change print foo to print(foo) --> python 3 works

models/pointnet2_cls_msg.py

@@ -7,6 +7,7 @@
 import numpy as np
 import tf_util
 from pointnet_util import pointnet_sa_module, pointnet_sa_module_msg
+from __future__ import print_function
 
 def placeholder_inputs(batch_size, num_point):
     pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))

models/pointnet2_cls_ssg.py

@@ -7,6 +7,7 @@
 import numpy as np
 import tf_util
 from pointnet_util import pointnet_sa_module
+from __future__ import print_function
 
 def placeholder_inputs(batch_size, num_point):
     pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))

models/pointnet2_part_seg.py

@@ -7,6 +7,7 @@
 import numpy as np
 import tf_util
 from pointnet_util import pointnet_sa_module, pointnet_fp_module
+from __future__ import print_function
 
 def placeholder_inputs(batch_size, num_point):
     pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 6))

models/pointnet2_sem_seg.py

@@ -7,6 +7,7 @@
 import numpy as np
 import tf_util
 from pointnet_util import pointnet_sa_module, pointnet_fp_module
+from __future__ import print_function
 
 def placeholder_inputs(batch_size, num_point):
     pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))

tf_ops/3d_interpolation/tf_interpolate.py

@@ -2,6 +2,7 @@
 from tensorflow.python.framework import ops
 import sys
 import os
+from __future__ import print_function
 BASE_DIR = os.path.dirname(__file__)
 sys.path.append(BASE_DIR)
 interpolate_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_interpolate_so.so'))
@@ -51,8 +52,8 @@ def _three_interpolate_grad(op, grad_out):
         now = time.time() 
         for _ in range(100):
             ret = sess.run(interpolated_points)
-        print time.time() - now
-        print ret.shape, ret.dtype
+        print(time.time() - now)
+        print(ret.shape, ret.dtype)
         #print ret
 
 

tf_ops/3d_interpolation/tf_interpolate_op_test.py

@@ -1,6 +1,7 @@
 import tensorflow as tf
 import numpy as np
 from tf_interpolate import three_nn, three_interpolate
+from __future__ import print_function
 
 class GroupPointTest(tf.test.TestCase):
   def test(self):
@@ -9,15 +10,15 @@ def test(self):
   def test_grad(self):
     with self.test_session():
       points = tf.constant(np.random.random((1,8,16)).astype('float32'))
-      print points
+      print(points)
       xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32'))
       xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32'))
       dist, idx = three_nn(xyz1, xyz2)
       weight = tf.ones_like(dist)/3.0
       interpolated_points = three_interpolate(points, idx, weight)
-      print interpolated_points
+      print(interpolated_points)
       err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
-      print err
+      print(err)
       self.assertLess(err, 1e-4) 
 
 if __name__=='__main__':

tf_ops/3d_interpolation/visu_interpolation.py

@@ -7,6 +7,7 @@
 import numpy as np
 from tf_interpolate import three_nn, three_interpolate
 import tensorflow as tf
+from __future__ import print_function
 
 
 pts2 = np.array([[0,0,1],[1,0,0],[0,1,0],[1,1,0]]).astype('float32')
@@ -23,7 +24,7 @@ def fun(xyz1,xyz2,pts2):
         dist = tf.maximum(dist, 1e-10)
         norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True)
         norm = tf.tile(norm, [1,1,3])
-        print norm
+        print(norm)
         weight = (1.0/dist) / norm
         interpolated_points = three_interpolate(points, idx, weight)
     with tf.Session('') as sess:

tf_ops/grouping/test_knn.py

@@ -1,40 +1,40 @@
 import tensorflow as tf
 import numpy as np
-
+from __future__ import print_function
 np.random.seed(0)
 
 
 a_val = np.random.random((2,5,3))
 b_val = np.random.random((2,2,3))
 for b in range(2):
-    print '--- ', b
+    print('--- ', b)
     t1 = a_val[b,:,:]
     t2 = b_val[b,:,:]
     for i in range(2): #npoint in b
-        print '-- point b: ', i
+        print('-- point b: ', i)
         for j in range(5): # npoint in a
             d = np.sum((t2[i,:]-t1[j,:])**2)
-            print d
+            print(d)
 
