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3062 lines
110 KiB
C++
3062 lines
110 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2019-2021 Intel Corporation
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#include "../test_precomp.hpp"
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#ifdef HAVE_INF_ENGINE
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#include <stdexcept>
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#include <mutex>
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#include <condition_variable>
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#include <inference_engine.hpp>
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#include <ade/util/iota_range.hpp>
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#include <opencv2/gapi/infer/ie.hpp>
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#include <opencv2/gapi/streaming/cap.hpp>
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#include "backends/ie/util.hpp"
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#include "backends/ie/giebackend/giewrapper.hpp"
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#ifdef HAVE_NGRAPH
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#if defined(__clang__) // clang or MSVC clang
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#pragma clang diagnostic push
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#pragma clang diagnostic ignored "-Wunused-parameter"
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#elif defined(_MSC_VER)
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#pragma warning(push)
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#pragma warning(disable : 4100)
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# if _MSC_VER < 1910
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# pragma warning(disable:4268) // Disable warnings of ngraph. OpenVINO recommends to use MSVS 2019.
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# pragma warning(disable:4800)
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# endif
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#elif defined(__GNUC__)
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wunused-parameter"
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#endif
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#include <ngraph/ngraph.hpp>
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#endif
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namespace opencv_test
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{
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namespace {
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class TestMediaBGR final: public cv::MediaFrame::IAdapter {
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cv::Mat m_mat;
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using Cb = cv::MediaFrame::View::Callback;
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Cb m_cb;
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public:
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explicit TestMediaBGR(cv::Mat m, Cb cb = [](){})
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: m_mat(m), m_cb(cb) {
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}
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cv::GFrameDesc meta() const override {
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return cv::GFrameDesc{cv::MediaFormat::BGR, cv::Size(m_mat.cols, m_mat.rows)};
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}
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cv::MediaFrame::View access(cv::MediaFrame::Access) override {
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cv::MediaFrame::View::Ptrs pp = { m_mat.ptr(), nullptr, nullptr, nullptr };
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cv::MediaFrame::View::Strides ss = { m_mat.step, 0u, 0u, 0u };
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return cv::MediaFrame::View(std::move(pp), std::move(ss), Cb{m_cb});
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}
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cv::util::any blobParams() const override {
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return std::make_pair<InferenceEngine::TensorDesc,
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InferenceEngine::ParamMap>({IE::Precision::U8,
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{1, 3, 300, 300},
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IE::Layout::NCHW},
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{{"HELLO", 42},
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{"COLOR_FORMAT",
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InferenceEngine::ColorFormat::NV12}});
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}
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};
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class TestMediaNV12 final: public cv::MediaFrame::IAdapter {
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cv::Mat m_y;
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cv::Mat m_uv;
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public:
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TestMediaNV12(cv::Mat y, cv::Mat uv) : m_y(y), m_uv(uv) {
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}
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cv::GFrameDesc meta() const override {
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return cv::GFrameDesc{cv::MediaFormat::NV12, cv::Size(m_y.cols, m_y.rows)};
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}
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cv::MediaFrame::View access(cv::MediaFrame::Access) override {
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cv::MediaFrame::View::Ptrs pp = {
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m_y.ptr(), m_uv.ptr(), nullptr, nullptr
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};
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cv::MediaFrame::View::Strides ss = {
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m_y.step, m_uv.step, 0u, 0u
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};
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return cv::MediaFrame::View(std::move(pp), std::move(ss));
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}
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};
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// FIXME: taken from DNN module
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static void initDLDTDataPath()
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{
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#ifndef WINRT
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static bool initialized = false;
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if (!initialized)
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{
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const char* omzDataPath = getenv("OPENCV_OPEN_MODEL_ZOO_DATA_PATH");
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if (omzDataPath)
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cvtest::addDataSearchPath(omzDataPath);
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const char* dnnDataPath = getenv("OPENCV_DNN_TEST_DATA_PATH");
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if (dnnDataPath) {
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// Add the dnnDataPath itself - G-API is using some images there directly
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cvtest::addDataSearchPath(dnnDataPath);
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cvtest::addDataSearchPath(dnnDataPath + std::string("/omz_intel_models"));
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}
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initialized = true;
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}
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#endif // WINRT
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}
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#if INF_ENGINE_RELEASE >= 2020010000
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static const std::string SUBDIR = "intel/age-gender-recognition-retail-0013/FP32/";
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#else
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static const std::string SUBDIR = "Retail/object_attributes/age_gender/dldt/";
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#endif
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// FIXME: taken from the DNN module
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void normAssert(cv::InputArray ref, cv::InputArray test,
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const char *comment /*= ""*/,
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double l1 = 0.00001, double lInf = 0.0001)
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{
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double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
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EXPECT_LE(normL1, l1) << comment;
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double normInf = cvtest::norm(ref, test, cv::NORM_INF);
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EXPECT_LE(normInf, lInf) << comment;
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}
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namespace IE = InferenceEngine;
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void setNetParameters(IE::CNNNetwork& net, bool is_nv12 = false) {
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auto ii = net.getInputsInfo().at("data");
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ii->setPrecision(IE::Precision::U8);
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ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
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if (is_nv12) {
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ii->getPreProcess().setColorFormat(IE::ColorFormat::NV12);
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}
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}
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bool checkDeviceIsAvailable(const std::string& device) {
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const static auto available_devices = [&](){
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auto devices = cv::gimpl::ie::wrap::getCore().GetAvailableDevices();
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return std::unordered_set<std::string>{devices.begin(), devices.end()};
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}();
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return available_devices.find(device) != available_devices.end();
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}
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void skipIfDeviceNotAvailable(const std::string& device) {
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if (!checkDeviceIsAvailable(device)) {
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throw SkipTestException("Device: " + device + " isn't available!");
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}
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}
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void compileBlob(const cv::gapi::ie::detail::ParamDesc& params,
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const std::string& output,
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const IE::Precision& ip) {
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auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
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auto net = cv::gimpl::ie::wrap::readNetwork(params);
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for (auto&& ii : net.getInputsInfo()) {
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ii.second->setPrecision(ip);
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}
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auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
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std::ofstream out_file{output, std::ios::out | std::ios::binary};
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GAPI_Assert(out_file.is_open());
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this_network.Export(out_file);
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}
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std::string compileAgeGenderBlob(const std::string& device) {
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const static std::string blob_path = [&](){
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cv::gapi::ie::detail::ParamDesc params;
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const std::string model_name = "age-gender-recognition-retail-0013";
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const std::string output = model_name + ".blob";
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params.model_path = findDataFile(SUBDIR + model_name + ".xml");
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params.weights_path = findDataFile(SUBDIR + model_name + ".bin");
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params.device_id = device;
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compileBlob(params, output, IE::Precision::U8);
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return output;
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}();
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return blob_path;
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}
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} // anonymous namespace
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// TODO: Probably DNN/IE part can be further parametrized with a template
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// NOTE: here ".." is used to leave the default "gapi/" search scope
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TEST(TestAgeGenderIE, InferBasicTensor)
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{
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initDLDTDataPath();
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cv::gapi::ie::detail::ParamDesc params;
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params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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params.device_id = "CPU";
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// Load IE network, initialize input data using that.
