Differences with torch.Tensor.min

torch.Tensor.min

torch.Tensor.min(dim=None, keepdim=False)

For more information, see torch.Tensor.min.

mindspore.Tensor.min

mindspore.Tensor.min(axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)

For more information, see mindspore.Tensor.min.

Differences

MindSpore is compatible with Numpy parameters initial and where based on PyTorch, added parameter return_indices is used to control whether indexes are returned.

Categories	Subcategories	PyTorch	MindSpore	Differences
Inputs	Input 1	dim	axis	Same function, different parameter names
	Input 2	keepdim	keepdims	Same function, different parameter names
	Input 3	-	initial	Not involved
	Input 4	-	where	Not involved
	Input 5	-	return_indices	Not involved

Code Example 1

When no dimension is specified, the two APIs implement the same functionality.

import mindspore as ms
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 = input_x.min()
print(output)
# -1.1348

# torch
input_x = torch.tensor(np_x)
output = input_x.min()
print(output)
# tensor(-1.1348)

Code Example 2

When specifying dimensions, MindSpore does not return an index by default and needs to be manually specified.

import mindspore as ms
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)
values, indices = input_x.min(axis=1, return_indices=True)
print(values)
# [-0.7814 -0.3848 -1.1348 -0.6668]
print(indices)
# [2 2 2 0]

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