| import os | |
| import sys | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.utils.checkpoint import checkpoint | |
| from torch.nn.utils.parametrizations import spectral_norm, weight_norm | |
| sys.path.append(os.getcwd()) | |
| from main.library.algorithm.commons import get_padding | |
| from main.library.algorithm.residuals import LRELU_SLOPE | |
| class MultiPeriodDiscriminator(torch.nn.Module): | |
| def __init__(self, version, use_spectral_norm=False, checkpointing=False): | |
| super(MultiPeriodDiscriminator, self).__init__() | |
| self.checkpointing = checkpointing | |
| periods = ([2, 3, 5, 7, 11, 17] if version == "v1" else [2, 3, 5, 7, 11, 17, 23, 37]) | |
| self.discriminators = torch.nn.ModuleList([DiscriminatorS(use_spectral_norm=use_spectral_norm, checkpointing=checkpointing)] + [DiscriminatorP(p, use_spectral_norm=use_spectral_norm, checkpointing=checkpointing) for p in periods]) | |
| def forward(self, y, y_hat): | |
| y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], [] | |
| for d in self.discriminators: | |
| if self.training and self.checkpointing: | |
| def forward_discriminator(d, y, y_hat): | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| return y_d_r, fmap_r, y_d_g, fmap_g | |
| y_d_r, fmap_r, y_d_g, fmap_g = checkpoint(forward_discriminator, d, y, y_hat, use_reentrant=False) | |
| else: | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| y_d_rs.append(y_d_r); fmap_rs.append(fmap_r) | |
| y_d_gs.append(y_d_g); fmap_gs.append(fmap_g) | |
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
| class DiscriminatorS(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False, checkpointing=False): | |
| super(DiscriminatorS, self).__init__() | |
| self.checkpointing = checkpointing | |
| norm_f = spectral_norm if use_spectral_norm else weight_norm | |
| self.convs = torch.nn.ModuleList([norm_f(torch.nn.Conv1d(1, 16, 15, 1, padding=7)), norm_f(torch.nn.Conv1d(16, 64, 41, 4, groups=4, padding=20)), norm_f(torch.nn.Conv1d(64, 256, 41, 4, groups=16, padding=20)), norm_f(torch.nn.Conv1d(256, 1024, 41, 4, groups=64, padding=20)), norm_f(torch.nn.Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), norm_f(torch.nn.Conv1d(1024, 1024, 5, 1, padding=2))]) | |
| self.conv_post = norm_f(torch.nn.Conv1d(1024, 1, 3, 1, padding=1)) | |
| self.lrelu = torch.nn.LeakyReLU(LRELU_SLOPE) | |
| def forward(self, x): | |
| fmap = [] | |
| for conv in self.convs: | |
| x = checkpoint(self.lrelu, checkpoint(conv, x, use_reentrant = False), use_reentrant = False) if self.training and self.checkpointing else self.lrelu(conv(x)) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| return torch.flatten(x, 1, -1), fmap | |
| class DiscriminatorP(torch.nn.Module): | |
| def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, checkpointing=False): | |
| super(DiscriminatorP, self).__init__() | |
| self.period = period | |
| self.checkpointing = checkpointing | |
| norm_f = spectral_norm if use_spectral_norm else weight_norm | |
| self.convs = torch.nn.ModuleList([norm_f(torch.nn.Conv2d(in_ch, out_ch, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))) for in_ch, out_ch in zip([1, 32, 128, 512, 1024], [32, 128, 512, 1024, 1024])]) | |
| self.conv_post = norm_f(torch.nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) | |
| self.lrelu = torch.nn.LeakyReLU(LRELU_SLOPE) | |
| def forward(self, x): | |
| fmap = [] | |
| b, c, t = x.shape | |
| if t % self.period != 0: x = F.pad(x, (0, (self.period - (t % self.period))), "reflect") | |
| x = x.view(b, c, -1, self.period) | |
| for conv in self.convs: | |
| x = checkpoint(self.lrelu, checkpoint(conv, x, use_reentrant = False), use_reentrant = False) if self.training and self.checkpointing else self.lrelu(conv(x)) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| return torch.flatten(x, 1, -1), fmap |