mindspore.ops.ScatterNdUpdate

class mindspore.ops.ScatterNdUpdate(use_locking=True)[source]

Updates tensor values by using input indices and value.

Using given values to update tensor value, along with the input indices.

input_x 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 input_x.

updates is a tensor of rank Q-1+P-N. Its shape is: \((i_0, i_1, ..., i_{Q-2}, x\_shape_N, ..., x\_shape_{P-1})\).

Inputs of input_x and updates comply with the implicit type conversion rules to make the data types consistent. If they have different data types, the lower priority data type will be converted to the relatively highest priority data type.

Parameters

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

Inputs:

  • input_x (Parameter) - The target tensor, with data type of Parameter. The shape is \((N,*)\) where \(*\) means,any number of additional dimensions.

  • indices (Tensor) - The index of input tensor, with int32 data type.

  • updates (Tensor) - The tensor to be updated to the input tensor, has the same type as input. The shape is indices.shape[:-1] + x.shape[indices.shape[-1]:].

Outputs:

Tensor, has the same shape and type as input_x.

Raises

  • TypeError – If use_locking is not a bool.

  • TypeError – If indices is not an int32.

  • RuntimeError – If the data type of input_x and updates conversion of Parameter is required when data type conversion of Parameter is not supported.

Supported Platforms:

Ascend GPU CPU

Examples

>>> np_x = np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]])
>>> input_x = mindspore.Parameter(Tensor(np_x, mindspore.float32), name="x")
>>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
>>> updates = Tensor(np.array([1.0, 2.2]), mindspore.float32)
>>> op = ops.ScatterNdUpdate()
>>> output = op(input_x, indices, updates)
>>> print(output)
[[1.   0.3   3.6]
 [0.4  2.2  -3.2]]