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"""PyTorch Lightning model for base Transformers."""
from typing import Any, Dict, Union, Tuple, Type
from omegaconf import DictConfig
from torch import nn
from torch import Tensor
import wandb
from text_recognizer.models.base import LitBaseModel
class LitVQVAEModel(LitBaseModel):
"""A PyTorch Lightning model for transformer networks."""
def __init__(
self,
network: Type[nn.Module],
optimizer: Union[DictConfig, Dict],
lr_scheduler: Union[DictConfig, Dict],
criterion: Union[DictConfig, Dict],
monitor: str = "val/loss",
*args: Any,
**kwargs: Dict,
) -> None:
super().__init__(network, optimizer, lr_scheduler, criterion, monitor)
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.network.predict(data)
def _log_prediction(
self, data: Tensor, reconstructions: Tensor, title: str
) -> None:
"""Logs prediction on image with wandb."""
try:
self.logger.experiment.log(
{
title: [
wandb.Image(data[0]),
wandb.Image(reconstructions[0]),
]
}
)
except AttributeError:
pass
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, _ = batch
reconstructions, vq_loss = self.network(data)
loss = self.loss_fn(reconstructions, data)
loss += vq_loss
self.log("train/loss", loss)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, _ = batch
reconstructions, vq_loss = self.network(data)
loss = self.loss_fn(reconstructions, data)
loss += vq_loss
self.log("val/loss", loss, prog_bar=True)
title = "val_pred_examples"
self._log_prediction(data, reconstructions, title)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, _ = batch
reconstructions, vq_loss = self.network(data)
loss = self.loss_fn(reconstructions, data)
loss += vq_loss
title = "test_pred_examples"
self._log_prediction(data, reconstructions, title)
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