facebook's implementation

Tune.py

@@ -34,7 +34,7 @@ TRAINING_SMPLE = 27
 
 # learning rate
 LR_RATE = 0.0001
-EPOCHS = 20
+EPOCHS = 10
 
 # depends on model architecture
 # ResNet, DenseNet = 224
@@ -109,7 +109,7 @@ print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}, epochs = {EP
 device = SetUp.get_device()
 
 
-for i in range(0,CLASS_SIZE):
+for i in range(0,1):#CLASS_SIZE):
     FORGET_CLASS_IDX = i
     # Create model using Factory
     model = Model.create(
@@ -118,13 +118,13 @@ for i in range(0,CLASS_SIZE):
         size = CLASS_SIZE)
 
     # we may need to load existing model or finetune
-    model.train(
-            epochs = EPOCHS, 
-            loader = train_loader, 
-            rate = LR_RATE)
+    #model.train(
+    #        epochs = EPOCHS, 
+    #        loader = train_loader, 
+    #        rate = LR_RATE)
 
         # save.
-    model.save(filename=arch.name.lower())
+    #model.save(filename=arch.name.lower())
 
 
     # done tuning
@@ -147,10 +147,10 @@ for i in range(0,CLASS_SIZE):
 
     # Evaluate
     current_mode = "Finetuned"
-    accuracy, report_dict = model.evaluate(
-        loader = test_loader,
-        mode=current_mode
-    )
+    #accuracy, report_dict = model.evaluate(
+    #    loader = test_loader,
+    #    mode=current_mode
+    #)
 
     Util._log_to_csv(
         arch=model.__class__.__name__, 
@@ -161,13 +161,13 @@ for i in range(0,CLASS_SIZE):
     )
 
     # unlearning algorithms
-    linear_filtration = LinearFiltration(target_class_idx=FORGET_CLASS_IDX)
+    #linear_filtration = LinearFiltration(target_class_index=FORGET_CLASS_IDX)
     #filtration.apply(reloaded.model)
 
-    weight_filtration = WeightFiltration(num_classes = CLASS_SIZE,target_class_idx=FORGET_CLASS_IDX)
+    #weight_filtration = WeightFiltration(num_classes = CLASS_SIZE,target_class_idx=FORGET_CLASS_IDX)
     #weight_filtration.apply(reloaded.model)
 
-    certified_removal = CertifiedRemoval(removal_bound=0.05, epsilon=0.5, l2_reg=0.1)
+    certified_removal = CertifiedRemoval(target_class_index=FORGET_CLASS_IDX,removal_bound=0.05, epsilon=0.5, l2_reg=15)
     #certified_removal.apply(reloaded.model)
 
     # to be unlearned
@@ -185,8 +185,8 @@ for i in range(0,CLASS_SIZE):
     )
 
 
-    strategies = [linear_filtration, weight_filtration, certified_removal]
-    #strategies = [linear_filtration]
+    #strategies = [linear_filtration, weight_filtration, certified_removal]
+    strategies = [certified_removal]
     for strategy in strategies:
         # test again
         reloaded = Model.create(

