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]