Source code for mindspore.nn.probability.bijector.power_transform
# Copyright 2020 Huawei Technologies Co., Ltd
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# ============================================================================
"""PowerTransform Bijector"""
from mindspore.ops import operations as P
from ..distribution._utils.utils import check_greater_equal_zero
from ..distribution._utils.custom_ops import exp_generic, log_generic
from .bijector import Bijector
[文档]class PowerTransform(Bijector):
r"""
PowerTransform Bijector.
This Bijector performs the operation:
.. math::
Y = g(X) = (1 + X * c)^{1 / c}, X >= -1 / c
where c >= 0 is the power.
The power transform maps inputs from `[-1/c, inf]` to `[0, inf]`.
This Bijector is equivalent to the `Exp` bijector when `c=0`.
Args:
power (float, list, numpy.ndarray, Tensor): The scale factor. Default: 0.
name (str): The name of the bijector. Default: 'PowerTransform'.
Inputs and Outputs of APIs:
The accessible APIs of the PowerTransform bijector are defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **inverse_log_jacobian**
It should be notice that the inputs to APIs of the PowerTransform bijector should be always a tensor,
with a shape that can be broadcasted to that of `power`.
For more details of all APIs, including the inputs and outputs of the PowerTransform bijector,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``
Note:
The dtype of `power` must be float.
Raises:
ValueError: When `power` is less than 0 or is not known statically.
TypeError: When the dtype of `power` is not float.
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import Tensor
>>> # To initialize a PowerTransform bijector of power 0.5.
>>> powertransform = msb.PowerTransform(0.5)
>>> value = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> ans1 = powertransform.forward(value)
>>> print(ans1.shape)
(3,)
>>> ans2 = powertransform.inverse(value)
>>> print(ans2.shape)
(3,)
>>> ans3 = powertransform.forward_log_jacobian(value)
>>> print(ans3.shape)
(3,)
>>> ans4 = powertransform.inverse_log_jacobian(value)
>>> print(ans4.shape)
(3,)
"""
def __init__(self,
power=0.,
name='PowerTransform'):
param = dict(locals())
param['param_dict'] = {'power': power}
super(PowerTransform, self).__init__(name=name, param=param)
self._power = self._add_parameter(power, 'power')
check_greater_equal_zero(self._power, 'Power')
self.pow = P.Pow()
self.dtypeop = P.DType()
self.cast = P.Cast()
self.equal_base = P.Equal()
self.exp = exp_generic
self.expm1 = P.Expm1()
self.fill = P.Fill()
self.log = log_generic
self.log1p = P.Log1p()
self.select_base = P.Select()
self.shape = P.Shape()
@property
def power(self):
"""
Return the power parameter of the bijector.
Output:
Tensor, the power parameter of the bijector.
"""
return self._power
def extend_repr(self):
"""Display instance object as string."""
if self.is_scalar_batch:
str_info = 'power = {}'.format(self.power)
else:
str_info = 'batch_shape = {}'.format(self.batch_shape)
return str_info
def _forward(self, x):
"""
Evaluate the forward mapping.
"""
x = self._check_value_dtype(x)
power_local = self.cast_param_by_value(x, self.power)
# broad cast the value of x and power
ones = self.fill(self.dtypeop(power_local),
self.shape(x + power_local), 1.)
power_local = power_local * ones
x = x * ones
safe_power = self.select_base(self.equal_base(power_local, 0.),
ones,
power_local)
forward_v = self.select_base(self.equal_base(power_local, 0.),
self.exp(x),
self.exp(self.log1p(x * safe_power) / safe_power))
return forward_v
def _inverse(self, y):
"""
Evaluate the inverse mapping.
"""
y = self._check_value_dtype(y)
power_local = self.cast_param_by_value(y, self.power)
# broad cast the value of x and power
ones = self.fill(self.dtypeop(power_local),
self.shape(y + power_local), 1.)
power_local = power_local * ones
y = y * ones
safe_power = self.select_base(self.equal_base(power_local, 0.),
ones,
power_local)
inverse_v = self.select_base(self.equal_base(power_local, 0.),
self.log(y),
self.expm1(self.log(y) * safe_power) / safe_power)
return inverse_v
def _forward_log_jacobian(self, x):
r"""
.. math:
if c == 0:
f(x) = e^x
f'(x) = e^x
\log(f'(x)) = \log(e^x) = x
else:
f(x) = e^\frac{\log(xc + 1)}{c}
f'(x) = e^\frac{\log(xc + 1)}{c} * \frac{1}{xc + 1}
\log(f'(x)) = (\frac{1}{c} - 1) * \log(xc + 1)
"""
x = self._check_value_dtype(x)
power_local = self.cast_param_by_value(x, self.power)
# broad cast the value of x and power
ones = self.fill(self.dtypeop(power_local),
self.shape(x + power_local), 1.)
power_local = power_local * ones
x = x * ones
forward_log_j = self.select_base(self.equal_base(power_local, 0.),
x,
(1. / power_local - 1) * self.log1p(x * power_local))
return forward_log_j
def _inverse_log_jacobian(self, y):
r"""
.. math:
if c == 0:
f(x) = \log(x)
f'(x) = \frac{1}{x}
\log(f'(x)) = \log(\frac{1}{x}) = -\log(x)
else:
f(x) = \frac{e^\log(y)*c + 1}{c}
f'(x) = \frac{e^c\log(y)}{y}
\log(f'(x)) = \log(\frac{e^c\log(y)}{y}) = (c-1) * \log(y)
"""
y = self._check_value_dtype(y)
power_local = self.cast_param_by_value(y, self.power)
inverse_log_j = (power_local - 1) * self.log(y)
return inverse_log_j