先搭建CNN 模型的卷积层， 再使用 Transformer 对图像数据和数值数据进行融合， 最后将融合的信息进行回归预测。pytorch实现代码

好的，以下是一个简单的示例代码：

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

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.pool = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(20 * 5 * 5, 50)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = x.view(-1, 20 * 5 * 5)
        x = F.relu(self.fc1(x))
        return x

class Transformer(nn.Module):
    def __init__(self):
        super(Transformer, self).__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(d_model=50, nhead=5)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=2)

    def forward(self, x):
        x = self.transformer_encoder(x)
        return x

class Regression(nn.Module):
    def __init__(self):
        super(Regression, self).__init__()
        self.linear1 = nn.Linear(50, 10)
        self.linear2 = nn.Linear(10, 1)

    def forward(self, x):
        x = F.relu(self.linear1(x))
        x = self.linear2(x)
        return x

class ImageRegression(nn.Module):
    def __init__(self):
        super(ImageRegression, self).__init__()
        self.cnn = CNN()
        self.transformer = Transformer()
        self.regression = Regression()

    def forward(self, image, numerical):
        image_feature = self.cnn(image)
        numerical_feature = self.transformer(numerical)
        fused_feature = torch.cat((image_feature, numerical_feature), dim=1)
        output = self.regression(fused_feature)
        return output

在这个示例中，我们首先定义了一个简单的CNN模型，将图像数据作为输入，并提取其特征。然后，我们定义了一个Transformer模型，将数值数据作为输入，并提取其特征。最后，我们将两个特征连接在一起，并通过一个线性层对它们进行回归预测。