DenseNet-Attention实现四分类代码

以下是使用DenseNet-Attention实现四分类的代码范例。这里使用的是PyTorch框架。

import torch
import torch.nn as nn
from torchvision.models import densenet121

class DenseNet_Attention(nn.Module):

    def __init__(self, num_classes=4):
        super(DenseNet_Attention, self).__init__()

        self.densenet = densenet121(pretrained=True)
        num_ftrs = self.densenet.classifier.in_features
        self.densenet.classifier = nn.Linear(num_ftrs, num_classes)
        
        self.attention = nn.Sequential(
            nn.Conv2d(in_channels=1024, out_channels=64, kernel_size=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        features = self.densenet.features(x)
        out = self.densenet.avgpool(features)
        out = torch.flatten(out, 1)
        out = self.densenet.classifier(out)

        # Attention Mechanism
        attention_weights = self.attention(features)
        out = out * attention_weights
        out = torch.sum(out, dim=(2,3)) / torch.sum(attention_weights, dim=(2,3))

        return out

这里使用了DenseNet-121网络作为基础模型，并添加了一个注意力机制来提高模型的性能。注意力机制的实现是一个简单的卷积神经网络，其输出的权重将与分类器的输出相乘，以获得最终的分类结果。模型的输出是一个四元组，每个元素对应一个类别的概率。