mindspore.ops.gather_elements
- mindspore.ops.gather_elements(input, dim, index)[source]
Gathers elements along an axis specified by dim.
For a 3-D tensor, the output is:
output[i][j][k] = x[index[i][j][k]][j][k] # if dim == 0 output[i][j][k] = x[i][index[i][j][k]][k] # if dim == 1 output[i][j][k] = x[i][j][index[i][j][k]] # if dim == 2
input and index have the same length of dimensions, and index.shape[axis] <= input.shape[axis] where axis goes through all dimensions of input except dim.
Warning
On Ascend, the behavior is unpredictable in the following cases:
the value of index is not in the range [-input.shape[dim], input.shape[dim]) in forward;
the value of index is not in the range [0, input.shape[dim]) in backward.
- Parameters
input (Tensor) – The input tensor.
dim (int) – The axis along which to index. It must be int32 or int64. The value range is [-input.ndim, input.ndim).
index (Tensor) – The indices of elements to gather. It can be one of the following data types: int32, int64. The value range of each index element is [-input.shape(dim), input.shape(dim)).
- Returns
Tensor, has the same shape as index and has the same data type with input.
- Raises
TypeError – If dtype of dim or index is neither int32 nor int64.
ValueError – If length of shape of input is not equal to length of shape of index.
ValueError – If the size of the dimension except dim in input is less than size in index.
ValueError – If the value of dim is not in the expected range.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> import mindspore >>> from mindspore import Tensor >>> x = Tensor(np.array([[1, 2], [3, 4]]), mindspore.int32) >>> index = Tensor(np.array([[0, 0], [1, 0]]), mindspore.int32) >>> dim = 1 >>> output = mindspore.ops.gather_elements(x, dim, index) >>> print(output) [[1 1] [4 3]]