mindspore.ops.unique_consecutive

mindspore.ops.unique_consecutive(x, 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

  • x (Tensor) – The input tensor.

  • 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 x 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 x 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

  • TypeError – If x is not a Tensor.

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

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindspore import ops
>>> 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 = ops.unique_consecutive(x, True, True, 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]