mindspore.ops.scatter_nd_sub

mindspore.ops.scatter_nd_sub(input_x, indices, updates, use_locking=False)[source]

Applies sparse subtraction to individual values or slices in a tensor.

Using given values to update tensor value through the subtraction operation, along with the input indices. This operation outputs the input_x after the update is done, which makes it convenient to use the updated value.

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: \((i_0, i_1, ..., i_{Q-2}, x\_shape_N, ..., x\_shape_{P-1})\).

Parameters

  • input_x (Parameter) – The target tensor, with data type of Parameter.

  • indices (Tensor) – The index of input tensor, with int32 or int64 data type. The rank of indices must be at least 2 and indices.shape[-1] <= len(shape).

  • updates (Tensor) – The tensor doing the subtraction operation with input_x, has the same type as input. The shape is indices.shape[:-1] + x.shape[indices.shape[-1]:].

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

Returns

Tensor, has the same shape and type as input_x.

Raises

  • TypeError – If the dtype of use_locking is not bool.

  • TypeError – If the dtype of indices is not int32 or int64.

  • TypeError – If dtype of input_x and updates are not the same.

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

  • 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

>>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
>>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
>>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
>>> output = ops.scatter_nd_sub(input_x, indices, updates, False)
>>> print(output)
[ 1. -6. -3.  4. -2.  6.  7. -1.]
>>> input_x = Parameter(Tensor(np.zeros((4, 4, 4)), mindspore.int32))
>>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
>>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
...                            [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32)
>>> output = ops.scatter_nd_sub(input_x, indices, updates, False)
>>> print(output)
[[[-1 -1 -1 -1]
  [-2 -2 -2 -2]
  [-3 -3 -3 -3]
  [-4 -4 -4 -4]]
 [[ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]]
 [[-5 -5 -5 -5]
  [-6 -6 -6 -6]
  [-7 -7 -7 -7]
  [-8 -8 -8 -8]]
 [[ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]]]