cifar10生成PGD对抗样本，如何分批生成 代码

以下是使用PyTorch生成CIFAR10数据集的PGD对抗样本，并分批生成的示例代码：

import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim

# 定义CIFAR10数据集
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)

# 定义PGD攻击函数
def pgd_attack(model, X, y, epsilon, alpha, num_iter):
    delta = torch.zeros_like(X, requires_grad=True)
    for t in range(num_iter):
        loss = nn.CrossEntropyLoss()(model(X + delta), y)
        loss.backward()
        delta.data = (delta + alpha*delta.grad.detach().sign()).clamp(-epsilon, epsilon)
        delta.grad.zero_()
    return delta.detach()

# 定义模型和优化器
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.fc1 = nn.Linear(128 * 4 * 4, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.relu(self.conv3(x))
        x = x.view(-1, 128 * 4 * 4)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 定义批大小和迭代次数
batch_size = 64
num_epochs = 10
num_iter = 40
epsilon = 0.1
alpha = 0.01

# 分批生成PGD对抗样本
for epoch in range(num_epochs):
    running_loss = 0.0
    for i in range(0, len(trainset), batch_size):
        X, y = trainset[i:i+batch_size]
        X, y = Variable(X), Variable(y)
        model.zero_grad()
        delta = pgd_attack(model, X, y, epsilon, alpha, num_iter)
        output = model(X + delta)
        loss = nn.CrossEntropyLoss()(output, y)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * batch_size
    print('Epoch [%d/%d], Loss: %.4f' % (epoch+1, num_epochs, running_loss / len(trainset)))

在以上代码中，我们使用`range`函数以`batch_size`的大小来分批生成PGD对抗样本。对于每个批次，我们使用`pgd_attack`函数来生成对抗样本，然后使用优化器来更新模型参数。最后，我们计算该批次的损失并打印出来。