feat: GaussianMixtureConditional

[lin-toto]

#238, Gaussian Mixture Model implementation which is used in Cheng2020 paper.

Implementation details follow the official code but I changed CDF generation into a parallel algorithm so that it's easier to work with parallel context models.

Also modified rans_interface to allow a torch tensor to be CDF, or ~80% of execution time is wasted on type conversion.

[fracape]

sorry for the late response, I was busy with other things. Can you give me push access to your fork for a quick fix before merge

[lin-toto]

sorry for the late response, I was busy with other things. Can you give me push access to your fork for a quick fix before merge

Thanks, though the "allow edits by maintainers" option seems already on my side. I've also sent an invitation to my fork if this doesn't work.

[fracape]

Correct, I messed-up and created a new branch, thanks

[fracape]

partially tested, approving. TODO (fracape): train, test and upload model

[fracape]

Have you tried with cheng2020-anchor+gmm or cheng2020-attn+gmm (and compared results)? I think you assembled the model outside compressai for testing. Can you add the model? I'd like to try a training in same conditions as the other models (vimeo90k, etc.) and also test the regression with K=1.

[lin-toto]

Sorry I must have missed this comment somehow and just came across it after 4 months.

I developed this GMM variant for some other model architecture so haven't tried on the exactly original Cheng2020.

I do used to work with the lab that published the Cheng2020 paper. Let me go ask a few guys if they have some pretrained models.