380 lines
18 KiB
C++
380 lines
18 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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#include <gflags/gflags.h>
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#include <iostream>
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#include <string>
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#include <memory>
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#include <vector>
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#include <algorithm>
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#include <map>
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#include <format_reader_ptr.h>
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#include <inference_engine.hpp>
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#include <ngraph/ngraph.hpp>
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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 <vpu/vpu_tools_common.hpp>
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#include <vpu/vpu_plugin_config.hpp>
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#include "object_detection_sample_ssd.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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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_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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return true;
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}
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static std::map<std::string, std::string> configure(const std::string& confFileName) {
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auto config = parseConfig(confFileName);
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return config;
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}
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/**
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* \brief The entry point for the Inference Engine object_detection sample application
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* \file object_detection_sample_ssd/main.cpp
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* \example object_detection_sample_ssd/main.cpp
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*/
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int main(int argc, char *argv[]) {
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try {
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/** This sample covers certain topology and cannot be generalized for any object detection one **/
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slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << "\n";
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// --------------------------- 1. 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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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 2. Read input -----------------------------------------------------------
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/** This vector stores paths to the processed images **/
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std::vector<std::string> images;
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parseInputFilesArguments(images);
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if (images.empty()) throw std::logic_error("No suitable images were found");
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 3. Load inference engine -------------------------------------
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slog::info << "Loading Inference Engine" << slog::endl;
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Core ie;
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slog::info << "Device info: " << slog::endl;
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std::cout << ie.GetVersions(FLAGS_d);
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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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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 4. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
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std::string binFileName = fileNameNoExt(FLAGS_m) + ".bin";
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slog::info << "Loading network files:"
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"\n\t" << FLAGS_m <<
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"\n\t" << binFileName <<
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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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// --------------------------- 5. 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 inputsInfo(network.getInputsInfo());
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/** SSD network has one input and one output **/
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if (inputsInfo.size() != 1 && inputsInfo.size() != 2) throw std::logic_error("Sample supports topologies only with 1 or 2 inputs");
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/**
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* Some networks have SSD-like output format (ending with DetectionOutput layer), but
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* having 2 inputs as Faster-RCNN: one for image and one for "image info".
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*
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* Although object_datection_sample_ssd's main task is to support clean SSD, it could score
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* the networks with two inputs as well. For such networks imInfoInputName will contain the "second" input name.
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*/
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std::string imageInputName, imInfoInputName;
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InputInfo::Ptr inputInfo = nullptr;
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SizeVector inputImageDims;
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/** Stores input image **/
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/** Iterating over all input blobs **/
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for (auto & item : inputsInfo) {
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/** Working with first input tensor that stores image **/
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if (item.second->getInputData()->getTensorDesc().getDims().size() == 4) {
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imageInputName = item.first;
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inputInfo = item.second;
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slog::info << "Batch size is " << std::to_string(network.getBatchSize()) << slog::endl;
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/** Creating first input blob **/
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Precision inputPrecision = Precision::U8;
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item.second->setPrecision(inputPrecision);
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} else if (item.second->getInputData()->getTensorDesc().getDims().size() == 2) {
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imInfoInputName = item.first;
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Precision inputPrecision = Precision::FP32;
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item.second->setPrecision(inputPrecision);
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if ((item.second->getTensorDesc().getDims()[1] != 3 && item.second->getTensorDesc().getDims()[1] != 6)) {
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throw std::logic_error("Invalid input info. Should be 3 or 6 values length");
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}
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}
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}
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if (inputInfo == nullptr) {
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inputInfo = inputsInfo.begin()->second;
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}
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 6. Prepare output blobs -------------------------------------------------
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slog::info << "Preparing output blobs" << slog::endl;
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OutputsDataMap outputsInfo(network.getOutputsInfo());
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std::string outputName;
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DataPtr outputInfo;
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if (auto ngraphFunction = network.getFunction()) {
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for (const auto& out : outputsInfo) {
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for (const auto & op : ngraphFunction->get_ops()) {
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if (op->get_type_info() == ngraph::op::DetectionOutput::type_info &&
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op->get_friendly_name() == out.second->getName()) {
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outputName = out.first;
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outputInfo = out.second;
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break;
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}
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}
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}
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} else {
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outputInfo = outputsInfo.begin()->second;
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outputName = outputInfo->getName();
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}
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if (outputInfo == nullptr) {
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throw std::logic_error("Can't find a DetectionOutput layer in the topology");
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}
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const SizeVector outputDims = outputInfo->getTensorDesc().getDims();
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const int maxProposalCount = outputDims[2];
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const int objectSize = outputDims[3];
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if (objectSize != 7) {
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throw std::logic_error("Output item should have 7 as a last dimension");
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}
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if (outputDims.size() != 4) {
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throw std::logic_error("Incorrect output dimensions for SSD model");
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}
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/** Set the precision of output data provided by the user, should be called before load of the network to the device **/
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outputInfo->setPrecision(Precision::FP32);
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 7. 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, configure(FLAGS_config));
