8139141456

Committed 04 Mar 2024 11:03AM UTC coverage: 37.923% (-60.6%) from 98.477%

Build # 8139141456

Build Type

Pull #722

github

Committed by

web-flow

Commit Message

Merge 5663238db into 17e0e9b31

Pull Request Pull Request #722: Fix guide compilation

Run Details

1344 of 3544 relevant lines covered (37.92%)

0.38 hits per line

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8.7

/foolbox/plot.py

from typing import Tuple, Any, Optional
import numpy as np
import eagerpy as ep


def images(
    images: Any,
    *,
    n: Optional[int] = None,
    data_format: Optional[str] = None,
    bounds: Tuple[float, float] = (0, 1),
    ncols: Optional[int] = None,
    nrows: Optional[int] = None,
    figsize: Optional[Tuple[float, float]] = None,
    scale: float = 1,
    **kwargs: Any,
) -> None:
    import matplotlib.pyplot as plt

    x: ep.Tensor = ep.astensor(images)
    if x.ndim != 4:
        raise ValueError(
            "expected images to have four dimensions: (N, C, H, W) or (N, H, W, C)"
        )
    if n is not None:
        x = x[:n]
    if data_format is None:
        channels_first = x.shape[1] == 1 or x.shape[1] == 3
        channels_last = x.shape[-1] == 1 or x.shape[-1] == 3
        if channels_first == channels_last:
            raise ValueError("data_format ambigous, please specify it explicitly")
    else:
        channels_first = data_format == "channels_first"
        channels_last = data_format == "channels_last"
        if not channels_first and not channels_last:
            raise ValueError(
                "expected data_format to be 'channels_first' or 'channels_last'"
            )
    assert channels_first != channels_last
    x_np = x.numpy()
    if channels_first:
        x_np = np.transpose(x_np, axes=(0, 2, 3, 1))
    min_, max_ = bounds
    x_np = (x_np - min_) / (max_ - min_)

    if nrows is None and ncols is None:
        nrows = 1
    if ncols is None:
        assert nrows is not None
        ncols = (len(x_np) + nrows - 1) // nrows
    elif nrows is None:
        nrows = (len(x_np) + ncols - 1) // ncols
    if figsize is None:
        figsize = (ncols * scale, nrows * scale)
    fig, axes = plt.subplots(
        ncols=ncols,
        nrows=nrows,
        figsize=figsize,
        squeeze=False,
        constrained_layout=True,
        **kwargs,
    )

    for row in range(nrows):
        for col in range(ncols):
            ax = axes[row][col]
            ax.set_xticks([])
            ax.set_yticks([])
            ax.axis("off")
            i = row * ncols + col
            if i < len(x):
                if x_np.shape[-1] == 1:
                    ax.imshow(x_np[i][:, :, 0])
                else:
                    ax.imshow(x_np[i])

1	from typing import Tuple, Any, Optional	1✔
2	import numpy as np	1✔
3	import eagerpy as ep	1✔
4
5
6	def images(	1✔
7	images: Any,
8	*,
9	n: Optional[int] = None,
10	data_format: Optional[str] = None,
11	bounds: Tuple[float, float] = (0, 1),
12	ncols: Optional[int] = None,
13	nrows: Optional[int] = None,
14	figsize: Optional[Tuple[float, float]] = None,
15	scale: float = 1,
16	**kwargs: Any,
17	) -> None:
18	import matplotlib.pyplot as plt	×
19
20	x: ep.Tensor = ep.astensor(images)	×
21	if x.ndim != 4:	×
22	raise ValueError(	×
23	"expected images to have four dimensions: (N, C, H, W) or (N, H, W, C)"
24	)
25	if n is not None:	×
26	x = x[:n]	×
27	if data_format is None:	×
28	channels_first = x.shape[1] == 1 or x.shape[1] == 3	×
29	channels_last = x.shape[-1] == 1 or x.shape[-1] == 3	×
30	if channels_first == channels_last:	×
31	raise ValueError("data_format ambigous, please specify it explicitly")	×
32	else:
33	channels_first = data_format == "channels_first"	×
34	channels_last = data_format == "channels_last"	×
35	if not channels_first and not channels_last:	×
36	raise ValueError(	×
37	"expected data_format to be 'channels_first' or 'channels_last'"
38	)
39	assert channels_first != channels_last	×
40	x_np = x.numpy()	×
41	if channels_first:	×
42	x_np = np.transpose(x_np, axes=(0, 2, 3, 1))	×
43	min_, max_ = bounds	×
44	x_np = (x_np - min_) / (max_ - min_)	×
45
46	if nrows is None and ncols is None:	×
47	nrows = 1	×
48	if ncols is None:	×
49	assert nrows is not None	×
50	ncols = (len(x_np) + nrows - 1) // nrows	×
51	elif nrows is None:	×
52	nrows = (len(x_np) + ncols - 1) // ncols	×
53	if figsize is None:	×
54	figsize = (ncols * scale, nrows * scale)	×
55	fig, axes = plt.subplots(	×
56	ncols=ncols,
57	nrows=nrows,
58	figsize=figsize,
59	squeeze=False,
60	constrained_layout=True,
61	**kwargs,
62	)
63
64	for row in range(nrows):	×
65	for col in range(ncols):	×
66	ax = axes[row][col]	×
67	ax.set_xticks([])	×
68	ax.set_yticks([])	×
69	ax.axis("off")	×
70	i = row * ncols + col	×
71	if i < len(x):	×
72	if x_np.shape[-1] == 1:	×
73	ax.imshow(x_np[i][:, :, 0])	×
74	else:
75	ax.imshow(x_np[i])	×

bethgelab / foolbox / 8139141456

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