mindspore.mint.nonzero

mindspore.mint.nonzero(input, as_tuple=False)[source]

Return the positions of all non-zero values.

Parameters

input (Tensor) – The input Tensor, its rank should be greater than or equal to 1.
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 input.
If as_tuple is True, return the Tuple of Tensor and data type is int64. The Tuple length is the dimension of the input tensor, and each element is the 1D tensor of the subscript of all non-zero elements of the input tensor in that dimension.

Raises

TypeError – If input is not Tensor.
TypeError – If as_tuple is not bool.
ValueError – If dim of input equals to 0.

Supported Platforms:: Ascend

Examples

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