Project-MONAI / Project-MONAI/MONAI
Batch support for saliency maps
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 8.7k
- Forks
- 1.6k
- Avg merge
- 5d 1h
- Merged PRs (30d)
- 20
Description
Is your feature request related to a problem? Please describe.
Currently, classes to generate saliency maps like VanillaGrad, SmoothGrad, etc. support only running on a single input sample.
Giving a batch of samples results in a ValueError due to this
https://github.com/Project-MONAI/MONAI/blob/90d2acb54d5325e6727f2cc2329cb40adc8577af/monai/visualize/gradient_based.py#L90
Describe the solution you'd like
Batch support for saliency maps can speed up calculation when running over an entire dataset.
Describe alternatives you've considered
I'm currently calculating saliency on a large number of output features over a large number of samples, and this is quite slow despite naive parallelization.
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 monai/visualize/gradient_based.py around line 90 and review the VanillaGrad and SmoothGrad entry points. Reproduce the ValueError with a batched input, then verify that saliency-map generation accepts batches and preserves the expected per-sample results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100