| 2023/05/31 17:27:44 - mmengine - INFO - | |
| ------------------------------------------------------------ | |
| System environment: | |
| sys.platform: linux | |
| Python: 3.10.9 (main, Mar 8 2023, 10:47:38) [GCC 11.2.0] | |
| CUDA available: True | |
| numpy_random_seed: 622910272 | |
| GPU 0,1,2,3: 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) 4.8.5 20150623 (Red Hat 4.8.5-44) | |
| 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: 0.7.3 | |
| Runtime environment: | |
| cudnn_benchmark: True | |
| mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} | |
| dist_cfg: {'backend': 'nccl'} | |
| seed: None | |
| deterministic: False | |
| Distributed launcher: slurm | |
| Distributed training: True | |
| GPU number: 4 | |
| ------------------------------------------------------------ | |
| 2023/05/31 17:27:44 - mmengine - INFO - Config: | |
| optim_wrapper = dict( | |
| optimizer=dict( | |
| type='AdamW', | |
| lr=0.004, | |
| weight_decay=0.05, | |
| eps=1e-08, | |
| betas=(0.9, 0.999), | |
| _scope_='mmpretrain'), | |
| paramwise_cfg=dict( | |
| norm_decay_mult=0.0, | |
| bias_decay_mult=0.0, | |
| flat_decay_mult=0.0, | |
| custom_keys=dict({ | |
| '.absolute_pos_embed': dict(decay_mult=0.0), | |
| '.relative_position_bias_table': dict(decay_mult=0.0) | |
| })), | |
| type='AmpOptimWrapper', | |
| dtype='bfloat16', | |
| clip_grad=None) | |
| param_scheduler = [ | |
| dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=True, begin=0) | |
| ] | |
| train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=1) | |
| val_cfg = dict() | |
| test_cfg = dict() | |
| auto_scale_lr = dict(base_batch_size=4096) | |
| model = dict( | |
| type='ImageClassifier', | |
| backbone=dict(type='ConvNeXt', arch='tiny', drop_path_rate=0.1), | |
| head=dict( | |
| type='LinearClsHead', | |
| num_classes=2, | |
| in_channels=768, | |
| loss=dict( | |
| type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), | |
| init_cfg=None), | |
| init_cfg=dict( | |
| type='TruncNormal', layer=['Conv2d', 'Linear'], std=0.02, bias=0.0), | |
| train_cfg=None) | |
| dataset_type = 'CustomDataset' | |
| data_preprocessor = dict( | |
| num_classes=2, | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True) | |
| bgr_mean = [103.53, 116.28, 123.675] | |
| bgr_std = [57.375, 57.12, 58.395] | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='PackInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='ResizeEdge', | |
| scale=256, | |
| edge='short', | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='CenterCrop', crop_size=224), | |
| dict(type='PackInputs') | |
| ] | |
| train_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=1024, | |
| num_workers=10, | |
| dataset=dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stablediffusionV1-5R2-dpmsolver-25-5m.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='PackInputs') | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=True)) | |
| val_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-dpmsolver-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='ResizeEdge', | |
| scale=256, | |
| edge='short', | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='CenterCrop', crop_size=224), | |
| dict(type='PackInputs') | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=False)) | |
| val_evaluator = [ | |
| dict(type='Accuracy', topk=1), | |
| dict(type='SingleLabelMetric', average=None) | |
| ] | |
| test_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-dpmsolver-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='ResizeEdge', | |
| scale=256, | |
| edge='short', | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='CenterCrop', crop_size=224), | |
| dict(type='PackInputs') | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=False)) | |
| test_evaluator = [ | |
| dict(type='Accuracy', topk=1), | |
| dict(type='SingleLabelMetric', average=None) | |
| ] | |
| custom_hooks = [dict(type='EMAHook', momentum=0.0001, priority='ABOVE_NORMAL')] | |
| default_scope = 'mmpretrain' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=100), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=1), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| visualization=dict(type='VisualizationHook', enable=True)) | |
| env_cfg = dict( | |
| cudnn_benchmark=True, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl')) | |
| vis_backends = [dict(type='LocalVisBackend')] | |
| visualizer = dict( | |
| type='UniversalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| dict(type='TensorboardVisBackend') | |
| ]) | |
| log_level = 'INFO' | |
| load_from = None | |
| resume = False | |
| randomness = dict(seed=None, deterministic=False) | |
| launcher = 'slurm' | |
| work_dir = 'workdir/convnext_tiny_4xb1024_4e-3lr_5m' | |
| 2023/05/31 17:27:48 - mmengine - INFO - Hooks will be executed in the following order: | |
| before_run: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (ABOVE_NORMAL) EMAHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| after_load_checkpoint: | |
| (ABOVE_NORMAL) EMAHook | |
| -------------------- | |
| before_train: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (ABOVE_NORMAL) EMAHook | |
| (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 | |
| (ABOVE_NORMAL) EMAHook | |
| (NORMAL ) IterTimerHook | |
| (BELOW_NORMAL) LoggerHook | |
| (LOW ) ParamSchedulerHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| after_train_epoch: | |
| (NORMAL ) IterTimerHook | |
| (LOW ) ParamSchedulerHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| before_val_epoch: | |
| (ABOVE_NORMAL) EMAHook | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| before_val_iter: | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| after_val_iter: | |
| (NORMAL ) IterTimerHook | |
| (NORMAL ) VisualizationHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| after_val_epoch: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (ABOVE_NORMAL) EMAHook | |
| (NORMAL ) IterTimerHook | |
| (BELOW_NORMAL) LoggerHook | |
| (LOW ) ParamSchedulerHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| before_save_checkpoint: | |
| (ABOVE_NORMAL) EMAHook | |
| -------------------- | |
| after_train: | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| before_test_epoch: | |
| (ABOVE_NORMAL) EMAHook | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| before_test_iter: | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| after_test_iter: | |
| (NORMAL ) IterTimerHook | |
| (NORMAL ) VisualizationHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| after_test_epoch: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (ABOVE_NORMAL) EMAHook | |
| (NORMAL ) IterTimerHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| after_run: | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.0.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.1.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.0.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.0.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.0.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.0.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.0.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.0.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.3.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.3.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.3.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.3.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.3.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.3.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.gamma:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.norm.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.norm.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.norm3.weight:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- backbone.norm3.bias:weight_decay=0.0 | |
| 2023/05/31 17:28:15 - mmengine - INFO - paramwise_options -- head.fc.bias:weight_decay=0.0 | |
| Name of parameter - Initialization information | |
