# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ============================================================================
"""Operators for function."""
from mindspore.ops.primitive import constexpr
from mindspore.ops import operations as P
@constexpr
def get_x_shape(x_shape):
s = 1
for i in x_shape:
s = s * i
return (s,)
[文档]def unique(x):
"""
Returns the unique elements of input tensor and also return a tensor containing the index of each value of input
tensor corresponding to the output unique tensor.
The output contains Tensor `y` and Tensor `idx`, the format is probably similar to (`y`, `idx`).
The shape of Tensor `y` and Tensor `idx` is different in most cases, because Tensor `y` will be deduplicated,
and the shape of Tensor `idx` is consistent with the input.
To get the same shape between `idx` and `y`, please ref to :class:'mindspore.ops.UniqueWithPad' operator.
.. warning::
This module is in beta.
Args:
x (Tensor): The input tensor.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Returns:
Tuple, containing Tensor objects `(y, idx), `y` is a tensor with the
same type as `x`, and contains the unique elements in `x`.
`idx` is a tensor containing indices of elements in
the input corresponding to the output tensor, have the same shape with `x`.
Raises:x
TypeError: If `x` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
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(x)
>>> print(output)
(Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 1]))
>>> y = output[0]
>>> print(y)
[1 2 5]
>>> idx = output[1]
>>> print(idx)
[0 1 2 1]
"""
unique_op = P.Unique()
reshape_op = P.Reshape()
shape_x = x.shape
length_x = get_x_shape(shape_x)
x = reshape_op(x, length_x)
y, idx = unique_op(x)
idx = reshape_op(idx, shape_x)
return y, idx