google-cloud-platform - Google Cloud Vision API 对象检测模型在 Raspberry Pi 上出现总线错误

Question

我使用 vision API 在 Google Cloud 上训练了一个简单的对象检测模型。导出为 tflite 模型后，我尝试在 Raspberry Pi 3B+ 上使用下面的简单入门代码和 tensorflow lite 2.6.0rc-2 运行它。该代码可以正常运行标准 MobileNet 模型，但在使用我的自定义模型分配张量时会出现总线错误。然后，我尝试在 WSL debian 上使用我的模型运行相同的代码，这很有效。视觉 API 说它支持 ARM 边缘设备，所以我不明白为什么它不起作用。树莓派是不是内存不够？如果是这样，为什么它运行更复杂的 MobileNet 模型？

从https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits修改的测试代码

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/highgui.hpp>
#include <fstream>
#include <iostream>
#include <opencv2/core/ocl.hpp>
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/string_util.h"
#include "tensorflow/lite/model.h"
#include <cmath>

using namespace cv;
using namespace std;

const size_t width = 192;
const size_t height = 192;

std::vector<std::string> Labels;
std::unique_ptr<tflite::Interpreter> interpreter;

static bool getFileContent(std::string fileName)
{
    // Open the File
    std::ifstream in(fileName.c_str());
    // Check if object is valid
    if(!in.is_open()) return false;

    std::string str;
    // Read the next line from File untill it reaches the end.
    while (std::getline(in, str))
    {
        // Line contains string of length > 0 then save it in vector
        if(str.size()>0) Labels.push_back(str);
    }
    // Close The File
    in.close();
    return true;
    
}

void detect_from_video(Mat &src)
{
    Mat image;
    int cam_width =src.cols;
    int cam_height=src.rows;

    // copy image to input as input tensor
    cv::resize(src, image, Size(width,height));
    memcpy(interpreter->typed_input_tensor<uchar>(0), image.data, image.total() * image.elemSize());

    interpreter->SetAllowFp16PrecisionForFp32(true);
    interpreter->SetNumThreads(4);      //quad core

//        cout << "tensors size: " << interpreter->tensors_size() << "\n";
//        cout << "nodes size: " << interpreter->nodes_size() << "\n";
//        cout << "inputs: " << interpreter->inputs().size() << "\n";
//        cout << "input(0) name: " << interpreter->GetInputName(0) << "\n";
//        cout << "outputs: " << interpreter->outputs().size() << "\n";

    interpreter->Invoke();      // run your model

    const float* detection_locations = interpreter->tensor(interpreter->outputs()[0])->data.f;
    const float* detection_classes=interpreter->tensor(interpreter->outputs()[1])->data.f;
    const float* detection_scores = interpreter->tensor(interpreter->outputs()[2])->data.f;
    const int    num_detections = *interpreter->tensor(interpreter->outputs()[3])->data.f;

    //there are ALWAYS 10 detections no matter how many objects are detectable
    //cout << "number of detections: " << num_detections << "\n";

    const float confidence_threshold = 0.5;
    for(int i = 0; i < num_detections; i++){
        if(detection_scores[i] > confidence_threshold){
            int  det_index = (int)detection_classes[i]+1;
            float y1=detection_locations[4*i  ]*cam_height;
            float x1=detection_locations[4*i+1]*cam_width;
            float y2=detection_locations[4*i+2]*cam_height;
            float x2=detection_locations[4*i+3]*cam_width;

            Rect rec((int)x1, (int)y1, (int)(x2 - x1), (int)(y2 - y1));
            rectangle(src,rec, Scalar(0, 0, 255), 1, 8, 0);
            putText(src, format("%s", Labels[det_index].c_str()), Point(x1, y1-5) ,FONT_HERSHEY_SIMPLEX,0.5, Scalar(0, 0, 255), 1, 8, 0);
        }
    }
}

int main(int argc,char ** argv)
{
    float f;
    float FPS[16];
    int i;
    int Fcnt=0;
    Mat frame;
    chrono::steady_clock::time_point Tbegin, Tend;

    for(i=0;i<16;i++) FPS[i]=0.0;

    // Load model
    std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile("detect.tflite");

    // Build the interpreter
    tflite::ops::builtin::BuiltinOpResolver resolver;
    tflite::InterpreterBuilder(*model.get(), resolver)(&interpreter);

    interpreter->AllocateTensors();

    // Get the names
    bool result = getFileContent("labels.txt");
    if(!result)
    {
        cout << "loading labels failed";
        exit(-1);
    }

    // VideoCapture cap("James.mp4");
    VideoCapture cap(0);
    if (!cap.isOpened()) {
        cerr << "ERROR: Unable to open the camera" << endl;
        return 0;
    }

    cout << "Start grabbing, press ESC on Live window to terminate" << endl;

    while(1){
//        frame=imread("Traffic.jpg");  //need to refresh frame before dnn class detection
        cap >> frame;
        if (frame.empty()) {
            cerr << "End of movie" << endl;
            break;
        }

        detect_from_video(frame);

        Tend = chrono::steady_clock::now();
        //calculate frame rate
        f = chrono::duration_cast <chrono::milliseconds> (Tend - Tbegin).count();

        Tbegin = chrono::steady_clock::now();

        FPS[((Fcnt++)&0x0F)]=1000.0/f;
        for(f=0.0, i=0;i<16;i++){ f+=FPS[i]; }
        putText(frame, format("FPS %0.2f",f/16),Point(10,20),FONT_HERSHEY_SIMPLEX,0.6, Scalar(0, 0, 255));

        //show output
        imshow("RPi 4 - 2.0 GHz - 2 Mb RAM", frame);

        char esc = waitKey(5);
        if(esc == 27) break;
    }

    cout << "Closing the camera" << endl;

    // When everything done, release the video capture and write object
    cap.release();

    destroyAllWindows();
    cout << "Bye!" << endl;

    return 0;
}

总线错误的堆栈跟踪，发生在张量分配期间

Program terminated with signal SIGBUS, Bus error.
#0  0x00134dd0 in tflite::ops::builtin::broadcastto::ResizeOutputTensor(TfLiteContext*, tflite::ops::builtin::broadcastto::BroadcastToContext*) ()
(gdb) bt
#0  0x00134dd0 in tflite::ops::builtin::broadcastto::ResizeOutputTensor(TfLiteContext*, tflite::ops::builtin::broadcastto::BroadcastToContext*) ()
#1  0x00135194 in tflite::ops::builtin::broadcastto::Prepare(TfLiteContext*, TfLiteNode*) ()
#2  0x000d36c4 in tflite::Subgraph::PrepareOpsStartingAt(int, std::vector<int, std::allocator<int> > const&, int*) ()
#3  0x000d386c in tflite::Subgraph::PrepareOpsAndTensors() ()
#4  0x000d5c64 in tflite::Subgraph::AllocateTensors() ()
#5  0x0001b2cc in tflite::Interpreter::AllocateTensors() ()
#6  0x000161d8 in main(int, char**) (argc=1, argv=0x7ebd0644)
    at MobileNetV1.cpp:106

Tflite 对象检测模型针对具有 50 个图像的单个标签类型进行了训练（我希望在添加更多图像之前让模型工作） https://storage.googleapis.com/ml-data-storage-bucket/models/model-export /iod/tflite-ping_pong_ball_1-2021-08-02T19%3A46%3A09.324008Z/model.tflite

标签： google-cloud-platformraspberry-piobject-detectiontensorflow-lite