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- # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- from .. import functional as F
- from .layers import Layer
- __all__ = []
- class PairwiseDistance(Layer):
- r"""
- It computes the pairwise distance between two vectors. The
- distance is calculated by p-oreder norm:
- .. math::
- \Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.
- Parameters:
- p (float, optional): The order of norm. Default: :math:`2.0`.
- epsilon (float, optional): Add small value to avoid division by zero.
- Default: :math:`1e-6`.
- keepdim (bool, optional): Whether to reserve the reduced dimension
- in the output Tensor. The result tensor is one dimension less than
- the result of ``|x-y|`` unless :attr:`keepdim` is True. Default: False.
- name (str, optional): For details, please refer to :ref:`api_guide_Name`.
- Generally, no setting is required. Default: None.
- Shape:
- - x: :math:`[N, D]` or :math:`[D]`, where :math:`N` is batch size, :math:`D`
- is the dimension of the data. Available data type is float16, float32, float64.
- - y: :math:`[N, D]` or :math:`[D]`, y have the same dtype as x.
- - output: The same dtype as input tensor.
- - If :attr:`keepdim` is True, the output shape is :math:`[N, 1]` or :math:`[1]`,
- depending on whether the input has data shaped as :math:`[N, D]`.
- - If :attr:`keepdim` is False, the output shape is :math:`[N]` or :math:`[]`,
- depending on whether the input has data shaped as :math:`[N, D]`.
- Examples:
- .. code-block:: python
- >>> import paddle
- >>> x = paddle.to_tensor([[1., 3.], [3., 5.]], dtype=paddle.float64)
- >>> y = paddle.to_tensor([[5., 6.], [7., 8.]], dtype=paddle.float64)
- >>> dist = paddle.nn.PairwiseDistance()
- >>> distance = dist(x, y)
- >>> print(distance)
- Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
- [4.99999860, 4.99999860])
- """
- def __init__(self, p=2.0, epsilon=1e-6, keepdim=False, name=None):
- super().__init__()
- self.p = p
- self.epsilon = epsilon
- self.keepdim = keepdim
- self.name = name
- def forward(self, x, y):
- return F.pairwise_distance(
- x, y, self.p, self.epsilon, self.keepdim, self.name
- )
- def extra_repr(self):
- main_str = 'p={p}'
- if self.epsilon != 1e-6:
- main_str += ', epsilon={epsilon}'
- if self.keepdim is not False:
- main_str += ', keepdim={keepdim}'
- if self.name is not None:
- main_str += ', name={name}'
- return main_str.format(**self.__dict__)
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