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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 |