mindspore.Tensor.nonzero

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Tensor.nonzero(*, as_tuple=False)[source]

Return the positions of all non-zero values.

Note

The rank of self.

  • Ascend: its rank can be equal to 0 except O2 mode.

  • CPU/GPU: its rank should be greater than or eaqual to 1.

Keyword Arguments

as_tuple (bool, optional) – Whether the output is tuple. If False , return Tensor. Default: False . If True , return Tuple of Tensor, only support Ascend .

Returns

  • If as_tuple is False, return the Tensor, a 2-D Tensor whose data type is int64, containing the positions of all non-zero values of the self .

  • If as_tuple is True, return the Tuple of Tensor and data type is int64. The Tuple length is the dimension of the self tensor, and each element is the 1D tensor of the subscript of all non-zero elements of the self tensor in that dimension.

Raises

  • TypeError – If self is not Tensor.

  • TypeError – If as_tuple is not bool.

  • RuntimeError – On GPU or CPU or Ascend O2 mode, if dim of input equals to 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> x = Tensor(np.array([[[1,  0], [-5, 0]]]), mindspore.int32)
>>> output = x.nonzero()
>>> print(output)
[[0 0 0]
[0 1 0]]
>>> x = Tensor(np.array([1, 0, 2, 0, 3]), mindspore.int32)
>>> output = x.nonzero(as_tuple=False)
>>> print(output)
[[0]
[2]
[4]]
>>> x = Tensor(np.array([[[1,  0], [-5, 0]]]), mindspore.int32)
>>> output = x.nonzero(as_tuple=True)
>>> print(output)
(Tensor(shape=[2], dtype=Int64, value=[0, 0]),
Tensor(shape=[2], dtype=Int64, value=[0, 1]),
Tensor(shape=[2], dtype=Int64, value=[0, 0]))
>>> x = Tensor(np.array([1, 0, 2, 0, 3]), mindspore.int32)
>>> output = x.nonzero(as_tuple=True)
>>> print(output)
(Tensor(shape=[3], dtype=Int64, value=[0, 2, 4]), )