'ov::Exception，位于内存位置 0x00000001000FF650 处'，局部变量ie显示<无可用信息，未为openvino.dll加载任何符号>

Question

'ov::Exception，位于内存位置 0x00000001000FF650 处'，局部变量ie显示<无可用信息，未为openvino.dll加载任何符号>

#include <iostream>
#include <string>

#include <openvino/openvino.hpp> //include openvino runtime header files
#include <opencv2/opencv.hpp>    //opencv header file

/* ---------  Please modify the path of yolov5 model and image -----------*/
std::string model_file = "yolov5s.xml";
std::string image_file = "zidane.jpg";
std::vector<cv::Scalar> colors = { cv::Scalar(0, 0, 255) , cv::Scalar(0, 255, 0) , cv::Scalar(255, 0, 0) ,
                                   cv::Scalar(255, 255, 0) , cv::Scalar(0, 255, 255) , cv::Scalar(255, 0, 255) };
const std::vector<std::string> class_names = {
    "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
    "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
    "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
    "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
    "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
    "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
    "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
    "hair drier", "toothbrush" };

cv::Mat letterbox(cv::Mat& img, std::vector<float>& paddings, std::vector<int> new_shape = { 640, 640 })
{
    // Get current image shape [height, width]
    // Refer to https://github.com/ultralytics/yolov5/blob/master/utils/augmentations.py#L111

    int img_h = img.rows;
    int img_w = img.cols;

    // Compute scale ratio(new / old) and target resized shape
    float scale = std::min(new_shape[1] * 1.0 / img_h, new_shape[0] * 1.0 / img_w);
    int resize_h = int(round(img_h * scale));
    int resize_w = int(round(img_w * scale));
    paddings[0] = scale;

    // Compute padding
    int pad_h = new_shape[1] - resize_h;
    int pad_w = new_shape[0] - resize_w;

    // Resize and pad image while meeting stride-multiple constraints
    cv::Mat resized_img;
    cv::resize(img, resized_img, cv::Size(resize_w, resize_h));

    // divide padding into 2 sides
    float half_h = pad_h * 1.0 / 2;
    float half_w = pad_w * 1.0 / 2;
    paddings[1] = half_h;
    paddings[2] = half_w;

    // Compute padding boarder
    int top = int(round(half_h - 0.1));
    int bottom = int(round(half_h + 0.1));
    int left = int(round(half_w - 0.1));
    int right = int(round(half_w + 0.1));

    // Add border
    cv::copyMakeBorder(resized_img, resized_img, top, bottom, left, right, 0, cv::Scalar(114, 114, 114));

    return resized_img;
}

int main(int argc, char* argv[]) {
    // -------- Get OpenVINO runtime version --------
    std::cout << ov::get_openvino_version().description << ':' << ov::get_openvino_version().buildNumber << std::endl;

    // -------- Step 1. Initialize OpenVINO Runtime Core --------
    ov::Core core;

    // -------- Step 2. Compile the Model --------
    auto compiled_model = core.compile_model(model_file, "GPU.1"); //GPU.1 is dGPU A770

    // -------- Step 3. Create an Inference Request --------
    ov::InferRequest infer_request = compiled_model.create_infer_request();

    // -------- Step 4. Read a picture file and do the preprocess --------
    cv::Mat img = cv::imread(image_file); //Load a picture into memory
    std::vector<float> paddings(3);       //scale, half_h, half_w
    cv::Mat resized_img = letterbox(img, paddings); //resize to (640,640) by letterbox
    // BGR->RGB, u8(0-255)->f32(0.0-1.0), HWC->NCHW
    cv::Mat blob = cv::dnn::blobFromImage(resized_img, 1 / 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true);

    // -------- Step 5. Feed the blob into the input node of YOLOv5 -------
    // Get input port for model with one input
    auto input_port = compiled_model.input();
    // Create tensor from external memory
    ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));
    // Set input tensor for model with one input
    infer_request.set_input_tensor(input_tensor);

    // -------- Step 6. Start inference --------
    infer_request.infer();

    // -------- Step 7. Get the inference result --------
    auto output = infer_request.get_output_tensor(0);
    auto output_shape = output.get_shape();
    std::cout << "The shape of output tensor:" << output_shape << std::endl;
    // 25200 x 85 Matrix 
    cv::Mat output_buffer(output_shape[1], output_shape[2], CV_32F, output.data());

