Remove VQVAE stuff, did not work...

3 files changed, 0 insertions, 255 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
diff --git a/text_recognizer/models/vqgan.py b/text_recognizer/models/vqgan.py
deleted file mode 100644
index 6a90e06..0000000
--- a/text_recognizer/models/vqgan.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""PyTorch Lightning model for base Transformers."""
-from typing import Tuple
-
-import attr
-from torch import Tensor
-
-from text_recognizer.criterion.vqgan_loss import VQGANLoss
-from text_recognizer.models.base import BaseLitModel
-
-
-@attr.s(auto_attribs=True, eq=False)
-class VQGANLitModel(BaseLitModel):
-    """A PyTorch Lightning model for transformer networks."""
-
-    loss_fn: VQGANLoss = attr.ib()
-    latent_loss_weight: float = attr.ib(default=0.25)
-
-    def forward(self, data: Tensor) -> Tensor:
-        """Forward pass with the transformer network."""
-        return self.network(data)
-
-    def training_step(
-        self, batch: Tuple[Tensor, Tensor], batch_idx: int, optimizer_idx: int
-    ) -> Tensor:
-        """Training step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-
-        if optimizer_idx == 0:
-            loss, log = self.loss_fn(
-                data=data,
-                reconstructions=reconstructions,
-                commitment_loss=commitment_loss,
-                decoder_last_layer=self.network.decoder.decoder[-1].weight,
-                optimizer_idx=optimizer_idx,
-                global_step=self.global_step,
-                stage="train",
-            )
-            self.log(
-                "train/loss", loss, prog_bar=True,
-            )
-            self.log_dict(log, logger=True, on_step=True, on_epoch=True)
-            return loss
-
-        if optimizer_idx == 1:
-            loss, log = self.loss_fn(
-                data=data,
-                reconstructions=reconstructions,
-                commitment_loss=commitment_loss,
-                decoder_last_layer=self.network.decoder.decoder[-1].weight,
-                optimizer_idx=optimizer_idx,
-                global_step=self.global_step,
-                stage="train",
-            )
-            self.log(
-                "train/discriminator_loss", loss, prog_bar=True,
-            )
-            self.log_dict(log, logger=True, on_step=True, on_epoch=True)
-            return loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-
-        loss, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=0,
-            global_step=self.global_step,
-            stage="val",
-        )
-        self.log(
-            "val/loss", loss, prog_bar=True,
-        )
-        self.log_dict(log)
-
-        _, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=1,
-            global_step=self.global_step,
-            stage="val",
-        )
-        self.log_dict(log)
-
-    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Test step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-
-        _, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=0,
-            global_step=self.global_step,
-            stage="test",
-        )
-        self.log_dict(log)
-
-        _, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=1,
-            global_step=self.global_step,
-            stage="test",
-        )
-        self.log_dict(log)
diff --git a/text_recognizer/models/vqvae.py b/text_recognizer/models/vqvae.py
deleted file mode 100644
index 4898852..0000000
--- a/text_recognizer/models/vqvae.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""PyTorch Lightning model for base Transformers."""
-from typing import Tuple
-
-import attr
-from torch import Tensor
-
-from text_recognizer.models.base import BaseLitModel
-
-
-@attr.s(auto_attribs=True, eq=False)
-class VQVAELitModel(BaseLitModel):
-    """A PyTorch Lightning model for transformer networks."""
-
-    commitment: float = attr.ib(default=0.25)
-
-    def forward(self, data: Tensor) -> Tensor:
-        """Forward pass with the transformer network."""
-        return self.network(data)
-
-    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
-        """Training step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-        loss = self.loss_fn(reconstructions, data)
-        loss = loss + self.commitment * commitment_loss
-        self.log("train/commitment_loss", commitment_loss)
-        self.log("train/loss", loss)
-        return loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-        loss = self.loss_fn(reconstructions, data)
-        self.log("val/commitment_loss", commitment_loss)
-        self.log("val/loss", loss, prog_bar=True)
-
-    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Test step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-        loss = self.loss_fn(reconstructions, data)
-        loss = loss + self.commitment * commitment_loss
-        self.log("test/commitment_loss", commitment_loss)
-        self.log("test/loss", loss)