比较与torch.Tensor.min的差异
torch.Tensor.min
torch.Tensor.min(dim=None, keepdim=False)
更多内容详见torch.Tensor.min。
mindspore.Tensor.min
mindspore.Tensor.min(axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)
更多内容详见mindspore.Tensor.min。
差异对比
MindSpore在PyTorch的基础上,兼容了Numpy的入参 initial
和 where
,新增了参数return_indices用于控制是否返回索引。
分类 |
子类 |
PyTorch |
MindSpore |
差异 |
---|---|---|---|---|
输入 |
输入1 |
dim |
axis |
功能一致,参数名不同 |
输入2 |
keepdim |
keepdims |
功能一致,参数名不同 |
|
输入3 |
- |
initial |
不涉及 |
|
输入4 |
- |
where |
不涉及 |
|
输入5 |
- |
return_indices |
不涉及 |
代码示例1
不指定维度时,两API实现功能一致。
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)
代码示例2
指定维度时,MindSpore默认不返回索引,需手动指定。
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])