mindspore.ops.select

mindspore.ops.select(cond, x, y)[source]

The conditional tensor determines whether the corresponding element in the output must be selected from \(x\) (if true) or \(y\) (if false) based on the value of each element.

It can be defined as:

\[\begin{split}out_i = \begin{cases} x_i, & \text{if } cond_i \\ y_i, & \text{otherwise} \end{cases}\end{split}\]
Parameters

  • cond (Tensor[bool]) – The condition tensor, decides which element is chosen. The shape is \((x_1, x_2, ..., x_N, ..., x_R)\).

  • x (Union[Tensor, int, float]) – The first Tensor or number to be selected. If x is a Tensor, the shape is \((x_1, x_2, ..., x_N, ..., x_R)\). If x is an int or a float, it will be cast to the type of int32 or float32, and broadcast to the same shape as y. One of x and y must be a Tensor.

  • y (Union[Tensor, int, float]) – The second Tensor or number to be selected. If y is a Tensor, The shape is \((x_1, x_2, ..., x_N, ..., x_R)\). If y is an int or a float, it will be cast to the type of int32 or float32, and broadcast to the same shape as x. One of x and y must be a Tensor.

Returns

Tensor, has the same shape as cond.

Raises

  • TypeError – If x or y is not a Tensor, int or float.

  • ValueError – The shapes of inputs are different.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # 1) Both inputs are Tensor
>>>
>>> cond = Tensor([True, False])
>>> x = Tensor([2,3], mindspore.float32)
>>> y = Tensor([1,2], mindspore.float32)
>>> output = ops.select(cond, x, y)
>>> print(output)
[2. 2.]
>>> # 2) y is a float
>>> cond = Tensor([True, False])
>>> x = Tensor([2,3], mindspore.float32)
>>> y = 2.0
>>> output = ops.select(cond, x, y)
>>> print(output)
[2. 2.]