Adding model with cycle consistency and VampPrior #2421
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Hrovatin
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Codecov ReportAttention: Patch coverage is
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## main #2421 +/- ##
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- Coverage 84.81% 84.62% -0.20%
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Files 173 183 +10
Lines 14793 15436 +643
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+ Hits 12547 13063 +516
- Misses 2246 2373 +127
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Hey, thanks for putting in this PR! A couple of high-level comments for now.
Could you restructure the files as follows?
csi/model/_model.py->csi/_model.pycsi/model/_utils.py->csi/_utils.pycsi/module/_module.py->csi/_module.pycsi/module/_priors.py->csi/_priors.pycsi/nn/_base_components.py->csi/_components.pycsi/train/_trainingplans.py->csi/_trainingplans.py- Refactor
csi/module/_module.pyto usescvi.module.base.LossOutputinstead of our previousLossRecorderand removecsi/module/_loss_recoder.py - If the plan is to directly inherit from
UnsupervisedTrainingMixinwithout any major changes except setting_training_plan_cls, this can be done within_model.pyby directly subclassingUnsupervisedTrainingMixinand setting_training_plan_clsthere instead, so I would recommend doing that and deletingcsi/model/_training.py
I can take a look at compatibility with scArches in the next round.
We're also slowly migrating to newer typing annotations in Python. Could you add a from __future__ import annotations to the top of every file (except __init__.pys) and migrate typing from:
Optional[x]->x | NoneUnion[x, y]->x | yList[x]->list[x]Tuple[x]->tuple[x]Callable->callable- There's more, I don't remember all of them. The pre-commit hooks will warn about missing ones.
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I added changes and adapted tutorial accrordingly |
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Thanks for addressing the comments! I'll take another pass at it soon. |
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The changes I pushed today are not yet ready for review. Missing docs, cleanup, and test on real data. |
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For code simplicity the use of mock covariates was replaced with real covariates in cycle. Needs testing on real data.
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@canergen @ori-kron-wis The PR is now ready for review. @ori-kron-wis Can you help me fix the cuda test err? For some reason, it can not import SysVI, although for me, it works locally. I also was not able to test on GPU since I only have MacOS access and I get the err where mps is detected but not used. - If you have any solutions for that also please let me know. |
| self.var_param = Parameter(torch.zeros(1, n_output)) | ||
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| raise ValueError("Mode not recognised.") | ||
| self.activation = torch.exp if activation is None else activation |
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Do you still need nan_to_num if you use here a softplus activation? You should consider changing it. It's just for backward compatibility still exp in scvi.
| super().train(*args, **kwargs) | ||
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| @torch.inference_mode() | ||
| def get_latent_representation( |
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Does it need it's own get_latent_representation or can it inherit from VAEMIXIN class?
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First look, looks good. Two comments. Should we wait for FClayers to be compatible and then use FClayers? Or do you think it will anyhow not work? |
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The FCLayers I use now are the a base for the suggestion that you are working of so it should work with minor modifications in how API is accessed in SysVI. If that is planned to be finished soon then lets wait, but otherwise I suggest merging so that we finally get it in main and then switching afterwards Also @canergen can you please help with tests. I can not check code on GPU locally (don't have any) and the CI tests fail - I think not due to my side as I can make the below import |
Closes #2383