文档反馈

问题文档片段

问题文档片段包含公式时，显示为空格。

提交类型

issue

有点复杂...

找人问问吧。

请选择提交类型

问题类型

规范和低错类

- 规范和低错类：

- 错别字或拼写错误，标点符号使用错误、公式错误或显示异常。

- 链接错误、空单元格、格式错误。

- 英文中包含中文字符。

- 界面和描述不一致，但不影响操作。

- 表述不通顺，但不影响理解。

- 版本号不匹配：如软件包名称、界面版本号。

易用性

- 易用性：

- 关键步骤错误或缺失，无法指导用户完成任务。

- 缺少主要功能描述、关键词解释、必要前提条件、注意事项等。

- 描述内容存在歧义指代不明、上下文矛盾。

- 逻辑不清晰，该分类、分项、分步骤的没有给出。

正确性

- 正确性：

- 技术原理、功能、支持平台、参数类型、异常报错等描述和软件实现不一致。

- 原理图、架构图等存在错误。

- 命令、命令参数等错误。

- 代码片段错误。

- 命令无法完成对应功能。

- 界面错误，无法指导操作。

- 代码样例运行报错、运行结果不符。

风险提示

- 风险提示：

- 对重要数据或系统存在风险的操作，缺少安全提示。

内容合规

- 内容合规：

- 违反法律法规，涉及政治、领土主权等敏感词。

- 内容侵权。

请选择问题类型

问题描述

点击输入详细问题描述，以帮助我们快速定位问题。

文档反馈

mindspore.ops.ScatterNd

class mindspore.ops.ScatterNd[source]

Scatters a tensor into a new tensor depending on the specified indices.

Creates an empty tensor with the given shape, and set values by scattering the update tensor depending on indices.

The empty tensor has rank P and indices has rank Q where Q >= 2.

indices has shape $(i_{0}, i_{1}, . . ., i_{Q - 2}, N)$ where N <= P.

The last dimension of indices (with length N ) indicates slices along the N th dimension of the empty tensor.

updates is a tensor of rank Q-1+P-N. Its shape is: $(i_{0}, i_{1}, . . ., i_{Q - 2}, s h a p e_{N}, . . ., s h a p e_{P - 1})$ .

The following figure shows the calculation process of inserting two slices in the first dimension of a rank-3 with two matrices of new values:

Inputs:

indices (Tensor) - The index of scattering in the new tensor with int32 or int64 data type. The rank of indices must be at least 2 and indices_shape[-1] <= len(shape).
updates (Tensor) - The source Tensor to be scattered. It has shape indices_shape[:-1] + shape[indices_shape[-1]:].
shape (tuple[int]) - Define the shape of the output tensor, has the same data type as indices. The shape of shape is $(x_{1}, x_{2}, . . ., x_{R})$ , and length of ‘shape’ is greater than or equal 2. In other words, the shape of shape is at least $(x_{1}, x_{2})$ . And the value of any element in shape must be greater than or equal 1. In other words, $x_{1}$ >= 1, $x_{2}$ >= 1.

Outputs:

Tensor, the new tensor, has the same type as update and the same shape as shape.

Raises

TypeError – If shape is not a tuple.
ValueError – If any element of shape is less than 1.

Supported Platforms:: Ascend GPU CPU

Examples

>>> op = ops.ScatterNd()
>>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
>>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2],
...                             [3, 3, 3, 3], [4, 4, 4, 4]],
...                            [[1, 1, 1, 1], [2, 2, 2, 2],
...                             [3, 3, 3, 3], [4, 4, 4, 4]]]), mindspore.float32)
>>> shape = (4, 4, 4)
>>> output = op(indices, updates, shape)
>>> print(output)
[[[1. 1. 1. 1.]
  [2. 2. 2. 2.]
  [3. 3. 3. 3.]
  [4. 4. 4. 4.]]
 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]
 [[1. 1. 1. 1.]
  [2. 2. 2. 2.]
  [3. 3. 3. 3.]
  [4. 4. 4. 4.]]
 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]]
>>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32)
>>> updates = Tensor(np.array([3.2, 1.1]), mindspore.float32)
>>> shape = (3, 3)
>>> output = op(indices, updates, shape)
>>> # In order to facilitate understanding, explain the operator pseudo-operation process step by step:
>>> # Step 1: Generate an empty Tensor of the specified shape according to the shape
>>> # [
>>> #     [0. 0. 0.]
>>> #     [0. 0. 0.]
>>> #     [0. 0. 0.]
>>> # ]
>>> # Step 2: Modify the data at the specified location according to the indicators
>>> # 0th row of indices is [0, 1], 0th row of updates is 3.2.
>>> # means that the empty tensor in the 0th row and 1st col set to 3.2
>>> # [
>>> #     [0. 3.2. 0.]
>>> #     [0. 0.   0.]
>>> #     [0. 0.   0.]
>>> # ]
>>> # 1th row of indices is [1, 1], 1th row of updates is 1.1.
>>> # means that the empty tensor in the 1th row and 1st col set to 1.1
>>> # [
>>> #     [0. 3.2. 0.]
>>> #     [0. 1.1  0.]
>>> #     [0. 0.   0.]
>>> # ]
>>> # The final result is as follows:
>>> print(output)
[[0. 3.2 0.]
 [0. 1.1 0.]
 [0. 0.  0.]]