dataset_type = 'UnconditionalImageDataset'

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        key='real_img',
        io_backend='disk',
    ),
    dict(type='Flip', keys=['real_img'], direction='horizontal'),
    dict(
        type='Normalize',
        keys=['real_img'],
        mean=[127.5] * 3,
        std=[127.5] * 3,
        to_rgb=False),
    dict(type='ImageToTensor', keys=['real_img']),
    dict(type='Collect', keys=['real_img'], meta_keys=['real_img_path'])
]

val_pipeline = [
    dict(
        type='LoadImageFromFile',
        key='real_img',
        io_backend='disk',
    ),
    dict(
        type='Normalize',
        keys=['real_img'],
        mean=[127.5] * 3,
        std=[127.5] * 3,
        to_rgb=True),
    dict(type='ImageToTensor', keys=['real_img']),
    dict(type='Collect', keys=['real_img'], meta_keys=['real_img_path'])
]

# `samples_per_gpu` and `imgs_root` need to be set.
data = dict(
    samples_per_gpu=None,
    workers_per_gpu=4,
    train=dict(
        type='RepeatDataset',
        times=100,
        dataset=dict(
            type=dataset_type, imgs_root=None, pipeline=train_pipeline)),
    val=dict(type=dataset_type, imgs_root=None, pipeline=val_pipeline))