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bc3c4a35ee
* [Feature]: Add caption * [Feature]: Update scienceqa * [CI] Add test mim CI. (#879) * refactor imagenet dataset * refactor imagenet dataset * refactor imagenet dataset * update imagenet21k * update configs * update mnist * update dataset_prepare.md * fix sun397 url and update user_guides/dataset_prepare.md * update dataset_prepare.md * fix sun397 dataset * fix sun397 * update chinese dataset_prepare.md * update dataset_prepare.md * [Refactor] update voc dataset * [Refactor] update voc dataset * refactor imagenet * refactor imagenet * use mmengine.fileio --------- Co-authored-by: liuyuan <3463423099@qq.com> Co-authored-by: Ma Zerun <mzr1996@163.com> Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com>
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
# dataset settings
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dataset_type = 'ImageNet'
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data_root = 'data/imagenet/'
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data_preprocessor = dict(
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type='TwoNormDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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second_mean=[127.5, 127.5, 127.5],
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second_std=[127.5, 127.5, 127.5],
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to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='ColorJitter',
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(
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type='RandomResizedCropAndInterpolationWithTwoPic',
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size=224,
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second_size=224,
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interpolation='bicubic',
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second_interpolation='bicubic',
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scale=(0.2, 1.0)),
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dict(
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type='BEiTMaskGenerator',
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input_size=(14, 14),
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num_masking_patches=75,
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max_num_patches=75,
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min_num_patches=16),
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dict(type='PackInputs')
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]
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train_dataloader = dict(
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batch_size=256,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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split='train',
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pipeline=train_pipeline))
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