1 files changed, 0 insertions, 124 deletions
diff --git a/text_recognizer/models/conformer.py b/text_recognizer/models/conformer.py
deleted file mode 100644
index 41a9e4d..0000000
--- a/text_recognizer/models/conformer.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""Lightning Conformer model."""
-import itertools
-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
-from text_recognizer.models.util import first_element
-
-
-class LitConformer(LitBase):
-    """A PyTorch Lightning model for transformer networks."""
-
-    def __init__(
-        self,
-        network: Type[nn.Module],
-        loss_fn: Type[nn.Module],
-        optimizer_configs: DictConfig,
-        lr_scheduler_configs: Optional[DictConfig],
-        mapping: EmnistMapping,
-        max_output_len: int = 451,
-        start_token: str = "<s>",
-        end_token: str = "<e>",
-        pad_token: str = "<p>",
-        blank_token: str = "<b>",
-    ) -> None:
-        super().__init__(
-            network, loss_fn, optimizer_configs, lr_scheduler_configs, mapping
-        )
-        self.max_output_len = max_output_len
-        self.start_token = start_token
-        self.end_token = end_token
-        self.pad_token = pad_token
-        self.blank_token = blank_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.blank_index = int(self.mapping.get_index(self.blank_token))
-        self.ignore_indices = set(
-            [self.start_index, self.end_index, self.pad_index, self.blank_index]
-        )
-        self.val_cer = CharacterErrorRate(self.ignore_indices)
-        self.test_cer = CharacterErrorRate(self.ignore_indices)
-
-    @torch.no_grad()
-    def predict(self, x: Tensor) -> str:
-        """Predicts a sequence of characters."""
-        logits = self(x)
-        logprobs = torch.log_softmax(logits, dim=1)
-        return self.decode(logprobs, self.max_output_len)
-
-    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
-        """Training step."""
-        data, targets = batch
-        logits = self(data)
-        logprobs = torch.log_softmax(logits, dim=1)
-        B, S, _ = logprobs.shape
-        input_length = torch.ones(B).type_as(logprobs).int() * S
-        target_length = first_element(targets, self.pad_index).type_as(targets)
-        loss = self.loss_fn(
-            logprobs.permute(1, 0, 2), targets, input_length, target_length
-        )
-        self.log("train/loss", loss)
-        return loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, targets = batch
-        logits = self(data)
-        logprobs = torch.log_softmax(logits, dim=1)
-        B, S, _ = logprobs.shape
-        input_length = torch.ones(B).type_as(logprobs).int() * S
-        target_length = first_element(targets, self.pad_index).type_as(targets)
-        loss = self.loss_fn(
-            logprobs.permute(1, 0, 2), targets, input_length, target_length
-        )
-        self.log("val/loss", loss)
-        preds = self.decode(logprobs, targets.shape[1])
-        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
-        logits = self(data)
-        logprobs = torch.log_softmax(logits, dim=1)
-        preds = self.decode(logprobs, targets.shape[1])
-        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 decode(self, logprobs: Tensor, max_length: int) -> Tensor:
-        """Greedly decodes a log prob sequence.
-
-        Args:
-            logprobs (Tensor): Log probabilities.
-            max_length (int): Max length of a sequence.
-
-        Shapes:
-            - x: :math: `(B, T, C)`
-            - output: :math: `(B, T)`
-
-        Returns:
-            Tensor: A predicted sequence of characters.
-        """
-        B = logprobs.shape[0]
-        argmax = logprobs.argmax(2)
-        decoded = torch.ones((B, max_length)).type_as(logprobs).int() * self.pad_index
-        for i in range(B):
-            seq = [
-                b
-                for b, _ in itertools.groupby(argmax[i].tolist())
-                if b != self.blank_index
-            ][:max_length]
-            for j, c in enumerate(seq):
-                decoded[i, j] = c
-        return decoded