262 lines
12 KiB
C++
262 lines
12 KiB
C++
// Copyright (C) 2018-2020 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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/**
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* @brief The entry point the Inference Engine sample application
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* @file classification_sample_async/main.cpp
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* @example classification_sample_async/main.cpp
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*/
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#include <fstream>
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#include <vector>
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#include <memory>
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#include <string>
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#include <map>
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#include <condition_variable>
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#include <mutex>
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#include <inference_engine.hpp>
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#include <format_reader_ptr.h>
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#include <samples/common.hpp>
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#include <samples/slog.hpp>
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#include <samples/args_helper.hpp>
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#include <samples/classification_results.h>
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#include <sys/stat.h>
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#include "classification_sample_async.h"
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using namespace InferenceEngine;
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bool ParseAndCheckCommandLine(int argc, char *argv[]) {
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// ---------------------------Parsing and validation of input args--------------------------------------
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slog::info << "Parsing input parameters" << slog::endl;
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gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
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if (FLAGS_h) {
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showUsage();
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showAvailableDevices();
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return false;
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}
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slog::info << "Parsing input parameters" << slog::endl;
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if (FLAGS_m.empty()) {
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throw std::logic_error("Model is required but not set. Please set -m option.");
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}
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if (FLAGS_i.empty()) {
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throw std::logic_error("Input is required but not set. Please set -i option.");
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}
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return true;
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}
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int main(int argc, char *argv[]) {
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try {
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slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << slog::endl;
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// ------------------------------ Parsing and validation of input args ---------------------------------
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if (!ParseAndCheckCommandLine(argc, argv)) {
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return 0;
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}
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/** This vector stores paths to the processed images **/
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std::vector<std::string> imageNames;
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parseInputFilesArguments(imageNames);
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if (imageNames.empty()) throw std::logic_error("No suitable images were found");
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 1. Load inference engine -------------------------------------
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slog::info << "Creating Inference Engine" << slog::endl;
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Core ie;
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if (!FLAGS_l.empty()) {
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// CPU(MKLDNN) extensions are loaded as a shared library and passed as a pointer to base extension
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IExtensionPtr extension_ptr = make_so_pointer<IExtension>(FLAGS_l);
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ie.AddExtension(extension_ptr);
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slog::info << "CPU Extension loaded: " << FLAGS_l << slog::endl;
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}
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if (!FLAGS_c.empty()) {
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// clDNN Extensions are loaded from an .xml description and OpenCL kernel files
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ie.SetConfig({{PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c}}, "GPU");
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slog::info << "GPU Extension loaded: " << FLAGS_c << slog::endl;
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}
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/** Printing device version **/
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std::cout << ie.GetVersions(FLAGS_d) << std::endl;
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// -----------------------------------------------------------------------------------------------------
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// 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
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slog::info << "Loading network files" << slog::endl;
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/** Read network model **/
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CNNNetwork network = ie.ReadNetwork(FLAGS_m);
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 3. Configure input & output ---------------------------------------------
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if (network.getOutputsInfo().size() != 1) throw std::logic_error("Sample supports topologies with 1 output only");
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// --------------------------- Prepare input blobs -----------------------------------------------------
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slog::info << "Preparing input blobs" << slog::endl;
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/** Taking information about all topology inputs **/
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InputsDataMap inputInfo(network.getInputsInfo());
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if (inputInfo.size() != 1) throw std::logic_error("Sample supports topologies with 1 input only");
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auto inputInfoItem = *inputInfo.begin();
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/** Specifying the precision and layout of input data provided by the user.
