C++ TensorflowLite模型驗證的過程詳解

private void saveFile(float[] pfImageData) {
        try {
            File file = new File(Environment.getExternalStoragePublicDirectory(Environment.DIRECTORY_DOWNLOADS).getAbsolutePath() + "/tfimg");
 
            StringBuilder sb = new StringBuilder();
            for (float val : pfImageData) {
                //保留4位小數，這裡可以改為其他值
                sb.append(String.format("%.4f", val));
                sb.append("\r\n");
            }
 
            FileWriter out = new FileWriter(file);  //文件寫入流
            out.write(sb.toString());
            out.close();
        } catch (Exception e) {
            e.printStackTrace();
            Log.e("Melon", "存儲文件異常," + e.getMessage());
        }
    }

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
 
#include "tensorflow/lite/examples/label_image/label_image.h"
 
#include <fcntl.h>     // NOLINT(build/include_order)
#include <getopt.h>    // NOLINT(build/include_order)
#include <sys/time.h>  // NOLINT(build/include_order)
#include <sys/types.h> // NOLINT(build/include_order)
#include <sys/uio.h>   // NOLINT(build/include_order)
#include <unistd.h>    // NOLINT(build/include_order)
 
#include <cstdarg>
#include <cstdio>
#include <cstdlib>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <map>
#include <memory>
#include <sstream>
#include <string>
#include <unordered_set>
#include <vector>
 
#include "absl/memory/memory.h"
#include "tensorflow/lite/examples/label_image/bitmap_helpers.h"
#include "tensorflow/lite/examples/label_image/get_top_n.h"
#include "tensorflow/lite/examples/label_image/log.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/optional_debug_tools.h"
#include "tensorflow/lite/profiling/profiler.h"
#include "tensorflow/lite/string_util.h"
#include "tensorflow/lite/tools/command_line_flags.h"
#include "tensorflow/lite/tools/delegates/delegate_provider.h"
 
namespace tflite
{
  namespace label_image
  {
 
    double get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); }
 
    using TfLiteDelegatePtr = tflite::Interpreter::TfLiteDelegatePtr;
    using ProvidedDelegateList = tflite::tools::ProvidedDelegateList;
 
    class DelegateProviders
    {
    public:
      DelegateProviders() : delegate_list_util_(&params_)
      {
        delegate_list_util_.AddAllDelegateParams();
      }
 
      // Initialize delegate-related parameters from parsing command line arguments,
      // and remove the matching arguments from (*argc, argv). Returns true if all
      // recognized arg values are parsed correctly.
      bool InitFromCmdlineArgs(int *argc, const char **argv)
      {
        std::vector<tflite::Flag> flags;
        // delegate_list_util_.AppendCmdlineFlags(&flags);
 
        const bool parse_result = Flags::Parse(argc, argv, flags);
        if (!parse_result)
        {
          std::string usage = Flags::Usage(argv[0], flags);
          LOG(ERROR) << usage;
        }
        return parse_result;
      }
 
      // According to passed-in settings `s`, this function sets corresponding
      // parameters that are defined by various delegate execution providers. See
      // lite/tools/delegates/README.md for the full list of parameters defined.
      void MergeSettingsIntoParams(const Settings &s)
      {
        // Parse settings related to GPU delegate.
        // Note that GPU delegate does support OpenCL. 'gl_backend' was introduced
        // when the GPU delegate only supports OpenGL. Therefore, we consider
        // setting 'gl_backend' to true means using the GPU delegate.
        if (s.gl_backend)
        {
          if (!params_.HasParam("use_gpu"))
          {
            LOG(WARN) << "GPU deleate execution provider isn't linked or GPU "
                         "delegate isn't supported on the platform!";
          }
          else
          {
            params_.Set<bool>("use_gpu", true);
            // The parameter "gpu_inference_for_sustained_speed" isn't available for
            // iOS devices.
            if (params_.HasParam("gpu_inference_for_sustained_speed"))
            {
              params_.Set<bool>("gpu_inference_for_sustained_speed", true);
            }
            params_.Set<bool>("gpu_precision_loss_allowed", s.allow_fp16);
          }
        }
 
        // Parse settings related to NNAPI delegate.
        if (s.accel)
        {
          if (!params_.HasParam("use_nnapi"))
          {
            LOG(WARN) << "NNAPI deleate execution provider isn't linked or NNAPI "
                         "delegate isn't supported on the platform!";
          }
          else
          {
            params_.Set<bool>("use_nnapi", true);
            params_.Set<bool>("nnapi_allow_fp16", s.allow_fp16);
          }
        }
 
