Python tensorflow.asin() Examples
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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