import tensorflow as tf
from tensorflow.python.framework import ops
import os.path as osp
base_dir = osp.dirname(osp.abspath(__file__))
nn_distance_module = tf.load_op_library(osp.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
'''
xyz1 = tf.expand_dims(xyz1, 0)
xyz2 = tf.expand_dims(xyz2, 0)
return nn_distance_module.nn_distance(xyz1,xyz2)
#@tf.RegisterShape('NnDistance')
@ops.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.kernel_tests.gradient_checker import compute_gradient
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'):
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()
