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55c1720719
* - headers in "infer/" and "infer/ie/" folders are included into gapi_ext_hdrs; + because of that a few #includes are required in the headers - HAVE_INF_ENGINE flag check in headers "infer/ie.hpp" and "infer/ie/util.hpp" is deleted * - the "ie/util.hpp" header is a private header now as it's used for tests; it's been moved to the scr directory to the place next to the implementation file "ie/giebackend.cpp" - the path to this header in files "ie/giebackend.cpp" and "test/infer/gapi_infer_ie_test.cpp" is updated - As it's private header now and explicitly depends on IE, the "HAVE_INF_ENGINE" flag check is returned
283 lines
9.8 KiB
C++
283 lines
9.8 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 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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////////////////////////////////////////////////////////////////////////////////
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// FIXME: Suppress deprecation warnings for OpenVINO 2019R2+
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// BEGIN {{{
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#if defined(__GNUC__)
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#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
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#endif
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#ifdef _MSC_VER
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#pragma warning(disable: 4996) // was declared deprecated
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#endif
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#if defined(__GNUC__)
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#pragma GCC visibility push(default)
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#endif
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#include <inference_engine.hpp>
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#if defined(__GNUC__)
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#pragma GCC visibility pop
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#endif
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// END }}}
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////////////////////////////////////////////////////////////////////////////////
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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 "backends/ie/util.hpp"
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namespace opencv_test
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{
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namespace {
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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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// 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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} // 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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const std::string path = "Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013";
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const auto topology_path = findDataFile(path + ".xml", false);
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const auto weights_path = findDataFile(path + ".bin", false);
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// Load IE network, initialize input data using that.
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namespace IE = InferenceEngine;
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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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IE::CNNNetReader reader;
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reader.ReadNetwork(topology_path);
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reader.ReadWeights(weights_path);
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auto net = reader.getNetwork();
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const auto &iedims = net.getInputsInfo().begin()->second->getDims();
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auto cvdims = cv::gapi::ie::util::to_ocv(iedims);
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std::reverse(cvdims.begin(), cvdims.end());
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in_mat.create(cvdims, CV_32F);
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cv::randu(in_mat, -1, 1);
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auto plugin = IE::PluginDispatcher().getPluginByDevice("CPU");
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auto plugin_net = plugin.LoadNetwork(net, {});
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auto infer_request = plugin_net.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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topology_path, weights_path, "CPU"
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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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const std::string path = "Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013";
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const auto topology_path = findDataFile(path + ".xml", false);
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const auto weights_path = findDataFile(path + ".bin", false);
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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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namespace IE = InferenceEngine;
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IE::Blob::Ptr ie_age, ie_gender;
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{
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IE::CNNNetReader reader;
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reader.ReadNetwork(topology_path);
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reader.ReadWeights(weights_path);
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auto net = reader.getNetwork();
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auto &ii = net.getInputsInfo().at("data");
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ii->setPrecision(IE::Precision::U8);
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ii->setLayout(IE::Layout::NHWC);
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ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
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auto plugin = IE::PluginDispatcher().getPluginByDevice("CPU");
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auto plugin_net = plugin.LoadNetwork(net, {});
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auto infer_request = plugin_net.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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topology_path, weights_path, "CPU"
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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, InferROIList)
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{
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initDLDTDataPath();
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const std::string path = "Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013";
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const auto topology_path = findDataFile(path + ".xml", false);
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const auto weights_path = findDataFile(path + ".bin", false);
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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(640, 480), CV_8UC3);
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cv::randu(in_mat, 0, 255);
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std::vector<cv::Rect> rois = {
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cv::Rect(cv::Point{ 0, 0}, cv::Size{80, 120}),
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cv::Rect(cv::Point{50, 100}, cv::Size{96, 160}),
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};
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std::vector<cv::Mat> gapi_age, gapi_gender;
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// Load & run IE network
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namespace IE = InferenceEngine;
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std::vector<cv::Mat> ie_age, ie_gender;
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{
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IE::CNNNetReader reader;
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reader.ReadNetwork(topology_path);
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reader.ReadWeights(weights_path);
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auto net = reader.getNetwork();
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auto &ii = net.getInputsInfo().at("data");
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ii->setPrecision(IE::Precision::U8);
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ii->setLayout(IE::Layout::NHWC);
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ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
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auto plugin = IE::PluginDispatcher().getPluginByDevice("CPU");
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auto plugin_net = plugin.LoadNetwork(net, {});
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auto infer_request = plugin_net.CreateInferRequest();
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auto frame_blob = cv::gapi::ie::util::to_ie(in_mat);
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for (auto &&rc : rois) {
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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(frame_blob, ie_rc));
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infer_request.Infer();
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using namespace cv::gapi::ie::util;
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ie_age.push_back(to_ocv(infer_request.GetBlob("age_conv3")).clone());
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ie_gender.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
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}
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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::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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topology_path, weights_path, "CPU"
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}.cfgOutputLayers({ "age_conv3", "prob" });
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comp.apply(cv::gin(in_mat, rois), 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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ASSERT_EQ(2u, ie_age.size() );
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ASSERT_EQ(2u, ie_gender.size());
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ASSERT_EQ(2u, gapi_age.size() );
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ASSERT_EQ(2u, gapi_gender.size());
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normAssert(ie_age [0], gapi_age [0], "0: Test age output");
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normAssert(ie_gender[0], gapi_gender[0], "0: Test gender output");
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normAssert(ie_age [1], gapi_age [1], "1: Test age output");
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normAssert(ie_gender[1], gapi_gender[1], "1: Test gender output");
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}
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} // namespace opencv_test
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#endif // HAVE_INF_ENGINE
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