Comments (7)
5had3z
commented on June 16, 2024
If I change the max_displacement to 1 or 2 and C=H=W=64, it works fine.
But if I have C=H=W=128 it doesn't work (with max_displacement 1 or 2)
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lliuz
commented on June 16, 2024
This is an issue about correlation_cuda package. I am not very familiar with cuda programming, so I may not be able to help you to solve this problem.
If you have trouble with this package during training, you can alternatively use my PyTorch implementation (It is correct although kind of slower.)
Since the correlation_cuda package is widely used in other projects, such as ClementPinard/Pytorch-Correlation-extension, NVIDIA/flownet2-pytorch, you can refer to these repos for help.
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5had3z
commented on June 16, 2024
From some testing that I did, there are access requests for index -1 during some operations.
correlaiton forward kernel during the element wise product sum: prod_sum += rInput1*rInput2
And in correlation_backward_input1 when reading from rInput2
In my own code I skip these operations during a boundary check and consequently don't have this issue anymore.
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lliuz
commented on June 16, 2024
Thanks for sharing and I'm glad you could find a workaround in the end!
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sun0215
commented on June 16, 2024
Hi, I have met the same issue as you. May I ask how do you use boundary check to skip these operations you mentioned above? @5had3z
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5had3z
commented on June 16, 2024
@sun0215 I've got the checks in my re-implementation but they're commented out as it turns out it this issue is due to insufficient padding (the pad_size variable). You won't access out of bounds if this is large enough, just search for the smallest number that works for you.
Bound checks are commented out as cuda cores are dumb afaik, there's no branch predicting, so you're paying the full cost of these checks, hence why I've commented out, but still there for future reference.
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sun0215
commented on June 16, 2024
I have solved this issue by setting padding as 20 for 256*256 image. Thank you very much.<html>
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Re: [lliuz/ARFlow] Illegal memory access during back propagation unit test (#10)
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<p dir="auto">Bound checks are commented out as cuda cores are dumb afaik, there's no branch predicting, so you're paying the full cost of these checks, hence why I've commented out, but still there for future reference.</p>
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