attck metrics

unlearning/CertifiedUnlearning.py

@@ -43,17 +43,12 @@ class CertifiedUnlearning(Strategy):
         InceptionV3 auxiliary layers and tracking gradients.
         """
         inner_model = getattr(model, "model", model)
-        
-        # Check if the current architecture is an Inception variant
-        is_inception = inner_model.__class__.__name__.lower() == "inception3"
-
+   
         params_list = []
         for name, p in inner_model.named_parameters():
             if p.requires_grad:
-                # Discard the disconnected auxiliary training branch weights
-                if is_inception and "AuxLogits" in name:
-                    continue
-                # CRITICAL: Append as a tuple so it can be unpacked as (name, param)
+                
+                # Append as a tuple so it can be unpacked as (name, param)
                 params_list.append((name, p))
                 
         return params_list if named else [e[1] for e in params_list]
@@ -92,7 +87,7 @@ class CertifiedUnlearning(Strategy):
         first_grads = grad(loss, params, retain_graph=True, create_graph=True)
         elemwise_products = sum(torch.sum(g_elem * v_elem) for g_elem, v_elem in zip(first_grads, v))
         return grad(elemwise_products, params, create_graph=False)
-
+    
     def _stochastic_newton_update(self, g, dataset, model, device):
         model.eval()
         criterion = nn.CrossEntropyLoss()
@@ -133,7 +128,6 @@ class CertifiedUnlearning(Strategy):
                 
                 h_s = self._hvp(loss, params, h_estimate)
                 
-                # OPTIMIZATION 4: Avoid deprecated .data, use detach() and in-place ops
                 with torch.no_grad():
                     for k in range(len(params)):
                         h_estimate[k].copy_(h_estimate[k] + g[k] - (h_s[k] / self.scale))
@@ -143,7 +137,7 @@ class CertifiedUnlearning(Strategy):
                 if global_step % step_interval == 0 and current_pct < 100:
                     current_pct += 1
                     print(f"\rProgress: {current_pct}% done", end="", flush=True)
-                        
+                    
             with torch.no_grad():
                 for k in range(len(params)):
                     h_res[k] += h_estimate[k] / self.scale