| backbone.downsample_layers.0.0.weight - torch.Size([96, 3, 4, 4]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.downsample_layers.0.0.bias - torch.Size([96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.downsample_layers.0.1.weight - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.0.1.bias - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.1.0.weight - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.1.0.bias - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.1.1.weight - torch.Size([192, 96, 2, 2]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.downsample_layers.1.1.bias - torch.Size([192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.downsample_layers.2.0.weight - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.2.0.bias - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.2.1.weight - torch.Size([384, 192, 2, 2]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.downsample_layers.2.1.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.downsample_layers.3.0.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.3.0.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.downsample_layers.3.1.weight - torch.Size([768, 384, 2, 2]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.downsample_layers.3.1.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.0.gamma - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.0.depthwise_conv.weight - torch.Size([96, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.0.depthwise_conv.bias - torch.Size([96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.0.norm.weight - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.0.norm.bias - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.0.pointwise_conv1.weight - torch.Size([384, 96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.0.pointwise_conv1.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.0.pointwise_conv2.weight - torch.Size([96, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.0.pointwise_conv2.bias - torch.Size([96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.1.gamma - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.1.depthwise_conv.weight - torch.Size([96, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.1.depthwise_conv.bias - torch.Size([96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.1.norm.weight - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.1.norm.bias - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.1.pointwise_conv1.weight - torch.Size([384, 96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.1.pointwise_conv1.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.1.pointwise_conv2.weight - torch.Size([96, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.1.pointwise_conv2.bias - torch.Size([96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.2.gamma - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.2.depthwise_conv.weight - torch.Size([96, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.2.depthwise_conv.bias - torch.Size([96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.2.norm.weight - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.2.norm.bias - torch.Size([96]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.0.2.pointwise_conv1.weight - torch.Size([384, 96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.2.pointwise_conv1.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.2.pointwise_conv2.weight - torch.Size([96, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.0.2.pointwise_conv2.bias - torch.Size([96]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.0.gamma - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.0.depthwise_conv.weight - torch.Size([192, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.0.depthwise_conv.bias - torch.Size([192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.0.norm.weight - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.0.norm.bias - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.0.pointwise_conv1.weight - torch.Size([768, 192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.0.pointwise_conv1.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.0.pointwise_conv2.weight - torch.Size([192, 768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.0.pointwise_conv2.bias - torch.Size([192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.1.gamma - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.1.depthwise_conv.weight - torch.Size([192, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.1.depthwise_conv.bias - torch.Size([192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.1.norm.weight - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.1.norm.bias - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.1.pointwise_conv1.weight - torch.Size([768, 192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.1.pointwise_conv1.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.1.pointwise_conv2.weight - torch.Size([192, 768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.1.pointwise_conv2.bias - torch.Size([192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.2.gamma - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.2.depthwise_conv.weight - torch.Size([192, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.2.depthwise_conv.bias - torch.Size([192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.2.norm.weight - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.2.norm.bias - torch.Size([192]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.1.2.pointwise_conv1.weight - torch.Size([768, 192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.2.pointwise_conv1.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.2.pointwise_conv2.weight - torch.Size([192, 768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.1.2.pointwise_conv2.bias - torch.Size([192]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.0.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.0.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.0.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.0.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.0.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.0.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.0.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.0.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.0.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.1.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.1.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.1.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.1.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.1.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.1.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.1.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.1.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.1.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.2.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.2.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.2.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.2.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.2.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.2.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.2.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.2.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.2.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.3.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.3.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.3.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.3.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.3.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.3.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.3.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.3.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.3.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.4.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.4.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.4.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.4.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.4.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.4.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.4.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.4.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.4.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.5.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.5.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.5.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.5.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.5.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.5.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.5.