Arbitrary Image Stylization 模块 Colab 示例错误
Arbitrary Image Stylization module Colab example error
在尝试 运行 Colab 上托管的 Tensorflow 团队的任意图像样式化示例代码时，我一直收到此错误。
这是代码。 （如 this notebook 中所见，块 5 给出了错误）。
from __future__ import absolute_import, division, print_function
import functools
import os
from matplotlib import gridspec
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
print("TF Version: ", tf.__version__)
print("TF-Hub version: ", hub.__version__)
print("Eager mode enabled: ", tf.executing_eagerly())
print("GPU available: ", tf.test.is_gpu_available())
# @title Define image loading and visualization functions { display-mode: "form" }
def crop_center(image):
"""Returns a cropped square image."""
shape = image.shape
new_shape = min(shape[1], shape[2])
offset_y = max(shape[1] - shape[2], 0) // 2
offset_x = max(shape[2] - shape[1], 0) // 2
image = tf.image.crop_to_bounding_box(
image, offset_y, offset_x, new_shape, new_shape)
return image
@functools.lru_cache(maxsize=None)
def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True):
"""Loads and preprocesses images."""
# Cache image file locally.
image_path = tf.keras.utils.get_file(os.path.basename(image_url)[-128:], image_url)
# Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1].
img = plt.imread(image_path).astype(np.float32)[np.newaxis, ...]
if img.max() > 1.0:
img = img / 255.
if len(img.shape) == 3:
img = tf.stack([img, img, img], axis=-1)
img = crop_center(img)
img = tf.image.resize(img, image_size, preserve_aspect_ratio=True)
return img
def show_n(images, titles=('',)):
n = len(images)
image_sizes = [image.shape[1] for image in images]
w = (image_sizes[0] * 6) // 320
plt.figure(figsize=(w * n, w))
gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)
for i in range(n):
plt.subplot(gs[i])
plt.imshow(images[i][0], aspect='equal')
plt.axis('off')
plt.title(titles[i] if len(titles) > i else '')
plt.show()
# @title Load example images { display-mode: "form" }
content_image_url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Golden_Gate_Bridge_from_Battery_Spencer.jpg/640px-Golden_Gate_Bridge_from_Battery_Spencer.jpg' # @param {type:"string"}
style_image_url = 'https://upload.wikimedia.org/wikipedia/commons/0/0a/The_Great_Wave_off_Kanagawa.jpg' # @param {type:"string"}
output_image_size = 384 # @param {type:"integer"}
# The content image size can be arbitrary.
content_img_size = (output_image_size, output_image_size)
# The style prediction model was trained with image size 256 and it's the
# recommended image size for the style image (though, other sizes work as
# well but will lead to different results).
style_img_size = (256, 256) # Recommended to keep it at 256.
content_image = load_image(content_image_url, content_img_size)
style_image = load_image(style_image_url, style_img_size)
style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
show_n([content_image, style_image], ['Content image', 'Style image'])
这是错误信息：
TypeError Traceback (most recent call last)
<ipython-input-8-b21290c301e4> in <module>()
14 style_image = load_image(style_image_url, style_img_size)
15 style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
---> 16 show_n([content_image, style_image], ['Content image', 'Style image'])
3 frames
<ipython-input-3-a1ddf5894992> in show_n(images, titles)
29 image_sizes = [image.shape[1] for image in images]
30 w = (image_sizes[0] * 6) // 320
---> 31 plt.figure(figsize=(w * n, w))
32 gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)
33 for i in range(n):
/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py in figure(num, figsize, dpi, facecolor, edgecolor, frameon, FigureClass, clear, **kwargs)
544 frameon=frameon,
545 FigureClass=FigureClass,
--> 546 **kwargs)
547
548 if figLabel:
/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py in new_figure_manager(cls, num, *args, **kwargs)
3322 from matplotlib.figure import Figure
3323 fig_cls = kwargs.pop('FigureClass', Figure)
-> 3324 fig = fig_cls(*args, **kwargs)
3325 return cls.new_figure_manager_given_figure(num, fig)
3326
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py in __init__(self, figsize, dpi, facecolor, edgecolor, linewidth, frameon, subplotpars, tight_layout, constrained_layout)
346 frameon = rcParams['figure.frameon']
347
--> 348 if not np.isfinite(figsize).all() or (np.array(figsize) <= 0).any():
349 raise ValueError('figure size must be positive finite not '
350 f'{figsize}')
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
谁能解释一下问题是什么以及解决方法？提前致谢。
使用您在 Google 托管的 Colab 中提供的代码，您也已链接。
我只是在 selects Tensorflow 版本 2.x.
