<hr>
<h3 dir="auto">System information</h3>
<ul dir="auto">
<li><strong>Have I written custom code (as opposed to using a stock example script provided in TensorFlow)</strong>: No</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>: CentOS</li>
<li><strong>TensorFlow installed from (source or binary)</strong>: source</li>
<li><strong>TensorFlow version (use command below)</strong>: master</li>
<li><strong>Python version</strong>:  2.7</li>
<li><strong>Bazel version (if compiling from source)</strong>:</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>:</li>
<li><strong>CUDA/cuDNN version</strong>:</li>
<li><strong>GPU model and memory</strong>:</li>
<li><strong>Exact command to reproduce</strong>:</li>
</ul>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">I found MultiWorkerMirroredStrategy has been supported in Estimator. However, it seems that only works with in-graph mode. And the comments here shows it will support between-graph mode in the future.</p>
<p dir="auto"><code class="notranslate"># TODO(yuefengz): support between-graph replication.</code></p>
<p dir="auto">I want to know the progress of the between-graph development. Whether it is in progress or even has not started yet？ If it has been in progress, when does it release in master branch？</p>
<p dir="auto">Thank you.</p>
<p dir="auto">The tf.image.resize_images() seems to use a strange padding option, which one is not clear to me at the moment. I tried to replicate the bilinear interpolation with various padding options in for example skimage, but cant replicate the behaviour.</p>
<p dir="auto">It would be nice to be able to set the padding option used in tf.images.resize_images(), or document what is used at least.</p>
<p dir="auto">Example code for comparing the results of  tf.images.resize_images() and skimage transform:<br>
Looks like  tf.images.resize_images() does some weird unsymmetrical padding!?<br>
Using tensorflow 0.12.1:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="import tensorflow as tf
import tensorlayer as tl
import numpy as np
import skimage
from scipy.misc import imread, imresize, imsave

sess = tf.InteractiveSession()

#create simple test image
imsize = 3
xa, ya = np.ogrid[:imsize, :imsize]
img = np.repeat((xa + ya)[..., np.newaxis], 3, 2) / float(imsize + imsize)

x = tf.placeholder(tf.float32, [1, imsize, imsize, 3])
y = tf.image.resize_images(x,(imsize*3, imsize*3))

sess.run(tf.global_variables_initializer())

upsampled_tf_result = sess.run(y, feed_dict={x: [img]})
upsampled_skimage_result = skimage.transform.rescale(img,
                                     3,
                                     mode='symmetric',
                                     cval=0,
                                     order=1,
                                     preserve_range=False)

print(np.allclose(upsampled_tf_result, upsampled_skimage_result))

imsave('upsampled_tf_result.png', np.squeeze(upsampled_tf_result))
imsave('upsampled_skimage_result.png', upsampled_skimage_result)"><pre class="notranslate"><code class="notranslate">import tensorflow as tf
import tensorlayer as tl
import numpy as np
import skimage
from scipy.misc import imread, imresize, imsave

sess = tf.InteractiveSession()

#create simple test image
imsize = 3
xa, ya = np.ogrid[:imsize, :imsize]
img = np.repeat((xa + ya)[..., np.newaxis], 3, 2) / float(imsize + imsize)

x = tf.placeholder(tf.float32, [1, imsize, imsize, 3])
y = tf.image.resize_images(x,(imsize*3, imsize*3))

sess.run(tf.global_variables_initializer())

upsampled_tf_result = sess.run(y, feed_dict={x: [img]})
upsampled_skimage_result = skimage.transform.rescale(img,
                                     3,
                                     mode='symmetric',
                                     cval=0,
                                     order=1,
                                     preserve_range=False)

print(np.allclose(upsampled_tf_result, upsampled_skimage_result))

imsave('upsampled_tf_result.png', np.squeeze(upsampled_tf_result))
imsave('upsampled_skimage_result.png', upsampled_skimage_result)
</code></pre></div>