Comments (2)
The general idea is to be able to accept a much wider range of implementations than we could put into the core. I think we'll try to mimic the package structure of torch (e.g. torchcontrib.optim
). The implementations will be maintained by the individual contributors, we'll only be reviewing and curating them. The releases will happen much more often than those of PyTorch (think every week or two), so you'll be able to use the latest shiny things. Eventually, if it turns out that a particular implementation is used often, we'll promote it into the core.
- There's no special process for contributing, just the same thing as usual. It's best if you open an issue before you write any code so that we can discuss if your feature request looks ok to us, and once this happens you can submit a PR.
- Not from our side I think. If you have any ideas please open feature requests.
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Awesome! Thank you for your answer!
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Related Issues (20)
- Implement PixelUnshuffle HOT 1
- Contribution Ideas/Wishlist
- [Feature Request] Add Squeeze-and-Excitation block
- [feature request] Singular values and spectral norm for convolutional layers
- entropy of a tensor HOT 1
- Wanted feature signaling / voting
- swa, type mismatch HOT 1
- Butterfly Transform [Feature Request]
- Will there be some doc introduce this repository? HOT 2
- swa with CyclicLR will get error
- [Bug] Unit test fails on multi-GPU setup HOT 2
- Memory problem
- [question] SWA BatchNorm momentum reset
- SWA wasn't applied to param {}; skipping it".format(p)) HOT 3
- Does swa support model with groupnorm?
- Can't pickle SWA: AttributeError: 'SWA' object has no attribute 'defaults' HOT 1
- Loss become nan with Adam and AdamW as base optimizers
- SWA test broken
- license for SWA / whole repo
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