mindspore.mint.unique

mindspore.mint.unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None)[source]

Return the unique elements of input tensor.

Parameters

input (Tensor) – The input tensor.
sorted (bool, optional) – Whether to sort the unique elements in ascending order before returning as output. Default True .
return_inverse (bool, optional) – Whether to additionally return a tensor indicating the indices of input corresponding to output. Default False .
return_counts (bool, optional) – Whether to additionally return a tensor indicating the count of each element in output within the input. Default False .
dim (int, optional) – Specify the dimension for computation.

Returns

A tensor or a tuple of tensors.

output (Tensor) - The unique elements of input tensor.
inverse_indices (Tensor) - Return when return_inverse is True. It represents the indices for where elements in input map to in output. When dim is None, it has same shape as input, otherwise, the shape is input.shape[dim].
counts (Tensor) - Return when return_counts is True. It represents the number of occurrences for each unique element from input within output. When dim is None, it has same shape as output, otherwise, the shape is output.shape[dim].

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> x = mindspore.tensor(np.array([1, 2, 5, 2]), mindspore.int32)
>>> output = mindspore.mint.unique(x, return_inverse=True, return_counts=True)
>>> print(output)
(Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int64,
value= [0, 1, 2, 1]), Tensor(shape=[3], dtype=Int64, value= [1, 2, 1]))
>>> y = output[0]
>>> print(y)
[1 2 5]
>>> inverse_indices = output[1]
>>> print(inverse_indices)
[0 1 2 1]
>>> counts = output[2]
>>> print(counts)
[1 2 1]