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Energy Task preventing over/under-flow #352

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merged 9 commits into from
Dec 1, 2022

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Aske-Rosted
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This task should be useful if experiencing over/underflow errors leading to NaN's in the training process.

@asogaard asogaard self-requested a review November 11, 2022 12:21
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Hi @Aske-Rosted,
Thanks for this contribution! I have made a few "inline" suggestions that you can consider using. Please also consider setting up pre-commit hooks, see our contributing guide as currently the automated black and flake8 checks are failing. Let me know if it would be beneficial to talk this over at some point. :)

src/graphnet/models/task/reconstruction.py Outdated Show resolved Hide resolved
src/graphnet/models/task/reconstruction.py Outdated Show resolved Hide resolved
@RasmusOrsoe
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@asogaard @Aske-Rosted Just wanted to add here that I also don't use the original class for the same reason. Perhaps we should consider if it should remain?

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Hi @Aske-Rosted,
I have added one suggestion based on the automated mypy check. Another point: Perhaps we should remove the exist EnergyReconstruction -> EnergyReconstructionWithPower and instead just call the Task you're proposing EnergyReconstruction, also based on @RasmusOrsoe's point?

src/graphnet/models/task/reconstruction.py Outdated Show resolved Hide resolved
Aske-Rosted and others added 3 commits November 14, 2022 14:40
Co-authored-by: Andreas Søgaard <andreas.sogaard@gmail.com>
…n graphnet/src/graphnet/models/task/reconstruction.py
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Brilliant!

@asogaard
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asogaard commented Dec 1, 2022

Psst, @Aske-Rosted ... you're free to merge this anytime you want. :)

@Aske-Rosted
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Not sure how to, thought it would happen automatically when approved...
it says "This branch has no conflicts with the base branch
Only those with write access to this repository can merge pull requests." on my end...

@asogaard asogaard merged commit 42857bb into graphnet-team:main Dec 1, 2022
RasmusOrsoe pushed a commit to RasmusOrsoe/graphnet that referenced this pull request Oct 25, 2023
Energy Task preventing over/under-flow
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3 participants