52 lines
1.4 KiB
Python
52 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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second_mean=[127.5, 127.5, 127.5],
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second_std=[127.5, 127.5, 127.5],
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bgr_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(
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type='PackSelfSupInputs',
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algorithm_keys=['mask'],
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meta_keys=['img_path'])
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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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