opencv/modules/gapi/test/infer/gapi_infer_ie_test.cpp
Anatoliy Talamanov 50a264d832
Merge pull request #19310 from TolyaTalamanov:at/generic-infer-overloads
[G-API] Support generic infer overloads

* Overloads for generic infer

* Fix build

* Refactoring

* Fix docs

* Put extra stuff to detail namespace

* Add doc for usings

* Remove uneccessary template in Priv
2021-03-19 12:50:45 +00:00

2011 lines
72 KiB
C++

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2019-2020 Intel Corporation
#include "../test_precomp.hpp"
#ifdef HAVE_INF_ENGINE
#include <stdexcept>
#include <inference_engine.hpp>
#include <ade/util/iota_range.hpp>
#include <opencv2/gapi/infer/ie.hpp>
#include <opencv2/gapi/streaming/cap.hpp>
#include "backends/ie/util.hpp"
#include "backends/ie/giebackend/giewrapper.hpp"
namespace opencv_test
{
namespace {
void initTestDataPath()
{
#ifndef WINRT
static bool initialized = false;
if (!initialized)
{
// Since G-API has no own test data (yet), it is taken from the common space
const char* testDataPath = getenv("OPENCV_TEST_DATA_PATH");
if (testDataPath) {
cvtest::addDataSearchPath(testDataPath);
}
initialized = true;
}
#endif // WINRT
}
class TestMediaBGR final: public cv::MediaFrame::IAdapter {
cv::Mat m_mat;
using Cb = cv::MediaFrame::View::Callback;
Cb m_cb;
public:
explicit TestMediaBGR(cv::Mat m, Cb cb = [](){})
: m_mat(m), m_cb(cb) {
}
cv::GFrameDesc meta() const override {
return cv::GFrameDesc{cv::MediaFormat::BGR, cv::Size(m_mat.cols, m_mat.rows)};
}
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), Cb{m_cb});
}
};
class TestMediaNV12 final: public cv::MediaFrame::IAdapter {
cv::Mat m_y;
cv::Mat m_uv;
public:
TestMediaNV12(cv::Mat y, cv::Mat uv) : m_y(y), m_uv(uv) {
}
cv::GFrameDesc meta() const override {
return cv::GFrameDesc{cv::MediaFormat::NV12, cv::Size(m_y.cols, m_y.rows)};
}
cv::MediaFrame::View access(cv::MediaFrame::Access) override {
cv::MediaFrame::View::Ptrs pp = {
m_y.ptr(), m_uv.ptr(), nullptr, nullptr
};
cv::MediaFrame::View::Strides ss = {
m_y.step, m_uv.step, 0u, 0u
};
return cv::MediaFrame::View(std::move(pp), std::move(ss));
}
};
// FIXME: taken from DNN module
static void initDLDTDataPath()
{
#ifndef WINRT
static bool initialized = false;
if (!initialized)
{
const char* omzDataPath = getenv("OPENCV_OPEN_MODEL_ZOO_DATA_PATH");
if (omzDataPath)
cvtest::addDataSearchPath(omzDataPath);
const char* dnnDataPath = getenv("OPENCV_DNN_TEST_DATA_PATH");
if (dnnDataPath) {
// Add the dnnDataPath itself - G-API is using some images there directly
cvtest::addDataSearchPath(dnnDataPath);
cvtest::addDataSearchPath(dnnDataPath + std::string("/omz_intel_models"));
}
initialized = true;
}
#endif // WINRT
}
#if INF_ENGINE_RELEASE >= 2020010000
static const std::string SUBDIR = "intel/age-gender-recognition-retail-0013/FP32/";
#else
static const std::string SUBDIR = "Retail/object_attributes/age_gender/dldt/";
#endif
// FIXME: taken from the DNN module
void normAssert(cv::InputArray ref, cv::InputArray test,
const char *comment /*= ""*/,
double l1 = 0.00001, double lInf = 0.0001)
{
double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
EXPECT_LE(normL1, l1) << comment;
double normInf = cvtest::norm(ref, test, cv::NORM_INF);
EXPECT_LE(normInf, lInf) << comment;
}
namespace IE = InferenceEngine;
void setNetParameters(IE::CNNNetwork& net, bool is_nv12 = false) {
auto ii = net.getInputsInfo().at("data");
ii->setPrecision(IE::Precision::U8);
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
if (is_nv12) {
ii->getPreProcess().setColorFormat(IE::ColorFormat::NV12);
}
}
} // anonymous namespace
// TODO: Probably DNN/IE part can be further parametrized with a template
// NOTE: here ".." is used to leave the default "gapi/" search scope
TEST(TestAgeGenderIE, InferBasicTensor)
{
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";
// Load IE network, initialize input data using that.
