Created NaiveTrainer for test purposes advances #2

trainers/__init__.py

@@ -1,4 +1,5 @@
 from trainers.basic_trainer import BasicTrainer
+from trainers.naive_trainer import NaiveTrainer
 
 def get_trainer(trainer_name, **kwargs):
     """
@@ -16,5 +17,7 @@ def get_trainer(trainer_name, **kwargs):
     """
     if trainer_name == "BasicTrainer":
         return BasicTrainer(**kwargs)
+    if trainer_name == "NaiveTrainer":
+        return NaiveTrainer(**kwargs)
     else:
         raise ValueError(f"Trainer {trainer_name} not recognized.")

trainers/naive_trainer.py

@@ -0,0 +1,34 @@
+from trainers.base_trainer import BaseTrainer
+
+
+class NaiveTrainer(BaseTrainer):
+    """
+    A complete naive trainer class for training a model. It main purpose is to
+    be used as a boilerplate to test some functionalities of the trainer.
+
+    Args:
+        model (nn.Module): The model to be trained.
+        device (torch.device): The device to be used for training.
+
+    Attributes:
+        model (nn.Module): The model to be trained.
+        device (torch.device): The device to be used for training.
+    """
+
+    def __init__(self, model, device):
+        super().__init__(model, device)
+
+    def _train_epoch(self, train_loader, epoch, num_epochs, verbose=True) -> float:
+        """
+        Simulates training the model for one epoch. It actually does nothing.
+
+        Args:
+            train_loader (DataLoader): The data loader for training data.
+            epoch (int): The current epoch number.
+            num_epochs (int): The total number of epochs.
+            verbose (bool, optional): Whether to display progress bar. Defaults to True.
+
+        Returns:
+            float: A mock loss value for the epoch of 0.0.
+        """
+        return 0.0