比较与torch.logical_xor的功能差异

torch.logical_xor

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

更多内容详见 torch.logical_xor。

mindspore.numpy.logical_xor

class mindspore.numpy.logical_xor(x1, x2, dtype=None)

更多内容详见 mindspore.numpy.logical_xor。

使用方式

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

MindSpore: 按元素计算输入张量的逻辑异或。输入应该是bool或数据类型为bool的张量。

代码示例

import mindspore.numpy as np
import torch

# MindSpore
x1 = np.array([True, False])
x2 = np.array([False, False])
print(np.logical_xor(x1, x2))
# [True False]
x1 = np.array([0, 1, 10, 0])
x2 = np.array([4, 0, 1, 0])
print(np.logical_xor(x1, x2))
# TypeError: For 'LogicalOr', the type of `x` should be subclass of Tensor[Bool], but got Tensor[Int32].

# PyTorch
print(torch.logical_xor(torch.tensor([True, False, True]), torch.tensor([True, False, False])))
# tensor([False, 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_xor(a, b))
# tensor([ True,  True, False, False])
print(torch.logical_xor(a.double(), b.double()))
# tensor([ True,  True, False, False])
print(torch.logical_xor(a.double(), b))
# tensor([ True,  True, False, False])
print(torch.logical_xor(a, b, out=torch.empty(4, dtype=torch.bool)))
# tensor([ True,  True, False, False])