350 lines
15 KiB
C++
350 lines
15 KiB
C++
// Copyright (C) 2018 Intel Corporation
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//
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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/main.cpp
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* @example classification_sample/main.cpp
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*/
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#include <fstream>
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#include <iomanip>
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#include <vector>
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#include <chrono>
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#include <memory>
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#include <string>
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#include <map>
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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 <sys/stat.h>
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#include <ext_list.hpp>
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#include "classification_sample_async.h"
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using namespace InferenceEngine;
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ConsoleErrorListener error_listener;
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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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return false;
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}
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slog::info << "Parsing input parameters" << slog::endl;
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if (FLAGS_ni < 1) {
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throw std::logic_error("Parameter -ni must be more than 0 ! (default 1)");
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}
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if (FLAGS_nireq < 1) {
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throw std::logic_error("Parameter -nireq must be more than 0 ! (default 1)");
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}
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if (FLAGS_i.empty()) {
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throw std::logic_error("Parameter -i is not set");
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}
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if (FLAGS_m.empty()) {
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throw std::logic_error("Parameter -m is not set");
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}
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if (FLAGS_ni < FLAGS_nireq) {
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throw std::logic_error("Number of iterations could not be less than requests quantity");
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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 Plugin for inference engine -------------------------------------
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slog::info << "Loading plugin" << slog::endl;
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InferencePlugin plugin = PluginDispatcher({ FLAGS_pp, "../../../lib/intel64" , "" }).getPluginByDevice(FLAGS_d);
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if (FLAGS_p_msg) {
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static_cast<InferenceEngine::InferenceEnginePluginPtr>(plugin)->SetLogCallback(error_listener);
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}
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/** Loading default extensions **/
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if (FLAGS_d.find("CPU") != std::string::npos) {
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/**
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* cpu_extensions library is compiled from "extension" folder containing
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* custom MKLDNNPlugin layer implementations. These layers are not supported
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* by mkldnn, but they can be useful for inferring custom topologies.
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**/
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plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());
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}
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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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plugin.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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plugin.SetConfig({{PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c}});
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slog::info << "GPU Extension loaded: " << FLAGS_c << slog::endl;
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}
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ResponseDesc resp;
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/** Printing plugin version **/
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printPluginVersion(plugin, std::cout);
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
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slog::info << "Loading network files" << slog::endl;
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CNNNetReader networkReader;
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/** Read network model **/
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networkReader.ReadNetwork(FLAGS_m);
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/** Extract model name and load weights **/
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std::string binFileName = fileNameNoExt(FLAGS_m) + ".bin";
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networkReader.ReadWeights(binFileName);
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CNNNetwork network = networkReader.getNetwork();
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 3. Configure input & output ---------------------------------------------
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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 only with 1 input");
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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 plugin **/
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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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for (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.get() != nullptr) {
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imagesData.push_back(data);
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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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// ------------------------------ Prepare output blobs -------------------------------------------------
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slog::info << "Preparing output blobs" << slog::endl;
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OutputsDataMap outputInfo(network.getOutputsInfo());
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std::vector <Blob::Ptr> outputBlobs;
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for (size_t i = 0; i < FLAGS_nireq; i++) {
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auto outputBlob = make_shared_blob<PrecisionTrait<Precision::FP32>::value_type>(outputInfo.begin()->second->getTensorDesc());
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outputBlob->allocate();
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outputBlobs.push_back(outputBlob);
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}
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 4. Loading model to the plugin ------------------------------------------
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slog::info << "Loading model to the plugin" << slog::endl;
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std::map<std::string, std::string> config;
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if (FLAGS_pc) {
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config[PluginConfigParams::KEY_PERF_COUNT] = PluginConfigParams::YES;
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}
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ExecutableNetwork executable_network = plugin.LoadNetwork(network, {});
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 5. Create infer request -------------------------------------------------
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std::vector<InferRequest> inferRequests;
