Tinsae/Finetuning
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cleaned up for submission

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Tinsae
2026-07-10 20:17:41 +02:00
parent 7eff030e89
commit 946de49b3e
2 changed files with 4 additions and 12 deletions
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6
eval/UnlearningAttack.py
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@@ -32,7 +32,7 @@ class UnlearningAttack:
if model1.__class__.__name__ == "WF_Module": if model1.__class__.__name__ == "WF_Module":
gate = torch.full((data.size(0),), target_class, device=device) gate = torch.full((data.size(0),), target_class, device=device)
p1 = torch.softmax(model1(data, target_class_indices=gate), dim=1) p1 = torch.softmax(model1(data, target_class_indices=gate), dim=1)
else: # its a normal nn.Module else: # its a normal nn.Module
p1 = torch.softmax(model1(data), dim=1) p1 = torch.softmax(model1(data), dim=1)
p2 = torch.softmax(model2(data), dim=1) p2 = torch.softmax(model2(data), dim=1)
@@ -43,12 +43,14 @@ class UnlearningAttack:
def calculate_a_dist(self, latent1, latent2): def calculate_a_dist(self, latent1, latent2):
accuracy_score = self._comput_adversarial_accuracy(latent1, latent2, axis=0) accuracy_score = self._comput_adversarial_accuracy(latent1, latent2, axis=0)
epsilon = 1 - accuracy_score epsilon = 1 - accuracy_score
return 2.0 * np.abs(0.5 - epsilon) return 2.0 * np.abs(0.5 - epsilon)
def _extract_attack_features(self, target_model, loader, device, target_class): def _extract_attack_features(self, target_model, loader, device, target_class):
target_model.eval() target_model.eval()

10
reports/LinearFiltration/ResNet/time_metrics_full sample.txt.txt
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@@ -299,16 +299,6 @@ execution_time_sec
0.001947 0.001947
0.001871 0.001871
0.003117 0.003117
16.738618
0.001900
0.002124
0.001867
0.001870
0.002135
0.001923
0.001955
0.001853
0.002758
16.945078 16.945078
0.001848 0.001848
0.001868 0.001868