Working training loop and testing of trained CharacterModel.

1 files changed, 18 insertions, 12 deletions

diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index fd69bf2..527fc7d 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -1,5 +1,5 @@
 """Defines the CharacterModel class."""
-from typing import Callable, Dict, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple, Type
 
 import numpy as np
 import torch
@@ -8,7 +8,6 @@ from torchvision.transforms import ToTensor
 
 from text_recognizer.datasets.emnist_dataset import load_emnist_mapping
 from text_recognizer.models.base import Model
-from text_recognizer.networks.mlp import mlp
 
 
 class CharacterModel(Model):
@@ -16,8 +15,9 @@ class CharacterModel(Model):
 
     def __init__(
         self,
-        network_fn: Callable,
+        network_fn: Type[nn.Module],
         network_args: Dict,
+        data_loader: Optional[Callable] = None,
         data_loader_args: Optional[Dict] = None,
         metrics: Optional[Dict] = None,
         criterion: Optional[Callable] = None,
@@ -33,6 +33,7 @@ class CharacterModel(Model):
         super().__init__(
             network_fn,
             network_args,
+            data_loader,
             data_loader_args,
             metrics,
             criterion,
@@ -43,13 +44,13 @@ class CharacterModel(Model):
             lr_scheduler_args,
             device,
         )
-        self.emnist_mapping = self.mapping()
-        self.eval()
+        self.load_mapping()
+        self.tensor_transform = ToTensor()
+        self.softmax = nn.Softmax(dim=0)
 
-    def mapping(self) -> Dict[int, str]:
+    def load_mapping(self) -> None:
         """Mapping between integers and classes."""
-        mapping = load_emnist_mapping()
-        return mapping
+        self._mapping = load_emnist_mapping()
 
     def predict_on_image(self, image: np.ndarray) -> Tuple[str, float]:
         """Character prediction on an image.
@@ -61,15 +62,20 @@ class CharacterModel(Model):
             Tuple[str, float]: The predicted character and the confidence in the prediction.
 
         """
+
         if image.dtype == np.uint8:
             image = (image / 255).astype(np.float32)
 
         # Conver to Pytorch Tensor.
-        image = ToTensor(image)
+        image = self.tensor_transform(image)
+
+        with torch.no_grad():
+            logits = self.network(image)
+
+        prediction = self.softmax(logits.data.squeeze())
 
-        prediction = self.network(image)
-        index = torch.argmax(prediction, dim=1)
+        index = int(torch.argmax(prediction, dim=0))
         confidence_of_prediction = prediction[index]
-        predicted_character = self.emnist_mapping[index]
+        predicted_character = self._mapping[index]
 
         return predicted_character, confidence_of_prediction