functional.py 文件源码

def pairwise_distance(x1, x2, p=2, eps=1e-6):
    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:
        x1: first input tensor
        x2: second input tensor
        p: the norm degree. Default: 2
        eps (float, optional): Small value to avoid division by zero. Default: 1e-6

    Shape:
        - Input: :math:`(N, D)` where `D = vector dimension`
        - Output: :math:`(N, 1)`

    Example::

        >>> input1 = autograd.Variable(torch.randn(100, 128))
        >>> input2 = autograd.Variable(torch.randn(100, 128))
        >>> output = F.pairwise_distance(input1, input2, p=2)
        >>> output.backward()
    """
    assert x1.size() == x2.size(), "Input sizes must be equal."
    assert x1.dim() == 2, "Input must be a 2D matrix."
    diff = torch.abs(x1 - x2)
    out = torch.pow(diff + eps, p).sum(dim=1, keepdim=True)
    return torch.pow(out, 1. / p)