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for (size_t i = 0; i < FLAGS_nireq; i++) {
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InferRequest inferRequest = executable_network.CreateInferRequest();
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inferRequests.push_back(inferRequest);
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}
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 6. Prepare input --------------------------------------------------------
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BlobMap inputBlobs;
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for (auto & item : inputInfo) {
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auto input = make_shared_blob<PrecisionTrait<Precision::U8>::value_type>(item.second->getTensorDesc());
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input->allocate();
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inputBlobs[item.first] = input;
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auto dims = input->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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/** 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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input->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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for (size_t i = 0; i < FLAGS_nireq; i++) {
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inferRequests[i].SetBlob(inputBlobs.begin()->first, inputBlobs.begin()->second);
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inferRequests[i].SetBlob(outputInfo.begin()->first, outputBlobs[i]);
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}
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 7. Do inference ---------------------------------------------------------
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slog::info << "Start inference (" << FLAGS_ni << " iterations)" << slog::endl;
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typedef std::chrono::high_resolution_clock Time;
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typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;
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typedef std::chrono::duration<float> fsec;
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double total = 0.0;
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/** Start inference & calc performance **/
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auto t0 = Time::now();
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size_t currentInfer = 0;
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size_t prevInfer = (FLAGS_nireq > 1) ? 1 : 0;
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// warming up
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inferRequests[0].StartAsync();
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inferRequests[0].Wait(10000);
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for (int iter = 0; iter < FLAGS_ni + FLAGS_nireq; ++iter) {
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if (iter < FLAGS_ni) {
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inferRequests[currentInfer].StartAsync();
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}
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inferRequests[prevInfer].Wait(10000);
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currentInfer++;
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if (currentInfer >= FLAGS_nireq) {
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currentInfer = 0;
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}
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prevInfer++;
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if (prevInfer >= FLAGS_nireq) {
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prevInfer = 0;
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}
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}
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auto t1 = Time::now();
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fsec fs = t1 - t0;
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ms d = std::chrono::duration_cast<ms>(fs);
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total = d.count();
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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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for (size_t i = 0; i < FLAGS_nireq; i++) {
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/** Validating -nt value **/
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const int resultsCnt = outputBlobs[i]->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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/** This vector stores id's of top N results **/
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std::vector<unsigned> results;
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TopResults(FLAGS_nt, *outputBlobs[i], results);
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std::cout << std::endl << "Top " << FLAGS_nt << " results:" << std::endl << std::endl;
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/** Read labels from file (e.x. AlexNet.labels) **/
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bool labelsEnabled = false;
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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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labelsEnabled = true;
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}
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/** Print the result iterating over each batch **/
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for (int image_id = 0; image_id < batchSize; ++image_id) {
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std::cout << "Image " << imageNames[image_id] << std::endl << std::endl;
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for (size_t id = image_id * FLAGS_nt, cnt = 0; cnt < FLAGS_nt; ++cnt, ++id) {
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std::cout.precision(7);
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/** Getting probability for resulting class **/
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auto result = outputBlobs[i]->buffer().
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as<PrecisionTrait<Precision::FP32>::value_type*>()[results[id] + image_id*(outputBlobs[i]->size() / batchSize)];
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std::cout << std::left << std::fixed << results[id] << " " << result;
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if (labelsEnabled) {
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std::cout << " label " << labels[results[id]] << std::endl;
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} else {
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std::cout << " label #" << results[id] << std::endl;
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}
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}
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std::cout << std::endl;
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}
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}
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// -----------------------------------------------------------------------------------------------------
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std::cout << std::endl << "total inference time: " << total << std::endl;
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std::cout << std::endl << "Throughput: " << 1000 * static_cast<double>(FLAGS_ni) * batchSize / total << " FPS" << std::endl;
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std::cout << std::endl;
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/** Show performance results **/
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std::map<std::string, InferenceEngineProfileInfo> performanceMap;
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if (FLAGS_pc) {
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for (size_t nireq = 0; nireq < FLAGS_nireq; nireq++) {
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performanceMap = inferRequests[nireq].GetPerformanceCounts();
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printPerformanceCounts(performanceMap, std::cout);
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}
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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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return 0;
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}
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