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.5.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.5.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.6.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.6.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.6.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.6.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.6.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.6.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.6.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.6.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.6.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.7.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.7.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.7.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.7.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.7.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.7.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.7.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.7.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.7.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.8.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.8.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.8.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.8.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.8.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.8.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.8.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.8.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.8.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.0.gamma - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.0.depthwise_conv.weight - torch.Size([768, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.0.depthwise_conv.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.0.norm.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.0.norm.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.0.pointwise_conv1.weight - torch.Size([3072, 768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.0.pointwise_conv1.bias - torch.Size([3072]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.0.pointwise_conv2.weight - torch.Size([768, 3072]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.0.pointwise_conv2.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.1.gamma - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.1.depthwise_conv.weight - torch.Size([768, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.1.depthwise_conv.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.1.norm.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.1.norm.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.1.pointwise_conv1.weight - torch.Size([3072, 768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.1.pointwise_conv1.bias - torch.Size([3072]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.1.pointwise_conv2.weight - torch.Size([768, 3072]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.1.pointwise_conv2.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.2.gamma - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.2.depthwise_conv.weight - torch.Size([768, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.2.depthwise_conv.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.2.norm.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.2.norm.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.3.2.pointwise_conv1.weight - torch.Size([3072, 768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.2.pointwise_conv1.bias - torch.Size([3072]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.2.pointwise_conv2.weight - torch.Size([768, 3072]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.3.2.pointwise_conv2.bias - torch.Size([768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.norm3.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.norm3.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| head.fc.weight - torch.Size([2, 768]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| head.fc.bias - torch.Size([2]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| 2023/05/31 17:28:15 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io | |
| 2023/05/31 17:28:15 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. | |
| 2023/05/31 17:28:15 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/convnext_tiny_4xb1024_4e-3lr_5m. | |
| 2023/05/31 17:38:14 - mmengine - INFO - Epoch(train) [1][ 100/1440] lr: 4.0000e-03 eta: 1 day, 23:42:27 time: 4.3614 data_time: 1.7131 memory: 61143 loss: 0.6281 | |
| 2023/05/31 17:45:17 - mmengine - INFO - Epoch(train) [1][ 200/1440] lr: 4.0000e-03 eta: 1 day, 16:36:00 time: 4.2238 data_time: 1.3604 memory: 61143 loss: 0.6048 | |
| 2023/05/31 17:51:44 - mmengine - INFO - Epoch(train) [1][ 300/1440] lr: 4.0000e-03 eta: 1 day, 13:10:09 time: 3.8278 data_time: 0.8225 memory: 61143 loss: 0.5771 | |
| 2023/05/31 17:58:18 - mmengine - INFO - Epoch(train) [1][ 400/1440] lr: 4.0000e-03 eta: 1 day, 11:33:08 time: 4.0650 data_time: 1.9558 memory: 61143 loss: 0.5526 | |
| 2023/05/31 18:04:48 - mmengine - INFO - Epoch(train) [1][ 500/1440] lr: 4.0000e-03 eta: 1 day, 10:28:34 time: 3.9281 data_time: 2.1526 memory: 61143 loss: 0.5663 | |
| 2023/05/31 18:10:47 - mmengine - INFO - Epoch(train) [1][ 600/1440] lr: 4.0000e-03 eta: 1 day, 9:18:32 time: 3.4568 data_time: 1.6816 memory: 61143 loss: 0.5426 | |
| 2023/05/31 18:16:16 - mmengine - INFO - Epoch(train) [1][ 700/1440] lr: 4.0000e-03 eta: 1 day, 8:07:34 time: 3.1568 data_time: 1.3889 memory: 61143 loss: 0.5292 | |
| 2023/05/31 18:22:13 - mmengine - INFO - Epoch(train) [1][ 800/1440] lr: 4.0000e-03 eta: 1 day, 7:28:23 time: 3.6402 data_time: 1.8797 memory: 61143 loss: 0.5095 | |
| 2023/05/31 18:28:22 - mmengine - INFO - Epoch(train) [1][ 900/1440] lr: 4.0000e-03 eta: 1 day, 7:03:20 time: 3.6294 data_time: 1.8447 memory: 61143 loss: 0.4994 | |
| 2023/05/31 18:34:00 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 18:34:00 - mmengine - INFO - Epoch(train) [1][1000/1440] lr: 4.0000e-03 eta: 1 day, 6:27:45 time: 3.4610 data_time: 1.6932 memory: 61143 loss: 0.4899 | |
| 2023/05/31 18:39:54 - mmengine - INFO - Epoch(train) [1][1100/1440] lr: 4.0000e-03 eta: 1 day, 6:04:05 time: 3.3571 data_time: 1.6030 memory: 61143 loss: 0.4797 | |
| 2023/05/31 18:45:49 - mmengine - INFO - Epoch(train) [1][1200/1440] lr: 4.0000e-03 eta: 1 day, 5:43:43 time: 3.6645 data_time: 1.8829 memory: 61143 loss: 0.4687 | |
| 2023/05/31 18:52:21 - mmengine - INFO - Epoch(train) [1][1300/1440] lr: 4.0000e-03 eta: 1 day, 5:38:58 time: 3.8143 data_time: 2.0433 memory: 61143 loss: 0.4437 | |
| 2023/05/31 18:58:50 - mmengine - INFO - Epoch(train) [1][1400/1440] lr: 4.0000e-03 eta: 1 day, 5:32:39 time: 3.8834 data_time: 2.1126 memory: 61143 loss: 0.4518 | |
| 2023/05/31 19:01:19 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 19:01:19 - mmengine - INFO - Saving checkpoint at 1 epochs | |
| 2023/05/31 19:01:37 - mmengine - INFO - Epoch(val) [1][16/16] accuracy/top1: 54.3337 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [54.33369445800781, 0.0] single-label/f1-score_classwise: [70.41067504882812, 0.0] data_time: 0.2721 time: 0.6429 | |
| 2023/05/31 19:08:07 - mmengine - INFO - Epoch(train) [2][ 100/1440] lr: 3.9754e-03 eta: 1 day, 5:22:19 time: 4.2048 data_time: 2.4369 memory: 61146 loss: 0.4514 | |
| 2023/05/31 19:14:33 - mmengine - INFO - Epoch(train) [2][ 200/1440] lr: 3.9754e-03 eta: 1 day, 5:15:25 time: 3.5233 data_time: 1.7614 memory: 61145 loss: 0.4308 | |
| 2023/05/31 19:20:27 - mmengine - INFO - Epoch(train) [2][ 300/1440] lr: 3.9754e-03 eta: 1 day, 5:00:07 time: 3.4567 data_time: 1.6979 memory: 61145 loss: 0.4170 | |
| 2023/05/31 19:26:28 - mmengine - INFO - Epoch(train) [2][ 400/1440] lr: 3.9754e-03 eta: 1 day, 4:47:31 time: 3.7817 data_time: 1.9897 memory: 61145 loss: 0.4038 | |
| 2023/05/31 19:32:26 - mmengine - INFO - Epoch(train) [2][ 500/1440] lr: 3.9754e-03 eta: 1 day, 4:35:04 time: 3.5252 data_time: 1.7586 memory: 61145 loss: 0.3877 | |
| 2023/05/31 19:35:57 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 19:38:06 - mmengine - INFO - Epoch(train) [2][ 600/1440] lr: 3.9754e-03 eta: 1 day, 4:19:21 time: 3.1745 data_time: 1.4087 memory: 61145 loss: 0.3851 | |
