Split _validate_trainable_layers into validator and num_of_layers selector
Open
Nobody has claimed this yet.
code quality
module: models
- Dominant language
- Python
- Stars
- 17.9k
- Forks
- 7.3k
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 13
Description
We use _validate_trainable_layers as a validator but also as a way to determine the actual number of trainable layers based on default values, pertained flag etc.
- It would be clearer to have separate checks for validation and another method to set the actual value instead of having a checker with side effects.
- Check if it makes sense to add the default value as a parameter default value instead of None?
- Should we use ValueError exception instead of assert?
cc @jdsgomes
cc @datumbox
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start in torchvision/models/detection/backbone_utils.py at _validate_trainable_layers and trace how its validation and trainable-layer selection behavior are used. Separate validation from selecting the actual layer count, and resolve the listed questions about the default parameter and ValueError versus assert.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100