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distance.py
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distance.py
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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(gpu:0), 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__)