生成一段基于CIFAR模型实现PGD算法的代码

以下是基于 CIFAR10 模型实现 PGD 算法的 Python 代码：

import torch
from torch import nn
from torchvision import models, datasets, transforms

# Load CIFAR10 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=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

# Load CIFAR10 model
model = models.resnet18(pretrained=False)
model.fc = nn.Linear(512, 10)
model.load_state_dict(torch.load('cifar10_model.pth'))

# Define PGD attack
def pgd_attack(model, x, y, epsilon=0.03, alpha=0.01, num_iter=40, randomize=True):
    if randomize:
        x_adv = x + torch.empty_like(x).uniform_(-epsilon, epsilon)
    else:
        x_adv = x.clone().detach()

    for i in range(num_iter):
        x_adv.requires_grad_()
        with torch.enable_grad():
            loss = nn.CrossEntropyLoss()(model(x_adv), y)
        grad = torch.autograd.grad(loss, [x_adv])[0]
        x_adv = x_adv.detach() + alpha * torch.sign(grad.detach())
        x_adv = torch.min(torch.max(x_adv, x - epsilon), x + epsilon)
        x_adv = torch.clamp(x_adv, 0, 1)

    return x_adv

# Evaluate model with PGD attack
def evaluate_pgd(model, dataset, epsilon=0.03, alpha=0.01, num_iter=40):
    model.eval()
    correct = 0
    total = 0
    for x, y in dataset:
        x_adv = pgd_attack(model, x.unsqueeze(0), y.unsqueeze(0), epsilon=epsilon, alpha=alpha, num_iter=num_iter)
        pred = model(x_adv).argmax().item()
        correct += int(pred == y)
        total += 1
    accuracy = correct / total
    return accuracy

# Evaluate model on test dataset
test_accuracy = evaluate_pgd(model, test_dataset)
print('Test accuracy:', test_accuracy)

在上面的代码中，我们首先加载 CIFAR10 数据集和预训练的 CIFAR10 模型。然后，我们定义了一个 `pgd_attack` 函数，该函数接受一个模型、一个输入 `x` 和一个真实标签 `y`，并返回一个对抗样本。该函数实现了 PGD 算法，其中 `epsilon` 和 `alpha` 是两个超参数，`num_iter` 是迭代次数。我们还定义了一个 `evaluate_pgd` 函数，该函数接受一个模型和一个数据集，并对数据集中的每个样本生成对抗样本并计算模型的准确度。最后，我们使用 `evaluate_pgd` 函数计算模型在测试数据集上的准确度。