1# Copyright 2019 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 19from mindspore import Tensor 20from mindspore.ops import operations as P 21 22context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") 23 24 25class Net(nn.Cell): 26 def __init__(self): 27 super(Net, self).__init__() 28 self.equal_count = P.EqualCount() 29 30 def construct(self, x_, y_): 31 return self.equal_count(x_, y_) 32 33 34x = np.random.randn(32).astype(np.int32) 35y = np.random.randn(32).astype(np.int32) 36 37 38def test_net(): 39 equal_count = Net() 40 output = equal_count(Tensor(x), Tensor(y)) 41 print(x) 42 print(y) 43 print(output.asnumpy()) 44