| 2023/05/31 19:43:33 - mmengine - INFO - Epoch(train) [2][ 700/1440] lr: 3.9754e-03 eta: 1 day, 4:01:49 time: 3.2064 data_time: 1.4148 memory: 61145 loss: 0.4002 | |
| 2023/05/31 19:48:46 - mmengine - INFO - Epoch(train) [2][ 800/1440] lr: 3.9754e-03 eta: 1 day, 3:42:32 time: 2.9946 data_time: 1.2440 memory: 61145 loss: 0.3552 | |
| 2023/05/31 19:53:54 - mmengine - INFO - Epoch(train) [2][ 900/1440] lr: 3.9754e-03 eta: 1 day, 3:23:29 time: 3.0410 data_time: 1.2612 memory: 61145 loss: 0.3580 | |
| 2023/05/31 19:59:05 - mmengine - INFO - Epoch(train) [2][1000/1440] lr: 3.9754e-03 eta: 1 day, 3:06:07 time: 3.3049 data_time: 1.5412 memory: 61145 loss: 0.3450 | |
| 2023/05/31 20:04:25 - mmengine - INFO - Epoch(train) [2][1100/1440] lr: 3.9754e-03 eta: 1 day, 2:51:25 time: 3.1558 data_time: 1.3897 memory: 61145 loss: 0.3331 | |
| 2023/05/31 20:10:05 - mmengine - INFO - Epoch(train) [2][1200/1440] lr: 3.9754e-03 eta: 1 day, 2:40:30 time: 3.2056 data_time: 1.4384 memory: 61145 loss: 0.3146 | |
| 2023/05/31 20:15:44 - mmengine - INFO - Epoch(train) [2][1300/1440] lr: 3.9754e-03 eta: 1 day, 2:29:56 time: 3.3135 data_time: 1.5567 memory: 61145 loss: 0.3398 | |
| 2023/05/31 20:21:24 - mmengine - INFO - Epoch(train) [2][1400/1440] lr: 3.9754e-03 eta: 1 day, 2:19:58 time: 3.4440 data_time: 1.6529 memory: 61145 loss: 0.3053 | |
| 2023/05/31 20:23:38 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 20:23:38 - mmengine - INFO - Saving checkpoint at 2 epochs | |
| 2023/05/31 20:23:53 - mmengine - INFO - Epoch(val) [2][16/16] accuracy/top1: 73.4486 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [73.44864654541016, 0.0] single-label/f1-score_classwise: [84.69210052490234, 0.0] data_time: 0.2554 time: 0.4853 | |
| 2023/05/31 20:29:47 - mmengine - INFO - Epoch(train) [3][ 100/1440] lr: 3.9024e-03 eta: 1 day, 2:07:58 time: 3.5298 data_time: 1.6939 memory: 61145 loss: 0.2797 | |
| 2023/05/31 20:30:58 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 20:35:32 - mmengine - INFO - Epoch(train) [3][ 200/1440] lr: 3.9024e-03 eta: 1 day, 1:59:13 time: 3.3690 data_time: 1.2517 memory: 61145 loss: 0.2745 | |
| 2023/05/31 20:41:09 - mmengine - INFO - Epoch(train) [3][ 300/1440] lr: 3.9024e-03 eta: 1 day, 1:49:37 time: 3.4114 data_time: 1.6347 memory: 61145 loss: 0.2831 | |
| 2023/05/31 20:46:45 - mmengine - INFO - Epoch(train) [3][ 400/1440] lr: 3.9024e-03 eta: 1 day, 1:40:02 time: 3.3154 data_time: 1.5658 memory: 61145 loss: 0.2657 | |
| 2023/05/31 20:52:20 - mmengine - INFO - Epoch(train) [3][ 500/1440] lr: 3.9024e-03 eta: 1 day, 1:30:40 time: 3.2786 data_time: 1.5092 memory: 61145 loss: 0.2652 | |
| 2023/05/31 20:57:55 - mmengine - INFO - Epoch(train) [3][ 600/1440] lr: 3.9024e-03 eta: 1 day, 1:21:24 time: 3.4873 data_time: 1.7051 memory: 61145 loss: 0.2564 | |
| 2023/05/31 21:03:24 - mmengine - INFO - Epoch(train) [3][ 700/1440] lr: 3.9024e-03 eta: 1 day, 1:11:43 time: 3.1659 data_time: 1.3825 memory: 61145 loss: 0.2584 | |
| 2023/05/31 21:08:54 - mmengine - INFO - Epoch(train) [3][ 800/1440] lr: 3.9024e-03 eta: 1 day, 1:02:22 time: 3.3623 data_time: 1.5842 memory: 61145 loss: 0.2746 | |
| 2023/05/31 21:14:22 - mmengine - INFO - Epoch(train) [3][ 900/1440] lr: 3.9024e-03 eta: 1 day, 0:52:57 time: 3.2797 data_time: 1.4913 memory: 61145 loss: 0.4432 | |
| 2023/05/31 21:19:51 - mmengine - INFO - Epoch(train) [3][1000/1440] lr: 3.9024e-03 eta: 1 day, 0:43:51 time: 3.3689 data_time: 1.6024 memory: 61145 loss: 0.3017 | |
| 2023/05/31 21:25:26 - mmengine - INFO - Epoch(train) [3][1100/1440] lr: 3.9024e-03 eta: 1 day, 0:35:36 time: 3.7910 data_time: 2.0036 memory: 61145 loss: 0.2460 | |
| 2023/05/31 21:26:34 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 21:31:02 - mmengine - INFO - Epoch(train) [3][1200/1440] lr: 3.9024e-03 eta: 1 day, 0:27:34 time: 3.4165 data_time: 1.6337 memory: 61145 loss: 0.2390 | |
| 2023/05/31 21:36:41 - mmengine - INFO - Epoch(train) [3][1300/1440] lr: 3.9024e-03 eta: 1 day, 0:19:59 time: 3.4488 data_time: 1.6870 memory: 61145 loss: 0.2319 | |
| 2023/05/31 21:42:17 - mmengine - INFO - Epoch(train) [3][1400/1440] lr: 3.9024e-03 eta: 1 day, 0:12:07 time: 3.4106 data_time: 1.6247 memory: 61145 loss: 0.2377 | |
| 2023/05/31 21:44:31 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 21:44:31 - mmengine - INFO - Saving checkpoint at 3 epochs | |
| 2023/05/31 21:44:46 - mmengine - INFO - Epoch(val) [3][16/16] accuracy/top1: 80.7588 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [80.7587890625, 0.0] single-label/f1-score_classwise: [89.35530853271484, 0.0] data_time: 0.2534 time: 0.4867 | |
| 2023/05/31 21:50:32 - mmengine - INFO - Epoch(train) [4][ 100/1440] lr: 3.7826e-03 eta: 1 day, 0:02:14 time: 3.2469 data_time: 1.4799 memory: 61145 loss: 0.2305 | |
| 2023/05/31 21:56:01 - mmengine - INFO - Epoch(train) [4][ 200/1440] lr: 3.7826e-03 eta: 23:53:56 time: 3.2973 data_time: 1.5249 memory: 61145 loss: 0.2314 | |
| 2023/05/31 22:01:36 - mmengine - INFO - Epoch(train) [4][ 300/1440] lr: 3.7826e-03 eta: 23:46:24 time: 3.3672 data_time: 1.5883 memory: 61145 loss: 0.2256 | |
| 2023/05/31 22:07:14 - mmengine - INFO - Epoch(train) [4][ 400/1440] lr: 3.7826e-03 eta: 23:39:07 time: 3.2423 data_time: 1.4663 memory: 61145 loss: 0.2337 | |
| 2023/05/31 22:12:32 - mmengine - INFO - Epoch(train) [4][ 500/1440] lr: 3.7826e-03 eta: 23:30:14 time: 3.1382 data_time: 1.3721 memory: 61145 loss: 0.2306 | |
| 2023/05/31 22:17:50 - mmengine - INFO - Epoch(train) [4][ 600/1440] lr: 3.7826e-03 eta: 23:21:36 time: 3.1335 data_time: 1.3680 memory: 61145 loss: 0.2205 | |
| 2023/05/31 22:22:07 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 22:23:09 - mmengine - INFO - Epoch(train) [4][ 700/1440] lr: 3.7826e-03 eta: 23:13:07 time: 3.1160 data_time: 1.3327 memory: 61145 loss: 0.2213 | |
| 2023/05/31 22:28:26 - mmengine - INFO - Epoch(train) [4][ 800/1440] lr: 3.7826e-03 eta: 23:04:34 time: 3.2489 data_time: 1.4970 memory: 61145 loss: 0.2181 | |
| 2023/05/31 22:33:44 - mmengine - INFO - Epoch(train) [4][ 900/1440] lr: 3.7826e-03 eta: 22:56:16 time: 3.1870 data_time: 1.4183 memory: 61145 loss: 0.4881 | |
| 2023/05/31 22:38:53 - mmengine - INFO - Epoch(train) [4][1000/1440] lr: 3.7826e-03 eta: 22:47:21 time: 3.0847 data_time: 1.3343 memory: 61145 loss: 0.3468 | |
| 2023/05/31 22:44:05 - mmengine - INFO - Epoch(train) [4][1100/1440] lr: 3.7826e-03 eta: 22:38:51 time: 3.2474 data_time: 1.4968 memory: 61145 loss: 0.2656 | |
| 2023/05/31 22:49:26 - mmengine - INFO - Epoch(train) [4][1200/1440] lr: 3.7826e-03 eta: 22:31:06 time: 3.2647 data_time: 1.4720 memory: 61145 loss: 0.2267 | |
| 2023/05/31 22:54:45 - mmengine - INFO - Epoch(train) [4][1300/1440] lr: 3.7826e-03 eta: 22:23:18 time: 3.0902 data_time: 0.0023 memory: 61145 loss: 0.2243 | |
| 2023/05/31 22:59:49 - mmengine - INFO - Epoch(train) [4][1400/1440] lr: 3.7826e-03 eta: 22:14:34 time: 3.0753 data_time: 0.7537 memory: 61145 loss: 0.2172 | |
| 2023/05/31 23:01:54 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 23:01:54 - mmengine - INFO - Saving checkpoint at 4 epochs | |
| 2023/05/31 23:02:09 - mmengine - INFO - Epoch(val) [4][16/16] accuracy/top1: 80.5000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [80.49996948242188, 0.0] single-label/f1-score_classwise: [89.1966552734375, 0.0] data_time: 0.2560 time: 0.4827 | |
| 2023/05/31 23:07:34 - mmengine - INFO - Epoch(train) [5][ 100/1440] lr: 3.6190e-03 eta: 22:04:08 time: 3.0755 data_time: 1.2481 memory: 61145 loss: 0.5187 | |
| 2023/05/31 23:12:43 - mmengine - INFO - Epoch(train) [5][ 200/1440] lr: 3.6190e-03 eta: 21:55:57 time: 3.0854 data_time: 0.6788 memory: 61145 loss: 0.4294 | |
| 2023/05/31 23:14:50 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/05/31 23:17:52 - mmengine - INFO - Epoch(train) [5][ 300/1440] lr: 3.6190e-03 eta: 21:47:52 time: 3.0641 data_time: 1.1823 memory: 61145 loss: 0.3695 | |
| 2023/05/31 23:23:05 - mmengine - INFO - Epoch(train) [5][ 400/1440] lr: 3.6190e-03 eta: 21:40:09 time: 3.0508 data_time: 1.2857 memory: 61145 loss: 0.2451 | |
| 2023/05/31 23:28:21 - mmengine - INFO - Epoch(train) [5][ 500/1440] lr: 3.6190e-03 eta: 21:32:43 time: 3.0749 data_time: 1.2892 memory: 61145 loss: 0.2295 | |
| 2023/05/31 23:33:38 - mmengine - INFO - Epoch(train) [5][ 600/1440] lr: 3.6190e-03 eta: 21:25:22 time: 3.0173 data_time: 1.2538 memory: 61145 loss: 0.2202 | |
