm: 6.2076 loss: 2.5719 loss_cls: 2.5719 2023/01/26 04:58:57 - mmengine - INFO - Saving checkpoint at 96 epochs 2023/01/26 04:59:56 - mmengine - INFO - Epoch(val) [96][100/155] eta: 0:00:30 time: 0.5561 data_time: 0.2180 memory: 4950 2023/01/26 05:00:24 - mmengine - INFO - Epoch(val) [96][155/155] acc/top1: 0.7659 acc/top5: 0.9229 acc/mean1: 0.7658 2023/01/26 05:04:07 - mmengine - INFO - Epoch(train) [97][ 100/1879] lr: 2.7608e-07 eta: 4:26:48 time: 2.1634 data_time: 0.0397 memory: 48866 grad_norm: 6.3780 loss: 2.4869 loss_cls: 2.4869 2023/01/26 05:07:43 - mmengine - INFO - Epoch(train) [97][ 200/1879] lr: 2.7031e-07 eta: 4:23:12 time: 2.1482 data_time: 0.0397 memory: 48866 grad_norm: 6.2878 loss: 2.4881 loss_cls: 2.4881 2023/01/26 05:11:18 - mmengine - INFO - Epoch(train) [97][ 300/1879] lr: 2.6462e-07 eta: 4:19:36 time: 2.1538 data_time: 0.0389 memory: 48866 grad_norm: 6.7460 loss: 2.7365 loss_cls: 2.7365 2023/01/26 05:14:54 - mmengine - INFO - Epoch(train) [97][ 400/1879] lr: 2.5900e-07 eta: 4:16:00 time: 2.1512 data_time: 0.0398 memory: 48866 grad_norm: 6.5203 loss: 2.6084 loss_cls: 2.6084 2023/01/26 05:18:30 - mmengine - INFO - Epoch(train) [97][ 500/1879] lr: 2.5346e-07 eta: 4:12:24 time: 2.1615 data_time: 0.0398 memory: 48866 grad_norm: 6.4365 loss: 2.6468 loss_cls: 2.6468 2023/01/26 05:22:06 - mmengine - INFO - Epoch(train) [97][ 600/1879] lr: 2.4800e-07 eta: 4:08:49 time: 2.1609 data_time: 0.0397 memory: 48866 grad_norm: 6.2719 loss: 2.5775 loss_cls: 2.5775 2023/01/26 05:22:41 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 05:25:42 - mmengine - INFO - Epoch(train) [97][ 700/1879] lr: 2.4262e-07 eta: 4:05:13 time: 2.1594 data_time: 0.0397 memory: 48866 grad_norm: 6.4324 loss: 2.4061 loss_cls: 2.4061 2023/01/26 05:29:18 - mmengine - INFO - Epoch(train) [97][ 800/1879] lr: 2.3731e-07 eta: 4:01:37 time: 2.1562 data_time: 0.0392 memory: 48866 grad_norm: 6.6991 loss: 2.4537 loss_cls: 2.4537 2023/01/26 05:32:54 - mmengine - INFO - Epoch(train) [97][ 900/1879] lr: 2.3208e-07 eta: 3:58:01 time: 2.1622 data_time: 0.0404 memory: 48866 grad_norm: 6.4561 loss: 2.6131 loss_cls: 2.6131 2023/01/26 05:36:31 - mmengine - INFO - Epoch(train) [97][1000/1879] lr: 2.2693e-07 eta: 3:54:25 time: 2.1713 data_time: 0.0410 memory: 48866 grad_norm: 6.1947 loss: 2.4819 loss_cls: 2.4819 2023/01/26 05:40:07 - mmengine - INFO - Epoch(train) [97][1100/1879] lr: 2.2186e-07 eta: 3:50:49 time: 2.1544 data_time: 0.0407 memory: 48866 grad_norm: 6.5037 loss: 2.4735 loss_cls: 2.4735 2023/01/26 05:43:44 - mmengine - INFO - Epoch(train) [97][1200/1879] lr: 2.1687e-07 eta: 3:47:13 time: 2.1603 data_time: 0.0401 memory: 48866 grad_norm: 6.4406 loss: 2.5525 loss_cls: 2.5525 2023/01/26 05:47:19 - mmengine - INFO - Epoch(train) [97][1300/1879] lr: 2.1195e-07 eta: 3:43:38 time: 2.1476 data_time: 0.0397 memory: 48866 grad_norm: 6.8403 loss: 2.7096 loss_cls: 2.7096 2023/01/26 05:50:55 - mmengine - INFO - Epoch(train) [97][1400/1879] lr: 2.0711e-07 eta: 3:40:02 time: 2.1664 data_time: 0.0405 memory: 48866 grad_norm: 6.5392 loss: 2.5198 loss_cls: 2.5198 2023/01/26 05:54:31 - mmengine - INFO - Epoch(train) [97][1500/1879] lr: 2.0235e-07 eta: 3:36:26 time: 2.1529 data_time: 0.0397 memory: 48866 grad_norm: 6.5662 loss: 2.6362 loss_cls: 2.6362 2023/01/26 05:58:07 - mmengine - INFO - Epoch(train) [97][1600/1879] lr: 1.9767e-07 eta: 3:32:50 time: 2.1543 data_time: 0.0405 memory: 48866 grad_norm: 6.4615 loss: 2.5838 loss_cls: 2.5838 2023/01/26 05:58:42 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 06:01:43 - mmengine - INFO - Epoch(train) [97][1700/1879] lr: 1.9307e-07 eta: 3:29:14 time: 2.1585 data_time: 0.0396 memory: 48866 grad_norm: 6.6617 loss: 2.4832 loss_cls: 2.4832 2023/01/26 06:05:19 - mmengine - INFO - Epoch(train) [97][1800/1879] lr: 1.8854e-07 eta: 3:25:38 time: 2.1725 data_time: 0.0409 memory: 48866 grad_norm: 6.4370 loss: 2.4646 loss_cls: 2.4646 2023/01/26 06:08:09 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 06:08:09 - mmengine - INFO - Epoch(train) [97][1879/1879] lr: 1.8502e-07 eta: 3:22:48 time: 2.1033 data_time: 0.0408 memory: 48866 grad_norm: 6.4727 loss: 2.5539 loss_cls: 2.5539 2023/01/26 06:09:02 - mmengine - INFO - Epoch(val) [97][100/155] eta: 0:00:29 time: 0.5441 data_time: 0.2092 memory: 4950 2023/01/26 06:09:33 - mmengine - INFO - Epoch(val) [97][155/155] acc/top1: 0.7671 acc/top5: 0.9227 acc/mean1: 0.7671 2023/01/26 06:09:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_92.pth is removed 2023/01/26 06:09:36 - mmengine - INFO - The best checkpoint with 0.7671 acc/top1 at 97 epoch is saved to best_acc/top1_epoch_97.pth. 2023/01/26 06:13:20 - mmengine - INFO - Epoch(train) [98][ 100/1879] lr: 1.8064e-07 eta: 3:19:12 time: 2.1496 data_time: 0.0397 memory: 48866 grad_norm: 6.6220 loss: 2.6436 loss_cls: 2.6436 2023/01/26 06:16:55 - mmengine - INFO - Epoch(train) [98][ 200/1879] lr: 1.7633e-07 eta: 3:15:36 time: 2.1662 data_time: 0.0401 memory: 48866 grad_norm: 6.6438 loss: 2.4044 loss_cls: 2.4044 2023/01/26 06:20:31 - mmengine - INFO - Epoch(train) [98][ 300/1879] lr: 1.7210e-07 eta: 3:12:00 time: 2.1485 data_time: 0.0398 memory: 48866 grad_norm: 6.5396 loss: 2.5604 loss_cls: 2.5604 2023/01/26 06:24:07 - mmengine - INFO - Epoch(train) [98][ 400/1879] lr: 1.6795e-07 eta: 3:08:24 time: 2.1641 data_time: 0.0400 memory: 48866 grad_norm: 6.3503 loss: 2.4260 loss_cls: 2.4260 2023/01/26 06:27:43 - mmengine - INFO - Epoch(train) [98][ 500/1879] lr: 1.6387e-07 eta: 3:04:49 time: 2.1519 data_time: 0.0399 memory: 48866 grad_norm: 6.5743 loss: 2.6187 loss_cls: 2.6187 2023/01/26 06:31:19 - mmengine - INFO - Epoch(train) [98][ 600/1879] lr: 1.5988e-07 eta: 3:01:13 time: 2.1672 data_time: 0.0405 memory: 48866 grad_norm: 6.2376 loss: 2.5899 loss_cls: 2.5899 2023/01/26 06:34:56 - mmengine - INFO - Epoch(train) [98][ 