| | 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 = [] |
| |
|
| | |
| | b, c, t = x.shape |
| | if t % self.period != 0: |
| | 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 |
| |
|
