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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2023-07-27 18:38:25 +03:00
parent 0e8748746f 677a28fd2a
commit 47188b7c7e
151 changed files with 5199 additions and 1111 deletions
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modules/gapi/test/infer/gapi_infer_ie_test.cpp
+126 -7
View File
@@ -62,6 +62,11 @@ public:
return cv::MediaFrame::View(std::move(pp), std::move(ss), Cb{m_cb});
}
cv::util::any blobParams() const override {
#if INF_ENGINE_RELEASE > 2023000000
// NB: blobParams() shouldn't be used in tests
// if OpenVINO versions is higher than 2023.0
GAPI_Assert(false && "NV12 feature has been deprecated in OpenVINO 1.0 API.");
#else
return std::make_pair<InferenceEngine::TensorDesc,
InferenceEngine::ParamMap>({IE::Precision::U8,
{1, 3, 300, 300},
@@ -69,6 +74,7 @@ public:
{{"HELLO", 42},
{"COLOR_FORMAT",
InferenceEngine::ColorFormat::NV12}});
#endif // INF_ENGINE_RELEASE > 2023000000
}
};
@@ -138,7 +144,13 @@ void setNetParameters(IE::CNNNetwork& net, bool is_nv12 = false) {
ii->setPrecision(IE::Precision::U8);
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
if (is_nv12) {
#if INF_ENGINE_RELEASE > 2023000000
// NB: NV12 feature shouldn't be used in tests
// if OpenVINO versions is higher than 2023.0
GAPI_Assert(false && "NV12 feature has been deprecated in OpenVINO 1.0 API.");
#else
ii->getPreProcess().setColorFormat(IE::ColorFormat::NV12);
#endif // INF_ENGINE_RELEASE > 2023000000
}
}
@@ -392,10 +404,14 @@ struct InferWithReshapeNV12: public InferWithReshape {
cv::randu(m_in_y, 0, 255);
m_in_uv = cv::Mat{sz / 2, CV_8UC2};
cv::randu(m_in_uv, 0, 255);
// NB: NV12 feature shouldn't be used in tests
// if OpenVINO versions is higher than 2023.0
#if INF_ENGINE_RELEASE <= 2023000000
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);
#endif // INF_ENGINE_RELEASE <= 2023000000
}
};
@@ -505,8 +521,11 @@ struct ROIListNV12: public ::testing::Test {
cv::Rect(cv::Point{50, 32}, cv::Size{128, 160}),
};
// Load & run IE network
// NB: NV12 feature shouldn't be used in tests
// if OpenVINO versions is higher than 2023.0
#if INF_ENGINE_RELEASE <= 2023000000
{
// Load & run IE network
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
auto net = cv::gimpl::ie::wrap::readNetwork(params);
setNetParameters(net, true);
@@ -530,9 +549,11 @@ struct ROIListNV12: public ::testing::Test {
m_out_ie_genders.push_back(to_ocv(infer_request.GetBlob("prob")).clone());
}
} // namespace IE = ..
#endif // INF_ENGINE_RELEASE <= 2023000000
} // ROIList()
void validate() {
#if INF_ENGINE_RELEASE <= 2023000000
// 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());
@@ -543,6 +564,10 @@ struct ROIListNV12: public ::testing::Test {
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");
#else
GAPI_Assert(false && "Reference hasn't been calculated because"
" NV12 feature has been deprecated.");
#endif // INF_ENGINE_RELEASE <= 2023000000
}
};
@@ -631,6 +656,9 @@ struct SingleROINV12: public ::testing::Test {
m_roi = cv::Rect(cv::Point{64, 60}, cv::Size{96, 96});
// NB: NV12 feature shouldn't be used in tests
// if OpenVINO versions is higher than 2023.0
#if INF_ENGINE_RELEASE <= 2023000000
// Load & run IE network
IE::Blob::Ptr ie_age, ie_gender;
{
@@ -657,12 +685,18 @@ struct SingleROINV12: public ::testing::Test {
m_out_ie_age = to_ocv(infer_request.GetBlob("age_conv3")).clone();
m_out_ie_gender = to_ocv(infer_request.GetBlob("prob")).clone();
}
#endif // INF_ENGINE_RELEASE <= 2023000000
}
void validate() {
#if INF_ENGINE_RELEASE <= 2023000000
// 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");
#else
GAPI_Assert(false && "Reference hasn't been calculated because"
" NV12 feature has been deprecated.");
#endif
}
};
@@ -962,11 +996,20 @@ TEST_F(ROIListNV12, MediaInputNV12)
auto pp = cv::gapi::ie::Params<AgeGender> {
params.model_path, params.weights_path, params.device_id
}.cfgOutputLayers({ "age_conv3", "prob" });
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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();
#else
EXPECT_ANY_THROW(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))));
#endif
