I have created a network with 2 inputs to pass to forward, which are then concatenated later to be fed into another network.
def forward(self, latent, image): # latent, image = input_dict["latent"], input_dict["image"] x1 = self.net1(latent) print(x1.size()) x2 = self.net2(image) print(x2.size()) x = torch.cat([x1, x2], 1) return self.merge(x)
After passing it to torchsummary, it is able to compile(?) the network with given inputs.
summary(netD, [(1024,1,1), (3,64,64)])
However, I have tried various methods to pass on 2 torch.Tensors to the network for feedfoward but they all throw errors.
dlXGenerated = netG(dlZ, LR)
Can anyone point me to the correct way of passing 2 tensors to a network?