Произвольная ошибка модуля Colab в произвольном изображении - PullRequest
1 голос
/ 23 марта 2020

Я получаю эту ошибку, когда пытаюсь запустить пример кода для произвольной стилизации изображения командой Tensorflow, размещенной на Colab.

Это код. (Как видно в этой записной книжке , блок 5 выдает ошибку).

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'])

Это сообщение об ошибке:

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''

Может кто-нибудь объяснить, в чем заключается проблема и способы ее устранения? Заранее спасибо.

1 Ответ

0 голосов
/ 25 марта 2020

Используя код, который вы предоставили в размещенном в Google Colab , который вы также связали.
Я только добавил одну строку кода в начале блокнота, которая выбирает Версия Tensorflow 2.x .

%tensorflow_version 2.x

Версия 2.x означает, что Google Colab выберет последний стабильный Tensorflow доступна версия .

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