added reports and params

sets/Data.py

@@ -40,10 +40,11 @@ def test_transform(res):
         )
     ])
 
-# Load data with 'identity' as target and transform it
+# Load data
 def get_set(set_name:Set_Name):
     return fetch_celeb_a() if set_name == Set_Name.CELEBA else fetch_casia_faces()
 
+# celebA
 def fetch_celeb_a():
     return datasets.CelebA(
         root='./data', 
@@ -53,6 +54,7 @@ def fetch_celeb_a():
         transform=None
     )
 
+# CASIA-WebFaces
 def fetch_casia_faces():
     # location of the data (path relative to project root)
     final_path = os.path.abspath("./data/casia-set")
@@ -191,12 +193,12 @@ def vertical_split(dataset, batch_size,num_classes):
         50% of each class goes to the Retain Set, 50% goes to the Forget Set.
         """
         
-        # 1. Group dataset indices by their respective ground-truth classes
+        # Dataset indices by their respective ground-truth classes
         class_to_indices = {c: [] for c in range(num_classes)}
         
         print("  [Vertical Split] Tracking class indices across the combined dataset...")
         for idx in range(len(dataset)):
-            # Extract the label cleanly from the underlying dataset structure
+            # Extract labels
             _, label = dataset[idx]
             if label in class_to_indices:
                 class_to_indices[label].append(idx)
@@ -204,14 +206,14 @@ def vertical_split(dataset, batch_size,num_classes):
         retain_indices = []
         forget_indices = []
 
-        # 2. Slice each class identity vertically (exactly 50/50)
+        # Slice each class identity vertically (exactly 50/50)
         for c, indices in class_to_indices.items():
             if len(indices) < 2:
                 print(f"  Warning: Class {c} has fewer than 2 samples. Cannot split vertically.")
                 retain_indices.extend(indices)
                 continue
                 
-            # Deterministic shuffle per class to ensure honest distribution before splitting
+            # Suffle to ensure honest distribution before splitting
             np.random.shuffle(indices)
             
             mid = len(indices) // 2
@@ -220,11 +222,10 @@ def vertical_split(dataset, batch_size,num_classes):
 
         print(f"  Vertical split complete: Retain Index Size = {len(retain_indices)} | Forget Index Size = {len(forget_indices)}")
 
-        # 3. Construct lightweight PyTorch Subsets using our sliced index maps
+        # Subsets using our sliced index maps
         retain_subset = Subset(dataset, retain_indices)
         forget_subset = Subset(dataset, forget_indices)
 
-        # 4. Return pristine, shuffled DataLoaders mirroring your environment's batch specifications
         retain_loader = DataLoader(retain_subset, batch_size=batch_size, shuffle=True)
         forget_loader = DataLoader(forget_subset, batch_size=batch_size, shuffle=True)