700/1879] lr: 1.5596e-07 eta: 2:57:37 time: 2.1684 data_time: 0.0406 memory: 48866 grad_norm: 6.3646 loss: 2.5298 loss_cls: 2.5298 2023/01/26 06:36:16 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 06:38:32 - mmengine - INFO - Epoch(train) [98][ 800/1879] lr: 1.5212e-07 eta: 2:54:01 time: 2.1632 data_time: 0.0403 memory: 48866 grad_norm: 6.3914 loss: 2.6600 loss_cls: 2.6600 2023/01/26 06:42:08 - mmengine - INFO - Epoch(train) [98][ 900/1879] lr: 1.4836e-07 eta: 2:50:25 time: 2.1550 data_time: 0.0404 memory: 48866 grad_norm: 6.2109 loss: 2.6304 loss_cls: 2.6304 2023/01/26 06:45:43 - mmengine - INFO - Epoch(train) [98][1000/1879] lr: 1.4468e-07 eta: 2:46:49 time: 2.1481 data_time: 0.0397 memory: 48866 grad_norm: 6.2836 loss: 2.4827 loss_cls: 2.4827 2023/01/26 06:49:19 - mmengine - INFO - Epoch(train) [98][1100/1879] lr: 1.4108e-07 eta: 2:43:13 time: 2.1500 data_time: 0.0408 memory: 48866 grad_norm: 6.3106 loss: 2.4759 loss_cls: 2.4759 2023/01/26 06:52:55 - mmengine - INFO - Epoch(train) [98][1200/1879] lr: 1.3755e-07 eta: 2:39:38 time: 2.1711 data_time: 0.0391 memory: 48866 grad_norm: 6.3158 loss: 2.5814 loss_cls: 2.5814 2023/01/26 06:56:31 - mmengine - INFO - Epoch(train) [98][1300/1879] lr: 1.3410e-07 eta: 2:36:02 time: 2.1624 data_time: 0.0409 memory: 48866 grad_norm: 6.4962 loss: 2.6852 loss_cls: 2.6852 2023/01/26 07:00:06 - mmengine - INFO - Epoch(train) [98][1400/1879] lr: 1.3074e-07 eta: 2:32:26 time: 2.1674 data_time: 0.0404 memory: 48866 grad_norm: 6.1766 loss: 2.4959 loss_cls: 2.4959 2023/01/26 07:03:43 - mmengine - INFO - Epoch(train) [98][1500/1879] lr: 1.2744e-07 eta: 2:28:50 time: 2.1562 data_time: 0.0391 memory: 48866 grad_norm: 6.5951 loss: 2.5115 loss_cls: 2.5115 2023/01/26 07:07:19 - mmengine - INFO - Epoch(train) [98][1600/1879] lr: 1.2423e-07 eta: 2:25:14 time: 2.1647 data_time: 0.0400 memory: 48866 grad_norm: 6.6001 loss: 2.7390 loss_cls: 2.7390 2023/01/26 07:10:55 - mmengine - INFO - Epoch(train) [98][1700/1879] lr: 1.2110e-07 eta: 2:21:38 time: 2.1618 data_time: 0.0399 memory: 48866 grad_norm: 6.1796 loss: 2.5529 loss_cls: 2.5529 2023/01/26 07:12:15 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 07:14:31 - mmengine - INFO - Epoch(train) [98][1800/1879] lr: 1.1804e-07 eta: 2:18:02 time: 2.1579 data_time: 0.0402 memory: 48866 grad_norm: 6.6240 loss: 2.5942 loss_cls: 2.5942 2023/01/26 07:17:21 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 07:17:21 - mmengine - INFO - Epoch(train) [98][1879/1879] lr: 1.1569e-07 eta: 2:15:12 time: 2.0941 data_time: 0.0421 memory: 48866 grad_norm: 6.7541 loss: 2.6482 loss_cls: 2.6482 2023/01/26 07:18:14 - mmengine - INFO - Epoch(val) [98][100/155] eta: 0:00:29 time: 0.5569 data_time: 0.1971 memory: 4950 2023/01/26 07:18:45 - mmengine - INFO - Epoch(val) [98][155/155] acc/top1: 0.7681 acc/top5: 0.9233 acc/mean1: 0.7681 2023/01/26 07:18:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_97.pth