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Source code for torch.nn.modules.distance

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

from torch import Tensor


[docs]class PairwiseDistance(Module): r""" Computes the 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)` or :math:`(D)` where `N = batch dimension` and `D = vector dimension` - Input2: :math:`(N, D)` or :math:`(D)`, same shape as the Input1 - Output: :math:`(N)` or :math:`()` based on input dimension. If :attr:`keepdim` is ``True``, then :math:`(N, 1)` or :math:`(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) """ __constants__ = ['norm', 'eps', 'keepdim'] norm: float eps: float keepdim: bool def __init__(self, p: float = 2., eps: float = 1e-6, keepdim: bool = False) -> None: super(PairwiseDistance, self).__init__() self.norm = p self.eps = eps self.keepdim = keepdim def forward(self, x1: Tensor, x2: Tensor) -> Tensor: return F.pairwise_distance(x1, x2, self.norm, self.eps, self.keepdim)
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 number of dimensions as x1, matching x1 size at dimension `dim`, and broadcastable with x1 at other dimensions. - 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'] dim: int eps: float def __init__(self, dim: int = 1, eps: float = 1e-8) -> None: super(CosineSimilarity, self).__init__() self.dim = dim self.eps = eps def forward(self, x1: Tensor, x2: Tensor) -> Tensor: return F.cosine_similarity(x1, x2, self.dim, self.eps)

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