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vector_quantization.py
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vector_quantization.py
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import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch
import math
from torch.autograd import Variable
class VectorQuantize(nn.Module):
def __init__(self,K,D,beta=0.25):
super().__init__()
self.K = K
self.D = D
self.embedding = nn.Embedding(K,D)
self.embedding.weight.data.uniform_(-1./K,1./K)
self.beta=beta
def forward(self,inputs):
inputs = inputs.permute(0,2,1).contiguous() # B T F
B, T ,F = inputs.size()
flat_inputs = inputs.view(-1,self.D)
in_sqr = torch.sum(flat_inputs**2,dim=1,keepdim=True)
embed_sqr = torch.sum(self.embedding.weight**2,dim=1)
dis = torch.addmm(embed_sqr + in_sqr, flat_inputs, self.embedding.weight.t(), alpha=-2.0,beta=1.0)
encoding_ind = torch.argmin(dis,dim=1).unsqueeze(1)
encodings = torch.zeros(B*T,self.K)#.type(torch.cuda.FloatTensor)
if torch.cuda.is_available():
encodings = encodings.type(torch.cuda.FloatTensor)
encodings.scatter_(1,encoding_ind,1)
quant = torch.matmul(encodings,self.embedding.weight).view(B,T,F)
vq_loss = torch.mean((quant.detach() - inputs) **2)
commit_loss = torch.mean((quant - inputs.detach()) **2)
vq_loss = self.beta * vq_loss + commit_loss
quant = inputs + (quant - inputs).detach()
avg_probs = torch.mean(encodings,dim=0)
perp = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
return quant.permute(0,2,1).contiguous(), vq_loss, perp
class SlicedVectorQuantize(nn.Module):
def __init__(self, K, D, beta = 0.25, decay = 0.99, n_d = 2, dropout = False, dropout_rate = 0.25, K1 = None ):
super().__init__()
self.K = K
if K1 is not None:
self.K1 = K1
else:
self.K1 = K
self.D = D
self.sub_D = self.D // n_d
self.embedding1 = nn.Embedding(K, self.sub_D)
self.embedding1.weight.data.uniform_(-1. / K, 1. / K)
self.embedding2 = nn.Embedding(self.K1, self.sub_D)
self.embedding2.weight.data.uniform_(-1. / self.K1, 1. / self.K1)
self.decay = decay
self.beta = beta
self.dropout = dropout
self.dropout_rate = dropout_rate
def forward(self, x):
x = x.permute(0,2,1).contiguous()
B, T, C = x.size()
flat_in = x.view(-1, C)
assert flat_in.size(1) == self.D
flat_in1, flat_in2 = flat_in[:, : self.sub_D], flat_in[:, self.sub_D :]
code_sqr1 = torch.sum(self.embedding1.weight **2, dim = 1)
code_sqr2 = torch.sum(self.embedding2.weight **2, dim = 1)
in_sqr1 = torch.sum(flat_in1**2, dim = 1, keepdim = True)
in_sqr2 = torch.sum(flat_in2**2, dim = 1, keepdim = True)
dis1 = torch.addmm(code_sqr1 + in_sqr1, flat_in1, self.embedding1.weight.t(), alpha = -2.0, beta = 1.0)
dis2 = torch.addmm(code_sqr2 + in_sqr2, flat_in2, self.embedding2.weight.t(), alpha = -2.0, beta = 1.0)
encoding_ind1 = torch.argmax(-1. * dis1, dim = 1).unsqueeze(1)
encoding_ind2 = torch.argmax(-1. * dis2, dim = 1).unsqueeze(1)
encodings1 = torch.zeros(encoding_ind1.size(0), self.K).type(torch.FloatTensor)
