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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import sys | |
| from ldm.util import exists | |
| sys.path.insert(0, '.') # nopep8 | |
| from ldm.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats, | |
| NLayerDiscriminator1dSpecs, | |
| weights_init) | |
| # from ldm.modules.losses_audio.lpaps import LPAPS | |
| from ldm.modules.losses.vqperceptual import l1, l2, measure_perplexity, hinge_d_loss, vanilla_d_loss, adopt_weight | |
| class DummyLoss(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| class VQLPAPSWithDiscriminator(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", n_classes=None, pixel_loss="l1"): | |
| super().__init__() | |
| assert disc_loss in ["hinge", "vanilla"] | |
| self.codebook_weight = codebook_weight | |
| self.pixel_weight = pixelloss_weight | |
| self.perceptual_loss = None # LPAPS().eval() | |
| self.perceptual_weight = perceptual_weight | |
| if pixel_loss == "l1": | |
| self.pixel_loss = l1 | |
| else: | |
| self.pixel_loss = l2 | |
| 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}'.") | |
| print(f"VQLPAPSWithDiscriminator running with {disc_loss} loss.") | |
| self.disc_factor = disc_factor | |
| self.discriminator_weight = disc_weight | |
| self.disc_conditional = disc_conditional | |
| self.n_classes = n_classes | |
| 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, codebook_loss, inputs, reconstructions, optimizer_idx, | |
| global_step, last_layer=None, cond=None, split="train", predicted_indices=None): | |
| if not exists(codebook_loss): | |
| codebook_loss = torch.tensor([0.]).to(inputs.device) | |
| 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 | |
| # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] | |
| nll_loss = torch.mean(nll_loss) | |
| # 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.mean() | |
| log = {"{}/total_loss".format(split): loss.clone().detach().mean(), | |
| "{}/quant_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(), | |
| } | |
| # if predicted_indices is not None: | |
| # assert self.n_classes is not None | |
| # with torch.no_grad(): | |
| # perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) | |
| # log[f"{split}/perplexity"] = perplexity | |
| # log[f"{split}/cluster_usage"] = cluster_usage | |
| 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) | |
| d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) | |
| 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 | |