Comments (3)
ctallec
commented on July 19, 2024
1
The latest push should fix this. torch.backends.cudnn.benchmark is set to True as a temporary fix, this slows down performances a bit, but you should not get NaNs while training under pytorch 1.0 anymore.
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xiaoschannel
commented on July 19, 2024
I had this error on GPU only but not CPU. If that's your case, it's probably due to underflow from using float tensors on the GPU. I changed torch's default tensor to double, and had the dataset loader load into double ndarraies, and it was fixed.
Edit: In case you're wondering, I am using pytorch 1.0 with cuda 10 on Ubuntu 18.04.
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leonardblier
commented on July 19, 2024
I have the same problem ... It seems to be a problem of conv2dtranspose with CUDNN. I wrote a minimal code reproducing this bug and opened an issue on the pytorch repo : pytorch/pytorch#16610
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