1 /** 2 * Copyright 2020 Huawei Technologies Co., Ltd 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * http://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 #include <string> 17 #include <vector> 18 #include "common/common_test.h" 19 #include "include/api/model.h" 20 #include "include/api/serialization.h" 21 #include "include/api/context.h" 22 23 using namespace mindspore; 24 25 static const char tensor_add_file[] = "/home/workspace/mindspore_dataset/mindir/add/add.mindir"; 26 static const std::vector<float> input_data_1 = {1, 2, 3, 4}; 27 static const std::vector<float> input_data_2 = {2, 3, 4, 5}; 28 29 class TestAdd : public ST::Common { 30 public: 31 TestAdd() {} 32 }; 33 34 TEST_F(TestAdd, InferMindIR) { 35 auto context = ContextAutoSet(); 36 37 Graph graph; 38 ASSERT_TRUE(Serialization::Load(tensor_add_file, ModelType::kMindIR, &graph)); 39 Model tensor_add; 40 ASSERT_TRUE(tensor_add.Build(GraphCell(graph), context) == kSuccess); 41 42 // get model inputs 43 std::vector<MSTensor> origin_inputs = tensor_add.GetInputs(); 44 ASSERT_EQ(origin_inputs.size(), 2); 45 46 // prepare input 47 std::vector<MSTensor> outputs; 48 std::vector<MSTensor> inputs; 49 inputs.emplace_back(origin_inputs[0].Name(), origin_inputs[0].DataType(), origin_inputs[0].Shape(), 50 input_data_1.data(), sizeof(float) * input_data_1.size()); 51 inputs.emplace_back(origin_inputs[1].Name(), origin_inputs[1].DataType(), origin_inputs[1].Shape(), 52 input_data_2.data(), sizeof(float) * input_data_2.size()); 53 54 // infer 55 ASSERT_TRUE(tensor_add.Predict(inputs, &outputs) == kSuccess); 56 57 // assert input 58 inputs = tensor_add.GetInputs(); 59 ASSERT_EQ(inputs.size(), 2); 60 auto after_input_data_1 = inputs[0].Data(); 61 auto after_input_data_2 = inputs[1].Data(); 62 const float *p = reinterpret_cast<const float *>(after_input_data_1.get()); 63 for (size_t i = 0; i < inputs[0].DataSize() / sizeof(float); ++i) { 64 ASSERT_LE(std::abs(p[i] - input_data_1[i]), 1e-4); 65 } 66 p = reinterpret_cast<const float *>(after_input_data_2.get()); 67 for (size_t i = 0; i < inputs[0].DataSize() / sizeof(float); ++i) { 68 ASSERT_LE(std::abs(p[i] - input_data_2[i]), 1e-4); 69 } 70 71 // assert output 72 for (auto &buffer : outputs) { 73 auto buffer_data = buffer.Data(); 74 p = reinterpret_cast<const float *>(buffer_data.get()); 75 for (size_t i = 0; i < buffer.DataSize() / sizeof(float); ++i) { 76 ASSERT_LE(std::abs(p[i] - (input_data_1[i] + input_data_2[i])), 1e-4); 77 } 78 } 79 } 80