mindspore.mint.meshgrid

mindspore.mint.meshgrid(*tensors, indexing=None)

Generate coordinate matrices from given coordinate tensors.

Given N 1-D coordinate tensors, returns a tuple outputs of N N-D

coordinate tensors for evaluating expressions on an N-D grid.

Warning

• This is an experimental API that is subject to change or deletion.

• Graph mode, dynamic shape scenario: Only supports input of N 1-D tensors, where N > 1.

Parameters

tensors (Union(tuple[Tensor], list[Tensor])) – In GRAPH_MODE, a tuple of N 1-D Tensor objects and the length of input should be greater than 1. In PYNATIVE_MODE, a tuple of N 0-D or 1-D Tensor objects and the length of input should be greater than 0. The data type is Number.

Keyword Arguments

indexing (str, optional) – Cartesian ('xy', default) or matrix ('ij') indexing of output. Valid options: xy' or 'ij'. In the 2-D case with inputs of length M and N, for 'xy' indexing, the shape of outputs is \((N, M)\) for 'ij' indexing, the shape of outputs is \((M, N)\). In the 3-D case with inputs of length M, N and P, for 'xy' indexing, the shape of outputs is \((N, M, P)\) and for 'ij' indexing, the shape of outputs is \((M, N, P)\). Default None , which is equivalent to the value 'ij' .

Returns

Tensors, a Tuple of N N-D Tensor objects. The data type is the same with the Inputs.

Raises

ValueError – If indexing is neither 'xy' nor 'ij'.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> x = mindspore.tensor([1, 2, 3, 4])
>>> y = mindspore.tensor([5, 6, 7])
>>> z = mindspore.tensor([8, 9, 0, 1, 2])
>>> mindspore.mint.meshgrid(x, y, z, indexing='xy')
(Tensor(shape=[3, 4, 5], dtype=Int64, value=
    [[[1, 1, 1, 1, 1],
      [2, 2, 2, 2, 2],
      [3, 3, 3, 3, 3],
      [4, 4, 4, 4, 4]],
     [[1, 1, 1, 1, 1],
      [2, 2, 2, 2, 2],
      [3, 3, 3, 3, 3],
      [4, 4, 4, 4, 4]],
     [[1, 1, 1, 1, 1],
      [2, 2, 2, 2, 2],
      [3, 3, 3, 3, 3],
      [4, 4, 4, 4, 4]]]), Tensor(shape=[3, 4, 5], dtype=Int64, value=
    [[[5, 5, 5, 5, 5],
      [5, 5, 5, 5, 5],
      [5, 5, 5, 5, 5],
      [5, 5, 5, 5, 5]],
     [[6, 6, 6, 6, 6],
      [6, 6, 6, 6, 6],
      [6, 6, 6, 6, 6],
      [6, 6, 6, 6, 6]],
     [[7, 7, 7, 7, 7],
      [7, 7, 7, 7, 7],
      [7, 7, 7, 7, 7],
      [7, 7, 7, 7, 7]]]), Tensor(shape=[3, 4, 5], dtype=Int64, value=
    [[[8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2]],
     [[8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2]],
     [[8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2],
      [8, 9, 0, 1, 2]]]))