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# CIFAR Dataset implementation in Pytorch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
def load_cifar10(batch_size=64, num_workers=2, download=True):
"""Load CIFAR-10 dataset."""
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=download, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=download, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, test_loader
def run_cifar10():
"""Run CIFAR-10 dataset loading and basic iteration."""
train_loader, test_loader = load_cifar10(batch_size=64, num_workers=2, download=True)
# Example: Iterate through the training data
for images, labels in train_loader:
print(f"Batch size: {images.size(0)}, Image shape: {images.shape}, Labels: {labels}")
break # Remove this break to iterate through all batches
if __name__ == "__main__":
train_loader, test_loader = load_cifar10(batch_size=64, num_workers=2, download=True)
# Example: Iterate through the training data
for images, labels in train_loader:
print(f"Batch size: {images.size(0)}, Image shape: {images.shape}, Labels: {labels}")
break # Remove this break to iterate through all batches
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