Python tf_interpolate.three_nn() Examples
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code examples of tf_interpolate.three_nn().
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Example #1
| Source File: visu_interpolation.py From deep-functional-dictionaries with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #2
| Source File: visu_interpolation.py From articulated-part-induction with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #3
| Source File: visu_interpolation.py From frustum-pointnets with Apache License 2.0 | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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)
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #4
| Source File: visu_interpolation.py From flownet3d with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #5
| Source File: visu_interpolation.py From JSNet with MIT License | 6 votes |
|
def fun(xyz1, xyz2, pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2, 0))
xyz1 = tf.constant(np.expand_dims(xyz1, 0))
xyz2 = tf.constant(np.expand_dims(xyz2, 0))
dist, idx = three_nn(xyz1, xyz2)
# weight = tf.ones_like(dist)/3.0
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)
weight = (1.0 / dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp, pts1, d, w = sess.run([xyz1, interpolated_points, dist, weight])
# print w
pts1 = pts1.squeeze()
return pts1
Example #6
| Source File: pointnet_util.py From JSNet with MIT License | 6 votes |
|
def pointnet_upsample(xyz1, xyz2, points2, scope):
""" PointNet Feature Propogation (FP) Module
Input:
xyz1: (batch_size, ndataset1, 3) TF tensor
xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1
points2: (batch_size, ndataset2, nchannel2) TF tensor
Return:
new_points: (batch_size, ndataset1, nchannel2) TF tensor
"""
with tf.variable_scope(scope) as sc:
dist, idx = three_nn(xyz1, xyz2)
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])
weight = (1.0 / dist) / norm
interpolated_points = three_interpolate(points2, idx, weight) # B x ndataset1 x nchannel2
return interpolated_points
Example #7
| Source File: visu_interpolation.py From CalibNet with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #8
| Source File: tf_util.py From scanobjectnn with MIT License | 6 votes |
|
def pc_upsampling(xyz_upsample,
xyz,
feat,
scope='upsampling'):
""" Fully connected layer with non-linear operation.
Args:
xyz_upsample: 3-D tensor B x N2 x 3
xyz: 3-D tensor B x N x 3
feat: 3-D tensor B x N x C
Returns:
feat_upsample: 3-D tensor B x N2 x C
"""
with tf.variable_scope(scope) as sc:
dist, idx_de = three_nn(xyz_upsample, xyz)
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])
weight = (1.0/dist) / norm
feat_upsample = three_interpolate(feat, idx_de, weight)
return feat_upsample
Example #9
| Source File: visu_interpolation.py From scanobjectnn with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #10
| Source File: visu_interpolation.py From dfc2019 with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #11
| Source File: visu_interpolation.py From reading-frustum-pointnets-code with Apache License 2.0 | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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)
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #12
| Source File: visu_interpolation.py From GSPN with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #13
| Source File: visu_interpolation.py From SpiderCNN with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #14
| Source File: tf_util.py From SpiderCNN with MIT License | 6 votes |
|
def pc_upsampling(xyz_upsample,
xyz,
feat,
scope='upsampling'):
""" Fully connected layer with non-linear operation.
