给出数据增强的pytorch代码

下面是一些常用的数据增强方法的 PyTorch 代码示例：

1. 随机裁剪：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.RandomCrop(size=224),
])

2. 随机旋转：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.RandomRotation(degrees=30),
])

3. 随机缩放：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.RandomResizedCrop(size=224),
])

4. 随机翻转：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
])

5. 随机亮度、对比度、饱和度调整：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5),
])

6. 随机噪声：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.RandomApply([transforms.GaussianBlur(kernel_size=3)], p=0.5),
])

7. 随机变形：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.RandomApply([transforms.RandomAffine(degrees=30)], p=0.5),
])

8. 随机颜色变换：

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.ColorJitter(hue=0.5),
])

9. Mixup：

import random
import torch
import numpy as np

def mixup_data(x, y, alpha=1.0):
    if alpha > 0:
        lam = np.random.beta(alpha, alpha)
    else:
        lam = 1
    batch_size = x.size()[0]
    index = torch.randperm(batch_size)
    mixed_x = lam * x + (1 - lam) * x[index, :]
    y_a, y_b = y, y[index]
    return mixed_x, y_a, y_b, lam

10. Cutout：

import random
import numpy as np
import torch

def cutout(image, n_holes=1, length=16):
    h, w = image.shape[1], image.shape[2]
    mask = np.ones((h, w), np.float32)
    for n in range(n_holes):
        y = np.random.randint(h)
        x = np.random.randint(w)
        y1 = np.clip(y - length // 2, 0, h)
        y2 = np.clip(y + length // 2, 0, h)
        x1 = np.clip(x - length // 2, 0, w)
        x2 = np.clip(x + length // 2, 0, w)
        mask[y1:y2, x1:x2] = 0.
    mask = torch.from_numpy(mask)
    mask = mask.expand_as(image)
    image = image * mask
    return image

这些代码示例可以在 PyTorch 中方便地进行数据增强，提高模型的泛化能力。