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Merge pull request #23799 from TolyaTalamanov:at/ov20-backend-implement-missing-kernels
G-API: Implement InferROI, InferList, InferList2 for OpenVINO backend #23799 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Anatoliy Talamanov
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GitHub
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0cf45b89ec
commit
71790e12ad
@@ -41,20 +41,6 @@ void initDLDTDataPath()
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static const std::string SUBDIR = "intel/age-gender-recognition-retail-0013/FP32/";
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void copyFromOV(ov::Tensor &tensor, cv::Mat &mat) {
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GAPI_Assert(tensor.get_byte_size() == mat.total() * mat.elemSize());
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std::copy_n(reinterpret_cast<uint8_t*>(tensor.data()),
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tensor.get_byte_size(),
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mat.ptr<uint8_t>());
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}
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void copyToOV(const cv::Mat &mat, ov::Tensor &tensor) {
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GAPI_Assert(tensor.get_byte_size() == mat.total() * mat.elemSize());
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std::copy_n(mat.ptr<uint8_t>(),
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tensor.get_byte_size(),
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reinterpret_cast<uint8_t*>(tensor.data()));
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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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@@ -74,7 +60,7 @@ ov::Core getCore() {
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// TODO: AGNetGenComp, AGNetTypedComp, AGNetOVComp, AGNetOVCompiled
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// can be generalized to work with any model and used as parameters for tests.
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struct AGNetGenComp {
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struct AGNetGenParams {
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static constexpr const char* tag = "age-gender-generic";
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using Params = cv::gapi::ov::Params<cv::gapi::Generic>;
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@@ -88,19 +74,9 @@ struct AGNetGenComp {
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const std::string &device) {
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return {tag, blob_path, device};
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}
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static cv::GComputation create() {
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cv::GMat in;
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GInferInputs inputs;
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inputs["data"] = in;
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auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, inputs);
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auto age = outputs.at("age_conv3");
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auto gender = outputs.at("prob");
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return cv::GComputation{cv::GIn(in), cv::GOut(age, gender)};
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}
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};
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struct AGNetTypedComp {
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struct AGNetTypedParams {
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using AGInfo = std::tuple<cv::GMat, cv::GMat>;
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G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "typed-age-gender");
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using Params = cv::gapi::ov::Params<AgeGender>;
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@@ -112,7 +88,9 @@ struct AGNetTypedComp {
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xml_path, bin_path, device
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}.cfgOutputLayers({ "age_conv3", "prob" });
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}
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};
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struct AGNetTypedComp : AGNetTypedParams {
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static cv::GComputation create() {
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cv::GMat in;
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cv::GMat age, gender;
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@@ -121,30 +99,104 @@ struct AGNetTypedComp {
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}
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};
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struct AGNetGenComp : public AGNetGenParams {
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static cv::GComputation create() {
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cv::GMat in;
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GInferInputs inputs;
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inputs["data"] = in;
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auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, inputs);
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auto age = outputs.at("age_conv3");
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auto gender = outputs.at("prob");
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return cv::GComputation{cv::GIn(in), cv::GOut(age, gender)};
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}
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};
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struct AGNetROIGenComp : AGNetGenParams {
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static cv::GComputation create() {
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cv::GMat in;
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cv::GOpaque<cv::Rect> roi;
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GInferInputs inputs;
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inputs["data"] = in;
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auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, roi, inputs);
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auto age = outputs.at("age_conv3");
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auto gender = outputs.at("prob");
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return cv::GComputation{cv::GIn(in, roi), cv::GOut(age, gender)};
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}
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};
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struct AGNetListGenComp : AGNetGenParams {
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static cv::GComputation create() {
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cv::GMat in;
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cv::GArray<cv::Rect> rois;
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GInferInputs inputs;
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inputs["data"] = in;
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auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, rois, inputs);
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auto age = outputs.at("age_conv3");
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auto gender = outputs.at("prob");
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return cv::GComputation{cv::GIn(in, rois), cv::GOut(age, gender)};
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}
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};
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struct AGNetList2GenComp : AGNetGenParams {
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static cv::GComputation create() {
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cv::GMat in;
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cv::GArray<cv::Rect> rois;
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GInferListInputs list;
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list["data"] = rois;
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auto outputs = cv::gapi::infer2<cv::gapi::Generic>(tag, in, list);
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auto age = outputs.at("age_conv3");
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auto gender = outputs.at("prob");
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return cv::GComputation{cv::GIn(in, rois), cv::GOut(age, gender)};
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}
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};
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class AGNetOVCompiled {
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public:
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AGNetOVCompiled(ov::CompiledModel &&compiled_model)
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: m_compiled_model(std::move(compiled_model)) {
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: m_compiled_model(std::move(compiled_model)),
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m_infer_request(m_compiled_model.create_infer_request()) {
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}
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void operator()(const cv::Mat &in_mat,
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const cv::Rect &roi,
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cv::Mat &age_mat,
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cv::Mat &gender_mat) {
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// FIXME: W & H could be extracted from model shape
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// but it's anyway used only for Age Gender model.
