mindspore.ops.ScatterNdMax

class mindspore.ops.ScatterNdMax(use_locking=False)

Applies sparse maximum to individual values or slices in a tensor.

Using given values to update parameter value through the maximum operation, along with the input indices. This operation outputs the input_x after the update is done, which makes it convenient to use the updated value.

Refer to mindspore.ops.scatter_nd_max() for more details.

Parameters

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

Inputs:

• input_x (Parameter) -The target tensor, with data type of Parameter.

• indices (Tensor) - The index to do maximum operation whose data type must be int32 or int64. The rank of indices must be at least 2 and indices.shape[-1] <= len(shape).

• updates (Tensor) - The tensor to do the max operation with input_x. The data type is same as input_x, and the shape is indices.shape[:-1] + x.shape[indices.shape[-1]:].

Outputs:

Tensor, the updated input_x, has the same shape and type as input_x.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops, Parameter
>>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
>>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
>>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
>>> scatter_nd_max = ops.ScatterNdMax()
>>> output = scatter_nd_max(input_x, indices, updates)
>>> print(output)
[ 1. 8. 6.  4. 7.  6.  7. 9.]
>>> input_x = Parameter(Tensor(np.ones((4, 4, 4)), mindspore.int32))
>>> 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]],
...                            [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32)
>>> scatter_nd_max = ops.ScatterNdMax()
>>> output = scatter_nd_max(input_x, indices, updates)
>>> print(output)
[[[1 1 1 1]
  [2 2 2 2]
  [3 3 3 3]
  [4 4 4 4]]
 [[1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]]
 [[5 5 5 5]
  [6 6 6 6]
  [7 7 7 7]
  [8 8 8 8]]
 [[1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]]]