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import math
import numpy as np
import torch
import torch.nn.functional as F
from torch.distributions.normal import Normal
def gaussian_loss(y_hat, y, log_std_min=-7.0):
assert y_hat.dim() == 3
assert y_hat.size(2) == 2
mean = y_hat[:, :, :1]
log_std = torch.clamp(y_hat[:, :, 1:], min=log_std_min)
# TODO: replace with pytorch dist
log_probs = -0.5 * (-math.log(2.0 * math.pi) - 2.0 * log_std - torch.pow(y - mean, 2) * torch.exp((-2.0 * log_std)))
return log_probs.squeeze().mean()
def sample_from_gaussian(y_hat, log_std_min=-7.0, scale_factor=1.0):
assert y_hat.size(2) == 2
mean = y_hat[:, :, :1]
log_std = torch.clamp(y_hat[:, :, 1:], min=log_std_min)
dist = Normal(
mean,
torch.exp(log_std),
)
sample = dist.sample()
sample = torch.clamp(torch.clamp(sample, min=-scale_factor), max=scale_factor)
del dist
return sample
def log_sum_exp(x):
"""numerically stable log_sum_exp implementation that prevents overflow"""
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis)
m2, _ = torch.max(x, dim=axis, keepdim=True)
return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
# It is adapted from https://github.com/r9y9/wavenet_vocoder/blob/master/wavenet_vocoder/mixture.py
def discretized_mix_logistic_loss(y_hat, y, num_classes=65536, log_scale_min=None, reduce=True):
if log_scale_min is None:
log_scale_min = float(np.log(1e-14))
y_hat = y_hat.permute(0, 2, 1)
assert y_hat.dim() == 3
assert y_hat.size(1) % 3 == 0
nr_mix = y_hat.size(1) // 3
# (B x T x C)
y_hat = y_hat.transpose(1, 2)
# unpack parameters. (B, T, num_mixtures) x 3
logit_probs = y_hat[:, :, :nr_mix]
means = y_hat[:, :, nr_mix : 2 * nr_mix]
log_scales = torch.clamp(y_hat[:, :, 2 * nr_mix : 3 * nr_mix], min=log_scale_min)
# B x T x 1 -> B x T x num_mixtures
y = y.expand_as(means)
centered_y = y - means
inv_stdv = torch.exp(-log_scales)
plus_in = inv_stdv * (centered_y + 1.0 / (num_classes - 1))
cdf_plus = torch.sigmoid(plus_in)
min_in = inv_stdv * (centered_y - 1.0 / (num_classes - 1))
cdf_min = torch.sigmoid(min_in)
# log probability for edge case of 0 (before scaling)
# equivalent: torch.log(F.sigmoid(plus_in))
log_cdf_plus = plus_in - F.softplus(plus_in)
# log probability for edge case of 255 (before scaling)
# equivalent: (1 - F.sigmoid(min_in)).log()
log_one_minus_cdf_min = -F.softplus(min_in)
# probability for all other cases
cdf_delta = cdf_plus - cdf_min
mid_in = inv_stdv * centered_y
# log probability in the center of the bin, to be used in extreme cases
# (not actually used in our code)
log_pdf_mid = mid_in - log_scales - 2.0 * F.softplus(mid_in)
# tf equivalent
# log_probs = tf.where(x < -0.999, log_cdf_plus,
# tf.where(x > 0.999, log_one_minus_cdf_min,
# tf.where(cdf_delta > 1e-5,
# tf.log(tf.maximum(cdf_delta, 1e-12)),
# log_pdf_mid - np.log(127.5))))
# TODO: cdf_delta <= 1e-5 actually can happen. How can we choose the value
# for num_classes=65536 case? 1e-7? not sure..
inner_inner_cond = (cdf_delta > 1e-5).float()
inner_inner_out = inner_inner_cond * torch.log(torch.clamp(cdf_delta, min=1e-12)) + (1.0 - inner_inner_cond) * (
log_pdf_mid - np.log((num_classes - 1) / 2)
)
inner_cond = (y > 0.999).float()
inner_out = inner_cond * log_one_minus_cdf_min + (1.0 - inner_cond) * inner_inner_out
cond = (y < -0.999).float()
log_probs = cond * log_cdf_plus + (1.0 - cond) * inner_out
log_probs = log_probs + F.log_softmax(logit_probs, -1)
if reduce:
return -torch.mean(log_sum_exp(log_probs))
return -log_sum_exp(log_probs).unsqueeze(-1)
def sample_from_discretized_mix_logistic(y, log_scale_min=None):
"""
Sample from discretized mixture of logistic distributions
Args:
y (Tensor): :math:`[B, C, T]`
log_scale_min (float): Log scale minimum value
Returns:
Tensor: sample in range of [-1, 1].
"""
if log_scale_min is None:
log_scale_min = float(np.log(1e-14))
assert y.size(1) % 3 == 0
nr_mix = y.size(1) // 3
# B x T x C
y = y.transpose(1, 2)
logit_probs = y[:, :, :nr_mix]
# sample mixture indicator from softmax
temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5)
temp = logit_probs.data - torch.log(-torch.log(temp))
_, argmax = temp.max(dim=-1)
# (B, T) -> (B, T, nr_mix)
one_hot = to_one_hot(argmax, nr_mix)
# select logistic parameters
means = torch.sum(y[:, :, nr_mix : 2 * nr_mix] * one_hot, dim=-1)
log_scales = torch.clamp(torch.sum(y[:, :, 2 * nr_mix : 3 * nr_mix] * one_hot, dim=-1), min=log_scale_min)
# sample from logistic & clip to interval
# we don't actually round to the nearest 8bit value when sampling
u = means.data.new(means.size()).uniform_(1e-5, 1.0 - 1e-5)
x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1.0 - u))
x = torch.clamp(torch.clamp(x, min=-1.0), max=1.0)
return x
def to_one_hot(tensor, n, fill_with=1.0):
# we perform one hot encore with respect to the last axis
one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_().type_as(tensor)
one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with)
return one_hot
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