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* hivit added * Update hivit.py * Update hivit.py * Add files via upload * Update __init__.py * Add files via upload * Update __init__.py * Add files via upload * Update hivit.py * Add files via upload * Add files via upload * Add files via upload * Add files via upload * Update itpn.py * Add files via upload * Update __init__.py * Update mae_hivit-base-p16.py * Delete mim_itpn-base-p16.py * Add files via upload * Update itpn_hivit-base-p16.py * Update itpn.py * Update hivit.py * Update __init__.py * Update mae.py * Delete hivit.py * Update __init__.py * Delete configs/itpn directory * Add files via upload * Add files via upload * Delete configs/hivit directory * Add files via upload * refactor and add metafile and readme * update clip * add ut * update ut * update * update docstring * update model.rst --------- Co-authored-by: 田运杰 <48153283+sunsmarterjie@users.noreply.github.com>
50 lines
1.4 KiB
Python
50 lines
1.4 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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# clip mean & std
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second_mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255],
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second_std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255],
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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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ann_file='meta/train.txt',
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data_prefix=dict(img_path='train/'),
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pipeline=train_pipeline))
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