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"""PyTorch Lightning model for base Transformers."""
from typing import Any, Dict, Union, Tuple, Type
import attr
from omegaconf import DictConfig
from torch import nn
from torch import Tensor
import wandb
from text_recognizer.models.base import BaseLitModel
@attr.s(auto_attribs=True)
class VQVAELitModel(BaseLitModel):
"""A PyTorch Lightning model for transformer networks."""
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.network.predict(data)
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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