Remove VQVAE stuff, did not work...

1 files changed, 0 insertions, 94 deletions

diff --git a/text_recognizer/models/vq_transformer.py b/text_recognizer/models/vq_transformer.py
deleted file mode 100644
index 8ec28fd..0000000
--- a/text_recognizer/models/vq_transformer.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""PyTorch Lightning model for base Transformers."""
-from typing import Tuple, Type
-
-import attr
-import torch
-from torch import Tensor
-
-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
-        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
-        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, 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