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cv::Mat in_mat;
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cv::Mat gapi_age, gapi_gender;
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IE::Blob::Ptr ie_age, ie_gender;
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{
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auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
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auto net = cv::gimpl::ie::wrap::readNetwork(params);
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auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
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auto infer_request = this_network.CreateInferRequest();
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const auto &iedims = net.getInputsInfo().begin()->second->getTensorDesc().getDims();
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auto cvdims = cv::gapi::ie::util::to_ocv(iedims);
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in_mat.create(cvdims, CV_32F);
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cv::randu(in_mat, -1, 1);
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infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
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infer_request.Infer();
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ie_age = infer_request.GetBlob("age_conv3");
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ie_gender = infer_request.GetBlob("prob");
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}
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// Configure & run G-API
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using AGInfo = std::tuple<cv::GMat, cv::GMat>;
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G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
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cv::GMat in;
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cv::GMat age, gender;
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std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
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cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
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auto pp = cv::gapi::ie::Params<AgeGender> {
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params.model_path, params.weights_path, params.device_id
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}.cfgOutputLayers({ "age_conv3", "prob" });
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comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate with IE itself (avoid DNN module dependency here)
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normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
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normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
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}
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TEST(TestAgeGenderIE, InferBasicImage)
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{
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initDLDTDataPath();
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cv::gapi::ie::detail::ParamDesc params;
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params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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params.device_id = "CPU";
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// FIXME: Ideally it should be an image from disk
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// cv::Mat in_mat = cv::imread(findDataFile("grace_hopper_227.png"));
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cv::Mat in_mat(cv::Size(320, 240), CV_8UC3);
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cv::randu(in_mat, 0, 255);
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cv::Mat gapi_age, gapi_gender;
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// Load & run IE network
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IE::Blob::Ptr ie_age, ie_gender;
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{
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auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
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auto net = cv::gimpl::ie::wrap::readNetwork(params);
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setNetParameters(net);
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auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
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auto infer_request = this_network.CreateInferRequest();
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infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
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infer_request.Infer();
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ie_age = infer_request.GetBlob("age_conv3");
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ie_gender = infer_request.GetBlob("prob");
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}
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// Configure & run G-API
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using AGInfo = std::tuple<cv::GMat, cv::GMat>;
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G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
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cv::GMat in;
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cv::GMat age, gender;
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std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
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cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
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auto pp = cv::gapi::ie::Params<AgeGender> {
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params.model_path, params.weights_path, params.device_id
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}.cfgOutputLayers({ "age_conv3", "prob" });
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comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate with IE itself (avoid DNN module dependency here)
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normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
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normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
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}
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struct InferWithReshape: public ::testing::Test {
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cv::gapi::ie::detail::ParamDesc params;
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cv::Mat m_in_mat;
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std::vector<cv::Rect> m_roi_list;
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std::vector<size_t> reshape_dims;
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std::vector<cv::Mat> m_out_ie_ages;
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std::vector<cv::Mat> m_out_ie_genders;
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std::vector<cv::Mat> m_out_gapi_ages;
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std::vector<cv::Mat> m_out_gapi_genders;
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using AGInfo = std::tuple<cv::GMat, cv::GMat>;
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G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
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InferenceEngine::CNNNetwork net;
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InferenceEngine::Core plugin;
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void SetUp() {
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// FIXME: it must be cv::imread(findDataFile("../dnn/grace_hopper_227.png", false));
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m_in_mat = cv::Mat(cv::Size(320, 240), CV_8UC3);
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cv::randu(m_in_mat, 0, 255);
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m_out_gapi_ages.resize(1);
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m_out_gapi_genders.resize(1);
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// both ROIs point to the same face, with a slightly changed geometry
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m_roi_list = {
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cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
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cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
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};
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// New dimensions for "data" input
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reshape_dims = {1, 3, 70, 70};
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initDLDTDataPath();
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params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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params.device_id = "CPU";
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plugin = cv::gimpl::ie::wrap::getPlugin(params);
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net = cv::gimpl::ie::wrap::readNetwork(params);
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setNetParameters(net);
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net.reshape({{"data", reshape_dims}});
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}
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void inferROIs(IE::Blob::Ptr blob) {
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auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
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auto infer_request = this_network.CreateInferRequest();
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for (auto &&rc : m_roi_list) {
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const auto ie_rc = IE::ROI {
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0u
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, static_cast<std::size_t>(rc.x)
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, static_cast<std::size_t>(rc.y)
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, static_cast<std::size_t>(rc.width)
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, static_cast<std::size_t>(rc.height)
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};
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infer_request.SetBlob("data", IE::make_shared_blob(blob, ie_rc));
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infer_request.Infer();
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using namespace cv::gapi::ie::util;
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m_out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
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m_out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
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}
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}
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void infer(cv::Mat& in, const bool with_roi = false) {
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if (!with_roi) {
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auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
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auto infer_request = this_network.CreateInferRequest();
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infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in));
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infer_request.Infer();
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using namespace cv::gapi::ie::util;
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m_out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
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m_out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
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} else {
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auto frame_blob = cv::gapi::ie::util::to_ie(in);
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inferROIs(frame_blob);
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}
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}
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void validate() {
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// Validate with IE itself (avoid DNN module dependency here)
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GAPI_Assert(!m_out_gapi_ages.empty());
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ASSERT_EQ(m_out_gapi_genders.size(), m_out_gapi_ages.size());
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ASSERT_EQ(m_out_gapi_ages.size(), m_out_ie_ages.size());
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ASSERT_EQ(m_out_gapi_genders.size(), m_out_ie_genders.size());
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const size_t size = m_out_gapi_ages.size();
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for (size_t i = 0; i < size; ++i) {
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normAssert(m_out_ie_ages [i], m_out_gapi_ages [i], "Test age output");
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normAssert(m_out_ie_genders[i], m_out_gapi_genders[i], "Test gender output");
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}
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}
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}; // InferWithReshape
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struct InferWithReshapeNV12: public InferWithReshape {
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cv::Mat m_in_uv;
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cv::Mat m_in_y;
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void SetUp() {
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InferWithReshape::SetUp();
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cv::Size sz{320, 240};
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m_in_y = cv::Mat{sz, CV_8UC1};
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cv::randu(m_in_y, 0, 255);
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m_in_uv = cv::Mat{sz / 2, CV_8UC2};
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cv::randu(m_in_uv, 0, 255);
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setNetParameters(net, true);
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net.reshape({{"data", reshape_dims}});
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auto frame_blob = cv::gapi::ie::util::to_ie(m_in_y, m_in_uv);
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inferROIs(frame_blob);
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}
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};
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struct ROIList: public ::testing::Test {
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cv::gapi::ie::detail::ParamDesc params;
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cv::Mat m_in_mat;
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std::vector<cv::Rect> m_roi_list;
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std::vector<cv::Mat> m_out_ie_ages;
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std::vector<cv::Mat> m_out_ie_genders;
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std::vector<cv::Mat> m_out_gapi_ages;
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std::vector<cv::Mat> m_out_gapi_genders;
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using AGInfo = std::tuple<cv::GMat, cv::GMat>;
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G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
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void SetUp() {
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initDLDTDataPath();
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params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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params.device_id = "CPU";
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// FIXME: it must be cv::imread(findDataFile("../dnn/grace_hopper_227.png", false));
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m_in_mat = cv::Mat(cv::Size(320, 240), CV_8UC3);
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cv::randu(m_in_mat, 0, 255);
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// both ROIs point to the same face, with a slightly changed geometry
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m_roi_list = {
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cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
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cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
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|
};
|
|
|
|
// Load & run IE network
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
auto frame_blob = cv::gapi::ie::util::to_ie(m_in_mat);
|
|
|
|
for (auto &&rc : m_roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
infer_request.SetBlob("data", IE::make_shared_blob(frame_blob, ie_rc));
|
|
infer_request.Infer();
|
|
|
|
using namespace cv::gapi::ie::util;
|
|
m_out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
m_out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
} // namespace IE = ..
|
|
} // ROIList()
|
|
|
|
void validate() {
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
ASSERT_EQ(2u, m_out_ie_ages.size());
|
|
ASSERT_EQ(2u, m_out_ie_genders.size());
|
|
ASSERT_EQ(2u, m_out_gapi_ages.size());
|
|
ASSERT_EQ(2u, m_out_gapi_genders.size());
|
|
|
|
normAssert(m_out_ie_ages [0], m_out_gapi_ages [0], "0: Test age output");
|
|
normAssert(m_out_ie_genders[0], m_out_gapi_genders[0], "0: Test gender output");
|
|
normAssert(m_out_ie_ages [1], m_out_gapi_ages [1], "1: Test age output");
|
|
normAssert(m_out_ie_genders[1], m_out_gapi_genders[1], "1: Test gender output");
|
|
}
|
|
}; // ROIList
|
|
|
|
struct ROIListNV12: public ::testing::Test {
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
|
|
cv::Mat m_in_uv;
|
|
cv::Mat m_in_y;
|
|
std::vector<cv::Rect> m_roi_list;
|
|
|
|
std::vector<cv::Mat> m_out_ie_ages;
|
|
std::vector<cv::Mat> m_out_ie_genders;
|
|
|
|
std::vector<cv::Mat> m_out_gapi_ages;
|
|
std::vector<cv::Mat> m_out_gapi_genders;
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
void SetUp() {
|
|
initDLDTDataPath();
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Size sz{320, 240};
|
|
m_in_y = cv::Mat{sz, CV_8UC1};
|
|
cv::randu(m_in_y, 0, 255);
|
|
m_in_uv = cv::Mat{sz / 2, CV_8UC2};
|
|
cv::randu(m_in_uv, 0, 255);
|
|
|
|
// both ROIs point to the same face, with a slightly changed geometry
|
|
m_roi_list = {
|
|
cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
|
|
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
|
|
};
|
|
|
|
// Load & run IE network
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net, true);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
auto frame_blob = cv::gapi::ie::util::to_ie(m_in_y, m_in_uv);
|
|
|
|
for (auto &&rc : m_roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
infer_request.SetBlob("data", IE::make_shared_blob(frame_blob, ie_rc));
|
|
infer_request.Infer();
|
|
|
|
using namespace cv::gapi::ie::util;
|
|
m_out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
m_out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
} // namespace IE = ..
