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Merge pull request #17668 from OrestChura:oc/giebackend_migration_to_core

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GAPI: Migration to IE Core API

* Migration to IE Core API
 - both versions are maintained
 - checked building with all the OpenVINO versions (2019.R1, R2, R3, 2020.4 (newest))

* commit to awake builders

* Addressing comments
 - migrated to Core API in 'gapi_ie_infer_test.cpp'
 - made Core a singleton object
 - dropped redundant steps

* Addressing comments
 - modified Mutex locking

* Update

* Addressing comments
 - remove getInitMutex()
 - reduce amount of #ifdef by abstracting into functions

* return to single IE::Core

* Divide functions readNet and loadNet to avoid warnings on GCC

* Fix deprecated code warnings

* Fix deprecated code warnings on CMake level

* Functions wrapped
 - All the functions depended on IE version wrapped into a cv::gapi::ie::wrap namesapace
 - All this contained to a new "giebackend/gieapi.hpp" header
 - The header shared with G-API infer tests to avoid code duplications

* Addressing comments
 - Renamed `gieapi.hpp` -> `giewrapper.hpp`, `cv::gapi::ie::wrap` -> `cv::gimpl::ie::wrap`
 - Created new `giewrapper.cpp` source file to avoid potential "multiple definition" problems
 - removed unnecessary step SetLayout() in tests

* Enabling two NN infer teest

* Two-NN infer test change for CI
 - deleted additional network
 - inference of two identical NN used instead

