Simply generic getting function to loss only

fltk/util/config/learning_config.py

@@ -167,12 +167,6 @@ def from_yaml(path: Path):
     "CROSSENTROPYLOSS": torch.nn.CrossEntropyLoss,
     "HUBERLOSS" : torch.nn.HuberLoss
 }
-_available_optimizer: Dict[str, Type[torch.optim.Optimizer]] = {
-    "SGD": torch.optim.SGD,
-    "ADAM": torch.optim.Adam,
-    "ADAMW": torch.optim.AdamW
-}
-
 
 @dataclass_json
 @dataclass
@@ -194,28 +188,14 @@ class DistLearningConfig(LearningConfig):  # pylint: disable=too-many-instance-a
     min_lr: float
     seed: int
 
-    @staticmethod
-    def __safe_get(lookup: Dict[str, T], keyword: str) -> T:
-        """
-        Static function to 'safe' get elements from a dictionary, to prevent issues with Capitalization in the code.
-        @param lookup: Lookup dictionary to 'safe get' from.
-        @type lookup: dict
-        @param keyword: Keyword to 'get' from the Lookup dictionary.
-        @type keyword: str
-        @return: Lookup value from 'safe get' request.
-        @rtype: T
-        """
-        safe_keyword = str.upper(keyword)
-        if safe_keyword not in lookup:
-            logging.fatal(f"Cannot find configuration parameter {keyword} in dictionary.")
-        return lookup.get(safe_keyword)
-
     def get_loss(self) -> Type:
         """
         Function to obtain the loss function Type that was given via commandline to be used during the training
         execution.
         @return: Type corresponding to the loss function that was passed as argument.
         @rtype: Type
         """
-        return self.__safe_get(_available_loss, self.loss)
-
+        safe_keyword = str.upper(self.loss)
+        if safe_keyword not in _available_loss:
+            logging.fatal(f"Cannot find configuration parameter {self.loss} in dictionary.")
+        return _available_loss.get(safe_keyword)