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Running
on
Zero
File size: 4,878 Bytes
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch.nn.utils import weight_norm
def WNConv1d(*args, **kwargs):
return weight_norm(nn.Conv1d(*args, **kwargs))
def WNConvTranspose1d(*args, **kwargs):
return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
class FactorizedVectorQuantize(nn.Module):
def __init__(
self,
input_dim,
codebook_size,
codebook_dim,
commitment=0.005,
codebook_loss_weight=1.0,
use_l2_normlize=True,
):
super().__init__()
self.input_dim = input_dim
self.codebook_size = codebook_size
self.codebook_dim = codebook_dim
self.commitment = commitment
self.codebook_loss_weight = codebook_loss_weight
self.use_l2_normlize = use_l2_normlize
if self.input_dim != self.codebook_dim:
self.in_project = WNConv1d(self.input_dim, self.codebook_dim, kernel_size=1)
self.out_project = WNConv1d(
self.codebook_dim, self.input_dim, kernel_size=1
)
else:
self.in_project = nn.Identity()
self.out_project = nn.Identity()
self.codebook = nn.Embedding(self.codebook_size, self.codebook_dim)
def forward(self, z):
"""
Parameters
----------
z: torch.Tensor[B x D x T]
Returns
-------
z_q: torch.Tensor[B x D x T]
Quantized continuous representation of input
commit_loss: Tensor[B]
Commitment loss to train encoder to predict vectors closer to codebook entries
codebook_loss: Tensor[B]
Codebook loss to update the codebook
indices: torch.Tensor[B x T]
Codebook indices (quantized discrete representation of input)
z_e: torch.Tensor[B x D x T]
Projected latents (continuous representation of input before quantization)
"""
# Factorized codes project input into low-dimensional space if self.input_dim != self.codebook_dim
z_e = self.in_project(z)
z_q, indices = self.decode_latents(z_e)
# Compute commitment loss and codebook loss
if self.training:
commit_loss = (
F.mse_loss(z_e, z_q.detach(), reduction="none").mean([1, 2])
* self.commitment
)
codebook_loss = (
F.mse_loss(z_q, z_e.detach(), reduction="none").mean([1, 2])
* self.codebook_loss_weight
)
else:
commit_loss = torch.zeros(z.shape[0], device=z.device)
codebook_loss = torch.zeros(z.shape[0], device=z.device)
z_q = z_e + (z_q - z_e).detach()
z_q = self.out_project(z_q)
return z_q, commit_loss, codebook_loss, indices, z_e
def embed_code(self, embed_id):
return F.embedding(embed_id, self.codebook.weight)
def decode_code(self, embed_id):
return self.embed_code(embed_id).transpose(1, 2)
def decode_latents(self, latents):
encodings = rearrange(latents, "b d t -> (b t) d")
codebook = self.codebook.weight
# L2 normalize encodings and codebook
if self.use_l2_normlize:
encodings = F.normalize(encodings)
codebook = F.normalize(codebook)
# Compute euclidean distance between encodings and codebook,
# if use_l2_normlize is True, the distance is equal to cosine distance
dist = (
encodings.pow(2).sum(1, keepdim=True)
- 2 * encodings @ codebook.t()
+ codebook.pow(2).sum(1, keepdim=True).t()
)
indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
z_q = self.decode_code(indices)
return z_q, indices
def vq2emb(self, vq, out_proj=True):
emb = self.decode_code(vq)
if out_proj:
emb = self.out_project(emb)
return emb
def latent2dist(self, latents):
encodings = rearrange(latents, "b d t -> (b t) d")
codebook = self.codebook.weight
# L2 normalize encodings and codebook
if self.use_l2_normlize:
encodings = F.normalize(encodings)
codebook = F.normalize(codebook)
# Compute euclidean distance between encodings and codebook,
# if use_l2_normlize is True, the distance is equal to cosine distance
dist = (
encodings.pow(2).sum(1, keepdim=True)
- 2 * encodings @ codebook.t()
+ codebook.pow(2).sum(1, keepdim=True).t()
) # (b*t, k)
indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
dist = rearrange(dist, "(b t) k -> b t k", b=latents.size(0))
z_q = self.decode_code(indices)
return -dist, indices, z_q
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