v_out = torch.cat((v_out_h + self.rel_h, v_out_w + self.rel_w), dim=1)

[scutzck033]

Hi, are the following codes wrong? "v_out_h, v_out_w = v_out.split(self.out_channels // 2, dim=1) v_out = torch.cat((v_out_h + self.rel_h, v_out_w + self.rel_w), dim=1)" shouldn't it be something as follows? "k_out_h, k_out_w = k_out.split(self.out_channels // 2, dim=1) k_out = torch.cat((k_out_h + self.rel_h, k_out_w + self.rel_w), dim=1)" Because the relative distance embedding is supposed to added to the "key" instead of the "value", right?

[leaderj1001]

Thanks for your comments. I'll check it and training it again. Thank you :)

[yassouali]

I think you're right, the relative embedding are added to the keys and not the values. As detailed in eq. 3 of the paper. But given that we unfold the keys, we need to compute the shifted keys before that. So we first shift the keys based on the relative positions and then unfold them.

k_out_h, k_out_w = k_out.split(self.out_channels // 2, dim=1)
k_out = torch.cat((k_out_h + self.rel_h, k_out_w + self.rel_w), dim=1)
k_out = F.unfold(k_out, kernel_size=(self.kernel_size, self.kernel_size), stride=self.stride)