Yolov9 C++ 推理输出 x、y、宽度和高度不符合预期

问题描述 投票：0回答：1

我正在尝试使用 opencv 库在 c++ 中进行 yolov9 推理。我使用 opencv 版本 4.9.0 和 cuda 11.8 和 cudnn 8.9.7.29。我还使用此存储库中的 yolov9-s-converted 模型 https://github.com/WongKinYiu/yolov9 以 onnx 格式。我使用 export.py 导出它，如下所示：

    python3 export.py --weights yolov9-s-converted.pt --imgsz 640 --include onnx

当对图片进行推理时，当我在 CPU 上进行推理时，我会得到正确的检测，但是当我在 GPU 上使用 cuda 进行推理时，我会得到相同数量的检测，具有相同的“类”预测和置信度，但是 x 、y、宽度和高度值均为 0。这意味着如果我处理带有飞盘和多人的图像，我会以高置信度对人和飞盘进行多次预测，并在 CPU 上获得正确的边界框，并且 x、y 均为 0，在 cuda 上运行时的宽度和高度。这里有一个最小的运行示例代码：

#include <opencv2/opencv.hpp>

int main(int argc, char **argv)
{
    cv::dnn::Net net = cv::dnn::readNet("../path/to/model/yolov9-s-converted-simplified.onnx");
    bool is_cuda = true;
    if (is_cuda)
    {
        std::cout << "Attempty to use CUDA\n";
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
    }
    else
    {
        std::cout << "Running on CPU\n";
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
    }
    std::string filename = "path_to_image.png";
    cv::Mat image = cv::imread(filename);
    int _max = MAX(image.cols, image.rows);
    cv::Mat input_image = cv::Mat::zeros(_max, _max, CV_8UC3);
    image.copyTo(input_image(cv::Rect(0, 0, image.cols, image.rows)));
    cv::Mat blob; 
    cv::dnn::blobFromImage(input_image, blob, 1./255., cv::Size(640, 640), cv::Scalar(), true, false); 
    net.setInput(blob); 
    std::vector<cv::Mat> outputs;
    net.forward(outputs, net.getUnconnectedOutLayersNames()); 
    cv::Mat preds = outputs.at(0); 
    cv::Mat det_output(preds.size[1], preds.size[2], CV_32F, preds.ptr<float>()); 
    det_output = det_output.t();
    for (int i = 0; i < det_output.rows; ++i) {
        cv::Mat classes_scores = det_output.row(i).colRange(4, det_output.cols);
        double score;
        cv::Point classIdPoint;
        minMaxLoc(classes_scores, 0, &score, 0, &classIdPoint);

        if(score > 0.4) {
            float cx = det_output.at<float>(i, 0);
            float cy = det_output.at<float>(i, 1);
            float ow = det_output.at<float>(i, 2);
            float oh = det_output.at<float>(i, 3);
            std::cout << "found index: " << classIdPoint.x << " at cx: " << cx << ", cy: " << cy << ", ow: " << ow << ", oh: " << oh << " with confidence: " << score << std::endl;
        }
    }
}

此代码是 [yolov5-opencv-cpp-python] 的修改版本，已根据 MIT 许可证获得许可。原始存储库可以在[此处](https://github.com/doleron/yolov5-opencv-cpp-python)找到。

在CPU上运行时的输出：

Attempty to use CUDA
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.815521
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.809074
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.758471
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.726192
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.796474
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.828768
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.854483
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.812704
found index: 29 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.608229
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.920465
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.930458
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.924879
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.91286
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.904521
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.904832
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.905469
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.901314
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.895667
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.911088
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.91148
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.909622
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.92291
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.926244
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.889289
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.914144
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.907706
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.904138
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.92109
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.926035
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.916511
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.906686
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.902697
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.901318
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.919215
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.924047
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.917313
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.887939
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.914409
found index: 0 at cx: 0, cy: 0, ow: 0, oh: 0 with confidence: 0.914886

