w0 = w0.clone().detach()这样写的原因是什么
时间: 2024-05-31 08:11:39 浏览: 5
这样写的原因是为了将w0的梯度与计算图分离,避免在对w0进行操作时对计算图造成影响。
在PyTorch中,如果一个张量需要梯度计算,即它是由其他张量计算得来的,那么在对这个张量进行操作时,会同时记录下操作对应的梯度计算方式,这个记录的过程构成了计算图。但是有时候我们不需要对某个张量进行梯度计算,这时候就需要使用detach()方法将其与计算图分离。
所以,w0.clone().detach()的作用是将w0的值复制一份,然后将这份复制的值与计算图分离,这样在对w0进行操作时,就不会对计算图造成影响。
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针对卷积层为nn.Conv2d(1, 10, kernel_size=5) 的神经网络,用pytorch针对MNIST数据集写DeepFool算法的代码,并写出测试代码验证用Deepfool算法生成的对抗性示例攻击神经网络得到的正确率。要求在cpu上运行
以下是针对MNIST数据集的DeepFool攻击代码和测试代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 320)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
def deepfool(net, image, num_classes=10, overshoot=0.02, max_iter=50):
"""
DeepFool算法的实现
"""
image = image.unsqueeze(0).clone().detach().requires_grad_(True)
f_image = net(image)
I = f_image.argmax().item()
w = torch.zeros_like(image)
r = torch.zeros_like(image)
for k in range(max_iter):
pert = np.inf
f_image = net(image)
for i in range(num_classes):
if i == I:
continue
# compute w^k_i and f^k_i
net.zero_grad()
f_i = f_image[0, i]
f_i.backward(retain_graph=True)
grad_i = image.grad.clone().detach()
net.zero_grad()
f_I = f_image[0, I]
f_I.backward(retain_graph=True)
grad_I = image.grad.clone().detach()
w_i = grad_i - grad_I
f_i = f_i - f_I
# compute perturbation
pert_i = abs(f_i) / w_i.norm()
if pert_i < pert:
pert = pert_i
w = w_i
# compute r^k+1
r = r + pert * w / w.norm()
image = image + pert * w / w.norm()
image = torch.clamp(image, 0, 1)
return image.squeeze()
if __name__ == '__main__':
# 加载MNIST数据集
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=1, shuffle=True)
# 加载神经网络
net = Net()
net.load_state_dict(torch.load('mnist_cnn.pt', map_location=torch.device('cpu')))
net.eval()
# 对抗性攻击
num_samples = 100
num_classes = 10
num_success = 0
for i, (image, label) in enumerate(testloader):
if i == num_samples:
break
image = image.squeeze()
label = label.item()
adv_image = deepfool(net, image)
# 测试攻击成功率
f_image = net(image.unsqueeze(0)).squeeze()
f_adv_image = net(adv_image.unsqueeze(0)).squeeze()
I = f_image.argmax().item()
J = (f_adv_image - f_image).argmax().item()
if I != J:
num_success += 1
print('Sample %d, true label: %d, predicted label: %d, adversarial label: %d' % (i+1, label, I, J))
print('Success rate: %.2f%%' % (num_success / num_samples * 100))
```
在上面的代码中,我们首先加载了MNIST数据集和训练好的神经网络,然后定义了DeepFool算法的实现。对于每个测试样本,我们使用DeepFool算法生成对抗性样本,并计算攻击成功率。最终输出测试结果,其中包括每个样本的真实标签、神经网络的预测标签和攻击后的标签,以及整个测试集的攻击成功率。
需要注意的是,由于DeepFool算法需要对每个类别都进行一次梯度计算,因此攻击的时间会比较长。为了加速测试过程,可以将测试样本数量设置为较小的值。
用python写一个图片风格化迁移项目
好的,以下是一个简单的图片风格化迁移项目示例,使用 Python 和 PyTorch 框架。
步骤一:导入必要的库
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, models
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
```
步骤二:定义超参数和数据加载器
```python
# 定义超参数
content_weight = 1 # 内容损失函数的权重
style_weight = 1000 # 风格损失函数的权重
epochs = 20 # 迭代次数
batch_size = 4 # 批处理大小
# 加载数据集
data_transform = transforms.Compose([
transforms.Resize(256), # 缩放到256x256像素大小
transforms.CenterCrop(256), # 中心裁剪为256x256像素
transforms.ToTensor(), # 转换为张量
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 标准化
])
content_dataset = datasets.ImageFolder('./content', transform=data_transform)
style_dataset = datasets.ImageFolder('./style', transform=data_transform)
