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:
p (real): the norm degree. Default: 2
eps (float, optional): Small value to avoid division by zero.
Default: 1e-6
Shape:
- Input1: :math:`(N, D)` where `D = vector dimension`
- Input2: :math:`(N, D)`, same shape as the Input1
- Output: :math:`(N, 1)`
Examples::
>>> pdist = nn.PairwiseDistance(p=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:
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 = 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)