normalise updates #699
normalise updates #699
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@fmassa would love to hear from you on this ! |
fmassa
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fmassa
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Hi,
Thanks for the PR!
I believe there is a potential simplifications in the code.
Let me know what you think.
torchvision/transforms/functional.py
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| else: | ||
| # This is faster than using broadcasting, don't change without benchmarking | ||
| for t, m, s in zip(tensor, mean, std): |
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I believe this can now be replaced with something simpler and as efficient as before
mean = torch.tensor(mean, dtype=torch.float32)
std = torch.tensor(std, dtype=torch.float32)
return (tensor - mean[:, None, None]) / std[:, None, None]This is out-of-place, but can be made in-place as well if needed
Just to double check, can you do some benchmarkings and report back if it's indeed now giving the same runtime speed?
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@fmassa i ran some tests and found that this approach was smewhat faster .
torchvision/transforms/functional.py
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| t.sub_(m).div_(s) | ||
| return tensor | ||
| if not inplace: | ||
| tensor_clone = tensor.clone() |
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Can't you just do
if not inplace:
tensor = tensor.clone()
# everything as before?
| t.sub_(m).div_(s) | ||
| return tensor | ||
| if not inplace: | ||
| tensor = tensor.clone() |
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Can we just have a single clone here, to avoid duplicated code?
like
if not inplace:
tensor = tensor.clone()
mean = ...
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@fmassa how does it look now ? |
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enhancement #680.