Some questions about the detach of the f in the ft_netAB

[Liu-1994]

Hello, thank you very much for providing the implementation code of the DG-Net model.
I encountered some problems during the implementation of the project. I will be honored if you can give me some suggestions.

I found the f=f.detch() in the ft_netAB. This causes the vector f to have no gradient. Then the loss_gen_recon_f_* has no no contribution to model parameter update. Is there something wrong with me or something wrong with the code?

I will be grateful if you can give me some suggestions. Thank you！

[layumi]

Thank you @Liu-1994 .
‘f’ is the appearance code for image generation; We do not want the generation losses to update f.
Thus, we use the detach here. In this way, f is mainly updated via the re-id related losses.

[Liu-1994]

@layumi Thanks for replying. I understood that.
And I have another small question. What is the role of the loss_gen_recon_f_a and loss_gen_recon_f_b ？As f is the input of the generator, I think the two losses may not update G.

[layumi]

Input -> Appearance Encoder(No Update) -> f (Detach)-> Decoder (Has Gradient) -> Generated Image (Has Gradient) -> Appearance Encoder(No Update, But has gradient) -> f

[Liu-1994]

@layumi Thanks for teh replying. I have understood the process except the last step.
As the f.detach() is executed in the ft_netAB.forward(). I think the final f in the above process is also detachso there is no back propagation for f -> Appearance Encoder. When I debug the project, the grad_fn of f_a and f_a_recon are both None.

[layumi]

Thanks for the great suggestion.
I have not checked it yet. It may not work.
In fact, the feature reconstruction loss has a similar role with the ID recon loss.

[Liu-1994]

Thank you very much for your reply. I have nothing else to ask about this issue.
By the way, if you are convenient, could you please take a look at another issue (#39 ) I raised.