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# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore.context as context 20import mindspore.nn as nn 21from mindspore import Tensor 22from mindspore.ops import operations as P 23 24 25class NetEqualCount(nn.Cell): 26 def __init__(self): 27 super(NetEqualCount, self).__init__() 28 self.equalcount = P.EqualCount() 29 30 def construct(self, x, y): 31 return self.equalcount(x, y) 32 33 34@pytest.mark.level0 35@pytest.mark.platform_x86_gpu_training 36@pytest.mark.env_onecard 37def test_equalcount(): 38 x = Tensor(np.array([1, 20, 5]).astype(np.int32)) 39 y = Tensor(np.array([2, 20, 5]).astype(np.int32)) 40 expect = np.array([2]).astype(np.int32) 41 42 context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") 43 equal_count = NetEqualCount() 44 output = equal_count(x, y) 45 assert (output.asnumpy() == expect).all() 46 47 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 48 equal_count = NetEqualCount() 49 output = equal_count(x, y) 50 assert (output.asnumpy() == expect).all() 51