比较与torch.Tensor.masked_scatter的差异

torch.Tensor.masked_scatter

torch.Tensor.masked_scatter(mask, tensor) -> Tensor

更多内容详见torch.Tensor.masked_scatter。

mindspore.Tensor.masked_scatter

mindspore.Tensor.masked_scatter(mask, tensor) -> Tensor

更多内容详见mindspore.Tensor.masked_scatter。

差异对比

PyTorch：返回一个Tensor。根据 mask ，使用 tensor 中的值，更新Tensor本身的值。

MindSpore：MindSpore此API实现功能与PyTorch基本一致。但是PyTorch支持 mask 与Tensor本身的双向广播， MindSpore只支持 mask 广播到Tensor本身。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	mask	mask	PyTorch支持 mask 与Tensor本身的双向广播，MindSpore只支持 mask 广播到Tensor本身
	参数2	tensor	tensor	-

代码示例1

# PyTorch
import torch

self = torch.tensor([0, 0, 0, 0, 0])
mask = torch.tensor([[0, 0, 0, 1, 1], [1, 1, 0, 1, 1]])
source = torch.tensor([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
output = self.masked_scatter(mask, source)
print(output)
# tensor([[0, 0, 0, 0, 1],
#         [2, 3, 0, 4, 5]])

# MindSpore
import mindspore
from mindspore import Tensor
import numpy as np

self = Tensor(np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]), mindspore.int32)
mask = Tensor(np.array([[False, False, False, True, True], [True, True, False, True, True]]), mindspore.bool_)
source = Tensor(np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]), mindspore.int32)
output = self.masked_scatter(mask, source)
print(output)
# [[0 0 0 0 1]
#  [2 3 0 4 5]]

代码示例2

# PyTorch
import torch

self = torch.tensor([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
mask = torch.tensor([0, 0, 0, 1, 1])
source = torch.tensor([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
output = self.masked_scatter(mask, source)
print(output)
# tensor([[0, 0, 0, 0, 1],
#         [0, 0, 0, 2, 3]])

# MindSpore
import mindspore
from mindspore import Tensor
import numpy as np

self = Tensor(np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]), mindspore.int32)
mask = Tensor(np.array([False, False, False, True, True]), mindspore.bool_)
source = Tensor(np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]), mindspore.int32)
output = self.masked_scatter(mask, source)
print(output)
# [[0 0 0 0 1]
#  [0 0 0 2 3]]