|
|
} // ROIList()
|
|
|
|
void validate() {
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
ASSERT_EQ(2u, m_out_ie_ages.size());
|
|
ASSERT_EQ(2u, m_out_ie_genders.size());
|
|
ASSERT_EQ(2u, m_out_gapi_ages.size());
|
|
ASSERT_EQ(2u, m_out_gapi_genders.size());
|
|
|
|
normAssert(m_out_ie_ages [0], m_out_gapi_ages [0], "0: Test age output");
|
|
normAssert(m_out_ie_genders[0], m_out_gapi_genders[0], "0: Test gender output");
|
|
normAssert(m_out_ie_ages [1], m_out_gapi_ages [1], "1: Test age output");
|
|
normAssert(m_out_ie_genders[1], m_out_gapi_genders[1], "1: Test gender output");
|
|
}
|
|
};
|
|
|
|
struct SingleROI: public ::testing::Test {
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
|
|
cv::Mat m_in_mat;
|
|
cv::Rect m_roi;
|
|
|
|
cv::Mat m_out_gapi_age;
|
|
cv::Mat m_out_gapi_gender;
|
|
|
|
cv::Mat m_out_ie_age;
|
|
cv::Mat m_out_ie_gender;
|
|
|
|
void SetUp() {
|
|
initDLDTDataPath();
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
// FIXME: it must be cv::imread(findDataFile("../dnn/grace_hopper_227.png", false));
|
|
m_in_mat = cv::Mat(cv::Size(320, 240), CV_8UC3);
|
|
cv::randu(m_in_mat, 0, 255);
|
|
|
|
m_roi = cv::Rect(cv::Point{64, 60}, cv::Size{96, 96});
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(m_roi.x)
|
|
, static_cast<std::size_t>(m_roi.y)
|
|
, static_cast<std::size_t>(m_roi.width)
|
|
, static_cast<std::size_t>(m_roi.height)
|
|
};
|
|
|
|
IE::Blob::Ptr roi_blob = IE::make_shared_blob(cv::gapi::ie::util::to_ie(m_in_mat), ie_rc);
|
|
infer_request.SetBlob("data", roi_blob);
|
|
infer_request.Infer();
|
|
|
|
using namespace cv::gapi::ie::util;
|
|
m_out_ie_age = to_ocv(infer_request.GetBlob("age_conv3")).clone();
|
|
m_out_ie_gender = to_ocv(infer_request.GetBlob("prob")).clone();
|
|
}
|
|
}
|
|
|
|
void validate() {
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(m_out_ie_age , m_out_gapi_age , "Test age output");
|
|
normAssert(m_out_ie_gender, m_out_gapi_gender, "Test gender output");
|
|
}
|
|
};
|
|
|
|
struct SingleROINV12: public ::testing::Test {
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
|
|
cv::Mat m_in_y;
|
|
cv::Mat m_in_uv;
|
|
cv::Rect m_roi;
|
|
|
|
cv::Mat m_out_gapi_age;
|
|
cv::Mat m_out_gapi_gender;
|
|
|
|
cv::Mat m_out_ie_age;
|
|
cv::Mat m_out_ie_gender;
|
|
|
|
void SetUp() {
|
|
initDLDTDataPath();
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Size sz{320, 240};
|
|
m_in_y = cv::Mat{sz, CV_8UC1};
|
|
cv::randu(m_in_y, 0, 255);
|
|
m_in_uv = cv::Mat{sz / 2, CV_8UC2};
|
|
cv::randu(m_in_uv, 0, 255);
|
|
|
|
m_roi = cv::Rect(cv::Point{64, 60}, cv::Size{96, 96});
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net, /* NV12 */ true);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
auto blob = cv::gapi::ie::util::to_ie(m_in_y, m_in_uv);
|
|
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(m_roi.x)
|
|
, static_cast<std::size_t>(m_roi.y)
|
|
, static_cast<std::size_t>(m_roi.width)
|
|
, static_cast<std::size_t>(m_roi.height)
|
|
};
|
|
|
|
IE::Blob::Ptr roi_blob = IE::make_shared_blob(blob, ie_rc);
|
|
infer_request.SetBlob("data", roi_blob);
|
|
infer_request.Infer();
|
|
|
|
using namespace cv::gapi::ie::util;
|
|
m_out_ie_age = to_ocv(infer_request.GetBlob("age_conv3")).clone();
|
|
m_out_ie_gender = to_ocv(infer_request.GetBlob("prob")).clone();
|
|
}
|
|
}
|
|
|
|
void validate() {
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(m_out_ie_age , m_out_gapi_age , "Test age output");
|
|
normAssert(m_out_ie_gender, m_out_gapi_gender, "Test gender output");
|
|
}
|
|
};
|
|
|
|
TEST_F(ROIList, TestInfer)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(m_in_mat, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIList, TestInfer2)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(m_in_mat, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
validate();
|
|
}
|
|
|
|
TEST(DISABLED_TestTwoIENNPipeline, InferBasicImage)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc AGparams;
|
|
AGparams.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml", false);
|
|
AGparams.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin", false);
|
|
AGparams.device_id = "MYRIAD";
|
|
|
|
// FIXME: Ideally it should be an image from disk
|
|
// cv::Mat in_mat = cv::imread(findDataFile("grace_hopper_227.png"));
|
|
cv::Mat in_mat(cv::Size(320, 240), CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age1, gapi_gender1, gapi_age2, gapi_gender2;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age1, ie_gender1, ie_age2, ie_gender2;
|
|
{
|
|
auto AGplugin1 = cv::gimpl::ie::wrap::getPlugin(AGparams);
|
|
auto AGnet1 = cv::gimpl::ie::wrap::readNetwork(AGparams);
|
|
setNetParameters(AGnet1);
|
|
auto AGplugin_network1 = cv::gimpl::ie::wrap::loadNetwork(AGplugin1, AGnet1, AGparams);
|
|
auto AGinfer_request1 = AGplugin_network1.CreateInferRequest();
|
|
AGinfer_request1.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
|
|
AGinfer_request1.Infer();
|
|
ie_age1 = AGinfer_request1.GetBlob("age_conv3");
|
|
ie_gender1 = AGinfer_request1.GetBlob("prob");
|
|
|
|
auto AGplugin2 = cv::gimpl::ie::wrap::getPlugin(AGparams);
|
|
auto AGnet2 = cv::gimpl::ie::wrap::readNetwork(AGparams);
|
|
setNetParameters(AGnet2);
|
|
auto AGplugin_network2 = cv::gimpl::ie::wrap::loadNetwork(AGplugin2, AGnet2, AGparams);
|
|
auto AGinfer_request2 = AGplugin_network2.CreateInferRequest();
|
|
AGinfer_request2.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
|
|
AGinfer_request2.Infer();
|
|
ie_age2 = AGinfer_request2.GetBlob("age_conv3");
|
|
ie_gender2 = AGinfer_request2.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender1, <AGInfo(cv::GMat)>, "test-age-gender1");
|
|
G_API_NET(AgeGender2, <AGInfo(cv::GMat)>, "test-age-gender2");
|
|
cv::GMat in;
|
|
cv::GMat age1, gender1;
|
|
std::tie(age1, gender1) = cv::gapi::infer<AgeGender1>(in);
|
|
|
|
cv::GMat age2, gender2;
|
|
// FIXME: "Multi-node inference is not supported!", workarounded 'till enabling proper tools
|
|
std::tie(age2, gender2) = cv::gapi::infer<AgeGender2>(cv::gapi::copy(in));
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age1, gender1, age2, gender2));
|
|
|
|
auto age_net1 = cv::gapi::ie::Params<AgeGender1> {
|
|
AGparams.model_path, AGparams.weights_path, AGparams.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
auto age_net2 = cv::gapi::ie::Params<AgeGender2> {
|
|
AGparams.model_path, AGparams.weights_path, AGparams.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
comp.apply(cv::gin(in_mat), cv::gout(gapi_age1, gapi_gender1, gapi_age2, gapi_gender2),
|
|
cv::compile_args(cv::gapi::networks(age_net1, age_net2)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age1), gapi_age1, "Test age output 1");
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender1), gapi_gender1, "Test gender output 1");
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age2), gapi_age2, "Test age output 2");
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender2), gapi_gender2, "Test gender output 2");
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, GenericInfer)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Mat in_mat(cv::Size(320, 240), CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
cv::GMat in;
|
|
GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", inputs);
|
|
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id};
|
|
|
|
comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, InvalidConfigGeneric)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
std::string weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
std::string device_id = "CPU";
|
|
|
|
// Configure & run G-API
|
|
cv::GMat in;
|
|
GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<cv::gapi::Generic>{
|
|
"age-gender-generic", model_path, weights_path, device_id
|
|
}.pluginConfig({{"unsupported_config", "some_value"}});
|
|
|
|
EXPECT_ANY_THROW(comp.compile(cv::GMatDesc{CV_8U,3,cv::Size{320, 240}},
|
|
cv::compile_args(cv::gapi::networks(pp))));
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, CPUConfigGeneric)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
std::string weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
std::string device_id = "CPU";
|
|
|
|
// Configure & run G-API
|
|
cv::GMat in;
|
|
GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<cv::gapi::Generic> {
|
|
"age-gender-generic", model_path, weights_path, device_id
|
|
}.pluginConfig({{IE::PluginConfigParams::KEY_CPU_THROUGHPUT_STREAMS,
|
|
IE::PluginConfigParams::CPU_THROUGHPUT_NUMA}});
|
|
|
|
EXPECT_NO_THROW(comp.compile(cv::GMatDesc{CV_8U,3,cv::Size{320, 240}},
|
|
cv::compile_args(cv::gapi::networks(pp))));
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, InvalidConfig)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
std::string weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
std::string device_id = "CPU";
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
model_path, weights_path, device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.pluginConfig({{"unsupported_config", "some_value"}});
|
|
|
|
EXPECT_ANY_THROW(comp.compile(cv::GMatDesc{CV_8U,3,cv::Size{320, 240}},
|
|
cv::compile_args(cv::gapi::networks(pp))));
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, CPUConfig)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
std::string weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
std::string device_id = "CPU";
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
model_path, weights_path, device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.pluginConfig({{IE::PluginConfigParams::KEY_CPU_THROUGHPUT_STREAMS,
|
|
IE::PluginConfigParams::CPU_THROUGHPUT_NUMA}});
