| 2023/06/01 16:15:27 - 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: 1120907987 | |
| 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 | |
| ------------------------------------------------------------ | |
| 2023/06/01 16:15:29 - mmengine - INFO - Config: | |
| optim_wrapper = dict( | |
| optimizer=dict( | |
| type='AdamW', | |
| lr=0.0004, | |
| 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=10, 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='small', drop_path_rate=0.4), | |
| head=dict( | |
| type='LinearClsHead', | |
| num_classes=2, | |
| in_channels=768, | |
| loss=dict(type='CrossEntropyLoss', loss_weight=1.0), | |
| 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=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| 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') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/if-dpmsolver++-50-20w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stablediffusionV2-1-dpmsolver-25-190w.tsv', | |
| 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_small_4xb256_fake7m-lr4e-4' | |
| 2023/06/01 16:15:35 - 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 | |
| -------------------- | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.0.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.1.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.0.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.0.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.0.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.0.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.0.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.0.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.0.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.1.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.1.2.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.0.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.pointwise_conv1.bias:weight_decay=0.0 | |
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| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.gamma:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.depthwise_conv.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.norm.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.norm.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.pointwise_conv1.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.pointwise_conv2.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.norm3.weight:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - mmengine - INFO - paramwise_options -- backbone.norm3.bias:weight_decay=0.0 | |
| 2023/06/01 16:16:14 - 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.2.9.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.9.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.9.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.9.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.9.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.9.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.9.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.9.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.9.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.10.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.10.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.10.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.10.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.10.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.10.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.10.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.10.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.10.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.11.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.11.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.11.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.11.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.11.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.11.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.11.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.11.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.11.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.12.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.12.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.12.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.12.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.12.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.12.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.12.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.12.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.12.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.13.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.13.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.13.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.13.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.13.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.13.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.13.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.13.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.13.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.14.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.14.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.14.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.14.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.14.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.14.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.14.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.14.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.14.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.15.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.15.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.15.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.15.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.15.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.15.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.15.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.15.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.15.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.16.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.16.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.16.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.16.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.16.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.16.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.16.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.16.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.16.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.17.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.17.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.17.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.17.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.17.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.17.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.17.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.17.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.17.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.18.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.18.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.18.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.18.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.18.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.18.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.18.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.18.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.18.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.19.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.19.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.19.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.19.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.19.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.19.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.19.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.19.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.19.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.20.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.20.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.20.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.20.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.20.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.20.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.20.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.20.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.20.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.21.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.21.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.21.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.21.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.21.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.21.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.21.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.21.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.21.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.22.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.22.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.22.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.22.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.22.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.22.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.22.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.22.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.22.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.23.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.23.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.23.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.23.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.23.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.23.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.23.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.23.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.23.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.24.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.24.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.24.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.24.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.24.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.24.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.24.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.24.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.24.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.25.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.25.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.25.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.25.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.25.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.25.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.25.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.25.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.25.pointwise_conv2.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.26.gamma - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.26.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.26.depthwise_conv.bias - torch.Size([384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.26.norm.weight - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.26.norm.bias - torch.Size([384]): | |
| The value is the same before and after calling `init_weights` of ImageClassifier | |
| backbone.stages.2.26.pointwise_conv1.weight - torch.Size([1536, 384]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.26.pointwise_conv1.bias - torch.Size([1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.26.pointwise_conv2.weight - torch.Size([384, 1536]): | |
| TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 | |
| backbone.stages.2.26.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 | |
| 2023/06/01 16:16:16 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io | |
| 2023/06/01 16:16:16 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. | |
| 2023/06/01 16:16:16 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/convnext_small_4xb256_fake7m-lr4e-4. | |
| 2023/06/01 16:17:39 - mmengine - INFO - Epoch(train) [1][ 100/7800] lr: 4.0000e-04 eta: 17:58:30 time: 0.7479 data_time: 0.0016 memory: 25071 loss: 0.5570 | |
| 2023/06/01 16:18:50 - mmengine - INFO - Epoch(train) [1][ 200/7800] lr: 4.0000e-04 eta: 16:40:25 time: 0.7111 data_time: 0.0013 memory: 25071 loss: 0.5156 | |
| 2023/06/01 16:20:02 - mmengine - INFO - Epoch(train) [1][ 300/7800] lr: 4.0000e-04 eta: 16:15:16 time: 0.7357 data_time: 0.0014 memory: 25071 loss: 0.4904 | |
| 2023/06/01 16:21:13 - mmengine - INFO - Epoch(train) [1][ 400/7800] lr: 4.0000e-04 eta: 16:00:42 time: 0.7196 data_time: 0.0015 memory: 25071 loss: 0.4712 | |