I am trying to run this on Amazon Sagemaker but I am getting this error while when I try to run it on my local machine, it works very fine.
this is my code:
import tensorflow as tf
import IPython.display as display
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (12,12)
mpl.rcParams['axes.grid'] = False
import numpy as np
import PIL.Image
import time
import functools
def tensor_to_image(tensor):
tensor = tensor*255
tensor = np.array(tensor, dtype=np.uint8)
if np.ndim(tensor)>3:
assert tensor.shape[0] == 1
tensor = tensor[0]
return PIL.Image.fromarray(tensor)
content_path = tf.keras.utils.get_file('YellowLabradorLooking_nw4.jpg', 'https://example.com/IMG_20200216_163015.jpg')
style_path = tf.keras.utils.get_file('kandinsky3.jpg','https://example.com/download+(2).png')
def load_img(path_to_img):
max_dim = 512
img = tf.io.read_file(path_to_img)
img = tf.image.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long_dim = max(shape)
scale = max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape)
img = img[tf.newaxis, :]
return img
def imshow(image, title=None):
if len(image.shape) > 3:
image = tf.squeeze(image, axis=0)
plt.imshow(image)
if title:
plt.title(title)
content_image = load_img(content_path)
style_image = load_img(style_path)
plt.subplot(1, 2, 1)
imshow(content_image, 'Content Image')
plt.subplot(1, 2, 2)
imshow(style_image, 'Style Image')
import tensorflow_hub as hub
hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]
tensor_to_image(stylized_image)
file_name = 'stylized-image5.png'
tensor_to_image(stylized_image).save(file_name)
This is the exact error I get:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-24-c47a4db4880c> in <module>()
53
54
---> 55 content_image = load_img(content_path)
56 style_image = load_img(style_path)
57
in load_img(path_to_img)
34
35 shape = tf.cast(tf.shape(img)[:-1], tf.float32)
---> 36 long_dim = max(shape)
37 scale = max_dim / long_dim
38
~/anaconda3/envs/amazonei_tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in iter (self)
475 if not context.executing_eagerly():
476 raise TypeError(
--> 477 "Tensor objects are only iterable when eager execution is "
478 "enabled. To iterate over this tensor use tf.map_fn.")
479 shape = self._shape_tuple()
TypeError: Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.
Your error is being raised in this function load_img
:
def load_img(path_to_img):
max_dim = 512
img = tf.io.read_file(path_to_img)
img = tf.image.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long_dim = max(shape)
scale = max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape)
img = img[tf.newaxis, :]
return img
Specifically, this line:
long_dim = max(shape)
You are passing a tensor to the built-in Python max function in graph execution mode. You can only iterate through tensors in eager-execution mode. You probably want to use tf.reduce_max instead:
long_dim = tf.reduce_max(shape)
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