is removed 2023/01/26 07:18:48 - mmengine - INFO - The best checkpoint with 0.7681 acc/top1 at 98 epoch is saved to best_acc/top1_epoch_98.pth. 2023/01/26 07:22:32 - mmengine - INFO - Epoch(train) [99][ 100/1879] lr: 1.1277e-07 eta: 2:11:36 time: 2.1544 data_time: 0.0401 memory: 48866 grad_norm: 6.2022 loss: 2.6561 loss_cls: 2.6561 2023/01/26 07:26:08 - mmengine - INFO - Epoch(train) [99][ 200/1879] lr: 1.0993e-07 eta: 2:08:00 time: 2.1560 data_time: 0.0389 memory: 48866 grad_norm: 6.2691 loss: 2.5348 loss_cls: 2.5348 2023/01/26 07:29:44 - mmengine - INFO - Epoch(train) [99][ 300/1879] lr: 1.0718e-07 eta: 2:04:24 time: 2.1606 data_time: 0.0379 memory: 48866 grad_norm: 6.4615 loss: 2.4934 loss_cls: 2.4934 2023/01/26 07:33:20 - mmengine - INFO - Epoch(train) [99][ 400/1879] lr: 1.0450e-07 eta: 2:00:49 time: 2.1585 data_time: 0.0396 memory: 48866 grad_norm: 6.4764 loss: 2.7172 loss_cls: 2.7172 2023/01/26 07:36:56 - mmengine - INFO - Epoch(train) [99][ 500/1879] lr: 1.0190e-07 eta: 1:57:13 time: 2.1636 data_time: 0.0404 memory: 48866 grad_norm: 6.4055 loss: 2.3045 loss_cls: 2.3045 2023/01/26 07:40:31 - mmengine - INFO - Epoch(train) [99][ 600/1879] lr: 9.9373e-08 eta: 1:53:37 time: 2.1572 data_time: 0.0394 memory: 48866 grad_norm: 6.4605 loss: 2.5593 loss_cls: 2.5593 2023/01/26 07:44:08 - mmengine - INFO - Epoch(train) [99][ 700/1879] lr: 9.6929e-08 eta: 1:50:01 time: 2.1532 data_time: 0.0399 memory: 48866 grad_norm: 6.6260 loss: 2.4341 loss_cls: 2.4341 2023/01/26 07:47:44 - mmengine - INFO - Epoch(train) [99][ 800/1879] lr: 9.4564e-08 eta: 1:46:25 time: 2.1562 data_time: 0.0391 memory: 48866 grad_norm: 6.7529 loss: 2.5650 loss_cls: 2.5650 2023/01/26 07:49:50 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 07:51:20 - mmengine - INFO - Epoch(train) [99][ 900/1879] lr: 9.2277e-08 eta: 1:42:49 time: 2.1489 data_time: 0.0394 memory: 48866 grad_norm: 6.6605 loss: 2.6254 loss_cls: 2.6254 2023/01/26 07:54:56 - mmengine - INFO - Epoch(train) [99][1000/1879] lr: 9.0068e-08 eta: 1:39:13 time: 2.1527 data_time: 0.0405 memory: 48866 grad_norm: 6.1835 loss: 2.5113 loss_cls: 2.5113 2023/01/26 07:58:32 - mmengine - INFO - Epoch(train) [99][1100/1879] lr: 8.7938e-08 eta: 1:35:37 time: 2.1556 data_time: 0.0401 memory: 48866 grad_norm: 6.5712 loss: 2.5362 loss_cls: 2.5362 2023/01/26 08:02:08 - mmengine - INFO - Epoch(train) [99][1200/1879] lr: 8.5887e-08 eta: 1:32:02 time: 2.1564 data_time: 0.0399 memory: 48866 grad_norm: 6.1211 loss: 2.3936 loss_cls: 2.3936 2023/01/26 08:05:45 - mmengine - INFO - Epoch(train) [99][1300/1879] lr: 8.3914e-08 eta: 1:28:26 time: 2.1505 data_time: 0.0403 memory: 48866 grad_norm: 6.4723 loss: 2.5275 loss_cls: 2.5275 2023/01/26 08:09:21 - mmengine - INFO - Epoch(train) [99][1400/1879] lr: 8.2020e-08 eta: 1:24:50 time: 2.1544 data_time: 0.0392 memory: 48866 grad_norm: 6.4930 loss: 2.6588 loss_cls: 2.6588 2023/01/26 