mindspore.ops.scatter_nd

mindspore.ops.scatter_nd(indices, updates, shape)

根据指定的索引将更新值散布到新tensor上。

使用给定的 shape 创建一个空tensor，并将 updates 的值通过索引（ indices ）来设置空tensor的值。空tensor的秩为 $P$ ， indices 的秩为 $Q$ 。

shape 为 $(s_0,s_1,...,s_{P-1})$ ，其中 $P\ge 1$ 。

indices 的shape为 $(i_0,i_1,...,i_{Q-2},N)$ ，其中 $Q\ge 2$ 且 $N\le P$ 。

indices 的最后一个维度（长度为 $N$ ）表示沿着空tensor的第 $N$ 个维度进行切片。

updates 是一个秩为 $Q-1+P-N$ 的tensor，shape为 $(i_0,i_1,...,i_{Q-2},s_N,s_{N+1},...,s_{P-1})$ 。

如果 indices 包含重复的下标，则 updates 的值会被累加到同一个位置上。

在秩为3的第一个维度中插入两个新值矩阵的计算过程如下图所示：

参数：

• indices (Tensor) - 指定新tensor中散布的索引。索引的秩须至少为2，并且 indices.shape[-1] <= len(shape) 。

• updates (Tensor) - 指定更新tensor。shape为 indices.shape[:-1] + shape[indices.shape[-1]:] 。

• shape (tuple[int]) - 指定输出tensor的shape。 shape 不能为空，且 shape 中的任何元素的值都必须大于等于1。

返回：

Tensor

支持平台：

Ascend GPU CPU

样例：

>>> import mindspore
>>> indices = mindspore.tensor([[0], [2]], mindspore.int32)
>>> updates = mindspore.tensor([[[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 = mindspore.ops.scatter_nd(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 = mindspore.tensor([[0, 1], [1, 1]], mindspore.int32)
>>> updates = mindspore.tensor([3.2, 1.1], mindspore.float32)
>>> shape = (3, 3)
>>> output = mindspore.ops.scatter_nd(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.]]