text: """ Compute Chamfer's Distance.
  
  Original author: Haoqiang Fan.
  Modified by Charles R. Qi
  """
  
  import tensorflow as tf
  from tensorflow.python.framework import ops
  import sys
  import os
  BASE_DIR = os.path.dirname(os.path.abspath(__file__))
  nn_distance_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_nndistance_so.so'))
  
  def nn_distance(xyz1,xyz2):
      '''
  Computes the distance of nearest neighbors for a pair of point clouds
  input: xyz1: (batch_size,#points_1,3)  the first point cloud
  input: xyz2: (batch_size,#points_2,3)  the second point cloud
  output: dist1: (batch_size,#point_1)   distance from first to second
  output: idx1:  (batch_size,#point_1)   nearest neighbor from first to second
  output: dist2: (batch_size,#point_2)   distance from second to first
  output: idx2:  (batch_size,#point_2)   nearest neighbor from second to first
      '''
      return nn_distance_module.nn_distance(xyz1,xyz2)
  #@tf.RegisterShape('NnDistance')
  #def _nn_distance_shape(op):
      #shape1=op.inputs[0].get_shape().with_rank(3)
      #shape2=op.inputs[1].get_shape().with_rank(3)
      #return [tf.TensorShape([shape1.dims[0],shape1.dims[1]]),tf.TensorShape([shape1.dims[0],shape1.dims[1]]),
          #tf.TensorShape([shape2.dims[0],shape2.dims[1]]),tf.TensorShape([shape2.dims[0],shape2.dims[1]])]
  @ops.RegisterGradient('NnDistance')
  def _nn_distance_grad(op,grad_dist1,grad_idx1,grad_dist2,grad_idx2):
      xyz1=op.inputs[0]
      xyz2=op.inputs[1]
      idx1=op.outputs[1]
      idx2=op.outputs[3]
      return nn_distance_module.nn_distance_grad(xyz1,xyz2,grad_dist1,idx1,grad_dist2,idx2)
  
  
  if __name__=='__main__':
      import numpy as np
      import random
      import time
      from tensorflow.python.ops.gradient_checker import compute_gradient
      random.seed(100)
      np.random.seed(100)
      with tf.Session('') as sess:
          xyz1=np.random.randn(32,16384,3).astype('float32')
          xyz2=np.random.randn(32,1024,3).astype('float32')
          #with tf.device('/gpu:0'):
          if True:
              inp1=tf.Variable(xyz1)
              inp2=tf.constant(xyz2)
              reta,retb,retc,retd=nn_distance(inp1,inp2)
              loss=tf.reduce_sum(reta)+tf.reduce_sum(retc)
              train=tf.train.GradientDescentOptimizer(learning_rate=0.05).minimize(loss)
          sess.run(tf.initialize_all_variables())
          t0=time.time()
          t1=t0
          best=1e100
          for i in xrange(100):
              trainloss,_=sess.run([loss,train])
              newt=time.time()
              best=min(best,newt-t1)
              print(i,trainloss,(newt-t0)/(i+1),best)
              t1=newt
          #print sess.run([inp1,retb,inp2,retd])
          #grads=compute_gradient([inp1,inp2],[(16,32,3),(16,32,3)],loss,(1,),[xyz1,xyz2])
          #for i,j in grads:
              #print i.shape,j.shape,np.mean(np.abs(i-j)),np.mean(np.abs(i)),np.mean(np.abs(j))
          #for i in xrange(10):
              #t0=time.time()
              #a,b,c,d=sess.run([reta,retb,retc,retd],feed_dict={inp1:xyz1,inp2:xyz2})
              #print 'time',time.time()-t0
          #print a.shape,b.shape,c.shape,d.shape
          #print a.dtype,b.dtype,c.dtype,d.dtype
          #samples=np.array(random.sample(range(xyz2.shape[1]),100),dtype='int32')
          #dist1=((xyz1[:,samples,None,:]-xyz2[:,None,:,:])**2).sum(axis=-1).min(axis=-1)
          #idx1=((xyz1[:,samples,None,:]-xyz2[:,None,:,:])**2).sum(axis=-1).argmin(axis=-1)
          #print np.abs(dist1-a[:,samples]).max()
          #print np.abs(idx1-b[:,samples]).max()
          #dist2=((xyz2[:,samples,None,:]-xyz1[:,None,:,:])**2).sum(axis=-1).min(axis=-1)
          #idx2=((xyz2[:,samples,None,:]-xyz1[:,None,:,:])**2).sum(axis=-1).argmin(axis=-1)
          #print np.abs(dist2-c[:,samples]).max()
          #print np.abs(idx2-d[:,samples]).max()
  
  
  