Args:
xyz_upsample: 3-D tensor B x N2 x 3
xyz: 3-D tensor B x N x 3
feat: 3-D tensor B x N x C
Returns:
feat_upsample: 3-D tensor B x N2 x C
"""
with tf.variable_scope(scope) as sc:
dist, idx_de = three_nn(xyz_upsample, xyz)
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])
weight = (1.0/dist) / norm
feat_upsample = three_interpolate(feat, idx_de, weight)
return feat_upsample
Example #15
| Source File: visu_interpolation.py From Geo-CNN with Apache License 2.0 | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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)
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #16
| Source File: visu_interpolation.py From ASIS with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #17
| Source File: visu_interpolation.py From SPFN with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #18
| Source File: visu_interpolation.py From meteornet with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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)
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #19
| Source File: visu_interpolation.py From PointRNN with MIT License | 6 votes |
|
def fun(xyz1,xyz2,pts2):
with tf.device('/cpu:0'):
points = tf.constant(np.expand_dims(pts2,0))
xyz1 = tf.constant(np.expand_dims(xyz1,0))
xyz2 = tf.constant(np.expand_dims(xyz2,0))
dist, idx = three_nn(xyz1, xyz2)
#weight = tf.ones_like(dist)/3.0
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
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points, idx, weight)
with tf.Session('') as sess:
tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
#print w
pts1 = pts1.squeeze()
return pts1
Example #20
| Source File: tf_interpolate_op_test.py From flownet3d with MIT License | 5 votes |
|
def test_grad(self):
with self.test_session():
points = tf.constant(np.random.random((1,8,16)).astype('float32'))
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
err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
print err
self.assertLess(err, 1e-4)
Example #21
| Source File: tf_interpolate_op_test.py From scanobjectnn with MIT License | 5 votes |
|
def test_grad(self):
with self.test_session():
points = tf.constant(np.random.random((1,8,16)).astype('float32'))
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
err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
print err
self.assertLess(err, 1e-4)
Example #22
| Source File: tf_interpolate_op_test.py From CalibNet with MIT License | 5 votes |
|
def test_grad(self):
with self.test_session():
points = tf.constant(np.random.random((1,8,16)).astype('float32'))
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
err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
print err
self.assertLess(err, 1e-4)
Example #23
| Source File: pointnet_util.py From ASIS with MIT License | 5 votes |
|
def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True):
''' PointNet Feature Propogation (FP) Module
Input:
xyz1: (batch_size, ndataset1, 3) TF tensor
xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1
points1: (batch_size, ndataset1, nchannel1) TF tensor
points2: (batch_size, ndataset2, nchannel2) TF tensor
mlp: list of int32 -- output size for MLP on each point
Return:
new_points: (batch_size, ndataset1, mlp[-1]) TF tensor
'''
with tf.variable_scope(scope) as sc:
dist, idx = three_nn(xyz1, xyz2)
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])
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points2, idx, weight)
if points1 is not None:
new_points1 = tf.concat(axis=2, values=[interpolated_points, points1]) # B,ndataset1,nchannel1+nchannel2
else:
new_points1 = interpolated_points
new_points1 = tf.expand_dims(new_points1, 2)
for i, num_out_channel in enumerate(mlp):
new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1,1],
padding='VALID', stride=[1,1],
bn=bn, is_training=is_training,
scope='conv_%d'%(i), bn_decay=bn_decay)
new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1]
return new_points1
Example #24
| Source File: pointnet_util.py From JSNet with MIT License | 5 votes |
|
def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True, is_dist=False):
''' PointNet Feature Propogation (FP) Module
Input:
xyz1: (batch_size, ndataset1, 3) TF tensor
xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1
points1: (batch_size, ndataset1, nchannel1) TF tensor
points2: (batch_size, ndataset2, nchannel2) TF tensor
mlp: list of int32 -- output size for MLP on each point
Return:
new_points: (batch_size, ndataset1, mlp[-1]) TF tensor
'''
with tf.variable_scope(scope) as sc:
dist, idx = three_nn(xyz1, xyz2)
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])
weight = (1.0 / dist) / norm
interpolated_points = three_interpolate(points2, idx, weight)