 
 
 a = tf.constant(a_val)
 b = tf.constant(b_val)
-print a.get_shape()
+print(a.get_shape())
 k = 3
 
 a = tf.tile(tf.reshape(a, (2,1,5,3)), [1,2,1,1])
 b = tf.tile(tf.reshape(b, (2,2,1,3)), [1,1,5,1])
 
 dist = -tf.reduce_sum((a-b)**2, -1)
-print dist
+print(dist)
 
 val, idx = tf.nn.top_k(dist, k=k)
-print val, idx
+print(val, idx)
 sess = tf.Session()
-print sess.run(a)
-print sess.run(b)
-print sess.run(dist)
-print sess.run(val)
-print sess.run(idx)
-print sess.run(idx).shape
+print(sess.run(a))
+print(sess.run(b))
+print(sess.run(dist))
+print(sess.run(val))
+print(sess.run(idx))
+print(sess.run(idx).shape)

tf_ops/grouping/tf_grouping.py

@@ -2,6 +2,7 @@
 from tensorflow.python.framework import ops
 import sys
 import os
+from __future__ import print_function
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(BASE_DIR)
 grouping_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_grouping_so.so'))
@@ -59,16 +60,16 @@ def knn_point(k, xyz1, xyz2):
     n = xyz1.get_shape()[1].value
     c = xyz1.get_shape()[2].value
     m = xyz2.get_shape()[1].value
-    print b, n, c, m
-    print xyz1, (b,1,n,c)
+    print(b, n, c, m)
+    print(xyz1, (b,1,n,c))
     xyz1 = tf.tile(tf.reshape(xyz1, (b,1,n,c)), [1,m,1,1])
     xyz2 = tf.tile(tf.reshape(xyz2, (b,m,1,c)), [1,1,n,1])
     dist = tf.reduce_sum((xyz1-xyz2)**2, -1)
-    print dist, k
+    print(dist, k)
     outi, out = select_top_k(k, dist)
     idx = tf.slice(outi, [0,0,0], [-1,-1,k])
     val = tf.slice(out, [0,0,0], [-1,-1,k])
-    print idx, val
+    print(idx, val)
     #val, idx = tf.nn.top_k(-dist, k=k) # ONLY SUPPORT CPU
     return val, idx
 
@@ -98,8 +99,8 @@ def knn_point(k, xyz1, xyz2):
         now = time.time() 
         for _ in range(100):
             ret = sess.run(grouped_points)
-        print time.time() - now
-        print ret.shape, ret.dtype
-        print ret
+        print(time.time() - now)
+        print(ret.shape, ret.dtype)
+        print(ret)
 
 

tf_ops/grouping/tf_grouping_op_test.py

@@ -1,6 +1,7 @@
 import tensorflow as tf
 import numpy as np
 from tf_grouping import query_ball_point, group_point
+from __future__ import print_function
 
 class GroupPointTest(tf.test.TestCase):
   def test(self):
@@ -9,19 +10,19 @@ def test(self):
   def test_grad(self):
     with tf.device('/gpu:0'):
       points = tf.constant(np.random.random((1,128,16)).astype('float32'))
-      print points
+      print(points)
       xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32'))
       xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32'))
       radius = 0.3 
       nsample = 32
       idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
       grouped_points = group_point(points, idx)
-      print grouped_points
+      print(grouped_points)
 
     with self.test_session():
-      print "---- Going to compute gradient error"
+      print("---- Going to compute gradient error")
       err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16))
-      print err
+      print(err)
       self.assertLess(err, 1e-4) 
 
 if __name__=='__main__':

tf_ops/sampling/tf_sampling.py

@@ -7,6 +7,7 @@
 from tensorflow.python.framework import ops
 import sys
 import os
+from __future__ import print_function
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(BASE_DIR)
 sampling_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_sampling_so.so'))
@@ -79,11 +80,11 @@ def farthest_point_sample(npoint,inp):
         us=(uplusv+uminusv)*0.5
         vs=(uplusv-uminusv)*0.5
         pt_sample=tria_sample+(trib_sample-tria_sample)*tf.expand_dims(us,-1)+(tric_sample-tria_sample)*tf.expand_dims(vs,-1)
-        print 'pt_sample: ', pt_sample
+        print('pt_sample: ', pt_sample)
         reduced_sample=gather_point(pt_sample,farthest_point_sample(1024,pt_sample))
-        print reduced_sample
+        print(reduced_sample)
     with tf.Session('') as sess:
         ret=sess.run(reduced_sample)
-    print ret.shape,ret.dtype
+    print(ret.shape,ret.dtype)
     import cPickle as pickle
     pickle.dump(ret,open('1.pkl','wb'),-1)