生成一段基于PGD攻击算法输入和输出图像都为32*32像素的代码

好的，以下是一个使用PGD攻击算法对CIFAR-10数据集中32*32像素图像进行攻击的示例代码：

import torch
import torchvision
import torchvision.transforms as transforms

from models import Net # 自定义的模型

def pgd_attack(model, images, labels, eps=0.3, alpha=2/255, iters=40):
    # PGD攻击函数
    loss = torch.nn.CrossEntropyLoss()
    ori_images = images.data

    for i in range(iters):
        images.requires_grad = True
        outputs = model(images)
        cost = loss(outputs, labels).to(device)
        grad = torch.autograd.grad(cost, images, 
                                   retain_graph=False, create_graph=False)[0]
        
        adv_images = images + alpha*grad.sign()
        eta = torch.clamp(adv_images - ori_images, min=-eps, max=eps)
        images = torch.clamp(ori_images + eta, min=0, max=1).detach_()
    
    return images

# 加载CIFAR-10数据集
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=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

# 实例化模型
model = Net()
model.to(device)

# 训练模型

# 对测试集中的图像进行PGD攻击
dataiter = iter(testloader)
images, labels = dataiter.next()
images = images.to(device)
labels = labels.to(device)
adv_images = pgd_attack(model, images, labels)

# 显示原始图像和攻击后的图像
import matplotlib.pyplot as plt
import numpy as np

def imshow(img):
    img = img / 2 + 0.5     # 非标准化
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

imshow(torchvision.utils.make_grid(images.cpu()))
imshow(torchvision.utils.make_grid(adv_images.cpu()))

需要注意的是，这里的`models.Net()`是一个自定义的模型，你需要根据你自己的模型进行相应的修改。同时，需要确保你的模型能够处理32*32像素的图像。