在resnet50加入GRU模块 pytorch

以下是在resnet50中加入GRU模块的PyTorch代码示例：

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

class ResnetGRU(nn.Module):
    def __init__(self, hidden_size, num_classes):
        super(ResnetGRU, self).__init__()
        self.hidden_size = hidden_size
        self.resnet = models.resnet50(pretrained=True)
        self.gru = nn.GRU(input_size=self.resnet.fc.in_features, hidden_size=self.hidden_size, num_layers=1, batch_first=True)
        self.fc = nn.Linear(self.hidden_size, num_classes)
    
    def forward(self, x):
        with torch.no_grad():
            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.resnet.avgpool(x)
            x = x.view(x.size(0), -1)
            x = self.resnet.fc(x)

        x = x.view(x.size(0), 1, -1)
        output, hidden = self.gru(x)
        x = self.fc(hidden[-1])
        return x

在这个模型中，我们首先使用ResNet50模型提取图像特征。然后，我们使用GRU模型来处理这些特征，并输出最后一个隐藏状态。最后，我们使用全连接层将隐藏状态映射到类别空间。