Repository: opencv/opencv
File: modules/gapi/src/backends/ov/govbackend.cpp (C++)

Code before:
    }
};

} // anonymous namespace

cv::gapi::GBackend cv::gapi::ov::backend() {
    static cv::gapi::GBackend this_backend(std::make_shared<GOVBackendImpl>());
    return this_backend;
}

static std::vector<::ov::InferRequest>
createInferRequests(::ov::CompiledModel &compiled_model,
                    size_t              num_infer_requests) {
    std::vector<::ov::InferRequest> infer_requests;
    for (size_t i = 0; i < num_infer_requests; ++i) {
        infer_requests.push_back(compiled_model.create_infer_request());
    }
    return infer_requests;
}

// GOVExecutable implementation //////////////////////////////////////////////
cv::gimpl::ov::GOVExecutable::GOVExecutable(const ade::Graph &g,
                                            const cv::GCompileArgs &compileArgs,
                                            const std::vector<ade::NodeHandle> &nodes)
    : m_g(g), m_gm(m_g) {
#if defined HAVE_INF_ENGINE && INF_ENGINE_RELEASE >= 2024030000
    auto workload_arg = cv::gapi::getCompileArg<cv::gapi::wip::ov::WorkloadTypeOvPtr>(compileArgs);
    if(workload_arg.has_value()) {
        m_workload_type = workload_arg.value();
        m_workload_listener_id = m_workload_type->addListener(std::bind(&GOVExecutable::setWorkLoadType, this, std::placeholders::_1));
    }
#endif
    m_options.inference_only =
        cv::gapi::getCompileArg<cv::gapi::wip::ov::benchmark_mode>(compileArgs).has_value();
    // FIXME: Currently this backend is capable to run a single inference node only.
    // Need to extend our island fusion with merge/not-to-merge decision making parametrization
    GConstGOVModel ovm(g);

    for (auto &nh : nodes) {
        switch (m_gm.metadata(nh).get<NodeType>().t) {
        case NodeType::OP:
            if (this_nh == nullptr) {
                this_nh = nh;
                const auto &unit = ovm.metadata(this_nh).get<OVUnit>();
                compiled = const_cast<OVUnit&>(unit).compile();
                m_reqPool.reset(new RequestPool(createInferRequests(
                                compiled.compiled_model, unit.params.nireq)));
            }
            else
                util::throw_error(std::logic_error("Multi-node inference is not supported!"));
            break;

Review comment (suggestion):
As we have `HAVE_INF_ENGINE` guard already, I suggest to use only the second one

Code after:
    }
};

} // anonymous namespace

cv::gapi::GBackend cv::gapi::ov::backend() {
    static cv::gapi::GBackend this_backend(std::make_shared<GOVBackendImpl>());
    return this_backend;
}

static std::vector<::ov::InferRequest>
createInferRequests(::ov::CompiledModel &compiled_model,
                    size_t              num_infer_requests) {
    std::vector<::ov::InferRequest> infer_requests;
    for (size_t i = 0; i < num_infer_requests; ++i) {
        infer_requests.push_back(compiled_model.create_infer_request());
    }
    return infer_requests;
}

// GOVExecutable implementation //////////////////////////////////////////////
cv::gimpl::ov::GOVExecutable::GOVExecutable(const ade::Graph &g,
                                            const cv::GCompileArgs &compileArgs,
                                            const std::vector<ade::NodeHandle> &nodes)
    : m_g(g), m_gm(m_g) {
    auto workload_arg = cv::gapi::getCompileArg<cv::gapi::wip::ov::WorkloadTypeOVPtr>(compileArgs);
    if(workload_arg.has_value()) {
#if INF_ENGINE_RELEASE >= 2024030000
        m_workload_type = workload_arg.value();
        m_workload_listener_id = m_workload_type->addListener(std::bind(&GOVExecutable::setWorkloadType, this, std::placeholders::_1));
#else
        util::throw_error(std::logic_error("Workload type not supported in this version of OpenVINO, use >= 2024.3.0"));
#endif

    }
    m_options.inference_only =
        cv::gapi::getCompileArg<cv::gapi::wip::ov::benchmark_mode>(compileArgs).has_value();
    // FIXME: Currently this backend is capable to run a single inference node only.
    // Need to extend our island fusion with merge/not-to-merge decision making parametrization
    GConstGOVModel ovm(g);

    for (auto &nh : nodes) {
        switch (m_gm.metadata(nh).get<NodeType>().t) {
        case NodeType::OP:
            if (this_nh == nullptr) {
                this_nh = nh;
                const auto &unit = ovm.metadata(this_nh).get<OVUnit>();
                compiled = const_cast<OVUnit&>(unit).compile();
                m_reqPool.reset(new RequestPool(createInferRequests(
                                compiled.compiled_model, unit.params.nireq)));
            }