mindspore.ops.ScatterUpdate

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

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

for each i, …, j in indices.shape:

\[\text{input_x}[\text{indices}[i, ..., j], :] = \text{updates}[i, ..., j, :]\]

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 (Union[Parameter, Tensor]) - The target tensor, with data type of Parameter or Tensor. The shape is 0-D or \((N, *)\) where \(*\) means any number of additional dimensions.

  • indices (Tensor) - The index of input tensor. With int32 data type. If there are duplicates in indices, the order for updating is undefined.

  • updates (Tensor) - The tensor to update the input tensor, has the same type as input, and updates.shape = indices.shape + input_x.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.

  • ValueError – If the shape of updates is not equal to indices.shape + input_x.shape[1:].

  • RuntimeError – If the data types of input_x and updates are inconsistent and require conversion, but the conversion of updates to the data type of input_x is not supported.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> 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, 1]), mindspore.int32)
>>> np_updates = np.array([[2.0, 1.2, 1.0], [3.0, 1.2, 1.0]])
>>> updates = Tensor(np_updates, mindspore.float32)
>>> op = ops.ScatterUpdate()
>>> output = op(input_x, indices, updates)
>>> print(output)
[[2. 1.2  1.]
 [3. 1.2  1.]]