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from modules.commons.common_layers import *
import random
class MixStyle(nn.Module):
"""MixStyle.
Reference:
Zhou et al. Domain Generalization with MixStyle. ICLR 2021.
"""
def __init__(self, p=0.5, alpha=0.1, eps=1e-6, hidden_size=256):
"""
Args:
p (float): probability of using MixStyle.
alpha (float): parameter of the Beta distribution.
eps (float): scaling parameter to avoid numerical issues.
mix (str): how to mix.
"""
super().__init__()
self.p = p
self.beta = torch.distributions.Beta(alpha, alpha)
self.eps = eps
self.alpha = alpha
self._activated = True
self.hidden_size = hidden_size
self.affine_layer = LinearNorm(
hidden_size,
2 * hidden_size, # For both b (bias) g (gain)
)
def __repr__(self):
return f'MixStyle(p={self.p}, alpha={self.alpha}, eps={self.eps})'
def set_activation_status(self, status=True):
self._activated = status
def forward(self, x, spk_embed):
if not self.training or not self._activated:
return x
if random.random() > self.p:
return x
B = x.size(0)
mu, sig = torch.mean(x, dim=-1, keepdim=True), torch.std(x, dim=-1, keepdim=True)
x_normed = (x - mu) / (sig + 1e-6) # [B, T, H_m]
lmda = self.beta.sample((B, 1, 1))
lmda = lmda.to(x.device)
# Get Bias and Gain
mu1, sig1 = torch.split(self.affine_layer(spk_embed), self.hidden_size, dim=-1) # [B, 1, 2 * H_m] --> 2 * [B, 1, H_m]
# MixStyle
perm = torch.randperm(B)
mu2, sig2 = mu1[perm], sig1[perm]
mu_mix = mu1*lmda + mu2 * (1-lmda)
sig_mix = sig1*lmda + sig2 * (1-lmda)
# Perform Scailing and Shifting
return sig_mix * x_normed + mu_mix # [B, T, H_m]
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