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// (Well won't work in case of reshape)
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const int W = 62;
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const int H = 62;
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cv::Mat resized_roi;
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cv::resize(in_mat(roi), resized_roi, cv::Size(W, H));
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(*this)(resized_roi, age_mat, gender_mat);
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}
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void operator()(const cv::Mat &in_mat,
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const std::vector<cv::Rect> &rois,
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std::vector<cv::Mat> &age_mats,
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std::vector<cv::Mat> &gender_mats) {
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for (size_t i = 0; i < rois.size(); ++i) {
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(*this)(in_mat, rois[i], age_mats[i], gender_mats[i]);
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}
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}
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void operator()(const cv::Mat &in_mat,
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cv::Mat &age_mat,
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cv::Mat &gender_mat) {
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auto infer_request = m_compiled_model.create_infer_request();
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auto input_tensor = infer_request.get_input_tensor();
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copyToOV(in_mat, input_tensor);
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auto input_tensor = m_infer_request.get_input_tensor();
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cv::gapi::ov::util::to_ov(in_mat, input_tensor);
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infer_request.infer();
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m_infer_request.infer();
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auto age_tensor = infer_request.get_tensor("age_conv3");
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auto age_tensor = m_infer_request.get_tensor("age_conv3");
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age_mat.create(cv::gapi::ov::util::to_ocv(age_tensor.get_shape()),
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cv::gapi::ov::util::to_ocv(age_tensor.get_element_type()));
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copyFromOV(age_tensor, age_mat);
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cv::gapi::ov::util::to_ocv(age_tensor, age_mat);
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auto gender_tensor = infer_request.get_tensor("prob");
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auto gender_tensor = m_infer_request.get_tensor("prob");
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gender_mat.create(cv::gapi::ov::util::to_ocv(gender_tensor.get_shape()),
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cv::gapi::ov::util::to_ocv(gender_tensor.get_element_type()));
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copyFromOV(gender_tensor, gender_mat);
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cv::gapi::ov::util::to_ocv(gender_tensor, gender_mat);
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}
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void export_model(const std::string &outpath) {
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@@ -155,6 +207,7 @@ public:
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private:
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ov::CompiledModel m_compiled_model;
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ov::InferRequest m_infer_request;
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};
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struct ImageInputPreproc {
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@@ -202,19 +255,78 @@ private:
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std::shared_ptr<ov::Model> m_model;
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};
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struct BaseAgeGenderOV: public ::testing::Test {
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BaseAgeGenderOV() {
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initDLDTDataPath();
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xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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device = "CPU";
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blob_path = "age-gender-recognition-retail-0013.blob";
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}
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cv::Mat getRandomImage(const cv::Size &sz) {
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cv::Mat image(sz, CV_8UC3);
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cv::randu(image, 0, 255);
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return image;
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}
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cv::Mat getRandomTensor(const std::vector<int> &dims,
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const int depth) {
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cv::Mat tensor(dims, depth);
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cv::randu(tensor, -1, 1);
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return tensor;
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}
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std::string xml_path;
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std::string bin_path;
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std::string blob_path;
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std::string device;
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};
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struct TestAgeGenderOV : public BaseAgeGenderOV {
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cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
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void validate() {
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normAssert(ov_age, gapi_age, "Test age output" );
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normAssert(ov_gender, gapi_gender, "Test gender output");
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}
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};
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struct TestAgeGenderListOV : public BaseAgeGenderOV {
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std::vector<cv::Mat> ov_age, ov_gender,
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gapi_age, gapi_gender;
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std::vector<cv::Rect> 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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TestAgeGenderListOV() {
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ov_age.resize(roi_list.size());
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ov_gender.resize(roi_list.size());
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gapi_age.resize(roi_list.size());
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gapi_gender.resize(roi_list.size());
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}
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void validate() {
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ASSERT_EQ(ov_age.size(), ov_gender.size());
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ASSERT_EQ(ov_age.size(), gapi_age.size());
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ASSERT_EQ(ov_gender.size(), gapi_gender.size());
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for (size_t i = 0; i < ov_age.size(); ++i) {
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normAssert(ov_age[i], gapi_age[i], "Test age output");
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normAssert(ov_gender[i], gapi_gender[i], "Test gender output");
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}
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}
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};
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} // anonymous namespace
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// TODO: Make all of tests below parmetrized to avoid code duplication
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TEST(TestAgeGenderOV, InferTypedTensor) {
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initDLDTDataPath();
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const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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const std::string device = "CPU";
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cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
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cv::randu(in_mat, -1, 1);
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cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
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TEST_F(TestAgeGenderOV, Infer_Tensor) {
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const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
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// OpenVINO
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AGNetOVComp ref(xml_path, bin_path, device);
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ref.apply(in_mat, ov_age, ov_gender);
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@@ -226,19 +338,11 @@ TEST(TestAgeGenderOV, InferTypedTensor) {
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cv::compile_args(cv::gapi::networks(pp)));
