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- /**
- * Copyright 2021 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
- #include <sys/time.h>
- #include <gflags/gflags.h>
- #include <dirent.h>
- #include <iostream>
- #include <string>
- #include <algorithm>
- #include <iosfwd>
- #include <vector>
- #include <fstream>
- #include <sstream>
-
- #include "include/api/model.h"
- #include "include/api/context.h"
- #include "include/api/types.h"
- #include "include/api/serialization.h"
- #include "include/dataset/execute.h"
- #include "include/dataset/vision.h"
- #include "inc/utils.h"
-
- using mindspore::Context;
- using mindspore::Serialization;
- using mindspore::Model;
- using mindspore::Status;
- using mindspore::MSTensor;
- using mindspore::dataset::Execute;
- using mindspore::ModelType;
- using mindspore::GraphCell;
- using mindspore::kSuccess;
-
- DEFINE_string(mindir_path, "", "mindir path");
- DEFINE_string(input0_path, ".", "input0 path");
- DEFINE_string(input1_path, ".", "input1 path");
- DEFINE_int32(device_id, 0, "device id");
-
- int main(int argc, char **argv) {
- gflags::ParseCommandLineFlags(&argc, &argv, true);
- if (RealPath(FLAGS_mindir_path).empty()) {
- std::cout << "Invalid mindir" << std::endl;
- return 1;
- }
-
- auto context = std::make_shared<Context>();
- auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
- ascend310->SetDeviceID(FLAGS_device_id);
- context->MutableDeviceInfo().push_back(ascend310);
- mindspore::Graph graph;
- Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
-
- Model model;
- Status ret = model.Build(GraphCell(graph), context);
- if (ret != kSuccess) {
- std::cout << "ERROR: Build failed." << std::endl;
- return 1;
- }
-
- std::vector<MSTensor> model_inputs = model.GetInputs();
- if (model_inputs.empty()) {
- std::cout << "Invalid model, inputs is empty." << std::endl;
- return 1;
- }
-
- auto input0_files = GetAllFiles(FLAGS_input0_path);
- auto input1_files = GetAllFiles(FLAGS_input1_path);
-
- if (input0_files.empty() || input1_files.empty()) {
- std::cout << "ERROR: input data empty." << std::endl;
- return 1;
- }
-
- std::map<double, double> costTime_map;
- size_t size = input0_files.size();
-
- for (size_t i = 0; i < size; ++i) {
- struct timeval start = {0};
- struct timeval end = {0};
- double startTimeMs;
- double endTimeMs;
- std::vector<MSTensor> inputs;
- std::vector<MSTensor> outputs;
- std::cout << "Start predict input files:" << input0_files[i] << std::endl;
-
- auto input0 = ReadFileToTensor(input0_files[i]);
- auto input1 = ReadFileToTensor(input1_files[i]);
- inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
- input0.Data().get(), input0.DataSize());
- inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
- input1.Data().get(), input1.DataSize());
-
- gettimeofday(&start, nullptr);
- ret = model.Predict(inputs, &outputs);
- gettimeofday(&end, nullptr);
- if (ret != kSuccess) {
- std::cout << "Predict " << input0_files[i] << " failed." << std::endl;
- return 1;
- }
- startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
- endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
- costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
- WriteResult(input0_files[i], outputs);
- }
- double average = 0.0;
- int inferCount = 0;
-
- for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
- double diff = 0.0;
- diff = iter->second - iter->first;
- average += diff;
- inferCount++;
- }
- average = average / inferCount;
- std::stringstream timeCost;
- timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
- std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
- std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
- std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
- fileStream << timeCost.str();
- fileStream.close();
- costTime_map.clear();
- return 0;
- }
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