resnet18做级联特征融合代码

好的，以下是使用ResNet18进行级联特征融合的示例代码：

import torch
import torch.nn as nn
import torchvision.models as models

class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.resnet = models.resnet18(pretrained=True)
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
        self.fc = nn.Linear(in_features=512, out_features=1)

    def forward(self, x):
        x = self.resnet.conv1(x)
        x = self.resnet.bn1(x)
        x = self.resnet.relu(x)
        x = self.resnet.maxpool(x)

        x = self.resnet.layer1(x)
        x = self.resnet.layer2(x)
        x = self.resnet.layer3(x)
        x = self.resnet.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

class CascadeFusion(nn.Module):
    def __init__(self):
        super(CascadeFusion, self).__init__()
        self.resnet1 = ResNet18()
        self.resnet2 = ResNet18()
        self.fc = nn.Linear(in_features=2, out_features=1)

    def forward(self, x):
        x1 = self.resnet1(x)
        x2 = self.resnet2(x)

        x = torch.cat((x1, x2), dim=1)
        x = self.fc(x)

        return x

这里定义了两个ResNet18，分别用于提取两个输入图像的特征。然后将两个特征级联起来，再通过一个全连接层进行融合。最终输出一个标量，表示两个图像之间的相似度。在实际使用中，需要根据具体情况进行修改和调整。例如，可以修改ResNet18的参数、修改级联方式、增加模型深度、增加正则化等。