mindspore.ops.index_add

mindspore.ops.index_add(x, indices, y, axis, use_lock=True, check_index_bound=True)[source]

Adds tensor y to specified axis and indices of Parameter x. The axis should be in [0, len(x.dim) - 1], and indices should be in [0, x.shape[axis] - 1] at the axis dimension.

Parameters

  • x (Parameter) – The input Parameter to add to.

  • indices (Tensor) – Add the value of x and y along the dimension of the axis according to the specified index value, with data type int32. The indices must be 1D with the same size as the size of y in the axis dimension. The values of indices should be in [0, b), where the b is the size of x in the axis dimension.

  • y (Tensor) – The input tensor with the value to add. Must have same data type as x. The shape must be the same as x except the axis th dimension.

  • axis (int) – The dimension along which to index.

  • use_lock (bool) – Whether to enable a lock to protect the updating process of variable tensors. If True , when updating the value of x, this process will be protected by a lock by using atomic operation. If False , the result may be unpredictable. Default: True .

  • check_index_bound (bool) – If True, check index boundary. If False , don’t check index boundary. Default: True .

Returns

Tensor, has the same shape and dtype as x.

Raises

  • TypeError – If x is not a Parameter.

  • TypeError – If neither indices nor y is a Tensor.

  • ValueError – If axis is out of x rank’s range.

  • ValueError – If x rank is not the same as y rank.

  • ValueError – If shape of indices is not 1D or size of indices is not equal to dimension of y[axis].

  • ValueError – If y’s shape is not the same as x except the axis th dimension.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, Parameter
>>> from mindspore import ops
>>> x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32), name="name_x")
>>> indices = Tensor(np.array([0, 2]), mindspore.int32)
>>> y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32)
>>> output = ops.index_add(x, indices, y, 1)
>>> print(output)
[[ 1.5  2.   4. ]
 [ 5.   5.   7.5]
 [ 9.   8.  11.5]]