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"""VQGAN loss for PyTorch Lightning."""
from typing import Dict, Optional
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,
) -> Optional[Tuple]:
"""Calculates the VQGAN loss."""
rec_loss: Tensor = 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: Tensor = (
rec_loss
+ self.discriminator_weight * g_loss
+ self.vq_loss_weight * vq_loss
)
log = {
f"{stage}/total_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.discriminator_weight * self.adversarial_loss(
logits_real=logits_real, logits_fake=logits_fake
)
log = {
f"{stage}/d_loss": d_loss,
}
return d_loss, log
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