写一个gnn与cnn相结合的图像识别代码

以下是一个使用GNN和CNN结合的图像分类示例代码，使用PyTorch框架：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class GNN_CNN(nn.Module):
    def __init__(self, input_channels, hidden_channels, output_channels):
        super(GNN_CNN, self).__init__()
        
        # GNN layers
        self.conv1 = GCNConv(input_channels, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)
        
        # CNN layers
        self.conv3 = nn.Conv2d(input_channels, hidden_channels, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(hidden_channels, hidden_channels, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # Classifier
        self.fc = nn.Linear(hidden_channels, output_channels)
        
    def forward(self, x, edge_index):
        # GNN layers
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        
        # CNN layers
        x = x.view(-1, x.shape[1], 1, 1)   # reshape for CNN input
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = self.pool(x)
        
        # Classifier
        x = x.view(x.size(0), -1)   # flatten for FC input
        x = self.fc(x)
        
        return x

在此代码中，我们使用了一个具有两个GCN层和两个卷积层（加池化）的模型，最后使用一个全连接层进行分类。该模型需要输入一个图像张量（大小为[batch_size, input_channels, height, width]）和一个图形结构边缘索引（大小为[2, num_edges]）。