    // -------- Step 8. Post-process the inference result -----------
    float conf_threshold = 0.25;
    float nms_threshold = 0.5;
    std::vector<cv::Rect> boxes;
    std::vector<int> class_ids;
    std::vector<float> class_scores;
    std::vector<float> confidences;
    // cx,cy,w,h,confidence,c1,c2,...c80
    for (int i = 0; i < output_buffer.rows; i++) {
        float confidence = output_buffer.at<float>(i, 4);
        if (confidence < conf_threshold) {
            continue;
        }
        cv::Mat classes_scores = output_buffer.row(i).colRange(5, 85);
        cv::Point class_id;
        double score;
        cv::minMaxLoc(classes_scores, NULL, &score, NULL, &class_id);

        // class score: 0~1
        if (score > 0.25)
        {
            float cx = output_buffer.at<float>(i, 0);
            float cy = output_buffer.at<float>(i, 1);
            float w = output_buffer.at<float>(i, 2);
            float h = output_buffer.at<float>(i, 3);
            int left = static_cast<int>((cx - 0.5 * w - paddings[2]) / paddings[0]);
            int top = static_cast<int>((cy - 0.5 * h - paddings[1]) / paddings[0]);
            int width = static_cast<int>(w / paddings[0]);
            int height = static_cast<int>(h / paddings[0]);
            cv::Rect box;
            box.x = left;
            box.y = top;
            box.width = width;
            box.height = height;

            boxes.push_back(box);
            class_ids.push_back(class_id.x);
            class_scores.push_back(score);
            confidences.push_back(confidence);
        }
    }
    // NMS
    std::vector<int> indices;
    cv::dnn::NMSBoxes(boxes, confidences, conf_threshold, nms_threshold, indices);

    // -------- Step 8. Visualize the detection results -----------
    for (size_t i = 0; i < indices.size(); i++) {
        int index = indices[i];
        int class_id = class_ids[index];
        cv::rectangle(img, boxes[index], colors[class_id % 6], 2, 8);
        std::string label = class_names[class_id] + ":" + std::to_string(class_scores[index]);
        cv::putText(img, label, cv::Point(boxes[index].tl().x, boxes[index].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, colors[class_id % 6]);
    }

    cv::namedWindow("YOLOv5 OpenVINO Inference C++ Demo", cv::WINDOW_AUTOSIZE);
    cv::imshow("YOLOv5 OpenVINO Inference C++ Demo", img);
    cv::waitKey(0);
    cv::destroyAllWindows();

    return 0;
}

这个问题怎么解决啊？

Answer 1

以下答案由GPT-3.5大模型与博主波罗歌共同编写：
这个问题是由于OpenVINO推理时发生异常引起的，具体原因需要查看OpenVINO异常的信息。

方法一：可以在运行代码时，直接让代码输出OpenVINO异常的信息，代码修改如下：

try {
   infer_request.infer();
} catch (const ov::Exception& e) {
   std::cout << "Inference error : " << e.what() << std::endl;
   return -1;
}

方法二：也可以在OpenVINO编译模型时，设置异常处理回调函数，代码修改如下：

// Define callback function for exceptions
void exception_callback(const char* msg) {
    std::cerr << "Exception raised: " << msg << std::endl;
}

int main(int argc, char* argv[]) {
    // -------- Step 1. Initialize OpenVINO Runtime Core --------
    ov::Core core;
    // Set exception callback function
    core.set_exception_callback(exception_callback);

    ...
}

这样能够方便地获取OpenVINO推理过程中发生异常的详细信息，以便排查问题。

另外，由于OpenVINO的版本和硬件环境等因素的影响，可能需要调整以下几个参数：

1. core.compile_model(model_file, "GPU.1") 的第二个参数（例如需要改成 "CPU"）；
2. inference_engine_cpu_extension 函数的参数，可以根据不同的硬件环境进行调整以获得更好的性能表现；
3. letterbox 函数中的 new_shape 参数和 paddings 数组，可以根据具体的模型进行调整以满足输入尺寸的要求。

希望能够帮到你！
如果我的回答解决了您的问题，请采纳！