TensorFlow Lite 开发手册（5）——TensorFlow Lite模型使用实例（分类模型）

  • （一）新建CLion工程
  • （二）编写Cmakelist
  • （三）编写main.cpp
  • （四）下载预训练模型
  • （五）修改模型配置
  • （六）运行实例

（一）新建CLion工程

到（https://download.csdn.net/download/weixin_42499236/11892106）下载该工程，解压后如下图所示：

（二）编写Cmakelist

cmake_minimum_required(VERSION 3.15)
project(testlite)set(CMAKE_CXX_STANDARD 14)include_directories(/home/ai/CLionProjects/tensorflow-master/)
include_directories(/home/ai/CLionProjects/tensorflow-master/tensorflow/lite/tools/make/downloads/flatbuffers/include)
include_directories(/home/ai/CLionProjects/tensorflow-master/tensorflow/lite/tools/make/downloads/absl)add_executable(testlite main.cpp bitmap_helpers.cc utils.cc)target_link_libraries(testlite /home/ai/CLionProjects/tensorflow-master/tensorflow/lite/tools/make/gen/linux_x86_64/lib/libtensorflow-lite.a -lpthread -ldl -lrt)

（三）编写main.cpp

  • 导入头文件
#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 <iostream>
#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 "bitmap_helpers.h"
#include "get_top_n.h"#include "tensorflow/lite/model.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/optional_debug_tools.h"
#include "tensorflow/lite/string_util.h"
#include "tensorflow/lite/profiling/profiler.h"
#include "tensorflow/lite/delegates/nnapi/nnapi_delegate.h"
#include "absl/memory/memory.h"
#include "utils.h"using namespace std;
  • 调用GPU、NNAPI加速（若无GPU，则默认使用CPU）
#define LOG(x) std::cerrdouble get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); }using TfLiteDelegatePtr = tflite::Interpreter::TfLiteDelegatePtr;
using TfLiteDelegatePtrMap = std::map<std::string, TfLiteDelegatePtr>;// 调用GPU
TfLiteDelegatePtr CreateGPUDelegate(tflite::label_image::Settings* s) {#if defined(__ANDROID__)TfLiteGpuDelegateOptionsV2 gpu_opts = TfLiteGpuDelegateOptionsV2Default();gpu_opts.inference_preference =TFLITE_GPU_INFERENCE_PREFERENCE_SUSTAINED_SPEED;gpu_opts.is_precision_loss_allowed = s->allow_fp16 ? 1 : 0;return evaluation::CreateGPUDelegate(s->model, &gpu_opts);
#elsereturn tflite::evaluation::CreateGPUDelegate(s->model);
#endif
}TfLiteDelegatePtrMap GetDelegates(tflite::label_image::Settings* s) {TfLiteDelegatePtrMap delegates;if (s->gl_backend) {auto delegate = CreateGPUDelegate(s);if (!delegate) {LOG(INFO) << "GPU acceleration is unsupported on this platform.";} else {delegates.emplace("GPU", std::move(delegate));}}if (s->accel) {auto delegate = tflite::evaluation::CreateNNAPIDelegate();if (!delegate) {LOG(INFO) << "NNAPI acceleration is unsupported on this platform.";} else {delegates.emplace("NNAPI", tflite::evaluation::CreateNNAPIDelegate());}}return delegates;
}
  • 读取标签文件
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;
}
  • 打印模型节点信息
void PrintProfilingInfo(const tflite::profiling::ProfileEvent* e,uint32_t subgraph_index, 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_2DLOG(INFO) << std::fixed << std::setw(10) << std::setprecision(3)<< (e->end_timestamp_us - e->begin_timestamp_us) / 1000.0<< ", Subgraph " << std::setw(3) << std::setprecision(3)<< subgraph_index << ", Node " << std::setw(3)<< std::setprecision(3) << op_index << ", OpCode " << std::setw(3)<< std::setprecision(3) << registration.builtin_code << ", "<< EnumNameBuiltinOperator(static_cast<tflite::BuiltinOperator>(registration.builtin_code))<< "\n";
}
  • 定义模型推理函数
void RunInference(tflite::label_image::Settings* s){if (!s->model_name.c_str()) {LOG(ERROR) << "no model file name\n";exit(-1);}// 读取.tflite模型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);}s->model = model.get();LOG(INFO) << "Loaded model " << s->model_name << "\n";model->error_reporter();LOG(INFO) << "resolved reporter\n";
// 生成解释器tflite::ops::builtin::BuiltinOpResolver resolver;tflite::InterpreterBuilder(*model, resolver)(&interpreter);if (!interpreter) {LOG(FATAL) << "Failed to construct interpreter\n";exit(-1);}interpreter->UseNNAPI(s->old_accel);interpreter->SetAllowFp16PrecisionForFp32(s->allow_fp16);
// 打印解释器参数，包括张量大小、输入节点名称等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);}// 定义输入图像参数int image_width = 224;int image_height = 224;int image_channels = 3;
// 读取bmp图像std::vector<uint8_t> in = tflite::label_image::read_bmp(s->input_bmp_name, &image_width,&image_height, &image_channels, s);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";}// 创建图auto delegates_ = GetDelegates(s);for (const auto& delegate : delegates_) {if (interpreter->ModifyGraphWithDelegate(delegate.second.get()) !=kTfLiteOk) {LOG(FATAL) << "Failed to apply " << delegate.first << " delegate.";} else {LOG(INFO) << "Applied " << delegate.first << " delegate.";}}if (interpreter->AllocateTensors() != kTfLiteOk) {LOG(FATAL) << "Failed to allocate tensors!";}if (s->verbose) PrintInterpreterState(interpreter.get());// 获取输入张量元数据的维度等信息TfLiteIntArray* dims = interpreter->tensor(input)->dims;int wanted_height = dims->data[1];int wanted_width = dims->data[2];int wanted_channels = dims->data[3];// 对图像进行resizeswitch (interpreter->tensor(input)->type) {case kTfLiteFloat32:s->input_floating = true;tflite::label_image::resize<float>(interpreter->typed_tensor<float>(input), in.data(),image_height, image_width, image_channels, wanted_height,wanted_width, wanted_channels, s);break;case kTfLiteUInt8:tflite::label_image::resize<uint8_t>(interpreter->typed_tensor<uint8_t>(input), in.data(),image_height, image_width, image_channels, wanted_height,wanted_width, wanted_channels, s);break;default:LOG(FATAL) << "cannot handle input type "<< interpreter->tensor(input)->type << " yet";exit(-1);}// 调用解释器auto profiler =absl::make_unique<tflite::profiling::Profiler>(s->max_profiling_buffer_entries);interpreter->SetProfiler(profiler.get());if (s->profiling) profiler->StartProfiling();if (s->loop_count > 1)for (int i = 0; i < s->number_of_warmup_runs; i++) {if (interpreter->Invoke() != kTfLiteOk) {LOG(FATAL) << "Failed to invoke tflite!\n";}}
// 进行模型推理并计算运行时间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) << "average time: "<< (get_us(stop_time) - get_us(start_time)) / (s->loop_count * 1000)<< " ms \n";
// 打印运行事件if (s->profiling) {profiler->StopProfiling();auto profile_events = profiler->GetProfileEvents();for (int i = 0; i < profile_events.size(); i++) {auto subgraph_index = profile_events[i]->event_subgraph_index;auto op_index = profile_events[i]->event_metadata;const auto subgraph = interpreter->subgraph(subgraph_index);const auto node_and_registration =subgraph->node_and_registration(op_index);const TfLiteRegistration registration = node_and_registration->second;PrintProfilingInfo(profile_events[i], subgraph_index, op_index,registration);}}const float threshold = 0.001f;std::vector<std::pair<float, int>> top_results;// 获取Top-N结果int output = interpreter->outputs()[0];TfLiteIntArray* output_dims = interpreter->tensor(output)->dims;// assume output dims to be something like (1, 1, ... ,size)auto output_size = output_dims->data[output_dims->size - 1];switch (interpreter->tensor(output)->type) {case kTfLiteFloat32:tflite::label_image::get_top_n<float>(interpreter->typed_output_tensor<float>(0), output_size,s->number_of_results, threshold, &top_results, true);break;case kTfLiteUInt8:tflite::label_image::get_top_n<uint8_t>(interpreter->typed_output_tensor<uint8_t>(0),output_size, s->number_of_results, threshold,&top_results, false);break;default:LOG(FATAL) << "cannot handle output type "<< interpreter->tensor(input)->type << " yet";exit(-1);}std::vector<string> labels;size_t label_count;if (ReadLabelsFile(s->labels_file_name, &labels, &label_count) != kTfLiteOk)exit(-1);
// 打印Top-N结果for (const auto& result : top_results) {const float confidence = result.first;const int index = result.second;LOG(INFO) << confidence << ": " << index << " " << labels[index] << "\n";}
}int main() {tflite::label_image::Settings s;RunInference(&s);
}

