mmpretrain/configs/_base_/datasets/imagenet_bs512_mae.py

# Copyright (c) OpenMMLab. All rights reserved.
# This is a BETA new format config file, and the usage may change recently.
from mmcv.transforms import LoadImageFromFile, RandomFlip
from mmengine.dataset.sampler import DefaultSampler

from mmpretrain.datasets import ImageNet, PackInputs, RandomResizedCrop
from mmpretrain.models import SelfSupDataPreprocessor

# 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)

train_pipeline = [
    dict(type=LoadImageFromFile),
    dict(
        type=RandomResizedCrop,
        scale=224,
        crop_ratio_range=(0.2, 1.0),
        backend='pillow',
        interpolation='bicubic'),
    dict(type=RandomFlip, prob=0.5),
    dict(type=PackInputs)
]

train_dataloader = dict(
    batch_size=512,
    num_workers=8,
    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))