About code question

[cocowy]

hi, congratulations! Nice work!
I have a question about /networks/deformable_refine.py/class GetValueV2(nn.Module)/line 72 :

        # clip p
        p_y = torch.clamp(p[..., :N], 0, h-1) / (h-1) * 2 - 1
        p_x = torch.clamp(p[..., N:], 0, w-1) / (w-1) * 2 - 1

According to the context of the code, the p_y and p_x shoud be

        # clip p
        p_x = torch.clamp(p[..., :N], 0, w-1) / (w-1) * 2 - 1
        p_y= torch.clamp(p[..., N:], 0, h-1) / (h-1) * 2 - 1

to achieve bilinear sample operation. Am I right?
THx!

[SpadeLiu]

hi, congratulations! Nice work! I have a question about /networks/deformable_refine.py/class GetValueV2(nn.Module)/line 72 :

        # clip p
        p_y = torch.clamp(p[..., :N], 0, h-1) / (h-1) * 2 - 1
        p_x = torch.clamp(p[..., N:], 0, w-1) / (w-1) * 2 - 1

According to the context of the code, the p_y and p_x shoud be

        # clip p
        p_x = torch.clamp(p[..., :N], 0, w-1) / (w-1) * 2 - 1
        p_y= torch.clamp(p[..., N:], 0, h-1) / (h-1) * 2 - 1

to achieve bilinear sample operation. Am I right? THx!

Hi, thanks for your suggestion.

Actually, it doesn't matter whether the first N points are 'x' or 'y', the model learns to regress the coordinates by itself.