Differences with torch.Tensor.max
torch.Tensor.max
torch.Tensor.max(dim=None, keepdim=False)
For more information, see torch.Tensor.max.
mindspore.Tensor.max
mindspore.Tensor.max(axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)
For more information, see mindspore.Tensor.max.
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.max()
print(output)
# 1.4201
# torch
input_x = torch.tensor(np_x)
output = input_x.max()
print(output)
# tensor(1.4201)
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.max(axis=1, return_indices=True)
print(values)
# [-0.0081 1.4201 0.5722 0.279 ]
print(indices)
# [0 0 3 2]
# torch
input_x = torch.tensor(np_x)
values, indices = input_x.max(dim=1)
print(values)
# tensor([-0.0081, 1.4201, 0.5722, 0.2790])
print(indices)
# tensor([0, 0, 3, 2])