BUG: Make distance metrics return tensors, fix #700 #701 #702
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Makes functions in `vak.transforms.distance.functional` return tensors so we don't cause errors when lightning tries to convert from numpy to tensors to log. Letting lightning do the conversion kind of works, but it can cause a fatal error for someone using an Apple M1 with 'mps' as the accelerator, see https://forum.vocalpy.org/t/vak-tweetynet-with-an-apple-m1-max/78/4?u=nicholdav I don't find any explicit statement in either the Lightning or Torchmetrics docs that metrics should always be tensors, and that this guarantees there won't be weird issues (right now we get a warning on start-up that all logged scalars should be float32, but I would expect one should be able to log integers too?). But from various issues I read, it seems like that should be the case, Lightning-AI/pytorch-lightning#2143 and I notice that torchmetrics classes tend to do things like convert to a float tensor
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Tests passed locally, merging |
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Makes functions in
vak.transforms.distance.functionalreturn tensors so we don't cause errors when lightning tries to convert from numpy to tensors to log.Letting lightning do the conversion kind of works, but it can cause a fatal error
for someone using an Apple M1 with 'mps' as the accelerator, see https://forum.vocalpy.org/t/vak-tweetynet-with-an-apple-m1-max/78/4?u=nicholdav
I don't find any explicit statement in either the Lightning or Torchmetrics docs that metrics should always be tensors, and that this guarantees there won't be weird issues (right now we get a warning on start-up that all logged scalars should be float32, but I would expect one should be able to log integers too?).
But from various issues I read, it seems like that should be the case, Lightning-AI/pytorch-lightning#2143 and I notice that torchmetrics classes tend to do things like convert to a float tensor