48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
# The distillation config is only for evaluation.
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_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 settings
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='DistilledVisionTransformer',
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arch='deit-tiny',
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img_size=224,
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patch_size=16),
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neck=None,
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head=dict(
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type='DeiTClsHead',
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num_classes=1000,
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in_channels=192,
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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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)
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# data settings
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train_dataloader = dict(batch_size=256)
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# schedule settings
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optim_wrapper = dict(
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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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clip_grad=dict(max_norm=5.0),
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)
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