How to perform data augmentation in PyTorch

In PyTorch, data augmentation is typically done using the torchvision.transforms module. This module offers many predefined data augmentation operations such as random cropping, flipping, rotating, and scaling. You can also customize data augmentation operations.

Here is a simple example demonstrating how to perform data augmentation in PyTorch.

import torch
from torchvision import datasets, transforms

# 定义数据增强操作
transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(degrees=10),
    transforms.ToTensor(),
])

# 加载数据集并应用数据增强
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# 遍历数据集
for images, labels in train_loader:
    # 在这里对图像进行训练
    pass

In this example, we defined some data augmentation operations and combined them into a transforms.Compose object. Later, we applied these data augmentation operations to the dataset while loading the CIFAR-10 dataset.

You have the ability to customize data augmentation operations according to your needs and combine them as shown above. PyTorch’s data augmentation capabilities are very powerful and can help improve the effectiveness of training models.

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