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>

Run Details

3475 of 3529 relevant lines covered (98.47%)

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100.0

/foolbox/tensorboard.py

"""Internal module for attacks that support logging to TensorBoard"""
from typing import Union, Callable, TypeVar, Any, cast
from typing_extensions import Literal
import eagerpy as ep
from functools import wraps


FuncType = Callable[..., None]
F = TypeVar("F", bound=FuncType)


def maybenoop(f: F) -> F:
    @wraps(f)
    def wrapper(self: "TensorBoard", *args: Any, **kwds: Any) -> None:
        if self.writer is None:
            return
        return f(self, *args, **kwds)

    return cast(F, wrapper)


class TensorBoard:
    """A custom TensorBoard class that accepts EagerPy tensors and that
    can be disabled by turned into a noop by passing logdir=False.

    This makes it possible to add tensorboard logging without any if
    statements and without any computational overhead if it's disabled.
    """

    def __init__(self, logdir: Union[Literal[False], None, str]):
        if logdir or (logdir is None):
            from tensorboardX import SummaryWriter

            self.writer = SummaryWriter(logdir=logdir)
        else:
            self.writer = None

    @maybenoop
    def close(self) -> None:
        self.writer.close()

    @maybenoop
    def scalar(self, tag: str, x: Union[int, float], step: int) -> None:
        self.writer.add_scalar(tag, x, step)

    @maybenoop
    def mean(self, tag: str, x: ep.Tensor, step: int) -> None:
        self.writer.add_scalar(tag, x.mean(axis=0).item(), step)

    @maybenoop
    def probability(self, tag: str, x: ep.Tensor, step: int) -> None:
        self.writer.add_scalar(tag, x.float32().mean(axis=0).item(), step)

    @maybenoop
    def conditional_mean(
        self, tag: str, x: ep.Tensor, cond: ep.Tensor, step: int
    ) -> None:
        cond_ = cond.numpy()
        if ~cond_.any():
            return
        x_ = x.numpy()
        x_ = x_[cond_]
        self.writer.add_scalar(tag, x_.mean(axis=0).item(), step)

    @maybenoop
    def probability_ratio(
        self, tag: str, x: ep.Tensor, y: ep.Tensor, step: int
    ) -> None:
        x_ = x.float32().mean(axis=0).item()
        y_ = y.float32().mean(axis=0).item()
        if y_ == 0:
            return
        self.writer.add_scalar(tag, x_ / y_, step)

    @maybenoop
    def histogram(
        self, tag: str, x: ep.Tensor, step: int, *, first: bool = True
    ) -> None:
        x = x.numpy()
        self.writer.add_histogram(tag, x, step)
        if first:
            self.writer.add_scalar(tag + "/0", x[0].item(), step)

1	"""Internal module for attacks that support logging to TensorBoard"""
2	from typing import Union, Callable, TypeVar, Any, cast	10✔
3	from typing_extensions import Literal	10✔
4	import eagerpy as ep	10✔
5	from functools import wraps	10✔
6
7
8	FuncType = Callable[..., None]	10✔
9	F = TypeVar("F", bound=FuncType)	10✔
10
11
12	def maybenoop(f: F) -> F:	10✔
13	@wraps(f)	10✔
14	def wrapper(self: "TensorBoard", args: Any, *kwds: Any) -> None:	10✔
15	if self.writer is None:	8✔
16	return	8✔
17	return f(self, args, *kwds)	8✔
18
19	return cast(F, wrapper)	10✔
20
21
22	class TensorBoard:	10✔
23	"""A custom TensorBoard class that accepts EagerPy tensors and that
24	can be disabled by turned into a noop by passing logdir=False.
25
26	This makes it possible to add tensorboard logging without any if
27	statements and without any computational overhead if it's disabled.
28	"""
29
30	def __init__(self, logdir: Union[Literal[False], None, str]):	10✔
31	if logdir or (logdir is None):	8✔
32	from tensorboardX import SummaryWriter	8✔
33
34	self.writer = SummaryWriter(logdir=logdir)	8✔
35	else:
36	self.writer = None	8✔
37
38	@maybenoop	10✔
39	def close(self) -> None:	10✔
40	self.writer.close()	8✔
41
42	@maybenoop	10✔
43	def scalar(self, tag: str, x: Union[int, float], step: int) -> None:	10✔
44	self.writer.add_scalar(tag, x, step)	8✔
45
46	@maybenoop	10✔
47	def mean(self, tag: str, x: ep.Tensor, step: int) -> None:	10✔
48	self.writer.add_scalar(tag, x.mean(axis=0).item(), step)	8✔
49
50	@maybenoop	10✔
51	def probability(self, tag: str, x: ep.Tensor, step: int) -> None:	10✔
52	self.writer.add_scalar(tag, x.float32().mean(axis=0).item(), step)	8✔
53
54	@maybenoop	10✔
55	def conditional_mean(	10✔
56	self, tag: str, x: ep.Tensor, cond: ep.Tensor, step: int
57	) -> None:
58	cond_ = cond.numpy()	8✔
59	if ~cond_.any():	8✔
60	return	8✔
61	x_ = x.numpy()	8✔
62	x_ = x_[cond_]	8✔
63	self.writer.add_scalar(tag, x_.mean(axis=0).item(), step)	8✔
64
65	@maybenoop	10✔
66	def probability_ratio(	10✔
67	self, tag: str, x: ep.Tensor, y: ep.Tensor, step: int
68	) -> None:
69	x_ = x.float32().mean(axis=0).item()	8✔
70	y_ = y.float32().mean(axis=0).item()	8✔
71	if y_ == 0:	8✔
72	return	8✔
73	self.writer.add_scalar(tag, x_ / y_, step)	8✔
74
75	@maybenoop	10✔
76	def histogram(	10✔
77	self, tag: str, x: ep.Tensor, step: int, *, first: bool = True
78	) -> None:
79	x = x.numpy()	8✔
80	self.writer.add_histogram(tag, x, step)	8✔
81	if first:	8✔
82	self.writer.add_scalar(tag + "/0", x[0].item(), step)	8✔

bethgelab / foolbox / 8137716344

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