Python tensorflow.asin() Examples
The following are 9
code examples of tensorflow.asin().
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Example #1
| Source File: euler.py From differentiable-point-clouds with MIT License | 6 votes |
|
def ypr_from_campos(cx, cy, cz):
camDist = math.sqrt(cx * cx + cy * cy + cz * cz)
cx = cx / camDist
cy = cy / camDist
cz = cz / camDist
t = math.sqrt(cx * cx + cy * cy)
tx = cx / t
ty = cy / t
yaw = math.acos(tx)
if ty > 0:
yaw = 2 * math.pi - yaw
roll = 0
pitch = math.asin(cz)
return yaw, pitch, roll
Example #2
| Source File: test_forward.py From incubator-tvm with Apache License 2.0 | 6 votes |
|
def test_forward_unary():
def _test_forward_unary(op, a_min=1, a_max=5, dtype=np.float32):
"""test unary operators"""
np_data = np.random.uniform(a_min, a_max, size=(2, 3, 5)).astype(dtype)
tf.reset_default_graph()
with tf.Graph().as_default():
in_data = tf.placeholder(dtype, (2, 3, 5), name="in_data")
out = op(in_data)
compare_tf_with_tvm([np_data], ['in_data:0'], out.name)
_test_forward_unary(tf.acos, -1, 1)
_test_forward_unary(tf.asin, -1, 1)
_test_forward_unary(tf.atanh, -1, 1)
_test_forward_unary(tf.sinh)
_test_forward_unary(tf.cosh)
_test_forward_unary(tf.acosh)
_test_forward_unary(tf.asinh)
_test_forward_unary(tf.atan)
_test_forward_unary(tf.sin)
_test_forward_unary(tf.cos)
_test_forward_unary(tf.tan)
_test_forward_unary(tf.tanh)
_test_forward_unary(tf.erf)
_test_forward_unary(tf.log)
_test_forward_unary(tf.log1p)
Example #3
| Source File: uniform_distribution.py From HyperGAN with MIT License | 5 votes |
|
def periodic_triangle_waveform(z, p):
return 2.0 / np.pi * tf.asin(tf.sin(2*np.pi*z/p))
Example #4
| Source File: ops.py From tfdeploy with MIT License | 5 votes |
|
def test_Asin(self):
t = tf.asin(self.random(4, 3))
self.check(t)
Example #5
| Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testFloatBasic(self):
x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32)
y = (x + .5).astype(np.float32) # no zero
z = (x + 15.5).astype(np.float32) # all positive
k = np.arange(-0.90, 0.90, 0.25).astype(np.float32) # between -1 and 1
self._compareBoth(x, np.abs, tf.abs)
self._compareBoth(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(y, self._inv, tf.inv)
self._compareBoth(x, np.square, tf.square)
self._compareBoth(z, np.sqrt, tf.sqrt)
self._compareBoth(z, self._rsqrt, tf.rsqrt)
self._compareBoth(x, np.exp, tf.exp)
self._compareBoth(z, np.log, tf.log)
self._compareBoth(z, np.log1p, tf.log1p)
self._compareBoth(x, np.tanh, tf.tanh)
self._compareBoth(x, self._sigmoid, tf.sigmoid)
self._compareBoth(y, np.sign, tf.sign)
self._compareBoth(x, np.sin, tf.sin)
self._compareBoth(x, np.cos, tf.cos)
self._compareBoth(k, np.arcsin, tf.asin)
self._compareBoth(k, np.arccos, tf.acos)
self._compareBoth(x, np.arctan, tf.atan)
self._compareBoth(x, np.tan, tf.tan)
self._compareBoth(
y,
np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
tf.lgamma)
self._compareBoth(x, np.vectorize(math.erf), tf.erf)
self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
self._compareBothSparse(y, np.sign, tf.sign)
