| from typing import Any, Union |
|
|
| import torch |
| import torch.nn as nn |
| from einops import rearrange |
| from taming.modules.discriminator.model import NLayerDiscriminator, weights_init |
| from taming.modules.losses.lpips import LPIPS |
| from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss |
|
|
| from ....util import default, instantiate_from_config |
|
|
|
|
| def adopt_weight(weight, global_step, threshold=0, value=0.0): |
| if global_step < threshold: |
| weight = value |
| return weight |
|
|
|
|
| class LatentLPIPS(nn.Module): |
| def __init__( |
| self, |
| decoder_config, |
| perceptual_weight=1.0, |
| latent_weight=1.0, |
| scale_input_to_tgt_size=False, |
| scale_tgt_to_input_size=False, |
| perceptual_weight_on_inputs=0.0, |
| ): |
| super().__init__() |
| self.scale_input_to_tgt_size = scale_input_to_tgt_size |
| self.scale_tgt_to_input_size = scale_tgt_to_input_size |
| self.init_decoder(decoder_config) |
| self.perceptual_loss = LPIPS().eval() |
| self.perceptual_weight = perceptual_weight |
| self.latent_weight = latent_weight |
| self.perceptual_weight_on_inputs = perceptual_weight_on_inputs |
|
|
| def init_decoder(self, config): |
| self.decoder = instantiate_from_config(config) |
| if hasattr(self.decoder, "encoder"): |
| del self.decoder.encoder |
|
|
| def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"): |
| log = dict() |
| loss = (latent_inputs - latent_predictions) ** 2 |
| log[f"{split}/latent_l2_loss"] = loss.mean().detach() |
| image_reconstructions = None |
| if self.perceptual_weight > 0.0: |
| image_reconstructions = self.decoder.decode(latent_predictions) |
| image_targets = self.decoder.decode(latent_inputs) |
| perceptual_loss = self.perceptual_loss( |
| image_targets.contiguous(), image_reconstructions.contiguous() |
| ) |
| loss = ( |
| self.latent_weight * loss.mean() |
| + self.perceptual_weight * perceptual_loss.mean() |
| ) |
| log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach() |
|
|
| if self.perceptual_weight_on_inputs > 0.0: |
| image_reconstructions = default( |
| image_reconstructions, self.decoder.decode(latent_predictions) |
| ) |
| if self.scale_input_to_tgt_size: |
| image_inputs = torch.nn.functional.interpolate( |
| image_inputs, |
| image_reconstructions.shape[2:], |
| mode="bicubic", |
| antialias=True, |
| ) |
| elif self.scale_tgt_to_input_size: |
| image_reconstructions = torch.nn.functional.interpolate( |
| image_reconstructions, |
| image_inputs.shape[2:], |
| mode="bicubic", |
| antialias=True, |
| ) |
|
|
| perceptual_loss2 = self.perceptual_loss( |
| image_inputs.contiguous(), image_reconstructions.contiguous() |
| ) |
| loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean() |
| log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach() |
| return loss, log |
|
|
|
|
| class GeneralLPIPSWithDiscriminator(nn.Module): |
| def __init__( |
| self, |
| disc_start: int, |
| logvar_init: float = 0.0, |
| pixelloss_weight=1.0, |
| disc_num_layers: int = 3, |
| disc_in_channels: int = 3, |
| disc_factor: float = 1.0, |
| disc_weight: float = 1.0, |
| perceptual_weight: float = 1.0, |
| disc_loss: str = "hinge", |
| scale_input_to_tgt_size: bool = False, |
| dims: int = 2, |
| learn_logvar: bool = False, |
| regularization_weights: Union[None, dict] = None, |
| ): |
| super().__init__() |
| self.dims = dims |
| if self.dims > 2: |
| print( |
| f"running with dims={dims}. This means that for perceptual loss calculation, " |
| f"the LPIPS loss will be applied to each frame independently. " |
| ) |
| self.scale_input_to_tgt_size = scale_input_to_tgt_size |
| assert disc_loss in ["hinge", "vanilla"] |
| 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.learn_logvar = learn_logvar |
|
|
| self.discriminator = NLayerDiscriminator( |
| input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=False |
| ).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.regularization_weights = default(regularization_weights, {}) |
|
|
| def get_trainable_parameters(self) -> Any: |
| return self.discriminator.parameters() |
|
|
| def get_trainable_autoencoder_parameters(self) -> Any: |
| if self.learn_logvar: |
| yield self.logvar |
| yield from () |
|
|
| 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, |
| regularization_log, |
| inputs, |
| reconstructions, |
| optimizer_idx, |
| global_step, |
| last_layer=None, |
| split="train", |
| weights=None, |
| ): |
| if self.scale_input_to_tgt_size: |
| inputs = torch.nn.functional.interpolate( |
| inputs, reconstructions.shape[2:], mode="bicubic", antialias=True |
| ) |
|
|
| if self.dims > 2: |
| inputs, reconstructions = map( |
| lambda x: rearrange(x, "b c t h w -> (b t) c h w"), |
| (inputs, reconstructions), |
| ) |
|
|
| 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] |
|
|
| |
| if optimizer_idx == 0: |
| |
| logits_fake = self.discriminator(reconstructions.contiguous()) |
| 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 + d_weight * disc_factor * g_loss |
| log = dict() |
| for k in regularization_log: |
| if k in self.regularization_weights: |
| loss = loss + self.regularization_weights[k] * regularization_log[k] |
| log[f"{split}/{k}"] = regularization_log[k].detach().mean() |
|
|
| log.update( |
| { |
| "{}/total_loss".format(split): loss.clone().detach().mean(), |
| "{}/logvar".format(split): self.logvar.detach(), |
| "{}/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: |
| |
| 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 |
|
|
