使用Tensorflow搭建回归预测模型之八：模型与外部接口对接

# Description:
# TensorFlow Lite A/C of Traffic Assist.

package(default_visibility = ["//visibility:public"])

licenses(["notice"])  # Apache 2.0

load("//tensorflow:tensorflow.bzl", "tf_cc_binary")
load("//tensorflow/contrib/lite:build_def.bzl", "tflite_linkopts")

exports_files(glob([
    "testdata/*.txt",
]))


tf_cc_binary(
    name = "ta_ac",
    srcs = [
        "get_ac_settings.h",
        "get_ac_settings_impl.h",
        "ta_ac.cc",
        "arm_caller.cc",
        "ta_ac.h",
    ],
    linkopts = tflite_linkopts() + select({
        "//tensorflow:android": [
            "-pie",  # Android 5.0 and later supports only PIE
            "-lm",  # some builtin ops, e.g., tanh, need -lm
        ],
        "//conditions:default": [],
    }),
    deps = [
        ":data_helpers",
        "//tensorflow/contrib/lite:framework",
        "//tensorflow/contrib/lite:string_util",
        "//tensorflow/contrib/lite/kernels:builtin_ops",
    ],
)

cc_library(
    name = "data_helpers",
    srcs = ["data_helpers.cc"],
    hdrs = [
        "data_helpers.h",
        "data_helpers_impl.h",
        "ta_ac.h",
    ],
    deps = [
        "//tensorflow/contrib/lite:builtin_op_data",
        "//tensorflow/contrib/lite:framework",
        "//tensorflow/contrib/lite:schema_fbs_version",
        "//tensorflow/contrib/lite:string",
        "//tensorflow/contrib/lite:string_util",
        "//tensorflow/contrib/lite/kernels:builtin_ops",
        "//tensorflow/contrib/lite/schema:schema_fbs",
    ],
)

cc_test(
    name = "ta_ac_test",
    srcs = [
        "get_ac_settings.h",
        "get_ac_settings_impl.h",
        "ta_ac_test.cc",
    ],
    data = [
        "testdata/ac_data_input.txt",
    ],
    tags = ["no_oss"],
    deps = [
        ":data_helpers",
        "@com_google_googletest//:gtest",
    ],
)

/* 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.
==============================================================================*/

#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_TA_AC_TA_AC_H
#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_TA_AC_TA_AC_H

#include "tensorflow/contrib/lite/string.h"

namespace tflite {
namespace ta_ac {

struct Settings {
  bool verbose = false;
  bool accel = false;
  bool input_floating = false;
  bool profiling = false;
  int loop_count = 1;
  float input_mean = 127.5f;
  float input_std = 127.5f;
  string model_name = "./model_ac.tflite";
  string input_data_name = "./ac_data_input.txt";
  string labels_file_name = "./ac_labels.txt";
  string input_layer_type = "uint8_t";
  int number_of_threads = 4;
};


struct ac_settings {
    float temp =0.0;
    int direct =0;
    int power = 0;
};


extern ac_settings RunInference(Settings* s,std::vector<float> ac_input,int data_width,int data_height,int data_channels);
extern Settings getopt(int argc, char** argv);

} // namespace ta_ac
}  // namespace tflite



#endif  // TENSORFLOW_CONTRIB_LITE_EXAMPLES_TA_AC_TA_AC_H

/* 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 <cstdarg>
#include <cstdio>
#include <cstdlib>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <memory>
#include <sstream>
#include <string>
#include <unordered_set>
#include <vector>

#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 "tensorflow/contrib/lite/kernels/register.h"
#include "tensorflow/contrib/lite/model.h"
#include "tensorflow/contrib/lite/optional_debug_tools.h"
#include "tensorflow/contrib/lite/string_util.h"

#include "tensorflow/contrib/lite/examples/ta_ac/data_helpers.h"
#include "tensorflow/contrib/lite/examples/ta_ac/data_helpers_impl.h"
#include "tensorflow/contrib/lite/examples/ta_ac/get_ac_settings.h"