| 2023/05/31 23:38:49 - mmengine - INFO - Epoch(train) [5][ 700/1440] lr: 3.6190e-03 eta: 21:17:48 time: 3.0805 data_time: 1.2854 memory: 61145 loss: 0.6604 | |
| 2023/05/31 23:44:02 - mmengine - INFO - Epoch(train) [5][ 800/1440] lr: 3.6190e-03 eta: 21:10:20 time: 3.1783 data_time: 1.4264 memory: 61145 loss: 0.4415 | |
| 2023/05/31 23:49:16 - mmengine - INFO - Epoch(train) [5][ 900/1440] lr: 3.6190e-03 eta: 21:03:03 time: 3.1799 data_time: 1.4117 memory: 61145 loss: 0.2370 | |
| 2023/05/31 23:54:20 - mmengine - INFO - Epoch(train) [5][1000/1440] lr: 3.6190e-03 eta: 20:55:18 time: 3.0851 data_time: 1.3251 memory: 61145 loss: 0.2167 | |
| 2023/05/31 23:59:27 - mmengine - INFO - Epoch(train) [5][1100/1440] lr: 3.6190e-03 eta: 20:47:43 time: 3.1024 data_time: 1.3410 memory: 61145 loss: 0.2216 | |
| 2023/06/01 00:04:36 - mmengine - INFO - Epoch(train) [5][1200/1440] lr: 3.6190e-03 eta: 20:40:20 time: 3.0994 data_time: 1.3417 memory: 61145 loss: 0.2179 | |
| 2023/06/01 00:06:38 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 00:09:41 - mmengine - INFO - Epoch(train) [5][1300/1440] lr: 3.6190e-03 eta: 20:32:51 time: 3.0856 data_time: 1.3213 memory: 61145 loss: 0.2147 | |
| 2023/06/01 00:14:47 - mmengine - INFO - Epoch(train) [5][1400/1440] lr: 3.6190e-03 eta: 20:25:28 time: 3.0591 data_time: 1.2700 memory: 61145 loss: 0.2130 | |
| 2023/06/01 00:16:47 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 00:16:47 - mmengine - INFO - Saving checkpoint at 5 epochs | |
| 2023/06/01 00:17:02 - mmengine - INFO - Epoch(val) [5][16/16] accuracy/top1: 73.1961 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [73.19613647460938, 0.0] single-label/f1-score_classwise: [84.52398681640625, 0.0] data_time: 0.2506 time: 0.4771 | |
| 2023/06/01 00:22:23 - mmengine - INFO - Epoch(train) [6][ 100/1440] lr: 3.4157e-03 eta: 20:15:47 time: 3.0956 data_time: 1.3272 memory: 61145 loss: 0.2109 | |
| 2023/06/01 00:27:24 - mmengine - INFO - Epoch(train) [6][ 200/1440] lr: 3.4157e-03 eta: 20:08:18 time: 3.0313 data_time: 1.2757 memory: 61145 loss: 0.2103 | |
| 2023/06/01 00:32:30 - mmengine - INFO - Epoch(train) [6][ 300/1440] lr: 3.4157e-03 eta: 20:01:06 time: 3.0531 data_time: 1.2686 memory: 61145 loss: 0.2119 | |
| 2023/06/01 00:37:33 - mmengine - INFO - Epoch(train) [6][ 400/1440] lr: 3.4157e-03 eta: 19:53:49 time: 3.1303 data_time: 1.3811 memory: 61145 loss: 0.2154 | |
| 2023/06/01 00:42:36 - mmengine - INFO - Epoch(train) [6][ 500/1440] lr: 3.4157e-03 eta: 19:46:36 time: 2.9587 data_time: 1.1833 memory: 61145 loss: 0.2110 | |
| 2023/06/01 00:47:46 - mmengine - INFO - Epoch(train) [6][ 600/1440] lr: 3.4157e-03 eta: 19:39:43 time: 3.1617 data_time: 1.4080 memory: 61145 loss: 0.2335 | |
| 2023/06/01 00:52:56 - mmengine - INFO - Epoch(train) [6][ 700/1440] lr: 3.4157e-03 eta: 19:32:56 time: 3.1356 data_time: 1.3428 memory: 61145 loss: 0.2092 | |
| 2023/06/01 00:58:02 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 00:58:02 - mmengine - INFO - Epoch(train) [6][ 800/1440] lr: 3.4157e-03 eta: 19:26:00 time: 3.1164 data_time: 1.3544 memory: 61145 loss: 0.2123 | |
| 2023/06/01 01:03:04 - mmengine - INFO - Epoch(train) [6][ 900/1440] lr: 3.4157e-03 eta: 19:18:56 time: 3.0944 data_time: 1.3150 memory: 61145 loss: 0.2122 | |
| 2023/06/01 01:08:42 - mmengine - INFO - Epoch(train) [6][1000/1440] lr: 3.4157e-03 eta: 19:13:25 time: 6.3566 data_time: 1.2379 memory: 61145 loss: 0.2294 | |
| 2023/06/01 01:13:37 - mmengine - INFO - Epoch(train) [6][1100/1440] lr: 3.4157e-03 eta: 19:06:07 time: 2.9337 data_time: 0.0022 memory: 61145 loss: 0.5894 | |
| 2023/06/01 01:18:28 - mmengine - INFO - Epoch(train) [6][1200/1440] lr: 3.4157e-03 eta: 18:58:45 time: 3.0414 data_time: 0.4232 memory: 61145 loss: 0.5871 | |
| 2023/06/01 01:23:33 - mmengine - INFO - Epoch(train) [6][1300/1440] lr: 3.4157e-03 eta: 18:51:57 time: 2.9096 data_time: 1.1399 memory: 61145 loss: 0.5092 | |
| 2023/06/01 01:28:35 - mmengine - INFO - Epoch(train) [6][1400/1440] lr: 3.4157e-03 eta: 18:45:07 time: 3.0509 data_time: 1.2904 memory: 61145 loss: 0.4725 | |
| 2023/06/01 01:30:35 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 01:30:35 - mmengine - INFO - Saving checkpoint at 6 epochs | |
| 2023/06/01 01:30:51 - mmengine - INFO - Epoch(val) [6][16/16] accuracy/top1: 78.4168 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [78.41676330566406, 0.0] single-label/f1-score_classwise: [87.90290832519531, 0.0] data_time: 0.2397 time: 0.4670 | |
| 2023/06/01 01:36:14 - mmengine - INFO - Epoch(train) [7][ 100/1440] lr: 3.1776e-03 eta: 18:36:22 time: 3.0124 data_time: 1.2088 memory: 61145 loss: 0.4403 | |
| 2023/06/01 01:41:16 - mmengine - INFO - Epoch(train) [7][ 200/1440] lr: 3.1776e-03 eta: 18:29:38 time: 3.1734 data_time: 1.3573 memory: 61145 loss: 0.3954 | |
| 2023/06/01 01:46:21 - mmengine - INFO - Epoch(train) [7][ 300/1440] lr: 3.1776e-03 eta: 18:23:00 time: 2.9848 data_time: 1.1978 memory: 61145 loss: 0.2557 | |
| 2023/06/01 01:49:28 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 01:51:33 - mmengine - INFO - Epoch(train) [7][ 400/1440] lr: 3.1776e-03 eta: 18:16:40 time: 3.1612 data_time: 1.3735 memory: 61145 loss: 0.2293 | |
| 2023/06/01 01:56:41 - mmengine - INFO - Epoch(train) [7][ 500/1440] lr: 3.1776e-03 eta: 18:10:13 time: 3.1020 data_time: 1.3456 memory: 61145 loss: 0.2181 | |
| 2023/06/01 02:01:53 - mmengine - INFO - Epoch(train) [7][ 600/1440] lr: 3.1776e-03 eta: 18:03:56 time: 3.0814 data_time: 1.2816 memory: 61145 loss: 0.2119 | |
| 2023/06/01 02:07:02 - mmengine - INFO - Epoch(train) [7][ 700/1440] lr: 3.1776e-03 eta: 17:57:34 time: 3.1496 data_time: 1.3925 memory: 61145 loss: 0.2117 | |
| 2023/06/01 02:12:13 - mmengine - INFO - Epoch(train) [7][ 800/1440] lr: 3.1776e-03 eta: 17:51:20 time: 3.0924 data_time: 1.2872 memory: 61145 loss: 0.2100 | |
| 2023/06/01 02:17:23 - mmengine - INFO - Epoch(train) [7][ 900/1440] lr: 3.1776e-03 eta: 17:45:02 time: 3.0860 data_time: 1.3338 memory: 61145 loss: 0.2105 | |
| 2023/06/01 02:22:36 - mmengine - INFO - Epoch(train) [7][1000/1440] lr: 3.1776e-03 eta: 17:38:53 time: 3.0671 data_time: 1.2958 memory: 61145 loss: 0.2075 | |
| 2023/06/01 02:27:49 - mmengine - INFO - Epoch(train) [7][1100/1440] lr: 3.1776e-03 eta: 17:32:46 time: 3.0555 data_time: 1.2907 memory: 61145 loss: 0.2066 | |
| 2023/06/01 02:33:05 - mmengine - INFO - Epoch(train) [7][1200/1440] lr: 3.1776e-03 eta: 17:26:44 time: 3.1656 data_time: 1.3843 memory: 61145 loss: 0.2134 | |
| 2023/06/01 02:38:18 - mmengine - INFO - Epoch(train) [7][1300/1440] lr: 3.1776e-03 eta: 17:20:38 time: 3.1757 data_time: 1.4102 memory: 61145 loss: 0.6153 | |
| 2023/06/01 02:41:28 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 02:43:34 - mmengine - INFO - Epoch(train) [7][1400/1440] lr: 3.1776e-03 eta: 17:14:39 time: 3.1776 data_time: 1.4122 memory: 61145 loss: 0.5245 | |
| 2023/06/01 02:45:40 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 02:45:40 - mmengine - INFO - Saving checkpoint at 7 epochs | |
| 2023/06/01 02:45:55 - mmengine - INFO - Epoch(val) [7][16/16] accuracy/top1: 93.3337 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [93.33375549316406, 0.0] single-label/f1-score_classwise: [96.55195617675781, 0.0] data_time: 0.2456 time: 0.4723 | |
| 2023/06/01 02:51:22 - mmengine - INFO - Epoch(train) [8][ 100/1440] lr: 2.9107e-03 eta: 17:06:36 time: 3.0902 data_time: 0.6951 memory: 61145 loss: 0.4658 | |
| 2023/06/01 02:56:35 - mmengine - INFO - Epoch(train) [8][ 200/1440] lr: 2.9107e-03 eta: 17:00:33 time: 3.2131 data_time: 0.8308 memory: 61145 loss: 0.4396 | |
| 2023/06/01 03:01:49 - mmengine - INFO - Epoch(train) [8][ 300/1440] lr: 2.9107e-03 eta: 16:54:32 time: 3.1430 data_time: 1.0770 memory: 61145 loss: 0.4115 | |
| 2023/06/01 03:06:58 - mmengine - INFO - Epoch(train) [8][ 400/1440] lr: 2.9107e-03 eta: 16:48:25 time: 3.0551 data_time: 1.2908 memory: 61145 loss: 0.4118 | |
| 2023/06/01 03:12:10 - mmengine - INFO - Epoch(train) [8][ 500/1440] lr: 2.9107e-03 eta: 16:42:23 time: 3.0869 data_time: 1.2974 memory: 61145 loss: 0.3912 | |
| 2023/06/01 03:17:24 - mmengine - INFO - Epoch(train) [8][ 600/1440] lr: 2.9107e-03 eta: 16:36:25 time: 3.0950 data_time: 1.2923 memory: 61145 loss: 0.3675 | |
| 2023/06/01 03:22:38 - mmengine - INFO - Epoch(train) [8][ 700/1440] lr: 2.9107e-03 eta: 16:30:29 time: 3.0924 data_time: 1.3209 memory: 61145 loss: 0.3533 | |
