How to perform data augmentation in the PaddlePaddle framework?

Various data augmentation methods can be utilized in the PaddlePaddle framework through the paddle.vision.transforms module. Here are some commonly used data augmentation methods:

1. Flip the image horizontally at random.

transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    # 其他数据增强方法
])

1. Randomly flip the input vertically.

transform = transforms.Compose([
    transforms.RandomVerticalFlip(),
    # 其他数据增强方法
])

1. Randomly rotating

transform = transforms.Compose([
    transforms.RandomRotation(degrees=45),
    # 其他数据增强方法
])

1. Randomly resizing and cropping in various sizes

transform = transforms.Compose([
    transforms.RandomResizedCrop(size=(224, 224)),
    # 其他数据增强方法
])

1. Convert to tensor

transform = transforms.Compose([
    transforms.ToTensor(),
    # 其他数据增强方法
])

Combining these data augmentation methods together can create a data augmentation transform, which can then be used to enhance images during data loading. For example:

train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True)