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129 lines
4.1 KiB
129 lines
4.1 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Scalar Affine Bijector"""
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from mindspore.ops import operations as P
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from mindspore._checkparam import Validator as validator
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from ..distribution._utils.utils import cast_to_tensor
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from ..distribution._utils.custom_ops import log_generic
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from .bijector import Bijector
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class ScalarAffine(Bijector):
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"""
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Scalar Affine Bijector.
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This Bijector performs the operation:
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.. math::
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Y = a * X + b
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where a is the scale factor and b is the shift factor.
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Args:
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scale (float): The scale factor. Default: 1.0.
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shift (float): The shift factor. Default: 0.0.
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name (str): The name of the bijector. Default: 'ScalarAffine'.
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Examples:
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>>> # To initialize a ScalarAffine bijector of scale 1 and shift 2
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>>> scalaraffine = nn.probability.bijector.ScalarAffine(1, 2)
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>>>
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>>> # To use ScalarAffine bijector in a network
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>>> class net(Cell):
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>>> def __init__(self):
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>>> super(net, self).__init__():
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>>> self.s1 = nn.probability.bijector.ScalarAffine(1, 2)
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>>>
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>>> def construct(self, value):
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>>> # Similar calls can be made to other probability functions
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>>> # by replacing 'forward' with the name of the function
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>>> ans1 = self.s1.forward(value)
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>>> ans2 = self.s1.inverse(value)
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>>> ans3 = self.s1.forward_log_jacobian(value)
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>>> ans4 = self.s1.inverse_log_jacobian(value)
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"""
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def __init__(self,
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scale=1.0,
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shift=0.0,
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name='ScalarAffine'):
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"""
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Constructor of scalar affine Bijector.
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"""
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param = dict(locals())
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validator.check_value_type('scale', scale, [int, float], type(self).__name__)
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validator.check_value_type('shift', shift, [int, float], type(self).__name__)
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self._scale = cast_to_tensor(scale)
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self._shift = cast_to_tensor(shift)
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super(ScalarAffine, self).__init__(
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is_constant_jacobian=True,
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is_injective=True,
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name=name,
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dtype=None,
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param=param)
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self.abs = P.Abs()
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self.oneslike = P.OnesLike()
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self.log = log_generic
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@property
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def scale(self):
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return self._scale
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@property
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def shift(self):
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return self._shift
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def extend_repr(self):
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str_info = f'scale = {self.scale}, shift = {self.shift}'
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return str_info
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def shape_mapping(self, shape):
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return shape
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def _forward(self, x):
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r"""
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.. math::
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f(x) = a * x + b
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"""
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x = self._check_value(x, 'value')
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return self.scale * x + self.shift * self.oneslike(x)
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def _inverse(self, y):
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r"""
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.. math::
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f(y) = \frac{y - b}{a}
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"""
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y = self._check_value(y, 'value')
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return (y - self.shift) / self.scale
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def _forward_log_jacobian(self, x):
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r"""
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.. math::
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f(x) = a * x + b
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f'(x) = a
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\log(f'(x)) = \log(a)
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"""
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x = self._check_value(x, 'value')
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return self.log(self.abs(self.scale))
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def _inverse_log_jacobian(self, y):
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r"""
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.. math::
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f(y) = \frac{(y - b)}{a}
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f'(x) = \frac{1.0}{a}
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\log(f'(x)) = - \log(a)
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"""
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y = self._check_value(y, 'value')
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return -1. * self.log(self.abs(self.scale))
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