| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677 |
- from .module import Module
- from .. import functional as F
- from torch import Tensor
- 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)
|
