Source code for diffsptk.modules.ivq
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import torch
from .base import BaseNonFunctionalModule
[docs]
class InverseVectorQuantization(BaseNonFunctionalModule):
"""See `this page <https://sp-nitech.github.io/sptk/latest/main/imsvq.html>`_
for details.
References
----------
.. [1] A. v. d. Oord et al., "Neural discrete representation learning," *Advances in
Neural Information Processing Systems*, pp. 6309-6318, 2017.
"""
def __init__(self):
super().__init__()
[docs]
def forward(self, indices: torch.Tensor, codebook: torch.Tensor) -> torch.Tensor:
"""Perform inverse vector quantization.
Parameters
----------
indices : Tensor [shape=(...,)]
The codebook indices.
codebook : Tensor [shape=(K, M+1)]
The codebook.
Returns
-------
xq : Tensor [shape=(..., M+1)]
The quantized vectors.
Examples
--------
>>> vq = diffsptk.VectorQuantization(4, 2)
>>> ivq = diffsptk.InverseVectorQuantization()
>>> indices = torch.tensor([[0, 1], [1, 0]])
>>> xq = ivq(indices, vq.codebook)
>>> xq
tensor([[[ 1.9246, -1.4498, -0.9959, 0.3809, 0.0478],
[-0.3040, 0.0568, 0.0113, -0.7284, -0.5103]],
[[-0.3040, 0.0568, 0.0113, -0.7284, -0.5103],
[ 1.9246, -1.4498, -0.9959, 0.3809, 0.0478]]])
"""
target_shape = list(indices.shape)
target_shape.append(codebook.size(-1))
xq = torch.index_select(codebook, 0, indices.view(-1).long())
xq = xq.view(target_shape)
return xq
