import torch import torch.nn as nn import torch.nn.functional as F from thirdparty.taming.losses.lpips import LPIPS from thirdparty.taming.discriminator.model import NLayerDiscriminator, weights_init class DummyLoss(nn.Module): def __init__(self): super().__init__() def adopt_weight(weight, global_step, threshold=0, value=0.): if global_step < threshold: weight = value return weight def hinge_d_loss(logits_real, logits_fake): loss_real = torch.mean(F.relu(1. - logits_real)) loss_fake = torch.mean(F.relu(1. + logits_fake)) d_loss = 0.5 * (loss_real + loss_fake) return d_loss def vanilla_d_loss(logits_real, logits_fake): d_loss = 0.5 * ( torch.mean(torch.nn.functional.softplus(-logits_real)) + torch.mean(torch.nn.functional.softplus(logits_fake))) return d_loss class VQLPIPSWithDiscriminator(nn.Module): def __init__(self, disc_start, codebook_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_ndf=64, disc_loss="hinge"): super().__init__() assert disc_loss in ["hinge", "vanilla"] self.codebook_weight = codebook_weight self.pixel_weight = pixelloss_weight self.perceptual_weight = perceptual_weight #self.perceptual_loss = LPIPS().eval() self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf ).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}'.") self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional 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, nll_loss, codebook_loss, inputs, reconstructions, optimizer_idx, global_step, last_layer=None, cond=None, split="train"): # 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 = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss log = {"{}/gan_loss".format(split): loss.clone().detach().mean(), #"{}/cb_loss".format(split): codebook_loss.detach().mean(), #"{}/nll_loss".format(split): nll_loss.detach().mean(), #"{}/rec_loss".format(split): rec_loss.detach().mean(), #"{}/p_loss".format(split): p_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: 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)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) #nll_loss * 0 to ensure entire graph is being used for DDP zero_loss = 0.0 * (nll_loss + codebook_loss.mean()) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + zero_loss 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() } return d_loss, log