Tune_new.py

@@ -0,0 +1,189 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from sklearn.metrics import classification_report
+
+# Framework and Utility Imports
+import SetUp
+import Util
+from sets.Data import *
+from sets.IdentitySubset import IdentitySubset
+from architectures.Model import Model, Architecture
+from unlearning.CertifiedRemoval import CertifiedRemoval
+
+# Global Hyperparameters
+CLASS_SIZE = 20
+BATCH_SIZE = 16
+SAMPLE_SIZE = 30
+TRAINING_SAMPLE = 27
+RESOLUTION = 224
+ARCH = Architecture.RESNET50
+
+
+# Data preparation and model setup
+def prepare_data_and_model_environment():
+    """
+    Handles environment discovery, downloads/loads datasets, generates 
+    train-test class splits, and configures the architecture base.
+    """
+    device = SetUp.get_device()
+    dataset_name = Set_Name.CELEBA
+
+    dataset = get_set(set_name=dataset_name)
+    print(f"> {dataset.__class__.__name__} dataset loaded")
+
+    # Select target identities (deterministic top sample identities)
+    selected_identities = select_top_ids(dataset=dataset, class_size=CLASS_SIZE)
+    print(f'> Selected {CLASS_SIZE} random identity classes from CelebA dataset.')
+    print(f'> A class has {TRAINING_SAMPLE} train and {SAMPLE_SIZE - TRAINING_SAMPLE} test samples')
+
+    # Isolate sample index partitions
+    train_indices, test_indices = get_indices(
+        dataset=dataset, 
+        identities=selected_identities, 
+        split_at=TRAINING_SAMPLE,
+        size=SAMPLE_SIZE
+    )
+
+    # Remap identities to 0 -> (N-1) range required by CrossEntropyLoss
+    id_map = {old_id: new_id for new_id, old_id in enumerate(selected_identities)}
+
+    # Build internal datasets using custom transforms
+    tr_transform = train_transform(RESOLUTION)
+    train_data = IdentitySubset(
+        dataset=dataset, 
+        indices=train_indices, 
+        id_mapping=id_map,
+        transform=tr_transform
+    )
+
+    te_transform = test_transform(RESOLUTION)
+    test_data = IdentitySubset(
+        dataset=dataset, 
+        indices=test_indices, 
+        id_mapping=id_map,
+        transform=te_transform
+    )
+
+    print(f"> Total training images: {len(train_data)}")
+    print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}')
+
+    # Create the base target model instance
+    base_model = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
+
+    return {
+        "device": device,
+        "train_data": train_data,
+        "test_data": test_data,
+        "base_model": base_model
+    }
+
+
+# Fine tunning and evaluation
+def run_finetuning_or_baseline_eval(env_dict, run_training=False, lr_rate=0.0001, epochs=10):
+    """
+    Handles model training (if flag is true) and logs the baseline fine-tuned 
+    performance to file metrics.
+    """
+    model = env_dict["base_model"]
+    train_data = env_dict["train_data"]
+    test_data = env_dict["test_data"]
+
+    test_loader = DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=False)
+
+    # Optional training configuration switch
+    if run_training:
+        train_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)
+        print(f"Starting training on {env_dict['device']}...")
+        model.train(epochs=epochs, loader=train_loader, rate=lr_rate)
+        model.save(filename=ARCH.name.lower())
+        print(f"Model saved to trained_models/{ARCH.name.lower()}.pth")
+
+    print(f"Total test images for these {CLASS_SIZE} classes: {len(test_data)}")
+
+    # Evaluate original base checkpoint performance
+    current_mode = "Finetuned"
+    
+    # Check if weights exist or model was trained before evaluating
+    try:
+        accuracy, report_dict = model.evaluate(loader=test_loader, mode=current_mode)
+        Util._log_to_csv(
+            arch=model.__class__.__name__, 
+            mode=current_mode, 
+            accuracy=accuracy, 
+            report_dict=report_dict, 
+            strategy="base"
+        )
+    except Exception as e:
+        print(f">> Skipping baseline log generation. Reason: {e}")
+
+
+# Unlearning and strategy eval
+def run_unlearning_and_strategy_eval(env_dict, forget_class_idx):
+    """
+    Reloads a clean model state, applies the isolated unlearning framework,
+    and runs specific target evaluation domain checks.
+    """
+    device = env_dict["device"]
+    train_data = env_dict["train_data"]
+    test_data = env_dict["test_data"]
+
+    # Initialize the strategy hyperparameters matching standard settings
+    certified_removal = CertifiedRemoval(
+        target_class_index=forget_class_idx, 
+        removal_bound=0.05, 
+        epsilon=0.5, 
+        l2_reg=15
+    )
+
+    # Segment specific unlearning loaders using class index boundaries
+    forget_train_loader, retain_train_loader = get_unlearning_loaders(
+        dataset=train_data, forget_class_idx=forget_class_idx, batch_size=BATCH_SIZE
+    )
+    forget_test_loader, retain_test_loader = get_unlearning_loaders(
+        dataset=test_data, forget_class_idx=forget_class_idx, batch_size=BATCH_SIZE
+    )
+
+    # Instantiate a clean copy of the model to keep weights isolated
+    reloaded = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
+    reloaded.load(arch=ARCH)
+    print("fine tunned model loaded into evaluation sandbox")
+
+    # Execute strategic parameter unlearning step
+    certified_removal.apply(reloaded.model, forget_train_loader, retain_train_loader)
+    strategy_in_use = certified_removal.__class__.__name__
+
+    # Define validation tracking steps dynamically
+    evaluation_domains = [
+        {"loader": retain_test_loader, "mode": "retain", "label": "\n--- Performance on Retained Classes"},
+        {"loader": forget_test_loader, "mode": "forget", "label": "\n--- Performance on Forgotten Class"},
+        {"loader": forget_train_loader, "mode": "forget_train", "label": "\n--- Performance on Forgotten Class (Train Set - Verifying Unlearning)"}
+    ]
+
+    # Process and append metrics to target reporting paths
+    for domain in evaluation_domains:
+        print(domain["label"])
+        accuracy, report_dict = reloaded.evaluate(loader=domain["loader"], mode=domain["mode"])
+        Util._log_to_csv(
+            arch=reloaded.__class__.__name__, 
+            mode=domain["mode"], 
+            accuracy=accuracy, 
+            report_dict=report_dict, 
+            strategy=strategy_in_use
+        )
+
+
+# entry
+if __name__ == "__main__":
+    
+    # Run Data Infrastructure and Architecture Builder
+    runtime_environment = prepare_data_and_model_environment()
+    
+    # Baseline Evaluation 
+    # switch finetuning for tests on strategies only
+    run_finetuning_or_baseline_eval(runtime_environment, run_training=True)
+    
+    # Unlearning Iterations
+    for i in range(0, 1):
+        print(f"\n>>> Executing Unlearning Framework for Target Identity Index: {i} <<<")
+        run_unlearning_and_strategy_eval(runtime_environment, forget_class_idx=i)

