Python tensorflow.tan方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import tan [as 别名]
def add_cen_x_output(self, output_key, pred_cen_z, pred_view_angs):

        output_type = self.output_config[output_key]
        print('\t{:30s}{}'.format(output_key, output_type))

        with tf.variable_scope(output_key):
            if output_type == 'from_view_ang_and_z':
                # Predict centroid x using viewing angle
                cam2_pred_cen_x = pred_cen_z * tf.tan(pred_view_angs)

                # Adjust for x_offset
                cam_p = self.cam_p
                x_offset = -cam_p[0, 3] / cam_p[0, 0]
                pred_cen_x = cam2_pred_cen_x + x_offset

            else:
                raise ValueError('Invalid output_type', output_type)

            self._output_dict.add_unique_to_dict({
                output_key: pred_cen_x,
            }) 

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

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import tan [as 别名]
def perspective(aspect_ratio, fov_y, near_clip, far_clip):
  """Computes perspective transformation matrices.

  Functionality mimes gluPerspective (third_party/GL/glu/include/GLU/glu.h).

  Args:
    aspect_ratio: float value specifying the image aspect ratio (width/height).
    fov_y: 1-D float32 Tensor with shape [batch_size] specifying output vertical
        field of views in degrees.
    near_clip: 1-D float32 Tensor with shape [batch_size] specifying near
        clipping plane distance.
    far_clip: 1-D float32 Tensor with shape [batch_size] specifying far clipping
        plane distance.

  Returns:
    A [batch_size, 4, 4] float tensor that maps from right-handed points in eye
    space to left-handed points in clip space.
  """
  # The multiplication of fov_y by pi/360.0 simultaneously converts to radians
  # and adds the half-angle factor of .5.
  focal_lengths_y = 1.0 / tf.tan(fov_y * (math.pi / 360.0))
  depth_range = far_clip - near_clip
  p_22 = -(far_clip + near_clip) / depth_range
  p_23 = -2.0 * (far_clip * near_clip / depth_range)

  zeros = tf.zeros_like(p_23, dtype=tf.float32)
  # pyformat: disable
  perspective_transform = tf.concat(
      [
          focal_lengths_y / aspect_ratio, zeros, zeros, zeros,
          zeros, focal_lengths_y, zeros, zeros,
          zeros, zeros, p_22, p_23,
          zeros, zeros, -tf.ones_like(p_23, dtype=tf.float32), zeros
      ], axis=0)
  # pyformat: enable
  perspective_transform = tf.reshape(perspective_transform, [4, 4, -1])
  return tf.transpose(perspective_transform, [2, 0, 1]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import tan [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 tan [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 tan [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 tan [as 别名]
def test_forward_atan2():
    """test operator tan """
    tf.disable_eager_execution()
    np_data_1 = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
    np_data_2 = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
    tf.reset_default_graph()
    in_data_1 = tf.placeholder(tf.float32, (2, 3, 5), name="in_data_1")
    in_data_2 = tf.placeholder(tf.float32, (2, 3, 5), name="in_data_2")
    tf.atan2(in_data_1, in_data_2, name="atan2")
    compare_tf_with_tvm([np_data_1, np_data_2], ['in_data_1:0', 'in_data_2:0'], 'atan2:0')