import tensorflow as tf
from tensorflow.python.framework import ops
import os.path as osp
base_dir = osp.dirname(osp.abspath(__file__))
approxmatch_module = tf.load_op_library(osp.join(base_dir, 'tf_approxmatch_so.so'))
def approx_match(xyz1,xyz2):
'''
input:
xyz1 : batch_size * #dataset_points * 3
xyz2 : batch_size * #query_points * 3
returns:
match : batch_size * #query_points * #dataset_points
'''
xyz1 = tf.expand_dims(xyz1, 0)
xyz2 = tf.expand_dims(xyz2, 0)
return approxmatch_module.approx_match(xyz1,xyz2)
ops.NoGradient('ApproxMatch')
#@tf.RegisterShape('ApproxMatch')
@ops.RegisterShape('ApproxMatch')
def _approx_match_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],shape2.dims[1],shape1.dims[1]])]
def match_cost(xyz1,xyz2,match):
'''
input:
xyz1 : batch_size * #dataset_points * 3
xyz2 : batch_size * #query_points * 3
match : batch_size * #query_points * #dataset_points
returns:
cost : batch_size
'''
xyz1 = tf.expand_dims(xyz1, 0)
xyz2 = tf.expand_dims(xyz2, 0)
return approxmatch_module.match_cost(xyz1,xyz2,match)
#@tf.RegisterShape('MatchCost')
@ops.RegisterShape('MatchCost')
def _match_cost_shape(op):
shape1=op.inputs[0].get_shape().with_rank(3)
shape2=op.inputs[1].get_shape().with_rank(3)
shape3=op.inputs[2].get_shape().with_rank(3)
return [tf.TensorShape([shape1.dims[0]])]
@tf.RegisterGradient('MatchCost')
def _match_cost_grad(op,grad_cost):
xyz1=op.inputs[0]
xyz2=op.inputs[1]
match=op.inputs[2]
grad_1,grad_2=approxmatch_module.match_cost_grad(xyz1,xyz2,match)
return [grad_1*tf.expand_dims(tf.expand_dims(grad_cost,1),2),grad_2*tf.expand_dims(tf.expand_dims(grad_cost,1),2),None]
if __name__=='__main__':
alpha=0.5
beta=2.0
import bestmatch
import numpy as np
import math
import random
import cv2
import tf_nndistance
npoint=100
with tf.device('/gpu:2'):
pt_in=tf.placeholder(tf.float32,shape=(1,npoint*4,3))
mypoints=tf.Variable(np.random.randn(1,npoint,3).astype('float32'))
match=approx_match(pt_in,mypoints)
loss=tf.reduce_sum(match_cost(pt_in,mypoints,match))
#match=approx_match(mypoints,pt_in)
#loss=tf.reduce_sum(match_cost(mypoints,pt_in,match))
#distf,_,distb,_=tf_nndistance.nn_distance(pt_in,mypoints)
#loss=tf.reduce_sum((distf+1e-9)**0.5)*0.5+tf.reduce_sum((distb+1e-9)**0.5)*0.5
#loss=tf.reduce_max((distf+1e-9)**0.5)*0.5*npoint+tf.reduce_max((distb+1e-9)**0.5)*0.5*npoint
optimizer=tf.train.GradientDescentOptimizer(1e-4).minimize(loss)
with tf.Session('') as sess:
sess.run(tf.initialize_all_variables())
while True:
meanloss=0
meantrueloss=0
for i in xrange(1001):
#phi=np.random.rand(4*npoint)*math.pi*2
#tpoints=(np.hstack([np.cos(phi)[:,None],np.sin(phi)[:,None],(phi*0)[:,None]])*random.random())[None,:,:]
#tpoints=((np.random.rand(400)-0.5)[:,None]*[0,2,0]+[(random.random()-0.5)*2,0,0]).astype('float32')[None,:,:]
tpoints=np.hstack([np.linspace(-1,1,400)[:,None],(random.random()*2*np.linspace(1,0,400)**2)[:,None],np.zeros((400,1))])[None,:,:]
trainloss,_=sess.run([loss,optimizer],feed_dict={pt_in:tpoints.astype('float32')})
trainloss,trainmatch=sess.run([loss,match],feed_dict={pt_in:tpoints.astype('float32')})
#trainmatch=trainmatch.transpose((0,2,1))
show=np.zeros((400,400,3),dtype='uint8')^255
trainmypoints=sess.run(mypoints)
for i in xrange(len(tpoints[0])):
u=np.random.choice(range(len(trainmypoints[0])),p=trainmatch[0].T[i])
cv2.line(show,
(int(tpoints[0][i,1]*100+200),int(tpoints[0][i,0]*100+200)),
(int(trainmypoints[0][u,1]*100+200),int(trainmypoints[0][u,0]*100+200)),
cv2.cv.CV_RGB(0,255,0))
for x,y,z in tpoints[0]:
cv2.circle(show,(int(y*100+200),int(x*100+200)),2,cv2.cv.CV_RGB(255,0,0))
for x,y,z in trainmypoints[0]:
cv2.circle(show,(int(y*100+200),int(x*100+200)),3,cv2.cv.CV_RGB(0,0,255))
cost=((tpoints[0][:,None,:]-np.repeat(trainmypoints[0][None,:,:],4,axis=1))**2).sum(axis=2)**0.5
#trueloss=bestmatch.bestmatch(cost)[0]
print trainloss#,trueloss
cv2.imshow('show',show)
cmd=cv2.waitKey(10)%256
if cmd==ord('q'):
break
