From 7f848b048550630d1afe0ac344cd328723359a96 Mon Sep 17 00:00:00 2001 From: Tinsae Date: Sat, 4 Jul 2026 10:34:51 +0200 Subject: [PATCH] reports from optimised linear filtration --- Tune_new.py | 21 +- Util.py | 1 - architectures/Model.py | 17 +- reports/LinearFiltration/RESNET34/forget.csv | 90 -- .../RESNET34/forget_train.csv | 90 -- .../RESNET34/full-set/forget.csv | 531 +++++++++++ .../RESNET34/full-set/forget_train.csv | 531 +++++++++++ .../RESNET34/full-set/retain.csv | 531 +++++++++++ reports/LinearFiltration/RESNET34/retain.csv | 868 ++++++++--------- .../LinearFiltration/ResNet/Report sample.txt | 3 + .../LinearFiltration/ResNet/time_metrics.txt | 882 ++++++++---------- .../ResNet/time_metrics_full sample.txt.txt | 492 ++++++++++ reports/base/RESNET34/Finetuned.csv | 20 + sets/Data.py | 58 +- unlearning/LinearFiltration.py | 32 +- unlearning/Retrain.py | 74 +- 16 files changed, 3014 insertions(+), 1227 deletions(-) create mode 100644 reports/LinearFiltration/RESNET34/full-set/forget.csv create mode 100644 reports/LinearFiltration/RESNET34/full-set/forget_train.csv create mode 100644 reports/LinearFiltration/RESNET34/full-set/retain.csv create mode 100644 reports/LinearFiltration/ResNet/Report sample.txt create mode 100644 reports/LinearFiltration/ResNet/time_metrics_full sample.txt.txt diff --git a/Tune_new.py b/Tune_new.py index fe73c31..a15c183 100644 --- a/Tune_new.py +++ b/Tune_new.py @@ -133,7 +133,7 @@ def run_finetuning_or_baseline_eval(env_dict, run_training=False, lr_rate=0.0001 # Evaluate original base checkpoint performance current_mode = "Finetuned" - # Check if weights exist or model was trained before evaluating + # evaluate finetuned model try: accuracy, report_dict = model.evaluate(loader=test_loader, mode=current_mode) Util._log_to_csv( @@ -178,6 +178,8 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu print("fine tunned model loaded into evaluation sandbox") # Execute strategic parameter unlearning step + # we are using only training data to unlearn. + # Test data is never touched here. unlearned = strategy.apply(reloaded.model, train_data) strategy_in_use = strategy.__class__.__name__ @@ -200,7 +202,7 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu print(domain["label"]) accuracy, report_dict = reloaded.evaluate(loader=domain["loader"], mode=domain["mode"]) Util._log_to_csv( - arch=ARCH.name,#reloaded.__class__.__name__, + arch=ARCH.name, mode=domain["mode"], accuracy=accuracy, report_dict=report_dict, @@ -211,7 +213,7 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu # entry if __name__ == "__main__": - outer_loop = 10 + outer_loop = 11 inner_loop = CLASS_SIZE for k in range(outer_loop): @@ -221,15 +223,17 @@ if __name__ == "__main__": runtime_environment = prepare_data_and_model_environment() # Baseline Evaluation + # switch finetuning for tests on strategies only, + # to avoid finetunning every time we test a strategy 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 = 20200 + scale = 20300 # batch 8 for resNet, unlearning_batches = 16 # regularis # strategies + # implementation of Certified Removal for DNNs certified_unlearning = CertifiedUnlearning( target_class_index=0, #arch ResNet18 GoogLeNet Inception l2_reg=0.000002 , # 0.000002 0.00001 0.0 @@ -241,12 +245,13 @@ if __name__ == "__main__": unlearn_bs=unlearning_batches # 8 32 8 ) - # works perfectly + # Normalisation Filtration linear_filtration = LinearFiltration( - target_class_index=0 + target_class_index=0, + num_classes=CLASS_SIZE ) - + # WF-Net weight_filtration = WeightFiltration( target_class_index=0, #arch ResNet18 GoogLeNet/Inception epochs=6, # diff --git a/Util.py b/Util.py index 2910054..d001518 100644 --- a/Util.py +++ b/Util.py @@ -1,6 +1,5 @@ from pathlib import Path -import