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
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 |
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]]