mindspore.ops.TensorScatterAdd

class mindspore.ops.TensorScatterAdd[source]

Creates a new tensor by adding the values from the positions in input_x indicated by indices, with values from updates. When multiple values are given for the same index, the updated result will be the sum of all values. This operation is almost equivalent to using mindspore.ops.ScatterNdAdd, except that the updates are applied on output Tensor instead of input Parameter.

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

Inputs:

  • input_x (Tensor) - The target tensor. The dimension of input_x must be no less than indices.shape[-1].

  • indices (Tensor) - The index of input tensor whose data type is int32 or int64. The rank must be at least 2.

  • updates (Tensor) - The tensor to update the input tensor, has the same type as input, and updates. Shape should be equal to indices.shape[:-1] + input_x.shape[indices.shape[-1]:].

Outputs:

Tensor, 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
>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> indices = Tensor(np.array([[0, 0], [0, 0]]), mindspore.int32)
>>> updates = Tensor(np.array([1.0, 2.2]), mindspore.float32)
>>> # Next, demonstrate the approximate operation process of this operator:
>>> # 1, indices[0] = [0, 0], indices[1] = [0, 0]
>>> # 2, And input_x[0, 0] = -0.1
>>> # 3, So input_x[indices] = [-0.1, -0.1]
>>> # 4, Satisfy the above formula: input_x[indices].shape=(2) == updates.shape=(2)
>>> op = ops.TensorScatterAdd()
>>> # 5, Perform the addition operation for the first time:
>>> #      first_input_x = input_x[0][0] + updates[0] = [[0.9, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> # 6, Perform the addition operation for the second time:
>>> #      second_input_x = input_x[0][0] + updates[1] = [[3.1, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> output = op(input_x, indices, updates)
>>> print(output)
[[ 3.1  0.3  3.6]
 [ 0.4  0.5 -3.2]]