if points1 is not None:
new_points1 = tf.concat(axis=2, values=[interpolated_points, points1]) # B,ndataset1,nchannel1+nchannel2
else:
new_points1 = interpolated_points
new_points1 = tf.expand_dims(new_points1, 2)
for i, num_out_channel in enumerate(mlp):
new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1, 1],
padding='VALID', stride=[1, 1],
bn=bn, is_training=is_training,
scope='conv_%d' % (i), bn_decay=bn_decay,
is_dist=is_dist)
new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1]
return new_points1
Example #25
| Source File: tf_interpolate_op_test.py From JSNet with MIT License | 5 votes |
|
def test_grad(self):
with self.test_session():
points = tf.constant(np.random.random((1, 8, 16)).astype('float32'))
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)
err = tf.test.compute_gradient_error(points, (1, 8, 16), interpolated_points, (1, 128, 16))
print(err)
self.assertLess(err, 1e-4)
Example #26
| Source File: tf_interpolate_op_test.py From meteornet with MIT License | 5 votes |
|
def test_grad(self):
with self.test_session():
points = tf.constant(np.random.random((1,8,16)).astype('float32'))
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
err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
print err
self.assertLess(err, 1e-4)
Example #27
| Source File: pointnet_util.py From flownet3d with MIT License | 5 votes |
|
def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True, last_mlp_activation=True):
''' PointNet Feature Propogation (FP) Module
Input:
xyz1: (batch_size, ndataset1, 3) TF tensor
xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1
points1: (batch_size, ndataset1, nchannel1) TF tensor
points2: (batch_size, ndataset2, nchannel2) TF tensor
mlp: list of int32 -- output size for MLP on each point
Return:
new_points: (batch_size, ndataset1, mlp[-1]) TF tensor
'''
with tf.variable_scope(scope) as sc:
dist, idx = three_nn(xyz1, xyz2)
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])
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points2, idx, weight)
if points1 is not None:
new_points1 = tf.concat(axis=2, values=[interpolated_points, points1]) # B,ndataset1,nchannel1+nchannel2
else:
new_points1 = interpolated_points
new_points1 = tf.expand_dims(new_points1, 2)
for i, num_out_channel in enumerate(mlp):
if i == len(mlp)-1 and not(last_mlp_activation):
activation_fn = None
else:
activation_fn = tf.nn.relu
new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1,1],
padding='VALID', stride=[1,1],
bn=bn, is_training=is_training,
scope='conv_%d'%(i), bn_decay=bn_decay, activation_fn=activation_fn)
new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1]
return new_points1
Example #28
| Source File: tf_interpolate_op_test.py From deep-functional-dictionaries with MIT License | 5 votes |
|
def test_grad(self):
with self.test_session():
points = tf.constant(np.random.random((1,8,16)).astype('float32'))
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
err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
print err
self.assertLess(err, 1e-4)
Example #29
| Source File: net_utils.py From meteornet with MIT License | 5 votes |
|
def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True):
''' PointNet Feature Propogation (FP) Module
Input:
xyz1: (batch_size, ndataset1, 3) TF tensor
xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1
points1: (batch_size, ndataset1, nchannel1) TF tensor
points2: (batch_size, ndataset2, nchannel2) TF tensor
mlp: list of int32 -- output size for MLP on each point
Return:
new_points: (batch_size, ndataset1, mlp[-1]) TF tensor
'''
with tf.variable_scope(scope) as sc:
dist, idx = three_nn(xyz1, xyz2)
dist = tf.maximum(dist, 1e-10)
norm = tf.reduce_sum((1.0/dist),axis=2, keepdims=True)
norm = tf.tile(norm,[1,1,3])
weight = (1.0/dist) / norm
interpolated_points = three_interpolate(points2, idx, weight)
if points1 is not None:
new_points1 = tf.concat(axis=2, values=[interpolated_points, points1]) # B,ndataset1,nchannel1+nchannel2
else:
new_points1 = interpolated_points
new_points1 = tf.expand_dims(new_points1, 2)
for i, num_out_channel in enumerate(mlp):
new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1,1],
padding='VALID', stride=[1,1],
bn=bn, is_training=is_training,
scope='conv_%d'%(i), bn_decay=bn_decay)
new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1]
return new_points1
Example #30
| Source File: tf_interpolate_op_test.py From frustum-pointnets with Apache License 2.0 | 5 votes |
|
def test_grad(self):
with self.test_session():
points = tf.constant(np.random.random((1,8,16)).astype('float32'))
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)
err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
print(err)
self.assertLess(err, 1e-4)
