from typing import Any, Union import torch import torch.nn as nn from einops import rearrange from ....util import default, instantiate_from_config from ..lpips.loss.lpips import LPIPS from ..lpips.model.model import NLayerDiscriminator, weights_init from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss def adopt_weight(weight, global_step, threshold=0, value=0.0): if global_step < threshold: weight = value return weight class LatentLPIPS(nn.Module): def __init__( self, decoder_config, perceptual_weight=1.0, latent_weight=1.0, scale_input_to_tgt_size=False, scale_tgt_to_input_size=False, perceptual_weight_on_inputs=0.0, ): super().__init__() self.scale_input_to_tgt_size = scale_input_to_tgt_size self.scale_tgt_to_input_size = scale_tgt_to_input_size self.init_decoder(decoder_config) self.perceptual_loss = LPIPS().eval() self.perceptual_weight = perceptual_weight self.latent_weight = latent_weight self.perceptual_weight_on_inputs = perceptual_weight_on_inputs def init_decoder(self, config): self.decoder = instantiate_from_config(config) if hasattr(self.decoder, "encoder"): del self.decoder.encoder def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"): log = dict() loss = (latent_inputs - latent_predictions) ** 2 log[f"{split}/latent_l2_loss"] = loss.mean().detach() image_reconstructions = None if self.perceptual_weight > 0.0: image_reconstructions = self.decoder.decode(latent_predictions) image_targets = self.decoder.decode(latent_inputs) perceptual_loss = self.perceptual_loss( image_targets.contiguous(), image_reconstructions.contiguous() ) loss = ( self.latent_weight * loss.mean() + self.perceptual_weight * perceptual_loss.mean() ) log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach() if self.perceptual_weight_on_inputs > 0.0: image_reconstructions = default( image_reconstructions, self.decoder.decode(latent_predictions) ) if self.scale_input_to_tgt_size: image_inputs = torch.nn.functional.interpolate( image_inputs, image_reconstructions.shape[2:], mode="bicubic", antialias=True, ) elif self.scale_tgt_to_input_size: image_reconstructions = torch.nn.functional.interpolate( image_reconstructions, image_inputs.shape[2:], mode="bicubic", antialias=True, ) perceptual_loss2 = self.perceptual_loss( image_inputs.contiguous(), image_reconstructions.contiguous() ) loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean() log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach() return loss, log class GeneralLPIPSWithDiscriminator(nn.Module): def __init__( self, disc_start: int, logvar_init: float = 0.0, pixelloss_weight=1.0, disc_num_layers: int = 3, disc_in_channels: int = 3, disc_factor: float = 1.0, disc_weight: float = 1.0, perceptual_weight: float = 1.0, disc_loss: str = "hinge", scale_input_to_tgt_size: bool = False, dims: int = 2, learn_logvar: bool = False, regularization_weights: Union[None, dict] = None, ): super().__init__() self.dims = dims if self.dims > 2: print( f"running with dims={dims}. This means that for perceptual loss calculation, " f"the LPIPS loss will be applied to each frame independently. " ) self.scale_input_to_tgt_size = scale_input_to_tgt_size assert disc_loss in ["hinge", "vanilla"] self.pixel_weight = pixelloss_weight self.perceptual_loss = LPIPS().eval() self.perceptual_weight = perceptual_weight # output log variance self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) self.learn_logvar = learn_logvar self.discriminator = NLayerDiscriminator( input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=False ).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.regularization_weights = default(regularization_weights, {}) def get_trainable_parameters(self) -> Any: return self.discriminator.parameters() def get_trainable_autoencoder_parameters(self) -> Any: if self.learn_logvar: yield self.logvar yield from () 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, regularization_log, inputs, reconstructions, optimizer_idx, global_step, last_layer=None, split="train", weights=None, ): if self.scale_input_to_tgt_size: inputs = torch.nn.functional.interpolate( inputs, reconstructions.shape[2:], mode="bicubic", antialias=True ) if self.dims > 2: inputs, reconstructions = map( lambda x: rearrange(x, "b c t h w -> (b t) c h w"), (inputs, reconstructions), ) 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] # now the GAN part if optimizer_idx == 0: # generator update logits_fake = self.discriminator(reconstructions.contiguous()) 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 + d_weight * disc_factor * g_loss log = dict() for k in regularization_log: if k in self.regularization_weights: loss = loss + self.regularization_weights[k] * regularization_log[k] log[f"{split}/{k}"] = regularization_log[k].detach().mean() log.update( { "{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_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 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".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