比较与torch.logical_or的功能差异

torch.logical_or

class torch.logical_or(input, other, out=None)

更多内容详见 torch.logical_or。

mindspore.ops.LogicalOr

class class mindspore.ops.LogicalOr()(x, y)

更多内容详见 mindspore.ops.LogicalOr。

使用方式

PyTorch: 计算给定输入张量的逐元素逻辑或。零被视为“False”，非零被视为“True”。

MindSpore: 按元素计算两个输入张量的逻辑或。输入可以是bool值或数据类型为bool的张量。

代码示例

import numpy as np
import torch
from mindspore import Tensor, Parameter
from mindspore import dtype as mstype

# MindSpore
x = Tensor(np.array([True, False, True]), mstype.bool_)
y = Tensor(np.array([True, True, False]), mstype.bool_)
logical_or = ops.LogicalOr()
print(logical_or(x, y))
# [ True  True  True]
x = Tensor(np.array([True, False, True]), mstype.bool_)
y = Tensor(np.array([True, True, False]), mstype.int32)
logical_or = ops.LogicalOr()
print(logical_or(x, y))
# TypeError: For 'LogicalOr', the type of `x` should be subclass of Tensor[Bool], but got Tensor[Int32] .

# PyTorch
print(torch.logical_or(torch.tensor([True, False, True]), torch.tensor([True, False, False])))
# tensor([ True, False,  True])
a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
print(torch.logical_or(a, b))
# tensor([ True,  True,  True, False])
print(torch.logical_or(a.double(), b.double()))
# tensor([ True,  True,  True, False])
print(torch.logical_or(a.double(), b))
# tensor([ True,  True,  True, False])
print(torch.logical_or(a, b, out=torch.empty(4, dtype=torch.bool)))
# tensor([ True,  True,  True, False])