在CPU上运行时的输出：

Running on CPU
found index: 29 at cx: 289.611, cy: 122.513, ow: 34.0658, oh: 20.5468 with confidence: 0.815522
found index: 29 at cx: 289.647, cy: 122.389, ow: 34.351, oh: 20.7311 with confidence: 0.809075
found index: 29 at cx: 289.749, cy: 122.344, ow: 34.2258, oh: 20.8556 with confidence: 0.758471
found index: 29 at cx: 289.82, cy: 122.272, ow: 34.193, oh: 20.9125 with confidence: 0.726192
found index: 29 at cx: 289.594, cy: 122.339, ow: 34.1768, oh: 20.8335 with confidence: 0.796475
found index: 29 at cx: 289.644, cy: 122.238, ow: 34.3859, oh: 20.7112 with confidence: 0.828769
found index: 29 at cx: 289.647, cy: 122.195, ow: 34.4007, oh: 20.8877 with confidence: 0.854484
found index: 29 at cx: 289.892, cy: 122.159, ow: 34.1567, oh: 20.8711 with confidence: 0.812705
found index: 29 at cx: 289.639, cy: 122.405, ow: 34.3852, oh: 21.2653 with confidence: 0.608234
found index: 0 at cx: 323.89, cy: 287.165, ow: 93.9451, oh: 269.682 with confidence: 0.920465
found index: 0 at cx: 323.976, cy: 287.407, ow: 93.6403, oh: 270.885 with confidence: 0.930458
found index: 0 at cx: 324.207, cy: 287.597, ow: 93.2349, oh: 271.135 with confidence: 0.924879
found index: 0 at cx: 323.889, cy: 287.495, ow: 93.7401, oh: 269.943 with confidence: 0.91286
found index: 0 at cx: 323.901, cy: 287.479, ow: 93.4483, oh: 270.616 with confidence: 0.904521
found index: 0 at cx: 324.074, cy: 287.59, ow: 92.9913, oh: 270.936 with confidence: 0.904831
found index: 0 at cx: 324.064, cy: 287.61, ow: 93.4742, oh: 270.561 with confidence: 0.905469
found index: 0 at cx: 323.926, cy: 287.633, ow: 93.6999, oh: 270.392 with confidence: 0.901314
found index: 0 at cx: 324.001, cy: 287.689, ow: 93.5008, oh: 270.508 with confidence: 0.895667
found index: 0 at cx: 465.54, cy: 312.337, ow: 116.727, oh: 210.617 with confidence: 0.911089
found index: 0 at cx: 465.757, cy: 312.078, ow: 116.757, oh: 210.52 with confidence: 0.91148
found index: 0 at cx: 466.246, cy: 311.914, ow: 118.289, oh: 210.543 with confidence: 0.909622
found index: 0 at cx: 231.701, cy: 326.317, ow: 90.925, oh: 186.438 with confidence: 0.92291
found index: 0 at cx: 231.629, cy: 326.256, ow: 90.8639, oh: 186.353 with confidence: 0.926244
found index: 0 at cx: 323.679, cy: 287.466, ow: 93.8954, oh: 270.34 with confidence: 0.889289
found index: 0 at cx: 465.627, cy: 312.421, ow: 116.809, oh: 210.823 with confidence: 0.914145
found index: 0 at cx: 465.722, cy: 312.388, ow: 116.932, oh: 210.904 with confidence: 0.907707
found index: 0 at cx: 466.115, cy: 312.279, ow: 117.909, oh: 210.981 with confidence: 0.904138
found index: 0 at cx: 231.786, cy: 326.117, ow: 91.1232, oh: 186.631 with confidence: 0.92109
found index: 0 at cx: 231.607, cy: 326.078, ow: 91.0004, oh: 186.639 with confidence: 0.926036
found index: 0 at cx: 231.571, cy: 326.173, ow: 91.1691, oh: 186.417 with confidence: 0.916511
found index: 0 at cx: 465.791, cy: 312.5, ow: 117.069, oh: 209.744 with confidence: 0.906686
found index: 0 at cx: 465.705, cy: 312.386, ow: 117.201, oh: 210.218 with confidence: 0.902697
found index: 0 at cx: 466.032, cy: 312.345, ow: 118.031, oh: 210.439 with confidence: 0.901318
found index: 0 at cx: 231.655, cy: 326.171, ow: 91.0509, oh: 186.889 with confidence: 0.919215
found index: 0 at cx: 231.596, cy: 326.172, ow: 91.0338, oh: 186.856 with confidence: 0.924047
found index: 0 at cx: 231.646, cy: 326.213, ow: 91.2963, oh: 186.845 with confidence: 0.917313
found index: 0 at cx: 465.667, cy: 312.452, ow: 117.44, oh: 210.378 with confidence: 0.887939
found index: 0 at cx: 231.719, cy: 326.015, ow: 91.4443, oh: 187.143 with confidence: 0.91441
found index: 0 at cx: 231.674, cy: 326.049, ow: 91.4489, oh: 187.108 with confidence: 0.914886

我在想这是否可能与我如何将模型导出为 onnx 格式有关，但我尝试了不同的导出配置，但没有成功。

c++ opencv machine-learning yolo onnx
1个回答
0
投票

  • 您提供的链接是将 Yolov5 pytorch 模型导出到 ONNX 格式。所以它也不支持导出版本 除了 Yolov5。

  • 要导出 Yolov9 的 ONNX，您可以尝试使用以下步骤 此处提供。这适用于 YOlov8，但它也适用于 Yolov9，如果 你按照步骤操作。

  • 一旦获得 ONNX 模型，您就可以继续使用 Yolov5
    github代码来测试它。

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