From 61da187012979737a3ab6c44b70d861fe5ded47c Mon Sep 17 00:00:00 2001 From: Tinsae Date: Fri, 3 Jul 2026 13:31:43 +0200 Subject: [PATCH] added reports and params --- .gitignore | 1 - LinearFiltration_metrics.txt | 7 - OOP.py | 48 -- Test.py | 0 Tune_new.py | 8 +- architectures/Model.py | 9 +- architectures/WFNet.py | 62 --- .../CertifiedUnlearning/RESNET34/forget.csv | 182 +++++++ .../RESNET34/forget_train.csv | 182 +++++++ .../CertifiedUnlearning/RESNET34/retain.csv | 182 +++++++ .../ResNet/time_metrics.txt | 233 +++++++++ reports/CertifiedUnlearning/params.txt | 5 + reports/LinearFiltration/RESNET34/forget.csv | 491 +++++++++++++++++ .../RESNET34/forget_train.csv | 491 +++++++++++++++++ reports/LinearFiltration/RESNET34/retain.csv | 491 +++++++++++++++++ .../LinearFiltration/ResNet/time_metrics.txt | 491 +++++++++++++++++ reports/WeightFiltration/Params.txt | 5 + reports/WeightFiltration/RESNET34/forget.csv | 492 ++++++++++++++++++ .../RESNET34/forget_train.csv | 492 ++++++++++++++++++ reports/WeightFiltration/RESNET34/retain.csv | 492 ++++++++++++++++++ .../WeightFiltration/ResNet/time_metrics.txt | 492 ++++++++++++++++++ reports/base/RESNET34/Finetuned.csv | 20 + retrain/resnet34/time_metrics.txt | 9 + sets/Casia.py | 167 ------ sets/CasiaFace.py | 21 - sets/CelebA.py | 20 - sets/Data.py | 15 +- DataAnalyser.py => sets/DataAnalyser.py | 0 sets/Data_OOP.py | 174 ------- unlearning/CertifiedUnlearning.py | 163 +----- unlearning/Retrain.py | 79 +++ unlearning/WeightFiltration.py | 17 +- unlearning/wf/WF_Net.py | 86 --- 33 files changed, 4872 insertions(+), 755 deletions(-) delete mode 100644 LinearFiltration_metrics.txt delete mode 100644 OOP.py delete mode 100644 Test.py create mode 100644 reports/CertifiedUnlearning/RESNET34/forget.csv create mode 100644 reports/CertifiedUnlearning/RESNET34/forget_train.csv create mode 100644 reports/CertifiedUnlearning/RESNET34/retain.csv create mode 100644 reports/CertifiedUnlearning/ResNet/time_metrics.txt create mode 100644 reports/CertifiedUnlearning/params.txt create mode 100644 reports/LinearFiltration/RESNET34/forget.csv create mode 100644 reports/LinearFiltration/RESNET34/forget_train.csv create mode 100644 reports/LinearFiltration/RESNET34/retain.csv create mode 100644 reports/LinearFiltration/ResNet/time_metrics.txt create mode 100644 reports/WeightFiltration/Params.txt create mode 100644 reports/WeightFiltration/RESNET34/forget.csv create mode 100644 reports/WeightFiltration/RESNET34/forget_train.csv create mode 100644 reports/WeightFiltration/RESNET34/retain.csv create mode 100644 reports/WeightFiltration/ResNet/time_metrics.txt create mode 100644 reports/base/RESNET34/Finetuned.csv create mode 100644 retrain/resnet34/time_metrics.txt delete mode 100644 sets/Casia.py delete mode 100644 sets/CasiaFace.py delete mode 100644 sets/CelebA.py rename DataAnalyser.py => sets/DataAnalyser.py (100%) delete mode 100644 sets/Data_OOP.py create mode 100644 unlearning/Retrain.py delete mode 100644 unlearning/wf/WF_Net.py diff --git a/.gitignore b/.gitignore index 39815d0..9ffc5d4 100644 --- a/.gitignore +++ b/.gitignore @@ -16,7 +16,6 @@ trained_models/ __pycache__/ *.py[cod] lib64 -/reports *.bin *.idx *.rec diff --git a/LinearFiltration_metrics.txt b/LinearFiltration_metrics.txt deleted file mode 100644 index ac054a6..0000000 --- a/LinearFiltration_metrics.txt +++ /dev/null @@ -1,7 +0,0 @@ -execution_time_sec -0.000996 -0.030071 -0.001182 -0.001176 -0.001229 -0.001257 diff --git a/OOP.py b/OOP.py deleted file mode 100644 index a7f69c6..0000000 --- a/OOP.py +++ /dev/null @@ -1,48 +0,0 @@ - -# This is from wikipedia pseudocode implementation of a single -# ThresholdLogic Unit. -# done to make me understand OOP the Python way - - -# no need for brackets if not inheriting -class ThresholdLogicUnit: - - # define members in init - def __init__(self, threshold, weights): - self.threshold = threshold - self.weights = weights - - - # If a function has to make use of member variables - # it has to have self as param - def fire(self,inputs): - tots = 0 - #for i in range(0,inputs.size()): - for val, weight in zip(inputs, self.weights): - if val: - tots+= weight - - return tots > self.threshold - - -def main(): - # data - weights = [0.5, -0.2, 0.8] - threshold = 1.0 - - # Instantiate the class - tlu = ThresholdLogicUnit(threshold, weights) - - # Test - test_inputs = [1, 1, 0] - result = tlu.fire(test_inputs) - - print(f"The unit fired: {result}") - -# The "Guard" -if __name__ == "__main__": - main() - - - - \ No newline at end of file diff --git a/Test.py b/Test.py deleted file mode 100644 index e69de29..0000000 diff --git a/Tune_new.py b/Tune_new.py index ca57e66..fe73c31 100644 --- a/Tune_new.py +++ b/Tune_new.py @@ -211,7 +211,7 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu # entry if __name__ == "__main__": - outer_loop = 1 + outer_loop = 10 inner_loop = CLASS_SIZE for k in range(outer_loop): @@ -221,13 +221,13 @@ if __name__ == "__main__": runtime_environment = prepare_data_and_model_environment() # Baseline Evaluation - finetuning = False + finetuning = True # switch finetuning for tests on strategies only run_finetuning_or_baseline_eval(runtime_environment, run_training = finetuning) # scale 16400.0 for ResNet - scale = 14500 + scale = 20200 # batch 8 for resNet, - unlearning_batches = 32 + unlearning_batches = 16 # regularis # strategies certified_unlearning = CertifiedUnlearning( diff --git a/architectures/Model.py b/architectures/Model.py index 855c4c6..38eaffb 100644 --- a/architectures/Model.py +++ b/architectures/Model.py @@ -46,19 +46,23 @@ class Model(ABC): file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt") Util._initialize_log_file(file_path) - #save_dir.mkdir(parents=True, exist_ok=True) print(f"Starting training on {self.device}...") start_time = time.time() + # training phase self.model.train() for epoch in range(epochs): total_loss = 0.0 for inputs, labels in loader: inputs, labels = inputs.to(self.device), labels.to(self.device) + # zero param gradients optimizer.zero_grad() + # forward pass outputs = self.model(inputs) + # comoutew loss loss = criterion(outputs, labels) + # backward pass loss.backward() optimizer.step() total_loss += loss.item() @@ -66,7 +70,8 @@ class Model(ABC): print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss / len(loader):.4f}") end_time = time.time() - Util.log_metric(log_file=file_path, execution_time=(end_time - start_time)) + execution_time = end_time - start_time + Util.log_metric(log_file=file_path, execution_time=execution_time) if self.device.type == 'cuda': torch.cuda.synchronize() print(f"Training completed in: {time.time() - start_time:.2f}s") diff --git a/architectures/WFNet.py b/architectures/WFNet.py index b4a20df..bd06a19 100644 --- a/architectures/WFNet.py +++ b/architectures/WFNet.py @@ -7,68 +7,6 @@ import numpy as np from sklearn.metrics import classification_report from architectures.Model import Model -'''class WF_Module(nn.Module): - """ - Pure PyTorch Neural Network module graph. - Keeps parameter registration and autograd tracking separate from - the framework's high-level Model abstractions to prevent recursion collisions. - """ - def __init__(self, original_model: nn.Module, num_classes: int): - super().__init__() - - self.original_model = original_model - - # Target layer for weight filtering (layer4 block 1 conv2 or conv3 depending on arch) - last_layer = original_model.layer4[1] - - # Some versions are limited to 2 convolutional layers - if hasattr(last_layer, "conv3"): - self.target_conv = last_layer.conv3 - else: - self.target_conv = last_layer.conv2 - - # Completely freeze the original ResNet parameters - for param in self.parameters(): - param.requires_grad = False - - # Initialize the alpha parameter matrix (Rows = Classes, Cols = Channels) - out_channels = self.target_conv.weight.shape[0] - self.alpha = nn.Parameter(torch.full((num_classes, out_channels), 3.0))''' - -''' - Poppi et_al's Single-shot multiclass unlearning. - This calculation happens only once to generate the mask. once the mask is generated, - Unlearning and remembering becomes a matter of switching gates on and off.''' -''' - def forward(self, x: torch.Tensor, target_class_indices: torch.Tensor) -> torch.Tensor: - # we linearly loop through layers 1 to 4[block 1] (for ResNet) - # for i in M_{|L|} do l <- l[i] - x = self.original_model.maxpool(self.original_model.relu(self.original_model.bn1(self.original_model.conv1(x)))) - x = self.original_model.layer1(x) - x = self.original_model.layer2(x) - x = self.original_model.layer3(x) - x = self.original_model.layer4[0](x) - - # The second block execute its internal transformations natively - # This handles conv1->conv2 (ResNet18) or conv1->conv2->conv3 (ResNet50) automatically! - # Xi+1 <- l(Xi, ˆwl) - x = self.original_model.layer4[1](x) - - # Apply mask dynamically to the completed block feature map - # wl <- αl[Yunl] ⊙ ˆwl - batch_alpha = self.alpha[target_class_indices] - mask = torch.sigmoid(batch_alpha).view(x.size(0), -1, 1, 1) - x = x * mask - - # Remaining standard head steps - x = self.original_model.avgpool(x) - x = torch.flatten(x, 1) - # so here we are returning the output logits - # the result of classification is then - # argmax(x) - return self.original_model.fc(x) -''' - class WF_Module(nn.Module): def __init__(self, original_model: nn.Module, num_classes: int, arch_enum): super().