        // Parse settings related to Hexagon delegate.
        if (s.hexagon_delegate)
        {
          if (!params_.HasParam("use_hexagon"))
          {
            LOG(WARN) << "Hexagon deleate execution provider isn't linked or "
                         "Hexagon delegate isn't supported on the platform!";
          }
          else
          {
            params_.Set<bool>("use_hexagon", true);
            params_.Set<bool>("hexagon_profiling", s.profiling);
          }
        }
 
        // Parse settings related to XNNPACK delegate.
        if (s.xnnpack_delegate)
        {
          if (!params_.HasParam("use_xnnpack"))
          {
            LOG(WARN) << "XNNPACK deleate execution provider isn't linked or "
                         "XNNPACK delegate isn't supported on the platform!";
          }
          else
          {
            params_.Set<bool>("use_xnnpack", true);
            params_.Set<bool>("num_threads", s.number_of_threads);
          }
        }
      }
 
      // Create a list of TfLite delegates based on what have been initialized (i.e.
      // 'params_').
      std::vector<ProvidedDelegateList::ProvidedDelegate> CreateAllDelegates()
          const
      {
        return delegate_list_util_.CreateAllRankedDelegates();
      }
 
    private:
      // Contain delegate-related parameters that are initialized from command-line
      // flags.
      tflite::tools::ToolParams params_;
 
      // A helper to create TfLite delegates.
      ProvidedDelegateList delegate_list_util_;
    };
 
    // Takes a file name, and loads a list of labels from it, one per line, and
    // returns a vector of the strings. It pads with empty strings so the length
    // of the result is a multiple of 16, because our model expects that.
 
    // std::vector<uint8_t> read_file(const std::string &input_bmp_name)
    // {
    //   int begin, end;
 
    //   std::ifstream file(input_bmp_name, std::ios::in | std::ios::binary);
    //   if (!file)
    //   {
    //     LOG(FATAL) << "input file " << input_bmp_name << " not found";
    //     exit(-1);
    //   }
 
    //   begin = file.tellg();
    //   file.seekg(0, std::ios::end);
    //   end = file.tellg();
    //   size_t len = end - begin;
 
    //   LOG(INFO) << "len: " << len;
    //   std::vector<uint8_t> img_bytes(len);
 
    //   file.seekg(0, std::ios::beg);
    //   file.read(reinterpret_cast<char *>(img_bytes.data()), len);
 
    //   return img_bytes;
    // }
 
    /**
     * 讀取文件
     */
    std::vector<float> read_file(const std::string &input_bmp_name)
    {
      int begin, end;
 
      std::ifstream file(input_bmp_name, std::ios::in | std::ios::binary);
      if (!file)
      {
        LOG(FATAL) << "input file " << input_bmp_name << " not found";
        exit(-1);
      }
 
      begin = file.tellg();
      file.seekg(0, std::ios::end);
      end = file.tellg();
      size_t len = end - begin;
 
      LOG(INFO) << "len: " << len;
      std::vector<float> img_bytes;
 
      file.seekg(0, std::ios::beg);
 
      string strLine = "";
      float temp;
      while (getline(file, strLine))
      {
        temp = atof(strLine.c_str());
        img_bytes.push_back(temp);
      }
 
      LOG(INFO) << "文件讀取完成：" << input_bmp_name;
      return img_bytes;
    }
 
    /**
     * 運行推理
     */
    void RunInference(Settings *settings)
    {
      if (!settings->model_name.c_str())
      {
        LOG(ERROR) << "no model file name";
        exit(-1);
      }
 
      std::unique_ptr<tflite::FlatBufferModel> model;
      std::unique_ptr<tflite::Interpreter> interpreter;
      model = tflite::FlatBufferModel::BuildFromFile(settings->model_name.c_str());
      if (!model)
      {
        LOG(ERROR) << "Failed to mmap model " << settings->model_name;
        exit(-1);
      }
      settings->model = model.get();
      LOG(INFO) << "Loaded model " << settings->model_name;
      model->error_reporter();
      LOG(INFO) << "resolved reporter";
 
      tflite::ops::builtin::BuiltinOpResolver resolver;
 
