PGD生成对抗样本 测试集的代码

以下是一个用PGD（Projected Gradient Descent）算法生成对抗样本并在测试集上进行评估的代码示例：

import torch
import torch.nn as nn

def pgd_attack(model, loss_fn, images, labels, epsilon, alpha, num_steps):
    perturbed_images = images.clone().detach()
    perturbed_images.requires_grad = True
    
    for _ in range(num_steps):
        # Forward pass
        outputs = model(perturbed_images)
        loss = loss_fn(outputs, labels)
        
        # Zeroing out gradients
        model.zero_grad()
        
        # Backward pass
        loss.backward()
        
        # Collecting the gradient of the input image
        grad = perturbed_images.grad.data
        
        # Generating adversarial example
        perturbed_images = perturbed_images + alpha * torch.sign(grad)
        
        # Projecting the perturbations to epsilon ball
        perturbations = torch.clamp(perturbed_images - images, -epsilon, epsilon)
        perturbed_images = torch.clamp(images + perturbations, 0, 1)
        
        # Resetting gradients
        perturbed_images.grad.zero_()
    
    return perturbed_images

# 示例用法
# 定义模型和损失函数
model = YourModel()
loss_fn = nn.CrossEntropyLoss()

# 定义输入图像和标签
test_images = torch.tensor(...)  # 测试集图像
test_labels = torch.tensor(...)  # 测试集标签

# 设置扰动大小 (epsilon)、步长 (alpha) 和迭代次数 (num_steps)
epsilon = 0.1
alpha = 0.01
num_steps = 10

# 生成对抗样本
perturbed_test_images = pgd_attack(model, loss_fn, test_images, test_labels, epsilon, alpha, num_steps)

# 在对抗样本上进行模型评估
outputs = model(perturbed_test_images)
predicted_labels = torch.argmax(outputs, dim=1)

# 计算准确率等评估指标
accuracy = (predicted_labels == test_labels).sum().item() / len(test_labels)
# 其他指标的计算...

# 打印评估结果
print(f"Accuracy: {accuracy:.4f}")
# 其他指标的打印...

请注意，这只是一个简单的示例，具体实现可能因你的模型结构和数据类型而有所不同。建议根据你的具体需求和模型架构来进行相应的修改和调整。另外，这里的代码示例是基于PyTorch框架的，如果你使用其他框架，可能需要进行相应的调整。