| import torch |
| import torchvision |
| import torchvision.transforms as transforms |
| import os |
|
|
| |
|
|
| 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 |
| """ |
| |
| 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)), |
| ]) |
|
|
| |
| if local_dataset_path: |
| print(f"使用本地数据集: {local_dataset_path}") |
| |
| 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' |
|
|
| |
| 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 |
|
|