__init__() diff --git a/reports/CertifiedUnlearning/RESNET34/forget.csv b/reports/CertifiedUnlearning/RESNET34/forget.csv new file mode 100644 index 0000000..f67c8cb --- /dev/null +++ b/reports/CertifiedUnlearning/RESNET34/forget.csv @@ -0,0 +1,182 @@ +accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1 +0.0500,1.0000,0.0500,0.0952,1.0000,0.0500,0.0952 +0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 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0c8e87c..0000000 --- a/sets/Casia.py +++ /dev/null @@ -1,167 +0,0 @@ - - -from torchvision import datasets, transforms -from torch.utils.data import Dataset, DataLoader, Subset -import torch -import numpy as np -import os - -# train set transform -def train_transform(res): - return transforms.Compose([ - transforms.Resize((res, res)), - transforms.RandomHorizontalFlip(p=0.5), - transforms.ColorJitter( - brightness=0.2, - contrast=0.2, - saturation=0.1 - ), - transforms.ToTensor(), - transforms.Normalize( - mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225] - ) - ]) - -# test set transform -def test_transform(res): - return transforms.Compose([ - transforms.Resize((res, res)), - transforms.ToTensor(), - transforms.Normalize( - mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225] - ) - ]) - -# Load data using ImageFolder for CASIA-WebFace -''' -def get_set(): - # This will check local cache first, then download if missing - print("Checking for CASIA-WebFace dataset...") - path = kagglehub.dataset_download("debarghamitraroy/casia-webface") - - # Kagglehub often downloads a nested structure (e.g., path/casia-webface/casia-webface) - # We need the folder that directly contains the identity subfolders - # We'll check if there's a 'casia-webface' subfolder inside the downloaded path - sub_path = os.path.join(path, "casia-webface") - final_path = sub_path if os.path.exists(sub_path) else path - - print(f"Loading dataset from: {final_path}") - - return datasets.ImageFolder( - root=final_path, - transform=None - )''' -# Load data using ImageFolder for your UNPACKED images -def get_set(): - # This must point to the folder created by Extractor.py - # NOT the kagglehub cache path - final_path = os.path.abspath("./data/casia-set") - - if not os.path.exists(final_path): - raise FileNotFoundError( - f"Unpacked dataset not found at {final_path}. " - "Please run Extractor.py first!" - ) - - print(f"Loading unpacked CASIA dataset from: {final_path}") - - return datasets.ImageFolder( - root=final_path, - transform=None - ) - -def get_ids_and_counts(dataset): - # ImageFolder stores labels in .targets - targets = torch.tensor(dataset.targets) - return torch.unique( - input = targets, - return_counts=True - ) - -def select_ids(dataset, sample_size, class_size): - ids, counts = get_ids_and_counts(dataset=dataset) - eligible_mask = counts >= sample_size - eligible_ids = ids[eligible_mask].numpy() - - if len(eligible_ids) < class_size: - raise ValueError( - f"Only found {len(eligible_ids)} identities with {sample_size}+ images." - ) - - return np.random.choice(eligible_ids, class_size, replace=False) - -def select_balanced_ids(dataset, class_size): - ids, counts = get_ids_and_counts(dataset=dataset) - sorted_indices = torch.argsort(counts, descending=True) - top_ids = ids[sorted_indices][:class_size].numpy() - return np.array(top_ids, dtype=int) - -def get_indices(dataset, identities, split_at): - train_indices = [] - test_indices = [] - - # We convert to numpy for faster searching with np.where - all_targets = np.array(dataset.targets) - - for person_id in identities: - # Get all indices for this specific person - indices = np.where(all_targets == person_id)[0] - - # Shuffle the indices for this person - np.random.shuffle(indices) - - # Split data based on your split_at value - train_indices.extend(indices[:split_at]) - test_indices.extend(indices[split_at:]) - - return train_indices, test_indices - - - -# optional function to get max amount of samples per class -def select_top_ids(dataset, class_size): - ids, counts = get_ids_and_counts(dataset=dataset) - - # sort by number of images (descending) - sorted_indices = torch.argsort(counts, descending=True) - - top_ids = ids[sorted_indices][:class_size].numpy() - - return np.array(top_ids, dtype=int) - - -def get_forget_retain_loaders(dataset: Dataset, forget_class_idx: int, batch_size: int = 32) -> tuple[DataLoader, DataLoader]: - """ - Splits an IdentitySubset or standard Dataset into forget and retain sets - based on a remapped target class index. - """ - # 1. Safely extract targets whether it's a standard dataset or a Subset wrapper - if hasattr(dataset, 'targets'): - targets = dataset.targets - elif hasattr(dataset, 'identity'): # Raw CelebA support - targets = dataset.identity - else: - # If it's an IdentitySubset or standard Subset, extract mapped targets sequentially - # This guarantees we get the 0 -> (n-1) remapped labels - targets = [dataset[i][1] for i in range(len(dataset))] - - if not isinstance(targets, torch.Tensor): - targets = torch.tensor(targets) - - # 2. Generate mask indices local to this subset - forget_indices = torch.where(targets == forget_class_idx)[0].tolist() - retain_indices = torch.where(targets != forget_class_idx)[0].tolist() - - # 3. Create PyTorch Subsets - forget_subset = Subset(dataset, forget_indices) - retain_subset = Subset(dataset, retain_indices) - - # 4. Wrap into clean DataLoaders - forget_loader = DataLoader(forget_subset, batch_size=batch_size, shuffle=False) - retain_loader = DataLoader(retain_subset, batch_size=batch_size, shuffle=True) - - print(f"[Data Split] Local Class {forget_class_idx}: {len(forget_subset)} samples | Remaining Classes: {len(retain_subset)} samples.") - - return forget_loader, retain_loader \ No newline at end of file diff --git a/sets/CasiaFace.py b/sets/CasiaFace.py deleted file mode 100644 index 51e7449..0000000 --- a/sets/CasiaFace.py +++ /dev/null @@ -1,21 +0,0 @@ -import os -from torchvision import datasets -from torch.utils.data import Dataset -import torch -from .Data import Data - -class CasiaSet(Data): - def __init__(self, resolution: int = 224, sample_size = 190): - super().__init__(resolution = resolution, sample_size = sample_size) - - def get_set(self) -> Data: - path_str = "./datasets/casia-set" - path = os.path.abspath(path_str) - - if not os.path.exists(path): - raise FileNotFoundError(f"Unpacked dataset missing at {self.final_path}. Run Extractor.py first!") - print(f"Loading unpacked CASIA dataset from: {self.final_path}") - set = datasets.ImageFolder(root=path, transform=None) - # we set the target here - self.target = torch.tensor(set.targets) - return set diff --git a/sets/CelebA.py b/sets/CelebA.py deleted file mode 100644 index 481dcc5..0000000 --- a/sets/CelebA.py +++ /dev/null @@ -1,20 +0,0 @@ -from torchvision import datasets -from torch.utils.data import Dataset -import torch -from .Data import Data - -class CelebA(Data): - def __init__(self, resolution: int = 224, sample_size = 30): - super().__init__(resolution, sample_size = sample_size) - - def get_set(self): - set = datasets.CelebA( - root = "../data", - split='all', - target_type='identity', - download=False, - transform=None - ) - # set the target first - self.target = set.identity - return set diff --git a/sets/Data.py b/sets/Data.py index ca2ff7e..a2ffdcf 100644 --- a/sets/Data.py +++ b/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) diff --git a/DataAnalyser.py b/sets/DataAnalyser.py similarity index 100% rename from DataAnalyser.py rename to sets/DataAnalyser.py diff --git a/sets/Data_OOP.py b/sets/Data_OOP.py deleted file mode 100644 index 25d6822..0000000 --- a/sets/Data_OOP.py +++ /dev/null @@ -1,174 +0,0 @@ -import torch -import numpy as np -from abc import ABC, abstractmethod -from torchvision import transforms, datasets -from torch.utils.data import Dataset, DataLoader, Subset - -class Data(ABC): - """ - Handles image pipelines, identity filtering, indexing, and unlearning splits. - """ - def __init__(self, res: int = 224, sample_size = 30, class_size = 20): - self.res = res - self.sample_size = sample_size - self.class_size = class_size - self.target = None # will have to be set in