#define LOG(x) std::cerr

namespace tflite {
namespace ta_ac {

double get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); }

// 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.
TfLiteStatus ReadLabelsFile(const string& file_name,
                            std::vector<string>* result,
                            size_t* found_label_count) {
  std::ifstream file(file_name);
  if (!file) {
    LOG(FATAL) << "Labels file " << file_name << " not found\n";
    return kTfLiteError;
  }
  result->clear();
  string line;
  while (std::getline(file, line)) {
    result->push_back(line);
  }
  *found_label_count = result->size();
  const int padding = 16;
  while (result->size() % padding) {
    result->emplace_back();
  }
  return kTfLiteOk;
}

void PrintProfilingInfo(const profiling::ProfileEvent* e, uint32_t op_index,
                        TfLiteRegistration registration) {
  // output something like
  // time (ms) , Node xxx, OpCode xxx, symblic name
  //      5.352, Node   5, OpCode   4, DEPTHWISE_CONV_2D

  LOG(INFO) << std::fixed << std::setw(10) << std::setprecision(3)
            << (e->end_timestamp_us - e->begin_timestamp_us) / 1000.0
            << ", Node " << std::setw(3) << std::setprecision(3) << op_index
            << ", OpCode " << std::setw(3) << std::setprecision(3)
            << registration.builtin_code << ", "
            << EnumNameBuiltinOperator(
                   static_cast<BuiltinOperator>(registration.builtin_code))
            << "\n";
}




ac_settings RunInference(Settings* s,std::vector<float> ac_input,int data_width,int data_height,int data_channels) {
    ac_settings ac;
  if (!s->model_name.c_str()) {
    LOG(ERROR) << "no model file name\n";
    exit(-1);
  }

  std::unique_ptr<tflite::FlatBufferModel> model;
  std::unique_ptr<tflite::Interpreter> interpreter;
  model = tflite::FlatBufferModel::BuildFromFile(s->model_name.c_str());
  if (!model) {
    LOG(FATAL) << "\nFailed to mmap model " << s->model_name << "\n";
    exit(-1);
  }
  LOG(INFO) << "Loaded tensorflow lite model " << s->model_name << "\n";
  model->error_reporter();
  //LOG(INFO) << "resolved reporter\n";

  tflite::ops::builtin::BuiltinOpResolver resolver;

  tflite::InterpreterBuilder(*model, resolver)(&interpreter);
  if (!interpreter) {
    LOG(FATAL) << "Failed to construct interpreter\n";
    exit(-1);
  }
  
  interpreter->UseNNAPI(s->accel);

  if (s->verbose) {
    LOG(INFO) << "tensors size: " << interpreter->tensors_size() << "\n";
    LOG(INFO) << "nodes size: " << interpreter->nodes_size() << "\n";
    LOG(INFO) << "inputs: " << interpreter->inputs().size() << "\n";
    LOG(INFO) << "input(0) name: " << interpreter->GetInputName(0) << "\n";

    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 << "\n";
    }
  }

  if (s->number_of_threads != -1) {
    interpreter->SetNumThreads(s->number_of_threads);
  }

  int input = interpreter->inputs()[0];

  if (s->verbose) 
  {
      LOG(INFO) << "input: " << input << "\n";
  }

  const std::vector<int> inputs = interpreter->inputs();
  const std::vector<int> outputs = interpreter->outputs();

  if (s->verbose) 
  {
    LOG(INFO) << "number of inputs: " << inputs.size() << "\n";;
    LOG(INFO) << "number of outputs: " << outputs.size() << "\n";
  }

  if (interpreter->AllocateTensors() != kTfLiteOk)
  {
    LOG(FATAL) << "Failed to allocate tensors!";
  }

