1 files changed, 0 insertions, 116 deletions
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)