time import os def _log_to_csv(arch, mode, accuracy, report_dict, strategy): diff --git a/architectures/Model.py b/architectures/Model.py index 38eaffb..7f5e7ee 100644 --- a/architectures/Model.py +++ b/architectures/Model.py @@ -43,12 +43,12 @@ class Model(ABC): scheduler = CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-6) # to save reports - file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt") - Util._initialize_log_file(file_path) + # file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt") + # Util._initialize_log_file(file_path) print(f"Starting training on {self.device}...") - start_time = time.time() + # start_time = time.time() # training phase self.model.train() @@ -69,11 +69,11 @@ class Model(ABC): scheduler.step() print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss / len(loader):.4f}") - end_time = time.time() - execution_time = end_time - start_time - Util.log_metric(log_file=file_path, execution_time=execution_time) + #end_time = time.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") + print(f"Training complete.") def save(self, filename=None): @@ -108,7 +108,7 @@ class Model(ABC): self.model.to(self.device) print(f'Model loaded from {file_path}') - + ''' def unlearn(self, strategy: 'Strategy', forget_loader, retain_loader): """ Executes a targeted unlearning strategy and profiles efficiency """ print(f"Executing: {strategy.__class__.__name__}...") @@ -122,6 +122,7 @@ class Model(ABC): print(f"{strategy.__class__.__name__} completed in {elapsed_time:.4f} seconds.") return elapsed_time + ''' def evaluate(self, loader, mode="eval"): """ diff --git a/reports/LinearFiltration/RESNET34/forget.csv b/reports/LinearFiltration/RESNET34/forget.csv index ec69d38..694c9cd 100644 --- a/reports/LinearFiltration/RESNET34/forget.csv +++ b/reports/LinearFiltration/RESNET34/forget.csv @@ -399,93 +399,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 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a/reports/LinearFiltration/RESNET34/forget_train.csv +++ b/reports/LinearFiltration/RESNET34/forget_train.csv @@ -399,93 +399,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 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0.9575,0.9585,0.9575,0.9575,0.9585,0.9575,0.9575 0.9537,0.9545,0.9538,0.9538,0.9545,0.9537,0.9538 0.9519,0.9529,0.9519,0.9518,0.9529,0.9519,0.9518 +0.9506,0.9519,0.9506,0.9507,0.9519,0.9506,0.9507 +0.9563,0.9573,0.9563,0.9563,0.9573,0.9563,0.9563 +0.9550,0.9555,0.9550,0.9549,0.9555,0.9550,0.9549 +0.9563,0.9573,0.9563,0.9564,0.9573,0.9563,0.9564 +0.9544,0.9551,0.9544,0.9544,0.9551,0.9544,0.9544 +0.9569,0.9575,0.9569,0.9569,0.9575,0.9569,0.9569 +0.9613,0.9618,0.9613,0.9613,0.9618,0.9613,0.9613 +0.9550,0.9561,0.9550,0.9551,0.9561,0.9550,0.9551 +0.9575,0.9599,0.9575,0.9580,0.9599,0.9575,0.9580 +0.9600,0.9605,0.9600,0.9600,0.9605,0.9600,0.9600 +0.9575,0.9583,0.9575,0.9576,0.9583,0.9575,0.9576 +0.9550,0.9559,0.9550,0.9550,0.9559,0.9550,0.9550 +0.9550,0.9560,0.9550,0.9550,0.9560,0.9550,0.9550 +0.9506,0.9523,0.9506,0.9508,0.9523,0.9506,0.9508 +0.9513,0.9528,0.9513,0.9514,0.9528,0.9513,0.9514 +0.9544,0.9555,0.9544,0.9544,0.9555,0.9544,0.9544 +0.9506,0.9519,0.9506,0.9507,0.9519,0.9506,0.9507 +0.9575,0.9586,0.9575,0.9576,0.9586,0.9575,0.9576 +0.9531,0.9548,0.9531,0.9534,0.9548,0.9531,0.9534 +0.9575,0.9586,0.9575,0.9576,0.9586,0.9575,0.9576 diff --git a/sets/Data.py b/sets/Data.py index a2ffdcf..f7e6ad1 100644 --- a/sets/Data.py +++ b/sets/Data.py @@ -186,7 +186,7 @@ def get_unlearning_loaders(dataset: Dataset, forget_class_idx: int, batch_size: return retain_loader, forget_loader -def vertical_split(dataset, batch_size,num_classes): +'''def vertical_split(dataset, batch_size,num_classes): """ Executes a class-wise vertical split. Divides the samples of