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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(x1, x2, p=2.0)
For more information, see mindspore.ops.cdist.
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 |
# 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]]
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