stash

Author: sivanov-work

demos/common/cpp/models/src/classification_model.cpp

@@ -44,16 +44,27 @@ ClassificationModel::ClassificationModel(const std::string& modelFileName,
 
 std::unique_ptr<ResultBase> ClassificationModel::postprocess(InferenceResult& infResult) {
     const ov::Tensor& indicesTensor = infResult.outputsData.find(outputsNames[0])->second;
+    const void* indicesTensorBuffer = reinterpret_cast<const void*>(indicesTensor.data());
+    std::cout << "-S- indices tensor data: " << indicesTensorBuffer <<  ", size: " << indicesTensor.get_size() << std::endl;
     const int* indicesPtr = indicesTensor.data<int>();
+    for (int i = 0; i < indicesTensor.get_size(); i++){
+        std::cout << "-S- index[" << i << "]: " << indicesPtr[i] <<std::endl;
+    }
     const ov::Tensor& scoresTensor = infResult.outputsData.find(outputsNames[1])->second;
     const float* scoresPtr = scoresTensor.data<float>();
+    const void* scoresTensorBuffer = reinterpret_cast<const void*>(scoresTensor.data());
+    std::cout << "-S- scores tensor data: " << scoresTensorBuffer << ", size: " << scoresTensor.get_size() <<std::endl;
+    for (int i = 0; i < scoresTensor.get_size(); i++){
+        std::cout << "-S- score[" << i << "]: " << scoresPtr[i] <<std::endl;
+    }
 
     ClassificationResult* result = new ClassificationResult(infResult.frameId, infResult.metaData);
     auto retVal = std::unique_ptr<ResultBase>(result);
 
     result->topLabels.reserve(scoresTensor.get_size());
     for (size_t i = 0; i < scoresTensor.get_size(); ++i) {
         int ind = indicesPtr[i];
+         std::cout << "-S- index???[" << i << "]: " << ind << ", labels size: " << labels.size() <<std::endl;
         if (ind < 0 || ind >= static_cast<int>(labels.size())) {
             throw std::runtime_error(std::string("Invalid index: ") + std::to_string(ind) + " for the class label is found during postprocessing, label size: " + std::to_string(labels.size()));
         }

demos/common/cpp/models/src/image_model.cpp

@@ -43,7 +43,7 @@ std::shared_ptr<InternalModelData> ImageModel::preprocess(std::vector<std::share
     const size_t width = tensorShape[ov::layout::width_idx(layout)];
     const size_t height = tensorShape[ov::layout::height_idx(layout)];
     const size_t channels = tensorShape[ov::layout::channels_idx(layout)];
-
+    std::cout << "ImageModel::preprocess: batch: " << batch << ", width: " << width << ", height: " << height << ", channels: " << channels << std::endl;
     char* memoryBlob = nullptr;
     size_t image_index = 0;
     bool isMatFloat = false;
@@ -75,6 +75,7 @@ std::shared_ptr<InternalModelData> ImageModel::preprocess(std::vector<std::share
             img = resizeImageExt(img, width, height, resizeMode, interpolationMode);
         }
         size_t sizeInBytes = img.total() * img.elemSize();
+        std::cout << "image size in bytes: " << sizeInBytes << std::endl;
         if (!memoryBlob) {
             memoryBlob = new char[sizeInBytes * batch]; // intended memory leak
         }
@@ -84,9 +85,10 @@ std::shared_ptr<InternalModelData> ImageModel::preprocess(std::vector<std::share
         image_index++;
     }
 
+    std::cout << "isMatFloat: " << isMatFloat << std::endl;
     auto precision = isMatFloat ? ov::element::f32 : ov::element::u8;
     auto batched_tensor =  ov::Tensor(precision, ov::Shape{ batch, height, width, channels }, memoryBlob);
-    request.set_tensor(inputsNames[0],batched_tensor);
+    request.set_tensor(inputsNames[0], batched_tensor);
     return std::make_shared<InternalImageModelData>(origImg_cols, origImg_rows);
 }
 

demos/common/cpp/pipelines/src/async_pipeline.cpp

@@ -108,6 +108,7 @@ int64_t AsyncPipeline::submitData(std::vector<std::shared_ptr<InputData>>::itera
 
                     for (const auto& outName : model->getOutputsNames()) {
                         auto tensor = request.get_tensor(outName);
+                        std::cout << "-S- output tensorName: " << outName << ", tensor ptr: " << reinterpret_cast<void*>(tensor.data()) << ", size: " << tensor.get_size() << std::endl;
                         result.outputsData.emplace(outName, tensor);
                     }