Create FC layer with multiple input channel and one output channel

deployment
5

Thanks, I saw similar answer to previous question here. I present my implementation for concatenating the encoders output and feed them as the input of linear layer.

class Res50_test(nn.Module):
  def __init__(self, st):
    super().__init__()
    self.encoders=nn.ModuleList()
    
    for i in range(3):
      self.encoders.append(torchvision.models.resnet50(zero_init_residual=True))
      self.encoders[i].load_state_dict(torch.load(st.trained_models[i]))
    for child in self.encoders.children():
                    for param in child.parameters():
                        param.requires_grad = False
    
    sizes = [2048*3,100]
    self.classifier = nn.Linear(sizes[-2], sizes[-1], bias=False)

    def forward(self, x):
            emb=[]
            for i,encoder in enumerate(self.encoders):
                emb.append(encoder(x))
                emb=torch.cat(emb,dim=1)
            logit=self.classifier(emb)
            return logit,emb

I wanted to differentiate between the output effect of each encoder in classifier.