| 2023/06/01 03:27:51 - mmengine - INFO - Epoch(train) [8][ 800/1440] lr: 2.9107e-03 eta: 16:24:32 time: 3.0916 data_time: 1.3250 memory: 61145 loss: 0.3378 | |
| 2023/06/01 03:33:05 - mmengine - INFO - Epoch(train) [8][ 900/1440] lr: 2.9107e-03 eta: 16:18:36 time: 3.1861 data_time: 1.3871 memory: 61145 loss: 0.3249 | |
| 2023/06/01 03:34:07 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 03:38:20 - mmengine - INFO - Epoch(train) [8][1000/1440] lr: 2.9107e-03 eta: 16:12:44 time: 3.1566 data_time: 1.3787 memory: 61145 loss: 0.3034 | |
| 2023/06/01 03:43:32 - mmengine - INFO - Epoch(train) [8][1100/1440] lr: 2.9107e-03 eta: 16:06:48 time: 3.1391 data_time: 1.3681 memory: 61145 loss: 0.3182 | |
| 2023/06/01 03:48:42 - mmengine - INFO - Epoch(train) [8][1200/1440] lr: 2.9107e-03 eta: 16:00:49 time: 3.1060 data_time: 1.3256 memory: 61145 loss: 0.2937 | |
| 2023/06/01 03:53:57 - mmengine - INFO - Epoch(train) [8][1300/1440] lr: 2.9107e-03 eta: 15:54:57 time: 3.0547 data_time: 1.2850 memory: 61145 loss: 0.2927 | |
| 2023/06/01 03:59:09 - mmengine - INFO - Epoch(train) [8][1400/1440] lr: 2.9107e-03 eta: 15:49:03 time: 3.2542 data_time: 1.4537 memory: 61145 loss: 0.2789 | |
| 2023/06/01 04:01:01 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 04:01:01 - mmengine - INFO - Saving checkpoint at 8 epochs | |
| 2023/06/01 04:01:16 - mmengine - INFO - Epoch(val) [8][16/16] accuracy/top1: 97.1340 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [97.13401794433594, 0.0] single-label/f1-score_classwise: [98.54617309570312, 0.0] data_time: 0.2475 time: 0.4747 | |
| 2023/06/01 04:06:45 - mmengine - INFO - Epoch(train) [9][ 100/1440] lr: 2.6215e-03 eta: 15:40:55 time: 3.1850 data_time: 0.8897 memory: 61145 loss: 0.2795 | |
| 2023/06/01 04:11:53 - mmengine - INFO - Epoch(train) [9][ 200/1440] lr: 2.6215e-03 eta: 15:34:56 time: 3.1250 data_time: 1.1256 memory: 61145 loss: 0.2640 | |
| 2023/06/01 04:17:03 - mmengine - INFO - Epoch(train) [9][ 300/1440] lr: 2.6215e-03 eta: 15:29:00 time: 3.1158 data_time: 1.3541 memory: 61145 loss: 0.2597 | |
| 2023/06/01 04:22:16 - mmengine - INFO - Epoch(train) [9][ 400/1440] lr: 2.6215e-03 eta: 15:23:10 time: 3.1486 data_time: 1.3617 memory: 61145 loss: 0.2476 | |
| 2023/06/01 04:26:25 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 04:27:27 - mmengine - INFO - Epoch(train) [9][ 500/1440] lr: 2.6215e-03 eta: 15:17:19 time: 3.0533 data_time: 1.2851 memory: 61145 loss: 0.2372 | |
| 2023/06/01 04:32:40 - mmengine - INFO - Epoch(train) [9][ 600/1440] lr: 2.6215e-03 eta: 15:11:30 time: 3.1301 data_time: 1.3581 memory: 61145 loss: 0.2365 | |
| 2023/06/01 04:37:50 - mmengine - INFO - Epoch(train) [9][ 700/1440] lr: 2.6215e-03 eta: 15:05:38 time: 3.0736 data_time: 1.2984 memory: 61145 loss: 0.2316 | |
| 2023/06/01 04:43:05 - mmengine - INFO - Epoch(train) [9][ 800/1440] lr: 2.6215e-03 eta: 14:59:54 time: 3.0875 data_time: 1.3187 memory: 61145 loss: 0.2319 | |
| 2023/06/01 04:48:16 - mmengine - INFO - Epoch(train) [9][ 900/1440] lr: 2.6215e-03 eta: 14:54:03 time: 3.1726 data_time: 1.3965 memory: 61145 loss: 0.2304 | |
| 2023/06/01 04:53:29 - mmengine - INFO - Epoch(train) [9][1000/1440] lr: 2.6215e-03 eta: 14:48:17 time: 3.0857 data_time: 1.3124 memory: 61145 loss: 0.2226 | |
| 2023/06/01 04:58:40 - mmengine - INFO - Epoch(train) [9][1100/1440] lr: 2.6215e-03 eta: 14:42:29 time: 3.0722 data_time: 1.2785 memory: 61145 loss: 0.2280 | |
| 2023/06/01 05:03:53 - mmengine - INFO - Epoch(train) [9][1200/1440] lr: 2.6215e-03 eta: 14:36:44 time: 3.0199 data_time: 1.2362 memory: 61145 loss: 0.2230 | |
| 2023/06/01 05:09:07 - mmengine - INFO - Epoch(train) [9][1300/1440] lr: 2.6215e-03 eta: 14:31:00 time: 3.1816 data_time: 1.4119 memory: 61145 loss: 0.2218 | |
| 2023/06/01 05:14:23 - mmengine - INFO - Epoch(train) [9][1400/1440] lr: 2.6215e-03 eta: 14:25:20 time: 3.1760 data_time: 1.3612 memory: 61145 loss: 0.2184 | |
| 2023/06/01 05:16:24 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 05:16:24 - mmengine - INFO - Saving checkpoint at 9 epochs | |
| 2023/06/01 05:16:39 - mmengine - INFO - Epoch(val) [9][16/16] accuracy/top1: 98.0178 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [98.0177993774414, 0.0] single-label/f1-score_classwise: [98.99898529052734, 0.0] data_time: 0.2387 time: 0.4705 | |
| 2023/06/01 05:19:00 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 05:22:04 - mmengine - INFO - Epoch(train) [10][ 100/1440] lr: 2.3171e-03 eta: 14:17:28 time: 3.1092 data_time: 1.1508 memory: 61145 loss: 0.2145 | |
| 2023/06/01 05:27:14 - mmengine - INFO - Epoch(train) [10][ 200/1440] lr: 2.3171e-03 eta: 14:11:41 time: 3.0942 data_time: 1.3323 memory: 61145 loss: 0.2162 | |
| 2023/06/01 05:32:24 - mmengine - INFO - Epoch(train) [10][ 300/1440] lr: 2.3171e-03 eta: 14:05:55 time: 3.0775 data_time: 1.2937 memory: 61145 loss: 0.2160 | |
| 2023/06/01 05:37:36 - mmengine - INFO - Epoch(train) [10][ 400/1440] lr: 2.3171e-03 eta: 14:00:11 time: 3.1045 data_time: 1.3234 memory: 61145 loss: 0.2188 | |
| 2023/06/01 05:42:45 - mmengine - INFO - Epoch(train) [10][ 500/1440] lr: 2.3171e-03 eta: 13:54:24 time: 3.1302 data_time: 1.3379 memory: 61145 loss: 0.2421 | |
| 2023/06/01 05:47:54 - mmengine - INFO - Epoch(train) [10][ 600/1440] lr: 2.3171e-03 eta: 13:48:39 time: 3.0870 data_time: 1.3331 memory: 61145 loss: 0.2091 | |
| 2023/06/01 05:53:07 - mmengine - INFO - Epoch(train) [10][ 700/1440] lr: 2.3171e-03 eta: 13:42:58 time: 3.1577 data_time: 1.3870 memory: 61145 loss: 0.2137 | |
| 2023/06/01 05:58:22 - mmengine - INFO - Epoch(train) [10][ 800/1440] lr: 2.3171e-03 eta: 13:37:19 time: 3.2528 data_time: 1.4971 memory: 61145 loss: 0.2121 | |
| 2023/06/01 06:03:34 - mmengine - INFO - Epoch(train) [10][ 900/1440] lr: 2.3171e-03 eta: 13:31:38 time: 3.1807 data_time: 1.4304 memory: 61145 loss: 0.2086 | |
| 2023/06/01 06:08:49 - mmengine - INFO - Epoch(train) [10][1000/1440] lr: 2.3171e-03 eta: 13:26:00 time: 3.1125 data_time: 1.3377 memory: 61145 loss: 0.2134 | |
| 2023/06/01 06:10:54 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 06:14:02 - mmengine - INFO - Epoch(train) [10][1100/1440] lr: 2.3171e-03 eta: 13:20:21 time: 3.2460 data_time: 1.4528 memory: 61145 loss: 0.2106 | |
| 2023/06/01 06:19:13 - mmengine - INFO - Epoch(train) [10][1200/1440] lr: 2.3171e-03 eta: 13:14:40 time: 3.1990 data_time: 1.4445 memory: 61145 loss: 0.2082 | |
| 2023/06/01 06:24:28 - mmengine - INFO - Epoch(train) [10][1300/1440] lr: 2.3171e-03 eta: 13:09:04 time: 3.1214 data_time: 1.3376 memory: 61145 loss: 0.4386 | |
| 2023/06/01 06:29:47 - mmengine - INFO - Epoch(train) [10][1400/1440] lr: 2.3171e-03 eta: 13:03:31 time: 3.1819 data_time: 1.3947 memory: 61145 loss: 0.2292 | |
| 2023/06/01 06:31:52 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 06:31:52 - mmengine - INFO - Saving checkpoint at 10 epochs | |
| 2023/06/01 06:32:07 - mmengine - INFO - Epoch(val) [10][16/16] accuracy/top1: 96.9383 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [96.93832397460938, 0.0] single-label/f1-score_classwise: [98.44535827636719, 0.0] data_time: 0.2335 time: 0.4679 | |
| 2023/06/01 06:37:32 - mmengine - INFO - Epoch(train) [11][ 100/1440] lr: 2.0050e-03 eta: 12:55:50 time: 3.0897 data_time: 1.0506 memory: 61145 loss: 0.2140 | |
| 2023/06/01 06:42:44 - mmengine - INFO - Epoch(train) [11][ 200/1440] lr: 2.0050e-03 eta: 12:50:11 time: 3.1347 data_time: 1.0325 memory: 61145 loss: 0.2098 | |
| 2023/06/01 06:48:00 - mmengine - INFO - Epoch(train) [11][ 300/1440] lr: 2.0050e-03 eta: 12:44:36 time: 3.1370 data_time: 1.3851 memory: 61145 loss: 0.2154 | |
| 2023/06/01 06:53:17 - mmengine - INFO - Epoch(train) [11][ 400/1440] lr: 2.0050e-03 eta: 12:39:03 time: 3.0547 data_time: 1.2575 memory: 61145 loss: 0.2089 | |
| 2023/06/01 06:58:31 - mmengine - INFO - Epoch(train) [11][ 500/1440] lr: 2.0050e-03 eta: 12:33:27 time: 3.0892 data_time: 1.3201 memory: 61145 loss: 0.2102 | |
| 2023/06/01 07:03:43 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 07:03:43 - mmengine - INFO - Epoch(train) [11][ 600/1440] lr: 2.0050e-03 eta: 12:27:50 time: 3.1587 data_time: 1.3789 memory: 61145 loss: 0.2074 | |
| 2023/06/01 07:08:53 - mmengine - INFO - Epoch(train) [11][ 700/1440] lr: 2.0050e-03 eta: 12:22:11 time: 3.0238 data_time: 1.2731 memory: 61145 loss: 0.2058 | |
| 2023/06/01 07:14:09 - mmengine - INFO - Epoch(train) [11][ 800/1440] lr: 2.0050e-03 eta: 12:16:37 time: 3.2337 data_time: 1.4580 memory: 61145 loss: 0.2100 | |
