0

我想使用 MaskRCNN 实现自定义图像分类器。

为了提高网络的速度,我想优化推理。

我已经使用过 OpenCV DNN 库,但我想用 OpenVINO 向前迈出一步。

我成功使用 OpenVINO 模型优化器 (python) 来构建代表我的网络的 .xml 和 .bin 文件。

我使用 Visual Studio 2017 成功构建了 OpenVINO 示例目录并运行了 MaskRCNNDemo 项目。

mask_rcnn_demo.exe -m .\Release\frozen_inference_graph.xml -i .\Release\input.jpg

InferenceEngine:
        API version ............ 1.4
        Build .................. 19154
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     .\Release\input.jpg
[ INFO ] Loading plugin

        API version ............ 1.5
        Build .................. win_20181005
        Description ....... MKLDNNPlugin
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (4288, 2848) to (800, 800)
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the plugin
[ INFO ] Start inference (1 iterations)

Average running time of one iteration: 2593.81 ms

[ INFO ] Processing output blobs
[ INFO ] Detected class 16 with probability 0.986519: [2043.3, 1104.9], [2412.87, 1436.52]
[ INFO ] Image out.png created!
[ INFO ] Execution successful

Oiseau VINO CPP

然后我试图在一个单独的项目中重现这个项目......首先我必须观察依赖关系......

<MaskRCNNDemo>
     //References
     <format_reader/>    => Open CV Images, resize it and get uchar data
     <ie_cpu_extension/> => CPU extension for un-managed layers (?)

     //Linker
     format_reader.lib         => Format Reader Lib (VINO Samples Compiled)
     cpu_extension.lib         => CPU extension Lib (VINO Samples Compiled)
     inference_engined.lib     => Inference Engine lib (VINO)
     opencv_world401d.lib      => OpenCV Lib
     libiomp5md.lib            => Dependancy
     ... (other libs)

有了它,我用我自己的类和打开图像的方式(多帧 tiff)构建了一个新项目。这项工作没有问题,我不会描述(我使用 CV DNN 推理引擎没有问题)。

我想构建与 MaskRCNNDemo 相同的项目:CustomIA

<CustomIA>
     //References
     None => I use my own libtiff way to open image and i resize with OpenCV
     None => I will just add include to cpu_extension source code.

     //Linker
     opencv_world345d.lib   => OpenCV 3.4.5 library
     tiffd.lib              => Libtiff Library
     cpu_extension.lib      => CPU extension compiled with sample
     inference_engined.lib  => Inference engine lib.

我将以下 dll 添加到项目目标目录:

cpu_extension.dll
inference_engined.dll
libiomp5md.dll
mkl_tiny_omp.dll
MKLDNNPlugind.dll
opencv_world345d.dll
tiffd.dll
tiffxxd.dll

我成功编译并执行,但我遇到了两个问题:

旧代码:

 slog::info << "Loading plugin" << slog::endl;
    InferencePlugin plugin = PluginDispatcher({ FLAGS_pp, "../../../lib/intel64" , "" }).getPluginByDevice(FLAGS_d);

    /** Loading default extensions **/
    if (FLAGS_d.find("CPU") != std::string::npos) {
        /**
         * cpu_extensions library is compiled from "extension" folder containing
         * custom MKLDNNPlugin layer implementations. These layers are not supported
         * by mkldnn, but they can be useful for inferring custom topologies.
        **/
        plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());
    }
    /** Printing plugin version **/
    printPluginVersion(plugin, std::cout);

输出 :

[ INFO ] Loading plugin
    API version ............ 1.5
    Build .................. win_20181005
    Description ....... MKLDNNPlugin

新代码:

    VINOEngine::VINOEngine()
{
    // Loading Plugin
    std::cout << std::endl;
    std::cout << "[INFO] - Loading VINO Plugin..." << std::endl;
    this->plugin= PluginDispatcher({ "", "../../../lib/intel64" , "" }).getPluginByDevice("CPU");
    this->plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());
    printPluginVersion(this->plugin, std::cout);

输出 :

[INFO] - Loading VINO Plugin...
000001A242280A18  // Like memory adress ???

第二期:

当我尝试从新代码中提取我的 ROI 和掩码时,如果我有一个“匹配”,我总是有:

  • 分数 =1.0
  • x1=x2=0.0
  • y1=y2=1.0

但是面具看起来很好提取......

新代码:

        float score = box_info[2];
        if (score > this->Conf_Threshold)
        {
            // On reconstruit les coordonnées de la box..
            float x1 = std::min(std::max(0.0f, box_info[3] * Image.cols), static_cast<float>(Image.cols));
            float y1 = std::min(std::max(0.0f, box_info[4] * Image.rows), static_cast<float>(Image.rows));
            float x2 = std::min(std::max(0.0f, box_info[5] * Image.cols), static_cast<float>(Image.cols));
            float y2 = std::min(std::max(0.0f, box_info[6] * Image.rows), static_cast<float>(Image.rows));
            int box_width = std::min(static_cast<int>(std::max(0.0f, x2 - x1)), Image.cols);
            int box_height = std::min(static_cast<int>(std::max(0.0f, y2 - y1)), Image.rows);

维诺面膜

Image is resized from (4288, 2848) to (800, 800)
Detected class 62 with probability 1: [4288, 0], [4288, 0]

然后,当我没有正确的 bbox 坐标时,我不可能将蒙版放置在图像中并调整其大小......

有人知道我做得不好吗?

如何使用 cpu_extension 正确创建和链接 OpenVINO 项目?

谢谢 !

4

1 回答 1

0

版本的第一个问题:查看 printPluginVersion 函数,您将看到 InferenceEngine 和插件版本信息的重载 std::ostream 运算符。

第二:您可以尝试通过比较原始框架和 OV 的第一个卷积和输出层后的输出来调试模型。确保它是逐个元素相等的。

在 OV 中,您可以使用 network.addOutput("layer_name") 将任何层添加到输出。然后使用以下命令读取输出: const Blob::Ptr debug_blob = infer_request.GetBlob("layer_name")。

大多数时候遇到这样的问题,我发现缺少输入预处理(均值、归一化等)

cpu_extensions 是一个动态库,但您仍然可以更改 cmake 脚本以使其成为静态并将其与您的应用程序链接。之后,您需要使用应用程序路径来调用 IExtensionPtr extension_ptr = make_so_pointer(argv[0])

于 2019-04-17T20:34:17.013 回答