ldm/modules/losses_audio/contperceptual_dis.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import sys

sys.path.insert(0, '.') # nopep8
from ldm.modules.losses_audio.vqperceptual import *
from ldm.modules.discriminator.multi_window_disc import Discriminator

class LPAPSWithDiscriminator(nn.Module):# 相比于contperceptual.py添加了MultiWindowDiscriminator
 def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
 disc_loss="hinge"):

 super().__init__()
 assert disc_loss in ["hinge", "vanilla"]
 self.kl_weight = kl_weight
 self.pixel_weight = pixelloss_weight
 self.perceptual_loss = LPAPS().eval()
 self.perceptual_weight = perceptual_weight
 # output log variance
 self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)

 self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
 n_layers=disc_num_layers,
 use_actnorm=use_actnorm,
 ).apply(weights_init)
 self.discriminator_iter_start = disc_start
 if disc_loss == "hinge":
 self.disc_loss = hinge_d_loss
 elif disc_loss == "vanilla":
 self.disc_loss = vanilla_d_loss
 else:
 raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
 print(f"LPAPSWithDiscriminator running with {disc_loss} loss.")
 self.disc_factor = disc_factor
 self.discriminator_weight = disc_weight
 self.disc_conditional = disc_conditional

 disc_win_num = 3
 mel_disc_hidden_size = 128
 self.discriminator_multi = Discriminator(time_lengths=[32, 64, 128][:disc_win_num],
 freq_length=80, hidden_size=mel_disc_hidden_size, kernel=(3, 3),
 cond_size=0, norm_type="in", reduction="stack")

 def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
 if last_layer is not None:
 nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
 g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
 else:
 nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
 g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]

 d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
 d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
 d_weight = d_weight * self.discriminator_weight
 return d_weight

 def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
 global_step, last_layer=None, cond=None, split="train", weights=None):
 rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
 if self.perceptual_weight > 0:
 p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
 rec_loss = rec_loss + self.perceptual_weight * p_loss
 else:
 p_loss = torch.tensor([0.0])

 nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
 weighted_nll_loss = nll_loss
 if weights is not None:
 weighted_nll_loss = weights*nll_loss
 weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
 nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
 kl_loss = posteriors.kl()
 kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]

 # now the GAN part
 if optimizer_idx == 0:
 # generator update
 if cond is None:
 assert not self.disc_conditional
 logits_fake = self.discriminator(reconstructions.contiguous())
 else:
 assert self.disc_conditional
 logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))

 logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().squeeze(1).transpose(1, 2))

 g_loss = -torch.mean(logits_fake)
 g_loss_multi = -torch.mean(logits_fake_multi['y'])

 try:
 d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
 d_weight_multi = self.calculate_adaptive_weight(nll_loss, g_loss_multi, last_layer=last_layer)
 except RuntimeError:
 assert not self.training
 d_weight = d_weight_multi = torch.tensor(0.0)

 disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
 loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss + d_weight_multi * disc_factor * g_loss_multi

 log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
 "{}/logvar".format(split): self.logvar.detach(),
 "{}/kl_loss".format(split): kl_loss.detach().mean(),
 "{}/nll_loss".format(split): nll_loss.detach().mean(),
 "{}/rec_loss".format(split): rec_loss.detach().mean(),
 "{}/d_weight".format(split): d_weight.detach(),
 "{}/disc_factor".format(split): torch.tensor(disc_factor),
 "{}/g_loss".format(split): g_loss.detach().mean(),
 "{}/g_loss_multi".format(split): g_loss_multi.detach().mean(),
 }
 return loss, log

 if optimizer_idx == 1:
 # second pass for discriminator update
 if cond is None:
 logits_real = self.discriminator(inputs.contiguous().detach())
 logits_fake = self.discriminator(reconstructions.contiguous().detach())
 else:
 logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
 logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))

 logits_real_multi = self.discriminator_multi(inputs.contiguous().detach().squeeze(1).transpose(1, 2))
 logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().detach().squeeze(1).transpose(1, 2))

 disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
 d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
 d_loss_multi = disc_factor * self.disc_loss(logits_real_multi['y'], logits_fake_multi['y'])

 log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
 "{}/disc_loss_multi".format(split): d_loss_multi.clone().detach().mean(),
 "{}/logits_real".format(split): logits_real.detach().mean(),
 "{}/logits_fake".format(split): logits_fake.detach().mean()
 }
 return d_loss+d_loss_multi, log