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"""PyTorch Lightning model for base Transformers."""
from typing import Tuple
import attr
from torch import Tensor
from text_recognizer.models.base import BaseLitModel
from text_recognizer.criterions.vqgan_loss import VQGANLoss
@attr.s(auto_attribs=True, eq=False)
class VQGANLitModel(BaseLitModel):
"""A PyTorch Lightning model for transformer networks."""
loss_fn: VQGANLoss = attr.ib()
latent_loss_weight: float = attr.ib(default=0.25)
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.network(data)
def training_step(
self, batch: Tuple[Tensor, Tensor], batch_idx: int, optimizer_idx: int
) -> Tensor:
"""Training step."""
data, _ = batch
reconstructions, commitment_loss = self(data)
if optimizer_idx == 0:
loss, log = self.loss_fn(
data=data,
reconstructions=reconstructions,
commitment_loss=commitment_loss,
decoder_last_layer=self.network.decoder.decoder[-1].weight,
optimizer_idx=optimizer_idx,
global_step=self.global_step,
stage="train",
)
self.log(
"train/loss", loss, prog_bar=True,
)
self.log_dict(log, logger=True, on_step=True, on_epoch=True)
return loss
if optimizer_idx == 1:
loss, log = self.loss_fn(
data=data,
reconstructions=reconstructions,
commitment_loss=commitment_loss,
decoder_last_layer=self.network.decoder.decoder[-1].weight,
optimizer_idx=optimizer_idx,
global_step=self.global_step,
stage="train",
)
self.log(
"train/discriminator_loss", loss, prog_bar=True,
)
self.log_dict(log, logger=True, on_step=True, on_epoch=True)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, _ = batch
reconstructions, commitment_loss = self(data)
loss, log = self.loss_fn(
data=data,
reconstructions=reconstructions,
commitment_loss=commitment_loss,
decoder_last_layer=self.network.decoder.decoder[-1].weight,
optimizer_idx=0,
global_step=self.global_step,
stage="val",
)
self.log(
"val/loss", loss, prog_bar=True,
)
self.log_dict(log)
_, log = self.loss_fn(
data=data,
reconstructions=reconstructions,
commitment_loss=commitment_loss,
decoder_last_layer=self.network.decoder.decoder[-1].weight,
optimizer_idx=1,
global_step=self.global_step,
stage="val",
)
self.log_dict(log)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, _ = batch
reconstructions, commitment_loss = self(data)
_, log = self.loss_fn(
data=data,
reconstructions=reconstructions,
commitment_loss=commitment_loss,
decoder_last_layer=self.network.decoder.decoder[-1].weight,
optimizer_idx=0,
global_step=self.global_step,
stage="test",
)
self.log_dict(log)
_, log = self.loss_fn(
data=data,
reconstructions=reconstructions,
commitment_loss=commitment_loss,
decoder_last_layer=self.network.decoder.decoder[-1].weight,
optimizer_idx=1,
global_step=self.global_step,
stage="test",
)
self.log_dict(log)
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