Are pretrained models available? about gnina-torch HOT 8 CLOSED

I'll add that there is now a dumpmodel binary built when you build gnina that will export the built-in caffe model weights (but not that actual caffe model file as you can get this easily from the source code).

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I actually have these weights files for all of the built-in models easily available.

The built-in model weights were pulled directly from the source code of GNINA and converted to a .caffemodel. Then https://github.com/vadimkantorov/caffemodel2pytorch was utilized to convert the .caffemodel files to .pt for PyTorch.

I have created PR #34 to add these weights files.

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Unfortunately, I never converted the original Caffe models weights to PyTorch. The examples folder contains some scripts to reproduce part of the results of your and Paul's work, but are meant mostly for testing.

However, I would be very interested in doing so (or incorporate PRs that will allow to do that). It would be a great feature, and would serve as double check that everything works as expected. Is there any recommended way of doing this Caffe to PyTorch conversion?

PS: For easier integration, I could push the package to PyPI and conda-forge, if it is of interest.

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That's fantastic, thanks @drewnutt ! I'll incorporate PR #34 and start adding the functionality to use them easily (including model ensemble, which is currently not implemented).

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Awesome! Thanks, @drewnutt.

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I opened #35 as a follow-up. I'll link relevant PRs there. Please feel free to comment with specific needs and missing features from your point of view.

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@mattragoza you can now easily load the pre-trained default2017 and *default2018* model as follows:

from gninatorch.gnina import load_gnina_model

model = load_gnina_model(MODEL_NAME)

where MODEL_NAME corresponds to name of the .pt files in gninatorch/weights (without the .pt extension); this is essentially equivalent to GNINA's --cnn option (but without model ensembles, which will follow).

The dense model is not yet supported because I haven't managed to obtain the same output as GNINA.

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An ensemble of models is now also available:

from gninatorch.gnina import load_gnina_models

ensemble = load_gnina_models([MODEL_NAME_1, MODEL_NAME_2, ...])

The ensemble of models returns log_CNNscore, CNNaffinity and CNNvariance.

Still no dense model for the time being (I suspect that there might be an issue with BatchNorm layer weights, but I'm still investigating), so it is not currently possible to reproduce GNINA's default model (since it contains two dense models). However, *_default2018_ensemble work as expected.

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