From 7eff030e898ba7353b2c28d4dd1ed601a3d30088 Mon Sep 17 00:00:00 2001 From: Tinsae Date: Fri, 10 Jul 2026 20:13:13 +0200 Subject: [PATCH] cleaned up for submission --- Note.md | 40 -- Predict.py | 67 -- SetUp.py | 14 - Tune.py | 14 +- Util.py | 3 +- architectures/EfficentNet.py | 4 +- architectures/Model.py | 25 +- architectures/WFNet.py | 63 -- eval/UnlearningAttack.py | 3 +- js_evaluator/JS_Evaluator.py | 111 ---- .../CertifiedUnlearning/RESNET34/forget.csv | 29 - .../RESNET34/forget_train.csv | 29 - .../CertifiedUnlearning/RESNET34/retain.csv | 29 - reports/CertifiedUnlearning/attack_values.csv | 6 - .../full-set/forget.csv | 50 -- .../full-set/forget_train.csv | 50 -- .../full-set/retain.csv | 52 +- .../LinearFiltration/RESNET34_old/forget.csv | 604 ------------------ .../RESNET34_old/forget_train.csv | 604 ------------------ .../LinearFiltration/RESNET34_old/retain.csv | 604 ------------------ .../LinearFiltration/ResNet/time_metrics.txt | 11 - .../time_metrics_full sample.txt.txt | 0 .../ResNet_old/Report sample.txt | 3 - reports/LinearFiltration/ResNet_old/old.txt | 590 ----------------- .../LinearFiltration/attack_values_old.csv | 108 ---- reports/Retrain/attack_values.csv | 12 - reports/Retrain/attack_values_int.csv | 38 -- reports/WeightFiltration/RESNET34/forget.csv | 199 ------ .../RESNET34/forget_train.csv | 199 ------ reports/WeightFiltration/RESNET34/retain.csv | 199 ------ .../WeightFiltration/ResNet/time_metrics.txt | 215 ------- reports/WeightFiltration/attack_values.csv | 7 - .../WeightFiltration/attack_values_old.csv | 101 --- retrain/resnet34/time_metrics.txt | 9 - unlearning/CertifiedUnlearning.py | 8 +- unlearning/LinearFiltration.py | 5 +- unlearning/Retrain.py | 2 +- unlearning/WF.py | 107 ---- 38 files changed, 39 insertions(+), 4175 deletions(-) delete mode 100644 Note.md delete mode 100644 Predict.py delete mode 100644 SetUp.py delete mode 100644 js_evaluator/JS_Evaluator.py rename reports/LinearFiltration/{RESNET34_old => RESNET34}/full-set/forget.csv (90%) rename reports/LinearFiltration/{RESNET34_old => RESNET34}/full-set/forget_train.csv (90%) rename reports/LinearFiltration/{RESNET34_old => RESNET34}/full-set/retain.csv (90%) delete mode 100644 reports/LinearFiltration/RESNET34_old/forget.csv delete mode 100644 reports/LinearFiltration/RESNET34_old/forget_train.csv delete mode 100644 reports/LinearFiltration/RESNET34_old/retain.csv rename reports/LinearFiltration/{ResNet_old => ResNet}/time_metrics_full sample.txt.txt (100%) delete mode 100644 reports/LinearFiltration/ResNet_old/Report sample.txt delete mode 100644 reports/LinearFiltration/ResNet_old/old.txt delete mode 100644 reports/LinearFiltration/attack_values_old.csv delete mode 100644 reports/Retrain/attack_values_int.csv delete mode 100644 reports/WeightFiltration/attack_values_old.csv delete mode 100644 retrain/resnet34/time_metrics.txt delete mode 100644 unlearning/WF.py diff --git a/Note.md b/Note.md deleted file mode 100644 index 67e3557..0000000 --- a/Note.md +++ /dev/null @@ -1,40 +0,0 @@ -Not at all! You are still completely faithful to Guo et al. Your current implementation does **not** break their framework. - -In fact, your setup matches the exact methodology of the paper. There is a common misconception about what Guo et al. mean when they caution against calculating the Hessian inverse, and understanding how your feature extraction relates to their theory explains why your code remains completely valid. - ---- - -## 1. What Guo et al. *Actually* Said - -In the original paper (*"Certified Removal from Linear Models"*), Guo et al. state that explicitly calculating and inverting the Hessian matrix becomes prohibitively expensive when the parameter count $d$ scales up. - -$$\text{Time Complexity to Invert } H = O(d^3)$$ - -However, the authors explicitly implemented and verified their approach on **linear classifiers** (like logistic regression) where the input feature dimension $d$ was small enough to handle directly. - -When you strip out the heavy ResNet50 convolutional layers and turn the backbone into a static feature extractor, **you transform your deep network into a linear classifier.** ``` -[Images] ──> [Frozen ResNet Backbone] ──> Extracted Feature Vector (d = 2048) ──> [Linear Head (model.fc)] - - -Because your feature vector is exactly 2,048 dimensions, your Hessian matrix is a modest $2048 \times 2048$. - -Inverting a $2048 \times 2048$ matrix takes your CPU less than **0.5 seconds** ($2048^3$ operations is tiny for a modern processor). You are executing the exact mathematics Guo et al. prescribed for linear systems. You haven't broken their implementation; you have successfully reduced a non-convex deep learning problem into their exact convex linear domain. - - -## 2. Where Hessian-Free Approximations (Like LiSSA) Apply - -The reason alternative methods like LiSSA or Conjugate Gradient exist is for scenarios where