08:12:56 - mmengine - INFO - Epoch(train) [99][1500/1879] lr: 8.0204e-08 eta: 1:21:14 time: 2.1545 data_time: 0.0397 memory: 48866 grad_norm: 6.8732 loss: 2.4458 loss_cls: 2.4458 2023/01/26 08:16:32 - mmengine - INFO - Epoch(train) [99][1600/1879] lr: 7.8467e-08 eta: 1:17:38 time: 2.1662 data_time: 0.0399 memory: 48866 grad_norm: 6.5266 loss: 2.5867 loss_cls: 2.5867 2023/01/26 08:20:08 - mmengine - INFO - Epoch(train) [99][1700/1879] lr: 7.6808e-08 eta: 1:14:02 time: 2.1548 data_time: 0.0395 memory: 48866 grad_norm: 6.6168 loss: 2.5816 loss_cls: 2.5816 2023/01/26 08:23:44 - mmengine - INFO - Epoch(train) [99][1800/1879] lr: 7.5228e-08 eta: 1:10:26 time: 2.1500 data_time: 0.0402 memory: 48866 grad_norm: 6.4395 loss: 2.2974 loss_cls: 2.2974 2023/01/26 08:25:49 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 08:26:33 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 08:26:33 - mmengine - INFO - Epoch(train) [99][1879/1879] lr: 7.4036e-08 eta: 1:07:36 time: 2.0992 data_time: 0.0412 memory: 48866 grad_norm: 6.9763 loss: 2.6769 loss_cls: 2.6769 2023/01/26 08:26:33 - mmengine - INFO - Saving checkpoint at 99 epochs 2023/01/26 08:27:33 - mmengine - INFO - Epoch(val) [99][100/155] eta: 0:00:30 time: 0.5804 data_time: 0.2358 memory: 4950 2023/01/26 08:27:59 - mmengine - INFO - Epoch(val) [99][155/155] acc/top1: 0.7689 acc/top5: 0.9234 acc/mean1: 0.7689 2023/01/26 08:27:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/fangyixiao/work_dirs/benchmarks/maskfeat/20230121_training_maskfeat-mvit-k400/best_acc/top1_epoch_98.pth is removed 2023/01/26 08:28:03 - mmengine - INFO - The best checkpoint with 0.7689 acc/top1 at 99 epoch is saved to best_acc/top1_epoch_99.pth. 2023/01/26 08:31:46 - mmengine - INFO - Epoch(train) [100][ 100/1879] lr: 7.2597e-08 eta: 1:04:00 time: 2.1611 data_time: 0.0399 memory: 48866 grad_norm: 6.4882 loss: 2.4859 loss_cls: 2.4859 2023/01/26 08:35:22 - mmengine - INFO - Epoch(train) [100][ 200/1879] lr: 7.1236e-08 eta: 1:00:24 time: 2.1593 data_time: 0.0392 memory: 48866 grad_norm: 6.6881 loss: 2.5681 loss_cls: 2.5681 2023/01/26 08:38:58 - mmengine - INFO - Epoch(train) [100][ 300/1879] lr: 6.9954e-08 eta: 0:56:48 time: 2.1603 data_time: 0.0394 memory: 48866 grad_norm: 6.4607 loss: 2.6782 loss_cls: 2.6782 2023/01/26 08:42:34 - mmengine - INFO - Epoch(train) [100][ 400/1879] lr: 6.8751e-08 eta: 0:53:12 time: 2.1685 data_time: 0.0396 memory: 48866 grad_norm: 6.1396 loss: 2.5065 loss_cls: 2.5065 2023/01/26 08:46:10 - mmengine - INFO - Epoch(train) [100][ 500/1879] lr: 6.7626e-08 eta: 0:49:36 time: 2.1665 data_time: 0.0400 memory: 48866 grad_norm: 7.1707 loss: 2.5746 loss_cls: 2.5746 2023/01/26 08:49:46 - mmengine - INFO - Epoch(train) [100][ 600/1879] lr: 6.6580e-08 eta: 0:46:01 time: 2.1590 data_time: 0.0401 memory: 48866 grad_norm: 6.3308 loss: 2.4758 loss_cls: 2.4758 2023/01/26 08:53:21 - mmengine - INFO - Epoch(train) [100][ 