45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
# In small and tiny arch, remove drop path and EMA hook comparing with the
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# original config
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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='VisionTransformer',
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arch='deit-small',
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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='VisionTransformerClsHead',
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num_classes=1000,
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in_channels=384,
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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='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
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dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
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]))
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# data settings
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data = dict(samples_per_gpu=256, workers_per_gpu=5)
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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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optimizer = dict(paramwise_cfg=paramwise_cfg)
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