Fix to suppress warnings of YOLOX

yolox_ros_cpp/yolox_cpp/CMakeLists.txt

@@ -37,13 +37,11 @@ set(ENABLE_TFLITE OFF)
 
 if(YOLOX_USE_OPENVINO)
   find_package(OpenVINO REQUIRED)
-  find_package(InferenceEngine REQUIRED)
-  find_package(ngraph REQUIRED)
 
   set(ENABLE_OPENVINO ON)
   set(TARGET_SRC src/yolox_openvino.cpp)
-  set(TARGET_LIBS InferenceEngine ngraph)
-  set(TARGET_DPENDENCIES OpenVINO InferenceEngine ngraph)
+  set(TARGET_LIBS openvino::runtime)
+  set(TARGET_DPENDENCIES OpenVINO)
 endif()
 
 if(YOLOX_USE_TENSORRT)

yolox_ros_cpp/yolox_cpp/include/yolox_cpp/yolox_openvino.hpp

@@ -7,7 +7,7 @@
 #include <vector>
 #include <iostream>
 #include <opencv2/opencv.hpp>
-#include <inference_engine.hpp>
+#include <openvino/openvino.hpp>
 
 #include "core.hpp"
 #include "coco_names.hpp"
@@ -22,11 +22,9 @@ namespace yolox_cpp{
 
         private:
             std::string device_name_;
-            std::string input_name_;
-            std::string output_name_;
-            InferenceEngine::CNNNetwork network_;
-            InferenceEngine::ExecutableNetwork executable_network_;
-            InferenceEngine::InferRequest infer_request_;
+            std::vector<float> blob_;
+            ov::Shape input_shape_;
+            ov::InferRequest infer_request_;
     };
 }
 

yolox_ros_cpp/yolox_cpp/src/yolox_openvino.cpp

@@ -1,8 +1,6 @@
 #include "yolox_cpp/yolox_openvino.hpp"
 
 namespace yolox_cpp{
-    using namespace InferenceEngine;
-
     YoloXOpenVINO::YoloXOpenVINO(file_name_t path_to_model, std::string device_name,
                                  float nms_th, float conf_th, std::string model_version,
                                  int num_classes, bool p6)
@@ -11,55 +9,36 @@ namespace yolox_cpp{
     {
         // Step 1. Initialize inference engine core
         std::cout << "Initialize Inference engine core" << std::endl;
-        Core ie;
+        ov::Core ie;
 
         // Step 2. Read a model in OpenVINO Intermediate Representation (.xml and
         // .bin files) or ONNX (.onnx file) format
         std::cout << "Read a model in OpenVINO Intermediate Representation: " << path_to_model << std::endl;
-        network_ = ie.ReadNetwork(path_to_model);
-        if (network_.getOutputsInfo().size() != 1)
-            throw std::logic_error("Sample supports topologies with 1 output only");
-        if (network_.getInputsInfo().size() != 1)
-            throw std::logic_error("Sample supports topologies with 1 input only");
+        const auto network = ie.read_model(path_to_model);
 
-        // Step 3. Configure input & output
-        std::cout << "Configuring input and output blobs" << std::endl;
-        // Prepare input blobs
-        InputInfo::Ptr input_info = network_.getInputsInfo().begin()->second;
-        input_name_ = network_.getInputsInfo().begin()->first;
+        //  Step 3. Loading a model to the device
+        std::cout << "Loading a model to the device: " << device_name_ << std::endl;
+        auto compiled_model = ie.compile_model(network, device_name);
+
+        // Step 4. Create an infer request
+        std::cout << "Create an infer request" << std::endl;
+        this->infer_request_ = compiled_model.create_infer_request();
 
