mmpretrain/configs/_base_/datasets/cifar10_bs16.py

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

from mmpretrain.datasets import CIFAR10, PackInputs, RandomCrop, RandomFlip
from mmpretrain.evaluation import Accuracy

# dataset settings
dataset_type = CIFAR10
data_preprocessor = dict(
    num_classes=10,
    # RGB format normalization parameters
    mean=[125.307, 122.961, 113.8575],
    std=[51.5865, 50.847, 51.255],
    # loaded images are already RGB format
    to_rgb=False)

train_pipeline = [
    dict(type=RandomCrop, crop_size=32, padding=4),
    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
    dict(type=PackInputs),
]

test_pipeline = [
    dict(type=PackInputs),
]

train_dataloader = dict(
    batch_size=16,
    num_workers=2,
    dataset=dict(
        type=dataset_type,
        data_root='data/cifar10',
        split='train',
        pipeline=train_pipeline),
    sampler=dict(type=DefaultSampler, shuffle=True),
)

val_dataloader = dict(
    batch_size=16,
    num_workers=2,
    dataset=dict(
        type=dataset_type,
        data_root='data/cifar10/',
        split='test',
        pipeline=test_pipeline),
    sampler=dict(type=DefaultSampler, shuffle=False),
)
val_evaluator = dict(type=Accuracy, topk=(1, ))

test_dataloader = val_dataloader
test_evaluator = val_evaluator