37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
_base_ = [
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'../_base_/models/vit-base-p16.py',
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'../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py',
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'../_base_/schedules/imagenet_bs4096_AdamW.py',
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'../_base_/default_runtime.py'
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]
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model = dict(backbone=dict(img_size=384))
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img_norm_cfg = dict(
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mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', size=384, backend='pillow'),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', size=(384, -1), backend='pillow'),
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dict(type='CenterCrop', crop_size=384),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline),
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)
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