PairwiseDistance — PyTorch 2.7 documentation (original) (raw)

class torch.nn.PairwiseDistance(p=2.0, eps=1e-06, keepdim=False)[source][source]¶

Computes the pairwise distance between input vectors, or between columns of input matrices.

Distances are computed using p-norm, with constant eps added to avoid division by zero if p is negative, i.e.:

dist(x,y)=∥x−y+ϵe∥p,\mathrm{dist}\left(x, y\right) = \left\Vert x-y + \epsilon e \right\Vert_p,

where ee is the vector of ones and the p-norm is given by.

∥x∥p=(∑i=1n∣xi∣p)1/p.\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.

Parameters

• p (real , optional) – the norm degree. Can be negative. 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 vector dimension. Default: False

Shape:

• Input1: (N,D)(N, D) or (D)(D) where N = batch dimension and D = vector dimension
• Input2: (N,D)(N, D) or (D)(D), same shape as the Input1
• Output: (N)(N) or ()() based on input dimension. If keepdim is True, then (N,1)(N, 1) or (1)(1) based on input dimension.

Examples::

pdist = nn.PairwiseDistance(p=2) input1 = torch.randn(100, 128) input2 = torch.randn(100, 128) output = pdist(input1, input2)