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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)] | |