| | import torch |
| | import torch.nn as nn |
| |
|
| | from taming.modules.losses.vqperceptual import * |
| |
|
| |
|
| | 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, |
| | disc_loss="hinge"): |
| |
|
| | 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) |
| |
|
| | 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): |
| | 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, split="train", |
| | weights=None): |
| | 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] |
| |
|
| | |
| | if optimizer_idx == 0: |
| | |
| | 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".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: |
| | |
| | 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".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 |
| |
|
| |
|
