Differences with torch.min

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torch.min

torch.min(input, dim, keepdim=False, *, out=None)

For more information, see torch.min.

mindspore.ops.min

mindspore.ops.min(input, axis=None, keepdims=False, *, initial=None, where=None)

For more information, see mindspore.ops.min.

Differences

PyTorch: Output tuple(min, index of min).

MindSpore: When the axis is None or the shape is empty in MindSpore, the keepdims and subsequent parameters are not effective, and the function is consistent with torch.min(input), and the index returned is fixed at 0. Otherwise, the output is a tuple (min, index of min), which is consistent with torch.min(input, dim, keepdim=False, *, out=None).

Categories

Subcategories

PyTorch

MindSpore

Difference

Parameters

Parameter 1

input

input

Consistent

Parameter 2

dim

axis

Same function, different parameter names

Parameter 3

keepdim

keepdims

Same function, different parameter names

Parameter 4

-

initial

Not involved

Parameter 5

-

where

Not involved

Parameter 6

out

-

Not involved

Code Example

import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np

np_x = np.array([[-0.0081, -0.3283, -0.7814, -0.0934],
                 [1.4201, -0.3566, -0.3848, -0.1608],
                 [-0.0446, -0.1843, -1.1348, 0.5722],
                 [-0.6668, -0.2368, 0.2790, 0.0453]]).astype(np.float32)
# mindspore
input_x = ms.Tensor(np_x)
output, index = ops.min(input_x, axis=1)
print(output)
# [-0.7814 -0.3848 -1.1348 -0.6668]
print(index)
# [2 2 2 0]

# torch
input_x = torch.tensor(np_x)
output, index = torch.min(input_x, dim=1)
print(output)
# tensor([-0.7814, -0.3848, -1.1348, -0.6668])
print(index)
# tensor([2, 2, 2, 0])