openvino/samples/cpp/common/utils/src/args_helper.cpp

// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

// clang-format off
#include <sys/stat.h>

#include <iostream>

#ifdef _WIN32
#    include "samples/os/windows/w_dirent.h"
#else
#    include <dirent.h>
#endif

#include "openvino/openvino.hpp"

#include "gflags/gflags.h"
#include "samples/args_helper.hpp"
#include "samples/slog.hpp"
// clang-format on

/**
 * @brief Checks input file argument and add it to files vector
 * @param files reference to vector to store file names
 * @param arg file or folder name
 * @return none
 */
void readInputFilesArguments(std::vector<std::string>& files, const std::string& arg) {
    struct stat sb;
    if (stat(arg.c_str(), &sb) != 0) {
        slog::warn << "File " << arg << " cannot be opened!" << slog::endl;
        return;
    }
    if (S_ISDIR(sb.st_mode)) {
        struct CloseDir {
            void operator()(DIR* d) const noexcept {
                if (d) {
                    closedir(d);
                }
            }
        };
        using Dir = std::unique_ptr<DIR, CloseDir>;
        Dir dp(opendir(arg.c_str()));
        if (dp == nullptr) {
            slog::warn << "Directory " << arg << " cannot be opened!" << slog::endl;
            return;
        }

        struct dirent* ep;
        while (nullptr != (ep = readdir(dp.get()))) {
            std::string fileName = ep->d_name;
            if (fileName == "." || fileName == "..")
                continue;
            files.push_back(arg + "/" + ep->d_name);
        }
    } else {
        files.push_back(arg);
    }
}

/**
 * @brief This function find -i key in input args. It's necessary to process multiple values for
 * single key
 * @param files reference to vector
 * @return none.
 */
void parseInputFilesArguments(std::vector<std::string>& files) {
    std::vector<std::string> args = gflags::GetArgvs();
    const auto is_image_arg = [](const std::string& s) {
        return s == "-i" || s == "--images";
    };
    const auto is_arg = [](const std::string& s) {
        return s.front() == '-';
    };
    const auto img_start = std::find_if(begin(args), end(args), is_image_arg);
    if (img_start == end(args)) {
        return;
    }
    const auto img_begin = std::next(img_start);
    const auto img_end = std::find_if(img_begin, end(args), is_arg);
    for (auto img = img_begin; img != img_end; ++img) {
        readInputFilesArguments(files, *img);
    }

    size_t max_files = 20;
    if (files.size() < max_files) {
        slog::info << "Files were added: " << files.size() << slog::endl;
        for (const auto& filePath : files) {
            slog::info << "    " << filePath << slog::endl;
        }
    } else {
        slog::info << "Files were added: " << files.size() << ". Too many to display each of them." << slog::endl;
    }
}

namespace {
std::vector<std::string> splitStringList(const std::string& str, char delim) {
    if (str.empty())
        return {};

    std::istringstream istr(str);

    std::vector<std::string> result;
    std::string elem;
    while (std::getline(istr, elem, delim)) {
        if (elem.empty()) {
            continue;
        }
        result.emplace_back(std::move(elem));
    }

    return result;
}

std::map<std::string, std::string> parseArgMap(std::string argMap) {
    argMap.erase(std::remove_if(argMap.begin(), argMap.end(), ::isspace), argMap.end());

    const auto pairs = splitStringList(argMap, ',');

    std::map<std::string, std::string> parsedMap;
    for (auto&& pair : pairs) {
        const auto keyValue = splitStringList(pair, ':');
        if (keyValue.size() != 2) {
            throw std::invalid_argument("Invalid key/value pair " + pair + ". Expected <layer_name>:<value>");
        }

        parsedMap[keyValue[0]] = keyValue[1];
    }

    return parsedMap;
}

using supported_precisions_t = std::unordered_map<std::string, InferenceEngine::Precision>;

