# Copyright 2019 Baidu Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Metric that tests models against contrast reductions."""
import numpy as np
from collections import Iterable
from tqdm import tqdm
from .base import Metric
from .base import call_decorator
"""Metric that tests models against brightness variations."""
def __call__(self, adv, annotation=None, unpack=True, abort_early=True,
"""Reduces the contrast of the image until it is misclassified.
adv : `numpy.ndarray`
The original, unperturbed input as a `numpy.ndarray`.
annotation : int
The reference label of the original input.
unpack : bool
If true, returns the adversarial input, otherwise returns
the Adversarial object.
abort_early : bool
If true, returns when got first adversarial, otherwise
returns when all the iterations are finished.
threshold : float
Upper bound for contrast factor
epsilons : int or Iterable[float]
Either Iterable of contrast levels or number of contrast
levels between 1 and 0 that should be tried. Epsilons are
one minus the contrast level.
a = adv
image = a.original_image
min_, max_ = a.bounds()
target = (max_ + min_) / 2
if not isinstance(epsilons, Iterable):
epsilons = np.linspace(0, threshold, num=epsilons + 1)[1:]
for epsilon in tqdm(epsilons):
perturbed = (1 - epsilon) * image + epsilon * target
_, is_adversarial = a.predictions(perturbed)
if is_adversarial and abort_early: