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-rw-r--r--text_recognizer/criterions/vqgan_loss.py85
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diff --git a/text_recognizer/criterions/vqgan_loss.py b/text_recognizer/criterions/vqgan_loss.py
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+++ b/text_recognizer/criterions/vqgan_loss.py
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+"""VQGAN loss for PyTorch Lightning."""
+from typing import Dict
+from click.types import Tuple
+
+import torch
+from torch import nn, Tensor
+import torch.nn.functional as F
+
+from text_recognizer.criterions.n_layer_discriminator import NLayerDiscriminator
+
+
+class VQGANLoss(nn.Module):
+ """VQGAN loss."""
+
+ def __init__(
+ self,
+ reconstruction_loss: nn.L1Loss,
+ discriminator: NLayerDiscriminator,
+ vq_loss_weight: float = 1.0,
+ discriminator_weight: float = 1.0,
+ ) -> None:
+ super().__init__()
+ self.reconstruction_loss = reconstruction_loss
+ self.discriminator = discriminator
+ self.vq_loss_weight = vq_loss_weight
+ self.discriminator_weight = discriminator_weight
+
+ @staticmethod
+ def adversarial_loss(logits_real: Tensor, logits_fake: Tensor) -> Tensor:
+ """Calculates the adversarial loss."""
+ loss_real = torch.mean(F.relu(1.0 - logits_real))
+ loss_fake = torch.mean(F.relu(1.0 + logits_fake))
+ d_loss = (loss_real + loss_fake) / 2.0
+ return d_loss
+
+ def forward(
+ self,
+ data: Tensor,
+ reconstructions: Tensor,
+ vq_loss: Tensor,
+ optimizer_idx: int,
+ stage: str,
+ ) -> Tuple[Tensor, Dict[str, Tensor]]:
+ """Calculates the VQGAN loss."""
+ rec_loss = self.reconstruction_loss(
+ data.contiguous(), reconstructions.contiguous()
+ )
+
+ # GAN part.
+ if optimizer_idx == 0:
+ logits_fake = self.discriminator(reconstructions.contiguous())
+ g_loss = -torch.mean(logits_fake)
+
+ loss = (
+ rec_loss
+ + self.discriminator_weight * g_loss
+ + self.vq_loss_weight * vq_loss
+ )
+ log = {
+ f"{stage}/loss": loss,
+ f"{stage}/vq_loss": vq_loss,
+ f"{stage}/rec_loss": rec_loss,
+ f"{stage}/g_loss": g_loss,
+ }
+ return loss, log
+
+ if optimizer_idx == 1:
+ logits_fake = self.discriminator(reconstructions.contiguous().detach())
+ logits_real = self.discriminator(data.contiguous().detach())
+
+ d_loss = self.adversarial_loss(
+ logits_real=logits_real, logits_fake=logits_fake
+ )
+ loss = (
+ rec_loss
+ + self.discriminator_weight * d_loss
+ + self.vq_loss_weight * vq_loss
+ )
+ log = {
+ f"{stage}/loss": loss,
+ f"{stage}/vq_loss": vq_loss,
+ f"{stage}/rec_loss": rec_loss,
+ f"{stage}/d_loss": d_loss,
+ }
+ return loss, log