}
TEST(TestAgeGenderIE, MediaInputNV12)
@@ -986,6 +1029,9 @@ TEST(TestAgeGenderIE, MediaInputNV12)
cv::Mat gapi_age, gapi_gender;
// NB: NV12 feature shouldn't be used in tests
// if OpenVINO versions is higher than 2023.0
#if INF_ENGINE_RELEASE <= 2023000000
// Load & run IE network
IE::Blob::Ptr ie_age, ie_gender;
{
@@ -999,6 +1045,7 @@ TEST(TestAgeGenderIE, MediaInputNV12)
ie_age = infer_request.GetBlob("age_conv3");
ie_gender = infer_request.GetBlob("prob");
}
#endif
// Configure & run G-API
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
@@ -1014,13 +1061,20 @@ TEST(TestAgeGenderIE, MediaInputNV12)
auto pp = cv::gapi::ie::Params<AgeGender> {
params.model_path, params.weights_path, params.device_id
}.cfgOutputLayers({ "age_conv3", "prob" });
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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");
#else
EXPECT_ANY_THROW(comp.apply(cv::gin(frame), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp))));
#endif
}
TEST(TestAgeGenderIE, MediaInputBGR)
@@ -1155,6 +1209,9 @@ TEST(InferROI, MediaInputNV12)
cv::Mat gapi_age, gapi_gender;
cv::Rect rect(cv::Point{64, 60}, cv::Size{96, 96});
// NB: NV12 feature shouldn't be used in tests
// if OpenVINO versions is higher than 2023.0
#if INF_ENGINE_RELEASE <= 2023000000
// Load & run IE network
IE::Blob::Ptr ie_age, ie_gender;
{
@@ -1176,6 +1233,7 @@ TEST(InferROI, MediaInputNV12)
ie_age = infer_request.GetBlob("age_conv3");
ie_gender = infer_request.GetBlob("prob");
}
#endif
// Configure & run G-API
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
@@ -1192,13 +1250,20 @@ TEST(InferROI, MediaInputNV12)
auto pp = cv::gapi::ie::Params<AgeGender> {
params.model_path, params.weights_path, params.device_id
}.cfgOutputLayers({ "age_conv3", "prob" });
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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");
#else
EXPECT_ANY_THROW(comp.apply(cv::gin(frame, rect), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp))));
#endif
}
TEST_F(ROIList, Infer2MediaInputBGR)
@@ -1233,10 +1298,20 @@ TEST_F(ROIListNV12, Infer2MediaInputNV12)
auto pp = cv::gapi::ie::Params<AgeGender> {
params.model_path, params.weights_path, params.device_id
}.cfgOutputLayers({ "age_conv3", "prob" });
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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();
#else
EXPECT_ANY_THROW(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))));
#endif
}
TEST_F(SingleROI, GenericInfer)
@@ -1310,10 +1385,19 @@ TEST_F(SingleROINV12, GenericInferMediaNV12)
pp.cfgNumRequests(2u);
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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();
#else
EXPECT_ANY_THROW(comp.apply(cv::gin(frame, m_roi),
cv::gout(m_out_gapi_age, m_out_gapi_gender),
cv::compile_args(cv::gapi::networks(pp))));
#endif
}
TEST_F(ROIList, GenericInfer)
@@ -1386,11 +1470,20 @@ TEST_F(ROIListNV12, GenericInferMediaNV12)
pp.cfgNumRequests(2u);
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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)));
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
cv::compile_args(cv::gapi::networks(pp)));
validate();
#else
EXPECT_ANY_THROW(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))));
#endif
}
TEST_F(ROIList, GenericInfer2)
@@ -1461,10 +1554,20 @@ TEST_F(ROIListNV12, GenericInfer2MediaInputNV12)
pp.cfgNumRequests(2u);
auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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)));
cv::gout(m_out_gapi_ages, m_out_gapi_genders),
cv::compile_args(cv::gapi::networks(pp)));
validate();
#else
EXPECT_ANY_THROW(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))));
#endif
}
TEST(Infer, SetInvalidNumberOfRequests)
@@ -2050,11 +2153,20 @@ TEST_F(InferWithReshapeNV12, TestInferListYUV)
auto pp = cv::gapi::ie::Params<AgeGender> {
params.model_path, params.weights_path, params.device_id
}.cfgOutputLayers({ "age_conv3", "prob" }).cfgInputReshape({{"data", reshape_dims}});
// NB: NV12 feature has been deprecated in OpenVINO versions higher
// than 2023.0 so G-API must throw error in that case.
