mindspore.ops.IndexAdd
- class mindspore.ops.IndexAdd(axis, use_lock=True, check_index_bound=True)[source]
Adds tensor y to specified axis and indices of tensor x. The axis should be in [-len(x.dim), len(x.dim) - 1], and indices should be in [0, the size of x - 1] at the axis dimension.
- Parameters
axis (int) – The dimension along which to index.
use_lock (bool, optional) – 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. IfFalse
, the result may be unpredictable. Default:True
.check_index_bound (bool, optional) – If
True
, check index boundary. IfFalse
, don’t check index boundary. Default:True
.
- Inputs:
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.
- Outputs:
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 mindspore >>> import numpy as np >>> from mindspore import Tensor, nn, ops, Parameter >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.index_add = ops.IndexAdd(axis=1) ... self.x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32), ... name="name_x") ... self.indices = Tensor(np.array([0, 2]), mindspore.int32) ... ... def construct(self, y): ... return self.index_add(self.x, self.indices, y) ... >>> y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32) >>> net = Net() >>> output = net(y) >>> print(output) [[ 1.5 2. 4. ] [ 5. 5. 7.5] [ 9. 8. 11.5]]