new_contents: import os
  import sys
  import cv2
  import tensorflow as tf
  import numpy as np
  import pgnet
  
  image_path = "/data/PASCAL_2012_cropped/2010_003078.jpg"
  image = cv2.imread(image_path)
  image1 = cv2.imread(image_path)
  
  images_ = tf.placeholder(tf.float32, [None, image.shape[0], image.shape[1], 3])
  
  #model = pgnet.get(images_)
  
  kernels = tf.get_variable("kernels", [3, 3, 3, 3],
                            initializer=tf.random_normal_initializer())
  
  reshaped_input_tensor = tf.reshape(
      tf.pack([tf.cast(image, tf.float32), tf.cast(image1, tf.float32)]),
      [-1, image.shape[0], image.shape[1], image.shape[2]])
  print(reshaped_input_tensor)
  
  eq_op = pgnet.eq_conv(reshaped_input_tensor, 3, 3, 2)
  init = tf.initialize_all_variables()
  
  with tf.Session() as sess:
      sess.run(init)
      print(image.shape)
      print(kernels.get_shape())
      reshaped_input = reshaped_input_tensor.eval()
      cv2.imshow("original", tf.cast(reshaped_input[0], tf.uint8).eval())
      """
      out = sess.run(tf.nn.relu(tf.nn.conv2d(reshaped_input, kernels,
                                             [1, 1, 1, 1], 'VALID').eval()))
  
      print("Conv2d")
      print(out.shape)
      print(tf.shape(out))
  
  
      cv2.imshow("im",
                 cv2.normalize(out[0],
                               out[0],
                               alpha=0,
                               beta=2**24,
                               norm_type=cv2.NORM_MINMAX))
      #cv2.imshow("im",out[0])
  
      out_tensor = tf.nn.relu(tf.nn.atrous_conv2d(
          reshaped_input, kernels, 20,
          padding='VALID'))
  
  
      out = out_tensor.eval()
      print("Atrous conv2d")
      print(out.shape)
      """
      """cv2.imshow("im1",
                 cv2.normalize(out[0],
                               out[0],
                               alpha=0,
                               beta=2**24,
                               norm_type=cv2.NORM_MINMAX))"""
      #cv2.imshow("atrous", out[0])
  
      #padded_tensor = padder(reshaped_input_tensor, out_tensor)
      #padded = padded_tensor.eval()
      #print(padded)
  
      #cv2.imshow("impadded", cv2.normalize(padded[0], padded[0], alpha=0, beta=2**24, norm_type=cv2.NORM_MINMAX))
      #cv2.imshow("imp", tf.cast(padded[0], tf.uint8).eval())
  
      #always remember to cast to tf.uint8 if you want to display the result with opencv
      eq_out = sess.run(eq_op)
      cv2.imshow("eq conv", tf.cast(eq_out[0], tf.uint8).eval())
  
      cv2.waitKey(0)
  
subject: Add visual-test.py for ideas exploration