import torch import torch.nn as nn import torch.nn.functional as F from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no? from ..vaemodules.discriminator import Discriminator3D class LPIPSWithDiscriminator(nn.Module): 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, outlier_penalty_loss_r=3.0, outlier_penalty_loss_weight=1e5, disc_loss="hinge", l2_loss_weight=0.0, l1_loss_weight=1.0): 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(size=()) * logvar_init) self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm ).apply(weights_init) self.discriminator3d = Discriminator3D( in_channels=disc_in_channels, block_out_channels=(64, 128, 256) ).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 self.outlier_penalty_loss_r = outlier_penalty_loss_r self.outlier_penalty_loss_weight = outlier_penalty_loss_weight self.l1_loss_weight = l1_loss_weight self.l2_loss_weight = l2_loss_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 outlier_penalty_loss(self, posteriors, r): batch_size, channels, frames, height, width = posteriors.shape mean_X = posteriors.mean(dim=(3, 4), keepdim=True) std_X = posteriors.std(dim=(3, 4), keepdim=True) diff = torch.abs(posteriors - mean_X) penalty = torch.maximum(diff - r * std_X, torch.zeros_like(diff)) opl = penalty.sum(dim=(3, 4)) / (height * width) opl_final = opl.mean(dim=(0, 1, 2)) return opl_final def forward(self, inputs, reconstructions, posteriors, optimizer_idx, global_step, last_layer=None, cond=None, split="train", weights=None): if inputs.ndim==4: inputs = inputs.unsqueeze(2) if reconstructions.ndim==4: reconstructions = reconstructions.unsqueeze(2) inputs_ori = inputs reconstructions_ori = reconstructions # get new loss_weight loss_weights = 1 inputs = inputs.permute(0, 2, 1, 3, 4).flatten(0, 1) reconstructions = reconstructions.permute(0, 2, 1, 3, 4).flatten(0, 1) rec_loss = 0 if self.l1_loss_weight > 0: rec_loss += torch.abs(inputs.contiguous() - reconstructions.contiguous()) * self.l1_loss_weight if self.l2_loss_weight > 0: rec_loss += F.mse_loss(inputs.contiguous(), reconstructions.contiguous(), reduction="none") * self.l2_loss_weight if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) rec_loss = rec_loss + self.perceptual_weight * p_loss rec_loss = rec_loss * loss_weights 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] outlier_penalty_loss = self.outlier_penalty_loss(posteriors.mode(), self.outlier_penalty_loss_r) * self.outlier_penalty_loss_weight # 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)) logits_fake_3d = self.discriminator3d(reconstructions_ori.contiguous()) g_loss = -torch.mean(logits_fake) - torch.mean(logits_fake_3d) 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 + outlier_penalty_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(), "{}/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 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)) logits_real_3d = self.discriminator3d(inputs_ori.contiguous().detach()) logits_fake_3d = self.discriminator3d(reconstructions_ori.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) + disc_factor * self.disc_loss(logits_real_3d, logits_fake_3d) 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