  if (s->verbose) 
  {
      PrintInterpreterState(interpreter.get());
  }

  //LOG(INFO) << "interpreter->tensor(input)->type: " << interpreter->tensor(input)->type << "\n";
  switch (interpreter->tensor(input)->type) {
    case kTfLiteFloat32:
      s->input_floating = true;
      setdata<float>(interpreter->typed_tensor<float>(input), ac_input.data(), data_height, data_width, data_channels, s);
      break;
    case kTfLiteUInt8:
      setdata<uint8_t>(interpreter->typed_tensor<uint8_t>(input), ac_input.data(), data_height, data_width, data_channels, s);
      break;
    default:
      LOG(FATAL) << "cannot handle input type " << interpreter->tensor(input)->type << " yet";
      exit(-1);
  }


  profiling::Profiler* profiler = new profiling::Profiler();
  interpreter->SetProfiler(profiler);

  if (s->profiling) profiler->StartProfiling();

  struct timeval start_time, stop_time;
  gettimeofday(&start_time, nullptr);
  for (int i = 0; i < s->loop_count; i++) {
    if (interpreter->Invoke() != kTfLiteOk) {
      LOG(FATAL) << "Failed to invoke tflite!\n";
    }
  }
  gettimeofday(&stop_time, nullptr);
  LOG(INFO) << "invoked \n";
  LOG(INFO) << "inference time: "
            << (get_us(stop_time) - get_us(start_time)) / (s->loop_count * 1000)
            << " ms \n";

  if (s->profiling) {
    profiler->StopProfiling();
    auto profile_events = profiler->GetProfileEvents();
    for (int i = 0; i < profile_events.size(); i++) {
      auto op_index = profile_events[i]->event_metadata;
      const auto node_and_registration =
          interpreter->node_and_registration(op_index);
      const TfLiteRegistration registration = node_and_registration->second;
      PrintProfilingInfo(profile_events[i], op_index, registration);
    }
  }

  int output = interpreter->outputs()[0];
  //LOG(INFO) << "RunInference interpreter->tensor(output)->type: " << interpreter->tensor(output)->type << "\n";

  float temp = interpreter->typed_output_tensor<float>(0)[0];
  float direct = interpreter->typed_output_tensor<float>(1)[0];
  float power = interpreter->typed_output_tensor<float>(2)[0];

  //LOG(INFO) << "RunInference temp: " << temp << "\n";
  //LOG(INFO) << "RunInference direct: " << direct << "\n";
  //LOG(INFO) << "RunInference power: " << power << "\n";

  LOG(INFO) << "temp:" << int(temp) << " C\n";
  LOG(INFO) << "direct:" << int(direct) << " direction(0:head,1:body,2:leg)\n";
  LOG(INFO) << "power:" << int(power) << " power(0:auto,9:large)\n";
    
    ac.temp = temp;
    ac.direct = direct;
    ac.power = power;
  LOG(INFO) << "RunInference ac.temp: " << ac.temp << "\n";
  LOG(INFO) << "RunInference ac.direct: " << ac.direct << "\n";
  LOG(INFO) << "RunInference ac.power: " << ac.power << "\n";

    return ac;
}



void display_usage() {
  LOG(INFO) << "ta_ac\n"
            << "--accelerated, -a: [0|1], use Android NNAPI or not\n"
            << "--count, -c: loop interpreter->Invoke() for certain times\n"
            << "--input_mean, -b: input mean\n"
            << "--input_std, -s: input standard deviation\n"
            << "--data, -d: data_name.txt\n"
            << "--labels, -l: labels for the model\n"
            << "--tflite_model, -m: model_name.tflite\n"
            << "--profiling, -p: [0|1], profiling or not\n"
            << "--threads, -t: number of threads\n"
            << "--verbose, -v: [0|1] print more information\n"
            << "\n";
}

