mindspore.ops.select

mindspore.ops.select(condition, input, other)[source]

The conditional tensor determines whether the corresponding element in the output must be selected from input (if True) or other (if False) based on the value of each element.

It can be defined as:

\[\begin{split}out_i = \begin{cases} input_i, & \text{if } condition_i \\ other_i, & \text{otherwise} \end{cases}\end{split}\]

Warning

Non-backward-compatible change after version 2.9.0: this interface currently has where-style conditional selection semantics, and will be changed to slice-along-dimension semantics. The new signature will be select(input, dim, index).

Parameters

condition (Tensor[bool]) – The condition tensor.
input (Union[Tensor, int, float]) – The first tensor or number to be selected.
other (Union[Tensor, int, float]) – The second tensor or number to be selected.

Returns

Tensor

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> # case1: Both `input` and `other` are tensor
>>> cond = mindspore.tensor([True, False])
>>> x = mindspore.tensor([2,3], mindspore.float32)
>>> y = mindspore.tensor([1,2], mindspore.float32)
>>> output1 = mindspore.ops.select(cond, x, y)
>>> print(output1)
[2. 2.]
>>> # case2: Both `input` and `other` are number
>>> output2 = mindspore.ops.select(cond, input=1, other=2)
>>> print(output2)
[1 2]
>>> # case3: `input` is tensor and `other` is number
>>> output3 = mindspore.ops.select(cond, x, other=3)
>>> print(output3)
[2. 3.]