encodings2 = torch.zeros(encoding_ind2.size(0), self.K1).type(torch.FloatTensor)
if torch.cuda.is_available():
encodings1 = encodings1.cuda()
encodings2 = encodings2.cuda()
encodings1.scatter_(1, encoding_ind1, 1)
encodings2.scatter_(1, encoding_ind2, 1)
quant1 = torch.matmul(encodings1, self.embedding1.weight).view(B, T, self.sub_D)
quant2 = torch.matmul(encodings2, self.embedding2.weight).view(B, T, self.sub_D)
quant = torch.cat([quant1, quant2], dim = 2)
vq_loss = torch.mean( (quant.detach() - x)**2 )
commit_loss = torch.mean((quant - x.detach()) **2)
vq_loss = vq_loss + self.beta * commit_loss
quant = x + (quant - x).detach()
avg_probs1 = torch.mean(encodings1, dim=0)
avg_probs2 = torch.mean(encodings2, dim=0)
perp1 = torch.exp( -1. * torch.sum(avg_probs1 * torch.log(avg_probs1 + 1e-10)))
perp2 = torch.exp( -1. * torch.sum(avg_probs2 * torch.log(avg_probs2 + 1e-10)))
perp = perp1 + perp2
return quant.permute(0,2,1), vq_loss, perp
class SlicedVectorQuantizeEMA(nn.Module):
def __init__(self, K, D, beta = 0.25, decay = 0.99, n_d = 2 ):
super().__init__()
self.K = K
self.D = D
self.sub_D = self.D // n_d
self.embedding1 = nn.Embedding(K, self.sub_D)
self.embedding1.weight.data.uniform_(-1. / K, 1. / K)
self.embedding2 = nn.Embedding(K, self.sub_D)
self.embedding2.weight.data.uniform_(-1. / K, 1. / K)
if self.training:
self.register_buffer('ema_cluster_size1', torch.zeros(K))
self.register_buffer('ema_w1', torch.zeros(K, self.sub_D))
self.register_buffer('ema_cluster_size2', torch.zeros(K))
self.register_buffer('ema_w2', torch.zeros(K, self.sub_D))
self.decay = decay
self.beta = beta
def forward(self, x):
x = x.permute(0,2,1).contiguous()
B, T, C = x.size()
flat_in = x.view(-1, C)
assert flat_in.size(1) == self.D
flat_in1, flat_in2 = flat_in[:, : self.sub_D], flat_in[:, self.sub_D :]
code_sqr1 = torch.sum(self.embedding1.weight **2, dim = 1)
code_sqr2 = torch.sum(self.embedding2.weight **2, dim = 1)
in_sqr1 = torch.sum(flat_in1**2, dim = 1, keepdim = True)
in_sqr2 = torch.sum(flat_in2**2, dim = 1, keepdim = True)
dis1 = torch.addmm(code_sqr1 + in_sqr1, flat_in1, self.embedding1.weight.t(), alpha = -2.0, beta = 1.0)
dis2 = torch.addmm(code_sqr2 + in_sqr2, flat_in2, self.embedding2.weight.t(), alpha = -2.0, beta = 1.0)
encoding_ind1 = torch.argmax(-1. * dis1, dim = 1).unsqueeze(1)
encoding_ind2 = torch.argmax(-1. * dis2, dim = 1).unsqueeze(1)
encodings1 = torch.zeros(encoding_ind1.size(0), self.K).type(torch.FloatTensor)
encodings2 = torch.zeros(encoding_ind2.size(0), self.K).type(torch.FloatTensor)
if torch.cuda.is_available:
encodings1 = encodings1.cuda()
encodings2 = encodings2.cuda()
encodings1.scatter_(1, encoding_ind1, 1)
encodings2.scatter_(1, encoding_ind2, 1)
if self.training:
with torch.no_grad():
self.ema_cluster_size1 = self.ema_cluster_size1 * self.decay + (1.0 - self.decay) * torch.sum(encodings1, 0)
self.ema_cluster_size2 = self.ema_cluster_size2 * self.decay + (1.0 - self.decay) * torch.sum(encodings2, 0)
n1 = torch.sum(self.ema_cluster_size1.data)
