8137716344

Committed 22 Jan 2024 10:53PM UTC coverage: 98.47%. Remained the same

Build # 8137716344

Build Type

push

github

Committed by

web-flow

Commit Message

Bump pillow from 10.1.0 to 10.2.0 in /tests (#718)

Bumps [pillow](https://github.com/python-pillow/Pillow) from 10.1.0 to 10.2.0.
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](https://github.com/python-pillow/Pillow/compare/10.1.0...10.2.0)

---
updated-dependencies:
- dependency-name: pillow
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

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3475 of 3529 relevant lines covered (98.47%)

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100.0

/foolbox/gradient_estimators.py

from typing import Callable, Tuple, Type
import eagerpy as ep
from .types import BoundsInput, Bounds
from .attacks.base import Attack


def evolutionary_strategies_gradient_estimator(
    AttackCls: Type[Attack],
    *,
    samples: int,
    sigma: float,
    bounds: BoundsInput,
    clip: bool,
) -> Type[Attack]:

    if not hasattr(AttackCls, "value_and_grad"):
        raise ValueError(
            "This attack does not support gradient estimators."
        )  # pragma: no cover

    bounds = Bounds(*bounds)

    class GradientEstimator(AttackCls):  # type: ignore
        def value_and_grad(
            self,
            loss_fn: Callable[[ep.Tensor], ep.Tensor],
            x: ep.Tensor,
        ) -> Tuple[ep.Tensor, ep.Tensor]:
            value = loss_fn(x)

            gradient = ep.zeros_like(x)
            for k in range(samples // 2):
                noise = ep.normal(x, shape=x.shape)

                pos_theta = x + sigma * noise
                neg_theta = x - sigma * noise

                if clip:
                    pos_theta = pos_theta.clip(*bounds)
                    neg_theta = neg_theta.clip(*bounds)

                pos_loss = loss_fn(pos_theta)
                neg_loss = loss_fn(neg_theta)

                gradient += (pos_loss - neg_loss) * noise

            gradient /= 2 * sigma * 2 * samples

            return value, gradient

    GradientEstimator.__name__ = AttackCls.__name__ + "WithESGradientEstimator"
    GradientEstimator.__qualname__ = AttackCls.__qualname__ + "WithESGradientEstimator"
    return GradientEstimator


es_gradient_estimator = evolutionary_strategies_gradient_estimator

1	from typing import Callable, Tuple, Type	10✔
2	import eagerpy as ep	10✔
3	from .types import BoundsInput, Bounds	10✔
4	from .attacks.base import Attack	10✔
5
6
7	def evolutionary_strategies_gradient_estimator(	10✔
8	AttackCls: Type[Attack],
9	*,
10	samples: int,
11	sigma: float,
12	bounds: BoundsInput,
13	clip: bool,
14	) -> Type[Attack]:
15
16	if not hasattr(AttackCls, "value_and_grad"):	10✔
17	raise ValueError(
18	"This attack does not support gradient estimators."
19	) # pragma: no cover
20
21	bounds = Bounds(*bounds)	10✔
22
23	class GradientEstimator(AttackCls): # type: ignore	10✔
24	def value_and_grad(	10✔
25	self,
26	loss_fn: Callable[[ep.Tensor], ep.Tensor],
27	x: ep.Tensor,
28	) -> Tuple[ep.Tensor, ep.Tensor]:
29	value = loss_fn(x)	8✔
30
31	gradient = ep.zeros_like(x)	8✔
32	for k in range(samples // 2):	8✔
33	noise = ep.normal(x, shape=x.shape)	8✔
34
35	pos_theta = x + sigma * noise	8✔
36	neg_theta = x - sigma * noise	8✔
37
38	if clip:	8✔
39	pos_theta = pos_theta.clip(*bounds)	8✔
40	neg_theta = neg_theta.clip(*bounds)	8✔
41
42	pos_loss = loss_fn(pos_theta)	8✔
43	neg_loss = loss_fn(neg_theta)	8✔
44
45	gradient += (pos_loss - neg_loss) * noise	8✔
46
47	gradient /= 2 * sigma * 2 * samples	8✔
48
49	return value, gradient	8✔
50
51	GradientEstimator.__name__ = AttackCls.__name__ + "WithESGradientEstimator"	10✔
52	GradientEstimator.__qualname__ = AttackCls.__qualname__ + "WithESGradientEstimator"	10✔
53	return GradientEstimator	10✔
54
55
56	es_gradient_estimator = evolutionary_strategies_gradient_estimator	10✔

bethgelab / foolbox / 8137716344

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