| import torch |
| import torch.nn as nn |
|
|
| from ....util import default, instantiate_from_config |
| from ..lpips.loss.lpips import LPIPS |
|
|
|
|
| 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 |
|
|
