pytorch / pytorch/vision

Add typing annotations to models/detection

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#4,582 3 comments 4 reactions 0 assignees View on GitHub

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code quality module: models topic: object detection
Dominant language
Python
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Avg merge
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Merged PRs (30d)
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Description

🚀 The feature

This is an issue to track down all the files in detection module and work up the chain.

  • _utils.py #4583
  • anchor_utils.py #4599
  • backbone_utils.py #4603
  • image_list.py #4602
  • roi_heads.py #4612
  • rpn.py #4619
  • generalized_rcnn.py #4631
  • transform.py #4630

To type the models, all the above files would need to have annotations.

  • faster_rcnn.py #4636
  • mask_rcnn.py
  • keypoint_rcnn.py
  • retinanet.py
  • ssd.py
  • ssdlite.py
Motivation, pitch

@khushi-411 and I will sync and work on these files.

Solving This issue also helps #2025

As first step I'm closing #4220 and splitting it up into multiple PRs preferably one file each.

Hopefully we should complete this soon.

Alternatives

Note that there are cases where we have make JIT happy over mypy.

Most of these cases occur as JIT does not Support Tuple fully. In most cases we should prefer List.

Additional context

cc @datumbox @pmeier @NicolasHug

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Review the unchecked files listed in the issue, including anchor_utils.py, roi_heads.py, transform.py, faster_rcnn.py, mask_rcnn.py, keypoint_rcnn.py, retinanet.py, ssd.py, and ssdlite.py, alongside the completed-file checklist. Start by comparing the existing annotation work in the checked files and account for the stated JIT-versus-mypy constraint. Done means the detection model files are annotated and the related work is complete.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
computer-vision, machine-learning
Issue type
Refactor
Difficulty
4/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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