mirror of
https://github.com/open-mmlab/mmpretrain.git
synced 2025-06-03 14:59:18 +08:00
08dc8c75d3
* remove basehead * add moco series * add byol simclr simsiam * add ut * update configs * add simsiam hook * add and refactor beit * update ut * add cae * update extract_feat * refactor cae * add mae * refactor data preprocessor * update heads * add maskfeat * add milan * add simmim * add mixmim * fix lint * fix ut * fix lint * add eva * add densecl * add barlowtwins * add swav * fix lint * update readtherdocs rst * update docs * update * Decrease UT memory usage * Fix docstring * update DALLEEncoder * Update model docs * refactor dalle encoder * update docstring * fix ut * fix config error * add val_cfg and test_cfg * refactor clip generator * fix lint * pass check * fix ut * add lars * update type of BEiT in configs * Use MMEngine style momentum in EMA. * apply mmpretrain solarize --------- Co-authored-by: mzr1996 <mzr1996@163.com>
36 lines
959 B
Python
36 lines
959 B
Python
_base_ = [
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'../../_base_/datasets/imagenet_bs64_swin_224.py',
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'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
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'../../_base_/default_runtime.py'
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]
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='BEiTViT',
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arch='base',
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img_size=224,
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patch_size=16,
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avg_token=True,
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output_cls_token=False,
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use_abs_pos_emb=False,
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use_rel_pos_bias=True,
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use_shared_rel_pos_bias=False,
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),
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neck=None,
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head=dict(
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type='LinearClsHead',
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num_classes=1000,
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in_channels=768,
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loss=dict(
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type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
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),
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init_cfg=[
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dict(type='TruncNormal', layer='Linear', std=.02),
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dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
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],
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train_cfg=dict(augments=[
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dict(type='Mixup', alpha=0.8),
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dict(type='CutMix', alpha=1.0)
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]))
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