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)
# TODO: Cosine # TODO: CosineDistance - make sure lua's CosineDistance isn't actually cosine similarity # TODO: Euclidean # TODO: WeightedEuclidean