Working bash scripts etc.

1 files changed, 1 insertions, 14 deletions

diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index f1dabb7..0a0ab2d 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -6,10 +6,6 @@ import torch
 from torch import nn
 from torchvision.transforms import ToTensor
 
-from text_recognizer.datasets.emnist_dataset import (
-    _augment_emnist_mapping,
-    _load_emnist_essentials,
-)
 from text_recognizer.models.base import Model
 
 
@@ -20,7 +16,6 @@ class CharacterModel(Model):
         self,
         network_fn: Type[nn.Module],
         network_args: Optional[Dict] = None,
-        data_loader: Optional[Callable] = None,
         data_loader_args: Optional[Dict] = None,
         metrics: Optional[Dict] = None,
         criterion: Optional[Callable] = None,
@@ -36,7 +31,6 @@ class CharacterModel(Model):
         super().__init__(
             network_fn,
             network_args,
-            data_loader,
             data_loader_args,
             metrics,
             criterion,
@@ -47,16 +41,9 @@ class CharacterModel(Model):
             lr_scheduler_args,
             device,
         )
-        if self.mapping is None:
-            self.load_mapping()
         self.tensor_transform = ToTensor()
         self.softmax = nn.Softmax(dim=0)
 
-    def load_mapping(self) -> None:
-        """Mapping between integers and classes."""
-        essentials = _load_emnist_essentials()
-        self._mapping = _augment_emnist_mapping(dict(essentials["mapping"]))
-
     def predict_on_image(
         self, image: Union[np.ndarray, torch.Tensor]
     ) -> Tuple[str, float]:
@@ -86,6 +73,6 @@ class CharacterModel(Model):
 
         index = int(torch.argmax(prediction, dim=0))
         confidence_of_prediction = prediction[index]
-        predicted_character = self._mapping[index]
+        predicted_character = self.mapper(index)
 
         return predicted_character, confidence_of_prediction