started unlearning setup

unlearning/LinearFiltration.py

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+
+import torch
+from Strategy import Strategy
+
+class NormalizingLinearFiltration(Strategy):
+    def __init__(self, target_class_idx):
+        self.target_class_idx = target_class_idx
+
+    def apply(self, model, forget_loader, retain_loader):
+        model.eval()
+        # Freeze parameters structurally
+        for param in model.parameters():
+            param.requires_grad = False
+            
+        with torch.no_grad():
+            # we modify only classification head
+            # Shape: [num_classes, feature_dim]
+            W = model.fc.weight.data
+            
+            # Compute the normalization transformation projection matrix (A)
+            # (In your full code, calculate A here matching Baumhauer et al.'s equations)
+            num_classes = W.shape[0]
+            A = torch.eye(num_classes, device=W.device)
+            # Mask/blend target class index distribution configurations here...
+            A[self.target_class_idx, :] = 0.0 
+            
+            # 3. Direct weight matrix override: W_filtered = A * W
+            sanitized_W = torch.mm(A, W)
+            model.fc.weight.copy_(sanitized_W)