Create reset_parameters method for ResNet blocks and models + move existing parameters init code there
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module: models
new feature
- Dominant language
- Python
- Stars
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- Avg merge
- 1d 15h
- Merged PRs (30d)
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Description
This would ease re-initialization some blocks that are not meant to be pretrained/frozen:
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py#L188-L203
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/resnet.py at the linked initialization code around lines 188-203. Trace how initialization is currently applied across the ResNet blocks and models, then define what reset_parameters should cover and how existing initialization should move there. Done means the requested blocks and models can be re-initialized through that method without losing the current initialization behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100