self._compareBothSparse(x, np.vectorize(math.erf), tf.erf)
Example #6
| Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testFloatEmpty(self):
x = np.empty((2, 0, 5), dtype=np.float32)
self._compareBoth(x, np.abs, tf.abs)
self._compareBoth(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(x, self._inv, tf.inv)
self._compareBoth(x, np.square, tf.square)
self._compareBoth(x, np.sqrt, tf.sqrt)
self._compareBoth(x, self._rsqrt, tf.rsqrt)
self._compareBoth(x, np.exp, tf.exp)
self._compareBoth(x, np.log, tf.log)
self._compareBoth(x, np.log1p, tf.log1p)
self._compareBoth(x, np.tanh, tf.tanh)
self._compareBoth(x, self._sigmoid, tf.sigmoid)
self._compareBoth(x, np.sign, tf.sign)
self._compareBoth(x, np.sin, tf.sin)
self._compareBoth(x, np.cos, tf.cos)
# Can't use vectorize below, so just use some arbitrary function
self._compareBoth(x, np.sign, tf.lgamma)
self._compareBoth(x, np.sign, tf.erf)
self._compareBoth(x, np.sign, tf.erfc)
self._compareBoth(x, np.tan, tf.tan)
self._compareBoth(x, np.arcsin, tf.asin)
self._compareBoth(x, np.arccos, tf.acos)
self._compareBoth(x, np.arctan, tf.atan)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(x, np.sqrt, tf.sqrt, tol=1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
self._compareBothSparse(x, np.sign, tf.sign)
self._compareBothSparse(x, np.sign, tf.erf)
Example #7
| Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testDoubleBasic(self):
x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64)
y = (x + .5).astype(np.float64) # no zero
z = (x + 15.5).astype(np.float64) # all positive
k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype(np.float64) # between -1 and 1
self._compareBoth(x, np.abs, tf.abs)
self._compareBoth(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(y, self._inv, tf.inv)
self._compareBoth(x, np.square, tf.square)
self._compareBoth(z, np.sqrt, tf.sqrt)
self._compareBoth(z, self._rsqrt, tf.rsqrt)
self._compareBoth(x, np.exp, tf.exp)
self._compareBoth(z, np.log, tf.log)
self._compareBoth(z, np.log1p, tf.log1p)
self._compareBoth(x, np.tanh, tf.tanh)
self._compareBoth(x, self._sigmoid, tf.sigmoid)
self._compareBoth(y, np.sign, tf.sign)
self._compareBoth(x, np.sin, tf.sin)
self._compareBoth(x, np.cos, tf.cos)
self._compareBoth(
y,
np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
tf.lgamma)
self._compareBoth(x, np.vectorize(math.erf), tf.erf)
self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)
self._compareBoth(x, np.arctan, tf.atan)
self._compareBoth(k, np.arcsin, tf.asin)
self._compareBoth(k, np.arccos, tf.acos)
self._compareBoth(k, np.tan, tf.tan)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
self._compareBothSparse(y, np.sign, tf.sign)
self._compareBothSparse(x, np.vectorize(math.erf), tf.erf)
Example #8
| Source File: ModelNet40.py From KPConv with MIT License | 4 votes |
|
def get_tf_mapping(self, config):
def tf_map(stacked_points, stacked_normals, labels, obj_inds, stack_lengths):
"""
From the input point cloud, this function compute all the point clouds at each layer, the neighbors
indices, the pooling indices and other useful variables.
