AlexNet-CIFAR10/Classification-normal/scripts/dataset_utils.py

import torch
import torchvision
import torchvision.transforms as transforms
import os

#加载数据集

def get_cifar10_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None, shuffle=False):
    """获取CIFAR10数据集的数据加载器
    
    Args:
        batch_size: 批次大小
        num_workers: 数据加载的工作进程数
        local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载
        
    Returns:
        trainloader: 训练数据加载器
        testloader: 测试数据加载器
    """
    # 数据预处理
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    # 设置数据集路径
    if local_dataset_path:
        print(f"使用本地数据集: {local_dataset_path}")
        # 检查数据集路径是否有数据集，没有的话则下载
        cifar_path = os.path.join(local_dataset_path, 'cifar-10-batches-py')
        download = not os.path.exists(cifar_path) or not os.listdir(cifar_path)
        dataset_path = local_dataset_path
    else:
        print("未指定本地数据集路径，将下载数据集")
        download = True
        dataset_path = '../dataset'

    # 创建数据集路径
    if not os.path.exists(dataset_path):
        os.makedirs(dataset_path)

    trainset = torchvision.datasets.CIFAR10(
        root=dataset_path, train=True, download=download, transform=transform_train)
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)

    testset = torchvision.datasets.CIFAR10(
        root=dataset_path, train=False, download=download, transform=transform_test)
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)

    return trainloader, testloader