unlearning/CertifiedRemoval.py

@@ -55,27 +55,9 @@ class CertifiedRemoval(Strategy):
         
         # Compute the Exact Hessian Matrix over the remaining (retained) features
         # Formula: H = (X^T * X) / N + lambda * I
-        # this will be done on CPU. requires more ram so we cant afford to do it on VRAM
-        # print(">> Computing exact Hessian matrix...")
         N_retain = retain_features.size(0)
-        # X_T_X = torch.matmul(retain_features.t(), retain_features)
-        # reg_matrix = self.l2_reg * torch.eye(retain_features.size(1))
         hessian = self._compute_hessian(retain_features=retain_features, retain_features_size = N_retain)
         
-        # Compute the gradient of the loss with respect to the forgotten data
-        # print(">> Calculating forget set gradients...")
-        # num_classes = w.size(0)
-        # Pass features through linear layer weights to get logits
-        # logits_forget = torch.matmul(forget_features, w.t()) 
-        # Apply softmax to get true class probabilities
-        # preds_softmax = torch.softmax(logits_forget, dim=1)
-        
-        # forget_labels_one_hot = torch.nn.functional.one_hot(forget_labels, num_classes=num_classes).float()
-        
-        #preds_forget = torch.matmul(forget_features, w.t())
-        #error = preds_forget - forget_labels_one_hot
-        # error = preds_softmax - forget_labels_one_hot
-        # grad_forget shape: [num_classes, 2048]
         grad_forget = self._compute_loss_gradient(
             forget_labels=forget_labels,
             forget_features=forget_features, 
@@ -87,14 +69,6 @@ class CertifiedRemoval(Strategy):
             tensor = hessian,
             gradient= grad_forget
         )
-        # print(">> Solving Newton step via system optimization...")
-        # try:
-        #     delta_w_t = torch.linalg.solve(Hessian, grad_forget.t())
-        #     delta_w = delta_w_t.t()
-        # except RuntimeError:
-        #     print(">> Warning: Hessian matrix is singular. Falling back to pseudo-inverse.")
-        #     delta_w = torch.matmul(grad_forget, torch.linalg.pinv(Hessian).t())
-
         # Apply the Certified Removal update rule: W_new = W + Delta_W
         new_w = w + delta_w
         # Calibrate noise based on your epsilon budget 