* Fix CI fileNotFound

* Disable MYRIAD test not to fail Custom CI runs
This commit is contained in:
Orest Chura
2020-07-16 21:33:35 +03:00
committed by GitHub
parent a216b8bf87
commit d17ab271e8
6 changed files with 301 additions and 148 deletions
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modules/gapi/test/infer/gapi_infer_ie_test.cpp
+107 -66
View File
@@ -2,7 +2,7 @@
// 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 Intel Corporation
// Copyright (C) 2019-2020 Intel Corporation
#include "../test_precomp.hpp"
@@ -10,33 +10,14 @@
#include <stdexcept>
////////////////////////////////////////////////////////////////////////////////
// FIXME: Suppress deprecation warnings for OpenVINO 2019R2+
// BEGIN {{{
#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
#ifdef _MSC_VER
#pragma warning(disable: 4996) // was declared deprecated
#endif
#if defined(__GNUC__)
#pragma GCC visibility push(default)
#endif
#include <inference_engine.hpp>
#if defined(__GNUC__)
#pragma GCC visibility pop
#endif
// END }}}
////////////////////////////////////////////////////////////////////////////////
#include <ade/util/iota_range.hpp>
#include <opencv2/gapi/infer/ie.hpp>
#include "backends/ie/util.hpp"
#include "backends/ie/giebackend/giewrapper.hpp"
namespace opencv_test
{
@@ -78,8 +59,10 @@ void normAssert(cv::InputArray ref, cv::InputArray test,
std::vector<std::string> modelPathByName(const std::string &model_name) {
// Handle OMZ model layout changes among OpenVINO versions here
static const std::unordered_multimap<std::string, std::string> map = {
#if INF_ENGINE_RELEASE >= 2019040000 // >= 2019.R4
{"age-gender-recognition-retail-0013",
"2020.3.0/intel/age-gender-recognition-retail-0013/FP32"},
#endif // INF_ENGINE_RELEASE >= 2019040000
{"age-gender-recognition-retail-0013",
"Retail/object_attributes/age_gender/dldt"},
};
@@ -113,6 +96,13 @@ std::tuple<std::string, std::string> findModel(const std::string &model_name) {
throw SkipTestException("Files for " + model_name + " were not found");
}
namespace IE = InferenceEngine;
void setNetParameters(IE::CNNNetwork& net) {
auto &ii = net.getInputsInfo().at("data");
ii->setPrecision(IE::Precision::U8);
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
}
} // anonymous namespace
// TODO: Probably DNN/IE part can be further parametrized with a template
@@ -121,30 +111,26 @@ TEST(TestAgeGenderIE, InferBasicTensor)
{
initDLDTDataPath();
std::string topology_path, weights_path;
std::tie(topology_path, weights_path) = findModel("age-gender-recognition-retail-0013");
cv::gapi::ie::detail::ParamDesc params;
std::tie(params.model_path, params.weights_path) = findModel("age-gender-recognition-retail-0013");
params.device_id = "CPU";
// Load IE network, initialize input data using that.
namespace IE = InferenceEngine;
cv::Mat in_mat;
cv::Mat gapi_age, gapi_gender;
IE::Blob::Ptr ie_age, ie_gender;
{
IE::CNNNetReader reader;
reader.ReadNetwork(topology_path);
reader.ReadWeights(weights_path);
auto net = reader.getNetwork();
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);
auto plugin = IE::PluginDispatcher().getPluginByDevice("CPU");
auto plugin_net = plugin.LoadNetwork(net, {});
auto infer_request = plugin_net.CreateInferRequest();
infer_request.SetBlob("data", cv::gapi::ie::util::to_ie(in_mat));
infer_request.Infer();
ie_age = infer_request.GetBlob("age_conv3");
@@ -161,7 +147,7 @@ TEST(TestAgeGenderIE, InferBasicTensor)
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
auto pp = cv::gapi::ie::Params<AgeGender> {
topology_path, weights_path, "CPU"
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)));
@@ -175,8 +161,9 @@ TEST(TestAgeGenderIE, InferBasicImage)
{
initDLDTDataPath();
std::string topology_path, weights_path;
std::tie(topology_path, weights_path) = findModel("age-gender-recognition-retail-0013");
cv::gapi::ie::detail::ParamDesc params;
std::tie(params.model_path, params.weights_path) = findModel("age-gender-recognition-retail-0013");
params.device_id = "CPU";
// FIXME: Ideally it should be an image from disk
// cv::Mat in_mat = cv::imread(findDataFile("grace_hopper_227.png"));
@@ -186,21 +173,13 @@ TEST(TestAgeGenderIE, InferBasicImage)
cv::Mat gapi_age, gapi_gender;
// Load & run IE network
namespace IE = InferenceEngine;
IE::Blob::Ptr ie_age, ie_gender;
{
IE::CNNNetReader reader;
reader.ReadNetwork(topology_path);
reader.ReadWeights(weights_path);
auto net = reader.getNetwork();
auto &ii = net.getInputsInfo().at("data");
ii->setPrecision(IE::Precision::U8);
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
auto plugin = IE::PluginDispatcher().getPluginByDevice("CPU");
auto plugin_net = plugin.LoadNetwork(net, {});
auto infer_request = plugin_net.CreateInferRequest();
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");
@@ -217,7 +196,7 @@ TEST(TestAgeGenderIE, InferBasicImage)
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
auto pp = cv::gapi::ie::Params<AgeGender> {
topology_path, weights_path, "CPU"
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)));
@@ -228,8 +207,7 @@ TEST(TestAgeGenderIE, InferBasicImage)
}
struct ROIList: public ::testing::Test {
std::string m_model_path;
std::string m_weights_path;
cv::gapi::ie::detail::ParamDesc params;
cv::Mat m_in_mat;
std::vector<cv::Rect> m_roi_list;
@@ -245,7 +223,8 @@ struct ROIList: public ::testing::Test {
ROIList() {
initDLDTDataPath();
std::tie(m_model_path, m_weights_path) = findModel("age-gender-recognition-retail-0013");
std::tie(params.model_path, params.weights_path) = findModel("age-gender-recognition-retail-0013");
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);
@@ -258,19 +237,12 @@ struct ROIList: public ::testing::Test {
};
// Load & run IE network
namespace IE = InferenceEngine;
{
IE::CNNNetReader reader;
reader.ReadNetwork(m_model_path);
reader.ReadWeights(m_weights_path);
auto net = reader.getNetwork();
auto &ii = net.getInputsInfo().at("data");
ii->setPrecision(IE::Precision::U8);
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
auto plugin = IE::PluginDispatcher().getPluginByDevice("CPU");
auto plugin_net = plugin.LoadNetwork(net, {});
auto infer_request = plugin_net.CreateInferRequest();
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) {
@@ -314,7 +286,7 @@ TEST_F(ROIList, TestInfer)
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
auto pp = cv::gapi::ie::Params<AgeGender> {
m_model_path, m_weights_path, "CPU"
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),
@@ -331,7 +303,7 @@ TEST_F(ROIList, TestInfer2)
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(age, gender));
auto pp = cv::gapi::ie::Params<AgeGender> {
m_model_path, m_weights_path, "CPU"
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),
@@ -339,6 +311,75 @@ TEST_F(ROIList, TestInfer2)
validate();
}
TEST(DISABLED_TestTwoIENNPipeline, InferBasicImage)
{
initDLDTDataPath();
cv::gapi::ie::detail::ParamDesc AGparams;
std::tie(AGparams.model_path, AGparams.weights_path) = findModel("age-gender-recognition-retail-0013");
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");
}
} // namespace opencv_test
#endif // HAVE_INF_ENGINE