import os import torch from torch import nn import torch.nn.functional as F from einops import rearrange from .modeling_lpips import LPIPS from .modeling_discriminator import NLayerDiscriminator, NLayerDiscriminator3D, weights_init from IPython import embed class AdaptiveLossWeight: def __init__(self, timestep_range=[0, 1], buckets=300, weight_range=[1e-7, 1e7]): self.bucket_ranges = torch.linspace(timestep_range[0], timestep_range[1], buckets-1) self.bucket_losses = torch.ones(buckets) self.weight_range = weight_range def weight(self, timestep): indices = torch.searchsorted(self.bucket_ranges.to(timestep.device), timestep) return (1/self.bucket_losses.to(timestep.device)[indices]).clamp(*self.weight_range) def update_buckets(self, timestep, loss, beta=0.99): indices = torch.searchsorted(self.bucket_ranges.to(timestep.device), timestep).cpu() self.bucket_losses[indices] = self.bucket_losses[indices]*beta + loss.detach().cpu() * (1-beta) def hinge_d_loss(logits_real, logits_fake): loss_real = torch.mean(F.relu(1.0 - logits_real)) loss_fake = torch.mean(F.relu(1.0 + 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 adopt_weight(weight, global_step, threshold=0, value=0.0): if global_step < threshold: weight = value return weight class LPIPSWithDiscriminator(nn.Module): def __init__( self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, perceptual_weight=1.0, # --- Discriminator Loss --- disc_num_layers=4, disc_in_channels=3, disc_factor=1.0, disc_weight=0.5, disc_loss="hinge", add_discriminator=True, using_3d_discriminator=False, ): 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 self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) if add_discriminator: disc_cls = NLayerDiscriminator3D if using_3d_discriminator else NLayerDiscriminator self.discriminator = disc_cls( input_nc=disc_in_channels, n_layers=disc_num_layers, ).apply(weights_init) else: self.discriminator = None 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.using_3d_discriminator = using_3d_discriminator 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, posteriors, optimizer_idx, global_step, split="train", last_layer=None, ): t = reconstructions.shape[2] inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous() reconstructions = rearrange(reconstructions, "b c t h w -> (b t) c h w").contiguous() if optimizer_idx == 0: # rec_loss = torch.mean(torch.abs(inputs - reconstructions), dim=(1,2,3), keepdim=True) rec_loss = torch.mean(F.mse_loss(inputs, reconstructions, reduction='none'), dim=(1,2,3), keepdim=True) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs, reconstructions) nll_loss = self.pixel_weight * rec_loss + self.perceptual_weight * p_loss nll_loss = nll_loss / torch.exp(self.logvar) + self.logvar weighted_nll_loss = 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.mean(kl_loss) disc_factor = adopt_weight( self.disc_factor, global_step, threshold=self.discriminator_iter_start ) if disc_factor > 0.0: if self.using_3d_discriminator: reconstructions = rearrange(reconstructions, '(b t) c h w -> b c t h w', t=t) 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) else: d_weight = torch.tensor(0.0) g_loss = torch.tensor(0.0) loss = ( weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss ) log = { "{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/perception_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(), } return loss, log if optimizer_idx == 1: if self.using_3d_discriminator: inputs = rearrange(inputs, '(b t) c h w -> b c t h w', t=t) reconstructions = rearrange(reconstructions, '(b t) c h w -> b c t h w', t=t) 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