Is there a built-in affine transformation module? Something of the form y = A @ x + b? I see that there is a lower Cholesky affine transformation, but it is not a module. Tensorflow has the sort of thing I’m looking for (their Affine Bijector), and I was a little surprised it doesn’t seem to exist in Pyro.
Hi, most common transformations live upstream in PyTorch Distributions (torch.distributions.transforms). There’s an AffineTransform there: Probability distributions - torch.distributions — PyTorch 1.13 documentation
Thanks for the replies @JamesTrick and @eb8680_2. Unfortunately, the pull-request wasn’t quite what I was look for, and it was actually rejected shortly after you posted, but I appreciate the pointer!
As for the AffineTransform in the PyTorch Distributions package, the main issue is that PyTorch’s Transforms aren’t trainable; they don’t subclass nn.Module. Pyro has a workaround for this, which provides a class that deals with the persnickety __hash__ function issue. I’d happily just throw together an implementation of AffineTransform subclassing Pyro’s TransformModule, but I was trying to avoid it because inverting affine transformations is an expensive operation unless you use some tricks that are a bit tougher to implement correctly.
Anyway, I was hoping there was an existing implementation of an affine transform that subclasses Pyro’s TransformModule. If there isn’t, maybe I should implement one and create a pull request myself?
Thanks again for the responses!