#if INF_ENGINE_RELEASE <= 2023000000
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();
#else
EXPECT_ANY_THROW(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))));
#endif
}
TEST_F(ROIList, CallInferMultipleTimes)
@@ -2079,6 +2191,7 @@ TEST_F(ROIList, CallInferMultipleTimes)
validate();
}
#if INF_ENGINE_RELEASE <= 2023000000
TEST(IEFrameAdapter, blobParams)
{
cv::Mat bgr = cv::Mat::eye(240, 320, CV_8UC3);
@@ -2093,6 +2206,7 @@ TEST(IEFrameAdapter, blobParams)
EXPECT_EQ(expected, actual);
}
#endif
namespace
{
@@ -2281,6 +2395,10 @@ TEST(TestAgeGenderIE, InferWithBatch)
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
}
// NB: All tests below use preprocessing for "Import" networks
// passed as the last argument to SetBLob. This overload has
// been deprecated in OpenVINO 1.0 API.
#if INF_ENGINE_RELEASE <= 2023000000
TEST(ImportNetwork, Infer)
{
const std::string device = "MYRIAD";
@@ -2820,6 +2938,7 @@ TEST(ImportNetwork, InferList2NV12)
normAssert(out_ie_genders[i], out_gapi_genders[i], "Test gender output");
}
}
#endif
TEST(TestAgeGender, ThrowBlobAndInputPrecisionMismatch)
{
modules/gapi/test/infer/gapi_infer_ov_tests.cpp
+296 -174
View File
@@ -41,20 +41,6 @@ void initDLDTDataPath()
static const std::string SUBDIR = "intel/age-gender-recognition-retail-0013/FP32/";
void copyFromOV(ov::Tensor &tensor, cv::Mat &mat) {
GAPI_Assert(tensor.get_byte_size() == mat.total() * mat.elemSize());
std::copy_n(reinterpret_cast<uint8_t*>(tensor.data()),
tensor.get_byte_size(),
mat.ptr<uint8_t>());
}
void copyToOV(const cv::Mat &mat, ov::Tensor &tensor) {
GAPI_Assert(tensor.get_byte_size() == mat.total() * mat.elemSize());
std::copy_n(mat.ptr<uint8_t>(),
tensor.get_byte_size(),
reinterpret_cast<uint8_t*>(tensor.data()));
}
// FIXME: taken from the DNN module
void normAssert(cv::InputArray ref, cv::InputArray test,
const char *comment /*= ""*/,
@@ -66,15 +52,10 @@ void normAssert(cv::InputArray ref, cv::InputArray test,
EXPECT_LE(normInf, lInf) << comment;
}
ov::Core getCore() {
static ov::Core core;
return core;
}
// TODO: AGNetGenComp, AGNetTypedComp, AGNetOVComp, AGNetOVCompiled
// can be generalized to work with any model and used as parameters for tests.
