mindspore.Tensor.unique_consecutive

Tensor.unique_consecutive(return_idx=False, return_counts=False, axis=None)[source]

Returns the elements that are unique in each consecutive group of equivalent elements in the input tensor.

Parameters

  • return_idx (bool, optional) – Whether to return the indices of the end position of each element in the original input list in the returned unique list. Default: False.

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

  • axis (int, optional) – The dimension to apply unique. If None, the unique of the flattened input is returned. If specified, it must be int32 or int64. Default: None.

Returns

A tensor or a tuple of tensors containing tensor objects (output, idx, counts). output has the same type as the input tensor and is used to represent the output list of unique scalar elements. If return_idx is True, there will be an additional returned tensor, idx, which has the same shape as the inupt tensor and represents the index of where the element in the original input maps to the position in the output. If return_counts is True, there will be an additional returned tensor, counts, which represents the number of occurrences for each unique value or tensor.

Raises

RuntimeError – If axis is not in the range of \([-ndim, ndim-1]\).

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import dtype as mstype
>>> x = Tensor(np.array([1, 1, 2, 2, 3, 1, 1, 2]), mstype.int32)
>>> output, idx, counts = x.unique_consecutive(return_idx=True, return_counts=True, axis=None)
>>> print(output)
[1 2 3 1 2]
>>> print(idx)
[0 0 1 1 2 3 3 4]
>>> print(counts)
[2 2 1 2 1]