Merge pull request #22957 from dkurt:new_openvino_api

Switch to new OpenVINO API after 2022.1 release * Pass Layer_Test_Convolution_DLDT.Accuracy/0 test * Pass test Test_Caffe_layers.Softmax * Failed 136 tests * Fix Concat. Failed 120 tests * Custom nGraph ops. 19 failed tests * Set and get properties from Core * Read model from buffer * Change MaxPooling layer output names. Restore reshape * Cosmetic changes * Cosmetic changes * Override getOutputsInfo * Fixes for OpenVINO < 2022.1 * Async inference for 2021.4 and less * Compile model with config * Fix serialize for 2022.1 * Asynchronous inference with 2022.1 * Handle 1d outputs * Work with model with dynamic output shape * Fixes with 1d output for old API * Control outputs by nGraph function for all OpenVINO versions * Refer inputs in PrePostProcessor by indices * Fix cycled dependency between InfEngineNgraphNode and InfEngineNgraphNet. Add InferRequest callback only for async inference. Do not capture InferRequest object. * Fix tests thresholds * Fix HETERO:GPU,CPU plugin issues with unsupported layer
2026-07-29 23:33:05 +04:00 · 2022-12-23 19:58:41 +03:00
parent 9012e6dd9b
commit 8681686d8f
14 changed files with 571 additions and 205 deletions
@@ -39,6 +39,86 @@ cv::String setInferenceEngineBackendType(const cv::String& newBackendType)

 CV__DNN_INLINE_NS_END

+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
+namespace InferenceEngine {
+
+CNNNetwork::CNNNetwork() {}
+
+CNNNetwork::CNNNetwork(std::shared_ptr<ov::Model> model) : model(model) {}
+
+std::shared_ptr<ov::Model> CNNNetwork::getFunction() const {
+    return model;
+}
+
+void CNNNetwork::serialize(const std::string& xmlPath, const std::string& binPath) {
+    ov::pass::Serialize(xmlPath, binPath).run_on_model(model);
+}
+
+void CNNNetwork::reshape(const std::map<std::string, std::vector<size_t> >& shapes) {
+    std::map<std::string, ov::PartialShape> partialShapes;
+    for (const auto& it : shapes) {
+        ov::PartialShape shape;
+        shape.insert(shape.begin(), it.second.begin(), it.second.end());
+        partialShapes.insert({it.first, shape});
+    }
+    model->reshape(partialShapes);
+}
+
+std::vector<std::string> Core::GetAvailableDevices() {
+    return get_available_devices();
+}
+
+void Core::UnregisterPlugin(const std::string& id) {
+    unload_plugin(id);
+}
+
+CNNNetwork Core::ReadNetwork(const std::string& xmlPath, const std::string& binPath) {
+    return read_model(xmlPath, binPath);
+}
+
+ExecutableNetwork Core::LoadNetwork(CNNNetwork net, const std::string& device,
+                                    const std::map<std::string, std::string>& config) {
+    ov::AnyMap props;
+    for (const auto& it : config) {
+        props.insert(it);
+    }
+    return compile_model(net.getFunction(), device, props);
+}
+
+ExecutableNetwork::ExecutableNetwork() {}
+
+ExecutableNetwork::ExecutableNetwork(const ov::CompiledModel& copy) : CompiledModel(copy) {}
+
+ov::InferRequest ExecutableNetwork::CreateInferRequest() { return create_infer_request(); }
+
+}  // namespace InferenceEngine
+
+Mat infEngineBlobToMat(const ov::Tensor& blob)
+{
+    std::vector<size_t> dims = blob.get_shape();
+    std::vector<int> size(dims.begin(), dims.end());
+    auto precision = blob.get_element_type();
+
+    int type = -1;
+    switch (precision)
+    {
+        case ov::element::f32: type = CV_32F; break;
+        case ov::element::u8: type = CV_8U; break;
+        default:
+            CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
+    }
+    return Mat(size, type, blob.data());
+}
+
+void infEngineBlobsToMats(const ov::TensorVector& blobs,
+                          std::vector<Mat>& mats)
+{
+    mats.resize(blobs.size());
+    for (int i = 0; i < blobs.size(); ++i)
+        mats[i] = infEngineBlobToMat(blobs[i]);
+}
+
+#else

 Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
 {
@@ -65,7 +145,7 @@ void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
    for (int i = 0; i < blobs.size(); ++i)
        mats[i] = infEngineBlobToMat(blobs[i]);
 }
-
+#endif // OpenVINO >= 2022.1

 static bool init_IE_plugins()
 {
@@ -130,7 +210,11 @@ static bool detectArmPlugin_()
    {
        if (i->find("CPU") != std::string::npos)
        {
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
+            const std::string name = ie.get_property(*i, ov::device::full_name);
+#else
            const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as<std::string>();
+#endif
            CV_LOG_INFO(NULL, "CPU plugin: " << name);
            return name.find("arm_compute::NEON") != std::string::npos;
        }
@@ -150,7 +234,11 @@ static bool detectMyriadX_(const std::string& device)
    {
        if (i->find(device) != std::string::npos)
        {
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
+            const std::string name = ie.get_property(*i, ov::device::full_name);
+#else
            const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as<std::string>();
+#endif
            CV_LOG_INFO(NULL, "Myriad device: " << name);
            return name.find("MyriadX") != std::string::npos || name.find("Myriad X") != std::string::npos || name.find("HDDL") != std::string::npos;
        }