mirror of
https://github.com/open-mmlab/mmclassification.git
synced 2025-06-03 21:53:55 +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>
75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
_base_ = [
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'../../_base_/datasets/imagenet_bs64_swin_224.py',
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'../../_base_/default_runtime.py',
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]
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# model settings
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='VisionTransformer',
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arch='large',
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img_size=224,
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patch_size=16,
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drop_path_rate=0.5,
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),
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neck=None,
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head=dict(
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type='VisionTransformerClsHead',
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num_classes=1000,
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in_channels=1024,
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loss=dict(
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type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
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init_cfg=[
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dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
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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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# optimizer
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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', lr=5e-4, eps=1e-8, betas=(0.9, 0.999),
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weight_decay=0.05),
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clip_grad=dict(max_norm=5.0),
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paramwise_cfg=dict(
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norm_decay_mult=0.0,
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bias_decay_mult=0.0,
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custom_keys={
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'.cls_token': dict(decay_mult=0.0),
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'.pos_embed': dict(decay_mult=0.0)
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}))
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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-3,
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begin=0,
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end=5,
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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=95,
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eta_min=1e-5,
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by_epoch=True,
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begin=5,
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end=100,
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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=100)
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val_cfg = dict()
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test_cfg = dict()
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default_hooks = dict(
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checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3))
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custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
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randomness = dict(seed=0)
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