| 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 |
|
|