（四）下载预训练模型

# Get model
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp# Get labels
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz  | tar xzv -C /tmp  mobilenet_v1_1.0_224/labels.txtmv /tmp/mobilenet_v1_1.0_224/labels.txt /tmp/

（五）修改模型配置

在label_image.h中修改Settings：

struct Settings {bool verbose = false;bool accel = false;bool old_accel = false;bool input_floating = false;bool profiling = false;bool allow_fp16 = false;bool gl_backend = false;int loop_count = 1;float input_mean = 127.5f;float input_std = 127.5f;string model_name = "/home/ai/CLionProjects/tflite/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224.tflite";tflite::FlatBufferModel* model;string input_bmp_name = "/home/ai/CLionProjects/tflite/grace_hopper.bmp";string labels_file_name = "/home/ai/CLionProjects/tflite/mobilenet_v1_1.0_224/labels.txt";string input_layer_type = "uint8_t";int number_of_threads = 4;int number_of_results = 5;int max_profiling_buffer_entries = 1024;int number_of_warmup_runs = 2;
};

（六）运行实例

Top5分类结果输出如下：

Loaded model /tmp/mobilenet_v1_1.0_224.tflite
resolved reporter
invoked
average time: 68.12 ms
0.860174: 653 653:military uniform
0.0481017: 907 907:Windsor tie
0.00786704: 466 466:bulletproof vest
0.00644932: 514 514:cornet, horn, trumpet, trump
0.00608029: 543 543:drumstick

结果显示该图像被正确分类，平均耗时68.12ms，速度非常快！

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