      tflite::InterpreterBuilder(*model, resolver)(&interpreter); //生成interpreter
      if (!interpreter)
      {
        LOG(ERROR) << "Failed to construct interpreter";
        exit(-1);
      }
 
      interpreter->SetAllowFp16PrecisionForFp32(settings->allow_fp16);
 
      if (settings->verbose)
      {
        LOG(INFO) << "tensors size: " << interpreter->tensors_size();
        LOG(INFO) << "nodes size: " << interpreter->nodes_size();
        LOG(INFO) << "inputs: " << interpreter->inputs().size();
        LOG(INFO) << "input(0) name: " << interpreter->GetInputName(0);
 
        int t_size = interpreter->tensors_size();
        for (int i = 0; i < t_size; i++)
        {
          if (interpreter->tensor(i)->name)
            LOG(INFO) << i << ": " << interpreter->tensor(i)->name << ", "
                      << interpreter->tensor(i)->bytes << ", "
                      << interpreter->tensor(i)->type << ", "
                      << interpreter->tensor(i)->params.scale << ", "
                      << interpreter->tensor(i)->params.zero_point;
        }
      }
 
      if (settings->number_of_threads != -1)
      {
        interpreter->SetNumThreads(settings->number_of_threads);
      }
 
      int image_width = 128;
      int image_height = 128;
      int image_channels = 3;
      // std::vector<uint8_t> in = read_bmp(settings->input_bmp_name, &image_width, &image_height, &image_channels, settings);
      std::vector<float> file_bytes = read_file(settings->input_bmp_name);
      for (int i = 0; i < 100; i++)
      {
        //和Android的輸入做對比
        LOG(INFO) << i << ": " << file_bytes[i];
      }
 
      /* inputs()[0]得到輸入張量數組中的第一個張量，也就是classifier中唯一的那個輸入張量;
      input是個整型值，是張量列表中的引索 */
      int input = interpreter->inputs()[0];
      LOG(INFO) << "input: " << input;
 
      const std::vector<int> inputs = interpreter->inputs();
      const std::vector<int> outputs = interpreter->outputs();
 
      LOG(INFO) << "number of inputs: " << inputs.size();
      LOG(INFO) << "input index: " << inputs[0];
      LOG(INFO) << "number of outputs: " << outputs.size();
      LOG(INFO) << "outputs index1: " << outputs[0] << "，outputs index2: " << outputs[1];
 
      if (interpreter->AllocateTensors() != kTfLiteOk)
      { //加載所有tensor
        LOG(ERROR) << "Failed to allocate tensors!";
        exit(-1);
      }
 
      if (settings->verbose)
        PrintInterpreterState(interpreter.get());
 
      // 從輸入張量的原數據中得到輸入尺寸
      TfLiteIntArray *dims = interpreter->tensor(input)->dims;
      int wanted_height = dims->data[1];
      int wanted_width = dims->data[2];
      int wanted_channels = dims->data[3];
 
      settings->input_type = interpreter->tensor(input)->type;
 
      //typed_tensor返回一個經過固定數據類型轉換的tensor指針
      //以input為索引，在TfLiteTensor* content_.tensors這個張量表得到具體的張量
      //返回該張量的data.raw，它指示張量正關聯著的內存塊
      // resize<float>(interpreter->typed_tensor<float>(input), in.data(),
      //               image_height, image_width, image_channels, wanted_height,
      //               wanted_width, wanted_channels, settings);
 
      //賦值給input tensor
      float *inputP = interpreter->typed_input_tensor<float>(0);
 
      LOG(INFO) << "file_bytes size: " << file_bytes.size();
      for (int i = 0; i < file_bytes.size(); i++)
      {
        inputP[i] = file_bytes[i];
      }
 
      struct timeval start_time, stop_time;
      gettimeofday(&start_time, nullptr);
      for (int i = 0; i < settings->loop_count; i++)
      { //調用模型進行推理
        if (interpreter->Invoke() != kTfLiteOk)
        {
          LOG(ERROR) << "Failed to invoke tflite!";
          exit(-1);
        }
      }
      gettimeofday(&stop_time, nullptr);
      LOG(INFO) << "invoked";
      LOG(INFO) << "average time: "
                << (get_us(stop_time) - get_us(start_time)) /
                       (settings->loop_count * 1000)
                << " ms";
 
      const float threshold = 0.001f;
 
      int output = interpreter->outputs()[1];
      LOG(INFO) << "output: " << output;
      LOG(INFO) << "interpreter->tensors_size: " << interpreter->tensors_size();
 