every single identity class exactly in half: @@ -229,7 +229,61 @@ def vertical_split(dataset, batch_size,num_classes): retain_loader = DataLoader(retain_subset, batch_size=batch_size, shuffle=True) forget_loader = DataLoader(forget_subset, batch_size=batch_size, shuffle=True) - return retain_loader, forget_loader + return retain_loader, forget_loader''' + +def vertical_split(dataset, batch_size, num_classes, ratio = 0.5): + """ + Optimized class-wise vertical split. + Divides the samples of every single identity class exactly in half. + """ + class_to_indices = {c: [] for c in range(num_classes)} + + # Read cached targets directly instead of decoding image files + if hasattr(dataset, 'targets'): + labels = dataset.targets + elif hasattr(dataset, 'labels'): + labels = dataset.labels + else: + # Fallback only if no cached targets attribute exists + labels = [dataset[i][1] for i in range(len(dataset))] + + print(" [Vertical Split] Fast-tracking class indices map...") + for idx, label in enumerate(labels): + if label in class_to_indices: + class_to_indices[label].append(idx) + + retain_indices = [] + forget_indices = [] + + for _, indices in class_to_indices.items(): + + np.random.shuffle(indices) + + mid = int(len(indices) * ratio) + + if mid == 0: + retain_indices.extend(indices) + continue + + forget_indices.extend(indices[:mid]) + retain_indices.extend(indices[mid:]) + + print(f" Vertical split complete: Retain = {len(retain_indices)} | Forget = {len(forget_indices)}") + + retain_subset = Subset(dataset, retain_indices) + forget_subset = Subset(dataset, forget_indices) + + # Performance flags keep workers alive between loop runs + retain_loader = DataLoader( + retain_subset, batch_size=batch_size, shuffle=True, + num_workers=2, pin_memory=True, persistent_workers=True + ) + forget_loader = DataLoader( + forget_subset, batch_size=batch_size, shuffle=True, + num_workers=2, pin_memory=True, persistent_workers=True + ) + + return retain_loader, forget_loader def _combine_set(loader_one, loader_two): full_train_dataset = ConcatDataset([loader_one.dataset, loader_two.dataset]) diff --git a/unlearning/LinearFiltration.py b/unlearning/LinearFiltration.py index e5331b4..10c5961 100644 --- a/unlearning/LinearFiltration.py +++ b/unlearning/LinearFiltration.py @@ -2,12 +2,13 @@ import torch import torch.nn as nn from .Strategy import Strategy from torch.utils.data import DataLoader -from sets.Data import get_unlearning_loaders, _combine_set +from sets.Data import get_unlearning_loaders, _combine_set, vertical_split class LinearFiltration(Strategy): - def __init__(self, target_class_index): + def __init__(self, target_class_index, num_classes = 20): super().__init__(target_class_index=target_class_index) self.A = None + self.num_classes = num_classes def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module: model.eval() @@ -51,13 +52,13 @@ class LinearFiltration(Strategy): - def _compute_A(self, model, num_classes, loader, device): + def _compute_A(self, model, loader, device): model.eval() # Initialize tracking tensors - sums = torch.zeros(num_classes, num_classes, device=device) - counts = torch.zeros(num_classes, device=device) + sums = torch.zeros(self.num_classes, self.num_classes, device=device) + counts = torch.zeros(self.num_classes, device=device) with torch.no_grad(): for inputs, targets in loader: @@ -67,7 +68,7 @@ class LinearFiltration(Strategy): outputs = model(inputs) # One-hot encode targets to act as a routing mask - one_hot = torch.nn.functional.one_hot(targets, num_classes=num_classes).float() + one_hot = torch.nn.functional.one_hot(targets, num_classes=self.num_classes).float() # add sums += torch.t(one_hot) @ outputs @@ -77,7 +78,6 @@ class LinearFiltration(Strategy): # means counts_safe = counts.unsqueeze(1) - print(f"COUNTS IS >>>>>>>>> {counts_safe}") self.A = torch.where( counts_safe > 0, sums / counts_safe, @@ -117,10 +117,10 @@ class LinearFiltration(Strategy): def normalise(self, model, retain_loader, forget_loader, device, forget_index): clf = self._get_classifier(model) W = clf.weight.data.clone() - num_classes = W.shape[0] + #num_classes = W.shape[0] # we combine the data so we can calculate the mean of prdictions - full_loader = _combine_set(retain_loader, forget_loader) + #full_loader = _combine_set(retain_loader, forget_loader) # 8 # Computing A is the most resource intensive part of this algorithm # and to optimise the process, we computr it only once and re-use it @@ -128,8 +128,8 @@ class LinearFiltration(Strategy): if self.A is None: self._compute_A( model = model, - num_classes = num_classes, - loader = full_loader, + #num_classes = num_classes, + loader = forget_loader, device = device ) @@ -137,7 +137,7 @@ class LinearFiltration(Strategy): Z = self._compute_z(tensor=self.A, forget_index=forget_index) B_Z_rows = [] - for i in range(num_classes): + for i in range(self.num_classes): if i == forget_index: B_Z_rows.append(Z) else: @@ -161,8 +161,14 @@ class LinearFiltration(Strategy): # overriden function def _split_data(self, dataset): - return get_unlearning_loaders( + '''return get_unlearning_loaders( dataset=dataset, forget_class_idx=self.target_class_index, batch_size = 32 + )''' + return vertical_split( + dataset= dataset, + batch_size=32, + num_classes=self.num_classes, + ratio=0.1 ) \ No newline at end of file diff --git a/unlearning/Retrain.py b/unlearning/Retrain.py index 8dc8510..e370836 100644 --- a/unlearning/Retrain.py +++ b/unlearning/Retrain.py @@ -2,75 +2,49 @@ 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 +from architectures.Model import Model, Architecture class Retrain(Strategy): """ - Implements the Exact Unlearning Baseline by retraining the model architecture - completely from scratch using only the retained dataset partition. + Implements the Exact Unlearning Baseline by re-instantiating a fresh, + pre-trained instance of the specific architecture and training it from scratch + on the retain set using the Model's internal train function. """ - def __init__(self, target_class_index: int, lr: float = 0.01, - weight_decay: float = 0.0005, epochs: int = 5): + def __init__(self, target_class_index: int, arch: Architecture, size: int, + lr: float = 0.001, epochs: int = 5): super().__init__(target_class_index) + self.arch = arch + self.size = size 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: + # 1. Determine the active execution device from the running sandbox device = next(model.parameters()).device - print(">> Triggering Exact Unlearning Baseline (Retraining from scratch)...") + print(f">> Triggering Exact Unlearning Baseline (Retraining {self.arch.name} from pristine state)...") - # 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() + # a new model with default params is created + fresh_meat = Model.create(self.arch, device, self.size) - # fresh optimizer for this clean environment - optimizer = optim.SGD( - model.parameters(), - lr=self.lr, - momentum=0.9, - weight_decay=self.weight_decay + # we train it with retain set + fresh_meat.train( + epochs=self.epochs, + loader=retain_loader, + rate=self.lr, + mode="retrain" ) - 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.") + + # 4. Extract the trained nn.Module parameter state dict + # In-place copy onto the existing sandbox model structure to seamlessly retain downstream evaluations + model.load_state_dict(fresh_meat.model.state_dict()) + + print(">> Retraining pipeline finished. Pristine baseline weights successfully 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,