在CNN模型中使用transformer将图像数据和数值数据融合，生成pytorch代码

以下是一个简单的示例代码，展示了如何使用PyTorch中的CNN和Transformer模型将图像和数值数据融合：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ImageTransformer(nn.Module):
    def __init__(self, img_channels, img_size, num_numerical_features, transformer_emb_size, transformer_num_layers):
        super(ImageTransformer, self).__init__()
        
        # 图像卷积层
        self.conv1 = nn.Conv2d(img_channels, 32, kernel_size=3, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2)
        self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=2)
        
        # 数值特征的线性层
        self.num_lin = nn.Linear(num_numerical_features, transformer_emb_size)
        
        # Transformer编码器层
        self.transformer = nn.TransformerEncoderLayer(d_model=transformer_emb_size, nhead=4, dim_feedforward=1024, num_layers=transformer_num_layers)
        
        # 最终的线性层
        self.fc = nn.Linear(transformer_emb_size + 512, 1)
        
        # 池化层
        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        
    def forward(self, x_img, x_num):
        # 图像特征提取
        x_img = F.relu(self.conv1(x_img))
        x_img = F.relu(self.conv2(x_img))
        x_img = F.relu(self.conv3(x_img))
        x_img = F.relu(self.conv4(x_img))
        x_img = F.relu(self.conv5(x_img))
        x_img = self.pool(x_img).view(x_img.size(0), -1)
        
        # 数值特征编码
        x_num = F.relu(self.num_lin(x_num))
        
        # Transformer编码器处理数值特征
        x_num = x_num.permute(1, 0, 2)
        x_num = self.transformer(x_num)
        x_num = x_num.permute(1, 0, 2)
        x_num = x_num.mean(dim=1)
        
        # 合并图像和数值特征
        x = torch.cat([x_img, x_num], dim=1)
        
        # 最终的线性层
        x = self.fc(x)
        return x

这个模型包括了一个图像卷积层和一个Transformer编码器层，用于处理数值特征。在forward方法中，我们首先提取图像特征，然后将数值特征编码成一个向量，最后将两个特征向量合并并输入到一个线性层中。