lucidrains / lucidrains/vector-quantize-pytorch
Residual simVQ loss
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- Dominant language
- Python
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- 4k
- Forks
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Description
For the residual simVQ or residualVQ, the commit_loss shape is (1,num_quantizers)
what should I do to manipulate the loss? Should I just sum?
Contributor guide
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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
The issue names residual simVQ/residualVQ and the commit_loss value, but no file or test. Start by locating those entry points and inspect how the (1,num_quantizers) loss is produced and consumed. Done means the expected loss aggregation is established and reflected in the relevant behavior or documentation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100