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INIT
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm, spectral_norm
class DiscriminatorP(nn.Module):
def __init__(self, hp, period):
super(DiscriminatorP, self).__init__()
self.LRELU_SLOPE = hp.mpd.lReLU_slope
self.period = period
kernel_size = hp.mpd.kernel_size
stride = hp.mpd.stride
norm_f = weight_norm if hp.mpd.use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList([
norm_f(nn.Conv2d(1, 64, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
norm_f(nn.Conv2d(64, 128, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
norm_f(nn.Conv2d(128, 256, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
norm_f(nn.Conv2d(256, 512, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), 1, padding=(kernel_size // 2, 0))),
])
self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, self.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return fmap, x
class MultiPeriodDiscriminator(nn.Module):
def __init__(self, hp):
super(MultiPeriodDiscriminator, self).__init__()
self.discriminators = nn.ModuleList(
[DiscriminatorP(hp, period) for period in hp.mpd.periods]
)
def forward(self, x):
ret = list()
for disc in self.discriminators:
ret.append(disc(x))
return ret # [(feat, score), (feat, score), (feat, score), (feat, score), (feat, score)]