distance.py

import torch
from .module import Module
from .. import functional as F
from ..._jit_internal import weak_module, weak_script_method


@weak_module
class PairwiseDistance(Module):
    r"""
    Computes the batchwise pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm:

    .. math ::
        \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}

    Args:
        p (real): the norm degree. Default: 2
        eps (float, optional): Small value to avoid division by zero.
            Default: 1e-6
        keepdim (bool, optional): Determines whether or not to keep the batch dimension.
            Default: False

    Shape:
        - Input1: :math:`(N, D)` where `D = vector dimension`
        - Input2: :math:`(N, D)`, same shape as the Input1
        - Output: :math:`(N)`. If :attr:`keepdim` is ``False``, then :math:`(N, 1)`.

    Examples::

        >>> pdist = nn.PairwiseDistance(p=2)
        >>> input1 = torch.randn(100, 128)
        >>> input2 = torch.randn(100, 128)
        >>> output = pdist(input1, input2)
    """
    __constants__ = ['norm', 'eps', 'keepdim']

    def __init__(self, p=2., eps=1e-6, keepdim=False):
        super(PairwiseDistance, self).__init__()
        self.norm = p
        self.eps = eps
        self.keepdim = keepdim

    @weak_script_method
    def forward(self, x1, x2):
        return F.pairwise_distance(x1, x2, self.norm, self.eps, self.keepdim)


@weak_module
class CosineSimilarity(Module):
    r"""Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along dim.

    .. math ::
        \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}

    Args:
        dim (int, optional): Dimension where cosine similarity is computed. Default: 1
        eps (float, optional): Small value to avoid division by zero.
            Default: 1e-8

    Shape:
        - Input1: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`
        - Input2: :math:`(\ast_1, D, \ast_2)`, same shape as the Input1
        - Output: :math:`(\ast_1, \ast_2)`

    Examples::

        >>> input1 = torch.randn(100, 128)
        >>> input2 = torch.randn(100, 128)
        >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6)
        >>> output = cos(input1, input2)
    """
    __constants__ = ['dim', 'eps']

    def __init__(self, dim=1, eps=1e-8):
        super(CosineSimilarity, self).__init__()
        self.dim = dim
        self.eps = eps

    @weak_script_method
    def forward(self, x1, x2):
        return F.cosine_similarity(x1, x2, self.dim, self.eps)