112 lines
5.5 KiB
Python
112 lines
5.5 KiB
Python
import torch
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import torch.nn as nn
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from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no?
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class LPIPSWithDiscriminator(nn.Module):
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def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
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disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
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perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
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disc_loss="hinge"):
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super().__init__()
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assert disc_loss in ["hinge", "vanilla"]
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self.kl_weight = kl_weight
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self.pixel_weight = pixelloss_weight
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self.perceptual_loss = LPIPS().eval()
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self.perceptual_weight = perceptual_weight
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# output log variance
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self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
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self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
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n_layers=disc_num_layers,
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use_actnorm=use_actnorm
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).apply(weights_init)
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self.discriminator_iter_start = disc_start
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self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
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self.disc_factor = disc_factor
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self.discriminator_weight = disc_weight
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self.disc_conditional = disc_conditional
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def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
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if last_layer is not None:
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nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
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g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
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else:
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nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
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g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
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d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
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d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
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d_weight = d_weight * self.discriminator_weight
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return d_weight
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def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
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global_step, last_layer=None, cond=None, split="train",
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weights=None):
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rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
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if self.perceptual_weight > 0:
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p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
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rec_loss = rec_loss + self.perceptual_weight * p_loss
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nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
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weighted_nll_loss = nll_loss
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if weights is not None:
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weighted_nll_loss = weights*nll_loss
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weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
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nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
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kl_loss = posteriors.kl()
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kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
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# now the GAN part
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if optimizer_idx == 0:
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# generator update
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if cond is None:
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assert not self.disc_conditional
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logits_fake = self.discriminator(reconstructions.contiguous())
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else:
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assert self.disc_conditional
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logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
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g_loss = -torch.mean(logits_fake)
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if self.disc_factor > 0.0:
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try:
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d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
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except RuntimeError:
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assert not self.training
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d_weight = torch.tensor(0.0)
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else:
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d_weight = torch.tensor(0.0)
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disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
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loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
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log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
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"{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
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"{}/rec_loss".format(split): rec_loss.detach().mean(),
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"{}/d_weight".format(split): d_weight.detach(),
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"{}/disc_factor".format(split): torch.tensor(disc_factor),
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"{}/g_loss".format(split): g_loss.detach().mean(),
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}
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return loss, log
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if optimizer_idx == 1:
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# second pass for discriminator update
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if cond is None:
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logits_real = self.discriminator(inputs.contiguous().detach())
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logits_fake = self.discriminator(reconstructions.contiguous().detach())
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else:
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logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
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logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
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disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
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d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
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log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
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"{}/logits_real".format(split): logits_real.detach().mean(),
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"{}/logits_fake".format(split): logits_fake.detach().mean()
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}
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return d_loss, log
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