lucidrains / lucidrains/vector-quantize-pytorch
Support perplexity to measure index collapse
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- Dominant language
- Python
- Stars
- 4k
- Forks
- 338
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Description
Hi! Thanks for you work. In the implementation of VQVAE (https://github.com/deepmind/sonnet/blob/v2/sonnet/src/nets/vqvae.py#L89C1-L89C1), perplexity is used as an evaluation measure for VQ codebook. It monitors whether index collapse happens. Does this project has similar parameter?
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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
Read the linked Sonnet VQ-VAE implementation to understand how perplexity measures codebook usage. Then locate the corresponding vector-quantization implementation in this repository and determine where the metric belongs. Done means the project exposes a perplexity measure that can monitor index collapse.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100