Add a new layers to a trained model

I have a (example) model shown in the left of a picture. The model is already trained, and I have a model_state_dict file.

I actully would like to add new layers to this trained model, which is shown in the right of the picture, where I added new output by concatinating two different features by scaling the dimension of the second feartures.

if I write code, which is something like

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.features1(x)
        out1 = x
        scale= out1.shape[2] # take a scaling factor

        x = self.features2(x)
        out2 = F.interpolate(x, (scale), mode='nearest') # scale the size of features
        outA =[out1, out2], dim=1) # concat two kinds of features

        x = self.features3(x)
        #return x
        return x, outA

So, I’d like to prepare the new model (right one) without any training. I guess that the new layers in the model do not include any new trainable paramters, where the scaling parameter is required, which is already determined by the dimentions of the features.

So, I guess that the model_state_dict file trained with the former model (left one) could be loaded into the new model although the name of the layer must be modified properly.

Then, I guess that the model could get availble.

Is this correct? Are there any useful information on this kind of transformation/displacement?

Your description sounds correct. The state_dicts would be compatible as long as no submodule or parameter names are changes. Adapting the forward method to your needs is also valid and doesn’t break the load_state_dict usage.

I see, thank you for your comment, I’ll try it.