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 8. Create infer request -------------------------------------------------
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slog::info << "Create infer request" << slog::endl;
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InferRequest infer_request = executable_network.CreateInferRequest();
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 9. Prepare input --------------------------------------------------------
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/** Collect images data ptrs **/
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std::vector<std::shared_ptr<unsigned char>> imagesData, originalImagesData;
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std::vector<size_t> imageWidths, imageHeights;
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for (auto & i : images) {
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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> originalData(reader->getData());
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std::shared_ptr<unsigned char> data(reader->getData(inputInfo->getTensorDesc().getDims()[3], inputInfo->getTensorDesc().getDims()[2]));
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if (data.get() != nullptr) {
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originalImagesData.push_back(originalData);
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imagesData.push_back(data);
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imageWidths.push_back(reader->width());
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imageHeights.push_back(reader->height());
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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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size_t batchSize = network.getBatchSize();
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slog::info << "Batch size is " << std::to_string(batchSize) << slog::endl;
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if (batchSize != imagesData.size()) {
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slog::warn << "Number of images " + std::to_string(imagesData.size()) + \
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" doesn't match batch size " + std::to_string(batchSize) << slog::endl;
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batchSize = std::min(batchSize, imagesData.size());
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slog::warn << "Number of images to be processed is "<< std::to_string(batchSize) << slog::endl;
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}
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/** Creating input blob **/
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Blob::Ptr imageInput = infer_request.GetBlob(imageInputName);
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/** Filling input tensor with images. First b channel, then g and r channels **/
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MemoryBlob::Ptr mimage = as<MemoryBlob>(imageInput);
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if (!mimage) {
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slog::err << "We expect image blob to be inherited from MemoryBlob, but by fact we were not able "
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"to cast imageInput 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 = mimage->wmap();
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size_t num_channels = mimage->getTensorDesc().getDims()[1];
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size_t image_size = mimage->getTensorDesc().getDims()[3] * mimage->getTensorDesc().getDims()[2];
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unsigned char *data = minputHolder.as<unsigned char *>();
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/** Iterate over all input images **/
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for (size_t image_id = 0; image_id < std::min(imagesData.size(), batchSize); ++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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if (imInfoInputName != "") {
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Blob::Ptr input2 = infer_request.GetBlob(imInfoInputName);
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auto imInfoDim = inputsInfo.find(imInfoInputName)->second->getTensorDesc().getDims()[1];
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/** Fill input tensor with values **/
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MemoryBlob::Ptr minput2 = as<MemoryBlob>(input2);
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if (!minput2) {
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slog::err << "We expect input2 blob to be inherited from MemoryBlob, but by fact we were not able "
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"to cast input2 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 minput2Holder = minput2->wmap();
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float *p = minput2Holder.as<PrecisionTrait<Precision::FP32>::value_type *>();
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for (size_t image_id = 0; image_id < std::min(imagesData.size(), batchSize); ++image_id) {
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p[image_id * imInfoDim + 0] = static_cast<float>(inputsInfo[imageInputName]->getTensorDesc().getDims()[2]);
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p[image_id * imInfoDim + 1] = static_cast<float>(inputsInfo[imageInputName]->getTensorDesc().getDims()[3]);
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for (size_t k = 2; k < imInfoDim; k++) {
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p[image_id * imInfoDim + k] = 1.0f; // all scale factors are set to 1.0
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}
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}
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}
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 10. Do inference ---------------------------------------------------------
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slog::info << "Start inference" << slog::endl;
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infer_request.Infer();
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 11. Process output -------------------------------------------------------
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slog::info << "Processing output blobs" << slog::endl;
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const Blob::Ptr output_blob = infer_request.GetBlob(outputName);
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MemoryBlob::CPtr moutput = as<MemoryBlob>(output_blob);
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if (!moutput) {
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throw std::logic_error("We expect output to be inherited from MemoryBlob, "
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"but by fact we were not able to cast output to MemoryBlob");
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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 moutputHolder = moutput->rmap();
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const float *detection = moutputHolder.as<const PrecisionTrait<Precision::FP32>::value_type *>();
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std::vector<std::vector<int> > boxes(batchSize);
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std::vector<std::vector<int> > classes(batchSize);
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/* Each detection has image_id that denotes processed image */
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for (int curProposal = 0; curProposal < maxProposalCount; curProposal++) {
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auto image_id = static_cast<int>(detection[curProposal * objectSize + 0]);
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if (image_id < 0) {
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break;
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}
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float confidence = detection[curProposal * objectSize + 2];
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auto label = static_cast<int>(detection[curProposal * objectSize + 1]);
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auto xmin = static_cast<int>(detection[curProposal * objectSize + 3] * imageWidths[image_id]);
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auto ymin = static_cast<int>(detection[curProposal * objectSize + 4] * imageHeights[image_id]);
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auto xmax = static_cast<int>(detection[curProposal * objectSize + 5] * imageWidths[image_id]);
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auto ymax = static_cast<int>(detection[curProposal * objectSize + 6] * imageHeights[image_id]);
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std::cout << "[" << curProposal << "," << label << "] element, prob = " << confidence <<
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" (" << xmin << "," << ymin << ")-(" << xmax << "," << ymax << ")" << " batch id : " << image_id;
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if (confidence > 0.5) {
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/** Drawing only objects with >50% probability **/
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classes[image_id].push_back(label);
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boxes[image_id].push_back(xmin);
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boxes[image_id].push_back(ymin);
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boxes[image_id].push_back(xmax - xmin);
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boxes[image_id].push_back(ymax - ymin);
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std::cout << " WILL BE PRINTED!";
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}
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std::cout << std::endl;
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}
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for (size_t batch_id = 0; batch_id < batchSize; ++batch_id) {
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addRectangles(originalImagesData[batch_id].get(), imageHeights[batch_id], imageWidths[batch_id], boxes[batch_id], classes[batch_id],
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BBOX_THICKNESS);
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const std::string image_path = "out_" + std::to_string(batch_id) + ".bmp";
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if (writeOutputBmp(image_path, originalImagesData[batch_id].get(), imageHeights[batch_id], imageWidths[batch_id])) {
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slog::info << "Image " + image_path + " created!" << slog::endl;
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} else {
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throw std::logic_error(std::string("Can't create a file: ") + image_path);
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}
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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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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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