you *cannot* reduce the model to a small linear head. - -If you decided to apply Certified Removal to the **entire ResNet50 network** (all 23.5 million parameters open), then you would be forced to abandon your exact matrix calculation. Inverting a $23.5\text{M} \times 23.5\text{M}$ matrix is impossible. In that specific scenario, you would have to use a Hessian-free approximation method to avoid breaking the budget. - ---- - -## 3. The Core Alignment with the Paper - -Your script implements the three pillars that define Guo et al.’s Certified Removal: - -1. **The Optimization Target:** It uses an $L_2$-regularized objective function (`self.l2_reg`). -2. **The Newton Step:** It takes the exact second-order curvature correction ($H^{-1} \nabla$) to adjust the parameters. -3. **The Indistinguishability Guarantee:** It applies a privacy perturbation boundary check (`self.removal_bound`). - -Your implementation is an elegant, academically sound adaptation of their linear model theory for a deep learning architecture. By handling the feature extraction step first, you made their exact algorithm run efficiently within a 4GB VRAM envelope. diff --git a/Predict.py b/Predict.py deleted file mode 100644 index 554d887..0000000 --- a/Predict.py +++ /dev/null @@ -1,67 +0,0 @@ - - - -import torch -import numpy as np - -@torch.inference_mode() # More memory-efficient than no_grad() -def get_loss_per_sample(model, data_loader, device): - """ - Returns a list of individual losses for every sample in the loader. - Useful for MIA to see how 'certain' the model is about specific images. - """ - model.eval() - criterion = torch.nn.CrossEntropyLoss(reduction='none') # Crucial: returns loss per image - all_losses = [] - - for inputs, labels in data_loader: - inputs, labels = inputs.to(device), labels.to(device) - - outputs = model(inputs) - - # Calculate loss for each image in the batch individually - loss = criterion(outputs, labels) - - all_losses.extend(loss.cpu().numpy()) - - return all_losses - - -@torch.inference_mode() -def get_losses_by_class(model, data_loader, device): - """ - Returns a dictionary: { class_id: [list_of_losses_for_this_class] } - """ - model.eval() - criterion = torch.nn.CrossEntropyLoss(reduction='none') - - class_losses = {} - - for inputs, labels in data_loader: - inputs, labels = inputs.to(device), labels.to(device) - outputs = model(inputs) - - # Get individual losses - losses = criterion(outputs, labels).cpu().numpy() - labels_np = labels.cpu().numpy() - - for i, class_id in enumerate(labels_np): - if class_id not in class_losses: - class_losses[class_id] = [] - class_losses[class_id].append(losses[i]) - - return class_losses - - -# evaluate MIA -def eval_MIA(forgotten_losses, never_seen_losses): - avg_f_loss = np.mean(forgotten_losses) - avg_ns_loss = np.mean(never_seen_losses) - - print(f"Average Loss on Forgotten Identity: {avg_f_loss:.4f}") - print(f"Average Loss on Unknown Identities: {avg_ns_loss:.4f}") - - if avg_f_loss < avg_ns_loss * 0.8: - print("MIA Warning: Model still shows high certainty on forgotten data.") - else: - print("MIA Success: Model treats forgotten data like unknown data.") diff --git a/SetUp.py b/SetUp.py deleted file mode 100644 index 939998c..0000000 --- a/SetUp.py +++ /dev/null @@ -1,14 +0,0 @@ -## -import torch -from torchvision import datasets, transforms, models - -def get_device(): - - if torch.cuda.is_available(): - # clear cach to boost memory - # for new round - torch.cuda.empty_cache() - return torch.device("cuda") - else: - return torch.device("cpu") - diff --git a/Tune.py b/Tune.py index d0de23f..995742e 100644 --- a/Tune.py +++ b/Tune.py @@ -5,7 +5,6 @@ from sklearn.metrics import classification_report import copy # Framework and Utility Imports -import SetUp import Util from sets.Data import * from sets.IdentitySubset import IdentitySubset @@ -43,6 +42,16 @@ RESOLUTION:int = 224 ''' ARCH = Architecture.RESNET34 +def get_device(): + + if torch.cuda.is_available(): + # clear cach to boost memory + # for new round + torch.cuda.empty_cache() + return torch.device("cuda") + else: + return torch.device("cpu") + # Data preparation and model setup def prepare_data_and_model_environment(): @@ -50,7 +59,8 @@ 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() + # get Cuda or CPU. + device = get_device() dataset_name = Set_Name.CASIAFACES if dataset_name == Set_Name.CASIAFACES: SAMPLE_SIZE = 400 diff --git a/Util.py b/Util.py index d001518..36a7114 100644 --- a/Util.py +++ b/Util.py @@ -2,6 +2,7 @@ from pathlib import Path import os +# used for logging metrics to files def _log_to_csv(arch, mode, accuracy, report_dict, strategy): """Handles directory structures, file setups, and distinct CSV column formatting.""" #arch_name = model.__class__.