pytorch修改ConvNeXt-T网络

 使用迁移学习，修改ConvNeXt-T网络，对特征进行融合

import torchimport torch.nn as nnimport torchvision.models as modelsclass CustomConvNeXtT(nn.Module):    def __init__(self, in_channels=3, num_classes=2, chunk=2, csv_shape=107, CSV=True):        super(CustomConvNeXtT, self).__init__()        self.chunk = chunk        self.num_classes = num_classes        self.CSV = CSV        # 加载预训练的ConvNeXt-Tiny模型        convnext = models.convnext_tiny(pretrained=True)        # 冻结预训练模型的所有参数        for name, param in convnext.named_parameters():            param.requires_grad = False        # 将修改后的模型赋值给自定义的ConvNeXt-T网络        self.model = convnext        # 修改第一个卷积层的输入通道数        self.model.features[0][0] = nn.Conv2d(in_channels, 96, kernel_size=4, stride=4)        # 获取特征提取器的输出特征维度        num_ftrs = self.model.classifier[2].in_features        # 修改分类头部        self.model.classifier = nn.Sequential(            nn.LayerNorm(num_ftrs * self.chunk + (csv_shape if CSV else 0), eps=1e-6, elementwise_affine=True),            nn.Linear(num_ftrs * self.chunk + (csv_shape if CSV else 0), num_classes)        )    def extract_features(self, x):        x = self.model.features(x)        x = self.model.avgpool(x)        x = torch.flatten(x, 1)        return x    def forward(self, data_DCE, data_T2, csv):        data_DCE = self.extract_features(data_DCE)        data_T2 = self.extract_features(data_T2)        if not self.CSV:            csv = torch.ones_like(csv)        x = torch.cat((data_DCE, data_T2, csv), dim=1)        print(f"Feature size after concatenation: {x.size()}")  # 打印特征拼接后的尺寸        output = self.model.classifier(x)        return outputif __name__ == '__main__':    net = CustomConvNeXtT(in_channels=3, num_classes=2, chunk=2, csv_shape=107, CSV=True)    for name, param in net.named_parameters():        print(name, ":", param.requires_grad)    data_DCE = torch.randn(64, 3, 224, 224)    data_T2 = torch.randn(64, 3, 224, 224)    csv = torch.randn(64, 107)    output = net(data_DCE, data_T2, csv)    print("输出特征尺寸:", output.size())