Function Differences with torch.cdist

torch.cdist

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

For more information, see torch.cdist.

mindspore.ops.cdist

mindspore.ops.cdist(x1, x2, p=2.0)

For more information, see mindspore.ops.cdist.

Differences

PyTorch: Compute the p-norm distance between each pair of column vectors of the two Tensors.

MindSpore: MindSpore API basically implements the same functionality as PyTorch, with a slight difference in accuracy.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	x1	x1	-
	Parameter 2	x2	x2	-
	Parameter 3	p	p	-
	Parameter 4	compute_mode	-	torch specifies whether to calculate the Euclidean distance by using matrix multiplication, which is not available in MindSpore

Code Example 1

# PyTorch
import torch
import numpy as np

x =  torch.tensor(np.array([[1.0, 1.0], [2.0, 2.0]]).astype(np.float32))
y =  torch.tensor(np.array([[3.0, 3.0], [3.0, 3.0]]).astype(np.float32))
output = torch.cdist(x, y, 2.0)
print(output)
# tensor([[2.8284, 2.8284],
#         [1.4142, 1.4142]])

# MindSpore
import mindspore.numpy as np
from mindspore import Tensor
from mindspore import ops

x = Tensor(np.array([[1.0, 1.0], [2.0, 2.0]]).astype(np.float32))
y = Tensor(np.array([[3.0, 3.0], [3.0, 3.0]]).astype(np.float32))
output = ops.cdist(x, y, 2.0)
print(output)
# [[2.828427  2.828427 ]
#  [1.4142135 1.4142135]]