Вывод Mxnet c ++ с ошибкой сегментации MXPredSetInput

Question

Вывод Mxnet c ++ с ошибкой сегментации MXPredSetInput

1.фон

Я пробовал https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/predict-cpp успешно.Но когда я пытался развернуть mxnet в c ++ с моей собственной моделью, я столкнулся с ошибкой сегментации:

[17:33:07] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v1.2.1. Attempting to upgrade...
Signal: SIGSEGV (Segmentation fault)

2.код с ошибкой:

  MXPredSetInput(pred_hnd, "data", image_data.data(), static_cast<mx_uint>(image_size));

3.Подсказки

Сначала я подумал, что это из-за того, что форма входных данных не совместима с входным слоем модели. Но я спрашиваю дизайнера моделей, что это модель реснета только с conv, поэтому любая форма ввода должна быть в порядке.

4.Загрузить модель:

Загрузите их и поместите в каталог моделей.https://drive.google.com/drive/folders/16MEKNOz_iwquVxHMk9c7igmBNuT6w7wz?usp=sharing

4.код: найти: https://github.com/jaysimon/mxnet_cpp_infere

#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <vector>
#include <memory>
#include <thread>
#include <iomanip>
#include <opencv2/opencv.hpp>
// Path for c_predict_api
#include <mxnet/c_predict_api.h>

const mx_float DEFAULT_MEAN = 117.0;

static std::string trim(const std::string& input) {
  auto not_space = [](int ch) {
    return !std::isspace(ch);
  };
  auto output = input;
  output.erase(output.begin(), std::find_if(output.begin(), output.end(), not_space));
  output.erase(std::find_if(output.rbegin(), output.rend(), not_space).base(), output.end());
  return output;
}

// Read file to buffer
class BufferFile {
public :
  std::string file_path_;
  std::size_t length_ = 0;
  std::unique_ptr<char[]> buffer_;

  explicit BufferFile(const std::string& file_path)
      : file_path_(file_path) {

    std::ifstream ifs(file_path.c_str(), std::ios::in | std::ios::binary);
    if (!ifs) {
      std::cerr << "Can't open the file. Please check " << file_path << ". \n";
      return;
    }

    ifs.seekg(0, std::ios::end);
    length_ = static_cast<std::size_t>(ifs.tellg());
    ifs.seekg(0, std::ios::beg);
    std::cout << file_path.c_str() << " ... " << length_ << " bytes\n";

    // Buffer as null terminated to be converted to string
    buffer_.reset(new char[length_ + 1]);
    buffer_[length_] = 0;
    ifs.read(buffer_.get(), length_);
    ifs.close();
  }

  std::size_t GetLength() {
    return length_;
  }

  char* GetBuffer() {
    return buffer_.get();
  }
};

void GetImageFile(const std::string& image_file,
                  mx_float* image_data, int channels,
                  cv::Size resize_size, const mx_float* mean_data = nullptr) {
  // Read all kinds of file into a BGR color 3 channels image
  cv::Mat im_ori = cv::imread(image_file, cv::IMREAD_COLOR);

  if (im_ori.empty()) {
    std::cerr << "Can't open the image. Please check " << image_file << ". \n";
    assert(false);
  }

  cv::Mat im;

  resize(im_ori, im, resize_size);

  int size = im.rows * im.cols * channels;

  mx_float* ptr_image_r = image_data;
  mx_float* ptr_image_g = image_data + size / 3;
  mx_float* ptr_image_b = image_data + size / 3 * 2;

  float mean_b, mean_g, mean_r;
  mean_b = mean_g = mean_r = DEFAULT_MEAN;
  mean_b = 103.06;
  mean_g = 115.9;
  mean_r = 123.15;

  for (int i = 0; i < im.rows; i++) {
    auto data = im.ptr<uchar>(i);

    for (int j = 0; j < im.cols; j++) {
      if (channels > 1) {
        *ptr_image_b++ = static_cast<mx_float>(*data++) - mean_b;
        *ptr_image_g++ = static_cast<mx_float>(*data++) - mean_g;
      }

      *ptr_image_r++ = static_cast<mx_float>(*data++) - mean_r;
    }
  }
}

// LoadSynsets
// Code from : https://github.com/pertusa/mxnet_predict_cc/blob/master/mxnet_predict.cc
std::vector<std::string> LoadSynset(const std::string& synset_file) {
  std::ifstream fi(synset_file.c_str());

  if (!fi.is_open()) {
    std::cerr << "Error opening synset file " << synset_file << std::endl;
    assert(false);
  }

  std::vector<std::string> output;

  std::string synset, lemma;
  while (fi >> synset) {
    getline(fi, lemma);
    output.push_back(lemma);
  }

  fi.close();

  return output;
}

void PrintOutputResult(const std::vector<float>& data, const std::vector<std::string>& synset) {
  if (data.size() != synset.size()) {
    std::cerr << "Result data and synset size do not match!" << std::endl;
  }

  float best_accuracy = 0.0;
  std::size_t best_idx = 0;

  for (std::size_t i = 0; i < data.size(); ++i) {
    std::cout << "Accuracy[" << i << "] = " << std::setprecision(8) << data[i] << std::endl;

    if (data[i] > best_accuracy) {
      best_accuracy = data[i];
      best_idx = i;
    }
  }

  std::cout << "Best Result: " << trim(synset[best_idx]) << " (id=" << best_idx << ", " <<
            "accuracy=" << std::setprecision(8) << best_accuracy << ")" << std::endl;
}

void predict(PredictorHandle pred_hnd, const std::vector<mx_float> &image_data,
             NDListHandle nd_hnd, const std::string &synset_file, int i) {
  auto image_size = image_data.size();
  // Set Input 
//>>>>>>>>>>>>>>>>>>>> Problem code <<<<<<<<<<<<<<<<<<<<<<<
  MXPredSetInput(pred_hnd, "data", image_data.data(), static_cast<mx_uint>(image_size));
// <<<<<<<<<<<<<<<<<<<<<<< Problem code <<<<<<<<<<<<<<<<<<<<<<<
  // Do Predict Forward
  MXPredForward(pred_hnd);

  mx_uint output_index = 0;

  mx_uint* shape = nullptr;
  mx_uint shape_len;

