Source code for perceptron.utils.criteria.base

# Copyright 2019 Baidu Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
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"""Provide base classes that define what is adversarial."""

import sys
from abc import ABC
from abc import abstractmethod
from perceptron.utils.func import softmax
import numpy as np

[docs]class Criterion(ABC): """Base class for criteria that define what is adversarial. The :class:`Criterion` class represents a criterion used to determine if predictions for an image are adversarial given a reference label. It shoud be subclassed when implementing new criteria. Subclasses must implement is_adversarial. """
[docs] def name(self): """Returns a human readable name.""" return self.__class__.__name__
[docs] @abstractmethod def is_adversarial(self, predictions, ground_truth): """Decides if predictions for an image are adversarial given a reference ground truth. """ raise NotImplementedError
def __and__(self, other): return CombinedCriteria(self, other)
[docs]class CombinedCriteria(Criterion): """Meta criterion that combines several criteria into a new one. Parameters ---------- *criteria : variable length list of :class:`Criterion` instances List of sub-criteria that will be combined. Notes ----- This class uses lazy evaluation of the criteria in the order they are passed to the constructor. """ def __init__(self, *criteria): super(CombinedCriteria, self).__init__() self._criteria = criteria
[docs] def name(self): """ Concatenates the names of the given criteria in alphabetical order.""" names = ( for criterion in self._criteria) return '__'.join(sorted(names))
[docs] def is_adversarial(self, predictions, ground_truth): for criterion in self._criteria: if not criterion.is_adversarial(predictions, ground_truth): return False return True