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zye1996 avatar zye1996 commented on May 27, 2024 1

hi, I've changed that line to
num = torch.sqrt( (y2-y1).square() + (x2-x1).square() +1e-14)
the 1e-8 is no more necessary as the sqrt is guaranteed positive. Please let me know if there are further issues.

Hi I have verified the fix work for the backprop. I can confirm that DIoU loss enhance the performance of the detector considerably.

from rotated_iou.

lilanxiao avatar lilanxiao commented on May 27, 2024

Thank you very much for the issue!

Yes, it's a bug. According to this link: pytorch/pytorch#6394, the backprop of torch.sqrt() would generate nan if the input is zero.

Actually, I'm surprised that Pytorch really puts an inf there. I thought the gradient was hard-coded to some very large but limited value. lol. My computer is now occupied by some other tasks and I cannot run tests. But I will fix this ASAP and let you know.

from rotated_iou.

lilanxiao avatar lilanxiao commented on May 27, 2024

hi, I've changed that line to
num = torch.sqrt( (y2-y1).square() + (x2-x1).square() +1e-14)
the 1e-8 is no more necessary as the sqrt is guaranteed positive. Please let me know if there are further issues.

from rotated_iou.

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