struct AGNetGenComp {
struct AGNetGenParams {
static constexpr const char* tag = "age-gender-generic";
using Params = cv::gapi::ov::Params<cv::gapi::Generic>;
@@ -88,19 +69,9 @@ struct AGNetGenComp {
const std::string &device) {
return {tag, blob_path, device};
}
static cv::GComputation create() {
cv::GMat in;
GInferInputs inputs;
inputs["data"] = in;
auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, inputs);
auto age = outputs.at("age_conv3");
auto gender = outputs.at("prob");
return cv::GComputation{cv::GIn(in), cv::GOut(age, gender)};
}
};
struct AGNetTypedComp {
struct AGNetTypedParams {
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "typed-age-gender");
using Params = cv::gapi::ov::Params<AgeGender>;
@@ -112,7 +83,9 @@ struct AGNetTypedComp {
xml_path, bin_path, device
}.cfgOutputLayers({ "age_conv3", "prob" });
}
};
struct AGNetTypedComp : AGNetTypedParams {
static cv::GComputation create() {
cv::GMat in;
cv::GMat age, gender;
@@ -121,30 +94,104 @@ struct AGNetTypedComp {
}
};
struct AGNetGenComp : public AGNetGenParams {
static cv::GComputation create() {
cv::GMat in;
GInferInputs inputs;
inputs["data"] = in;
auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, inputs);
auto age = outputs.at("age_conv3");
auto gender = outputs.at("prob");
return cv::GComputation{cv::GIn(in), cv::GOut(age, gender)};
}
};
struct AGNetROIGenComp : AGNetGenParams {
static cv::GComputation create() {
cv::GMat in;
cv::GOpaque<cv::Rect> roi;
GInferInputs inputs;
inputs["data"] = in;
auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, roi, inputs);
auto age = outputs.at("age_conv3");
auto gender = outputs.at("prob");
return cv::GComputation{cv::GIn(in, roi), cv::GOut(age, gender)};
}
};
struct AGNetListGenComp : AGNetGenParams {
static cv::GComputation create() {
cv::GMat in;
cv::GArray<cv::Rect> rois;
GInferInputs inputs;
inputs["data"] = in;
auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, rois, inputs);
auto age = outputs.at("age_conv3");
auto gender = outputs.at("prob");
return cv::GComputation{cv::GIn(in, rois), cv::GOut(age, gender)};
}
};
struct AGNetList2GenComp : AGNetGenParams {
static cv::GComputation create() {
cv::GMat in;
cv::GArray<cv::Rect> rois;
GInferListInputs list;
list["data"] = rois;
auto outputs = cv::gapi::infer2<cv::gapi::Generic>(tag, in, list);
auto age = outputs.at("age_conv3");
auto gender = outputs.at("prob");
return cv::GComputation{cv::GIn(in, rois), cv::GOut(age, gender)};
}
};
class AGNetOVCompiled {
public:
AGNetOVCompiled(ov::CompiledModel &&compiled_model)
: m_compiled_model(std::move(compiled_model)) {
: m_compiled_model(std::move(compiled_model)),
m_infer_request(m_compiled_model.create_infer_request()) {
}
void operator()(const cv::Mat &in_mat,
const cv::Rect &roi,
cv::Mat &age_mat,
cv::Mat &gender_mat) {
// FIXME: W & H could be extracted from model shape
// but it's anyway used only for Age Gender model.
// (Well won't work in case of reshape)
const int W = 62;
const int H = 62;
cv::Mat resized_roi;
cv::resize(in_mat(roi), resized_roi, cv::Size(W, H));
(*this)(resized_roi, age_mat, gender_mat);
}
void operator()(const cv::Mat &in_mat,
const std::vector<cv::Rect> &rois,
std::vector<cv::Mat> &age_mats,
std::vector<cv::Mat> &gender_mats) {
for (size_t i = 0; i < rois.size(); ++i) {
(*this)(in_mat, rois[i], age_mats[i], gender_mats[i]);
}
}
void operator()(const cv::Mat &in_mat,
cv::Mat &age_mat,
cv::Mat &gender_mat) {
auto infer_request = m_compiled_model.create_infer_request();
auto input_tensor = infer_request.get_input_tensor();
copyToOV(in_mat, input_tensor);
auto input_tensor = m_infer_request.get_input_tensor();
cv::gapi::ov::util::to_ov(in_mat, input_tensor);
infer_request.infer();
m_infer_request.infer();
auto age_tensor = infer_request.get_tensor("age_conv3");
auto age_tensor = m_infer_request.get_tensor("age_conv3");
age_mat.create(cv::gapi::ov::util::to_ocv(age_tensor.get_shape()),
cv::gapi::ov::util::to_ocv(age_tensor.get_element_type()));
copyFromOV(age_tensor, age_mat);
cv::gapi::ov::util::to_ocv(age_tensor, age_mat);
auto gender_tensor = infer_request.get_tensor("prob");
