cleaned up for submission

eval/UnlearningAttack.py

@@ -32,7 +32,7 @@ class UnlearningAttack:
                 if model1.__class__.__name__ == "WF_Module":
                     gate = torch.full((data.size(0),), target_class, device=device)
                     p1 = torch.softmax(model1(data, target_class_indices=gate), dim=1)
-                    
+
                 else: # its a normal nn.Module
                     p1 = torch.softmax(model1(data), dim=1)
                 p2 = torch.softmax(model2(data), dim=1)
@@ -43,12 +43,14 @@ class UnlearningAttack:
     
 
     def calculate_a_dist(self, latent1, latent2):
-
+        
         accuracy_score = self._comput_adversarial_accuracy(latent1, latent2, axis=0)
         epsilon = 1 - accuracy_score
         
         return 2.0 * np.abs(0.5 - epsilon)
 
+
+
     def _extract_attack_features(self, target_model, loader, device, target_class):
         
         target_model.eval()

reports/LinearFiltration/ResNet/time_metrics_full sample.txt.txt

@@ -299,16 +299,6 @@ execution_time_sec
 0.001947
 0.001871
 0.003117
-16.738618
-0.001900
-0.002124
-0.001867
-0.001870
-0.002135
-0.001923
-0.001955
-0.001853
-0.002758
 16.945078
 0.001848
 0.001868