mindspore.ops.coo_concat

mindspore.ops.coo_concat(sp_input, concat_dim=0)[source]

concatenates the input SparseTensor(COO format) along the specified dimension.

Warning

This is an experimental API that is subjected to change or deletion. Only supported on CPU now.

Parameters

  • sp_input (Union[list(COOTensor), tuple(COOTensor)]) – for COOTensor input.

  • concat_dim (scalar) – decide the dimension to concatenation along. The value must be in range [-rank, rank), where rank is the number of dimensions in each input SparseTensor. Default is 0.

Returns

  • output (COOtensor) - the result of concatenates the input SparseTensor along the specified dimension. OutShape: OutShape[non concat_dim] is equal to InShape[non concat_dim] and OutShape[concat_dim] is all input concat_dim axis shape accumulate.

Raises

  • ValueError – If only one sparse tensor input.

  • ValueError – If Input COOTensor shape dim > 3. COOtensor shape dim size must be 2 now

Supported Platforms:

CPU

Examples

>>> indices0 = Tensor([[0, 1], [1, 2]], dtype=mstype.int64)
>>> values0 = Tensor([1, 2], dtype=mstype.int32)
>>> shape0 = (3, 4)
>>> input0 = COOTensor(indices0, values0, shape0)
>>> indices1 = Tensor([[0, 0], [1, 1]], dtype=mstype.int64)
>>> values1 = Tensor([3, 4], dtype=mstype.int32)
>>> shape1 = (3, 4)
>>> input1 = COOTensor(indices1, values1, shape1)
>>> concat_dim = 1
>>> out = ops.coo_concat((input0, input1), concat_dim)
>>> print(out)
COOTensor(shape=[3, 8], dtype=Int32, indices=Tensor(shape=[4, 2], dtype=Int64, value=
[[0 1]
 [0 4]
 [1 2]
 [1 5]]), values=Tensor(shape=[4], dtype=Int32, value=[1 3 2 4]))