reports from optimised linear filtration

architectures/Model.py

@@ -43,12 +43,12 @@ class Model(ABC):
         
         scheduler = CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-6)
         # to save reports
-        file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
-        Util._initialize_log_file(file_path)
+        # file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
+        # Util._initialize_log_file(file_path)
         
 
         print(f"Starting training on {self.device}...")
-        start_time = time.time()
+        # start_time = time.time()
         # training phase
         self.model.train()
 
@@ -69,11 +69,11 @@ class Model(ABC):
             scheduler.step()
         
             print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss / len(loader):.4f}")
-        end_time = time.time()
-        execution_time = end_time - start_time
-        Util.log_metric(log_file=file_path, execution_time=execution_time)
+        #end_time = time.time()
+        #execution_time = end_time - start_time
+        #Util.log_metric(log_file=file_path, execution_time=execution_time)
         if self.device.type == 'cuda': torch.cuda.synchronize()
-        print(f"Training completed in: {time.time() - start_time:.2f}s")
+        print(f"Training complete.")
 
 
     def save(self, filename=None):
@@ -108,7 +108,7 @@ class Model(ABC):
         self.model.to(self.device)
         print(f'Model loaded from {file_path}')
 
-    
+    '''
     def unlearn(self, strategy: 'Strategy', forget_loader, retain_loader):
         """ Executes a targeted unlearning strategy and profiles efficiency """
         print(f"Executing: {strategy.__class__.__name__}...")
@@ -122,6 +122,7 @@ class Model(ABC):
         print(f"{strategy.__class__.__name__} completed in {elapsed_time:.4f} seconds.")
         
         return elapsed_time
+        '''
 
     def evaluate(self, loader, mode="eval"):
         """