1 files changed, 0 insertions, 97 deletions
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
deleted file mode 100644
index 4dd5cdf..0000000
--- a/text_recognizer/models/base.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""Base PyTorch Lightning model."""
-from typing import Any, Dict, Optional, Tuple, Type
-
-import hydra
-import torch
-from loguru import logger as log
-from omegaconf import DictConfig
-from pytorch_lightning import LightningModule
-from torch import nn, Tensor
-from torchmetrics import Accuracy
-
-from text_recognizer.data.tokenizer import Tokenizer
-
-
-class LitBase(LightningModule):
-    """Abstract PyTorch Lightning class."""
-
-    def __init__(
-        self,
-        network: Type[nn.Module],
-        loss_fn: Type[nn.Module],
-        optimizer_config: DictConfig,
-        lr_scheduler_config: Optional[DictConfig],
-        tokenizer: Tokenizer,
-    ) -> None:
-        super().__init__()
-
-        self.network = network
-        self.loss_fn = loss_fn
-        self.optimizer_config = optimizer_config
-        self.lr_scheduler_config = lr_scheduler_config
-        self.tokenizer = tokenizer
-        ignore_index = int(self.tokenizer.get_value("<p>"))
-        # Placeholders
-        self.train_acc = Accuracy(mdmc_reduce="samplewise", ignore_index=ignore_index)
-        self.val_acc = Accuracy(mdmc_reduce="samplewise", ignore_index=ignore_index)
-        self.test_acc = Accuracy(mdmc_reduce="samplewise", ignore_index=ignore_index)
-
-    def optimizer_zero_grad(
-        self,
-        epoch: int,
-        batch_idx: int,
-        optimizer: Type[torch.optim.Optimizer],
-        optimizer_idx: int,
-    ) -> None:
-        """Optimal way to set grads to zero."""
-        optimizer.zero_grad(set_to_none=True)
-
-    def _configure_optimizer(self) -> Type[torch.optim.Optimizer]:
-        """Configures the optimizer."""
-        log.info(f"Instantiating optimizer <{self.optimizer_config._target_}>")
-        return hydra.utils.instantiate(
-            self.optimizer_config, params=self.network.parameters()
-        )
-
-    def _configure_lr_schedulers(
-        self, optimizer: Type[torch.optim.Optimizer]
-    ) -> Optional[Dict[str, Any]]:
-        """Configures the lr scheduler."""
-        log.info(
-            f"Instantiating learning rate scheduler <{self.lr_scheduler_config._target_}>"
-        )
-        monitor = self.lr_scheduler_config.pop("monitor")
-        interval = self.lr_scheduler_config.pop("interval")
-        return {
-            "monitor": monitor,
-            "interval": interval,
-            "scheduler": hydra.utils.instantiate(
-                self.lr_scheduler_config, optimizer=optimizer
-            ),
-        }
-
-    def configure_optimizers(
-        self,
-    ) -> Dict[str, Any]:
-        """Configures optimizer and lr scheduler."""
-        optimizer = self._configure_optimizer()
-        if self.lr_scheduler_config is not None:
-            scheduler = self._configure_lr_schedulers(optimizer)
-            return {"optimizer": optimizer, "lr_scheduler": scheduler}
-        return {"optimizer": optimizer}
-
-    def forward(self, data: Tensor) -> Tensor:
-        """Feedforward pass."""
-        return self.network(data)
-
-    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
-        """Training step."""
-        pass
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        pass
-
-    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Test step."""
-        pass