get_set() - - def train_transform(self): - return transforms.Compose([ - # ResNet expects 224 x 224 res - # Inception expects 299 x 299 - transforms.Resize((self.res, self.res)), - transforms.RandomHorizontalFlip(p=0.5), - transforms.ColorJitter( - brightness=0.2, - contrast=0.2, - saturation=0.1 - ), - transforms.ToTensor(), - transforms.Normalize( - mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225] - ) - ]) - - - def test_transform(self): - return transforms.Compose([ - transforms.Resize((self.res, self.res)), - transforms.ToTensor(), - transforms.Normalize( - mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225] - ) - ]) - - @abstractmethod - def get_set(self)-> datasets.TorchDataset: - """Loads and returns the raw underlying PyTorch Dataset instance.""" - pass - - def get_targets(self) -> torch.Tensor: - return self.target - - def get_ids_and_counts(self) -> tuple[torch.Tensor, torch.Tensor]: - if self.target is None: - raise ValueError ("This should be called after the 'target' variable has been set.") - return torch.unique( - self.target, - return_counts=True - ) - - def select_ids(self) -> np.ndarray: - ids, counts = self.get_ids_and_counts() - eligible_mask = counts >= self.sample_size - eligible_ids = ids[eligible_mask].numpy() - - if len(eligible_ids) < self.class_size: - raise ValueError( - f"Only found {len(eligible_ids)} identities with {self.sample_size}+ images." - ) - - return np.random.choice(eligible_ids, self.class_size, replace=False) - - # Function to get max amount of samples per class - def select_top_ids(self) -> np.ndarray: - - ids, counts = self.get_ids_and_counts() - # sort by number of images (descending) - sorted_indices = torch.argsort(counts, descending=True) - top_ids = ids[sorted_indices][:self.class_size].numpy() - return np.array(top_ids, dtype=int) - - def get_indices(self, identities: np.ndarray, split_at: int, max_size: int = None) -> tuple[list, list]: - '''train_indices = [] - test_indices = [] - max_size = self.sample_size if max_size is None else max_size - - # Pull raw target tensor array using concrete implementation rules - all_targets = np.array(self.get_targets().cpu()) - np.random.seed(42) - - for person_id in identities: - indices = np.where(all_targets == person_id)[0] - np.random.shuffle(indices) - - # Constrain total sample tracking size if requested (e.g. CelebA ceiling) - current_pool = indices[:max_size] if max_size else indices - - if split_at >= len(current_pool): - raise ValueError(f"Split point ({split_at}) exceeds slice size ({len(current_pool)}) for class {person_id}.") - - train_indices.extend(current_pool[:split_at]) - test_indices.extend(current_pool[split_at:]) - - return train_indices, test_indices''' - if split_at >= self.sample_size: # debug safety - raise ValueError(f"Split point ({split_at}) must be less than total size ({self.sample_size}).") - - train_indices = [] - test_indices = [] - - #training_sample = int(sample_size * training_ratio) - np.random.seed(42) - target = self.get_targets() - for person_id in identities: - # Get all indices for this specific person - indices = torch.where(target == person_id)[0].numpy() - - # Shuffle the indices for this person - np.random.shuffle(indices) - - # split data to testing and training - train_indices.extend(indices[:split_at]) - test_indices.extend(indices[split_at:self.sample_size]) - - return train_indices, test_indices - - @staticmethod - def get_unlearn_loaders( - dataset: Dataset, - forget_class_idx: int, - batch_size: int = 32 - ) -> tuple[DataLoader, DataLoader]: - - """Splits an IdentitySubset into forget/retain parts based on local class index.""" - if hasattr(dataset, 'targets'): - targets = dataset.targets - elif hasattr(dataset, 'identity'): - targets = dataset.identity - else: - targets = [dataset[i][1] for i in range(len(dataset))] - - if not isinstance(targets, torch.Tensor): - targets = torch.tensor(targets) - - forget_indices = torch.where(targets == forget_class_idx)[0].tolist() - retain_indices = torch.where(targets != forget_class_idx)[0].tolist() - - forget_subset = Subset(dataset, forget_indices) - retain_subset = Subset(dataset, retain_indices) - - forget_loader = DataLoader(forget_subset, batch_size=batch_size, shuffle=False) - retain_loader = DataLoader(retain_subset, batch_size=batch_size, shuffle=True) - - print(f"[Data Split] Local Class {forget_class_idx}: {len(forget_subset)} samples | Remaining Classes: {len(retain_subset)} samples.") - - return forget_loader, retain_loader - - - @staticmethod - def getDataSet(set:SetType, sample_size): - # some test - if set == SetType.CASIA: - from sets.CasiaFace import CasiaFace - return CasiaFace(sample_size = sample_size) - if set == SetType.CELEBA: - from sets.CelebA import CelebA - return CelebA(sample_size=sample_size) - - -from enum import Enum, auto -class SetType(Enum): - CASIA = auto() - CELEBA = auto() diff --git a/unlearning/CertifiedUnlearning.py b/unlearning/CertifiedUnlearning.py index 92a0820..1578ff9 100644 --- a/unlearning/CertifiedUnlearning.py +++ b/unlearning/CertifiedUnlearning.py @@ -14,9 +14,18 @@ class CertifiedUnlearning(Strategy): Uses a modified, stabilized stochastic Newton step using Taylor-expansion HVP estimation across the entire parameter space, capped with calibrated noise. """ - def __init__(self, target_class_index: int, l2_reg: float = 0.0005, - gamma: float = 0.01, scale: float = 50000.0, - s1: int = 2, s2: int = 350, std: float = 0.001, unlearn_bs: int = 2): + def __init__( + self, + target_class_index: int, + l2_reg: float = 0.0005, + gamma: float = 0.01, + scale: float = 50000.0, + s1: int = 2, + s2: int = 350, + std: float = 0.001, + unlearn_bs: int = 2 + ): + super().__init__(target_class_index) self.l2_reg = l2_reg self.gamma = gamma @@ -49,151 +58,12 @@ class CertifiedUnlearning(Strategy): return params_list if named else [e[1] for e in params_list] - ''' - def _compute_loss_gradient(self, model, loader, device: torch.device): - - model.eval() - criterion = nn.CrossEntropyLoss(reduction='sum') - params = self.get_params(model, False) # [p for name, p in model.named_parameters() if p.requires_grad and "AuxLogits" not in name] - - - grad_accumulator = [torch.zeros_like(p, device = device) for p in params] - total_samples = 0''' - - # Accumulate true data cross-entropy gradients - ''' - for data, targets in loader: - total_samples += targets.shape[0] - data, targets = data.to(device), targets.to(device) - outputs = model(data) - loss = criterion(outputs, targets) - - mini_grads = list(grad(loss, params, retain_graph=False)) - for i in range(len(grad_accumulator)): - grad_accumulator[i] += mini_grads[i].cpu().detach() - - # Empirical data mean conversion - for i in range(len(grad_accumulator)): - grad_accumulator[i] /= total_samples - - # L2 weight regularization - l2_reg_term = 0.0 - for param in params: - if param.requires_grad: - l2_reg_term += torch.sum(param ** 2) - - reg_grads = list(grad(self.l2_reg * l2_reg_term, params)) - for i in range(len(grad_accumulator)): - grad_accumulator[i] += reg_grads[i].cpu().detach() - - return [p.to(device) for p in grad_accumulator] - ''' - ''' - with torch.set_grad_enabled(True): - for data, targets in loader: - total_samples += targets.shape[0] - data, targets = data.to(device), targets.to(device) - outputs = model(data) - loss = criterion(outputs, targets) - - mini_grads = grad(loss, params, retain_graph=False) - for i in range(len(grad_accumulator)): - grad_accumulator[i] += mini_grads[i] - - # Empirical data mean conversion - for i in range(len(grad_accumulator)): - grad_accumulator[i] /= total_samples - - # OPTIMIZATION 2: Analytical L2 Regularization Gradient instead of autograd - # d/dx (l2_reg * x^2) = 2 * l2_reg * x - for i, param in enumerate(params): - grad_accumulator[i] += 2 * self.l2_reg * param.detach() - - return grad_accumulator - - def _hvp(self, loss, params, v): - first_grads = grad(loss, params, retain_graph=True, create_graph=True) - elemwise_products = 0 - ''' - ''' - for grad_elem, v_elem in zip(first_grads, v): - elemwise_products += torch.sum(grad_elem * v_elem) - 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() - params = self.get_params(model, False) # [p for p in model.parameters() if p.requires_grad] - h_res = [torch.zeros_like(p) for p in g] - - # progress - total_steps = self.s1 * self.s2 - step_interval = max(1, total_steps // 100) - - global_step = 0 - current_pct = 0 - - sampler = RandomSampler(dataset, replacement=True, num_samples=self.unlearn_bs * self.s2) - res_loader = DataLoader(dataset, batch_size=self.unlearn_bs, sampler=sampler) - res_iter = iter(res_loader) - - for _ in range(self.s1): - h_estimate = [p.clone() for p in g] - sampler = RandomSampler(dataset, replacement=True, num_samples=self.unlearn_bs * self.s2) - res_loader = DataLoader(dataset, batch_size=self.unlearn_bs, sampler=sampler) - res_iter = iter(res_loader) - - for _ in range(self.s2): - - global_step += 1 - - if global_step % step_interval == 0 and current_pct < 100: - current_pct += 1 - print(f"\rProgress: {current_pct}% done", end="", flush=True) - - try: - data, target = next(res_iter) - except StopIteration: - res_iter = iter(res_loader) - data, target = next(res_iter) - - data, target = data.to(device), target.to(device) - - outputs = model(data) - - loss = criterion(outputs, target) - l2_reg_term = sum(p.pow(2).sum() for p in params) - 'for param in params: - #if param.requires_grad: - l2_reg_term += torch.sum(param ** 2) - loss += (self.l2_reg + self.gamma) * l2_reg_term - - h_s = self._hvp(loss, params, h_estimate) - - with torch.no_grad(): - for k in range(len(params)): - h_estimate[k].copy_(h_estimate[k] + g[k] - (h_s[k] / self.scale)) - #h_res[k] += h_estimate[k] / self.scale - #next_estimate = h_estimate[k].data + g[k].data - (h_s[k].data / self.scale) - #h_estimate[k] = next_estimate.clone() - del h_s, loss, outputs - - #for k in range(len(params)): - # h_res[k] = h_res[k] + h_estimate[k] / self.scale - with torch.no_grad(): - for k in range(len(params)): - h_res[k] += h_estimate[k] / self.scale - - return [p / self.s1 for p in h_res] - ''' def _compute_loss_gradient(self, model, loader, device: torch.device): model.eval() criterion = nn.CrossEntropyLoss(reduction='sum') params = self.get_params(model, False) - # OPTIMIZATION 1: Keep accumulator on GPU device directly grad_accumulator = [torch.zeros_like(p, device=device) for p in params] total_samples = 0 @@ -208,12 +78,11 @@ class CertifiedUnlearning(Strategy): for i in range(len(grad_accumulator)): grad_accumulator[i] += mini_grads[i] - # Empirical data mean conversion + # Data mean conversion for i in range(len(grad_accumulator)): grad_accumulator[i] /= total_samples - # OPTIMIZATION 2: Analytical L2 Regularization Gradient instead of autograd - # d/dx (l2_reg * x^2) = 2 * l2_reg * x + # regularisation gradient for i, param in enumerate(params): grad_accumulator[i] += 2 * self.l2_reg * param.detach() @@ -243,7 +112,7 @@ class CertifiedUnlearning(Strategy): for _ in range(self.s1): h_estimate = [p.clone() for p in g] - + # hesian estimation for _ in range(self.s2): global_step += 1 @@ -255,7 +124,7 @@ class CertifiedUnlearning(Strategy): data, target = data.to(device), target.to(device) - # OPTIMIZATION 3: Clean up graph creation for loss & L2 + # forward outputs = model(data) loss = criterion(outputs, target) diff --git a/unlearning/Retrain.py b/unlearning/Retrain.py new file mode 100644 index 0000000..8dc8510 --- /dev/null +++ b/unlearning/Retrain.py @@ -0,0 +1,79 @@ +import time +from pathlib import Path +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader +from unlearning.Strategy import Strategy + +class Retrain(Strategy): + """ + Implements the Exact Unlearning Baseline by retraining the model architecture + completely from scratch using only the retained dataset partition. + """ + def __init__(self, target_class_index: int, lr: float = 0.01, + weight_decay: float = 0.0005, epochs: int = 5): + super().__init__(target_class_index) + self.lr = lr + self.weight_decay = weight_decay + self.epochs = epochs + + def _reset_weights(self, model: nn.Module): + """ + Re-initializes all learnable parameters of the model to clear pre-trained memories. + """ + inner_model = getattr(model, "model", model) + for layer in inner_model.modules(): + if hasattr(layer, 'reset_parameters'): + layer.reset_parameters() + + def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module: + device = next(model.parameters()).device + + print(">> Triggering Exact Unlearning Baseline (Retraining from scratch)...") + + # 1. Clear the pre-trained state completely + self._reset_weights(model) # model should be loaded here or weights reset to ImageNet (pretrained default) + model.train() + + # fresh optimizer for this clean environment + optimizer = optim.SGD( + model.parameters(), + lr=self.lr, + momentum=0.9, + weight_decay=self.weight_decay + ) + criterion = nn.CrossEntropyLoss() + + # 3. Standard training loop over the Retain set + for epoch in range(self.epochs): + running_loss = 0.0 + total_samples = 0 + + for data, targets in retain_loader: + data, targets = data.to(device), targets.to(device) + + optimizer.zero_grad() + outputs = model(data) + loss = criterion(outputs, targets) + + loss.backward() + optimizer.step() + + running_loss += loss.item() * targets.size(0) + total_samples += targets.size(0) + + epoch_loss = running_loss / total_samples + print(f" [Retrain] Epoch {epoch+1}/{self.epochs} completed. Loss: {epoch_loss:.4f}") + + print(">> Retraining pipeline finished. Baseline weights established.") + return model + + def _split_data(self, dataset): + # Dynamically pulls loaders from your Data.py script + from sets.Data import get_unlearning_loaders + return get_unlearning_loaders( + dataset=dataset, + forget_class_idx=self.target_class_index, + batch_size=32 + ) \ No newline at end of file diff --git a/unlearning/WeightFiltration.py b/unlearning/WeightFiltration.py index 3ee5bd0..9cb7424 100644 --- a/unlearning/WeightFiltration.py +++ b/unlearning/WeightFiltration.py @@ -11,15 +11,14 @@ from sets.Data import vertical_split class WeightFiltration(Strategy): def __init__(self, - target_class_index: int, - arch, - num_classes: int = 20, - epochs: int = 6, - lr: float = 100.0, - gamma: float = 0.01, - lambda_1 = 25 - - ): + target_class_index: int, + arch, + num_classes: int = 20, + epochs: int = 6, + lr: float = 100.0, + gamma: float = 0.01, + lambda_1 = 25 + ): super().__init__(target_class_index=target_class_index) self.epochs = epochs self.lr = lr diff --git a/unlearning/wf/WF_Net.py b/unlearning/wf/WF_Net.py deleted file mode 100644 index eb50180..0000000 --- a/unlearning/wf/WF_Net.py +++ /dev/null @@ -1,86 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class WF_Net(nn.Module): - """ - Implements Poppi et al.'s WF Model structure. - Wraps a pre-trained ResNet-18 and dynamically applies - weight-space gating matrix multiplication during the forward step. - """ - def __init__(self, original_model: nn.Module, num_classes: int): - super().__init__() - # Extract the sequence of blocks/layers L from the original model - self.conv1 = original_model.conv1 - self.bn1 = original_model.bn1 - self.relu = original_model.relu - self.maxpool = original_model.maxpool - - self.layer1 = original_model.layer1 - self.layer2 = original_model.layer2 - self.layer3 = original_model.layer3 - self.layer4 = original_model.layer4 - - self.avgpool = original_model.avgpool - self.fc = original_model.fc - - # Target layer for filtering: layer4 block 1 conv2 - # We extract its static tensor data out of the autograd parameter pool - self.target_conv = self.layer4[1].conv2 - self.original_w = nn.Parameter(self.target_conv.weight.data.clone().detach(), requires_grad=False) - - # Require: Alpha gating matrix. Shape: (num_classes, out_channels) - # Initialized to 1.5 as per Poppi et al.'s verbatim specification - out_channels = self.original_w.shape[0] - #self.alpha = nn.Parameter(torch.ones(num_classes, out_channels) * 1.5) - self.alpha = nn.Parameter(torch.ones(num_classes, out_channels)) - - def forward(self, x: torch.Tensor, target_class_indices: torch.Tensor) -> torch.Tensor: - # 1. Run through early sequence of layers undisturbed - x = self.maxpool(self.relu(self.bn1(self.conv1(x)))) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - - # Run layer4 block 0 normally - x = self.layer4[0](x) - - # ------------------------------------------------------------- - # HERE IT IS: Save the structural skip connection (identity) - # BEFORE modifying features via block 1's convolutions - # ------------------------------------------------------------- - identity = x - - # Now enter layer4 block 1 - x = self.layer4[1].conv1(x) - x = self.layer4[1].bn1(x) - x = self.layer4[1].relu(x) - - # [Your Step 1 Masking Math happens right here...] - batch_alpha = self.alpha[target_class_indices] - mask = torch.sigmoid(batch_alpha).view(x.size(0), -1, 1, 1) - - # Run the functional convolution - x = F.conv2d( - x, - weight=self.original_w, - bias=self.target_conv.bias, - stride=self.target_conv.stride, - padding=self.target_conv.padding - ) - - # Apply your WF-Net channel mask - x = x * mask - x = self.layer4[1].bn2(x) - - # ------------------------------------------------------------- - # HERE IT IS USED: Add the pristine identity back to the gated output - # ------------------------------------------------------------- - x = self.layer4[1].relu(x + identity) - - # Final Classification Head Sequence - x = self.avgpool(x) - x = torch.flatten(x, 1) - y_out = self.fc(x) - - return y_out