# dataset settings
dataset_type = 'ImageNet'
data_root = 'data/imagenet/'
data_preprocessor = dict(
    type='SelfSupDataPreprocessor',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    to_rgb=True)

view_pipeline1 = [
    dict(
        type='RandomResizedCrop',
        scale=224,
        interpolation='bicubic',
        backend='pillow'),
    dict(type='RandomFlip', prob=0.5),
    dict(
        type='RandomApply',
        transforms=[
            dict(
                type='ColorJitter',
                brightness=0.4,
                contrast=0.4,
                saturation=0.2,
                hue=0.1)
        ],
        prob=0.8),
    dict(
        type='RandomGrayscale',
        prob=0.2,
        keep_channels=True,
        channel_weights=(0.114, 0.587, 0.2989)),
    dict(
        type='GaussianBlur',
        magnitude_range=(0.1, 2.0),
        magnitude_std='inf',
        prob=1.),
    dict(type='Solarize', thr=128, prob=0.),
]
view_pipeline2 = [
    dict(
        type='RandomResizedCrop',
        scale=224,
        interpolation='bicubic',
        backend='pillow'),
    dict(type='RandomFlip', prob=0.5),
    dict(
        type='RandomApply',
        transforms=[
            dict(
                type='ColorJitter',
                brightness=0.4,
                contrast=0.4,
                saturation=0.2,
                hue=0.1)
        ],
        prob=0.8),
    dict(
        type='RandomGrayscale',
        prob=0.2,
        keep_channels=True,
        channel_weights=(0.114, 0.587, 0.2989)),
    dict(
        type='GaussianBlur',
        magnitude_range=(0.1, 2.0),
        magnitude_std='inf',
        prob=0.1),
    dict(type='Solarize', thr=128, prob=0.2)
]
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiView',
        num_views=[1, 1],
        transforms=[view_pipeline1, view_pipeline2]),
    dict(type='PackInputs')
]

train_dataloader = dict(
    batch_size=32,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='default_collate'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        split='train',
        pipeline=train_pipeline))