cv::Mat in_mat;
cv::Mat 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);
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
auto infer_request = this_network.CreateInferRequest();
const auto &iedims = net.getInputsInfo().begin()->second->getTensorDesc().getDims();
auto cvdims = cv::gapi::ie::util::to_ocv(iedims);
in_mat.create(cvdims, CV_32F);
cv::randu(in_mat, -1, 1);
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" });
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, InferBasicImage)
{
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";
// 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_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::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" });
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");
}
struct InferWithReshape: public ::testing::Test {
cv::gapi::ie::detail::ParamDesc params;
cv::Mat m_in_mat;
std::vector<cv::Rect> m_roi_list;
std::vector<size_t> reshape_dims;
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");
InferenceEngine::CNNNetwork net;
InferenceEngine::Core plugin;
InferWithReshape() {
// 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_out_gapi_ages.resize(1);
m_out_gapi_genders.resize(1);
// 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}),
};
// New dimensions for "data" input
reshape_dims = {1, 3, 70, 70};
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";
plugin = cv::gimpl::ie::wrap::getPlugin(params);
net = cv::gimpl::ie::wrap::readNetwork(params);
setNetParameters(net);
net.reshape({{"data", reshape_dims}});
}
void inferROIs(IE::Blob::Ptr blob) {
auto this_network = cv::gimpl::ie::wrap::loadNetwork(plugin, net, params);
auto infer_request = this_network.CreateInferRequest();
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(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());
}
}
void infer(cv::Mat& in, const bool with_roi = false) {
if (!with_roi) {
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));
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());
} else {
auto frame_blob = cv::gapi::ie::util::to_ie(in);
inferROIs(frame_blob);
}
}
void validate() {
// Validate with IE itself (avoid DNN module dependency here)
GAPI_Assert(!m_out_gapi_ages.empty());
ASSERT_EQ(m_out_gapi_genders.size(), m_out_gapi_ages.size());
ASSERT_EQ(m_out_gapi_ages.size(), m_out_ie_ages.size());
ASSERT_EQ(m_out_gapi_genders.size(), m_out_ie_genders.size());
const size_t size = m_out_gapi_ages.size();
for (size_t i = 0; i < size; ++i) {
normAssert(m_out_ie_ages [i], m_out_gapi_ages [i], "Test age output");
normAssert(m_out_ie_genders[i], m_out_gapi_genders[i], "Test gender output");
}
}
}; // InferWithReshape
struct InferWithReshapeNV12: public InferWithReshape {
cv::Mat m_in_uv;
cv::Mat m_in_y;
void SetUp() {
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);
setNetParameters(net, true);
net.reshape({{"data", reshape_dims}});
auto frame_blob = cv::gapi::ie::util::to_ie(m_in_y, m_in_uv);
inferROIs(frame_blob);
}
};
struct ROIList: public ::testing::Test {
cv::gapi::ie::detail::ParamDesc params;
cv::Mat m_in_mat;
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";
// 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);
// 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);
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)
{
initTestDataPath();
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)
{
initTestDataPath();
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)
{
initTestDataPath();
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)
{
initTestDataPath();
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)
{
initTestDataPath();
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)
{
initTestDataPath();
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();
}
} // namespace opencv_test
#endif // HAVE_INF_ENGINE