生成一段基于CIFAR模型实现PGD算法输入和输出32*32像素的图片并保存的代码
时间: 2024-02-13 08:03:19 浏览: 22
以下是基于 CIFAR10 模型实现 PGD 算法并保存对抗样本的 Python 代码:
```python
import torch
from torch import nn
from torchvision import models, datasets, transforms
from PIL import Image
# 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
# Generate and save PGD attack image
def save_pgd_image(model, x, y, filename, epsilon=0.03, alpha=0.01, num_iter=40):
model.eval()
x_adv = pgd_attack(model, x.unsqueeze(0), y.unsqueeze(0), epsilon=epsilon, alpha=alpha, num_iter=num_iter)
x_adv = x_adv.squeeze(0).detach().cpu()
x_adv = transforms.ToPILImage()(x_adv)
x_adv.save(filename)
# Load and preprocess input image
image = Image.open('input_image.png')
transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
x = transform(image)
# Generate and save PGD attack image
y = torch.tensor([0]) # replace 0 with the true label of the input image
save_pgd_image(model, x, y, 'pgd_image.png')
```
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 `save_pgd_image` 函数,该函数接受一个模型、一个输入 `x`、一个真实标签 `y` 和一个文件名 `filename`,并生成一个对抗样本并将其保存为 PNG 图像。最后,我们加载输入图像并使用 `save_pgd_image` 函数生成并保存对抗样本。请注意,输入图像必须是 32x32 像素大小。