torch.cdist

torch.cdist(x1, x2, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary')

Computes batched the p-norm distance between each pair of the two collections of row vectors.

Parameters

• x1 (Tensor) – input tensor of shape $B \times P \times M$ .

• x2 (Tensor) – input tensor of shape $B \times R \times M$ .

• p – p value for the p-norm distance to calculate between each vector pair $\in [0, \infty]$ .

• compute_mode – ‘use_mm_for_euclid_dist_if_necessary’ - will use matrix multiplication approach to calculate euclidean distance (p = 2) if P > 25 or R > 25 ‘use_mm_for_euclid_dist’ - will always use matrix multiplication approach to calculate euclidean distance (p = 2) ‘donot_use_mm_for_euclid_dist’ - will never use matrix multiplication approach to calculate euclidean distance (p = 2) Default: use_mm_for_euclid_dist_if_necessary.

If x1 has shape $B \times P \times M$ and x2 has shape $B \times R \times M$ then the output will have shape $B \times P \times R$ .

This function is equivalent to scipy.spatial.distance.cdist(input,’minkowski’, p=p) if $p \in (0, \infty)$ . When $p = 0$ it is equivalent to scipy.spatial.distance.cdist(input, ‘hamming’) * M. When $p = \infty$ , the closest scipy function is scipy.spatial.distance.cdist(xn, lambda x, y: np.abs(x - y).max()).

Example

>>> a = torch.tensor([[0.9041,  0.0196], [-0.3108, -2.4423], [-0.4821,  1.059]])
>>> a
tensor([[ 0.9041,  0.0196],
        [-0.3108, -2.4423],
        [-0.4821,  1.0590]])
>>> b = torch.tensor([[-2.1763, -0.4713], [-0.6986,  1.3702]])
>>> b
tensor([[-2.1763, -0.4713],
        [-0.6986,  1.3702]])
>>> torch.cdist(a, b, p=2)
tensor([[3.1193, 2.0959],
        [2.7138, 3.8322],
        [2.2830, 0.3791]])