n2 = torch.sum(self.ema_cluster_size2.data)
self.ema_cluster_size1 =(
(self.ema_cluster_size1 + 1e-5)
/ (n1 + self.K*1e-5) * n1 )
self.ema_cluster_size2 =(
(self.ema_cluster_size2 + 1e-5)
/ (n2 + self.K*1e-5) * n2 )
dw1 = torch.matmul(encodings1.t(), flat_in1)
dw2 = torch.matmul(encodings2.t(), flat_in2)
self.ema_w1 = self.ema_w1 * self.decay + (1 - self.decay) * dw1
self.ema_w2 = self.ema_w2 * self.decay + (1 - self.decay) * dw2
self.embedding1.weight.data.copy_( self.ema_w1 / self.ema_cluster_size1.unsqueeze(1) )
self.embedding2.weight.data.copy_( self.ema_w2 / self.ema_cluster_size2.unsqueeze(1) )
quant1 = torch.matmul(encodings1, self.embedding1.weight).view(B, T, self.sub_D)
quant2 = torch.matmul(encodings2, self.embedding2.weight).view(B, T, self.sub_D)
quant = torch.cat([quant1, quant2], dim = 2)
vq_loss = self.beta * torch.mean( (quant.detach() - x)**2 )
quant = x + (quant - x).detach()
avg_probs1 = torch.mean(encodings1, dim=0)
avg_probs2 = torch.mean(encodings2, dim=0)
perp1 = torch.exp( -1. * torch.sum(avg_probs1 * torch.log(avg_probs1 + 1e-10)))
perp2 = torch.exp( -1. * torch.sum(avg_probs2 * torch.log(avg_probs2 + 1e-10)))
perp = perp1 + perp2
return quant.permute(0,2,1), vq_loss, perp
class VectorQuantizeEMA(nn.Module):
def __init__(self, K, D, beta = 0.25, decay = 0.99 ):
super().__init__()
self.K = K
self.D = D
self.embedding = nn.Embedding(K, D)
self.embedding.weight.data.uniform_(-1. / K, 1. / K)
if self.training:
self.register_buffer('ema_cluster_size', torch.zeros(K))
self.register_buffer('ema_w', torch.zeros(K, D))
self.decay = decay
self.beta = beta
def forward(self, x):
x = x.permute(0,2,1).contiguous()
in_size = x.size()
flat_in = x.view(-1,in_size[2])
assert flat_in.size(1) == self.D
code_sqr = torch.sum(self.embedding.weight **2, dim = 1)
in_sqr = torch.sum(flat_in**2, dim = 1, keepdim = True)
dis = torch.addmm(code_sqr + in_sqr, flat_in, self.embedding.weight.t(), alpha = -2.0, beta = 1.0)
encoding_ind = torch.argmax(-1. * dis, dim = 1).unsqueeze(1)
encodings = torch.zeros(encoding_ind.size(0), self.K).type(torch.FloatTensor)
if torch.cuda.is_available:
encodings = encodings.cuda()
encodings.scatter_(1, encoding_ind, 1)
if self.training:
with torch.no_grad():
self.ema_cluster_size = self.ema_cluster_size * self.decay + (1.0 - self.decay) * torch.sum(encodings, 0)
n = torch.sum(self.ema_cluster_size.data)
self.ema_cluster_size =(
(self.ema_cluster_size + 1e-5)
/ (n + self.K*1e-5) * n )
dw = torch.matmul(encodings.t(), flat_in)
self.ema_w = self.ema_w * self.decay + (1 - self.decay) * dw
self.embedding.weight.data.copy_( self.ema_w / self.ema_cluster_size.unsqueeze(1) )
quant = torch.matmul(encodings, self.embedding.weight).view(in_size)
vq_loss = self.beta * torch.mean( (quant.detach() - x)**2 )
quant = x + (quant - x).detach()
avg_probs = torch.mean(encodings, dim=0)
perp = torch.exp( -1. * torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
return quant.permute(0,2,1), vq_loss, perp