Inital commit for refactoring to lightning

1 files changed, 88 insertions, 0 deletions

diff --git a/text_recognizer/models/character_model.py b/text_recognizer/models/character_model.py
new file mode 100644
index 0000000..f9944f3
--- /dev/null
+++ b/text_recognizer/models/character_model.py
@@ -0,0 +1,88 @@
+"""Defines the CharacterModel class."""
+from typing import Callable, Dict, Optional, Tuple, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+from text_recognizer.datasets import EmnistMapper
+from text_recognizer.models.base import Model
+
+
+class CharacterModel(Model):
+    """Model for predicting characters from images."""
+
+    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:
+        """Initializes the CharacterModel."""
+
+        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()
+        self.softmax = nn.Softmax(dim=0)
+
+    @torch.no_grad()
+    def predict_on_image(
+        self, image: Union[np.ndarray, torch.Tensor]
+    ) -> Tuple[str, float]:
+        """Character prediction on an image.
+
+        Args:
+            image (Union[np.ndarray, torch.Tensor]): An image containing a character.
+
+        Returns:
+            Tuple[str, float]: The predicted character and the confidence in the prediction.
+
+        """
+        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)
+        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)
+        logits = self.forward(image)
+
+        prediction = self.softmax(logits.squeeze(0))
+
+        index = int(torch.argmax(prediction, dim=0))
+        confidence_of_prediction = prediction[index]
+        predicted_character = self.mapper(index)
+
+        return predicted_character, confidence_of_prediction