mindspore.ops.scatter_div

mindspore.ops.scatter_div(input_x, indices, updates)

Perform a division update on input_x based on the specified indices and update values.

$$\text{input\_x}[\text{indices}[i,...,j],:]/=\text{updates}[i,...,j,:]$$

Note

• Support implicit type conversion and type promotion.

• Since Parameter objects do not support type conversion, an exception will be thrown when input_x is of a low-precision data type.

• The shape of updates is indices.shape + input_x.shape[1:].

Parameters

• input_x (Union[Parameter, Tensor]) – The input parameter or tensor.

• indices (Tensor) – The specified indices.

• updates (Tensor) – The update values.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> input_x = mindspore.Parameter(mindspore.tensor([[6.0, 6.0, 6.0], [2.0, 2.0, 2.0]],
...                               mindspore.float32), name="x")
>>> indices = mindspore.tensor([0, 1], mindspore.int32)
>>> updates = mindspore.tensor([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]], mindspore.float32)
>>> output = mindspore.ops.scatter_div(input_x, indices, updates)
>>> print(output)
[[3. 3. 3.]
 [1. 1. 1.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = mindspore.Parameter(mindspore.tensor(np.array([[105.0, 105.0, 105.0],
...                                                [315.0, 315.0, 315.0]]), mindspore.float32), name="x")
>>> # for indices = [[0, 1], [1, 1]]
>>> # step 1: [0, 1]
>>> # input_x[0] = [105.0, 105.0, 105.0] / [1.0, 1.0, 1.0] = [105.0, 105.0, 105.0]
>>> # input_x[1] = [315.0, 315.0, 315.0] / [3.0, 3.0, 3.0] = [105.0, 105.0, 105.0]
>>> # step 2: [1, 1]
>>> # input_x[1] = [105.0, 105.0, 105.0] / [5.0, 5.0, 5.0] = [21.0, 21.0, 21.0]
>>> # input_x[1] = [21.0, 21.0, 21.0] / [7.0, 7.0, 7.0] = [3.0, 3.0, 3.0]
>>> indices = mindspore.tensor(np.array([[0, 1], [1, 1]]), mindspore.int32)
>>> updates = mindspore.tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[5.0, 5.0, 5.0], [7.0, 7.0, 7.0]]]), mindspore.float32)
>>> output = mindspore.ops.scatter_div(input_x, indices, updates)
>>> print(output)
[[105. 105. 105.]
 [  3.   3.   3.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = mindspore.Parameter(mindspore.tensor(np.array([[105.0, 105.0, 105.0],
...                                                [315.0, 315.0, 315.0]]), mindspore.float32), name="x")
>>> # for indices = [[1, 0], [1, 1]]
>>> # step 1: [1, 0]
>>> # input_x[0] = [105.0, 105.0, 105.0] / [3.0, 3.0, 3.0] = [35.0, 35.0, 35.0]
>>> # input_x[1] = [315.0, 315.0, 315.0] / [1.0, 1.0, 1.0] = [315.0, 315.0, 315.0]
>>> # step 2: [1, 1]
>>> # input_x[1] = [315.0, 315.0, 315.0] / [5.0, 5.0, 5.0] = [63.0 63.0 63.0]
>>> # input_x[1] = [63.0 63.0 63.0] / [7.0, 7.0, 7.0] = [9.0, 9.0, 9.0]
>>> indices = mindspore.tensor(np.array([[1, 0], [1, 1]]), mindspore.int32)
>>> updates = mindspore.tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[5.0, 5.0, 5.0], [7.0, 7.0, 7.0]]]), mindspore.float32)
>>> output = mindspore.ops.scatter_div(input_x, indices, updates)
>>> print(output)
[[35. 35. 35.]
 [ 9.  9.  9.]]