1 files changed, 0 insertions, 99 deletions
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
deleted file mode 100644
index bbfaac0..0000000
--- a/text_recognizer/models/transformer.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""Lightning model for base Transformers."""
-from typing import Callable, Optional, Sequence, Tuple, Type
-from text_recognizer.models.greedy_decoder import GreedyDecoder
-
-import torch
-from omegaconf import DictConfig
-from torch import nn, Tensor
-from torchmetrics import CharErrorRate, WordErrorRate
-
-from text_recognizer.data.tokenizer import Tokenizer
-from text_recognizer.models.base import LitBase
-
-
-class LitTransformer(LitBase):
-    """A PyTorch Lightning model for transformer networks."""
-
-    def __init__(
-        self,
-        network: Type[nn.Module],
-        loss_fn: Type[nn.Module],
-        optimizer_config: DictConfig,
-        tokenizer: Tokenizer,
-        decoder: Callable = GreedyDecoder,
-        lr_scheduler_config: Optional[DictConfig] = None,
-        max_output_len: int = 682,
-    ) -> None:
-        super().__init__(
-            network,
-            loss_fn,
-            optimizer_config,
-            lr_scheduler_config,
-            tokenizer,
-        )
-        self.max_output_len = max_output_len
-        self.val_cer = CharErrorRate()
-        self.test_cer = CharErrorRate()
-        self.val_wer = WordErrorRate()
-        self.test_wer = WordErrorRate()
-        self.decoder = decoder
-
-    def forward(self, data: Tensor) -> Tensor:
-        """Autoregressive forward pass."""
-        return self.predict(data)
-
-    def teacher_forward(self, data: Tensor, targets: Tensor) -> Tensor:
-        """Non-autoregressive forward pass."""
-        return self.network(data, targets)
-
-    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
-        """Training step."""
-        data, targets = batch
-        logits = self.teacher_forward(data, targets[:, :-1])
-        loss = self.loss_fn(logits, targets[:, 1:])
-        self.log("train/loss", loss)
-        return loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, targets = batch
-
-        logits = self.teacher_forward(data, targets[:, :-1])
-        loss = self.loss_fn(logits, targets[:, 1:])
-        preds = self.predict(data)
-        pred_text, target_text = self._to_tokens(preds), self._to_tokens(targets)
-
-        self.val_acc(preds, targets)
-        self.val_cer(pred_text, target_text)
-        self.val_wer(pred_text, target_text)
-        self.log("val/loss", loss, on_step=False, on_epoch=True)
-        self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
-        self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
-        self.log("val/wer", self.val_wer, on_step=False, on_epoch=True, prog_bar=True)
-
-    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Test step."""
-        data, targets = batch
-
-        logits = self.teacher_forward(data, targets[:, :-1])
-        loss = self.loss_fn(logits, targets[:, 1:])
-        preds = self(data)
-        pred_text, target_text = self._to_tokens(preds), self._to_tokens(targets)
-
-        self.test_acc(preds, targets)
-        self.test_cer(pred_text, target_text)
-        self.test_wer(pred_text, target_text)
-        self.log("test/loss", loss, on_step=False, on_epoch=True)
-        self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
-        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
-        self.log("test/wer", self.test_wer, on_step=False, on_epoch=True, prog_bar=True)
-
-    def _to_tokens(
-        self,
-        indecies: Tensor,
-    ) -> Sequence[str]:
-        return [self.tokenizer.decode(i) for i in indecies]
-
-    @torch.no_grad()
-    def predict(self, x: Tensor) -> Tensor:
-        return self.decoder(x)