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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.
import paddle
from paddle import _C_ops
from paddle.framework import in_dynamic_mode
from ...fluid.data_feeder import check_type, check_variable_and_dtype
from ...fluid.layer_helper import LayerHelper
__all__ = []
def pairwise_distance(x, y, p=2.0, epsilon=1e-6, keepdim=False, name=None):
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:
x (Tensor): Tensor, shape is :math:`[N, D]` or :math:`[D]`, where :math:`N`
is batch size, :math:`D` is the dimension of vector. Available dtype is
float16, float32, float64.
y (Tensor): Tensor, shape is :math:`[N, D]` or :math:`[D]`, where :math:`N`
is batch size, :math:`D` is the dimension of vector. Available dtype is
float16, float32, float64.
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.
Returns:
Tensor, the dtype is same 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)
distance = paddle.nn.functional.pairwise_distance(x, y)
print(distance)
# Tensor(shape=[2], dtype=float64, place=Place(gpu:0), stop_gradient=True,
# [4.99999860, 4.99999860])
"""
if in_dynamic_mode():
sub = _C_ops.subtract(x, y)
# p_norm op has not used epsilon, so change it to the following.
if epsilon != 0.0:
epsilon = paddle.fluid.dygraph.base.to_variable(
[epsilon], dtype=sub.dtype
)
sub = _C_ops.add(sub, epsilon)
return _C_ops.p_norm(sub, p, -1, 0.0, keepdim, False)
else:
check_type(p, 'porder', (float, int), 'PairwiseDistance')
check_type(epsilon, 'epsilon', (float), 'PairwiseDistance')
check_type(keepdim, 'keepdim', (bool), 'PairwiseDistance')
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'PairwiseDistance'
)
check_variable_and_dtype(
y, 'y', ['float16', 'float32', 'float64'], 'PairwiseDistance'
)
sub = paddle.subtract(x, y)
if epsilon != 0.0:
epsilon_var = sub.block.create_var(dtype=sub.dtype)
epsilon_var = paddle.full(
shape=[1], fill_value=epsilon, dtype=sub.dtype
)
sub = paddle.add(sub, epsilon_var)
helper = LayerHelper("PairwiseDistance", name=name)
attrs = {
'axis': -1,
'porder': p,
'keepdim': keepdim,
'epsilon': 0.0,
}
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='p_norm', inputs={'X': sub}, outputs={'Out': out}, attrs=attrs
)
return out