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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import typing as tp
import torch
from torch import nn
from .core_vq import ResidualVectorQuantization
################################################################################
# Residual quantization module
################################################################################
class ResidualVectorQuantizer(nn.Module):
"""Residual Vector Quantizer.
Args:
dimension (int): Dimension of the codebooks.
n_q (int): Number of residual vector quantizers used.
bins (int): Codebook size.
decay (float): Decay for exponential moving average over the codebooks.
kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
kmeans_iters (int): Number of iterations used for kmeans initialization.
threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
that have an exponential moving average cluster size less than the specified threshold with
randomly selected vector from the current batch.
"""
def __init__(
self,
dimension: int = 256,
n_q: int = 8,
bins: int = 1024,
decay: float = 0.99,
kmeans_init: bool = True,
kmeans_iters: int = 50,
threshold_ema_dead_code: int = 2,
):
super().__init__()
self.n_q = n_q
self.dimension = dimension
self.bins = bins
self.decay = decay
self.kmeans_init = kmeans_init
self.kmeans_iters = kmeans_iters
self.threshold_ema_dead_code = threshold_ema_dead_code
self.vq = ResidualVectorQuantization(
dim=self.dimension,
codebook_size=self.bins,
num_quantizers=self.n_q,
decay=self.decay,
kmeans_init=self.kmeans_init,
kmeans_iters=self.kmeans_iters,
threshold_ema_dead_code=self.threshold_ema_dead_code,
)
def get_num_quantizers_for_bandwidth(
self, frame_rate: int, bandwidth: tp.Optional[float] = None
) -> int:
"""Return n_q based on specified target bandwidth."""
bw_per_q = self.get_bandwidth_per_quantizer(frame_rate)
n_q = self.n_q
if bandwidth and bandwidth > 0.0:
# bandwidth is represented as a thousandth of what it is, e.g. 6kbps bandwidth is represented as
# bandwidth == 6.0
n_q = int(max(1, math.floor(bandwidth * 1000 / bw_per_q)))
return n_q
def get_bandwidth_per_quantizer(self, frame_rate: int):
"""Return bandwidth per quantizer for a given input frame rate.
Each quantizer encodes a frame with lg(bins) bits.
"""
return math.log2(self.bins) * frame_rate
def encode(
self, x: torch.Tensor, frame_rate: int, bandwidth: tp.Optional[float] = None
) -> torch.Tensor:
"""Encode a given input tensor with the specified frame rate at the given bandwidth.
The RVQ encode method sets the appropriate number of quantizers to use
and returns indices for each quantizer.
"""
n_q = self.get_num_quantizers_for_bandwidth(frame_rate, bandwidth)
codes, z_O, z_o = self.vq.encode(x, n_q=n_q)
return codes, z_O, z_o
def decode(self, codes: torch.Tensor) -> torch.Tensor:
"""
Decode the given codes to the quantized representation.
"""
quantized = self.vq.decode(codes)
return quantized
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