请用pytorch写出用于图像分类任务的cnn-attention模型的示例代码

好的，下面是一个简单的用于图像分类任务的CNN-Attention模型的PyTorch代码示例：

import torch
import torch.nn as nn

class CNN_Attention(nn.Module):
    def __init__(self, num_classes):
        super(CNN_Attention, self).__init__()

        # CNN layers
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)

        # Attention layers
        self.attention = nn.Sequential(
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 1),
            nn.Sigmoid()
        )

        # Classifier layers
        self.fc1 = nn.Linear(128, 64)
        self.fc2 = nn.Linear(64, num_classes)

    def forward(self, x):
        # CNN layers
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.conv3(x))

        # Attention layers
        attention_weights = self.attention(x.view(x.size(0), -1))
        attention_weights = attention_weights.view(-1, 1, x.size(2) * x.size(3))
        x = x.view(x.size(0), x.size(1), -1)
        x = torch.bmm(attention_weights, x)
        x = x.view(x.size(0), -1)

        # Classifier layers
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)

        return x

这个模型包含了三个卷积层和两个全连接层，其中 Attention 层用于计算每个特征图的权重，然后将这些特征图加权求和。注意，这里的 Attention 层使用了一个简单的全连接神经网络，但是可以根据任务的要求来调整 Attention 层的结构。