# 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 __future__ import annotations import paddle from paddle import _C_ops from paddle.framework import in_dynamic_or_pir_mode from paddle.utils.decorator_utils import ParamAliasDecorator from ...base.data_feeder import check_type, check_variable_and_dtype from ...base.layer_helper import LayerHelper __all__ = [] @ParamAliasDecorator( { "x": ["x1"], "y": ["x2"], "epsilon": ["eps"], } ) def pairwise_distance( x: paddle.Tensor, y: paddle.Tensor, p: float = 2.0, epsilon: float = 1e-6, keepdim: bool = False, name: str | None = None, ) -> paddle.Tensor: r""" It computes the pairwise distance between two vectors. The distance is calculated by p-order 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|None, 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:: pycon >>> import paddle >>> x = paddle.to_tensor([[1.0, 3.0], [3.0, 5.0]], dtype=paddle.float64) >>> y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype=paddle.float64) >>> distance = paddle.nn.functional.pairwise_distance(x, y) >>> print(distance) Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True, [4.99999860, 4.99999860]) """ if in_dynamic_or_pir_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.to_tensor([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 def pdist( x: paddle.Tensor, p: float = 2.0, name: str | None = None ) -> paddle.Tensor: r''' Computes the p-norm distance between every pair of row vectors in the input. Args: x (Tensor): The input tensor with shape :math:`N \times M`. p (float, optional): The value for the p-norm distance to calculate between each vector pair. Default: :math:`2.0`. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor with shape :math:`N(N-1)/2` , the dtype is same as input tensor. Examples: .. code-block:: pycon >>> import paddle >>> paddle.seed(2023) >>> a = paddle.randn([4, 5]) >>> print(a) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.06132207, 1.11349595, 0.41906244, -0.24858207, -1.85169315], [-1.50370061, 1.73954511, 0.13331604, 1.66359663, -0.55764782], [-0.59911072, -0.57773495, -1.03176904, -0.33741450, -0.29695082], [-1.50258386, 0.67233968, -1.07747352, 0.80170447, -0.06695852]]) >>> pdist_out = paddle.pdist(a) >>> print(pdist_out) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [2.87295413, 2.79758120, 3.02793980, 3.40844536, 1.89435327, 1.93171620]) ''' x_shape = list(x.shape) assert len(x_shape) == 2, "The x must be 2-dimensional" d = paddle.linalg.norm(x[..., None, :] - x[..., None, :, :], p=p, axis=-1) mask = ~paddle.tril(paddle.ones(d.shape, dtype='bool')) return paddle.masked_select(d, mask)