mindspore.Tensor.unique

Tensor.unique(sorted=True, return_inverse=False, return_counts=False, dim=None)

Returns the unique elements of self.

when return_inverse=True, also return a tensor containing the index of each value of self corresponding to the output unique tensor. when return_counts=True, also return a tensor containing the number of occurrences for each unique value or tensor.

Parameters

  • sorted (bool, optional) – Whether to sort the unique elements in ascending order before returning as output. Default: True .

  • return_inverse (bool, optional) – Whether to also return the indices for where elements in self ended up in the returned unique list. Default: False .

  • return_counts (bool, optional) – Whether to also return the counts for each unique element. Default: False .

  • dim (int, optional) – the dimension to operate upon. If None, the unique of the flattened self is returned. Otherwise, each of the tensors indexed by the given dimension is treated as one of the elements to apply the unique operation upon. Default: None .

Returns

A tensor or a tuple of tensors containing some of tensor objects (output, inverse_indices, counts).

  • output(Tensor) - The output tensor including the unique elements of self, it has same dtype as self.

  • inverse_indices(Tensor) - Return when return_inverse is True. It represents the indices for where elements in self map to in the output. When dim is None, it has same shape as self, otherwise, the shape is self.shape[dim].

  • counts(Tensor) - Return when return_counts is True. It represents the number of occurrences for each unique value or tensor. 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
>>> from mindspore import Tensor, nn
>>> from mindspore import ops
>>> x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32)
>>> output = ops.unique_ext(x, return_inverse=True)
>>> print(output)
(Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int64, value= [0, 1, 2, 1]))
>>> y = output[0]
>>> print(y)
[1 2 5]
>>> idx = output[1]
>>> print(idx)
[0 1 2 1]