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Refactor tests

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This commit is contained in:
TolyaTalamanov 2022-10-04 06:59:22 +00:00
parent 9fd877acc9
commit 5a0c85b3ef
1 changed files with 15 additions and 92 deletions
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107
modules/gapi/test/infer/gapi_infer_ie_test.cpp
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@ -3027,109 +3027,32 @@ TEST_F(AgeGenderInferTest, ThrowSyncWithNireqNotEqualToOne) {
cv::compile_args(cv::gapi::networks(pp)))); cv::compile_args(cv::gapi::networks(pp))));
} }
TEST(TestAgeGenderIE, ChangeOutputPrecision) TEST_F(AgeGenderInferTest, ChangeOutputPrecision) {
{
initDLDTDataPath();
cv::gapi::ie::detail::ParamDesc params;
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
params.device_id = "CPU";
cv::Mat in_mat(cv::Size(320, 240), CV_8UC3);
cv::randu(in_mat, 0, 255);
cv::Mat gapi_age, gapi_gender;
// Load & run IE network
IE::Blob::Ptr ie_age, ie_gender;
{
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
auto net = cv::gimpl::ie::wrap::readNetwork(params);
setNetParameters(net);
for (auto it : net.getOutputsInfo()) {
it.second->setPrecision(IE::Precision::U8);
}
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");
ie_gender = infer_request.GetBlob("prob");
}
// Configure & run G-API
using AGInfo = std::tuple<cv::GMat, cv::GMat>;
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender");
cv::GMat in;
cv::GMat age, gender;
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
auto pp = cv::gapi::ie::Params<AgeGender> { auto pp = cv::gapi::ie::Params<AgeGender> {
params.model_path, params.weights_path, params.device_id m_params.model_path, m_params.weights_path, m_params.device_id
}.cfgOutputLayers({ "age_conv3", "prob" }) }.cfgOutputLayers({ "age_conv3", "prob" })
.cfgOutputPrecision(CV_8U); .cfgOutputPrecision(CV_8U);
comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp)));
// Validate with IE itself (avoid DNN module dependency here) for (auto it : m_net.getOutputsInfo()) {
normAssert(cv::gapi::ie::util::to_ocv(ie_age), gapi_age, "Test age output" ); it.second->setPrecision(IE::Precision::U8);
normAssert(cv::gapi::ie::util::to_ocv(ie_gender), gapi_gender, "Test gender output");
}
TEST(TestAgeGenderIE, ChangeSpecificOutputPrecison)
{
initDLDTDataPath();
cv::gapi::ie::detail::ParamDesc params;
params.model_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
params.weights_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
params.device_id = "CPU";
cv::Mat in_mat(cv::Size(320, 240), CV_8UC3);
cv::randu(in_mat, 0, 255);
cv::Mat gapi_age, gapi_gender;
// Load & run IE network
IE::Blob::Ptr ie_age, ie_gender;
{
auto plugin = cv::gimpl::ie::wrap::getPlugin(params);
auto net = cv::gimpl::ie::wrap::readNetwork(params);
setNetParameters(net);
// NB: Specify precision only for "prob" output.
net.getOutputsInfo().at("prob")->setPrecision(IE::Precision::U8);
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");
ie_gender = infer_request.GetBlob("prob");
} }
// Configure & run G-API buildGraph().apply(cv::gin(m_in_mat), cv::gout(m_gapi_age, m_gapi_gender),
using AGInfo = std::tuple<cv::GMat, cv::GMat>; cv::compile_args(cv::gapi::networks(pp)));
G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "test-age-gender"); validate();
}
cv::GMat in;
cv::GMat age, gender;
std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
cv::GComputation comp(cv::GIn(in), cv::GOut(age, gender));
TEST_F(AgeGenderInferTest, ChangeSpecificOutputPrecison) {
auto pp = cv::gapi::ie::Params<AgeGender> { auto pp = cv::gapi::ie::Params<AgeGender> {
params.model_path, params.weights_path, params.device_id m_params.model_path, m_params.weights_path, m_params.device_id
}.cfgOutputLayers({ "age_conv3", "prob" }) }.cfgOutputLayers({ "age_conv3", "prob" })
.cfgOutputPrecision({{"prob", CV_8U}}); .cfgOutputPrecision({{"prob", CV_8U}});
comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
cv::compile_args(cv::gapi::networks(pp)));
// Validate with IE itself (avoid DNN module dependency here) m_net.getOutputsInfo().at("prob")->setPrecision(IE::Precision::U8);
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"); buildGraph().apply(cv::gin(m_in_mat), cv::gout(m_gapi_age, m_gapi_gender),
cv::compile_args(cv::gapi::networks(pp)));
validate();
} }
} // namespace opencv_test } // namespace opencv_test