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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)] | |