|
|
|
|
EXPECT_NO_THROW(comp.compile(cv::GMatDesc{CV_8U,3,cv::Size{320, 240}},
|
|
cv::compile_args(cv::gapi::networks(pp))));
|
|
}
|
|
|
|
TEST_F(ROIList, MediaInputBGR)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::GFrame in;
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(m_in_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIListNV12, MediaInputNV12)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::GFrame in;
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, MediaInputNV12)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Size sz{320, 240};
|
|
cv::Mat in_y_mat(sz, CV_8UC1);
|
|
cv::randu(in_y_mat, 0, 255);
|
|
cv::Mat in_uv_mat(sz / 2, CV_8UC2);
|
|
cv::randu(in_uv_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net, true);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_y_mat, in_uv_mat));
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GFrame in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(in_y_mat, in_uv_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, MediaInputBGR)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Size sz{320, 240};
|
|
cv::Mat in_mat(sz, CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GFrame in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(InferROI, MediaInputBGR)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Size sz{320, 240};
|
|
cv::Mat in_mat(sz, CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
cv::Rect rect(cv::Point{64, 60}, cv::Size{96, 96});
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rect.x)
|
|
, static_cast<std::size_t>(rect.y)
|
|
, static_cast<std::size_t>(rect.width)
|
|
, static_cast<std::size_t>(rect.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob = IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_mat), ie_rc);
|
|
infer_request.SetBlob("data", roi_blob);
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GFrame in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(roi, in);
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame, rect), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(InferROI, MediaInputNV12)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Size sz{320, 240};
|
|
auto in_y_mat = cv::Mat{sz, CV_8UC1};
|
|
cv::randu(in_y_mat, 0, 255);
|
|
auto in_uv_mat = cv::Mat{sz / 2, CV_8UC2};
|
|
cv::randu(in_uv_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
cv::Rect rect(cv::Point{64, 60}, cv::Size{96, 96});
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net, true);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rect.x)
|
|
, static_cast<std::size_t>(rect.y)
|
|
, static_cast<std::size_t>(rect.width)
|
|
, static_cast<std::size_t>(rect.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob = IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_y_mat, in_uv_mat), ie_rc);
|
|
infer_request.SetBlob("data", roi_blob);
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GFrame in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(roi, in);
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(in_y_mat, in_uv_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame, rect), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST_F(ROIList, Infer2MediaInputBGR)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GFrame in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(m_in_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIListNV12, Infer2MediaInputNV12)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GFrame in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
validate();
|
|
}
|
|
|
|
TEST_F(SingleROI, GenericInfer)
|
|
{
|
|
// Configure & run G-API
|
|
cv::GMat in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", roi, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
comp.apply(cv::gin(m_in_mat, m_roi), cv::gout(m_out_gapi_age, m_out_gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST_F(SingleROI, GenericInferMediaBGR)
|
|
{
|
|
// Configure & run G-API
|
|
cv::GFrame in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", roi, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(m_in_mat);
|
|
comp.apply(cv::gin(frame, m_roi), cv::gout(m_out_gapi_age, m_out_gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST_F(SingleROINV12, GenericInferMediaNV12)
|
|
{
|
|
// Configure & run G-API
|
|
cv::GFrame in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", roi, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
|
|
comp.apply(cv::gin(frame, m_roi), cv::gout(m_out_gapi_age, m_out_gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIList, GenericInfer)
|
|
{
|
|
cv::GMat in;
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", rr, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
comp.apply(cv::gin(m_in_mat, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIList, GenericInferMediaBGR)
|
|
{
|
|
cv::GFrame in;
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", rr, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(m_in_mat);
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIListNV12, GenericInferMediaNV12)
|
|
{
|
|
cv::GFrame in;
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GInferInputs inputs;
|
|
inputs["data"] = in;
|
|
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>("age-gender-generic", rr, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIList, GenericInfer2)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
GInferListInputs list;
|
|
list["data"] = rr;
|
|
|
|
auto outputs = cv::gapi::infer2<cv::gapi::Generic>("age-gender-generic", in, list);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
comp.apply(cv::gin(m_in_mat, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIList, GenericInfer2MediaInputBGR)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GFrame in;
|
|
GInferListInputs inputs;
|
|
inputs["data"] = rr;
|
|
|
|
auto outputs = cv::gapi::infer2<cv::gapi::Generic>("age-gender-generic", in, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(m_in_mat);
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIListNV12, GenericInfer2MediaInputNV12)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GFrame in;
|
|
GInferListInputs inputs;
|
|
inputs["data"] = rr;
|
|
|
|
auto outputs = cv::gapi::infer2<cv::gapi::Generic>("age-gender-generic", in, inputs);
|
|
auto age = outputs.at("age_conv3");
|
|
auto gender = outputs.at("prob");
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
cv::gapi::ie::Params<cv::gapi::Generic> pp{
|
|
"age-gender-generic", params.model_path, params.weights_path, params.device_id
|
|
};
|
|
pp.cfgNumRequests(2u);
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
validate();
|
|
}
|
|
|
|
TEST(Infer, SetInvalidNumberOfRequests)
|
|
{
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::gapi::ie::Params<AgeGender> pp{"model", "weights", "device"};
|
|
|
|
EXPECT_ANY_THROW(pp.cfgNumRequests(0u));
|
|
}
|
|
|
|
TEST(Infer, TestStreamingInfer)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string filepath = findDataFile("cv/video/768x576.avi");
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
// Load IE network, initialize input data using that.
|
|
cv::Mat in_mat;
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgNumRequests(4u);
|
|
|
|
|
|
std::size_t num_frames = 0u;
|
|
std::size_t max_frames = 10u;
|
|
|
|
cv::VideoCapture cap;
|
|
cap.open(filepath);
|
|
if (!cap.isOpened())
|
|
throw SkipTestException("Video file can not be opened");
|
|
|
|
cap >> in_mat;
|
|
auto pipeline = comp.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
pipeline.setSource<cv::gapi::wip::GCaptureSource>(filepath);
|
|
|
|
pipeline.start();
|
|
while (num_frames < max_frames && pipeline.pull(cv::gout(gapi_age, gapi_gender)))
|
|
{
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
++num_frames;
|
|
cap >> in_mat;
|
|
}
|
|
pipeline.stop();
|
|
}
|
|
|
|
TEST(InferROI, TestStreamingInfer)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string filepath = findDataFile("cv/video/768x576.avi");
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
// Load IE network, initialize input data using that.
|
|
cv::Mat in_mat;
|
|
cv::Mat gapi_age, gapi_gender;
|
|
cv::Rect rect(cv::Point{64, 60}, cv::Size{96, 96});
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GMat age, gender;
|
|
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(roi, in);
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgNumRequests(4u);
|
|
|
|
|
|
std::size_t num_frames = 0u;
|
|
std::size_t max_frames = 10u;
|
|
|
|
cv::VideoCapture cap;
|
|
cap.open(filepath);
|
|
if (!cap.isOpened())
|
|
throw SkipTestException("Video file can not be opened");
|
|
|
|
cap >> in_mat;
|
|
auto pipeline = comp.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
pipeline.setSource(
|
|
cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(filepath), rect));
|
|
|
|
pipeline.start();
|
|
while (num_frames < max_frames && pipeline.pull(cv::gout(gapi_age, gapi_gender)))
|
|
{
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rect.x)
|
|
, static_cast<std::size_t>(rect.y)
|
|
, static_cast<std::size_t>(rect.width)
|
|
, static_cast<std::size_t>(rect.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob = IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_mat), ie_rc);
|
|
infer_request.SetBlob("data", roi_blob);
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
++num_frames;
|
|
cap >> in_mat;
|
|
}
|
|
pipeline.stop();
|
|
}
|
|
|
|
TEST(InferList, TestStreamingInfer)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string filepath = findDataFile("cv/video/768x576.avi");
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
// Load IE network, initialize input data using that.