+        // Step 5. Configure input & output
+        std::cout << "Configuring input and output blobs" << std::endl;
+        this->input_shape_ = compiled_model.input(0).get_shape();
         /* Mark input as resizable by setting of a resize algorithm.
         * In this case we will be able to set an input blob of any shape to an
         * infer request. Resize and layout conversions are executed automatically
         * during inference */
-        //input_info->getPreProcess().setResizeAlgorithm(RESIZE_BILINEAR);
-        //input_info->setLayout(Layout::NHWC);
-        input_info->setPrecision(Precision::FP32);
-        auto input_dims = input_info->getInputData()->getDims();
-        this->input_h_ = input_dims[2];
-        this->input_w_ = input_dims[3];
+        this->blob_.resize(
+            this->input_shape_.at(0) * this->input_shape_.at(1) *
+            this->input_shape_.at(2) * this->input_shape_.at(3));
+        this->input_h_ = this->input_shape_.at(2);
+        this->input_w_ = this->input_shape_.at(3);
         std::cout << "INPUT_HEIGHT: " << this->input_h_ << std::endl;
         std::cout << "INPUT_WIDTH: " << this->input_w_ << std::endl;
 
-        // Prepare output blobs
-        if (network_.getOutputsInfo().empty()) {
-            std::cerr << "Network outputs info is empty" << std::endl;
-            throw std :: runtime_error( "Network outputs info is empty" );
-        }
-        DataPtr output_info = network_.getOutputsInfo().begin()->second;
-        output_name_ = network_.getOutputsInfo().begin()->first;
-
-        // output_info->setPrecision(Precision::FP16);
-        output_info->setPrecision(Precision::FP32);
-
-        //  Step 4. Loading a model to the device
-        std::cout << "Loading a model to the device: " << device_name_ << std::endl;
-        executable_network_ = ie.LoadNetwork(network_, device_name_);
-
-        //  Step 5. Create an infer request
-        std::cout << "Create an infer request" << std::endl;
-        infer_request_ = executable_network_.CreateInferRequest();
-
         // Prepare GridAndStrides
         if(this->p6_)
         {
@@ -75,34 +54,18 @@ namespace yolox_cpp{
     {
         // preprocess
         cv::Mat pr_img = static_resize(frame);
-        InferenceEngine::Blob::Ptr imgBlob = infer_request_.GetBlob(input_name_);
-        InferenceEngine::MemoryBlob::Ptr mblob = InferenceEngine::as<InferenceEngine::MemoryBlob>(imgBlob);
-        if (!mblob)
-        {
-            THROW_IE_EXCEPTION << "We expect blob to be inherited from MemoryBlob in matU8ToBlob, "
-                << "but by fact we were not able to cast inputBlob to MemoryBlob";
-        }
         // locked memory holder should be alive all time while access to its buffer happens
-        auto mblobHolder = mblob->wmap();
-        float *blob_data = mblobHolder.as<float *>();
-        blobFromImage(pr_img, blob_data);
+        blobFromImage(pr_img, this->blob_.data());
 
         // do inference
         /* Running the request synchronously */
-        infer_request_.Infer();
+        this->infer_request_.set_input_tensor(
+            ov::Tensor{ov::element::f32, this->input_shape_, reinterpret_cast<float *>(this->blob_.data())});
+        infer_request_.infer();
 
-        // Process output
-        const InferenceEngine::Blob::Ptr output_blob = infer_request_.GetBlob(output_name_);
-        InferenceEngine::MemoryBlob::CPtr moutput = as<InferenceEngine::MemoryBlob>(output_blob);
-        if (!moutput) {
-            throw std::logic_error("We expect output to be inherited from MemoryBlob, "
-                                "but by fact we were not able to cast output to MemoryBlob");
-        }
 
-        // locked memory holder should be alive all time while access to its buffer
-        // happens
-        auto moutputHolder = moutput->rmap();
-        const float* net_pred = moutputHolder.as<const PrecisionTrait<Precision::FP32>::value_type*>();
+        const auto &output_tensor = this->infer_request_.get_output_tensor();
+        const float* net_pred = reinterpret_cast<float *>(output_tensor.data());
 
         float scale = std::min(input_w_ / (frame.cols*1.0), input_h_ / (frame.rows*1.0));
         std::vector<Object> objects;