@Oli thank you very much for your help. Yes, you are right. the pic shows the output channel. I read in the paper “Please note that the kernel size value corresponds to the number of feature maps (dimensionality of the output of the layer)”.
the snippet you posted first if we want to keep it as pic, the maxpooling should be after conv. also if we agree that those filter size are output channel, still up-sampling sections up4.conv_trans1.weight torch.Size([1152, 384, 2, 2]) giving the wrong number of channels.
in the second model you posted is causing an error RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same which is about x1, down = self.down1(x)