# Source code for torch.nn.modules.distance

import torch
from .module import Module
from .. import functional as F

[docs]class PairwiseDistance(Module): r""" Computes the batchwise pairwise distance between vectors v1,v2: .. math :: \Vert x \Vert _p := \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p} Args: x (Tensor): input tensor containing the two input batches p (real): the norm degree. Default: 2 Shape: - Input: :math:(N, D) where D = vector dimension - Output: :math:(N, 1) >>> pdist = nn.PairwiseDistance(2) >>> input1 = autograd.Variable(torch.randn(100, 128)) >>> input2 = autograd.Variable(torch.randn(100, 128)) >>> output = pdist(input1, input2) """ def __init__(self, p=2, eps=1e-6): super(PairwiseDistance, self).__init__() self.norm = p self.eps = eps def forward(self, x1, x2): return F.pairwise_distance(x1, x2, self.norm, self.eps)
[docs]class CosineSimilarity(Module): r"""Returns cosine similarity between x1 and x2, 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: x1 (Variable): First input. x2 (Variable): Second input (of size matching x1). dim (int, optional): Dimension of vectors. Default: 1 eps (float, optional): Small value to avoid division by zero. Default: 1e-8 Shape: - Input: :math:(\ast_1, D, \ast_2) where D is at position dim. - Output: :math:(\ast_1, \ast_2) where 1 is at position dim. >>> input1 = autograd.Variable(torch.randn(100, 128)) >>> input2 = autograd.Variable(torch.randn(100, 128)) >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6) >>> output = cos(input1, input2) >>> print(output) """ def __init__(self, dim=1, eps=1e-8): super(CosineSimilarity, self).__init__() self.dim = dim self.eps = eps def forward(self, x1, x2): return F.cosine_similarity(x1, x2, self.dim, self.eps)
# TODO: Cosine # TODO: Euclidean # TODO: WeightedEuclidean