:param stacked_points: Tensor with size [None, 3] where None is the total number of points
:param labels: Tensor with size [None] where None is the number of batch
:param stack_lengths: Tensor with size [None] where None is the number of batch
"""
# Get batch indice for each point
batch_inds = self.tf_get_batch_inds(stack_lengths)
# Augment input points
stacked_points, scales, rots = self.tf_augment_input(stacked_points,
batch_inds,
config)
# First add a column of 1 as feature for the network to be able to learn 3D shapes
stacked_features = tf.ones((tf.shape(stacked_points)[0], 1), dtype=tf.float32)
# Then use positions or not
if config.in_features_dim == 1:
pass
elif config.in_features_dim == 3:
stacked_features = tf.concat((stacked_features, stacked_points), axis=1)
elif config.in_features_dim == 4:
stacked_features = tf.concat((stacked_features, stacked_normals), axis=1)
elif config.in_features_dim == 5:
angles = tf.asin(tf.abs(stacked_normals)) * (2 / np.pi)
stacked_features = tf.concat((stacked_features, angles), axis=1)
elif config.in_features_dim == 7:
stacked_features = tf.concat((stacked_features, stacked_points, stacked_normals), axis=1)
else:
raise ValueError('Only accepted input dimensions are 1, 4 and 7 (without and with XYZ)')
# Get the whole input list
input_list = self.tf_classification_inputs(config,
stacked_points,
stacked_features,
labels,
stack_lengths,
batch_inds)
# Add scale and rotation for testing
input_list += [scales, rots, obj_inds]
return input_list
return tf_map
# Debug methods
# ------------------------------------------------------------------------------------------------------------------
Example #9
| Source File: euler.py From differentiable-point-clouds with MIT License | 4 votes |
|
def quaternion2euler_full_tf(q, rotseq="yzy"):
def twoaxisrot_tf(r11, r12, r21, r31, r32):
a0 = tf.atan2(r11, r12)
a1 = tf.acos(r21)
a2 = tf.atan2(r31, r32)
return tf.stack([a0, a1, a2], axis=-1)
def threeaxisrot_tf(r11, r12, r21, r31, r32):
a0 = tf.atan2(r31, r32)
a1 = tf.asin(tf.clip_by_value(r21, -1.0, 1.0))
a2 = tf.atan2(r11, r12)
return tf.stack([a0, a1, a2], axis=-1)
q_norm = tf.expand_dims(tf.norm(q, axis=-1), axis=-1)
q /= q_norm
if rotseq == "yzy":
angles = twoaxisrot_tf(2 * (q[:, 2] * q[:, 3] + q[:, 0] * q[:, 1]),
-2 * (q[:, 1] * q[:, 2] - q[:, 0] * q[:, 3]),
q[:, 0] * q[:, 0] - q[:, 1] * q[:, 1] + q[:, 2] * q[:, 2] - q[:, 3] * q[:, 3],
2 * (q[:, 2] * q[:, 3] - q[:, 0] * q[:, 1]),
2 * (q[:, 1] * q[:, 2] + q[:, 0] * q[:, 3]))
yaw = angles[:, 2]
pitch = angles[:, 1]
elif rotseq == "xzy":
angles = threeaxisrot_tf(2 * (q[:, 2] * q[:, 3] + q[:, 0] * q[:, 1]),
q[:, 0] * q[:, 0] - q[:, 1] * q[:, 1] + q[:, 2] * q[:, 2] - q[:, 3] * q[:, 3],
-2 * (q[:, 1] * q[:, 2] - q[:, 0] * q[:, 3]),
2 * (q[:, 1] * q[:, 3] + q[:, 0] * q[:, 2]),
q[:, 0] * q[:, 0] + q[:, 1] * q[:, 1] - q[:, 2] * q[:, 2] - q[:, 3] * q[:, 3])
yaw = angles[:, 0]
pitch = angles[:, 1]
elif rotseq == "zxy":
angles = threeaxisrot_tf(-2 * (q[:, 1] * q[:, 2] - q[:, 0] * q[:, 3]),
q[:, 0] * q[:, 0] - q[:, 1] * q[:, 1] + q[:, 2] * q[:, 2] - q[:, 3] * q[:, 3],
2 * (q[:, 2] * q[:, 3] + q[:, 0] * q[:, 1]),
-2 * (q[:, 1] * q[:, 3] - q[:, 0] * q[:, 2]),
q[:, 0] * q[:, 0] - q[:, 1] * q[:, 1] - q[:, 2] * q[:, 2] + q[:, 3] * q[:, 3])
yaw = angles[:, 0]
pitch = angles[:, 2]
return yaw, pitch
