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naivete5656 / WSISPDR

Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response, in MICCAI2019.
MIT License
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Where is the regional backpropagation implemented? #6

Closed erik-koynov closed 2 years ago

erik-koynov commented 2 years ago

Dear Mr. Nishimura, I have been reading your paper with interest and decided to look into the implementation (thank you very much for providing legible code) of the region backprop as defined in Eq. 5 in your paper "Weakly supervised cell instance segmentation under various conditions". However, I could not find it. The closest I got to a backpropagation based region proposal method was in the guided_model.py aroung lines 160-180, namely in the function call: img_grad.sum(1).clone().clamp(min=0).cpu().numpy(). What I read from this line is namely - the gradient used is the gradient w.r.t. the input, but with ReLU applied on it, which as far as I can see is not the same as in the paper. Where am I wrong? Your paper is really interesting and I would really appreciate your clarification. :) Yours, Erik

naivete5656 commented 2 years ago

Hi! I appreciate your interest in my paper. As you said, the "img_grad.sum(1).clone().clamp(min=0).cpu().numpy()" is not directly indicates Eq.5. I think the code you are looking for is the guided_relu function. To implement Eq. 5, I changed the backward function of the ReLU operation to the guided relu function by the following code.

def _patch(self):
        for module in self.modules():
            if isinstance(module, nn.ReLU):
                module._original_forward = module.forward
                module.forward = MethodType(guide_relu, module)

Does my answer make sense? Kazuya

erik-koynov commented 2 years ago

Hi Kazuya, thank you for the quick reply. Yes, this was exactly what I was looking for, thanks a lot! Erik :)