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| """VQGAN Loss | |
| - Adapted from https://github.com/CompVis/taming-transformers | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from .discriminator import NLayerDiscriminator, weights_init | |
| from .blocks import LossCriterion, LossCriterionMask | |
| 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 | |
| def fft_loss(pred, tgt): | |
| return ((torch.fft.fftn(pred, dim=(-2,-1)) - torch.fft.fftn(tgt, dim=(-2,-1)))).abs().mean() | |
| class LPIPSWithDiscriminator(nn.Module): | |
| def __init__(self, disc_start, model_path, pixelloss_weight=1.0, | |
| disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=0.8, | |
| perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, | |
| disc_ndf=64, disc_loss="hinge", rec_loss="FFT", | |
| style_layers = [], content_layers = ['r41']): | |
| super().__init__() | |
| assert disc_loss in ["hinge", "vanilla"] | |
| self.pixel_weight = pixelloss_weight | |
| self.perceptual_loss = LossCriterion(style_layers, content_layers, | |
| 0, perceptual_weight, | |
| model_path = model_path) | |
| 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} and {rec_loss} loss.") | |
| self.disc_factor = disc_factor | |
| self.discriminator_weight = disc_weight | |
| self.disc_conditional = disc_conditional | |
| self.rec_loss = rec_loss | |
| self.perceptual_weight = perceptual_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, optimizer_idx, | |
| global_step, last_layer=None, cond=None, split="train"): | |
| if self.rec_loss == "L1": | |
| rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()).mean() | |
| elif self.rec_loss == "MSE": | |
| rec_loss = F.mse_loss(reconstructions, inputs) | |
| elif self.rec_loss == "FFT": | |
| rec_loss = fft_loss(inputs, reconstructions) | |
| elif self.rec_loss is None: | |
| rec_loss = 0 | |
| else: | |
| raise ValueError("Unkown reconstruction loss, choices are [FFT, L1]") | |
| if self.perceptual_weight > 0: | |
| loss_dict = self.perceptual_loss(reconstructions, inputs, style = False) | |
| p_loss = loss_dict['content'] | |
| rec_loss = rec_loss + p_loss | |
| else: | |
| p_loss = torch.zeros(1).cuda() | |
| nll_loss = rec_loss | |
| # adversarial loss for both branches | |
| if optimizer_idx == 0: | |
| log = {} | |
| disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) | |
| # generator update | |
| if disc_factor > 0: | |
| logits_fake = self.discriminator(reconstructions.contiguous()) | |
| 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) | |
| loss = nll_loss + d_weight * disc_factor * g_loss | |
| log["d_weight"] = d_weight.detach() | |
| log["disc_factor"] = torch.tensor(disc_factor) | |
| log["g_loss"] = g_loss.detach().mean() | |
| else: | |
| loss = nll_loss | |
| log["total_loss"] = loss.clone().detach().mean() | |
| log["nll_loss"] = nll_loss.detach().mean() | |
| log["rec_loss"] = rec_loss.detach().mean() | |
| log["p_loss"] = p_loss.detach().mean() | |
| return loss, log | |
| if optimizer_idx == 1: | |
| # second pass for discriminator update | |
| logits_real = self.discriminator(inputs.contiguous().detach()) | |
| logits_fake = self.discriminator(reconstructions.contiguous().detach()) | |
| 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": d_loss.clone().detach().mean(), | |
| "logits_real": logits_real.detach().mean(), | |
| "logits_fake": logits_fake.detach().mean() | |
| } | |
| return d_loss, log | |
| class LPIPSWithDiscriminatorMask(nn.Module): | |
| def __init__(self, disc_start, model_path, pixelloss_weight=1.0, | |
| disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=0.8, | |
| perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, | |
| disc_ndf=64, disc_loss="hinge", rec_loss="FFT", | |
| style_layers = [], content_layers = ['r41']): | |
| super().__init__() | |
| assert disc_loss in ["hinge", "vanilla"] | |
| self.pixel_weight = pixelloss_weight | |
| self.perceptual_loss = LossCriterionMask(style_layers, content_layers, | |
| 0.2, perceptual_weight, | |
| model_path = model_path) | |
| 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} and {rec_loss} loss.") | |
| self.disc_factor = disc_factor | |
| self.discriminator_weight = disc_weight | |
| self.disc_conditional = disc_conditional | |
| self.rec_loss = rec_loss | |
| self.perceptual_weight = perceptual_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, optimizer_idx, | |
| global_step, mask, last_layer=None, cond=None, split="train"): | |
| if self.rec_loss == "L1": | |
| rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()).mean() | |
| elif self.rec_loss == "MSE": | |
| rec_loss = F.mse_loss(reconstructions, inputs) | |
| elif self.rec_loss == "FFT": | |
| rec_loss = fft_loss(inputs, reconstructions) | |
| elif self.rec_loss is None: | |
| rec_loss = 0 | |
| else: | |
| raise ValueError("Unkown reconstruction loss, choices are [FFT, L1]") | |
| if self.perceptual_weight > 0: | |
| loss_dict = self.perceptual_loss(reconstructions, inputs, mask, style = True) | |
| p_loss = loss_dict['content'] | |
| s_loss = loss_dict['style'] | |
| rec_loss = rec_loss + p_loss + s_loss | |
| else: | |
| p_loss = torch.zeros(1).cuda() | |
| nll_loss = rec_loss | |
| # adversarial loss for both branches | |
| if optimizer_idx == 0: | |
| # generator update | |
| logits_fake = self.discriminator(reconstructions.contiguous()) | |
| 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 | |
| log = {"total_loss": loss.clone().detach().mean(), | |
| "nll_loss": nll_loss.detach().mean(), | |
| "rec_loss": rec_loss.detach().mean(), | |
| "p_loss": p_loss.detach().mean(), | |
| "s_loss": s_loss, | |
| "d_weight": d_weight.detach(), | |
| "disc_factor": torch.tensor(disc_factor), | |
| "g_loss": g_loss.detach().mean(), | |
| } | |
| return loss, log | |
| if optimizer_idx == 1: | |
| # second pass for discriminator update | |
| logits_real = self.discriminator(inputs.contiguous().detach()) | |
| logits_fake = self.discriminator(reconstructions.contiguous().detach()) | |
| 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": d_loss.clone().detach().mean(), | |
| "logits_real": logits_real.detach().mean(), | |
| "logits_fake": logits_fake.detach().mean() | |
| } | |
| return d_loss, log |