1 files changed, 99 insertions, 0 deletions
diff --git a/text_recognizer/models/vq_transformer.py b/text_recognizer/models/vq_transformer.py
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+++ b/text_recognizer/models/vq_transformer.py
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+"""PyTorch Lightning model for base Transformers."""
+from typing import Tuple, Type, Set
+
+import attr
+import torch
+from torch import Tensor
+
+from text_recognizer.models.metrics import CharacterErrorRate
+from text_recognizer.models.transformer import TransformerLitModel
+
+
+@attr.s(auto_attribs=True, eq=False)
+class VqTransformerLitModel(TransformerLitModel):
+    """A PyTorch Lightning model for transformer networks."""
+
+    def forward(self, data: Tensor) -> Tensor:
+        """Forward pass with the transformer network."""
+        return self.predict(data)
+
+    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
+        """Training step."""
+        data, targets = batch
+        logits, commitment_loss = self.network(data, targets[:-1])
+        loss = self.loss_fn(logits, targets[1:]) + commitment_loss
+        self.log("train/loss", loss)
+        self.log("train/commitment_loss", commitment_loss)
+        return loss
+
+    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+        """Validation step."""
+        data, targets = batch
+
+        # Compute the loss.
+        logits, commitment_loss = self.network(data, targets[:-1])
+        loss = self.loss_fn(logits, targets[1:]) + commitment_loss
+        self.log("val/loss", loss, prog_bar=True)
+        self.log("val/commitment_loss", commitment_loss)
+
+        # Get the token prediction.
+        pred = self(data)
+        self.val_cer(pred, targets)
+        self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
+        self.test_acc(pred, targets)
+        self.log("val/acc", self.test_acc, on_step=False, on_epoch=True)
+
+    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+        """Test step."""
+        data, targets = batch
+
+        # Compute the text prediction.
+        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)
+
+    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, Sy, C)
+            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