Settings getopt(int argc, char** argv)
{
        Settings s;
      int c;
        while (1) {
    static struct option long_options[] = {
        {"accelerated", required_argument, nullptr, ‘a‘},
        {"count", required_argument, nullptr, ‘c‘},
        {"verbose", required_argument, nullptr, ‘v‘},
        {"data", required_argument, nullptr, ‘d‘},
        {"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‘},
        {nullptr, 0, nullptr, 0}};

    /* getopt_long stores the option index here. */
    int option_index = 0;

    c = getopt_long(argc, argv, "a:b:c:d:f:l:m:p:s:t:v:", 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 ‘d‘:
        s.input_data_name = 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 ‘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 ‘h‘:
      case ‘?‘:
        /* getopt_long already printed an error message. */
        display_usage();
        exit(-1);
      default:
        exit(-1);
    }
  }
    return s;
}

#if 0
int Main(int argc, char** argv) {
  Settings s;
    ac_settings ac;
    s = getopt(argc, argv);

//input data
  int data_width = 4;
  int data_height = 3;
  int data_channels = 1;

  std::vector<float> ac_in(data_height * data_width * data_channels);
// test code {2018,11,16,14.88,31.21549,121.30741,15.18,31.20742,121.44468,14.5,14.4,14});
  ac_in[0] = 2018;
  ac_in[1] = 11;
  ac_in[2] = 16;
  ac_in[3] = 14.88f;
  ac_in[4] = 31.21549f;
  ac_in[5] = 121.30741f;
  ac_in[6] = 15.18;
  ac_in[7] = 31.20742;
  ac_in[8] = 121.44468;
  ac_in[9] = 14.5f;
  ac_in[10] = 14.4f;
  ac_in[11] = 14;

//inference
  ac = RunInference(&s,ac_in,data_width,data_height,data_channels);

//output data
  LOG(INFO) << "Main ac.temp: " << ac.temp << "\n";
  LOG(INFO) << "Main ac.direct: " << ac.direct << "\n";
  LOG(INFO) << "Main ac.power: " << ac.power << "\n";
  return 0;
}
#endif

}  // namespace ta_ac
}  // namespace tflite

#if 0
int main(int argc, char** argv) { 
  printf("-----------------------\n");
  printf("-         ta_ac      --\n");  
  printf("-     tflite   ok!   --\n");
  printf("-----------------------\n");    
  return tflite::ta_ac::Main(argc, argv);  
}

#endif

/* 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 <cstdarg>
#include <cstdio>
#include <cstdlib>
#include <iomanip>
#include <string>
#include <vector>
#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 "tensorflow/contrib/lite/examples/ta_ac/ta_ac.h"



int main(int argc, char** argv) {
  printf("-----------------------\n");
  printf("-         ta_ac      --\n");  
  printf("-     tflite   ok!   --\n");
  printf("-----------------------\n");
  tflite::ta_ac::Settings s;
    tflite::ta_ac::ac_settings ac;
    s = tflite::ta_ac::getopt(argc, argv);

//input data
  int data_width = 4;
  int data_height = 3;
  int data_channels = 1;

  std::vector<float> ac_in(data_height * data_width * data_channels);
// test code {2018,11,16,14.88,31.21549,121.30741,15.18,31.20742,121.44468,14.5,14.4,14});
  ac_in[0] = 2018;
  ac_in[1] = 11;
  ac_in[2] = 16;
  ac_in[3] = 14.88f;
  ac_in[4] = 31.21549f;
  ac_in[5] = 121.30741f;
  ac_in[6] = 15.18;
  ac_in[7] = 31.20742;
  ac_in[8] = 121.44468;
  ac_in[9] = 14.5f;
  ac_in[10] = 14.4f;
  ac_in[11] = 14;

//inference
  ac = tflite::ta_ac::RunInference(&s,ac_in,data_width,data_height,data_channels);

//output data
  printf("arm_caller ac.temp:%d\n", int(ac.temp));
  printf("arm_caller ac.direct:%d\n", ac.direct);
  printf("arm_caller ac.power:%d\n", ac.power);

  return 0;
}