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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
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