auto gender_tensor = m_infer_request.get_tensor("prob");
gender_mat.create(cv::gapi::ov::util::to_ocv(gender_tensor.get_shape()),
cv::gapi::ov::util::to_ocv(gender_tensor.get_element_type()));
copyFromOV(gender_tensor, gender_mat);
cv::gapi::ov::util::to_ocv(gender_tensor, gender_mat);
}
void export_model(const std::string &outpath) {
@@ -155,6 +202,7 @@ public:
private:
ov::CompiledModel m_compiled_model;
ov::InferRequest m_infer_request;
};
struct ImageInputPreproc {
@@ -175,7 +223,8 @@ public:
const std::string &bin_path,
const std::string &device)
: m_device(device) {
m_model = getCore().read_model(xml_path, bin_path);
m_model = cv::gapi::ov::wrap::getCore()
.read_model(xml_path, bin_path);
}
using PrePostProcessF = std::function<void(ov::preprocess::PrePostProcessor&)>;
@@ -187,7 +236,8 @@ public:
}
AGNetOVCompiled compile() {
auto compiled_model = getCore().compile_model(m_model, m_device);
auto compiled_model = cv::gapi::ov::wrap::getCore()
.compile_model(m_model, m_device);
return {std::move(compiled_model)};
}
@@ -202,19 +252,78 @@ private:
std::shared_ptr<ov::Model> m_model;
};
struct BaseAgeGenderOV: public ::testing::Test {
BaseAgeGenderOV() {
initDLDTDataPath();
xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
device = "CPU";
blob_path = "age-gender-recognition-retail-0013.blob";
}
cv::Mat getRandomImage(const cv::Size &sz) {
cv::Mat image(sz, CV_8UC3);
cv::randu(image, 0, 255);
return image;
}
cv::Mat getRandomTensor(const std::vector<int> &dims,
const int depth) {
cv::Mat tensor(dims, depth);
cv::randu(tensor, -1, 1);
return tensor;
}
std::string xml_path;
std::string bin_path;
std::string blob_path;
std::string device;
};
struct TestAgeGenderOV : public BaseAgeGenderOV {
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
void validate() {
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
}
};
struct TestAgeGenderListOV : public BaseAgeGenderOV {
std::vector<cv::Mat> ov_age, ov_gender,
gapi_age, gapi_gender;
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}),
};
TestAgeGenderListOV() {
ov_age.resize(roi_list.size());
ov_gender.resize(roi_list.size());
gapi_age.resize(roi_list.size());
gapi_gender.resize(roi_list.size());
}
void validate() {
ASSERT_EQ(ov_age.size(), ov_gender.size());
ASSERT_EQ(ov_age.size(), gapi_age.size());
ASSERT_EQ(ov_gender.size(), gapi_gender.size());
for (size_t i = 0; i < ov_age.size(); ++i) {
normAssert(ov_age[i], gapi_age[i], "Test age output");
normAssert(ov_gender[i], gapi_gender[i], "Test gender output");
}
}
};
} // anonymous namespace
// TODO: Make all of tests below parmetrized to avoid code duplication
TEST(TestAgeGenderOV, InferTypedTensor) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
cv::randu(in_mat, -1, 1);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, Infer_Tensor) {
const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
ref.apply(in_mat, ov_age, ov_gender);
@@ -226,19 +335,11 @@ TEST(TestAgeGenderOV, InferTypedTensor) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, InferTypedImage) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
cv::Mat in_mat(300, 300, CV_8UC3);
cv::randu(in_mat, 0, 255);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, Infer_Image) {
const auto in_mat = getRandomImage({300, 300});
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
@@ -252,19 +353,11 @@ TEST(TestAgeGenderOV, InferTypedImage) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, InferGenericTensor) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
cv::randu(in_mat, -1, 1);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, InferGeneric_Tensor) {
const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
@@ -277,19 +370,11 @@ TEST(TestAgeGenderOV, InferGenericTensor) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, InferGenericImage) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
cv::Mat in_mat(300, 300, CV_8UC3);
cv::randu(in_mat, 0, 255);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, InferGenericImage) {
const auto in_mat = getRandomImage({300, 300});
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
