1# Copyright 2020 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 16 17import mindspore.context as context 18import mindspore.nn as nn 19import mindspore.common.dtype as mstype 20from mindspore import Tensor 21from mindspore.ops import operations as P 22 23context.set_context(mode=context.GRAPH_MODE, 24 device_target="Ascend") 25 26 27class Net(nn.Cell): 28 def __init__(self, pad_num): 29 super(Net, self).__init__() 30 self.unique_with_pad = P.UniqueWithPad() 31 self.pad_num = pad_num 32 33 def construct(self, x): 34 return self.unique_with_pad(x, self.pad_num) 35 36 37def test_unique_with_pad(): 38 x = Tensor(np.array([1, 1, 5, 5, 4, 4, 3, 3, 2, 2]), mstype.int32) 39 pad_num = 8 40 unique_with_pad = Net(pad_num) 41 out = unique_with_pad(x) 42 expect_val = ([1, 5, 4, 3, 2, 8, 8, 8, 8, 8], [0, 0, 1, 1, 2, 2, 3, 3, 4, 4]) 43 assert(out[0].asnumpy() == expect_val[0]).all() 44 assert(out[1].asnumpy() == expect_val[1]).all() 45