mindspore.ops.scatter_nd_div

mindspore.ops.scatter_nd_div(input_x, indices, updates, use_locking=False)

Perform a sparse 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.

• The dimension of indices is at least 2, and its shape must be indices.shape[-1] <= len(indices.shape).

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

Parameters

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

• indices (Tensor) – The specified indices.

• updates (Tensor) – The update values.

• use_locking (bool) – Whether to protect the assignment by a lock. Default: False .

Returns

Tensor

Supported Platforms:

GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> input_x = mindspore.Parameter(mindspore.tensor([1, 2, 3, 4, 5, 6, 7, 8],
...                               mindspore.float32), name="x")
>>> indices = mindspore.tensor([[2], [4], [1], [7]], mindspore.int32)
>>> updates = mindspore.tensor([6, 7, 8, 9], mindspore.float32)
>>> output = mindspore.ops.scatter_nd_div(input_x, indices, updates, False)
>>> print(output)
[1.         0.25       0.5        4.         0.71428573 6.
 7.         0.8888889 ]
>>> input_x = mindspore.Parameter(mindspore.tensor(np.ones((4, 4, 4)), mindspore.float32))
>>> indices = mindspore.tensor(np.array([[0], [2]]), mindspore.int32)
>>> updates = mindspore.tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
...                            [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.float32)
>>> output = mindspore.ops.scatter_nd_div(input_x, indices, updates, False)
>>> print(output)
[[[1.         1.         1.         1.        ]
  [0.5        0.5        0.5        0.5       ]
  [0.33333334 0.33333334 0.33333334 0.33333334]
  [0.25       0.25       0.25       0.25      ]]
 [[1.         1.         1.         1.        ]
  [1.         1.         1.         1.        ]
  [1.         1.         1.         1.        ]
  [1.         1.         1.         1.        ]]
 [[0.2        0.2        0.2        0.2       ]
  [0.16666667 0.16666667 0.16666667 0.16666667]
  [0.14285715 0.14285715 0.14285715 0.14285715]
  [0.125      0.125      0.125      0.125     ]]
 [[1.         1.         1.         1.        ]
  [1.         1.         1.         1.        ]
  [1.         1.         1.         1.        ]
  [1.         1.         1.         1.        ]]]