__name__.lower() @@ -33,7 +34,7 @@ def _log_to_csv(arch, mode, accuracy, report_dict, strategy): print(f">> Direct CSV metrics appended to {csv_path}") - +# creates a file for def _initialize_log_file(log_file): """Creates a unique log file for this strategy with a header if it doesn't exist.""" log_file.parent.mkdir(parents=True, exist_ok=True) diff --git a/architectures/EfficentNet.py b/architectures/EfficentNet.py index 9fbfc97..ede9da7 100644 --- a/architectures/EfficentNet.py +++ b/architectures/EfficentNet.py @@ -11,8 +11,8 @@ class EfficientNet(Model): m = models.efficientnet_b1(weights=models.EfficientNet_B1_Weights.DEFAULT) - # Unfreeze the last block for a lighter touch - for param in m.features[-1].parameters(): param.requires_grad = True + # Unfreeze the last block + # for param in m.features[-1].parameters(): param.requires_grad = True # Standard classifier fix m.classifier[1] = nn.Linear(m.classifier[1].in_features, self.size) diff --git a/architectures/Model.py b/architectures/Model.py index 094d76e..9d9cea9 100644 --- a/architectures/Model.py +++ b/architectures/Model.py @@ -31,13 +31,13 @@ class Model(ABC): optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()), lr=rate, weight_decay=0.1) 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) + # to save training time to reports + 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() @@ -49,7 +49,7 @@ class Model(ABC): optimizer.zero_grad() # forward pass outputs = self.model(inputs) - # comoutew loss + # comopute loss loss = criterion(outputs, labels) # backward pass loss.backward() @@ -58,9 +58,9 @@ 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 complete.") @@ -123,10 +123,10 @@ class Model(ABC): print(f"Test Accuracy: {accuracy:.2f}%") - # 1. Print standard text report to terminal - print(classification_report(all_labels, all_preds, labels=classes, zero_division=0)) + # Print standard text report to terminal + #print(classification_report(all_labels, all_preds, labels=classes, zero_division=0)) - # 2. Extract structured dictionary metrics + # Structured dictionary metrics report_dict = classification_report( all_labels, all_preds, @@ -135,8 +135,7 @@ class Model(ABC): zero_division=0 ) - # 3. Delegate file tracking to isolated helper method - #self._log_to_csv(mode, accuracy,report_dict) + # report metrics to choriographer return accuracy, report_dict diff --git a/architectures/WFNet.py b/architectures/WFNet.py index 4d0db37..2e6d7cb 100644 --- a/architectures/WFNet.py +++ b/architectures/WFNet.py @@ -6,69 +6,6 @@ from torch.utils.data import DataLoader 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/eval/UnlearningAttack.py b/eval/UnlearningAttack.py index e4ba698..19d02db 100644 --- a/eval/UnlearningAttack.py +++ b/eval/UnlearningAttack.py @@ -32,7 +32,8 @@ class UnlearningAttack: if model1.__class__.__name__ == "WF_Module": gate = torch.full((data.size(0),), target_class, device=device) p1 = torch.softmax(model1(data, target_class_indices=gate), dim=1) - else: + + else: # its a normal nn.Module p1 = torch.softmax(model1(data), dim=1) p2 = torch.softmax(model2(data), dim=1) probs1.extend(p1.cpu().numpy()); probs2.extend(p2.cpu().numpy()) diff --git a/js_evaluator/JS_Evaluator.py b/js_evaluator/JS_Evaluator.py deleted file mode 100644 index 3961e9b..0000000 --- a/js_evaluator/JS_Evaluator.py +++ /dev/null @@ -1,111 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader -import torchvision.models as models - -class ZeroRetrainForgettingEvaluator: - def __init__(self, unlearned_model: nn.Module, num_classes: int): - """ - Initializes the ZRF Evaluator. - - Args: - unlearned_model (nn.Module): Your fine-tuned & unlearned ResNet-50. - num_classes (int): Number of classes used in your CelebA task. - """ - # select device - if torch.cuda.is_available(): - self.device = torch.device("cuda") - elif hasattr(torch, "xpu") and torch.xpu.is_available(): - self.device = torch.device("xpu") # For Intel GPUs using IPEX - else: - self.device = torch.device("cpu") - - print(f"[INFO] Using device: {self.device}") - - # prepare the unlearned model - self.unlearned_model = unlearned_model.to(self.device) - self.unlearned_model.eval() - - # Instantiate a structurally matching, completely random model - print(f"[INFO] Initializing random baseline ResNet-50 with {num_classes} classes...") - self.random_model = self.get_random_model(num_classes) - self.random_model = self.random_model.to(self.device) - self.random_model.eval() - - # gets randomly initialised model - # for comparison with unlearned model - def get_random_model(num_classes): - print(f"[INFO] Initializing random baseline ResNet-50 with {num_classes} classes...") - model = models.resnet50(weights=None) - model.fc = nn.Linear(model.fc.in_features, num_classes) - return model - - - # compute divergence - def _compute_js_divergence(self, p: torch.Tensor, q: torch.Tensor) -> float: - """ - Computes the Jensen-Shannon (JS) Divergence between two probability distributions. - - Args: - p, q (Tensor): Tensors of shape (batch_size, num_classes) containing