InferenceEngine::Precision getPrecision(std::string value, const supported_precisions_t& supported_precisions) {
    std::transform(value.begin(), value.end(), value.begin(), ::toupper);

    const auto precision = supported_precisions.find(value);
    if (precision == supported_precisions.end()) {
        throw std::logic_error("\"" + value + "\"" + " is not a valid precision");
    }

    return precision->second;
}

InferenceEngine::Precision getPrecision(const std::string& value) {
    static const supported_precisions_t supported_precisions = {
        {"FP32", InferenceEngine::Precision::FP32},
        {"FP16", InferenceEngine::Precision::FP16},
        {"BF16", InferenceEngine::Precision::BF16},
        {"U64", InferenceEngine::Precision::U64},
        {"I64", InferenceEngine::Precision::I64},
        {"U32", InferenceEngine::Precision::U32},
        {"I32", InferenceEngine::Precision::I32},
        {"U16", InferenceEngine::Precision::U16},
        {"I16", InferenceEngine::Precision::I16},
        {"U8", InferenceEngine::Precision::U8},
        {"I8", InferenceEngine::Precision::I8},
        {"BOOL", InferenceEngine::Precision::BOOL},
    };

    return getPrecision(value, supported_precisions);
}

void setPrecisions(const InferenceEngine::CNNNetwork& network, const std::string& iop) {
    const auto user_precisions_map = parseArgMap(iop);

    auto inputs = network.getInputsInfo();
    auto outputs = network.getOutputsInfo();

    for (auto&& item : user_precisions_map) {
        const auto& layer_name = item.first;
        const auto& user_precision = item.second;

        const auto input = inputs.find(layer_name);
        const auto output = outputs.find(layer_name);

        if (input != inputs.end()) {
            input->second->setPrecision(getPrecision(user_precision));
        } else if (output != outputs.end()) {
            output->second->setPrecision(getPrecision(user_precision));
        } else {
            throw std::logic_error(layer_name + " is not an input neither output");
        }
    }
}

}  // namespace

void processPrecision(InferenceEngine::CNNNetwork& network,
                      const std::string& ip,
                      const std::string& op,
                      const std::string& iop) {
    if (!ip.empty()) {
        const auto user_precision = getPrecision(ip);
        for (auto&& layer : network.getInputsInfo()) {
            layer.second->setPrecision(user_precision);
        }
    }

    if (!op.empty()) {
        auto user_precision = getPrecision(op);
        for (auto&& layer : network.getOutputsInfo()) {
            layer.second->setPrecision(user_precision);
        }
    }

    if (!iop.empty()) {
        setPrecisions(network, iop);
    }
}

namespace {
using supported_layouts_t = std::unordered_map<std::string, InferenceEngine::Layout>;
using matchLayoutToDims_t = std::unordered_map<size_t, size_t>;

InferenceEngine::Layout getLayout(std::string value, const supported_layouts_t& supported_layouts) {
    std::transform(value.begin(), value.end(), value.begin(), ::toupper);

    const auto layout = supported_layouts.find(value);
    if (layout == supported_layouts.end()) {
        throw std::logic_error("\"" + value + "\"" + " is not a valid layout");
    }

    return layout->second;
}

InferenceEngine::Layout getLayout(const std::string& value) {
    static const supported_layouts_t supported_layouts = {
        {"NCDHW", InferenceEngine::Layout::NCDHW},
        {"NDHWC", InferenceEngine::Layout::NDHWC},
        {"NCHW", InferenceEngine::Layout::NCHW},
        {"NHWC", InferenceEngine::Layout::NHWC},
        {"CHW", InferenceEngine::Layout::CHW},
        {"HWC", InferenceEngine::Layout::HWC},
        {"NC", InferenceEngine::Layout::NC},
        {"C", InferenceEngine::Layout::C},
    };

    return getLayout(value, supported_layouts);
}

bool isMatchLayoutToDims(InferenceEngine::Layout layout, size_t dimension) {
    static const matchLayoutToDims_t matchLayoutToDims = {{static_cast<size_t>(InferenceEngine::Layout::NCDHW), 5},
                                                          {static_cast<size_t>(InferenceEngine::Layout::NDHWC), 5},
                                                          {static_cast<size_t>(InferenceEngine::Layout::NCHW), 4},
                                                          {static_cast<size_t>(InferenceEngine::Layout::NHWC), 4},
                                                          {static_cast<size_t>(InferenceEngine::Layout::CHW), 3},
                                                          {static_cast<size_t>(InferenceEngine::Layout::NC), 2},
                                                          {static_cast<size_t>(InferenceEngine::Layout::C), 1}};

