1 files changed, 0 insertions, 76 deletions
diff --git a/text_recognizer/models/perceiver.py b/text_recognizer/models/perceiver.py
deleted file mode 100644
index c482235..0000000
--- a/text_recognizer/models/perceiver.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""Lightning model for base Perceiver."""
-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.base import LitBase
-from text_recognizer.models.metrics import CharacterErrorRate
-
-
-class LitPerceiver(LitBase):
-    """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>",
-    ) -> None:
-        super().__init__(
-            network, loss_fn, optimizer_config, lr_scheduler_config, mapping
-        )
-        self.max_output_len = max_output_len
-        self.start_token = start_token
-        self.end_token = end_token
-        self.pad_token = pad_token
-        self.start_index = int(self.mapping.get_index(self.start_token))
-        self.end_index = int(self.mapping.get_index(self.end_token))
-        self.pad_index = int(self.mapping.get_index(self.pad_token))
-        self.ignore_indices = set([self.start_index, self.end_index, self.pad_index])
-        self.val_cer = CharacterErrorRate(self.ignore_indices)
-        self.test_cer = CharacterErrorRate(self.ignore_indices)
-
-    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 = self.network(data)
-        loss = self.loss_fn(logits, targets)
-        self.log("train/loss", loss)
-        return 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
-
-        # 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)
-
-    @torch.no_grad()
-    def predict(self, x: Tensor) -> Tensor:
-        return self.network(x).argmax(dim=1)