| import torch | |
| import torchvision | |
| import torchvision.transforms as transforms | |
| def load_data(batch_size=64): | |
| """ | |
| Loads and preprocesses the CIFAR-10 dataset using PyTorch. | |
| Returns: | |
| tuple: (trainloader, testloader) | |
| """ | |
| transform = transforms.Compose( | |
| [transforms.ToTensor(), | |
| transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) | |
| trainset = torchvision.datasets.CIFAR10(root='./data', train=True, | |
| download=True, transform=transform) | |
| trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, | |
| shuffle=True, num_workers=0) | |
| testset = torchvision.datasets.CIFAR10(root='./data', train=False, | |
| download=True, transform=transform) | |
| testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, | |
| shuffle=False, num_workers=0) | |
| classes = ('plane', 'car', 'bird', 'cat', | |
| 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') | |
| return trainloader, testloader, classes | |