笔记本的开头添加了一行代码
%tensorflow_version 2.x
版本 2.x 表示 Google Colab 将 select 最新稳定的 Tensorflow 版本可用。
在尝试 运行 Colab 上托管的 Tensorflow 团队的任意图像样式化示例代码时，我一直收到此错误。
这是代码。 （如 this notebook 中所见，块 5 给出了错误）。
from __future__ import absolute_import, division, print_function
import functools
import os
from matplotlib import gridspec
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
print("TF Version: ", tf.__version__)
print("TF-Hub version: ", hub.__version__)
print("Eager mode enabled: ", tf.executing_eagerly())
print("GPU available: ", tf.test.is_gpu_available())
# @title Define image loading and visualization functions { display-mode: "form" }
def crop_center(image):
"""Returns a cropped square image."""
shape = image.shape
new_shape = min(shape[1], shape[2])
offset_y = max(shape[1] - shape[2], 0) // 2
offset_x = max(shape[2] - shape[1], 0) // 2
image = tf.image.crop_to_bounding_box(
image, offset_y, offset_x, new_shape, new_shape)
return image
@functools.lru_cache(maxsize=None)
def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True):
"""Loads and preprocesses images."""
# Cache image file locally.
image_path = tf.keras.utils.get_file(os.path.basename(image_url)[-128:], image_url)
# Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1].
img = plt.imread(image_path).astype(np.float32)[np.newaxis, ...]
if img.max() > 1.0:
img = img / 255.
if len(img.shape) == 3:
img = tf.stack([img, img, img], axis=-1)
img = crop_center(img)
img = tf.image.resize(img, image_size, preserve_aspect_ratio=True)
return img
def show_n(images, titles=('',)):
n = len(images)
image_sizes = [image.shape[1] for image in images]
w = (image_sizes[0] * 6) // 320
plt.figure(figsize=(w * n, w))
gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)
for i in range(n):
plt.subplot(gs[i])
plt.imshow(images[i][0], aspect='equal')
plt.axis('off')
plt.title(titles[i] if len(titles) > i else '')
plt.show()
# @title Load example images { display-mode: "form" }
content_image_url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Golden_Gate_Bridge_from_Battery_Spencer.jpg/640px-Golden_Gate_Bridge_from_Battery_Spencer.jpg' # @param {type:"string"}
style_image_url = 'https://upload.wikimedia.org/wikipedia/commons/0/0a/The_Great_Wave_off_Kanagawa.jpg' # @param {type:"string"}
output_image_size = 384 # @param {type:"integer"}
# The content image size can be arbitrary.
content_img_size = (output_image_size, output_image_size)
# The style prediction model was trained with image size 256 and it's the
# recommended image size for the style image (though, other sizes work as
# well but will lead to different results).
style_img_size = (256, 256) # Recommended to keep it at 256.
content_image = load_image(content_image_url, content_img_size)
style_image = load_image(style_image_url, style_img_size)
style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
show_n([content_image, style_image], ['Content image', 'Style image'])
这是错误信息：
TypeError Traceback (most recent call last)
<ipython-input-8-b21290c301e4> in <module>()
14 style_image = load_image(style_image_url, style_img_size)
15 style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
---> 16 show_n([content_image, style_image], ['Content image', 'Style image'])
3 frames
<ipython-input-3-a1ddf5894992> in show_n(images, titles)
29 image_sizes = [image.shape[1] for image in images]
30 w = (image_sizes[0] * 6) // 320
---> 31 plt.figure(figsize=(w * n, w))
32 gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)
33 for i in range(n):
/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py in figure(num, figsize, dpi, facecolor, edgecolor, frameon, FigureClass, clear, **kwargs)
544 frameon=frameon,
545 FigureClass=FigureClass,
--> 546 **kwargs)
547
548 if figLabel:
/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py in new_figure_manager(cls, num, *args, **kwargs)
3322 from matplotlib.figure import Figure
3323 fig_cls = kwargs.pop('FigureClass', Figure)
-> 3324 fig = fig_cls(*args, **kwargs)
3325 return cls.new_figure_manager_given_figure(num, fig)
3326
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py in __init__(self, figsize, dpi, facecolor, edgecolor, linewidth, frameon, subplotpars, tight_layout, constrained_layout)
346 frameon = rcParams['figure.frameon']
347
--> 348 if not np.isfinite(figsize).all() or (np.array(figsize) <= 0).any():
349 raise ValueError('figure size must be positive finite not '
350 f'{figsize}')
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
谁能解释一下问题是什么以及解决方法？提前致谢。
使用您在 Google 托管的 Colab 中提供的代码，您也已链接。
我只是在 selects Tensorflow 版本 2.x.
%tensorflow_version 2.x
版本 2.x 表示 Google Colab 将 select 最新稳定的 Tensorflow 版本可用。