probabilities. - """ - # Avoid log(0) issues by adding a tiny epsilon - eps = 1e-12 - p = torch.clamp(p, eps, 1.0) - q = torch.clamp(q, eps, 1.0) - - # Calculate the midpoint distribution - m = 0.5 * (p + q) - - # Compute KL Divergence natively: KL(P || M) and KL(Q || M) - kl_pm = torch.sum(p * (torch.log(p) - torch.log(m)), dim=1) - kl_qm = torch.sum(q * (torch.log(q) - torch.log(m)), dim=1) - - # JS Divergence is the average of both KL divergences - js_div = 0.5 * (kl_pm + kl_qm) - - # Return the mean divergence across the entire batch - return js_div.mean().item() - - def evaluate_forget_class(self, dataset, batch_size: int = 32) -> float: - """ - Evaluates the unlearned model against the random model using images - from the forgotten class/identity. - - Args: - dataset (Dataset): A PyTorch Dataset containing images of the forget set. - batch_size (int): Batch size for evaluation. - - Returns: - float: The ZRF score (JS Divergence). A lower divergence means - the unlearned model is behaving exactly like a random model. - """ - dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False) - - total_js_div = 0.0 - total_samples = 0 - - # No gradients needed for evaluation - with torch.no_grad(): - for images, _ in dataloader: - images = images.to(self.device) - batch_len = images.size(0) - - # Get raw outputs (logits) - unlearned_logits = self.unlearned_model(images) - random_logits = self.random_model(images) - - # Convert logits to probability distributions via Softmax - unlearned_probs = F.softmax(unlearned_logits, dim=1) - random_probs = F.softmax(random_logits, dim=1) - - # Calculate JS divergence for this batch - batch_js = self._compute_js_divergence(unlearned_probs, random_probs) - - # Weighted average based on batch size (handles final smaller batches perfectly) - total_js_div += batch_js * batch_len - total_samples += batch_len - - final_zrf_score = total_js_div / total_samples - return final_zrf_score \ No newline at end of file diff --git a/reports/CertifiedUnlearning/RESNET34/forget.csv b/reports/CertifiedUnlearning/RESNET34/forget.csv index 25e34de..8c361a0 100644 --- a/reports/CertifiedUnlearning/RESNET34/forget.csv +++ b/reports/CertifiedUnlearning/RESNET34/forget.csv @@ -471,32 +471,3 @@ 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 -0.1500,1.0000,0.1500,0.2609,1.0000,0.1500,0.2609 -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 -0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723 -0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488 -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 -0.1125,1.0000,0.1125,0.2022,1.0000,0.1125,0.2022 diff --git a/reports/CertifiedUnlearning/RESNET34/forget_train.csv b/reports/CertifiedUnlearning/RESNET34/forget_train.csv index 7f24ebf..52c24c5 100644 --- a/reports/CertifiedUnlearning/RESNET34/forget_train.csv +++ b/reports/CertifiedUnlearning/RESNET34/forget_train.csv @@ -471,32 +471,3 @@ 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 -0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488 -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 -0.1031,1.0000,0.1031,0.1870,1.0000,0.1031,0.1870 -0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723 -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 -0.0656,1.0000,0.0656,0.1232,1.0000,0.0656,0.1232 -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 -0.0094,1.0000,0.0094,0.0186,1.0000,0.0094,0.0186 -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 -0.0875,1.0000,0.0875,0.1609,1.0000,0.0875,0.1609 diff --git a/reports/CertifiedUnlearning/RESNET34/retain.csv b/reports/CertifiedUnlearning/RESNET34/retain.csv index 346d70c..5e2d277 100644 --- a/reports/CertifiedUnlearning/RESNET34/retain.csv +++ b/reports/CertifiedUnlearning/RESNET34/retain.csv @@ -471,32 +471,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal 0.1026,0.1456,0.1026,0.0392,0.1456,0.1026,0.0392 0.6928,0.8887,0.6928,0.7218,0.8887,0.6928,0.7218 0.9191,0.9270,0.9191,0.9197,0.9270,0.9191,0.9197 -0.4316,0.8326,0.4316,0.4030,0.8326,0.4316,0.4030 -0.9289,0.9391,0.9289,0.9310,0.9391,0.9289,0.9310 -0.9454,0.9476,0.9454,0.9456,0.9476,0.9454,0.9456 -0.9046,0.9221,0.9046,0.9073,0.9221,0.9046,0.9073 -0.9322,0.9393,0.9322,0.9332,0.9393,0.9322,0.9332 -0.8842,0.9196,0.8842,0.8916,0.9196,0.8842,0.8916 -0.9072,0.9258,0.9072,0.9111,0.9258,0.9072,0.9111 -0.1322,0.3672,0.1322,0.0844,0.3672,0.1322,0.0844 -0.9447,0.9469,0.9447,0.9451,0.9469,0.9447,0.9451 -0.9336,0.9384,0.9336,0.9339,0.9384,0.9336,0.9339 -0.7941,0.8921,0.7941,0.8103,0.8921,0.7941,0.8103 -0.9447,0.9477,0.9447,0.9449,0.9477,0.9447,0.9449 -0.1329,0.2409,0.1329,0.0850,0.2409,0.1329,0.0850 -0.9513,0.9546,0.9513,0.9519,0.9546,0.9513,0.9519 -0.9408,0.9464,0.9408,0.9416,0.9464,0.9408,0.9416 -0.8987,0.9157,0.8987,0.8994,0.9157,0.8987,0.8994 -0.8987,0.9205,0.8987,0.9035,0.9205,0.8987,0.9035 -0.0750,0.0175,0.0750,0.0233,0.0175,0.0750,0.0233 -0.4493,0.7334,0.4493,0.4059,0.7334,0.4493,0.4059 -0.9322,0.9380,0.9322,0.9327,0.9380,0.9322,0.9327 -0.7204,0.8401,0.7204,0.7178,0.8401,0.7204,0.7178 -0.9428,0.9486,0.9428,0.9439,0.9486,0.9428,0.9439 -0.9401,0.9450,0.9401,0.9408,0.9450,0.9401,0.9408 -0.5875,0.8750,0.5875,0.6154,0.8750,0.5875,0.6154 -0.9336,0.9402,0.9336,0.9342,0.9402,0.9336,0.9342 -0.8257,0.8913,0.8257,0.8352,0.8913,0.8257,0.8352 -0.8987,0.9353,0.8987,0.9086,0.9353,0.8987,0.9086 -0.8882,0.9162,0.8882,0.8931,0.9162,0.8882,0.8931 -0.9388,0.9419,0.9388,0.9392,0.9419,0.9388,0.9392 diff --git a/reports/CertifiedUnlearning/attack_values.csv b/reports/CertifiedUnlearning/attack_values.csv index 4e14b2c..7aa45ae 100644 --- a/reports/CertifiedUnlearning/attack_values.csv +++ b/reports/CertifiedUnlearning/attack_values.csv @@ -19,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist 17,0.500000,0.989583, 0.062500, 0.503747 18,0.550000,1.000000, 0.166667, 0.495459 19,0.500000,0.916667, 0.052083, 0.551996 - -0,0.500000,0.937500, 0.020833, 0.532267 -1,0.500000,0.937500, 0.093750, 0.503973 -2,0.500000,0.989583, 0.104167, 0.570063 0,0.500000,0.402083, 0.031250, 0.511158 1,0.500000,0.154167, 0.312500, 0.496193 2,0.500000,0.602083, 0.145833, 0.530453 @@ -103,5 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist 17,0.500000,0.377083, 0.166667, 0.459667 18,0.600000,0.881250, 0.083333, 0.488006 19,0.500000,0.568750, 0.187500, 0.546058 -0,0.500000,0.635417, 0.156250, 0.527750 -1,0.500000,0.193750, 0.333333, 0.447474 diff --git a/reports/LinearFiltration/RESNET34_old/full-set/forget.csv b/reports/LinearFiltration/RESNET34/full-set/forget.csv similarity index 90% rename from reports/LinearFiltration/RESNET34_old/full-set/forget.csv rename to reports/LinearFiltration/RESNET34/full-set/forget.csv index 2b99a71..53cab8d 100644 --- a/reports/LinearFiltration/RESNET34_old/full-set/forget.csv +++ b/reports/LinearFiltration/RESNET34/full-set/forget.csv @@ -479,53 +479,3 @@ 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 -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 -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 -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 diff --git a/reports/LinearFiltration/RESNET34_old/full-set/forget_train.csv b/reports/LinearFiltration/RESNET34/full-set/forget_train.csv similarity index 90% rename from reports/LinearFiltration/RESNET34_old/full-set/forget_train.csv rename to reports/LinearFiltration/RESNET34/full-set/forget_train.csv index 03fc9de..3c1dc5d 100644 --- a/reports/LinearFiltration/RESNET34_old/full-set/forget_train.csv +++ b/reports/LinearFiltration/RESNET34/full-set/forget_train.csv @@ -479,53 +479,3 @@ 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 -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 -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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reports/LinearFiltration/ResNet_old/time_metrics_full sample.txt.txt rename to reports/LinearFiltration/ResNet/time_metrics_full sample.txt.txt diff --git a/reports/LinearFiltration/ResNet_old/Report sample.txt b/reports/LinearFiltration/ResNet_old/Report sample.txt deleted file mode 100644 index fb9ac5e..0000000 --- a/reports/LinearFiltration/ResNet_old/Report sample.txt +++ /dev/null @@ -1,3 +0,0 @@ -on full data: - -- average first run = 16.495034 diff --git a/reports/LinearFiltration/ResNet_old/old.txt b/reports/LinearFiltration/ResNet_old/old.txt deleted file mode 100644 index d1c1797..0000000 --- a/reports/LinearFiltration/ResNet_old/old.txt +++ /dev/null @@ -1,590 +0,0 @@ -execution_time_sec -1.635622 -0.002531 -0.001979 -0.002586 -0.002610 -0.002725 -0.002553 -0.002423 -0.002479 -0.002613 -0.002302 -0.002516 -0.002445 -0.002463 -0.002466 -0.002304 -0.002904 -0.002558 -0.002211 -0.004793 -1.558785 -0.001836 -0.001835 -0.001834 -0.001844 -0.001856 -0.001870 -0.001834 -0.001856 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-14,0.500000,1.000000, 0.041667, 0.470029 -15,0.500000,1.000000, 0.020833, 0.479780 -16,0.500000,1.000000, 0.208333, 0.468624 -17,0.500000,1.000000, 0.291667, 0.465381 -18,0.493750,1.000000, 0.145833, 0.449917 -19,0.500000,1.000000, 0.250000, 0.493480 -0,0.500000,1.000000, 0.145833, 0.477865 -1,0.500000,1.000000, 0.072917, 0.476946 -2,0.500000,1.000000, 0.187500, 0.530780 diff --git a/reports/Retrain/attack_values.csv b/reports/Retrain/attack_values.csv index 68d9f6c..f8e9f3d 100644 --- a/reports/Retrain/attack_values.csv +++ b/reports/Retrain/attack_values.csv @@ -1,7 +1,4 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist -0,0.500000,0.000000, 0.156250, 0.000000 -1,0.500000,0.000000, 0.104167, 0.000000 -2,0.500000,0.000000, 0.114583, 0.000000 0,0.500000,0.000000, 0.125000, 0.000000 1,0.500000,0.000000, 0.072917, 0.000000 2,0.500000,0.000000, 0.020833, 0.000000 @@ -22,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist 17,0.500000,0.000000, 0.093750, 0.000000 18,0.531250,0.000000, 0.010417, 