Function Differences with torch.logical_xor

torch.logical_xor

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

For more information, see torch.logical_xor.

mindspore.numpy.logical_xor

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

For more information, see mindspore.numpy.logical_xor.

Differences

PyTorch: Computes the element-wise logical XOR of the given input tensors. Zeros are treated as False and nonzeros are treated as True.

MindSpore: Computes the truth value of x1 XOR x2, element-wise. The input should be a bool or a tensor whose data type is bool.

Code Example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 primitive[LogicalOr], the input argument[x, y, ] must be a type of {Tensor[Bool],}, but got 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])