@@ -303,20 +388,11 @@ TEST(TestAgeGenderOV, InferGenericImage) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, InferGenericImageBlob) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string blob_path = "age-gender-recognition-retail-0013.blob";
const std::string device = "CPU";
cv::Mat in_mat(300, 300, CV_8UC3);
cv::randu(in_mat, 0, 255);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, InferGeneric_ImageBlob) {
const auto in_mat = getRandomImage({300, 300});
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
@@ -333,20 +409,11 @@ TEST(TestAgeGenderOV, InferGenericImageBlob) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, InferGenericTensorBlob) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string blob_path = "age-gender-recognition-retail-0013.blob";
const std::string device = "CPU";
cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
cv::randu(in_mat, -1, 1);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, InferGeneric_TensorBlob) {
const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
@@ -361,19 +428,11 @@ TEST(TestAgeGenderOV, InferGenericTensorBlob) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, InferBothOutputsFP16) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
cv::randu(in_mat, -1, 1);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, InferGeneric_BothOutputsFP16) {
const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
@@ -392,19 +451,11 @@ TEST(TestAgeGenderOV, InferBothOutputsFP16) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, InferOneOutputFP16) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
cv::randu(in_mat, -1, 1);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, InferGeneric_OneOutputFP16) {
const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
// OpenVINO
const std::string fp16_output_name = "prob";
@@ -423,17 +474,10 @@ TEST(TestAgeGenderOV, InferOneOutputFP16) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST(TestAgeGenderOV, ThrowCfgOutputPrecForBlob) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string blob_path = "age-gender-recognition-retail-0013.blob";
const std::string device = "CPU";
TEST_F(TestAgeGenderOV, InferGeneric_ThrowCfgOutputPrecForBlob) {
// OpenVINO (Just for blob compilation)
AGNetOVComp ref(xml_path, bin_path, device);
auto cc_ref = ref.compile();
@@ -446,12 +490,7 @@ TEST(TestAgeGenderOV, ThrowCfgOutputPrecForBlob) {
EXPECT_ANY_THROW(pp.cfgOutputTensorPrecision(CV_16F));
}
TEST(TestAgeGenderOV, ThrowInvalidConfigIR) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
TEST_F(TestAgeGenderOV, InferGeneric_ThrowInvalidConfigIR) {
// G-API
auto comp = AGNetGenComp::create();
auto pp = AGNetGenComp::params(xml_path, bin_path, device);
@@ -461,13 +500,7 @@ TEST(TestAgeGenderOV, ThrowInvalidConfigIR) {
cv::compile_args(cv::gapi::networks(pp))));
}
TEST(TestAgeGenderOV, ThrowInvalidConfigBlob) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string blob_path = "age-gender-recognition-retail-0013.blob";
const std::string device = "CPU";
TEST_F(TestAgeGenderOV, InferGeneric_ThrowInvalidConfigBlob) {
// OpenVINO (Just for blob compilation)
AGNetOVComp ref(xml_path, bin_path, device);
auto cc_ref = ref.compile();
@@ -482,16 +515,8 @@ TEST(TestAgeGenderOV, ThrowInvalidConfigBlob) {
cv::compile_args(cv::gapi::networks(pp))));
}
TEST(TestAgeGenderOV, ThrowInvalidImageLayout) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
// NB: This mat may only have "NHWC" layout.
cv::Mat in_mat(300, 300, CV_8UC3);
cv::randu(in_mat, 0, 255);
cv::Mat gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, Infer_ThrowInvalidImageLayout) {
const auto in_mat = getRandomImage({300, 300});
auto comp = AGNetTypedComp::create();
auto pp = AGNetTypedComp::params(xml_path, bin_path, device);
@@ -501,15 +526,8 @@ TEST(TestAgeGenderOV, ThrowInvalidImageLayout) {
cv::compile_args(cv::gapi::networks(pp))));
}
TEST(TestAgeGenderOV, InferTensorWithPreproc) {
initDLDTDataPath();
const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
const std::string device = "CPU";
cv::Mat in_mat({1, 240, 320, 3}, CV_32F);
cv::randu(in_mat, -1, 1);
cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