|
|
cv::Mat in_mat;
|
|
std::vector<cv::Mat> ie_ages, ie_genders, gapi_ages, gapi_genders;
|
|
|
|
std::vector<cv::Rect> roi_list = {
|
|
cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
|
|
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
|
|
};
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GArray<cv::Rect> roi;
|
|
cv::GArray<GMat> age, gender;
|
|
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(roi, in);
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgNumRequests(4u);
|
|
|
|
std::size_t num_frames = 0u;
|
|
std::size_t max_frames = 10u;
|
|
|
|
cv::VideoCapture cap;
|
|
cap.open(filepath);
|
|
if (!cap.isOpened())
|
|
throw SkipTestException("Video file can not be opened");
|
|
|
|
cap >> in_mat;
|
|
auto pipeline = comp.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
pipeline.setSource(
|
|
cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(filepath), roi_list));
|
|
|
|
pipeline.start();
|
|
while (num_frames < max_frames && pipeline.pull(cv::gout(gapi_ages, gapi_genders)))
|
|
{
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
auto frame_blob = cv::gapi::ie::util::to_ie(in_mat);
|
|
|
|
for (auto &&rc : roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
infer_request.SetBlob("data", IE::make_shared_blob(frame_blob, ie_rc));
|
|
infer_request.Infer();
|
|
|
|
using namespace cv::gapi::ie::util;
|
|
ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
} // namespace IE = ..
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(ie_ages [0], gapi_ages [0], "0: Test age output");
|
|
normAssert(ie_genders[0], gapi_genders[0], "0: Test gender output");
|
|
normAssert(ie_ages [1], gapi_ages [1], "1: Test age output");
|
|
normAssert(ie_genders[1], gapi_genders[1], "1: Test gender output");
|
|
|
|
ie_ages.clear();
|
|
ie_genders.clear();
|
|
|
|
++num_frames;
|
|
cap >> in_mat;
|
|
}
|
|
}
|
|
|
|
TEST(Infer2, TestStreamingInfer)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string filepath = findDataFile("cv/video/768x576.avi");
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
// Load IE network, initialize input data using that.
|
|
cv::Mat in_mat;
|
|
std::vector<cv::Mat> ie_ages, ie_genders, gapi_ages, gapi_genders;
|
|
|
|
std::vector<cv::Rect> roi_list = {
|
|
cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
|
|
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
|
|
};
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgNumRequests(4u);
|
|
|
|
std::size_t num_frames = 0u;
|
|
std::size_t max_frames = 10u;
|
|
|
|
cv::VideoCapture cap;
|
|
cap.open(filepath);
|
|
if (!cap.isOpened())
|
|
throw SkipTestException("Video file can not be opened");
|
|
|
|
cap >> in_mat;
|
|
auto pipeline = comp.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
pipeline.setSource(
|
|
cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(filepath), roi_list));
|
|
|
|
pipeline.start();
|
|
while (num_frames < max_frames && pipeline.pull(cv::gout(gapi_ages, gapi_genders)))
|
|
{
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
auto frame_blob = cv::gapi::ie::util::to_ie(in_mat);
|
|
|
|
for (auto &&rc : roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
infer_request.SetBlob("data", IE::make_shared_blob(frame_blob, ie_rc));
|
|
infer_request.Infer();
|
|
|
|
using namespace cv::gapi::ie::util;
|
|
ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
} // namespace IE = ..
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(ie_ages [0], gapi_ages [0], "0: Test age output");
|
|
normAssert(ie_genders[0], gapi_genders[0], "0: Test gender output");
|
|
normAssert(ie_ages [1], gapi_ages [1], "1: Test age output");
|
|
normAssert(ie_genders[1], gapi_genders[1], "1: Test gender output");
|
|
|
|
ie_ages.clear();
|
|
ie_genders.clear();
|
|
|
|
++num_frames;
|
|
cap >> in_mat;
|
|
}
|
|
pipeline.stop();
|
|
}
|
|
|
|
TEST(InferEmptyList, TestStreamingInfer)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string filepath = findDataFile("cv/video/768x576.avi");
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
// Load IE network, initialize input data using that.
|
|
cv::Mat in_mat;
|
|
std::vector<cv::Mat> ie_ages, ie_genders, gapi_ages, gapi_genders;
|
|
|
|
// NB: Empty list of roi
|
|
std::vector<cv::Rect> roi_list;
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GArray<cv::Rect> roi;
|
|
cv::GArray<GMat> age, gender;
|
|
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(roi, in);
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgNumRequests(4u);
|
|
|
|
std::size_t num_frames = 0u;
|
|
std::size_t max_frames = 1u;
|
|
|
|
cv::VideoCapture cap;
|
|
cap.open(filepath);
|
|
if (!cap.isOpened())
|
|
throw SkipTestException("Video file can not be opened");
|
|
|
|
cap >> in_mat;
|
|
auto pipeline = comp.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
pipeline.setSource(
|
|
cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(filepath), roi_list));
|
|
|
|
pipeline.start();
|
|
while (num_frames < max_frames && pipeline.pull(cv::gout(gapi_ages, gapi_genders)))
|
|
{
|
|
EXPECT_TRUE(gapi_ages.empty());
|
|
EXPECT_TRUE(gapi_genders.empty());
|
|
}
|
|
}
|
|
|
|
TEST(Infer2EmptyList, TestStreamingInfer)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
std::string filepath = findDataFile("cv/video/768x576.avi");
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
// Load IE network, initialize input data using that.
|
|
cv::Mat in_mat;
|
|
std::vector<cv::Mat> ie_ages, ie_genders, gapi_ages, gapi_genders;
|
|
|
|
// NB: Empty list of roi
|
|
std::vector<cv::Rect> roi_list;
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgNumRequests(4u);
|
|
|
|
std::size_t num_frames = 0u;
|
|
std::size_t max_frames = 1u;
|
|
|
|
cv::VideoCapture cap;
|
|
cap.open(filepath);
|
|
if (!cap.isOpened())
|
|
throw SkipTestException("Video file can not be opened");
|
|
|
|
cap >> in_mat;
|
|
auto pipeline = comp.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
pipeline.setSource(
|
|
cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(filepath), roi_list));
|
|
|
|
pipeline.start();
|
|
while (num_frames < max_frames && pipeline.pull(cv::gout(gapi_ages, gapi_genders)))
|
|
{
|
|
EXPECT_TRUE(gapi_ages.empty());
|
|
EXPECT_TRUE(gapi_genders.empty());
|
|
}
|
|
}
|
|
|
|
TEST_F(InferWithReshape, TestInfer)
|
|
{
|
|
// IE code
|
|
infer(m_in_mat);
|
|
// G-API code
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" }).cfgInputReshape({{"data", reshape_dims}});
|
|
comp.apply(cv::gin(m_in_mat), cv::gout(m_out_gapi_ages.front(), m_out_gapi_genders.front()),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
// Validate
|
|
validate();
|
|
}
|
|
|
|
TEST_F(InferWithReshape, TestInferInImage)
|
|
{
|
|
// Input image already has 70x70 size
|
|
cv::Mat rsz;
|
|
cv::resize(m_in_mat, rsz, cv::Size(70, 70));
|
|
// IE code
|
|
infer(rsz);
|
|
// G-API code
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" }).cfgInputReshape({"data"});
|
|
// Reshape CNN input by input image size
|
|
comp.apply(cv::gin(rsz), cv::gout(m_out_gapi_ages.front(), m_out_gapi_genders.front()),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
// Validate
|
|
validate();
|
|
}
|
|
|
|
TEST_F(InferWithReshape, TestInferForSingleLayer)
|
|
{
|
|
// IE code
|
|
infer(m_in_mat);
|
|
// G-API code
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgInputReshape("data", reshape_dims);
|
|
comp.apply(cv::gin(m_in_mat), cv::gout(m_out_gapi_ages.front(), m_out_gapi_genders.front()),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
// Validate
|
|
validate();
|
|
}
|
|
|
|
TEST_F(InferWithReshape, TestInferList)
|
|
{
|
|
// IE code
|
|
infer(m_in_mat, true);
|
|
// G-API code
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" }).cfgInputReshape({{"data", reshape_dims}});
|
|
comp.apply(cv::gin(m_in_mat, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
// Validate
|
|
validate();
|
|
}
|
|
|
|
TEST_F(InferWithReshape, TestInferList2)
|
|
{
|
|
// IE code
|
|
infer(m_in_mat, true);
|
|
// G-API code
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" }).cfgInputReshape({{"data", reshape_dims}});
|
|