| 2023/06/01 07:19:19 - mmengine - INFO - Epoch(train) [11][ 900/1440] lr: 2.0050e-03 eta: 12:10:59 time: 3.0961 data_time: 1.3105 memory: 61145 loss: 0.2043 | |
| 2023/06/01 07:24:33 - mmengine - INFO - Epoch(train) [11][1000/1440] lr: 2.0050e-03 eta: 12:05:25 time: 3.1072 data_time: 1.3269 memory: 61145 loss: 0.2049 | |
| 2023/06/01 07:29:46 - mmengine - INFO - Epoch(train) [11][1100/1440] lr: 2.0050e-03 eta: 11:59:50 time: 3.1478 data_time: 1.3773 memory: 61145 loss: 0.2150 | |
| 2023/06/01 07:35:00 - mmengine - INFO - Epoch(train) [11][1200/1440] lr: 2.0050e-03 eta: 11:54:16 time: 3.2485 data_time: 1.4597 memory: 61145 loss: 0.2066 | |
| 2023/06/01 07:40:12 - mmengine - INFO - Epoch(train) [11][1300/1440] lr: 2.0050e-03 eta: 11:48:41 time: 3.1004 data_time: 1.3403 memory: 61145 loss: 0.2052 | |
| 2023/06/01 07:45:29 - mmengine - INFO - Epoch(train) [11][1400/1440] lr: 2.0050e-03 eta: 11:43:10 time: 3.1783 data_time: 1.3930 memory: 61145 loss: 0.3890 | |
| 2023/06/01 07:47:37 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 07:47:37 - mmengine - INFO - Saving checkpoint at 11 epochs | |
| 2023/06/01 07:47:52 - mmengine - INFO - Epoch(val) [11][16/16] accuracy/top1: 92.3932 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [92.39315795898438, 0.0] single-label/f1-score_classwise: [96.04619598388672, 0.0] data_time: 0.2547 time: 0.4821 | |
| 2023/06/01 07:53:17 - mmengine - INFO - Epoch(train) [12][ 100/1440] lr: 1.6929e-03 eta: 11:35:34 time: 3.1339 data_time: 1.1678 memory: 61145 loss: 0.2115 | |
| 2023/06/01 07:56:21 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 07:58:27 - mmengine - INFO - Epoch(train) [12][ 200/1440] lr: 1.6929e-03 eta: 11:29:58 time: 3.1980 data_time: 1.4426 memory: 61145 loss: 0.2072 | |
| 2023/06/01 08:03:48 - mmengine - INFO - Epoch(train) [12][ 300/1440] lr: 1.6929e-03 eta: 11:24:31 time: 3.2064 data_time: 1.4528 memory: 61145 loss: 0.2081 | |
| 2023/06/01 08:09:09 - mmengine - INFO - Epoch(train) [12][ 400/1440] lr: 1.6929e-03 eta: 11:19:04 time: 3.2709 data_time: 1.4935 memory: 61145 loss: 0.2063 | |
| 2023/06/01 08:14:31 - mmengine - INFO - Epoch(train) [12][ 500/1440] lr: 1.6929e-03 eta: 11:13:38 time: 3.2489 data_time: 1.4901 memory: 61145 loss: 0.2068 | |
| 2023/06/01 08:19:54 - mmengine - INFO - Epoch(train) [12][ 600/1440] lr: 1.6929e-03 eta: 11:08:13 time: 3.0888 data_time: 1.3309 memory: 61145 loss: 0.2061 | |
| 2023/06/01 08:25:19 - mmengine - INFO - Epoch(train) [12][ 700/1440] lr: 1.6929e-03 eta: 11:02:49 time: 3.2565 data_time: 1.4864 memory: 61145 loss: 0.2067 | |
| 2023/06/01 08:30:38 - mmengine - INFO - Epoch(train) [12][ 800/1440] lr: 1.6929e-03 eta: 10:57:20 time: 3.1482 data_time: 1.3911 memory: 61145 loss: 0.2045 | |
| 2023/06/01 08:36:03 - mmengine - INFO - Epoch(train) [12][ 900/1440] lr: 1.6929e-03 eta: 10:51:56 time: 3.3144 data_time: 1.5198 memory: 61145 loss: 0.2034 | |
| 2023/06/01 08:41:28 - mmengine - INFO - Epoch(train) [12][1000/1440] lr: 1.6929e-03 eta: 10:46:33 time: 3.2255 data_time: 1.4723 memory: 61145 loss: 0.2042 | |
| 2023/06/01 08:46:53 - mmengine - INFO - Epoch(train) [12][1100/1440] lr: 1.6929e-03 eta: 10:41:09 time: 3.2166 data_time: 1.4404 memory: 61145 loss: 0.2078 | |
| 2023/06/01 08:50:08 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 08:52:18 - mmengine - INFO - Epoch(train) [12][1200/1440] lr: 1.6929e-03 eta: 10:35:44 time: 3.2394 data_time: 1.4543 memory: 61145 loss: 0.2035 | |
| 2023/06/01 08:57:43 - mmengine - INFO - Epoch(train) [12][1300/1440] lr: 1.6929e-03 eta: 10:30:20 time: 3.2678 data_time: 1.4798 memory: 61145 loss: 0.2025 | |
| 2023/06/01 09:03:08 - mmengine - INFO - Epoch(train) [12][1400/1440] lr: 1.6929e-03 eta: 10:24:57 time: 3.2344 data_time: 1.4094 memory: 61145 loss: 0.2038 | |
| 2023/06/01 09:05:15 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 09:05:15 - mmengine - INFO - Saving checkpoint at 12 epochs | |
| 2023/06/01 09:05:31 - mmengine - INFO - Epoch(val) [12][16/16] accuracy/top1: 75.1720 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [75.1720199584961, 0.0] single-label/f1-score_classwise: [85.8265151977539, 0.0] data_time: 0.2597 time: 0.4906 | |
| 2023/06/01 09:11:14 - mmengine - INFO - Epoch(train) [13][ 100/1440] lr: 1.3885e-03 eta: 10:17:33 time: 3.0909 data_time: 0.8689 memory: 61145 loss: 0.2030 | |
| 2023/06/01 09:17:09 - mmengine - INFO - Epoch(train) [13][ 200/1440] lr: 1.3885e-03 eta: 10:12:29 time: 3.2317 data_time: 0.5883 memory: 61145 loss: 0.2036 | |
| 2023/06/01 09:22:35 - mmengine - INFO - Epoch(train) [13][ 300/1440] lr: 1.3885e-03 eta: 10:07:05 time: 3.1851 data_time: 0.5509 memory: 61145 loss: 0.2052 | |
| 2023/06/01 09:27:58 - mmengine - INFO - Epoch(train) [13][ 400/1440] lr: 1.3885e-03 eta: 10:01:39 time: 3.2718 data_time: 1.3912 memory: 61145 loss: 0.2074 | |
| 2023/06/01 09:33:37 - mmengine - INFO - Epoch(train) [13][ 500/1440] lr: 1.3885e-03 eta: 9:56:23 time: 4.0069 data_time: 2.2406 memory: 61145 loss: 0.2060 | |
| 2023/06/01 09:39:01 - mmengine - INFO - Epoch(train) [13][ 600/1440] lr: 1.3885e-03 eta: 9:50:58 time: 3.2260 data_time: 1.4577 memory: 61145 loss: 0.2040 | |
| 2023/06/01 09:44:23 - mmengine - INFO - Epoch(train) [13][ 700/1440] lr: 1.3885e-03 eta: 9:45:32 time: 3.1958 data_time: 1.4013 memory: 61145 loss: 0.2033 | |
| 2023/06/01 09:45:27 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 09:49:46 - mmengine - INFO - Epoch(train) [13][ 800/1440] lr: 1.3885e-03 eta: 9:40:06 time: 3.2272 data_time: 1.4415 memory: 61145 loss: 0.2040 | |
| 2023/06/01 09:55:08 - mmengine - INFO - Epoch(train) [13][ 900/1440] lr: 1.3885e-03 eta: 9:34:40 time: 3.1846 data_time: 1.4219 memory: 61145 loss: 0.2033 | |
| 2023/06/01 10:00:31 - mmengine - INFO - Epoch(train) [13][1000/1440] lr: 1.3885e-03 eta: 9:29:14 time: 3.2232 data_time: 1.4633 memory: 61145 loss: 0.2035 | |
| 2023/06/01 10:06:01 - mmengine - INFO - Epoch(train) [13][1100/1440] lr: 1.3885e-03 eta: 9:23:53 time: 3.2273 data_time: 1.4749 memory: 61145 loss: 0.2013 | |
| 2023/06/01 10:11:27 - mmengine - INFO - Epoch(train) [13][1200/1440] lr: 1.3885e-03 eta: 9:18:29 time: 3.2653 data_time: 1.4675 memory: 61145 loss: 0.2028 | |
| 2023/06/01 10:16:54 - mmengine - INFO - Epoch(train) [13][1300/1440] lr: 1.3885e-03 eta: 9:13:05 time: 3.2732 data_time: 1.5046 memory: 61145 loss: 0.2019 | |
| 2023/06/01 10:22:21 - mmengine - INFO - Epoch(train) [13][1400/1440] lr: 1.3885e-03 eta: 9:07:42 time: 3.1675 data_time: 1.3992 memory: 61145 loss: 0.2039 | |
| 2023/06/01 10:24:31 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 10:24:31 - mmengine - INFO - Saving checkpoint at 13 epochs | |
| 2023/06/01 10:24:45 - mmengine - INFO - Epoch(val) [13][16/16] accuracy/top1: 61.7701 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [61.77008819580078, 0.0] single-label/f1-score_classwise: [76.36775207519531, 0.0] data_time: 0.2610 time: 0.4888 | |
| 2023/06/01 10:30:24 - mmengine - INFO - Epoch(train) [14][ 100/1440] lr: 1.0993e-03 eta: 9:00:14 time: 3.1806 data_time: 1.1026 memory: 61145 loss: 0.2025 | |
| 2023/06/01 10:35:49 - mmengine - INFO - Epoch(train) [14][ 200/1440] lr: 1.0993e-03 eta: 8:54:50 time: 3.2537 data_time: 1.4801 memory: 61145 loss: 0.2027 | |
| 2023/06/01 10:40:12 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 10:41:18 - mmengine - INFO - Epoch(train) [14][ 300/1440] lr: 1.0993e-03 eta: 8:49:27 time: 3.3251 data_time: 1.5635 memory: 61145 loss: 0.2014 | |
| 2023/06/01 10:46:43 - mmengine - INFO - Epoch(train) [14][ 400/1440] lr: 1.0993e-03 eta: 8:44:02 time: 3.2223 data_time: 1.4504 memory: 61145 loss: 0.2016 | |
| 2023/06/01 10:52:14 - mmengine - INFO - Epoch(train) [14][ 500/1440] lr: 1.0993e-03 eta: 8:38:41 time: 3.3354 data_time: 1.5506 memory: 61145 loss: 0.2016 | |
| 2023/06/01 10:57:46 - mmengine - INFO - Epoch(train) [14][ 600/1440] lr: 1.0993e-03 eta: 8:33:19 time: 3.3589 data_time: 1.5880 memory: 61145 loss: 0.2029 | |
| 2023/06/01 11:03:16 - mmengine - INFO - Epoch(train) [14][ 700/1440] lr: 1.0993e-03 eta: 8:27:57 time: 3.2776 data_time: 1.4978 memory: 61145 loss: 0.2031 | |
| 2023/06/01 11:08:45 - mmengine - INFO - Epoch(train) [14][ 800/1440] lr: 1.0993e-03 eta: 8:22:34 time: 3.3003 data_time: 1.5182 memory: 61145 loss: 0.2038 | |
