alternating convolution & upsampling in TensorFlow

tf_upsample.py

#!/bin/env python

# uses tf-slim from release 0.10.0

import tensorflow as tf
slim = tf.contrib.slim

batch = 13
in_height, in_width, in_channels = 7, 7, 512

x = tf.ones([batch, in_height, in_width, in_channels])
print(x.get_shape())
b,h,w,c = x.get_shape().as_list()
for i in range(3):
  c /= 2
  h *= 2
  w *= 2
  # kernel = tf.Variable(tf.truncated_normal([3, 3, in_channels, out_channels]))
  # conv = tf.nn.conv2d(x, kernel, [1, 1, 1, 1], padding='SAME')
  x = slim.conv2d(x,c,[3,3])
  x = tf.image.resize_images(x,h,w)
  print(x.get_shape())