59 lines
1.5 KiB
Python
59 lines
1.5 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='SelfSupDataPreprocessor',
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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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to_rgb=True)
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# The difference between mocov2 and mocov1 is the transforms in the pipeline
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view_pipeline = [
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dict(
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type='RandomResizedCrop',
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scale=224,
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crop_ratio_range=(0.2, 1.),
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backend='pillow'),
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dict(
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type='RandomApply',
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transforms=[
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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=0.1)
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],
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prob=0.8),
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dict(
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type='RandomGrayscale',
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prob=0.2,
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keep_channels=True,
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channel_weights=(0.114, 0.587, 0.2989)),
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dict(
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type='GaussianBlur',
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magnitude_range=(0.1, 2.0),
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magnitude_std='inf',
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prob=0.5),
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dict(type='RandomFlip', prob=0.5),
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]
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='MultiView', num_views=2, transforms=[view_pipeline]),
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dict(type='PackInputs')
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]
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train_dataloader = dict(
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batch_size=32,
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num_workers=8,
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drop_last=True,
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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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