请基于pytorch框架，编写一段获取CNN卷积核并绘制为图像的代码

好的，以下是基于pytorch框架，获取CNN卷积核并绘制为图像的代码：

import torch
import matplotlib.pyplot as plt

# 定义CNN模型
class CNNModel(torch.nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 6, 5)
        self.pool = torch.nn.MaxPool2d(2, 2)
        self.conv2 = torch.nn.Conv2d(6, 16, 5)
        self.fc1 = torch.nn.Linear(16 * 5 * 5, 120)
        self.fc2 = torch.nn.Linear(120, 84)
        self.fc3 = torch.nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(torch.nn.functional.relu(self.conv1(x)))
        x = self.pool(torch.nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.nn.functional.relu(self.fc1(x))
        x = torch.nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = CNNModel()

# 获取第一个卷积层的卷积核
conv1_weights = model.conv1.weight.detach().numpy()

# 绘制卷积核图像
fig, axs = plt.subplots(6, 3, figsize=(10, 10))
for i in range(6):
    for j in range(3):
        axs[i, j].imshow(conv1_weights[i, j])
        axs[i, j].axis('off')
plt.show()

以上代码会获取一个使用pytorch框架定义的简单CNN模型的第一个卷积层的卷积核，并将其绘制成6x3的图像。如果您需要绘制其他卷积层的卷积核，只需更改`model.conv1`为其他卷积层即可。希望能够帮助到您。