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* This should be called before load of the network to the device **/
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inputInfoItem.second->setPrecision(Precision::U8);
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inputInfoItem.second->setLayout(Layout::NCHW);
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std::vector<std::shared_ptr<unsigned char>> imagesData = {};
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std::vector<std::string> validImageNames = {};
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for (const auto & i : imageNames) {
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FormatReader::ReaderPtr reader(i.c_str());
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if (reader.get() == nullptr) {
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slog::warn << "Image " + i + " cannot be read!" << slog::endl;
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continue;
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}
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/** Store image data **/
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std::shared_ptr<unsigned char> data(
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reader->getData(inputInfoItem.second->getTensorDesc().getDims()[3],
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inputInfoItem.second->getTensorDesc().getDims()[2]));
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if (data != nullptr) {
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imagesData.push_back(data);
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validImageNames.push_back(i);
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}
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}
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if (imagesData.empty()) throw std::logic_error("Valid input images were not found!");
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/** Setting batch size using image count **/
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network.setBatchSize(imagesData.size());
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size_t batchSize = network.getBatchSize();
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slog::info << "Batch size is " << std::to_string(batchSize) << slog::endl;
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 4. Loading model to the device ------------------------------------------
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slog::info << "Loading model to the device" << slog::endl;
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ExecutableNetwork executable_network = ie.LoadNetwork(network, FLAGS_d);
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 5. Create infer request -------------------------------------------------
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slog::info << "Create infer request" << slog::endl;
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InferRequest inferRequest = executable_network.CreateInferRequest();
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 6. Prepare input --------------------------------------------------------
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for (auto & item : inputInfo) {
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Blob::Ptr inputBlob = inferRequest.GetBlob(item.first);
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SizeVector dims = inputBlob->getTensorDesc().getDims();
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/** Fill input tensor with images. First b channel, then g and r channels **/
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size_t num_channels = dims[1];
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size_t image_size = dims[3] * dims[2];
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MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
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if (!minput) {
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slog::err << "We expect MemoryBlob from inferRequest, but by fact we were not able to cast inputBlob to MemoryBlob" << slog::endl;
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return 1;
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}
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// locked memory holder should be alive all time while access to its buffer happens
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auto minputHolder = minput->wmap();
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auto data = minputHolder.as<PrecisionTrait<Precision::U8>::value_type *>();
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/** Iterate over all input images **/
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for (size_t image_id = 0; image_id < imagesData.size(); ++image_id) {
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/** Iterate over all pixel in image (b,g,r) **/
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for (size_t pid = 0; pid < image_size; pid++) {
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/** Iterate over all channels **/
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for (size_t ch = 0; ch < num_channels; ++ch) {
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/** [images stride + channels stride + pixel id ] all in bytes **/
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data[image_id * image_size * num_channels + ch * image_size + pid] = imagesData.at(image_id).get()[pid*num_channels + ch];
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}
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}
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}
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}
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 7. Do inference ---------------------------------------------------------
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size_t numIterations = 10;
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size_t curIteration = 0;
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std::condition_variable condVar;
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inferRequest.SetCompletionCallback(
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[&] {
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curIteration++;
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slog::info << "Completed " << curIteration << " async request execution" << slog::endl;
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if (curIteration < numIterations) {
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/* here a user can read output containing inference results and put new input
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to repeat async request again */
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inferRequest.StartAsync();
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} else {
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/* continue sample execution after last Asynchronous inference request execution */
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condVar.notify_one();
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}
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});
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/* Start async request for the first time */
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slog::info << "Start inference (" << numIterations << " asynchronous executions)" << slog::endl;
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inferRequest.StartAsync();
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/* Wait all repetitions of the async request */
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std::mutex mutex;
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std::unique_lock<std::mutex> lock(mutex);
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condVar.wait(lock, [&]{ return curIteration == numIterations; });
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 8. Process output -------------------------------------------------------
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slog::info << "Processing output blobs" << slog::endl;
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OutputsDataMap outputInfo(network.getOutputsInfo());
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Blob::Ptr outputBlob = inferRequest.GetBlob(outputInfo.begin()->first);
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/** Validating -nt value **/
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const size_t resultsCnt = outputBlob->size() / batchSize;
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if (FLAGS_nt > resultsCnt || FLAGS_nt < 1) {
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slog::warn << "-nt " << FLAGS_nt << " is not available for this network (-nt should be less than " \
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<< resultsCnt+1 << " and more than 0)\n will be used maximal value : " << resultsCnt << slog::endl;
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FLAGS_nt = resultsCnt;
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}
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/** Read labels from file (e.x. AlexNet.labels) **/
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std::string labelFileName = fileNameNoExt(FLAGS_m) + ".labels";
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std::vector<std::string> labels;
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std::ifstream inputFile;
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inputFile.open(labelFileName, std::ios::in);
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if (inputFile.is_open()) {
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std::string strLine;
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while (std::getline(inputFile, strLine)) {
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trim(strLine);
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labels.push_back(strLine);
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}
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}
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ClassificationResult classificationResult(outputBlob, validImageNames,
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batchSize, FLAGS_nt,
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labels);
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classificationResult.print();
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// -----------------------------------------------------------------------------------------------------
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}
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catch (const std::exception& error) {
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slog::err << error.what() << slog::endl;
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return 1;
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}
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catch (...) {
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slog::err << "Unknown/internal exception happened." << slog::endl;
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return 1;
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}
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slog::info << "Execution successful" << slog::endl;
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slog::info << slog::endl << "This sample is an API example, for any performance measurements "
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"please use the dedicated benchmark_app tool" << slog::endl;
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return 0;
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}
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