lucidrains / lucidrains/vector-quantize-pytorch
How to get the original code by saved codebook and index
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Description
Hi, I am trying to get quantized by saved codebook and index, but self.codebooks will always get 0. How can I do it?
```
import torch
from vector_quantize_pytorch import ResidualVQ
residual_vq = ResidualVQ(
dim = 256,
codebook_size = 256,
num_quantizers = 2,
kmeans_init = True,
kmeans_iters = 10
)
x = torch.randn(1, 1024, 256)
residual_vq.eval()
quantized, indices, commit_loss = residual_vq(x)
codebook = residual_vq.codebooks # get the codebook
torch.save(dict(codebook = codebook, indices = indices), 'codebook.pt')
codebook = torch.load('codebook.pt')['codebook']
indices = torch.load('codebook.pt')['indices']
residual_vq_reinit = ResidualVQ(
dim = 256,
codebook_size = 256,
num_quantizers = 2,
kmeans_init = True,
kmeans_iters = 10
)
residual_vq_reinit.codebook = codebook
quantized_out = residual_vq_reinit.get_codes_from_indices(indices)
assert torch.all(quantized == quantized_out.sum(dim = 0))
```
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
Start by running the provided reproduction with ResidualVQ and inspect the codebooks attribute alongside get_codes_from_indices. Trace how the saved codebook is stored and how the reinitialized instance reads its codebook, then verify that reconstruction from the saved indices matches the original quantized output.
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
- Mostly clear
- Newbie friendliness
- 25/100