0.000000 19,0.500000,0.000000, 0.031250, 0.000000 -0,0.500000,0.000000, 0.062500, 0.000000 -1,0.500000,0.000000, 0.072917, 0.000000 -2,0.500000,0.000000, 0.031250, 0.000000 -3,0.500000,0.000000, 0.083333, 0.000000 0,0.500000,0.000000, 0.010417, 0.000000 1,0.500000,0.000000, 0.062500, 0.000000 2,0.500000,0.000000, 0.104167, 0.000000 @@ -106,8 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist 17,0.500000,0.000000, 0.125000, 0.000000 18,0.500000,0.000000, 0.114583, 0.000000 19,0.500000,0.000000, 0.104167, 0.000000 -0,0.500000,0.000000, 0.031250, 0.000000 -1,0.500000,0.000000, 0.072917, 0.000000 -2,0.500000,0.000000, 0.104167, 0.000000 -0,0.500000,0.000000, 0.041667, 0.000000 -0,0.500000,0.000000, 0.052083, 0.000000 diff --git a/reports/Retrain/attack_values_int.csv b/reports/Retrain/attack_values_int.csv deleted file mode 100644 index 9d2f4a3..0000000 --- a/reports/Retrain/attack_values_int.csv +++ /dev/null @@ -1,38 +0,0 @@ -target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy -0,0.500000,11.573492,0.000000 -1,0.500000,12.793120,0.000000 -2,0.500000,12.951434,0.000000 -3,0.500000,10.942259,0.000000 -4,0.500000,10.675704,0.000000 -5,0.500000,13.046491,0.000000 -6,0.500000,11.343709,0.000000 -7,0.500000,10.020437,0.000000 -8,0.500000,12.896643,0.000000 -9,0.500000,12.220088,0.000000 -10,0.500000,11.315407,0.000000 -11,0.450000,10.556920,0.000000 -12,0.500000,10.925443,0.000000 -13,0.500000,12.007375,0.000000 -14,0.500000,11.471087,0.000000 -15,0.500000,12.301955,0.000000 -16,0.500000,10.831913,0.000000 -17,0.500000,10.942777,0.000000 -18,0.500000,11.699934,0.000000 -19,0.500000,12.526654,0.000000 -0,0.500000,11.601441,0.000000 -0,0.500000,11.639872,0.000000 -1,0.500000,12.723348,0.000000 -2,0.500000,13.151769,0.000000 -3,0.500000,10.969514,0.000000 -4,0.500000,10.674448,0.000000 -5,0.500000,13.068334,0.000000 -6,0.500000,11.328772,0.000000 -7,0.500000,9.926997,0.000000 -8,0.500000,12.859781,0.000000 -9,0.500000,12.061253,0.000000 -10,0.500000,11.358070,0.000000 -11,0.500000,10.484346,0.000000 -12,0.500000,10.926878,0.000000 -13,0.500000,12.002632,0.000000 -0,0.500000,11.557904,0.000000 -1,0.500000,12.819191,0.000000 diff --git a/reports/WeightFiltration/RESNET34/forget.csv b/reports/WeightFiltration/RESNET34/forget.csv index 10d041a..756987f 100644 --- a/reports/WeightFiltration/RESNET34/forget.csv +++ b/reports/WeightFiltration/RESNET34/forget.csv @@ -471,202 +471,3 @@ 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 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b/reports/WeightFiltration/RESNET34/forget_train.csv @@ -471,202 +471,3 @@ 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 0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062 -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 -0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062 -0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 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b/reports/WeightFiltration/ResNet/time_metrics.txt @@ -299,17 +299,6 @@ execution_time_sec 0.000427 0.000398 0.000403 -183.682534 -0.000395 -0.000388 -0.000383 -0.000515 -0.000383 -0.000385 -0.000386 -0.000450 -0.000374 -0.000908 177.630187 0.000413 0.000469 @@ -450,207 +439,3 @@ execution_time_sec 0.000431 0.000414 0.000410 -176.474485 -0.000407 -0.000406 -0.000407 -0.000406 -0.000407 -0.000451 -0.000399 -0.000415 -0.000411 -0.000407 -0.000428 -0.000411 -0.000408 -0.000408 -0.000402 -0.000407 -0.000411 -0.000402 -0.000411 -176.875764 -0.000397 -0.000400 -0.000404 -0.000404 -0.000397 -0.000411 -0.000397 -0.000408 -0.000409 -0.000406 -0.000426 -0.000454 -0.000416 -0.000380 -0.000419 -0.000408 -0.000396 -0.000410 -0.000435 -87.953725 -89.953191 -88.555181 -87.086629 -86.067240 -0.001263 -0.000383 -0.000499 -0.000494 -0.000391 -0.000393 -0.000394 -87.004814 -88.485772 -0.000770 -0.001780 -0.000391 -0.000384 -0.000495 -0.000501 -0.000405 -0.001766 -0.000508 -87.975398 -0.000514 -0.000446 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-0.000433 -0.000432 -0.000430 -0.000424 -0.000421 -0.000435 -0.000428 -0.000398 -0.000432 -0.000427 -0.000407 -0.000425 -0.000433 -0.000430 -0.000422 -0.000418 -89.527112 -87.450551 -0.002478 -0.000423 -0.000417 -0.000433 -0.000426 -0.000440 -0.000826 -0.000426 -0.000440 -0.000437 -0.000425 -0.000425 -0.000422 -0.000450 -0.000423 -0.000422 -0.000422 -0.000426 -0.000428 -86.247649 -0.000436 diff --git a/reports/WeightFiltration/attack_values.csv b/reports/WeightFiltration/attack_values.csv index 464c2dc..783b0d3 100644 --- a/reports/WeightFiltration/attack_values.csv +++ b/reports/WeightFiltration/attack_values.csv @@ -19,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist 17,0.500000,0.927083, 0.114583, 0.536984 18,0.500000,0.947917, 0.072917, 0.529271 19,0.500000,0.958333, 0.062500, 0.552560 -0,0.500000,0.968750, 0.208333, 0.536183 -1,0.500000,0.968750, 0.125000, 0.538881 -2,0.500000,0.958333, 0.031250, 0.567163 -3,0.500000,0.979167, 0.114583, 0.562516 0,0.500000,0.952083, 0.000000, 0.537566 1,0.500000,0.912500, 0.083333, 0.535581 2,0.500000,0.958333, 0.010417, 0.564744 @@ -103,6 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist 17,0.500000,0.937500, 0.093750, 0.532926 18,0.500000,0.937500, 0.031250, 0.532182 19,0.500000,0.962500, 0.041667, 0.552156 -0,0.500000,0.960417, 0.031250, 0.534253 -1,0.500000,0.910417, 0.000000, 0.534778 -2,0.500000,0.922917, 0.000000, 0.560311 diff --git a/reports/WeightFiltration/attack_values_old.csv b/reports/WeightFiltration/attack_values_old.csv deleted file mode 100644 index 8848236..0000000 --- a/reports/WeightFiltration/attack_values_old.csv +++ /dev/null @@ -1,101 +0,0 @@ -target_class,attack_mia_accuracy,latent_distance_tell -0,0.500000,1.107324 -1,0.500000,1.182681 -2,0.500000,1.628934 -3,0.500000,1.260251 -4,0.500000,1.399319 -5,0.500000,2.046023 -6,0.500000,1.750646 -7,0.500000,1.243093 -8,0.500000,1.809917 -9,0.500000,1.702536 -10,0.500000,1.291788 -11,0.500000,1.434109 -12,0.500000,1.448272 -13,0.500000,1.694034 -14,0.500000,1.717611 -15,0.500000,1.758781 -16,0.500000,1.188805 -17,0.500000,1.591978 -18,0.500000,1.445776 -19,0.500000,1.626087 -0,0.500000,1.158666 -1,0.500000,1.245154 -2,0.500000,1.558492 -3,0.500000,1.358110 -4,0.500000,1.339859 -5,0.500000,2.025961 -6,0.500000,1.828531 -7,0.500000,1.186695 -8,0.500000,1.920701 -9,0.500000,1.718948 -10,0.500000,1.374345 -11,0.500000,1.495150 -12,0.500000,1.368306 -13,0.500000,1.710072 -14,0.500000,1.700057 -15,0.500000,1.766020 -16,0.500000,1.160263 -17,0.500000,1.634570 -18,0.500000,1.461136 -19,0.500000,1.697268 -0,0.500000,1.092890 -1,0.500000,1.238615 -2,0.500000,1.704648 -3,0.500000,1.394107 -4,0.500000,1.365094 -5,0.500000,2.032884 -6,0.500000,1.833764 -7,0.500000,1.225851 -8,0.500000,1.807006 -9,0.500000,1.704291 -10,0.500000,1.358446 -11,0.500000,1.555449 -12,0.500000,1.387334 -13,0.500000,1.693131 -14,0.500000,1.736060 -15,0.500000,1.768330 -16,0.500000,1.190044 -17,0.500000,1.585899 -18,0.500000,1.482916 -19,0.500000,1.691146 -0,0.500000,1.141869 -1,0.500000,1.352442 -2,0.500000,1.695588 -3,0.500000,1.432673 -4,0.500000,1.314509 -5,0.500000,2.010463 -6,0.500000,1.817650 -7,0.500000,1.291032 -8,0.500000,1.703021 -9,0.500000,1.802832 -10,0.500000,1.355631 -11,0.500000,1.485411 -12,0.500000,1.441830 -13,0.500000,1.728542 -14,0.500000,1.740982 -15,0.500000,1.764840 -16,0.500000,1.210430 -17,0.500000,1.645152 -18,0.500000,1.471922 -19,0.500000,1.709163 -0,0.500000,1.122816 -1,0.500000,1.332376 -2,0.500000,1.646908 -3,0.500000,1.429030 -4,0.500000,1.321270 -5,0.500000,2.033827 -6,0.500000,1.863828 -7,0.500000,1.242626 -8,0.500000,1.924087 -9,0.500000,1.760985 -10,0.500000,1.423025 -11,0.500000,1.428449 -12,0.500000,1.390632 -13,0.500000,1.619642 -14,0.500000,1.745749 -15,0.500000,1.734899 -16,0.500000,1.144821 -17,0.500000,1.548540 -18,0.500000,1.452088 -19,0.500000,1.721123 diff --git a/retrain/resnet34/time_metrics.txt b/retrain/resnet34/time_metrics.txt deleted file mode 100644 index 9f06bbc..0000000 --- a/retrain/resnet34/time_metrics.txt +++ /dev/null @@ -1,9 +0,0 @@ -execution_time_sec -991.073163 -978.072894 -982.032442 -1081.642368 -985.512192 -979.091341 -978.852345 -978.963139 diff --git a/unlearning/CertifiedUnlearning.py b/unlearning/CertifiedUnlearning.py index f929f5b..43b047c 100644 --- a/unlearning/CertifiedUnlearning.py +++ b/unlearning/CertifiedUnlearning.py @@ -7,7 +7,6 @@ from unlearning.Strategy import Strategy from sets.Data import * # Single-Batch Certified Unlearning for DNNs - class CertifiedUnlearning(Strategy): """ Implements Certified Unlearning for non-convex DNNs (Zhang et al.). @@ -47,8 +46,7 @@ class CertifiedUnlearning(Strategy): params_list = [] for name, p in inner_model.named_parameters(): if p.requires_grad: - - # Append as a tuple so it can be unpacked as (name, param) + # so it can be unpacked as (name, param) params_list.append((name, p)) return params_list if named else [e[1] for e in params_list] @@ -133,7 +131,7 @@ class CertifiedUnlearning(Strategy): h_estimate[k].copy_(h_estimate[k] + g[k] - (h_s[k] / self.scale)) - # feed back on status + # feed back because this took agaes and it was not clear if things were working if global_step % step_interval == 0 and current_pct < 100: current_pct += 1 print(f"\rProgress: {current_pct}% done", end="", flush=True) @@ -167,7 +165,7 @@ class CertifiedUnlearning(Strategy): # Keep early low-level vision filters entirely pristine pass - # Move to the next calculated Hessian vector block only after a valid update step + # to the next calculated Hessian vector block delta_idx += 1 return model diff --git a/unlearning/LinearFiltration.py b/unlearning/LinearFiltration.py index 0a762ca..90ddec6 100644 --- a/unlearning/LinearFiltration.py +++ b/unlearning/LinearFiltration.py @@ -124,7 +124,6 @@ class LinearFiltration(Strategy): if self.A is None: self._compute_A( model = model, - #num_classes = num_classes, loader = forget_loader, device = device ) @@ -147,6 +146,9 @@ class LinearFiltration(Strategy): A_inv = torch.linalg.pinv(A_t, rcond=1e-2) # 11 W_temp = B_z @ A_inv @ W + # Linear (Normalisation)filtration is done here, + # next steps are added for safe automated evaluations + # with one class removed, we have a head W_temp for 19 classes. # but we have loaders for 20 classes for evaluation. so , # map K-1 W back to K x K. @@ -164,6 +166,7 @@ class LinearFiltration(Strategy): # neutralise forget bias if exists if clf.bias is not None: n_bias = clf.bias.data.clone() + # push it down to negative n_bias[forget_index] = -1e9 clf.bias.copy_(n_bias) diff --git a/unlearning/Retrain.py b/unlearning/Retrain.py index cd9e9e4..cfd18c9 100644 --- a/unlearning/Retrain.py +++ b/unlearning/Retrain.py @@ -42,7 +42,7 @@ class Retrain(Strategy): print(f"No naive model found for class {self.target_class_index} retraining a new one") - print(f">> Triggering Exact Unlearning Baseline (Retraining {self.arch.name} from pristine state)...") + print(f"Retraining {self.arch.name} from pristine state)...") inner_model = getattr(model, "model", model) if hasattr(inner_model, "fc"): total_classes = inner_model.fc.out_features diff --git a/unlearning/WF.py b/unlearning/WF.py deleted file mode 100644 index e3776d3..0000000 --- a/unlearning/WF.py +++ /dev/null @@ -1,107 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -from torch.utils.data import DataLoader -from unlearning.Strategy import Strategy -from .wf.WF_Net import WF_Net - -class WeightF(Strategy): - """ - Verbatim implementation of Poppi et al.'s WF-Net framework modified - for static, single-class unlearning extraction. - """ - def __init__(self, target_class_index: int, epochs: int = 10, lr: float = 0.2, gamma: float = 10.0): - super().__init__(target_class_index=target_class_index) - self.epochs = epochs - self.lr = lr - self.gamma = gamma - - def _optimise_filter(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader, device): - num_classes = model.fc.out_features - wf_model = WF_Net(original_model=model, num_classes=num_classes).to(device) - - # Optimize only the specific alpha masks - optimizer = optim.Adam([wf_model.alpha], lr=self.lr) - criterion = nn.CrossEntropyLoss() # Default reduction is 'mean' - - for epoch in range(self.epochs): - forget_iter = iter(forget_loader) - t_loss_r, t_loss_f = 0.0, 0.0 - steps = 0 - - for r_inputs, r_labels in retain_loader: - r_inputs, r_labels = r_inputs.to(device), r_labels.to(device) - - try: - f_inputs, _ = next(forget_iter) - except StopIteration: - forget_iter = iter(forget_loader) - f_inputs, _ = next(forget_iter) - f_inputs = f_inputs.to(device) - - optimizer.zero_grad() - - # Forward Pass - outputs_r = wf_model(r_inputs, target_unlearn_class=self.target_class_index) - outputs_f = wf_model(f_inputs, target_unlearn_class=self.target_class_index) - - # Retain Loss (Mean over batch) - loss_r = criterion(outputs_r, r_labels) - - # Forget Loss (Corrected to Mean over batch) - temperature = 1.0 - logits_f_scaled = outputs_f / temperature - - # Compute uniform target entropy per-sample, then average over the batch - log_probs_f = torch.log_softmax(logits_f_scaled, dim=-1) - uniform_target = torch.ones_like(logits_f_scaled) / num_classes - loss_f = -torch.sum(uniform_target * log_probs_f, dim=-1).mean() - - total_loss = loss_r + (self.gamma * loss_f) - total_loss.backward() - optimizer.step() - - t_loss_r += loss_r.item() - t_loss_f += loss_f.item() - steps += 1 - - print(f" Epoch {epoch+1}/{self.epochs} | Retain Loss: {t_loss_r/steps:.4f} | Forget Loss: {t_loss_f/steps:.4f}") - return wf_model - - def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module: - device = next(model.parameters()).device - model.eval() - - if hasattr(model, 'layer4') and len(model.layer4) > 1: - target_conv = model.layer4[1].conv2 - else: - raise AttributeError("Model architecture does not match expected ResNet-18 structure.") - - original_weights = target_conv.weight.data.clone().detach() - out_channels = original_weights.shape[0] - - # Freeze global network layers - for p in model.parameters(): - p.requires_grad = False - - wf_model = self._optimise_filter( - model, - forget_loader=forget_loader, - retain_loader=retain_loader, - device=device, - ) - - # --- PERMANENT BAKING STEP --- - with torch.no_grad(): - # Grab the alpha mask vector for the forgotten class and cast to 4D tensor shape - final_mask = torch.sigmoid(wf_model.alpha[self.target_class_index]).view(-1, 1, 1, 1) - - # Apply filter masking permanently back onto the base layer - target_conv.weight.copy_(original_weights * final_mask) - - # Unfreeze architecture parameters for evaluations downstream - for p in model.parameters(): - p.requires_grad = True - - print(f">> Permanently altered {out_channels} convolutional filters in layer4 via WF-Net.") - return model