Inital commit for refactoring to lightning

1 files changed, 0 insertions, 120 deletions

diff --git a/src/text_recognizer/models/ctc_transformer_model.py b/src/text_recognizer/models/ctc_transformer_model.py
deleted file mode 100644
index 25925f2..0000000
--- a/src/text_recognizer/models/ctc_transformer_model.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""Defines the CTC Transformer Model class."""
-from typing import Callable, Dict, Optional, Tuple, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets import EmnistMapper
-from text_recognizer.models.base import Model
-from text_recognizer.networks import greedy_decoder
-
-
-class CTCTransformerModel(Model):
-    """Model for predicting a sequence of characters from an image of a text line."""
-
-    def __init__(
-        self,
-        network_fn: Type[nn.Module],
-        dataset: Type[Dataset],
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        super().__init__(
-            network_fn,
-            dataset,
-            network_args,
-            dataset_args,
-            metrics,
-            criterion,
-            criterion_args,
-            optimizer,
-            optimizer_args,
-            lr_scheduler,
-            lr_scheduler_args,
-            swa_args,
-            device,
-        )
-        self.pad_token = dataset_args["args"]["pad_token"]
-        self.lower = dataset_args["args"]["lower"]
-
-        if self._mapper is None:
-            self._mapper = EmnistMapper(pad_token=self.pad_token, lower=self.lower,)
-
-        self.tensor_transform = ToTensor()
-
-    def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
-        """Computes the CTC loss.
-
-        Args:
-            output (Tensor): Model predictions.
-            targets (Tensor): Correct output sequence.
-
-        Returns:
-            Tensor: The CTC loss.
-
-        """
-        # Input lengths on the form [T, B]
-        input_lengths = torch.full(
-            size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,
-        )
-
-        # Configure target tensors for ctc loss.
-        targets_ = Tensor([]).to(self.device)
-        target_lengths = []
-        for t in targets:
-            # Remove padding symbol as it acts as the blank symbol.
-            t = t[t < 53]
-            targets_ = torch.cat([targets_, t])
-            target_lengths.append(len(t))
-
-        targets = targets_.type(dtype=torch.long)
-        target_lengths = (
-            torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device)
-        )
-
-        return self._criterion(output, targets, input_lengths, target_lengths)
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
-        """Predict on a single input."""
-        self.eval()
-
-        if image.dtype == np.uint8:
-            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
-            image = self.tensor_transform(image)
-
-        # Rescale image between 0 and 1.
-        if image.dtype == torch.uint8:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-        log_probs = self.forward(image)
-
-        raw_pred, _ = greedy_decoder(
-            predictions=log_probs,
-            character_mapper=self.mapper,
-            blank_label=53,
-            collapse_repeated=True,
-        )
-
-        log_probs, _ = log_probs.max(dim=2)
-
-        predicted_characters = "".join(raw_pred[0])
-        confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item()
-
-        return predicted_characters, confidence_of_prediction