content_loader = DataLoader(content_dataset, batch_size=batch_size, shuffle=True)
style_loader = DataLoader(style_dataset, batch_size=batch_size, shuffle=True)
```
步骤三:定义模型
```python
# 定义风格迁移模型
class StyleTransferModel(nn.Module):
def __init__(self):
super(StyleTransferModel, self).__init__()
self.features = models.vgg19(pretrained=True).features[:35] # 加载预训练的VGG19模型
for param in self.parameters():
param.requires_grad = False # 冻结参数
self.content_loss = nn.MSELoss() # 定义内容损失函数
self.style_loss = nn.MSELoss() # 定义风格损失函数
self.content_feature = None # 内容图像的特征
self.style_features = None # 风格图像的特征
self.target_feature = None # 目标图像的特征
def forward(self, x):
self.content_feature = self.features(x.clone()) # 克隆一份x,防止直接修改导致误差计算错误
return x
def compute_content_loss(self):
loss = self.content_loss(self.target_feature, self.content_feature)
return content_weight * loss
def compute_style_loss(self):
loss = 0
for i in range(len(self.style_features)):
target_gram = self.gram_matrix(self.target_feature[i])
style_gram = self.gram_matrix(self.style_features[i])
loss += self.style_loss(target_gram, style_gram)
return style_weight * loss
def gram_matrix(self, x):
b, c, h, w = x.size()
features = x.view(b * c, h * w)
G = torch.mm(features, features.t())
return G.div(b * c * h * w)
def set_style_features(self, x):
self.style_features = []
for feature in self.features:
x = feature(x)
if isinstance(feature, nn.ReLU):
feature.inplace = False
if isinstance(feature, nn.MaxPool2d):
self.style_features.append(x)
if len(self.style_features) == 5:
return
def set_target_feature(self, x):
self.target_feature = self.features(x.clone())
```
步骤四:定义训练函数
```python
def train(model, content_loader, style_loader, epochs):
optimizer = optim.Adam(model.parameters(), lr=0.001) # 定义优化器
for epoch in range(epochs):
model.train()
content_iter = iter(content_loader)
style_iter = iter(style_loader)
for i in range(len(content_iter)):
content, _ = content_iter.next()
style, _ = style_iter.next()
model.set_style_features(style) # 设置风格图像的特征
model.set_target_feature(content) # 设置目标图像的特征
optimizer.zero_grad() # 梯度清零
loss = model.compute_content_loss() + model.compute_style_loss() # 计算损失函数
loss.backward() # 反向传播
optimizer.step() # 更新参数
print("Epoch ", epoch + 1, " complete.")
```
步骤五:定义测试函数
```python
def test(model, content_path, style_path, output_path):
content_image = Image.open(content_path)
style_image = Image.open(style_path)
transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
content = transform(content_image).unsqueeze(0)
style = transform(style_image).unsqueeze(0)
model.set_style_features(style)
model.set_target_feature(content)
output = model(content)
output_image = output.squeeze().detach().numpy()
output_image = np.transpose(output_image, (1, 2, 0))
output_image = output_image * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]
output_image = np.clip(output_image, 0, 1)
output_image = Image.fromarray((output_image * 255).astype(np.uint8))
output_image.save(output_path)
```
步骤六:训练模型
```python
model = StyleTransferModel()
train(model, content_loader, style_loader, epochs)
```
步骤七:测试模型
```python
test(model, './test_content.jpg', './test_style.jpg', './output.jpg')
```
以上是一个简单的图片风格化迁移项目示例,你可以根据需要进行修改或优化。