43 lines
1.6 KiB
Python
43 lines
1.6 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):
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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.log_file = Path(f"reports/{self.strategy_name}/metrics.txt")
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Util._initialize_log_file(log_file= self.log_file)
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def apply(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
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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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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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Util.log_metric(
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log_file=self.log_file,
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execution_time=execution_time
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)
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print(f"[{self.strategy_name}] Completed in {execution_time:.6f} seconds. Saved to {self.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 |