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// Assert
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normAssert(ov_age, gapi_age, "Test age output" );
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normAssert(ov_gender, gapi_gender, "Test gender output");
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validate();
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}
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TEST(TestAgeGenderOV, InferTypedImage) {
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initDLDTDataPath();
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const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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const std::string device = "CPU";
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cv::Mat in_mat(300, 300, CV_8UC3);
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cv::randu(in_mat, 0, 255);
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cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
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TEST_F(TestAgeGenderOV, Infer_Image) {
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const auto in_mat = getRandomImage({300, 300});
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// OpenVINO
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AGNetOVComp ref(xml_path, bin_path, device);
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@@ -252,19 +356,11 @@ TEST(TestAgeGenderOV, InferTypedImage) {
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cv::compile_args(cv::gapi::networks(pp)));
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// Assert
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normAssert(ov_age, gapi_age, "Test age output" );
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normAssert(ov_gender, gapi_gender, "Test gender output");
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validate();
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}
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TEST(TestAgeGenderOV, InferGenericTensor) {
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initDLDTDataPath();
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const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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const std::string device = "CPU";
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cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
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cv::randu(in_mat, -1, 1);
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cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
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TEST_F(TestAgeGenderOV, InferGeneric_Tensor) {
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const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
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// OpenVINO
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AGNetOVComp ref(xml_path, bin_path, device);
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@@ -277,19 +373,11 @@ TEST(TestAgeGenderOV, InferGenericTensor) {
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cv::compile_args(cv::gapi::networks(pp)));
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// Assert
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normAssert(ov_age, gapi_age, "Test age output" );
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normAssert(ov_gender, gapi_gender, "Test gender output");
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validate();
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}
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TEST(TestAgeGenderOV, InferGenericImage) {
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initDLDTDataPath();
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const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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const std::string device = "CPU";
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cv::Mat in_mat(300, 300, CV_8UC3);
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cv::randu(in_mat, 0, 255);
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cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
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TEST_F(TestAgeGenderOV, InferGenericImage) {
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const auto in_mat = getRandomImage({300, 300});
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// OpenVINO
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AGNetOVComp ref(xml_path, bin_path, device);
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@@ -303,20 +391,11 @@ TEST(TestAgeGenderOV, InferGenericImage) {
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cv::compile_args(cv::gapi::networks(pp)));
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// Assert
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normAssert(ov_age, gapi_age, "Test age output" );
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normAssert(ov_gender, gapi_gender, "Test gender output");
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validate();
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}
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TEST(TestAgeGenderOV, InferGenericImageBlob) {
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initDLDTDataPath();
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const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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const std::string blob_path = "age-gender-recognition-retail-0013.blob";
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const std::string device = "CPU";
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cv::Mat in_mat(300, 300, CV_8UC3);
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cv::randu(in_mat, 0, 255);
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cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
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TEST_F(TestAgeGenderOV, InferGeneric_ImageBlob) {
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const auto in_mat = getRandomImage({300, 300});
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// OpenVINO
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AGNetOVComp ref(xml_path, bin_path, device);
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@@ -333,20 +412,11 @@ TEST(TestAgeGenderOV, InferGenericImageBlob) {
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cv::compile_args(cv::gapi::networks(pp)));
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// Assert
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normAssert(ov_age, gapi_age, "Test age output" );
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normAssert(ov_gender, gapi_gender, "Test gender output");
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validate();
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}
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TEST(TestAgeGenderOV, InferGenericTensorBlob) {
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initDLDTDataPath();
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const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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const std::string blob_path = "age-gender-recognition-retail-0013.blob";
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const std::string device = "CPU";
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cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
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cv::randu(in_mat, -1, 1);
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cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
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TEST_F(TestAgeGenderOV, InferGeneric_TensorBlob) {
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const auto in_mat = getRandomTensor({1, 3, 62, 62}, CV_32F);
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// OpenVINO
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AGNetOVComp ref(xml_path, bin_path, device);
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@@ -361,19 +431,11 @@ TEST(TestAgeGenderOV, InferGenericTensorBlob) {
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cv::compile_args(cv::gapi::networks(pp)));
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// Assert
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normAssert(ov_age, gapi_age, "Test age output" );
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normAssert(ov_gender, gapi_gender, "Test gender output");
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validate();
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}
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TEST(TestAgeGenderOV, InferBothOutputsFP16) {
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initDLDTDataPath();
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const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
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const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
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const std::string device = "CPU";
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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 +454,11 @@ 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 +477,10 @@ 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 +493,7 @@ 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 +503,7 @@ 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 +518,8 @@ 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 +529,8 @@ 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 +552,112 @@ 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
|
||||
|
||||
Reference in New Issue
Block a user