mindspore.ops.unique_with_pad

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mindspore.ops.unique_with_pad(x, pad_num)[source]

Returns unique elements and relative indexes in 1-D tensor, filled with padding num.

The basic function is the same as the Unique operator, but the UniqueWithPad operator adds a Pad function. The returned tuple(y, idx) after the input Tensor x is processed by the unique operator, in which the shapes of y and idx are mostly not equal. Therefore, in order to solve the above situation, the UniqueWithPad operator will fill the y Tensor with the pad_num specified by the user to make it have the same shape as the Tensor idx.

Warning

mindspore.ops.unique_with_pad() is deprecated from version 2.4 and will be removed in a future version.

Parameters

  • x (Tensor) – The tensor need to be unique. Must be 1-D vector with types: int32, int64.

  • pad_num (int) – Pad num. The data type is an int.

Returns

tuple(Tensor), tuple of 2 tensors, y and idx.

  • y (Tensor) - The unique elements filled with pad_num, the shape and data type same as x.

  • idx (Tensor) - The index of each value of x in the unique output y, the shape and data type same as x.

Raises

  • TypeError – If dtype of x is neither int32 nor int64.

  • ValueError – If x is not a 1-D Tensor.

Supported Platforms:

Deprecated

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, nn
>>> from mindspore import ops
>>> x = Tensor(np.array([1, 2, 2, 3, 5, 5]), mindspore.int32)
>>> output = ops.unique_with_pad(x, 0)
>>> print(output)
(Tensor(shape=[6], dtype=Int32, value= [1, 2, 3, 5, 0, 0]),
 Tensor(shape=[6], dtype=Int32, value= [0, 1, 1, 2, 3, 3]))
>>> y = output[0]
>>> print(y)
[1 2 3 5 0 0]
>>> idx = output[1]
>>> print(idx)
[0 1 1 2 3 3]