TEST_F(TestAgeGenderOV, Infer_TensorWithPreproc) {
const auto in_mat = getRandomTensor({1, 240, 320, 3}, CV_32F);
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
@@ -531,8 +549,112 @@ TEST(TestAgeGenderOV, InferTensorWithPreproc) {
cv::compile_args(cv::gapi::networks(pp)));
// Assert
normAssert(ov_age, gapi_age, "Test age output" );
normAssert(ov_gender, gapi_gender, "Test gender output");
validate();
}
TEST_F(TestAgeGenderOV, InferROIGeneric_Image) {
const auto in_mat = getRandomImage({300, 300});
cv::Rect roi(cv::Rect(cv::Point{64, 60}, cv::Size{96, 96}));
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
ref.cfgPrePostProcessing([](ov::preprocess::PrePostProcessor &ppp) {
ppp.input().tensor().set_element_type(ov::element::u8);
ppp.input().tensor().set_layout("NHWC");
});
ref.compile()(in_mat, roi, ov_age, ov_gender);
// G-API
auto comp = AGNetROIGenComp::create();
auto pp = AGNetROIGenComp::params(xml_path, bin_path, device);
comp.apply(cv::gin(in_mat, roi), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp)));
// Assert
validate();
}
TEST_F(TestAgeGenderOV, InferROIGeneric_ThrowIncorrectLayout) {
const auto in_mat = getRandomImage({300, 300});
cv::Rect roi(cv::Rect(cv::Point{64, 60}, cv::Size{96, 96}));
// G-API
auto comp = AGNetROIGenComp::create();
auto pp = AGNetROIGenComp::params(xml_path, bin_path, device);
pp.cfgInputTensorLayout("NCHW");
EXPECT_ANY_THROW(comp.apply(cv::gin(in_mat, roi), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp))));
}
TEST_F(TestAgeGenderOV, InferROIGeneric_ThrowTensorInput) {
const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
cv::Rect roi(cv::Rect(cv::Point{64, 60}, cv::Size{96, 96}));
// G-API
auto comp = AGNetROIGenComp::create();
auto pp = AGNetROIGenComp::params(xml_path, bin_path, device);
EXPECT_ANY_THROW(comp.apply(cv::gin(in_mat, roi), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp))));
}
TEST_F(TestAgeGenderOV, InferROIGeneric_ThrowExplicitResize) {
const auto in_mat = getRandomImage({300, 300});
cv::Rect roi(cv::Rect(cv::Point{64, 60}, cv::Size{96, 96}));
// G-API
auto comp = AGNetROIGenComp::create();
auto pp = AGNetROIGenComp::params(xml_path, bin_path, device);
pp.cfgResize(cv::INTER_LINEAR);
EXPECT_ANY_THROW(comp.apply(cv::gin(in_mat, roi), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp))));
}
TEST_F(TestAgeGenderListOV, InferListGeneric_Image) {
const auto in_mat = getRandomImage({300, 300});
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
ref.cfgPrePostProcessing([](ov::preprocess::PrePostProcessor &ppp) {
ppp.input().tensor().set_element_type(ov::element::u8);
ppp.input().tensor().set_layout("NHWC");
});
ref.compile()(in_mat, roi_list, ov_age, ov_gender);
// G-API
auto comp = AGNetListGenComp::create();
auto pp = AGNetListGenComp::params(xml_path, bin_path, device);
comp.apply(cv::gin(in_mat, roi_list), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp)));
// Assert
validate();
}
TEST_F(TestAgeGenderListOV, InferList2Generic_Image) {
const auto in_mat = getRandomImage({300, 300});
// OpenVINO
AGNetOVComp ref(xml_path, bin_path, device);
ref.cfgPrePostProcessing([](ov::preprocess::PrePostProcessor &ppp) {
ppp.input().tensor().set_element_type(ov::element::u8);
ppp.input().tensor().set_layout("NHWC");
});
ref.compile()(in_mat, roi_list, ov_age, ov_gender);
// G-API
auto comp = AGNetList2GenComp::create();
auto pp = AGNetList2GenComp::params(xml_path, bin_path, device);
comp.apply(cv::gin(in_mat, roi_list), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp)));
// Assert
validate();
}
} // namespace opencv_test
modules/gapi/test/internal/gapi_int_gmodel_builder_test.cpp
+4 -2
View File
@@ -30,7 +30,8 @@ namespace
, nullptr
, { GShape::GMAT }
, { D::OpaqueKind::CV_UNKNOWN }
, { cv::detail::HostCtor{cv::util::monostate{}} }
, { D::HostCtor{cv::util::monostate{}} }
, { D::OpaqueKind::CV_UNKNOWN }
}).pass(m).yield(0);
}
@@ -41,7 +42,8 @@ namespace
, nullptr
, { GShape::GMAT }
, { D::OpaqueKind::CV_UNKNOWN, D::OpaqueKind::CV_UNKNOWN }
, { cv::detail::HostCtor{cv::util::monostate{}} }
, { D::HostCtor{cv::util::monostate{}} }
, { D::OpaqueKind::CV_UNKNOWN}
}).pass(m1, m2).yield(0);
}