      TfLiteTensor *tensor = interpreter->tensor(output);
 
      TfLiteIntArray *output_dims = tensor->dims;
      // assume output dims to be something like (1, 1, ... ,size)
      auto output_size = output_dims->data[output_dims->size - 1];
      LOG(INFO) << "索引為" << output << "的輸出張量的-"
                << "output_size: " << output_size;
 
      for (int i = 0; i < output_dims->size; i++)
      {
        LOG(INFO) << "元數據有：" << output_dims->data[i];
      }
 
      float *prediction = interpreter->typed_output_tensor<float>(1);
 
      float classificators[1][896][1];
      memcpy(classificators, prediction, 896 * 1 * sizeof(float));
      // float classificators[1][896][18];
      // memcpy(classificators, prediction, 896 * 18 * sizeof(float));
 
      //輸出分類結果
      for (float(&r)[896][1] : classificators)
      {
        for (float(&p)[1] : r)
        {
          for (float &q : p)
          {
            std::cout << q << ' ';
          }
          std::cout << std::endl;
        }
        std::cout << std::endl;
      }
    }
 
    int Main(int argc, char **argv)
    {
      DelegateProviders delegate_providers;
      bool parse_result = delegate_providers.InitFromCmdlineArgs(
          &argc, const_cast<const char **>(argv));
      if (!parse_result)
      {
        return EXIT_FAILURE;
      }
 
      Settings s;
 
      int c;
      while (true)
      {
        static struct option long_options[] = {
            {"accelerated", required_argument, nullptr, 'a'},
            {"allow_fp16", required_argument, nullptr, 'f'},
            {"count", required_argument, nullptr, 'c'},
            {"verbose", required_argument, nullptr, 'v'},
            {"image", required_argument, nullptr, 'i'},
            {"labels", required_argument, nullptr, 'l'},
            {"tflite_model", required_argument, nullptr, 'm'},
            {"profiling", required_argument, nullptr, 'p'},
            {"threads", required_argument, nullptr, 't'},
            {"input_mean", required_argument, nullptr, 'b'},
            {"input_std", required_argument, nullptr, 's'},
            {"num_results", required_argument, nullptr, 'r'},
            {"max_profiling_buffer_entries", required_argument, nullptr, 'e'},
            {"warmup_runs", required_argument, nullptr, 'w'},
            {"gl_backend", required_argument, nullptr, 'g'},
            {"hexagon_delegate", required_argument, nullptr, 'j'},
            {"xnnpack_delegate", required_argument, nullptr, 'x'},
            {nullptr, 0, nullptr, 0}};
 
        /* getopt_long stores the option index here. */
        int option_index = 0;
 
        c = getopt_long(argc, argv,
                        "a:b:c:d:e:f:g:i:j:l:m:p:r:s:t:v:w:x:", long_options,
                        &option_index);
 
        /* Detect the end of the options. */
        if (c == -1)
          break;
 
        switch (c)
        {
        case 'a':
          s.accel = strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'b':
          s.input_mean = strtod(optarg, nullptr);
          break;
        case 'c':
          s.loop_count =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'e':
          s.max_profiling_buffer_entries =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'f':
          s.allow_fp16 =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'g':
          s.gl_backend =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'i':
          s.input_bmp_name = optarg;
          break;
        case 'j':
          s.hexagon_delegate = optarg;
          break;
        case 'l':
          s.labels_file_name = optarg;
          break;
        case 'm':
          s.model_name = optarg;
          break;
        case 'p':
          s.profiling =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'r':
          s.number_of_results =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 's':
          s.input_std = strtod(optarg, nullptr);
          break;
        case 't':
          s.number_of_threads = strtol( // NOLINT(runtime/deprecated_fn)
              optarg, nullptr, 10);
          break;
        case 'v':
          s.verbose =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'w':
          s.number_of_warmup_runs =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'x':
          s.xnnpack_delegate =
              strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn)
          break;
        case 'h':
        case '?':
          /* getopt_long already printed an error message. */
          exit(-1);
        default:
          exit(-1);
        }
      }
 
      delegate_providers.MergeSettingsIntoParams(s);
      RunInference(&s);
      return 0;
    }
 
  } // namespace label_image
} // namespace tflite
 
int main(int argc, char **argv)
{
  return tflite::label_image::Main(argc, argv);
}