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change print foo to print(foo) --> python 3 works #6

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change print foo to print(foo) --> python 3 works #6

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change print foo to print(foo) --> python 3 works
TimoFriedri Sep 8, 2017
c10b48c
further 2+3 print function changes
TimoFriedri Mar 2, 2018
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4 changes: 2 additions & 2 deletions tf_ops/3d_interpolation/tf_interpolate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,8 +51,8 @@ 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)
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This is not quite right. When you are printing multiple values separated by a comma then you must from __future__ import print_function at the top of the import statements to get identical output in Python 2.

$ python2 -c "print 'a', 'b' ; print('a', 'b')"

a b
('a', 'b')

#print ret


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6 changes: 3 additions & 3 deletions tf_ops/3d_interpolation/tf_interpolate_op_test.py
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Expand Up @@ -9,15 +9,15 @@ 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__':
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2 changes: 1 addition & 1 deletion tf_ops/3d_interpolation/visu_interpolation.py
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Expand Up @@ -23,7 +23,7 @@ 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:
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14 changes: 7 additions & 7 deletions tf_ops/grouping/tf_grouping.py
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Expand Up @@ -59,16 +59,16 @@ 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

Expand Down Expand Up @@ -98,8 +98,8 @@ 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)


8 changes: 4 additions & 4 deletions tf_ops/grouping/tf_grouping_op_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,19 +9,19 @@ 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__':
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6 changes: 3 additions & 3 deletions tf_ops/sampling/tf_sampling.py
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Expand Up @@ -79,11 +79,11 @@ 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)