本文整理汇总了Python中tf_grouping.query_ball_point方法的典型用法代码示例。如果您正苦于以下问题:Python tf_grouping.query_ball_point方法的具体用法?Python tf_grouping.query_ball_point怎么用?Python tf_grouping.query_ball_point使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tf_grouping
的用法示例。
在下文中一共展示了tf_grouping.query_ball_point方法的12个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_grad
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def test_grad(self):
with tf.device('/gpu:0'):
points = tf.constant(np.random.random((1,128,16)).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'))
radius = 0.3
nsample = 32
idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
grouped_points = group_point(points, idx)
print grouped_points
with self.test_session():
print "---- Going to compute gradient error"
err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16))
print err
self.assertLess(err, 1e-4)
示例2: test_grad
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def test_grad(self):
with tf.device('/gpu:0'):
points = tf.constant(np.random.random((1,128,16)).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'))
radius = 0.3
nsample = 32
idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
grouped_points = group_point(points, idx)
print(grouped_points)
with self.test_session():
print("---- Going to compute gradient error")
err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16))
print(err)
self.assertLess(err, 1e-4)
示例3: test_grad
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def test_grad(self):
with tf.device('/gpu:0'):
points = tf.constant(np.random.random((1,128,16)).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'))
radius = 0.3
nsample = 32
idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
grouped_points = group_point(points, idx)
print(grouped_points)
with self.test_session():
print("---- Going to compute gradient error")
err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16))
print(err)
self.assertLess(err, 1e-4)
示例4: test_grad
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def test_grad(self):
with tf.device('/gpu:1'):
points = tf.constant(np.random.random((4,256,8)).astype('float32'))
print (points)
xyz1 = tf.constant(np.random.random((4,256,3)).astype('float32'))
xyz2 = tf.constant(np.random.random((4,256,3)).astype('float32'))
radius = 0.3
nsample = 256
idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
grouped_points = group_point(points, idx)
#grouped_points, max_idx = group_maxpool(points, idx)
print (grouped_points)
with self.test_session():
print ("---- Going to compute gradient error")
err = tf.test.compute_gradient_error(points, (4,256,8), grouped_points, (4,256,256,8))
#err = tf.test.compute_gradient_error(points, (1,8,4), grouped_points, (1,2,4))
print (err)
self.assertLess(err, 1e-4)
示例5: test_grad
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def test_grad(self):
with tf.device('/gpu:0'):
points = tf.constant(np.random.random((1, 128, 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'))
radius = 0.3
nsample = 32
idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
grouped_points = group_point(points, idx)
print(grouped_points)
with self.test_session():
print("---- Going to compute gradient error")
err = tf.test.compute_gradient_error(points, (1, 128, 16), grouped_points, (1, 8, 32, 16))
print(err)
self.assertLess(err, 1e-4)
示例6: test_grad
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def test_grad(self):
with tf.device('/gpu:0'):
points = tf.constant(np.random.random((1,128,16)).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'))
radius = 0.3
nsample = 32
idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
grouped_points = group_point(points, idx)
print(grouped_points)
# with self.test_session():
with tf.Session() as sess:
print("---- Going to compute gradient error")
err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16))
print(err)
self.assertLess(err, 1e-4)
示例7: test_grad
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def test_grad(self):
with tf.device('/gpu:3'):
points = tf.constant(np.random.random((1,128,16)).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'))
radius = 0.3
nsample = 32
idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
grouped_points = group_point(points, idx)
grouped_xyz = group_point(xyz1, idx)
print grouped_points
#sess = tf.Session()
#xyz1_eval, xyz2_eval, grouped_xyz_eval = sess.run([xyz1,xyz2,grouped_xyz])
#print(xyz1_eval, xyz2_eval, grouped_xyz_eval)
with self.test_session():
print "---- Going to compute gradient error"
err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16))
print err
self.assertLess(err, 1e-4)
示例8: sample_and_group
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
'''
Input:
npoint: int32
radius: float32
nsample: int32
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
knn: bool, if True use kNN instead of radius search
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
Output:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
(subtracted by seed point XYZ) in local regions
'''
