1 files changed, 0 insertions, 113 deletions
diff --git a/text_recognizer/models/vq_transformer.py b/text_recognizer/models/vq_transformer.py
deleted file mode 100644
index 99f69c0..0000000
--- a/text_recognizer/models/vq_transformer.py
+++ /dev/null
@@ -1,113 +0,0 @@
-"""Lightning model for Vector Quantized Transformers."""
-from typing import Optional, Tuple, Type
-
-from omegaconf import DictConfig
-import torch
-from torch import nn, Tensor
-
-from text_recognizer.data.mappings import EmnistMapping
-from text_recognizer.models.transformer import LitTransformer
-
-
-class LitVqTransformer(LitTransformer):
-    """A PyTorch Lightning model for transformer networks."""
-
-    def __init__(
-        self,
-        network: Type[nn.Module],
-        loss_fn: Type[nn.Module],
-        optimizer_config: DictConfig,
-        lr_scheduler_config: Optional[DictConfig],
-        mapping: EmnistMapping,
-        max_output_len: int = 682,
-        start_token: str = "<s>",
-        end_token: str = "<e>",
-        pad_token: str = "<p>",
-        vq_loss_weight: float = 0.1,
-    ) -> None:
-        super().__init__(
-            network,
-            loss_fn,
-            optimizer_config,
-            lr_scheduler_config,
-            mapping,
-            max_output_len,
-            start_token,
-            end_token,
-            pad_token,
-        )
-        self.vq_loss_weight = vq_loss_weight
-
-    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
-        """Training step."""
-        data, targets = batch
-        logits, vq_loss = self.network(data, targets[:, :-1])
-        loss = self.loss_fn(logits, targets[:, 1:])
-        total_loss = loss + self.vq_loss_weight * vq_loss
-        self.log("train/vq_loss", vq_loss)
-        self.log("train/loss", loss)
-        self.log("train/total_loss", total_loss)
-        return total_loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, targets = batch
-        preds = self.predict(data)
-        self.val_acc(preds, targets)
-        self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
-        self.val_cer(preds, targets)
-        self.log("val/cer", self.val_cer, 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
-        pred = self(data)
-        self.test_cer(pred, targets)
-        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
-        self.test_acc(pred, targets)
-        self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
-
-    @torch.no_grad()
-    def predict(self, x: Tensor) -> Tensor:
-        """Predicts text in image.
-
-        Args:
-            x (Tensor): Image(s) to extract text from.
-
-        Shapes:
-            - x: :math: `(B, H, W)`
-            - output: :math: `(B, S)`
-
-        Returns:
-            Tensor: A tensor of token indices of the predictions from the model.
-        """
-        bsz = x.shape[0]
-
-        # Encode image(s) to latent vectors.
-        z, _ = self.network.encode(x)
-
-        # Create a placeholder matrix for storing outputs from the network
-        output = torch.ones((bsz, self.max_output_len), dtype=torch.long).to(x.device)
-        output[:, 0] = self.start_index
-
-        for Sy in range(1, self.max_output_len):
-            context = output[:, :Sy]  # (B, Sy)
-            logits = self.network.decode(z, context)  # (B, C, Sy)
-            tokens = torch.argmax(logits, dim=1)  # (B, Sy)
-            output[:, Sy : Sy + 1] = tokens[:, -1:]
-
-            # Early stopping of prediction loop if token is end or padding token.
-            if (
-                (output[:, Sy - 1] == self.end_index)
-                | (output[:, Sy - 1] == self.pad_index)
-            ).all():
-                break
-
-        # Set all tokens after end token to pad token.
-        for Sy in range(1, self.max_output_len):
-            idx = (output[:, Sy - 1] == self.end_index) | (
-                output[:, Sy - 1] == self.pad_index
-            )
-            output[idx, Sy] = self.pad_index
-
-        return output