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@ -657,6 +657,187 @@ TEST_F(TestAgeGenderListOV, InferList2Generic_Image) { |
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validate(); |
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} |
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static ov::element::Type toOV(int depth) { |
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switch (depth) { |
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case CV_8U: return ov::element::u8; |
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case CV_32S: return ov::element::i32; |
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case CV_32F: return ov::element::f32; |
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case CV_16F: return ov::element::f16; |
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default: GAPI_Error("OV Backend: Unsupported data type"); |
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} |
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return ov::element::undefined; |
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} |
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struct TestMeanScaleOV : public ::testing::TestWithParam<int>{ |
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G_API_NET(IdentityNet, <cv::GMat(cv::GMat)>, "test-identity-net"); |
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static cv::GComputation create() { |
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cv::GMat in; |
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cv::GMat out; |
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out = cv::gapi::infer<IdentityNet>(in); |
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return cv::GComputation{cv::GIn(in), cv::GOut(out)}; |
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} |
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using Params = cv::gapi::ov::Params<IdentityNet>; |
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static Params params(const std::string &xml_path, |
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const std::string &bin_path, |
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const std::string &device) { |
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return Params { |
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xml_path, bin_path, device |
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}.cfgInputModelLayout("NHWC") |
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.cfgOutputLayers({ "output" }); |
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} |
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TestMeanScaleOV() { |
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initDLDTDataPath(); |
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m_model_path = findDataFile("gapi/ov/identity_net_100x100.xml"); |
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m_weights_path = findDataFile("gapi/ov/identity_net_100x100.bin"); |
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m_device_id = "CPU"; |
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m_ov_model = cv::gapi::ov::wrap::getCore() |
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.read_model(m_model_path, m_weights_path); |
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auto input_depth = GetParam(); |
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auto input = cv::imread(findDataFile("gapi/gapi_logo.jpg")); |
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input.convertTo(m_in_mat, input_depth); |
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} |
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void addPreprocToOV( |
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std::function<void(ov::preprocess::PrePostProcessor&)> f) { |
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auto input_depth = GetParam(); |
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ov::preprocess::PrePostProcessor ppp(m_ov_model); |
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ppp.input().tensor().set_layout(ov::Layout("NHWC")) |
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.set_element_type(toOV(input_depth)) |
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.set_shape({ 1, 100, 100, 3 }); |
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ppp.input().model().set_layout(ov::Layout("NHWC")); |
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f(ppp); |
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m_ov_model = ppp.build(); |
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} |
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void runOV() { |
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auto compiled_model = cv::gapi::ov::wrap::getCore() |
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.compile_model(m_ov_model, m_device_id); |
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auto infer_request = compiled_model.create_infer_request(); |
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auto input_tensor = infer_request.get_input_tensor(); |
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cv::gapi::ov::util::to_ov(m_in_mat, input_tensor); |
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infer_request.infer(); |
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auto out_tensor = infer_request.get_tensor("output"); |
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m_out_mat_ov.create(cv::gapi::ov::util::to_ocv(out_tensor.get_shape()), |
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cv::gapi::ov::util::to_ocv(out_tensor.get_element_type())); |
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cv::gapi::ov::util::to_ocv(out_tensor, m_out_mat_ov); |
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} |
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std::string m_model_path; |
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std::string m_weights_path; |
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std::string m_device_id; |
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std::shared_ptr<ov::Model> m_ov_model; |
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cv::Mat m_in_mat; |
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cv::Mat m_out_mat_gapi; |
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cv::Mat m_out_mat_ov; |
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}; |
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TEST_P(TestMeanScaleOV, Mean) |
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{ |
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int input_depth = GetParam(); |
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std::vector<float> mean_values{ 220.1779, 218.9857, 217.8986 }; |
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// Run OV reference pipeline:
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{ |
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addPreprocToOV([&](ov::preprocess::PrePostProcessor& ppp) { |
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if (input_depth == CV_8U || input_depth == CV_32S) { |
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ppp.input().preprocess().convert_element_type(ov::element::f32); |
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} |
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ppp.input().preprocess().mean(mean_values); |
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}); |
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runOV(); |
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} |
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// Run G-API
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GComputation comp = create(); |
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auto pp = params(m_model_path, m_weights_path, m_device_id); |
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pp.cfgMean(mean_values); |
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comp.apply(cv::gin(m_in_mat), cv::gout(m_out_mat_gapi), |
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cv::compile_args(cv::gapi::networks(pp))); |
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// Validate OV results against G-API ones:
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normAssert(m_out_mat_ov, m_out_mat_gapi, "Test output"); |
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} |
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TEST_P(TestMeanScaleOV, Scale) |
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{ |
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int input_depth = GetParam(); |
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std::vector<float> scale_values{ 2., 2., 2. }; |
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// Run OV reference pipeline:
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{ |
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addPreprocToOV([&](ov::preprocess::PrePostProcessor& ppp) { |
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if (input_depth == CV_8U || input_depth == CV_32S) { |
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ppp.input().preprocess().convert_element_type(ov::element::f32); |
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} |
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ppp.input().preprocess().scale(scale_values); |
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}); |
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runOV(); |
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} |
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// Run G-API
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GComputation comp = create(); |
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auto pp = params(m_model_path, m_weights_path, m_device_id); |
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pp.cfgScale(scale_values); |
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comp.apply(cv::gin(m_in_mat), cv::gout(m_out_mat_gapi), |
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cv::compile_args(cv::gapi::networks(pp))); |
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// Validate OV results against G-API ones:
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normAssert(m_out_mat_ov, m_out_mat_gapi, "Test output"); |
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} |
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TEST_P(TestMeanScaleOV, MeanAndScale) |
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{ |
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int input_depth = GetParam(); |
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std::vector<float> mean_values{ 220.1779, 218.9857, 217.8986 }; |
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std::vector<float> scale_values{ 2., 2., 2. }; |
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// Run OV reference pipeline:
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{ |
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addPreprocToOV([&](ov::preprocess::PrePostProcessor& ppp) { |
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if (input_depth == CV_8U || input_depth == CV_32S) { |
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ppp.input().preprocess().convert_element_type(ov::element::f32); |
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} |
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ppp.input().preprocess().mean(mean_values); |
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ppp.input().preprocess().scale(scale_values); |
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}); |
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runOV(); |
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} |
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// Run G-API
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GComputation comp = create(); |
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auto pp = params(m_model_path, m_weights_path, m_device_id); |
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pp.cfgMean(mean_values); |
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pp.cfgScale(scale_values); |
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comp.apply(cv::gin(m_in_mat), cv::gout(m_out_mat_gapi), |
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cv::compile_args(cv::gapi::networks(pp))); |
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// Validate OV results against G-API ones:
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normAssert(m_out_mat_ov, m_out_mat_gapi, "Test output"); |
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} |
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INSTANTIATE_TEST_CASE_P(Instantiation, TestMeanScaleOV, |
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Values(CV_8U, CV_32S, CV_16F, CV_32F)); |
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} // namespace opencv_test
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#endif // HAVE_INF_ENGINE && INF_ENGINE_RELEASE >= 2022010000
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Alexander Smorkalov
10 months ago