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Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| 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", | |
| max_bs=None, | |
| ): | |
| 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.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.max_bs = max_bs | |
| 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, | |
| ): | |
| if inputs.dim() == 5: | |
| inputs = rearrange(inputs, "b c t h w -> (b t) c h w") | |
| if reconstructions.dim() == 5: | |
| reconstructions = rearrange(reconstructions, "b c t h w -> (b t) c h w") | |
| rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) | |
| if self.perceptual_weight > 0: | |
| if self.max_bs is not None and self.max_bs < inputs.shape[0]: | |
| input_list = torch.split(inputs, self.max_bs, dim=0) | |
| reconstruction_list = torch.split(reconstructions, self.max_bs, dim=0) | |
| p_losses = [ | |
| self.perceptual_loss( | |
| inputs.contiguous(), reconstructions.contiguous() | |
| ) | |
| for inputs, reconstructions in zip(input_list, reconstruction_list) | |
| ] | |
| p_loss = torch.cat(p_losses, dim=0) | |
| else: | |
| 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] | |
| # 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) | |
| ) | |
| 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: | |
| # 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) | |
| ) | |
| 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 | |