64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
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import torch.nn as nn
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import time
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import os
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from pathlib import Path
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from torch.utils.data import DataLoader
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import Util
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class Strategy:
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"""Abstract base class for unlearning algorithms with automated, strategy-specific logging."""
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def __init__(self, target_class_index):
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# Dynamically set file name based on the class name (e.g., 'NormalizingLinearFiltration.txt')
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self.strategy_name = self.__class__.__name__
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self.target_class_index = target_class_index
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def set_target_class(self, target_class_index: int):
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"""Dynamically switch the unlearning target without retraining."""
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self.target_class_index = target_class_index
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def apply(self, model: nn.Module, dataset) -> nn.Module:
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log_file = Path(f"reports/{self.strategy_name}/{model.__class__.__name__}/time_metrics.txt")
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Util._initialize_log_file(log_file=log_file)
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"""
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Wraps the unlearning execution with automated timing and strategy-specific logging.
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DO NOT override this method in subclasses. Override _run instead.
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"""
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retain_loader, forget_loader = self._split_data(dataset)
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# record start time to evaluate efficiency
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start_time = time.perf_counter()
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# Execute core unlearning logic
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processed_model = self._run(model, forget_loader, retain_loader)
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end_time = time.perf_counter()
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execution_time = end_time - start_time
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# Log to the strategy's specific file
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'''
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Util.log_metric(
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log_file=log_file,
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execution_time=execution_time
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)
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'''
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print(f"[{self.strategy_name}] Completed in {execution_time:.6f} seconds. Saved to {log_file}")
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return processed_model
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def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
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"""Subclasses implement their core unlearning logic here."""
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raise NotImplementedError
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'''
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different strategies split data in to different partitions differently.
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So a strategy will implement its own and since this part is startegy specific.
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not all should compute it the same.
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'''
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def _split_data(self,dataset):
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pass |