1 files changed, 119 insertions, 0 deletions
diff --git a/src/text_recognizer/models/crnn_model.py b/src/text_recognizer/models/crnn_model.py
new file mode 100644
index 0000000..1e01a83
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+++ b/src/text_recognizer/models/crnn_model.py
@@ -0,0 +1,119 @@
+"""Defines the CRNNModel 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 CRNNModel(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"]
+        if self._mapper is None:
+            self._mapper = EmnistMapper(pad_token=self.pad_token,)
+        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 < 79]
+            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=79,
+            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