comp.apply(cv::gin(m_in_mat, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
// Validate
|
|
validate();
|
|
}
|
|
|
|
TEST_F(InferWithReshape, TestInferListBGR)
|
|
{
|
|
// IE code
|
|
infer(m_in_mat, true);
|
|
// G-API code
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GFrame in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaBGR>(m_in_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" }).cfgInputReshape({{"data", reshape_dims}});
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
// Validate
|
|
validate();
|
|
}
|
|
|
|
TEST_F(InferWithReshapeNV12, TestInferListYUV)
|
|
{
|
|
// G-API code
|
|
cv::GFrame in;
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" }).cfgInputReshape({{"data", reshape_dims}});
|
|
comp.apply(cv::gin(frame, m_roi_list),
|
|
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
// Validate
|
|
validate();
|
|
}
|
|
|
|
TEST_F(ROIList, CallInferMultipleTimes)
|
|
{
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
auto cc = comp.compile(cv::descr_of(cv::gin(m_in_mat, m_roi_list)),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
for (int i = 0; i < 10; ++i) {
|
|
cc(cv::gin(m_in_mat, m_roi_list), cv::gout(m_out_gapi_ages, m_out_gapi_genders));
|
|
}
|
|
|
|
validate();
|
|
}
|
|
|
|
TEST(IEFrameAdapter, blobParams)
|
|
{
|
|
cv::Mat bgr = cv::Mat::eye(240, 320, CV_8UC3);
|
|
cv::MediaFrame frame = cv::MediaFrame::Create<TestMediaBGR>(bgr);
|
|
|
|
auto expected = std::make_pair(IE::TensorDesc{IE::Precision::U8, {1, 3, 300, 300},
|
|
IE::Layout::NCHW},
|
|
IE::ParamMap{{"HELLO", 42}, {"COLOR_FORMAT",
|
|
IE::ColorFormat::NV12}});
|
|
|
|
auto actual = cv::util::any_cast<decltype(expected)>(frame.blobParams());
|
|
|
|
EXPECT_EQ(expected, actual);
|
|
}
|
|
|
|
namespace
|
|
{
|
|
|
|
struct Sync {
|
|
std::mutex m;
|
|
std::condition_variable cv;
|
|
int counter = 0;
|
|
};
|
|
|
|
class GMockMediaAdapter final: public cv::MediaFrame::IAdapter {
|
|
public:
|
|
explicit GMockMediaAdapter(cv::Mat m, std::shared_ptr<Sync> sync)
|
|
: m_mat(m), m_sync(sync) {
|
|
}
|
|
|
|
cv::GFrameDesc meta() const override {
|
|
return cv::GFrameDesc{cv::MediaFormat::BGR, m_mat.size()};
|
|
}
|
|
|
|
cv::MediaFrame::View access(cv::MediaFrame::Access) override {
|
|
cv::MediaFrame::View::Ptrs pp = { m_mat.ptr(), nullptr, nullptr, nullptr };
|
|
cv::MediaFrame::View::Strides ss = { m_mat.step, 0u, 0u, 0u };
|
|
return cv::MediaFrame::View(std::move(pp), std::move(ss));
|
|
}
|
|
|
|
~GMockMediaAdapter() {
|
|
{
|
|
std::lock_guard<std::mutex> lk{m_sync->m};
|
|
m_sync->counter--;
|
|
}
|
|
m_sync->cv.notify_one();
|
|
}
|
|
|
|
private:
|
|
cv::Mat m_mat;
|
|
std::shared_ptr<Sync> m_sync;
|
|
};
|
|
|
|
// NB: This source is needed to simulate real
|
|
// cases where the memory resources are limited.
|
|
// GMockSource(int limit) - accept the number of MediaFrames that
|
|
// the source can produce until resources are over.
|
|
class GMockSource : public cv::gapi::wip::IStreamSource {
|
|
public:
|
|
explicit GMockSource(int limit)
|
|
: m_limit(limit), m_mat(cv::Size(1920, 1080), CV_8UC3),
|
|
m_sync(new Sync{}) {
|
|
cv::randu(m_mat, cv::Scalar::all(0), cv::Scalar::all(255));
|
|
}
|
|
|
|
bool pull(cv::gapi::wip::Data& data) {
|
|
std::unique_lock<std::mutex> lk(m_sync->m);
|
|
m_sync->counter++;
|
|
// NB: Can't produce new frames until old ones are released.
|
|
m_sync->cv.wait(lk, [this]{return m_sync->counter <= m_limit;});
|
|
|
|
data = cv::MediaFrame::Create<GMockMediaAdapter>(m_mat, m_sync);
|
|
return true;
|
|
}
|
|
|
|
GMetaArg descr_of() const override {
|
|
return GMetaArg{cv::GFrameDesc{cv::MediaFormat::BGR, m_mat.size()}};
|
|
}
|
|
|
|
private:
|
|
int m_limit;
|
|
cv::Mat m_mat;
|
|
std::shared_ptr<Sync> m_sync;
|
|
};
|
|
|
|
struct LimitedSourceInfer: public ::testing::Test {
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
LimitedSourceInfer()
|
|
: comp([](){
|
|
cv::GFrame in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
return cv::GComputation(cv::GIn(in), cv::GOut(age, gender));
|
|
}) {
|
|
initDLDTDataPath();
|
|
}
|
|
|
|
GStreamingCompiled compileStreaming(int nireq) {
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id }
|
|
.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgNumRequests(nireq);
|
|
|
|
return comp.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
}
|
|
|
|
void run(const int max_frames, const int limit, const int nireq) {
|
|
auto pipeline = compileStreaming(nireq);
|
|
pipeline.setSource<GMockSource>(limit);
|
|
pipeline.start();
|
|
|
|
int num_frames = 0;
|
|
while (num_frames != max_frames &&
|
|
pipeline.pull(cv::gout(out_age, out_gender))) {
|
|
++num_frames;
|
|
}
|
|
}
|
|
|
|
cv::GComputation comp;
|
|
cv::Mat out_age, out_gender;
|
|
};
|
|
|
|
} // anonymous namespace
|
|
|
|
TEST_F(LimitedSourceInfer, ReleaseFrame)
|
|
{
|
|
constexpr int max_frames = 50;
|
|
constexpr int resources_limit = 1;
|
|
constexpr int nireq = 1;
|
|
|
|
run(max_frames, resources_limit, nireq);
|
|
}
|
|
|
|
TEST_F(LimitedSourceInfer, ReleaseFrameAsync)
|
|
{
|
|
constexpr int max_frames = 50;
|
|
constexpr int resources_limit = 4;
|
|
constexpr int nireq = 8;
|
|
|
|
run(max_frames, resources_limit, nireq);
|
|
}
|
|
|
|
TEST(TestAgeGenderIE, InferWithBatch)
|
|
{
|
|
initDLDTDataPath();
|
|
|
|
constexpr int batch_size = 4;
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
params.device_id = "CPU";
|
|
|
|
cv::Mat in_mat({batch_size, 3, 320, 240}, CV_8U);
|
|
cv::randu(in_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto net = cv::gimpl::ie::wrap::readNetwork(params);
|
|
setNetParameters(net);
|
|
net.setBatchSize(batch_size);
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.weights_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgBatchSize(batch_size);
|
|
|
|
comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(ImportNetwork, Infer)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Mat in_mat(320, 240, CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat), info);
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(ImportNetwork, InferNV12)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path= compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Size sz{320, 240};
|
|
cv::Mat in_y_mat(sz, CV_8UC1);
|
|
cv::randu(in_y_mat, 0, 255);
|
|
cv::Mat in_uv_mat(sz / 2, CV_8UC2);
|
|
cv::randu(in_uv_mat, 0, 255);
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
info.setColorFormat(IE::ColorFormat::NV12);
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_y_mat, in_uv_mat), info);
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GFrame in;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(in_y_mat, in_uv_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
comp.apply(cv::gin(frame), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(ImportNetwork, InferROI)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Mat in_mat(320, 240, CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
cv::Mat gapi_age, gapi_gender;
|
|
cv::Rect rect(cv::Point{64, 60}, cv::Size{96, 96});
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rect.x)
|
|
, static_cast<std::size_t>(rect.y)
|
|
, static_cast<std::size_t>(rect.width)
|
|
, static_cast<std::size_t>(rect.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob = IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_mat), ie_rc);
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
infer_request.SetBlob("data", roi_blob, info);
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(roi, in);
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
comp.apply(cv::gin(in_mat, rect), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(ImportNetwork, InferROINV12)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Size sz{320, 240};
|
|
cv::Mat in_y_mat(sz, CV_8UC1);
|
|
cv::randu(in_y_mat, 0, 255);
|
|
cv::Mat in_uv_mat(sz / 2, CV_8UC2);
|
|
cv::randu(in_uv_mat, 0, 255);
|
|
cv::Rect rect(cv::Point{64, 60}, cv::Size{96, 96});
|
|
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// Load & run IE network
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rect.x)
|
|
, static_cast<std::size_t>(rect.y)
|
|
