本文主要是介绍pytorch修改ConvNeXt-T网络，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
使用迁移学习，修改ConvNeXt-T网络，对特征进行融合
import torch
import torch.nn as nn
import 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 = chunkself.num_classes = num_classesself.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 xdef 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())
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