比较与torch.max的差异

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torch.max

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

torch.max(input, other, *, out=None)

更多内容详见torch.max

mindspore.ops.max

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

更多内容详见mindspore.ops.max

差异对比

PyTorch:torch.max(input, dim, keepdim=False, *, out=None) 输出为元组(最大值, 最大值的索引)。

MindSpore:axis为None或者shape为空时,keepdims以及后面的参数均不生效,功能与torch.max(input)一致,此时索引固定返回0;否则,输出为元组(最大值, 最大值的索引),功能与torch.max(input, dim, keepdim=False, *, out=None)一致。

分类

子类

PyTorch

MindSpore

差异

参数

参数1

input

input

一致

参数2

dim

axis

功能一致,参数名不同

参数3

keepdim

keepdims

功能一致,参数名不同

参数4

-

initial

不涉及

参数5

-

where

不涉及

参数6

out

-

不涉及

PyTorch: torch.max(input, other, *, out=None)mindspore.ops.maximum 用法一致。

代码示例

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)

# torch.max(input, dim, keepdim=False, *, out=None)
input_x = torch.tensor(np_x)
output, index = torch.max(input_x, dim=1)
print(output)
# tensor([-0.0081,  1.4201,  0.5722,  0.2790])
print(index)
# tensor([0, 0, 3, 2])

# mindspore.ops.max
input_x = ms.Tensor(np_x)
output, index = ops.max(input_x, axis=1)
print(output)
# [-0.0081  1.4201  0.5722  0.279 ]
print(index)
# [0 0 3 2]

# torch.max(input, other, *, out=None)
torch_x = torch.tensor([1.0, 5.0, 3.0], dtype=torch.float32)
torch_y = torch.tensor([4.0, 2.0, 6.0], dtype=torch.float32)
torch_output = torch.max(torch_x, torch_y)
print(torch_output)
# tensor([4., 5., 6.])

# mindspore.ops.maximum
x = ms.Tensor([1.0, 5.0, 3.0], ms.float32)
y = ms.Tensor([4.0, 2.0, 6.0], ms.float32)
output = ms.ops.maximum(x, y)
print(output)
# [4. 5. 6.]