text: import numpy as np
  
  if __name__ == "__main__":
  	import tensorflow as tf
  	from .ROIPoolingWrapper import *
  
  	with tf.Session() as sess:
  		img = np.zeros((1,8,8, 9), np.float32)
  		boxes = tf.constant([[0,0,2*16,5*16]], dtype=tf.float32)
  		print(boxes.get_shape().as_list())
  
  		yOffset=0
  		xOffset=0
  		chOffset=0
  		img[0,yOffset+0:yOffset+1,xOffset+0:xOffset+1,chOffset+0:chOffset+1]=1;
  		#img[:,:,:,:]=1
  		p = tf.placeholder(tf.float32, shape=img.shape)
  
  		np.set_printoptions(threshold=5000, linewidth=150)
  
  		pooled=positionSensitiveRoiPooling(p, boxes)
  		pooled=tf.Print(pooled,[tf.shape(pooled)],"pooled shape", summarize=100)
  		print(sess.run(pooled, feed_dict={p: img}))
  
  
  		loss = tf.reduce_sum(pooled)
  
  		g = tf.gradients(loss, p)
  
  		print(img)
  		print(sess.run(g, feed_dict={p: img})[0])
  		print(sess.run(g, feed_dict={p: img})[0][:,:,:,1])
  
  
  