1# Copyright 2022 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15import numpy as np 16import mindspore.context as context 17import mindspore.nn as nn 18from mindspore import Tensor 19import mindspore.common.dtype as mstype 20from mindspore.ops import operations as P 21 22 23context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") 24 25 26class Net(nn.Cell): 27 def __init__(self): 28 super(Net, self).__init__() 29 self.unique = P.Unique() 30 31 def construct(self, x): 32 x = self.unique(x) 33 return (x[0], x[1]) 34 35 36def test_unique(): 37 """ 38 Feature: for Unique op 39 Description: inputs are integers 40 Expectation: the result is correct 41 """ 42 x = Tensor(np.array([1, 1, 2, 3, 3, 3]), mstype.int32) 43 unique = Net() 44 output = unique(x) 45 expect1 = np.array([1, 2, 3]) 46 expect2 = np.array([0, 0, 1, 2, 2, 2]) 47 assert (output[0].asnumpy() == expect1).all() 48 assert (output[1].asnumpy() == expect2).all() 49 50 51if __name__ == "__main__": 52 test_unique() 53