Python tensorflow.asin方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
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 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
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) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
def periodic_triangle_waveform(z, p):
    return 2.0 / np.pi * tf.asin(tf.sin(2*np.pi*z/p)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
def test_Asin(self):
        t = tf.asin(self.random(4, 3))
        self.check(t) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
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) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
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) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
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) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
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
    # ------------------------------------------------------------------------------------------------------------------ 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import asin [as 别名]
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