unlearning/Certified_facebook.py

@@ -0,0 +1,123 @@
+import torch
+import torch.nn as nn
+import math
+from torch.utils.data import DataLoader
+from unlearning.Strategy import Strategy
+
+class CertifiedRemovalFacebook(Strategy):
+    """
+    Implements Certified Removal (Guo et al.) mapped for Multi-Class models
+    by executing a single-class One-vs-Rest (OvR) block-removal update step.
+    Math matches the facebookresearch/certified-removal reference repository.
+    """
+    def __init__(self, target_class_index: int, removal_bound: float, epsilon: float, l2_reg: float = 0.1):
+        super().__init__(target_class_index=target_class_index)
+        self.removal_bound = removal_bound  # gamma in the paper
+        self.epsilon = epsilon              # Privacy budget
+        self.l2_reg = l2_reg                # Lambda (regularization term)
+
+    def _get_features(self, backbone: nn.Module, loader: DataLoader, device: torch.device):
+        """Passes data through the frozen ResNet backbone to extract embedding features."""
+        backbone.eval()
+        all_features = []
+        
+        with torch.no_grad():
+            for inputs, _ in loader:
+                inputs = inputs.to(device)
+                # Pass through frozen backbone to get the 2048-dimensional embedding
+                features = backbone(inputs)
+                all_features.append(features.cpu())
+                
+        return torch.cat(all_features, dim=0)
+
+    def _fb_lr_grad(self, w, X, y, lam):
+        """
+        Replicates exact lr_grad calculation from Facebook's codebase.
+        Note: The resulting gradient has a flipped sign due to the structure of (z - 1).
+        """
+        # X.mv(w) computes raw linear margins
+        z = torch.sigmoid(y * X.mv(w))
+        # Gradient formula: X^T * ((z - 1) * y) + lambda * N * w
+        return X.t().mv((z - 1) * y) + lam * X.size(0) * w
+
+    def _fb_lr_hessian_inv(self, w, X, y, lam, device, batch_size=50000):
+        """
+        Replicates exact lr_hessian_inv calculation from Facebook's codebase.
+        Scales the L2 regularization matrix explicitly by dataset row count (N * lambda * I).
+        """
+        z = torch.sigmoid(X.mv(w).mul_(y))
+        D = z * (1 - z) # Element-wise variance vector
+        
+        H = None
+        num_batch = int(math.ceil(X.size(0) / batch_size))
+        for i in range(num_batch):
+            lower = i * batch_size
+            upper = min((i + 1) * batch_size, X.size(0))
+            X_i = X[lower:upper]
+            
+            # Stepwise feature weighting via element-wise variance columns
+            if H is None:
+                H = X_i.t().mm(D[lower:upper].unsqueeze(1) * X_i)
+            else:
+                H += X_i.t().mm(D[lower:upper].unsqueeze(1) * X_i)
+        
+        # Scale identity buffer by dataset split size: lambda * N_retain
+        reg_matrix = lam * X.size(0) * torch.eye(X.size(1), device=device).float()
+        return torch.linalg.inv(H + reg_matrix)
+
+    def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
+        """
+        Applies Certified Removal strictly to the target class parameters 
+        belonging to the final fully connected layer (model.fc).
+        """
+        device = next(model.parameters()).device
+        k = self.target_class_index
+        
+        # Isolate final layer and extract raw deep embeddings using frozen backbone
+        linear_head = model.fc
+        model.fc = nn.Identity() 
+        
+        print(">> Extracting deep features from model backbone...")
+        X_retain = self._get_features(model, retain_loader, device).to(device)
+        X_forget = self._get_features(model, forget_loader, device).to(device)
+        
+        # Restore the classification head back
+        model.fc = linear_head
+        
+        # Extract current model weight row for the target class channel
+        w_k = model.fc.weight.data[k].clone().to(device)
+
+        # Create One-vs-Rest binary target indicator arrays (+1.0 / -1.0)
+        # Retain dataset instances are negative labels (-1.0) for the target class channel
+        y_retain_binary = torch.full((X_retain.size(0),), -1.0, device=device)
+        # Forget dataset instances are positive labels (+1.0) for the target class channel
+        y_forget_binary = torch.full((X_forget.size(0),), 1.0, device=device)
+
+        # Compute Inverse Hessian (on Retain Data) and Gradient (on Forget Data)
+        H_inv = self._fb_lr_hessian_inv(w_k, X_retain, y_retain_binary, self.l2_reg, device)
+        grad_forget = self._fb_lr_grad(w_k, X_forget, y_forget_binary, self.l2_reg)
+
+        # 5. Compute the Weight Update Step Vector (Delta)
+        multiplier = 0.5
+        delta_w_k = torch.mv(H_inv, grad_forget) * multiplier
+
+        # Verify Theoretical Removal Bound Criteria
+        norm_delta = torch.norm(delta_w_k).item()
+        if norm_delta > self.removal_bound:
+            print(f"!! Warning: Removal budget exceeded! Norm: {norm_delta:.4f} > Bound: {self.removal_bound}")
+        else:
+            print(f">> Certificate valid. Norm: {norm_delta:.4f} <= Bound: {self.removal_bound}")
+
+        # Apply Update (Using '+' since Facebook's grad calculation yields a negative sign output)
+        new_w_k = w_k + delta_w_k
+
+        # Calibrate and Inject Perturbation Noise (Objective Perturbation Verification)
+        sigma = 2.0 / (self.l2_reg * self.epsilon)
+        noise = torch.randn_like(new_w_k, device=device) * (sigma / X_retain.size(0))
+        new_w_k = new_w_k + noise
+
+        # Commit updated weight vector row back into model head parameters in-place
+        model.fc.weight.data[k] = new_w_k
+        
+        print(">> Certified Removal process completed successfully.")
+        return model

