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| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from einops import repeat | |
| from taming.modules.discriminator.model import NLayerDiscriminator, weights_init | |
| from taming.modules.losses.lpips import LPIPS | |
| from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss | |
| def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): | |
| assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0] | |
| loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3]) | |
| loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3]) | |
| loss_real = (weights * loss_real).sum() / weights.sum() | |
| loss_fake = (weights * loss_fake).sum() / weights.sum() | |
| d_loss = 0.5 * (loss_real + loss_fake) | |
| return d_loss | |
| def adopt_weight(weight, global_step, threshold=0, value=0.): | |
| if global_step < threshold: | |
| weight = value | |
| return weight | |
| def measure_perplexity(predicted_indices, n_embed): | |
| # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py | |
| # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally | |
| encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed) | |
| avg_probs = encodings.mean(0) | |
| perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp() | |
| cluster_use = torch.sum(avg_probs > 0) | |
| return perplexity, cluster_use | |
| def l1(x, y): | |
| return torch.abs(x-y) | |
| def l2(x, y): | |
| return torch.pow((x-y), 2) | |
| 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", n_classes=None, perceptual_loss="lpips", | |
| pixel_loss="l1"): | |
| super().__init__() | |
| assert disc_loss in ["hinge", "vanilla"] | |
| assert perceptual_loss in ["lpips", "clips", "dists"] | |
| assert pixel_loss in ["l1", "l2"] | |
| self.codebook_weight = codebook_weight | |
| self.pixel_weight = pixelloss_weight | |
| if perceptual_loss == "lpips": | |
| print(f"{self.__class__.__name__}: Running with LPIPS.") | |
| self.perceptual_loss = LPIPS().eval() | |
| else: | |
| raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<") | |
| 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"VQLPIPSWithDiscriminator 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()) | |
| rec_loss = self.pixel_loss(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 | |