700/1879] lr: 6.5612e-08 eta: 0:42:25 time: 2.1538 data_time: 0.0402 memory: 48866 grad_norm: 6.5698 loss: 2.4924 loss_cls: 2.4924 2023/01/26 08:56:58 - mmengine - INFO - Epoch(train) [100][ 800/1879] lr: 6.4723e-08 eta: 0:38:49 time: 2.1617 data_time: 0.0402 memory: 48866 grad_norm: 6.2995 loss: 2.3704 loss_cls: 2.3704 2023/01/26 09:00:34 - mmengine - INFO - Epoch(train) [100][ 900/1879] lr: 6.3913e-08 eta: 0:35:13 time: 2.1650 data_time: 0.0402 memory: 48866 grad_norm: 6.9044 loss: 2.7961 loss_cls: 2.7961 2023/01/26 09:03:24 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 09:04:09 - mmengine - INFO - Epoch(train) [100][1000/1879] lr: 6.3181e-08 eta: 0:31:37 time: 2.1474 data_time: 0.0402 memory: 48866 grad_norm: 6.1858 loss: 2.5448 loss_cls: 2.5448 2023/01/26 09:07:45 - mmengine - INFO - Epoch(train) [100][1100/1879] lr: 6.2529e-08 eta: 0:28:01 time: 2.1639 data_time: 0.0412 memory: 48866 grad_norm: 6.5384 loss: 2.6740 loss_cls: 2.6740 2023/01/26 09:11:21 - mmengine - INFO - Epoch(train) [100][1200/1879] lr: 6.1954e-08 eta: 0:24:25 time: 2.1604 data_time: 0.0401 memory: 48866 grad_norm: 6.7725 loss: 2.6193 loss_cls: 2.6193 2023/01/26 09:14:57 - mmengine - INFO - Epoch(train) [100][1300/1879] lr: 6.1459e-08 eta: 0:20:49 time: 2.1564 data_time: 0.0402 memory: 48866 grad_norm: 6.2221 loss: 2.4926 loss_cls: 2.4926 2023/01/26 09:18:33 - mmengine - INFO - Epoch(train) [100][1400/1879] lr: 6.1042e-08 eta: 0:17:14 time: 2.1538 data_time: 0.0404 memory: 48866 grad_norm: 6.2757 loss: 2.3577 loss_cls: 2.3577 2023/01/26 09:22:08 - mmengine - INFO - Epoch(train) [100][1500/1879] lr: 6.0704e-08 eta: 0:13:38 time: 2.1565 data_time: 0.0404 memory: 48866 grad_norm: 6.2253 loss: 2.4139 loss_cls: 2.4139 2023/01/26 09:25:45 - mmengine - INFO - Epoch(train) [100][1600/1879] lr: 6.0444e-08 eta: 0:10:02 time: 2.1645 data_time: 0.0393 memory: 48866 grad_norm: 6.8015 loss: 2.3761 loss_cls: 2.3761 2023/01/26 09:29:21 - mmengine - INFO - Epoch(train) [100][1700/1879] lr: 6.0263e-08 eta: 0:06:26 time: 2.1609 data_time: 0.0401 memory: 48866 grad_norm: 6.4173 loss: 2.6470 loss_cls: 2.6470 2023/01/26 09:32:57 - mmengine - INFO - Epoch(train) [100][1800/1879] lr: 6.0161e-08 eta: 0:02:50 time: 2.1640 data_time: 0.0400 memory: 48866 grad_norm: 6.1387 loss: 2.4865 loss_cls: 2.4865 2023/01/26 09:35:47 - mmengine - INFO - Exp name: mvit-small_ft-8xb16-coslr-100e_k400_20230121_142927 2023/01/26 09:35:47 - mmengine - INFO - Epoch(train) [100][1879/1879] lr: 6.0136e-08 eta: 0:00:00 time: 2.1171 data_time: 0.0416 memory: 48866 grad_norm: 6.7513 loss: 2.6393 loss_cls: 2.6393 2023/01/26 09:35:47 - mmengine - INFO - Saving checkpoint at 100 epochs 2023/01/26 09:36:47 - mmengine - INFO - Epoch(val) [100][100/155] eta: 0:00:30 time: 0.5833 data_time: 0.2355 memory: 4950 2023/01/26 09:37:15 - mmengine - INFO - Epoch(val) [100][155/155] acc/top1: 0.7676 acc/top5: 0.9235 acc/mean1: 0.7676