, static_cast<std::size_t>(rect.width)
|
|
, static_cast<std::size_t>(rect.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob =
|
|
IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_y_mat, in_uv_mat), ie_rc);
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
info.setColorFormat(IE::ColorFormat::NV12);
|
|
infer_request.SetBlob("data", roi_blob, info);
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GFrame in;
|
|
cv::GOpaque<cv::Rect> roi;
|
|
cv::GMat age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(roi, in);
|
|
cv::GComputation comp(cv::GIn(in, roi), cv::GOut(age, gender));
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(in_y_mat, in_uv_mat);
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
comp.apply(cv::gin(frame, rect), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
|
|
}
|
|
|
|
TEST(ImportNetwork, InferList)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Mat in_mat(320, 240, CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
std::vector<cv::Rect> roi_list = {
|
|
cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
|
|
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
|
|
};
|
|
std::vector<cv::Mat> out_ie_ages, out_ie_genders, out_gapi_ages, out_gapi_genders;
|
|
|
|
// Load & run IE network
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
for (auto &&rc : roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob =
|
|
IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_mat), ie_rc);
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
infer_request.SetBlob("data", roi_blob, info);
|
|
infer_request.Infer();
|
|
using namespace cv::gapi::ie::util;
|
|
out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
comp.apply(cv::gin(in_mat, roi_list), cv::gout(out_gapi_ages, out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
GAPI_Assert(!out_gapi_ages.empty());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_gapi_ages.size());
|
|
ASSERT_EQ(out_gapi_ages.size(), out_ie_ages.size());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_ie_genders.size());
|
|
|
|
const size_t size = out_gapi_ages.size();
|
|
for (size_t i = 0; i < size; ++i) {
|
|
normAssert(out_ie_ages [i], out_gapi_ages [i], "Test age output");
|
|
normAssert(out_ie_genders[i], out_gapi_genders[i], "Test gender output");
|
|
}
|
|
}
|
|
|
|
TEST(ImportNetwork, InferListNV12)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Size sz{320, 240};
|
|
cv::Mat in_y_mat(sz, CV_8UC1);
|
|
cv::randu(in_y_mat, 0, 255);
|
|
cv::Mat in_uv_mat(sz / 2, CV_8UC2);
|
|
cv::randu(in_uv_mat, 0, 255);
|
|
std::vector<cv::Rect> roi_list = {
|
|
cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
|
|
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
|
|
};
|
|
std::vector<cv::Mat> out_ie_ages, out_ie_genders, out_gapi_ages, out_gapi_genders;
|
|
|
|
// Load & run IE network
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
for (auto &&rc : roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob =
|
|
IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_y_mat, in_uv_mat), ie_rc);
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
info.setColorFormat(IE::ColorFormat::NV12);
|
|
infer_request.SetBlob("data", roi_blob, info);
|
|
infer_request.Infer();
|
|
using namespace cv::gapi::ie::util;
|
|
out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GFrame in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(in_y_mat, in_uv_mat);
|
|
|
|
comp.apply(cv::gin(frame, roi_list), cv::gout(out_gapi_ages, out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
GAPI_Assert(!out_gapi_ages.empty());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_gapi_ages.size());
|
|
ASSERT_EQ(out_gapi_ages.size(), out_ie_ages.size());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_ie_genders.size());
|
|
|
|
const size_t size = out_gapi_ages.size();
|
|
for (size_t i = 0; i < size; ++i) {
|
|
normAssert(out_ie_ages [i], out_gapi_ages [i], "Test age output");
|
|
normAssert(out_ie_genders[i], out_gapi_genders[i], "Test gender output");
|
|
}
|
|
}
|
|
|
|
TEST(ImportNetwork, InferList2)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Mat in_mat(320, 240, CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
std::vector<cv::Rect> roi_list = {
|
|
cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
|
|
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
|
|
};
|
|
std::vector<cv::Mat> out_ie_ages, out_ie_genders, out_gapi_ages, out_gapi_genders;
|
|
|
|
// Load & run IE network
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
for (auto &&rc : roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob =
|
|
IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_mat), ie_rc);
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
infer_request.SetBlob("data", roi_blob, info);
|
|
infer_request.Infer();
|
|
using namespace cv::gapi::ie::util;
|
|
out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GMat in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
comp.apply(cv::gin(in_mat, roi_list), cv::gout(out_gapi_ages, out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
GAPI_Assert(!out_gapi_ages.empty());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_gapi_ages.size());
|
|
ASSERT_EQ(out_gapi_ages.size(), out_ie_ages.size());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_ie_genders.size());
|
|
|
|
const size_t size = out_gapi_ages.size();
|
|
for (size_t i = 0; i < size; ++i) {
|
|
normAssert(out_ie_ages [i], out_gapi_ages [i], "Test age output");
|
|
normAssert(out_ie_genders[i], out_gapi_genders[i], "Test gender output");
|
|
}
|
|
}
|
|
|
|
TEST(ImportNetwork, InferList2NV12)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
cv::Size sz{320, 240};
|
|
cv::Mat in_y_mat(sz, CV_8UC1);
|
|
cv::randu(in_y_mat, 0, 255);
|
|
cv::Mat in_uv_mat(sz / 2, CV_8UC2);
|
|
cv::randu(in_uv_mat, 0, 255);
|
|
std::vector<cv::Rect> roi_list = {
|
|
cv::Rect(cv::Point{64, 60}, cv::Size{ 96, 96}),
|
|
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
|
|
};
|
|
std::vector<cv::Mat> out_ie_ages, out_ie_genders, out_gapi_ages, out_gapi_genders;
|
|
|
|
// Load & run IE network
|
|
{
|
|
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
|
|
auto this_network = cv::gimpl::ie::wrap::importNetwork(plugin, params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
for (auto &&rc : roi_list) {
|
|
const auto ie_rc = IE::ROI {
|
|
0u
|
|
, static_cast<std::size_t>(rc.x)
|
|
, static_cast<std::size_t>(rc.y)
|
|
, static_cast<std::size_t>(rc.width)
|
|
, static_cast<std::size_t>(rc.height)
|
|
};
|
|
IE::Blob::Ptr roi_blob =
|
|
IE::make_shared_blob(cv::gapi::ie::util::to_ie(in_y_mat, in_uv_mat), ie_rc);
|
|
IE::PreProcessInfo info;
|
|
info.setResizeAlgorithm(IE::RESIZE_BILINEAR);
|
|
info.setColorFormat(IE::ColorFormat::NV12);
|
|
infer_request.SetBlob("data", roi_blob, info);
|
|
infer_request.Infer();
|
|
using namespace cv::gapi::ie::util;
|
|
out_ie_ages.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
|
|
out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
|
|
}
|
|
}
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GArray<cv::Rect> rr;
|
|
cv::GFrame in;
|
|
cv::GArray<cv::GMat> age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
|
|
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
auto frame = MediaFrame::Create<TestMediaNV12>(in_y_mat, in_uv_mat);
|
|
|
|
comp.apply(cv::gin(frame, roi_list), cv::gout(out_gapi_ages, out_gapi_genders),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
GAPI_Assert(!out_gapi_ages.empty());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_gapi_ages.size());
|
|
ASSERT_EQ(out_gapi_ages.size(), out_ie_ages.size());
|
|
ASSERT_EQ(out_gapi_genders.size(), out_ie_genders.size());
|
|
|
|
const size_t size = out_gapi_ages.size();
|
|
for (size_t i = 0; i < size; ++i) {
|
|
normAssert(out_ie_ages [i], out_gapi_ages [i], "Test age output");
|
|
normAssert(out_ie_genders[i], out_gapi_genders[i], "Test gender output");
|
|
}
|
|
}
|
|
|
|
TEST(TestAgeGender, ThrowBlobAndInputPrecisionMismatch)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
// NB: Precision for inputs is U8.