unlearning/LinearFiltration.py

@@ -5,9 +5,8 @@ from .Strategy import Strategy
 from torch.utils.data import DataLoader
 
 class LinearFiltration(Strategy):
-    def __init__(self, target_class_idx: int):
-        super().__init__()
-        self.target_class_idx = target_class_idx
+    def __init__(self,target_class_index):
+        super().__init__(target_class_index = target_class_index)
 
     def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
         model.eval()
@@ -18,7 +17,7 @@ class LinearFiltration(Strategy):
             W = model.fc.weight.data.clone()
             num_classes = W.shape[0]
             
-            A = self._calculate_filtration_matrix(num_classes, self.target_class_idx, W.device)
+            A = self._calculate_filtration_matrix(num_classes, self.target_class_index, W.device)
             sanitized_W = torch.mm(A, W)
             model.fc.weight.copy_(sanitized_W)
             # Filter the bias (if the layer uses one)

unlearning/Strategy.py

@@ -9,9 +9,10 @@ import Util
 class Strategy:
     """Abstract base class for unlearning algorithms with automated, strategy-specific logging."""
     
-    def __init__(self):
+    def __init__(self, target_class_index):
         # Dynamically set file name based on the class name (e.g., 'NormalizingLinearFiltration.txt')
         self.strategy_name = self.__class__.__name__
+        self.target_class_index = target_class_index
         self.log_file = Path(f"reports/{self.strategy_name}/metrics.txt")
         Util._initialize_log_file(log_file= self.log_file)
 

unlearning/WeightFiltration.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 import torch.optim as optim
 from torch.utils.data import DataLoader
 from unlearning.Strategy import Strategy
@@ -10,10 +9,9 @@ class WeightFiltration(Strategy):
     Implements Poppi et al.'s Weight Filtering framework for linear layers.
     Uses a standard functional hook to guarantee native PyTorch autograd tracking.
     """
-    def __init__(self, num_classes: int, target_class_idx: int, epochs: int = 10, lr: float = 0.2, gamma: float = 10.0):
-        super().__init__()
+    def __init__(self, target_class_index,num_classes: int, epochs: int = 10, lr: float = 0.2, gamma: float = 10.0):
+        super().__init__(target_class_index = target_class_index)
         self.num_classes = num_classes
-        self.target_class_idx = target_class_idx
         self.epochs = epochs
         self.lr = lr
         self.gamma = gamma 
@@ -52,13 +50,13 @@ class WeightFiltration(Strategy):
         
         # Transfer the channel suppression permanently into model.fc
         with torch.no_grad():
-            mask = torch.sigmoid(self.alpha[self.target_class_idx]) # Shape: (num_features,)
+            mask = torch.sigmoid(self.alpha[self.target_class_index]) # Shape: (num_features,)
             
             # Suppress the channels ONLY for the target class row in fc
-            fc_layer.weight[self.target_class_idx].copy_(
-                fc_layer.weight[self.target_class_idx] * mask
+            fc_layer.weight[self.target_class_index].copy_(
+                fc_layer.weight[self.target_class_index] * mask
             )
-            print(f">> Baked deep channel filter into Class {self.target_class_idx} weights.")
+            print(f">> Baked deep channel filter into Class {self.target_class_index} weights.")
             
         return model
 
@@ -72,7 +70,7 @@ class WeightFiltration(Strategy):
             # self.alpha shape: (num_classes, channels) -> e.g., (20, 2048)
             
             # Extract 1D mask for the target class: (channels,)
-            mask = torch.sigmoid(self.alpha[self.target_class_idx])
+            mask = torch.sigmoid(self.alpha[self.target_class_index])
             
             # Reshape mask to (1, channels, 1, 1) so it broadcasts over batch, height, and width
             mask = mask.view(1, -1, 1, 1)
@@ -87,7 +85,7 @@ class WeightFiltration(Strategy):
         optimizer = optim.Adam([self.alpha], lr=self.lr)
         criterion = nn.CrossEntropyLoss()
 
-        print(f"[{self.__class__.__name__}] Unlearning Class {self.target_class_idx} with gamma={self.gamma}...")
+        print(f"[{self.__class__.__name__}] Unlearning Class {self.target_class_index} with gamma={self.gamma}...")
 
         # To optimise this loop we will watch improvements after each optimisation
         temp_forget_loss = None