dataloader.py

import torch
from torchvision import datasets
import torchvision.transforms as transforms

batch_size = 128

def data_transform():
    transform_train = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.RandomCrop(32, padding=4),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])

    return transform_train, transform_test

def data_loader(transform_train, transform_test):
    train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
    test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)

    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)
    return train_loader, test_loader