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
cv::GMat in, age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
cv::Mat in_mat(320, 240, CV_32FC3);
|
|
cv::randu(in_mat, 0, 1);
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
// NB: Blob precision is U8, but user pass FP32 data, so exception will be thrown.
|
|
// Now exception comes directly from IE, but since G-API has information
|
|
// about data precision at the compile stage, consider the possibility of
|
|
// throwing exception from there.
|
|
EXPECT_ANY_THROW(comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
|
|
cv::compile_args(cv::gapi::networks(pp))));
|
|
}
|
|
|
|
#ifdef HAVE_NGRAPH
|
|
|
|
TEST(Infer, ModelWith2DInputs)
|
|
{
|
|
const std::string model_name = "ModelWith2DInputs";
|
|
const std::string model_path = model_name + ".xml";
|
|
const std::string weights_path = model_name + ".bin";
|
|
const std::string device_id = "CPU";
|
|
const int W = 10;
|
|
const int H = 5;
|
|
|
|
// NB: Define model with 2D inputs.
|
|
auto in1 = std::make_shared<ngraph::op::Parameter>(
|
|
ngraph::element::Type_t::u8,
|
|
ngraph::Shape(std::vector<size_t>{(size_t)H, (size_t)W})
|
|
);
|
|
auto in2 = std::make_shared<ngraph::op::Parameter>(
|
|
ngraph::element::Type_t::u8,
|
|
ngraph::Shape(std::vector<size_t>{(size_t)H, (size_t)W})
|
|
);
|
|
auto result = std::make_shared<ngraph::op::v1::Add>(in1, in2);
|
|
auto func = std::make_shared<ngraph::Function>(
|
|
ngraph::OutputVector{result},
|
|
ngraph::ParameterVector{in1, in2}
|
|
);
|
|
|
|
cv::Mat in_mat1(std::vector<int>{H, W}, CV_8U),
|
|
in_mat2(std::vector<int>{H, W}, CV_8U),
|
|
gapi_mat, ref_mat;
|
|
|
|
cv::randu(in_mat1, 0, 100);
|
|
cv::randu(in_mat2, 0, 100);
|
|
cv::add(in_mat1, in_mat2, ref_mat, cv::noArray(), CV_32F);
|
|
|
|
// Compile xml file
|
|
IE::CNNNetwork(func).serialize(model_path);
|
|
|
|
// Configure & run G-API
|
|
cv::GMat g_in1, g_in2;
|
|
cv::GInferInputs inputs;
|
|
inputs[in1->get_name()] = g_in1;
|
|
inputs[in2->get_name()] = g_in2;
|
|
auto outputs = cv::gapi::infer<cv::gapi::Generic>(model_name, inputs);
|
|
auto out = outputs.at(result->get_name());
|
|
|
|
cv::GComputation comp(cv::GIn(g_in1, g_in2), cv::GOut(out));
|
|
|
|
auto pp = cv::gapi::ie::Params<cv::gapi::Generic>(model_name,
|
|
model_path,
|
|
weights_path,
|
|
device_id);
|
|
|
|
comp.apply(cv::gin(in_mat1, in_mat2), cv::gout(gapi_mat),
|
|
cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
normAssert(ref_mat, gapi_mat, "Test model output");
|
|
}
|
|
|
|
#endif // HAVE_NGRAPH
|
|
|
|
TEST(TestAgeGender, ThrowBlobAndInputPrecisionMismatchStreaming)
|
|
{
|
|
const std::string device = "MYRIAD";
|
|
skipIfDeviceNotAvailable(device);
|
|
|
|
initDLDTDataPath();
|
|
|
|
cv::gapi::ie::detail::ParamDesc params;
|
|
// NB: Precision for inputs is U8.
|
|
params.model_path = compileAgeGenderBlob(device);
|
|
params.device_id = device;
|
|
|
|
// Configure & run G-API
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
params.model_path, params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" });
|
|
|
|
cv::GMat in, age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(age, gender))
|
|
.compileStreaming(cv::compile_args(cv::gapi::networks(pp)));
|
|
|
|
cv::Mat in_mat(320, 240, CV_32FC3);
|
|
cv::randu(in_mat, 0, 1);
|
|
cv::Mat gapi_age, gapi_gender;
|
|
|
|
pipeline.setSource(cv::gin(in_mat));
|
|
pipeline.start();
|
|
|
|
// NB: Blob precision is U8, but user pass FP32 data, so exception will be thrown.
|
|
// Now exception comes directly from IE, but since G-API has information
|
|
// about data precision at the compile stage, consider the possibility of
|
|
// throwing exception from there.
|
|
for (int i = 0; i < 10; ++i) {
|
|
EXPECT_ANY_THROW(pipeline.pull(cv::gout(gapi_age, gapi_gender)));
|
|
}
|
|
}
|
|
|
|
struct AgeGenderInferTest: public ::testing::Test {
|
|
cv::Mat m_in_mat;
|
|
cv::Mat m_gapi_age;
|
|
cv::Mat m_gapi_gender;
|
|
|
|
cv::gimpl::ie::wrap::Plugin m_plugin;
|
|
IE::CNNNetwork m_net;
|
|
cv::gapi::ie::detail::ParamDesc m_params;
|
|
|
|
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
|
|
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
|
|
|
|
void SetUp() {
|
|
initDLDTDataPath();
|
|
m_params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
|
|
m_params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
|
|
m_params.device_id = "CPU";
|
|
|
|
m_plugin = cv::gimpl::ie::wrap::getPlugin(m_params);
|
|
m_net = cv::gimpl::ie::wrap::readNetwork(m_params);
|
|
setNetParameters(m_net);
|
|
|
|
m_in_mat = cv::Mat(cv::Size(320, 240), CV_8UC3);
|
|
cv::randu(m_in_mat, 0, 255);
|
|
}
|
|
|
|
cv::GComputation buildGraph() {
|
|
cv::GMat in, age, gender;
|
|
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
|
|
return cv::GComputation(cv::GIn(in), cv::GOut(age, gender));
|
|
}
|
|
|
|
void validate() {
|
|
IE::Blob::Ptr ie_age, ie_gender;
|
|
{
|
|
auto this_network = cv::gimpl::ie::wrap::loadNetwork(m_plugin, m_net, m_params);
|
|
auto infer_request = this_network.CreateInferRequest();
|
|
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(m_in_mat));
|
|
infer_request.Infer();
|
|
ie_age = infer_request.GetBlob("age_conv3");
|
|
ie_gender = infer_request.GetBlob("prob");
|
|
}
|
|
// Validate with IE itself (avoid DNN module dependency here)
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_age), m_gapi_age, "Test age output" );
|
|
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), m_gapi_gender, "Test gender output");
|
|
}
|
|
};
|
|
|
|
TEST_F(AgeGenderInferTest, SyncExecution) {
|
|
auto pp = cv::gapi::ie::Params<AgeGender> {
|
|
m_params.model_path, m_params.weights_path, m_params.device_id
|
|
}.cfgOutputLayers({ "age_conv3", "prob" })
|
|
.cfgInferMode(cv::gapi::ie::InferMode::Sync);
|
|
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buildGraph().apply(cv::gin(m_in_mat), cv::gout(m_gapi_age, m_gapi_gender),
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cv::compile_args(cv::gapi::networks(pp)));
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validate();
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}
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TEST_F(AgeGenderInferTest, ThrowSyncWithNireqNotEqualToOne) {
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auto pp = cv::gapi::ie::Params<AgeGender> {
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m_params.model_path, m_params.weights_path, m_params.device_id
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}.cfgOutputLayers({ "age_conv3", "prob" })
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.cfgInferMode(cv::gapi::ie::InferMode::Sync)
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.cfgNumRequests(4u);
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EXPECT_ANY_THROW(buildGraph().apply(cv::gin(m_in_mat), cv::gout(m_gapi_age, m_gapi_gender),
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cv::compile_args(cv::gapi::networks(pp))));
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}
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TEST_F(AgeGenderInferTest, ChangeOutputPrecision) {
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auto pp = cv::gapi::ie::Params<AgeGender> {
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m_params.model_path, m_params.weights_path, m_params.device_id
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}.cfgOutputLayers({ "age_conv3", "prob" })
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.cfgOutputPrecision(CV_8U);
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for (auto it : m_net.getOutputsInfo()) {
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it.second->setPrecision(IE::Precision::U8);
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}
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buildGraph().apply(cv::gin(m_in_mat), cv::gout(m_gapi_age, m_gapi_gender),
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cv::compile_args(cv::gapi::networks(pp)));
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validate();
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}
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TEST_F(AgeGenderInferTest, ChangeSpecificOutputPrecison) {
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auto pp = cv::gapi::ie::Params<AgeGender> {
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m_params.model_path, m_params.weights_path, m_params.device_id
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}.cfgOutputLayers({ "age_conv3", "prob" })
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.cfgOutputPrecision({{"prob", CV_8U}});
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m_net.getOutputsInfo().at("prob")->setPrecision(IE::Precision::U8);
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buildGraph().apply(cv::gin(m_in_mat), cv::gout(m_gapi_age, m_gapi_gender),
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cv::compile_args(cv::gapi::networks(pp)));
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validate();
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}
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} // namespace opencv_test
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#endif // HAVE_INF_ENGINE
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