    const auto dims = matchLayoutToDims.find(static_cast<size_t>(layout));
    if (dims == matchLayoutToDims.end()) {
        throw std::logic_error("Layout is not valid.");
    }

    return dimension == dims->second;
}

void setLayouts(const InferenceEngine::CNNNetwork& network, const std::string iol) {
    const auto user_layouts_map = parseArgMap(iol);

    auto inputs = network.getInputsInfo();
    auto outputs = network.getOutputsInfo();

    for (auto&& item : user_layouts_map) {
        const auto& layer_name = item.first;
        const auto& user_layout = getLayout(item.second);

        const auto input = inputs.find(layer_name);
        const auto output = outputs.find(layer_name);

        if (input != inputs.end()) {
            if (!isMatchLayoutToDims(user_layout, input->second->getTensorDesc().getDims().size())) {
                throw std::logic_error(item.second + " layout is not applicable to " + layer_name);
            }

            input->second->setLayout(user_layout);
        } else if (output != outputs.end()) {
            if (!isMatchLayoutToDims(user_layout, output->second->getTensorDesc().getDims().size())) {
                throw std::logic_error(item.second + " layout is not applicable to " + layer_name);
            }

            output->second->setLayout(user_layout);
        } else {
            throw std::logic_error(layer_name + " is not an input neither output");
        }
    }
}

}  // namespace

void processLayout(InferenceEngine::CNNNetwork& network,
                   const std::string& il,
                   const std::string& ol,
                   const std::string& iol) {
    if (!il.empty()) {
        const auto layout = getLayout(il);
        for (auto&& layer : network.getInputsInfo()) {
            if (isMatchLayoutToDims(layout, layer.second->getTensorDesc().getDims().size())) {
                layer.second->setLayout(layout);
            }
        }
    }

    if (!ol.empty()) {
        const auto layout = getLayout(ol);
        for (auto&& layer : network.getOutputsInfo()) {
            if (isMatchLayoutToDims(layout, layer.second->getTensorDesc().getDims().size())) {
                layer.second->setLayout(layout);
            }
        }
    }

    if (!iol.empty()) {
        setLayouts(network, iol);
    }
}

void printInputAndOutputsInfo(const InferenceEngine::CNNNetwork& network) {
    std::cout << "Network inputs:" << std::endl;
    for (auto&& layer : network.getInputsInfo()) {
        std::cout << "    " << layer.first << " : " << layer.second->getPrecision() << " / "
                  << layer.second->getLayout() << std::endl;
    }
    std::cout << "Network outputs:" << std::endl;
    for (auto&& layer : network.getOutputsInfo()) {
        std::cout << "    " << layer.first << " : " << layer.second->getPrecision() << " / "
                  << layer.second->getLayout() << std::endl;
    }
}

void printInputAndOutputsInfo(const ov::Model& network) {
    slog::info << "model name: " << network.get_friendly_name() << slog::endl;

    const std::vector<ov::Output<const ov::Node>> inputs = network.inputs();
    for (const ov::Output<const ov::Node> input : inputs) {
        slog::info << "    inputs" << slog::endl;

        const std::string name = input.get_names().empty() ? "NONE" : input.get_any_name();
        slog::info << "        input name: " << name << slog::endl;

        const ov::element::Type type = input.get_element_type();
        slog::info << "        input type: " << type << slog::endl;

        const ov::Shape shape = input.get_shape();
        slog::info << "        input shape: " << shape << slog::endl;
    }

    const std::vector<ov::Output<const ov::Node>> outputs = network.outputs();
    for (const ov::Output<const ov::Node> output : outputs) {
        slog::info << "    outputs" << slog::endl;

        const std::string name = output.get_names().empty() ? "NONE" : output.get_any_name();
        slog::info << "        output name: " << name << slog::endl;

        const ov::element::Type type = output.get_element_type();
        slog::info << "        output type: " << type << slog::endl;

        const ov::Shape shape = output.get_shape();
        slog::info << "        output shape: " << shape << slog::endl;
    }
}