# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn as nn from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no? class LPIPSWithDiscriminator(nn.Module): """ A perceptual loss module that combines LPIPS with an adversarial discriminator for improved reconstruction quality in variational autoencoders. This class calculates a combination of pixel-level, perceptual (LPIPS), KL, and adversarial losses for training a VAE model with a discriminator. """ 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_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_loss="hinge", ): """ Initializes the LPIPSWithDiscriminator module. Args: disc_start (int): Iteration at which to start discriminator updates. logvar_init (float): Initial value for the log variance parameter. kl_weight (float): Weight for the KL divergence term. pixelloss_weight (float): Weight for the pixel-level reconstruction loss. disc_num_layers (int): Number of layers in the discriminator. disc_in_channels (int): Number of input channels for the discriminator. disc_factor (float): Scaling factor for the discriminator loss. disc_weight (float): Weight applied to the discriminator gradient balancing. perceptual_weight (float): Weight for the LPIPS perceptual loss. use_actnorm (bool): Whether to use actnorm in the discriminator. disc_conditional (bool): Whether the discriminator is conditional on an additional input. disc_loss (str): Type of GAN loss to use ("hinge" or "vanilla"). """ super().__init__() assert disc_loss in ["hinge", "vanilla"] self.kl_weight = kl_weight self.pixel_weight = pixelloss_weight self.perceptual_loss = LPIPS().eval() self.perceptual_weight = perceptual_weight # output log variance self.logvar = nn.Parameter(torch.ones(1) * logvar_init) self.discriminator = NLayerDiscriminator( input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm ).apply(weights_init) self.discriminator_iter_start = disc_start self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_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): """ Computes an adaptive weight that balances the reconstruction (NLL) and the adversarial (GAN) losses. This ensures stable training by adjusting the impact of the discriminator’s gradient on the generator. Args: nll_loss (torch.Tensor): The negative log-likelihood loss. g_loss (torch.Tensor): The generator (adversarial) loss. last_layer (torch.nn.Parameter, optional): Last layer parameters of the model for gradient-based calculations. If None, uses self.last_layer[0]. Returns: torch.Tensor: The computed adaptive weight for balancing the discriminator. """ 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, weights=None ): """ Forward pass for computing the combined loss. Depending on the optimizer index, this either computes the generator loss (including pixel, perceptual, KL, and adversarial terms) or the discriminator loss. Args: inputs (torch.Tensor): Original inputs to reconstruct. reconstructions (torch.Tensor): Reconstructed outputs from the model. posteriors (object): Posteriors from the VAE model for KL computation. optimizer_idx (int): Indicates which optimizer is being updated (0 for generator, 1 for discriminator). global_step (int): Current training iteration step. last_layer (torch.nn.Parameter, optional): The last layer's parameters for adaptive weight calculation. cond (torch.Tensor, optional): Conditional input for the discriminator. weights (torch.Tensor, optional): Sample-wise weighting for the losses. Returns: (torch.Tensor, dict): A tuple of (loss, log_dict) where loss is the computed loss for the current optimizer and log_dict is a dictionary of intermediate values for logging and debugging. """ 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 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) if self.disc_factor > 0.0: 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) else: 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": loss.clone().detach().mean(), "logvar": self.logvar.detach().item(), "kl_loss": kl_loss.detach().mean(), "nll_loss": nll_loss.detach().mean(), "rec_loss": rec_loss.detach().mean(), "d_weight": d_weight.detach(), "disc_factor": torch.tensor(disc_factor), "g_loss": g_loss.detach().mean(), } return loss, log if optimizer_idx == 1: # 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": d_loss.clone().detach().mean(), "logits_real": logits_real.detach().mean(), "logits_fake": logits_fake.detach().mean(), } return d_loss, log