bethgelab / bethgelab/model-vs-human
Feature request: Simpler loading of custom models
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- Dominant language
- Python
- Stars
- 363
- Forks
- 55
- PR merge metrics
- No merged PRs in 30d
Description
Using your toolbox with the built-in models is straightforward, but we would like to compare some custom pytorch models.
It would be great to have a routine to add these models (i.e. subclasses of `nn.Module`) to the toolbox registry from your own script. If this is already possible, it would be great if you could share an example.
Currently, we add the model inside the toolbox's files which makes extensions complicated and redundant (e.g. name of
model in the path, the function name, the plotting routine).
Thanks
David
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First steps
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Research direction
Start by tracing how built-in models are registered across the toolbox files, including the model path, function name, and plotting routine. Determine the existing benchmark entry points and how an nn.Module could be supplied from an external script. Done means a custom model can be registered without editing toolbox files and an example shows the workflow.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, tooling
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100