Comments (2)
Hi, can you try setting torch.backends.cudnn.deterministic = True
in your main script?
They were some updates in pytorch 1.8 that produces undeterministic behavior in some convolution ops. Usually the NaN are caused by the hyperprior decoding, the scales prediction need to be 100% reproducible otherwise the entropy decoding fails to decode the correct latent values.
A better solution would be to have a quantized hyperprior decoder for the scales. We'll release a model soon.
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Thanks for your reply!
I set torch.backends.cudnn.deterministic = True
, and it works.
Thanks!
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