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 * def discriminator_loss_mse(disc_real_outputs, disc_generated_outputs): r_losses = 0 g_losses = 0 for dr, dg in zip(disc_real_outputs, disc_generated_outputs): r_loss = torch.mean((1 - dr) ** 2) g_loss = torch.mean(dg ** 2) r_losses += r_loss g_losses += g_loss r_losses = r_losses / len(disc_real_outputs) g_losses = g_losses / len(disc_real_outputs) total = 0.5 * (r_losses + g_losses) return total class LPAPSWithDiscriminator(nn.Module): 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_hidden_size=64, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_loss="hinge",r1_reg_weight=5): super().__init__() assert disc_loss in ["hinge", "vanilla","mse"] self.kl_weight = kl_weight self.pixel_weight = pixelloss_weight self.perceptual_weight = perceptual_weight if self.perceptual_weight > 0: raise RuntimeError("don't use perceptual loss") # self.perceptual_loss = LPAPS().eval()# LPIPS用于日常图像，而LPAPS用于梅尔谱图 # output log variance self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, ndf = disc_hidden_size, n_layers=disc_num_layers, use_actnorm=use_actnorm, ).apply(weights_init) # h=8,w/(2**disc_num_layers) - 2 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 elif disc_loss == 'mse': self.disc_loss = discriminator_loss_mse 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 self.r1_reg_weight = r1_reg_weight 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): if len(inputs.shape) == 3: inputs,reconstructions = inputs.unsqueeze(1),reconstructions.unsqueeze(1) rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) # print(f"p_loss {p_loss}") 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)) g_loss = -torch.mean(logits_fake) try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = 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 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(), } return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: d_real_in = inputs.contiguous().detach() d_real_in.requires_grad = True logits_real = self.discriminator(d_real_in) 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)) 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) # logits_real越大，logits_fake越小说明discriminator越强 if self.r1_reg_weight > 0 and split=='train': r1_grads = torch.autograd.grad(outputs=[logits_real.sum()], inputs=[d_real_in], create_graph=True, only_inputs=True) r1_grads = r1_grads[0] r1_penalty = r1_grads.square().mean() d_loss += self.r1_reg_weight * r1_penalty log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } if self.r1_reg_weight and split=='train': log["{}/r1_prnalty".format(split)] = r1_penalty return d_loss, log