new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3)
if knn:
_,idx = knn_point(nsample, xyz, new_xyz)
else:
idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
if points is not None:
grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
if use_xyz:
new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
else:
new_points = grouped_points
else:
new_points = grouped_xyz
return new_xyz, new_points, idx, grouped_xyz
示例9: pointnet_sa_module_msg
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_list, is_training, bn_decay, scope, bn=True, use_xyz=True, use_nchw=False):
''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG)
Input:
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor
npoint: int32 -- #points sampled in farthest point sampling
radius: list of float32 -- search radius in local region
nsample: list of int32 -- how many points in each local region
mlp: list of list of int32 -- output size for MLP on each point
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format
Return:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, \sum_k{mlp[k][-1]}) TF tensor
'''
data_format = 'NCHW' if use_nchw else 'NHWC'
with tf.variable_scope(scope) as sc:
new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))
new_points_list = []
for i in range(len(radius_list)):
radius = radius_list[i]
nsample = nsample_list[i]
idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = group_point(xyz, idx)
grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1])
if points is not None:
grouped_points = group_point(points, idx)
if use_xyz:
grouped_points = tf.concat([grouped_points, grouped_xyz], axis=-1)
else:
grouped_points = grouped_xyz
if use_nchw: grouped_points = tf.transpose(grouped_points, [0,3,1,2])
for j,num_out_channel in enumerate(mlp_list[i]):
grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1,1],
padding='VALID', stride=[1,1], bn=bn, is_training=is_training,
scope='conv%d_%d'%(i,j), bn_decay=bn_decay)
if use_nchw: grouped_points = tf.transpose(grouped_points, [0,2,3,1])
new_points = tf.reduce_max(grouped_points, axis=[2])
new_points_list.append(new_points)
new_points_concat = tf.concat(new_points_list, axis=-1)
return new_xyz, new_points_concat
示例10: sample_and_group
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
'''
Input:
npoint: int32
radius: float32
nsample: int32
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
knn: bool, if True use kNN instead of radius search
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
Output:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
(subtracted by seed point XYZ) in local regions
'''
sample_idx = farthest_point_sample(npoint, xyz)
new_xyz = gather_point(xyz, sample_idx) # (batch_size, npoint, 3)
if knn:
_,idx = knn_point(nsample, xyz, new_xyz)
else:
idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
if points is not None:
grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
if use_xyz:
new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
else:
new_points = grouped_points
else:
new_points = grouped_xyz
return new_xyz, new_points, idx, sample_idx, grouped_xyz
示例11: sample_and_group
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
'''
Input:
npoint: int32
radius: float32
nsample: int32
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
knn: bool, if True use kNN instead of radius search
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
Output:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs (subtracted by seed point XYZ) in local regions
'''
new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3)
if knn:
_,idx = knn_point(nsample, xyz, new_xyz)
else:
idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
if points is not None:
grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
if use_xyz:
new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
else:
new_points = grouped_points
else:
new_points = grouped_xyz
return new_xyz, new_points, idx, grouped_xyz
示例12: sample_and_group
# 需要导入模块: import tf_grouping [as 别名]
# 或者: from tf_grouping import query_ball_point [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, tnet_spec=None, knn=False, use_xyz=True):
'''
Input:
npoint: int32
radius: float32
nsample: int32
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
tnet_spec: dict (keys: mlp, mlp2, is_training, bn_decay), if None do not apply tnet
knn: bool, if True use kNN instead of radius search
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
Output:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
(subtracted by seed point XYZ) in local regions
'''
new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3)
if knn:
_,idx = knn_point(nsample, xyz, new_xyz)
else:
idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
if tnet_spec is not None:
grouped_xyz = tnet(grouped_xyz, tnet_spec)
if points is not None:
grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
if use_xyz:
new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
else:
new_points = grouped_points
else:
new_points = grouped_xyz
return new_xyz, new_points, idx, grouped_xyz