wehub-resource-sync
164 lines
6.4 KiB
Python
164 lines
6.4 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import annotations
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import paddle
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from paddle import _C_ops
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from paddle.framework import in_dynamic_or_pir_mode
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from paddle.utils.decorator_utils import ParamAliasDecorator
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from ...base.data_feeder import check_type, check_variable_and_dtype
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from ...base.layer_helper import LayerHelper
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__all__ = []
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@ParamAliasDecorator(
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{
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"x": ["x1"],
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"y": ["x2"],
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"epsilon": ["eps"],
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}
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)
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def pairwise_distance(
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x: paddle.Tensor,
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y: paddle.Tensor,
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p: float = 2.0,
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epsilon: float = 1e-6,
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keepdim: bool = False,
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name: str | None = None,
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) -> paddle.Tensor:
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r"""
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It computes the pairwise distance between two vectors. The
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distance is calculated by p-order norm:
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.. math::
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\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.
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Parameters:
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x (Tensor): Tensor, shape is :math:`[N, D]` or :math:`[D]`, where :math:`N`
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is batch size, :math:`D` is the dimension of vector. Available dtype is
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float16, float32, float64.
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y (Tensor): Tensor, shape is :math:`[N, D]` or :math:`[D]`, where :math:`N`
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is batch size, :math:`D` is the dimension of vector. Available dtype is
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float16, float32, float64.
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p (float, optional): The order of norm. Default: :math:`2.0`.
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epsilon (float, optional): Add small value to avoid division by zero.
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Default: :math:`1e-6`.
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keepdim (bool, optional): Whether to reserve the reduced dimension
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in the output Tensor. The result tensor is one dimension less than
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the result of ``|x-y|`` unless :attr:`keepdim` is True. Default: False.
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name (str|None, optional): For details, please refer to :ref:`api_guide_Name`.
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Generally, no setting is required. Default: None.
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Returns:
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Tensor, the dtype is same as input tensor.
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- If :attr:`keepdim` is True, the output shape is :math:`[N, 1]` or :math:`[1]`,
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depending on whether the input has data shaped as :math:`[N, D]`.
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- If :attr:`keepdim` is False, the output shape is :math:`[N]` or :math:`[]`,
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depending on whether the input has data shaped as :math:`[N, D]`.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> x = paddle.to_tensor([[1.0, 3.0], [3.0, 5.0]], dtype=paddle.float64)
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>>> y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype=paddle.float64)
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>>> distance = paddle.nn.functional.pairwise_distance(x, y)
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>>> print(distance)
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Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
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[4.99999860, 4.99999860])
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"""
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if in_dynamic_or_pir_mode():
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sub = _C_ops.subtract(x, y)
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# p_norm op has not used epsilon, so change it to the following.
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if epsilon != 0.0:
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epsilon = paddle.to_tensor([epsilon], dtype=sub.dtype)
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sub = _C_ops.add(sub, epsilon)
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return _C_ops.p_norm(sub, p, -1, 0.0, keepdim, False)
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else:
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check_type(p, 'porder', (float, int), 'PairwiseDistance')
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check_type(epsilon, 'epsilon', (float), 'PairwiseDistance')
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check_type(keepdim, 'keepdim', (bool), 'PairwiseDistance')
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check_variable_and_dtype(
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x, 'x', ['float16', 'float32', 'float64'], 'PairwiseDistance'
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)
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check_variable_and_dtype(
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y, 'y', ['float16', 'float32', 'float64'], 'PairwiseDistance'
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)
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sub = paddle.subtract(x, y)
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if epsilon != 0.0:
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epsilon_var = sub.block.create_var(dtype=sub.dtype)
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epsilon_var = paddle.full(
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shape=[1], fill_value=epsilon, dtype=sub.dtype
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)
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sub = paddle.add(sub, epsilon_var)
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helper = LayerHelper("PairwiseDistance", name=name)
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attrs = {
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'axis': -1,
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'porder': p,
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'keepdim': keepdim,
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'epsilon': 0.0,
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}
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out = helper.create_variable_for_type_inference(dtype=x.dtype)
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helper.append_op(
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type='p_norm', inputs={'X': sub}, outputs={'Out': out}, attrs=attrs
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)
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return out
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def pdist(
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x: paddle.Tensor, p: float = 2.0, name: str | None = None
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) -> paddle.Tensor:
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r'''
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Computes the p-norm distance between every pair of row vectors in the input.
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Args:
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x (Tensor): The input tensor with shape :math:`N \times M`.
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p (float, optional): The value for the p-norm distance to calculate between each vector pair. Default: :math:`2.0`.
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name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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Returns:
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Tensor with shape :math:`N(N-1)/2` , the dtype is same as input tensor.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.seed(2023)
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>>> a = paddle.randn([4, 5])
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>>> print(a)
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Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
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[[ 0.06132207, 1.11349595, 0.41906244, -0.24858207, -1.85169315],
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[-1.50370061, 1.73954511, 0.13331604, 1.66359663, -0.55764782],
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[-0.59911072, -0.57773495, -1.03176904, -0.33741450, -0.29695082],
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[-1.50258386, 0.67233968, -1.07747352, 0.80170447, -0.06695852]])
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>>> pdist_out = paddle.pdist(a)
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>>> print(pdist_out)
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Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True,
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[2.87295413, 2.79758120, 3.02793980, 3.40844536, 1.89435327, 1.93171620])
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'''
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x_shape = list(x.shape)
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assert len(x_shape) == 2, "The x must be 2-dimensional"
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d = paddle.linalg.norm(x[..., None, :] - x[..., None, :, :], p=p, axis=-1)
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mask = ~paddle.tril(paddle.ones(d.shape, dtype='bool'))
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return paddle.masked_select(d, mask)
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