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Open Source Computer Vision Library
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281 lines
9.8 KiB
281 lines
9.8 KiB
// 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->getTensorDesc().getDims(); |
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auto cvdims = cv::gapi::ie::util::to_ocv(iedims); |
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in_mat.create(cvdims, CV_32F); |
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cv::randu(in_mat, -1, 1); |
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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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