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# Source code for torch.nn.modules.distance

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

[docs]@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 vector 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 True, 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)

[docs]@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)


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