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Merge pull request #18240 from mpashchenkov:mp/ocv-gapi-input-cnn-reshape
[G-API]: Adding reshape for CNN input. * Added CNN input IE reshape * rbs * Added unordered_set instead vector * Alignment
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Maxim Pashchenkov
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GitHub
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ddd2447192
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12fa8d8444
@@ -233,6 +233,115 @@ TEST(TestAgeGenderIE, InferBasicImage)
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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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InferWithReshape() {
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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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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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@@ -1403,6 +1512,153 @@ TEST(Infer2EmptyList, TestStreamingInfer)
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}
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}
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TEST_F(InferWithReshape, TestInfer)
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{
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// IE code
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infer(m_in_mat);
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// G-API code
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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" }).cfgInputReshape({{"data", reshape_dims}});
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comp.apply(cv::gin(m_in_mat), cv::gout(m_out_gapi_ages.front(), m_out_gapi_genders.front()),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate
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validate();
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}
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TEST_F(InferWithReshape, TestInferInImage)
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{
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// Input image already has 70x70 size
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cv::Mat rsz;
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cv::resize(m_in_mat, rsz, cv::Size(70, 70));
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// IE code
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infer(rsz);
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// G-API code
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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" }).cfgInputReshape({"data"});
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// Reshape CNN input by input image size
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comp.apply(cv::gin(rsz), cv::gout(m_out_gapi_ages.front(), m_out_gapi_genders.front()),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate
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validate();
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}
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TEST_F(InferWithReshape, TestInferForSingleLayer)
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{
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// IE code
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infer(m_in_mat);
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// G-API code
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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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.cfgInputReshape("data", reshape_dims);
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comp.apply(cv::gin(m_in_mat), cv::gout(m_out_gapi_ages.front(), m_out_gapi_genders.front()),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate
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validate();
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}
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TEST_F(InferWithReshape, TestInferList)
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{
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// IE code
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infer(m_in_mat, true);
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// G-API code
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cv::GArray<cv::Rect> rr;
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cv::GMat in;
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cv::GArray<cv::GMat> age, gender;
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std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
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cv::GComputation comp(cv::GIn(in, rr), 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" }).cfgInputReshape({{"data", reshape_dims}});
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comp.apply(cv::gin(m_in_mat, m_roi_list),
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cv::gout(m_out_gapi_ages, m_out_gapi_genders),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate
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validate();
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}
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TEST_F(InferWithReshape, TestInferList2)
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{
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// IE code
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infer(m_in_mat, true);
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// G-API code
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cv::GArray<cv::Rect> rr;
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cv::GMat in;
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cv::GArray<cv::GMat> age, gender;
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std::tie(age, gender) = cv::gapi::infer2<AgeGender>(in, rr);
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cv::GComputation comp(cv::GIn(in, rr), 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" }).cfgInputReshape({{"data", reshape_dims}});
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comp.apply(cv::gin(m_in_mat, m_roi_list),
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cv::gout(m_out_gapi_ages, m_out_gapi_genders),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate
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validate();
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}
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TEST_F(InferWithReshape, TestInferListBGR)
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{
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// IE code
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infer(m_in_mat, true);
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// G-API code
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cv::GArray<cv::Rect> rr;
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cv::GFrame in;
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cv::GArray<cv::GMat> age, gender;
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std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
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cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
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auto frame = MediaFrame::Create<TestMediaBGR>(m_in_mat);
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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" }).cfgInputReshape({{"data", reshape_dims}});
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comp.apply(cv::gin(frame, m_roi_list),
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cv::gout(m_out_gapi_ages, m_out_gapi_genders),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate
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validate();
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}
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TEST_F(InferWithReshapeNV12, TestInferListYUV)
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{
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// G-API code
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cv::GFrame in;
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cv::GArray<cv::Rect> rr;
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cv::GArray<cv::GMat> age, gender;
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std::tie(age, gender) = cv::gapi::infer<AgeGender>(rr, in);
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cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
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auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
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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" }).cfgInputReshape({{"data", reshape_dims}});
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comp.apply(cv::gin(frame, m_roi_list),
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cv::gout(m_out_gapi_ages, m_out_gapi_genders),
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cv::compile_args(cv::gapi::networks(pp)));
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// Validate
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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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