mirror of
https://github.com/open-mmlab/mmpretrain.git
synced 2025-06-02 22:31:23 +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>
89 lines
2.3 KiB
Python
89 lines
2.3 KiB
Python
_base_ = [
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'../_base_/datasets/imagenet_bs512_mae.py',
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'../_base_/default_runtime.py',
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]
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# dataset settings
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train_dataloader = dict(batch_size=256)
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# model settings
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model = dict(
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type='MILAN',
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backbone=dict(
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type='MILANViT',
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arch='b',
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patch_size=16,
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mask_ratio=0.75,
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init_cfg=[
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dict(type='Xavier', distribution='uniform', layer='Linear'),
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dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
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]),
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neck=dict(
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type='MILANPretrainDecoder',
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init_cfg=[
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dict(type='Xavier', distribution='uniform', layer='Linear'),
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dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
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]),
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head=dict(
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type='MIMHead',
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loss=dict(
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type='CosineSimilarityLoss', shift_factor=2.0, scale_factor=2.0),
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),
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target_generator=dict(
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type='CLIPGenerator',
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tokenizer_path= # noqa
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'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/clip_vit_base_16.pth.tar' # noqa
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),
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init_cfg=None)
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# optimizer wrapper
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(
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type='AdamW',
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lr=1.5e-4 * 4096 / 256,
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betas=(0.9, 0.95),
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weight_decay=0.05),
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paramwise_cfg=dict(
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custom_keys={
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'ln': dict(decay_mult=0.0),
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'bias': dict(decay_mult=0.0),
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'pos_embed': dict(decay_mult=0.),
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'mask_token': dict(decay_mult=0.),
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'cls_token': dict(decay_mult=0.)
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}))
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find_unused_parameters = True
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# learning rate scheduler
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-4,
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by_epoch=True,
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begin=0,
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end=40,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=360,
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by_epoch=True,
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begin=40,
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end=400,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=400)
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default_hooks = dict(
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
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randomness = dict(seed=0, diff_rank_seed=True)
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# auto resume
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resume = True
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=2048)
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