mindspore.ops.ScatterAddWithAxis

class mindspore.ops.ScatterAddWithAxis(axis=0)[source]

The output of the operation is produced by creating a copy of the input input_x, and then add updating its value to values specified by updates at specific index positions specified by indices.

Note

The three inputs input_x, updates and indices must have the same rank r >= 1.

Parameters

axis (int, optional) – Specifies which axis to do scatter add, default: 0.

Inputs:

  • input_x (Tensor) - The target tensor to be added.

  • indices (Tensor) - The index of input tensor whose data type is int32 or int64.

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

Outputs:

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

Raises

  • TypeError – If dtype of indices is neither int32 nor int64.

  • ValueError – If the shape of indices is not equal to the shape of updates.

Supported Platforms:

CPU

Examples

>>> op = ops.ScatterAddWithAxis(0)
>>> input_x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32)
>>> indices = Tensor(np.array([[1, 0, 2], [0, 2, 1]]), mindspore.int32)
>>> updates = Tensor(np.array([[1, 1, 1], [1, 1, 1]]), mindspore.float32)
>>> output = op(input_x, indices, updates)
>>> print(output)
[[ 2.  3.  3.]
 [ 5.  5.  7.]
 [ 7.  9.  10.]]
>>> op = ops.ScatterAddWithAxis(1)
>>> input_x = Tensor(np.array([[1, 2, 3, 4, 5]]), mindspore.int32)
>>> indices = Tensor(np.array([[2, 4]]), mindspore.int32)
>>> updates = Tensor(np.array([[8, 8]]), mindspore.int32)
>>> output = op(input_x, indices, updates)
>>> print(output)
[[ 1  2  11  4  13]]