incorrect iou?

Hi @lilanxiao ,

Maybe I’m missing something but:

device = torch.device('cuda:0')
box_0 = torch.tensor([.0, .0, 2., 2., .0], device=device)
box_1 = torch.tensor([.0, 2., 2., 2., .0], device=device)
print(cal_iou(box_0[None, None, ...], box_1[None, None, ...])[0].cpu().numpy())

returns [[0.33333334]]. However, these 2 boxes are not intersecting.

[lilanxiao]

@kevinghst thank you for the issue! Yeah, it seems my dark magic didn’t work. The true reason of the error was the vertices of the intersection polygon were duplicated (we only need 4, but would get 8) if the two boxes were exactly the same. In this case, the shoelace formula was broken. Also, there was still some numerically instability in my code.

I hope the bug is now properly fixed in commit 6267cefca26cbcd742bfb3cc4ae8064ba0b7a1da . This time I’ve modified the CUDA part and removed the magic. BTW, please remember to re-compile the CUDA extension.

[filaPro]

Probably this EPSILON stuff is not the best idea here. After replacing

t = den_t / (num + EPSILON) 
mask_t = (t >= 0) * (t < 1)
...
u = - den_u / (num + EPSILON)
mask_u = (u >= 0) * (u < 1)  

with

t = den_t / num
t[num == .0] = -1.
mask_t = (t >= 0) * (t <= 1)
...
u = -den_u / num
u[num == .0] = -1.
mask_u = (u >= 0) * (u <= 1)  

everything looks fine.