| 2023/06/01 11:14:23 - mmengine - INFO - Epoch(train) [14][ 900/1440] lr: 1.0993e-03 eta: 8:17:15 time: 3.4478 data_time: 1.6688 memory: 61145 loss: 0.2018 | |
| 2023/06/01 11:20:01 - mmengine - INFO - Epoch(train) [14][1000/1440] lr: 1.0993e-03 eta: 8:11:56 time: 3.2508 data_time: 1.4506 memory: 61145 loss: 0.2023 | |
| 2023/06/01 11:25:40 - mmengine - INFO - Epoch(train) [14][1100/1440] lr: 1.0993e-03 eta: 8:06:37 time: 3.3529 data_time: 1.5441 memory: 61145 loss: 0.2021 | |
| 2023/06/01 11:31:10 - mmengine - INFO - Epoch(train) [14][1200/1440] lr: 1.0993e-03 eta: 8:01:14 time: 3.4700 data_time: 1.6898 memory: 61145 loss: 0.2015 | |
| 2023/06/01 11:35:40 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 11:36:48 - mmengine - INFO - Epoch(train) [14][1300/1440] lr: 1.0993e-03 eta: 7:55:55 time: 3.4183 data_time: 1.6409 memory: 61145 loss: 0.5079 | |
| 2023/06/01 11:42:26 - mmengine - INFO - Epoch(train) [14][1400/1440] lr: 1.0993e-03 eta: 7:50:35 time: 3.3625 data_time: 1.5968 memory: 61145 loss: 0.2150 | |
| 2023/06/01 11:44:34 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 11:44:34 - mmengine - INFO - Saving checkpoint at 14 epochs | |
| 2023/06/01 11:44:49 - mmengine - INFO - Epoch(val) [14][16/16] accuracy/top1: 73.1014 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [73.1014404296875, 0.0] single-label/f1-score_classwise: [84.46080780029297, 0.0] data_time: 0.2505 time: 0.4782 | |
| 2023/06/01 11:50:20 - mmengine - INFO - Epoch(train) [15][ 100/1440] lr: 8.3237e-04 eta: 7:43:01 time: 3.2008 data_time: 1.4396 memory: 61145 loss: 0.2063 | |
| 2023/06/01 11:55:35 - mmengine - INFO - Epoch(train) [15][ 200/1440] lr: 8.3237e-04 eta: 7:37:32 time: 3.1488 data_time: 1.3790 memory: 61145 loss: 0.2038 | |
| 2023/06/01 12:00:51 - mmengine - INFO - Epoch(train) [15][ 300/1440] lr: 8.3237e-04 eta: 7:32:02 time: 3.1446 data_time: 1.3874 memory: 61145 loss: 0.2030 | |
| 2023/06/01 12:06:05 - mmengine - INFO - Epoch(train) [15][ 400/1440] lr: 8.3237e-04 eta: 7:26:32 time: 3.0224 data_time: 1.2620 memory: 61145 loss: 0.2024 | |
| 2023/06/01 12:11:15 - mmengine - INFO - Epoch(train) [15][ 500/1440] lr: 8.3237e-04 eta: 7:21:02 time: 3.0775 data_time: 1.2974 memory: 61145 loss: 0.2039 | |
| 2023/06/01 12:16:33 - mmengine - INFO - Epoch(train) [15][ 600/1440] lr: 8.3237e-04 eta: 7:15:34 time: 3.1038 data_time: 1.3352 memory: 61145 loss: 0.2050 | |
| 2023/06/01 12:21:55 - mmengine - INFO - Epoch(train) [15][ 700/1440] lr: 8.3237e-04 eta: 7:10:07 time: 3.2604 data_time: 1.4990 memory: 61145 loss: 0.2004 | |
| 2023/06/01 12:27:11 - mmengine - INFO - Epoch(train) [15][ 800/1440] lr: 8.3237e-04 eta: 7:04:39 time: 3.1540 data_time: 1.3388 memory: 61145 loss: 0.2008 | |
| 2023/06/01 12:29:20 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 12:32:28 - mmengine - INFO - Epoch(train) [15][ 900/1440] lr: 8.3237e-04 eta: 6:59:11 time: 3.1246 data_time: 1.3477 memory: 61145 loss: 0.2016 | |
| 2023/06/01 12:37:41 - mmengine - INFO - Epoch(train) [15][1000/1440] lr: 8.3237e-04 eta: 6:53:42 time: 3.1804 data_time: 1.4056 memory: 61145 loss: 0.2012 | |
| 2023/06/01 12:42:54 - mmengine - INFO - Epoch(train) [15][1100/1440] lr: 8.3237e-04 eta: 6:48:13 time: 3.1857 data_time: 1.3900 memory: 61145 loss: 0.2018 | |
| 2023/06/01 12:48:08 - mmengine - INFO - Epoch(train) [15][1200/1440] lr: 8.3237e-04 eta: 6:42:44 time: 3.1122 data_time: 1.3087 memory: 61145 loss: 0.2034 | |
| 2023/06/01 12:53:20 - mmengine - INFO - Epoch(train) [15][1300/1440] lr: 8.3237e-04 eta: 6:37:15 time: 3.0883 data_time: 1.3111 memory: 61145 loss: 0.2014 | |
| 2023/06/01 12:58:30 - mmengine - INFO - Epoch(train) [15][1400/1440] lr: 8.3237e-04 eta: 6:31:45 time: 3.0851 data_time: 1.3131 memory: 61145 loss: 0.2014 | |
| 2023/06/01 13:00:25 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 13:00:25 - mmengine - INFO - Saving checkpoint at 15 epochs | |
| 2023/06/01 13:00:40 - mmengine - INFO - Epoch(val) [15][16/16] accuracy/top1: 94.0155 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [94.0155258178711, 0.0] single-label/f1-score_classwise: [96.91546630859375, 0.0] data_time: 0.2400 time: 0.4684 | |
| 2023/06/01 13:06:09 - mmengine - INFO - Epoch(train) [16][ 100/1440] lr: 5.9432e-04 eta: 6:24:07 time: 3.0457 data_time: 1.0311 memory: 61145 loss: 0.2003 | |
| 2023/06/01 13:11:27 - mmengine - INFO - Epoch(train) [16][ 200/1440] lr: 5.9432e-04 eta: 6:18:41 time: 3.1365 data_time: 0.2847 memory: 61145 loss: 0.2004 | |
| 2023/06/01 13:16:46 - mmengine - INFO - Epoch(train) [16][ 300/1440] lr: 5.9432e-04 eta: 6:13:14 time: 3.6608 data_time: 1.3309 memory: 61145 loss: 0.2009 | |
| 2023/06/01 13:22:16 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 13:22:16 - mmengine - INFO - Epoch(train) [16][ 400/1440] lr: 5.9432e-04 eta: 6:07:51 time: 3.1618 data_time: 1.4055 memory: 61145 loss: 0.2004 | |
| 2023/06/01 13:27:46 - mmengine - INFO - Epoch(train) [16][ 500/1440] lr: 5.9432e-04 eta: 6:02:28 time: 3.4606 data_time: 1.7095 memory: 61145 loss: 0.2003 | |
| 2023/06/01 13:33:13 - mmengine - INFO - Epoch(train) [16][ 600/1440] lr: 5.9432e-04 eta: 5:57:05 time: 3.2758 data_time: 1.5108 memory: 61145 loss: 0.2016 | |
| 2023/06/01 13:38:35 - mmengine - INFO - Epoch(train) [16][ 700/1440] lr: 5.9432e-04 eta: 5:51:39 time: 3.1949 data_time: 1.4147 memory: 61145 loss: 0.2009 | |
| 2023/06/01 13:43:53 - mmengine - INFO - Epoch(train) [16][ 800/1440] lr: 5.9432e-04 eta: 5:46:13 time: 3.1362 data_time: 1.3603 memory: 61145 loss: 0.2001 | |
| 2023/06/01 13:49:15 - mmengine - INFO - Epoch(train) [16][ 900/1440] lr: 5.9432e-04 eta: 5:40:47 time: 3.2374 data_time: 1.4581 memory: 61145 loss: 0.1996 | |
| 2023/06/01 13:54:32 - mmengine - INFO - Epoch(train) [16][1000/1440] lr: 5.9432e-04 eta: 5:35:21 time: 3.1935 data_time: 1.4217 memory: 61145 loss: 0.2007 | |
| 2023/06/01 13:59:53 - mmengine - INFO - Epoch(train) [16][1100/1440] lr: 5.9432e-04 eta: 5:29:55 time: 3.3168 data_time: 1.5334 memory: 61145 loss: 0.2016 | |
| 2023/06/01 14:05:09 - mmengine - INFO - Epoch(train) [16][1200/1440] lr: 5.9432e-04 eta: 5:24:28 time: 3.1746 data_time: 1.3878 memory: 61145 loss: 0.2012 | |
| 2023/06/01 14:10:30 - mmengine - INFO - Epoch(train) [16][1300/1440] lr: 5.9432e-04 eta: 5:19:03 time: 3.1452 data_time: 1.3828 memory: 61145 loss: 0.2011 | |
| 2023/06/01 14:15:52 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 14:15:52 - mmengine - INFO - Epoch(train) [16][1400/1440] lr: 5.9432e-04 eta: 5:13:38 time: 3.2832 data_time: 1.5014 memory: 61145 loss: 0.2013 | |
| 2023/06/01 14:18:02 - mmengine - INFO - Exp name: convnext_tiny_4xb1024_fake5m_20230531_172740 | |
| 2023/06/01 14:18:02 - mmengine - INFO - Saving checkpoint at 16 epochs | |
| 2023/06/01 14:18:17 - mmengine - INFO - Epoch(val) [16][16/16] accuracy/top1: 98.2261 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [98.22612762451172, 0.0] single-label/f1-score_classwise: [99.10511779785156, 0.0] data_time: 0.2630 time: 0.4892 | |
| 2023/06/01 14:23:55 - mmengine - INFO - Epoch(train) [17][ 100/1440] lr: 3.9101e-04 eta: 5:06:07 time: 3.1291 data_time: 1.3749 memory: 61145 loss: 0.2003 | |
| 2023/06/01 14:29:22 - mmengine - INFO - Epoch(train) [17][ 200/1440] lr: 3.9101e-04 eta: 5:00:43 time: 3.4322 data_time: 1.6813 memory: 61145 loss: 0.1996 | |
| 2023/06/01 14:35:02 - mmengine - INFO - Epoch(train) [17][ 300/1440] lr: 3.9101e-04 eta: 4:55:22 time: 3.4481 data_time: 1.2939 memory: 61145 loss: 0.2001 | |
| 2023/06/01 14:40:30 - mmengine - INFO - Epoch(train) [17][ 400/1440] lr: 3.9101e-04 eta: 4:49:58 time: 3.2638 data_time: 1.5064 memory: 61145 loss: 0.2002 | |
| 2023/06/01 14:45:53 - mmengine - INFO - Epoch(train) [17][ 500/1440] lr: 3.9101e-04 eta: 4:44:33 time: 3.1671 data_time: 1.3796 memory: 61145 loss: 0.2016 | |
| 2023/06/01 14:51:12 - mmengine - INFO - Epoch(train) [17][ 600/1440] lr: 3.9101e-04 eta: 4:39:08 time: 3.0302 data_time: 1.2577 memory: 61145 loss: 0.1997 | |
| 2023/06/01 14:56:17 - mmengine - INFO - Epoch(train) [17][ 700/1440] lr: 3.9101e-04 eta: 4:33:39 time: 3.1236 data_time: 1.3265 memory: 61145 loss: 0.1996 | |
| 2023/06/01 15:01:27 - mmengine - INFO - Epoch(train) [17][ 800/1440] lr: 3.9101e-04 eta: 4:28:11 time: 2.9701 data_time: 1.1868 memory: 61145 loss: 0.1999 | |
| 2023/06/01 15:06:28 - mmengine - INFO - Epoch(train) [17][ 900/1440] lr: 3.9101e-04 eta: 4:22:42 time: 3.0264 data_time: 1.2686 memory: 61145 loss: 0.2004 | |