from abc import ABC, abstractmethod import torch import torch.nn as nn import torch.optim as optim import time import numpy as np from sklearn.metrics import classification_report from pathlib import Path #from unlearning.Strategy import Strategy import copy from torch.optim.lr_scheduler import CosineAnnealingLR import Util class Model(ABC): # need to add a weight decay here def __init__(self, device, size): self.device = device self.size = size self.model = self.get().to(self.device) @abstractmethod def get(self): pass ''' Have to have a new param here as mode, for example it would be base, or retrain param mode = "base" or "retrain" that way I can save time it takes to train and retrain. file would be solved with Util functions log_file = Path(f"reports/{mode}/{self.__class__.__name__}/time_metrics.txt") Util._initialize_log_file(log_file): strt = time.perf_counter() end = time.perf_counter() and then save logs execution_time = end -strt Util.log_metric(log_file, execution_time: float): ''' def train(self, epochs, loader, rate, mode = "retrain"): criterion = nn.CrossEntropyLoss() 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) 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() 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) if self.device.type == 'cuda': torch.cuda.synchronize() print(f"Training completed in: {time.time() - start_time:.2f}s") def save(self, filename=None): save_dir = Path("trained_models") save_dir.mkdir(parents=True, exist_ok=True) # Filename (Default to class name if not provided) if filename is None: filename = f"{self.__class__.__name__.lower()}.pth" if not filename.endswith('.pth'): filename += '.pth' save_path = save_dir / filename torch.save(self.model.state_dict(), save_path) print(f'Model saved to {save_path}') def load(self, arch): file_path = Path("trained_models") / f'{arch.name.lower()}.pth' # does file exist if not file_path.exists(): raise FileNotFoundError(f'No checkpoint found at: {file_path}') # Load the weights state_dict = torch.load(file_path, map_location=self.device, weights_only=True) self.model.load_state_dict(state_dict) 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__}...") start_time = time.time() # Delegate the actual algorithmic weight/logit manipulation to the strategy strategy.apply(self.model, forget_loader, retain_loader) elapsed_time = time.time() - start_time print(f"{strategy.__class__.__name__} completed in {elapsed_time:.4f} seconds.") return elapsed_time def evaluate(self, loader, mode="eval"): """ Evaluates the model, prints terminal reports, and routes metrics to a file logger based on the current context mode. """ self.model.eval() all_preds, all_labels = [], [] print(f"\nEvaluating Domain: [{mode}]...") with torch.no_grad(): for inputs, labels in loader: inputs, labels = inputs.to(self.device), labels.to(self.device) outputs = self.model(inputs) _, predicted = torch.max(outputs, 1) all_preds.extend(predicted.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) # Extract only the active classes evaluated in this loader slice classes = sorted(list(set(all_labels))) accuracy = 100 * (np.array(all_preds) == np.array(all_labels)).sum() / len(all_labels) 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)) # 2. Extract structured dictionary metrics report_dict = classification_report( all_labels, all_preds, labels=classes, output_dict=True, zero_division=0 ) # 3. Delegate file tracking to isolated helper method #self._log_to_csv(mode, accuracy,report_dict) return accuracy, report_dict # factory @staticmethod def create(arch, device, size): print(f'>> MODEL ARCHITECTURE >> {arch.name}.') match arch: # ResNet18 case Architecture.RESNET18: from architectures.ResNet18 import ResNet18 return ResNet18(device, size) # ResNet34 case Architecture.RESNET34: from architectures.ResNet34 import ResNet34 return ResNet34(device, size) # ResNet50 case Architecture.RESNET50: from architectures.ResNet50 import ResNet50 return ResNet50(device, size) # INCEPTION case Architecture.INCEPTION: from architectures.Inception import Inception return Inception(device, size) # DENSENET121 case Architecture.DENSENET121: from architectures.DenseNet121 import DenseNet121 return DenseNet121(device, size) # googleNet case Architecture.GOOGLENET: from architectures.GoogleNet import GoogleNet return GoogleNet(device, size) # EfficientNet case Architecture.EFFICIENTNET: from architectures.EfficentNet import EfficientNet return EfficientNet(device, size) #ShuffleNet case Architecture.SHUFFLENET: from architectures.ShuffleNet import ShuffleNet return ShuffleNet(device, size) # wide ResNet case Architecture.WIDE_RESNET: from architectures.WideResNet import WideResNet return WideResNet(device, size) case _: raise ValueError(f"Unknown model: {arch}") # model architectures from enum import Enum, auto class Architecture(Enum): RESNET18 = auto() RESNET50 = auto() RESNET34 = auto() INCEPTION = auto() DENSENET121 = auto() GOOGLENET = auto() EFFICIENTNET = auto() SHUFFLENET = auto() WIDE_RESNET = auto()