  // Get Output Result
  MXPredGetOutputShape(pred_hnd, output_index, &shape, &shape_len);

  std::size_t size = 1;
  for (mx_uint i = 0; i < shape_len; ++i) { size *= shape[i]; }

  std::vector<float> data(size);

  MXPredGetOutput(pred_hnd, output_index, &(data[0]), static_cast<mx_uint>(size));

  // Release NDList
  if (nd_hnd) {
    MXNDListFree(nd_hnd);
  }

  // Release Predictor
  MXPredFree(pred_hnd);

  // Synset path for your model, you have to modify it
  auto synset = LoadSynset(synset_file);

  // Print Output Data
  PrintOutputResult(data, synset);
}

int main(int argc, char* argv[]) {
  if (argc < 2) {
    std::cout << "No test image here." << std::endl
              << "Usage: ./image-classification-predict apple.jpg [num_threads]" << std::endl;
    return EXIT_FAILURE;
  }

  std::string test_file(argv[1]);
  int num_threads = 1;
  if (argc == 3)
    num_threads = std::atoi(argv[2]);

  // Models path for your model, you have to modify it
  std::string json_file = "../model/rfcn_dcn_chicken-0000.json";
  std::string param_file = "../model/rfcn_dcn_chicken-0000.params";
  std::string synset_file = "../model/synset.txt";
  std::string nd_file = "../model/mean_224.nd";

  BufferFile json_data(json_file);
  BufferFile param_data(param_file);

  // Parameters
  int dev_type = 1;  // 1: cpu, 2: gpu
  int dev_id = 0;  // arbitrary.
  mx_uint num_input_nodes = 1;  // 1 for feedforward
  const char* input_key[1] = { "data" };
  const char** input_keys = input_key;

  // Image size and channels
  int width = 1000;
  int height = 562;
  int channels = 3;

  const mx_uint input_shape_indptr[2] = { 0, 4 };
  const mx_uint input_shape_data[4] = { 1,
                                        static_cast<mx_uint>(channels),
                                        static_cast<mx_uint>(height),
                                        static_cast<mx_uint>(width) };

  if (json_data.GetLength() == 0 || param_data.GetLength() == 0) {
    return EXIT_FAILURE;
  }

  auto image_size = static_cast<std::size_t>(width * height * channels);

  // Read Mean Data
  const mx_float* nd_data = nullptr;
  NDListHandle nd_hnd = nullptr;
  BufferFile nd_buf(nd_file);

  if (nd_buf.GetLength() > 0) {
    mx_uint nd_index = 0;
    mx_uint nd_len;
    const mx_uint* nd_shape = nullptr;
    const char* nd_key = nullptr;
    mx_uint nd_ndim = 0;

    MXNDListCreate(static_cast<const char*>(nd_buf.GetBuffer()),
                   static_cast<int>(nd_buf.GetLength()),
                   &nd_hnd, &nd_len);

    MXNDListGet(nd_hnd, nd_index, &nd_key, &nd_data, &nd_shape, &nd_ndim);
  }

  // Read Image Data
  std::vector<mx_float> image_data(image_size);

  GetImageFile(test_file, image_data.data(), channels, cv::Size(width, height), nd_data);

  if (num_threads == 1) {
    // Create Predictor
    PredictorHandle pred_hnd;
    MXPredCreate(static_cast<const char*>(json_data.GetBuffer()),
                 static_cast<const char*>(param_data.GetBuffer()),
                 static_cast<int>(param_data.GetLength()),
                 dev_type,
                 dev_id,
                 num_input_nodes,
                 input_keys,
                 input_shape_indptr,
                 input_shape_data,
                 &pred_hnd);
    assert(pred_hnd);

    predict(pred_hnd, image_data, nd_hnd, synset_file, 0);
  } else {
    // Create Predictor
    std::vector<PredictorHandle> pred_hnds(num_threads, nullptr);
    MXPredCreateMultiThread(static_cast<const char*>(json_data.GetBuffer()),
                            static_cast<const char*>(param_data.GetBuffer()),
                            static_cast<int>(param_data.GetLength()),
                            dev_type,
                            dev_id,
                            num_input_nodes,
                            input_keys,
                            input_shape_indptr,
                            input_shape_data,
                            pred_hnds.size(),
                            pred_hnds.data());
    for (auto hnd : pred_hnds)
      assert(hnd);

    std::vector<std::thread> threads;
    for (int i = 0; i < num_threads; i++)
      threads.emplace_back(predict, pred_hnds[i], image_data, nd_hnd, synset_file, i);
    for (int i = 0; i < num_threads; i++)
      threads[i].join();
  }
  printf("run successfully\n");

  return EXIT_SUCCESS;
}