PaddleClas学习3——使用PPLCNet模型对车辆朝向进行识别（c++）

1 准备环境

参考上一篇：Windows PaddleSeg c++部署

2 准备模型

2.1 模型导出

对上一篇 使用PPLCNet模型对车辆朝向进行识别 训练得到模型进行转换。将该模型转为 inference 模型只需运行如下命令：

python tools\export_model.py -c .\ppcls\configs\PULC\vehicle_attribute\PPLCNet_x1_0.yaml -o Global.pretrained_model=output/PPLCNet_x1_0/best_model -o Global.save_inference_dir=./deploy/models/class_vehicle_attribute_infer

图2.1 训练得到的模型

图2.2 导出的模型

2.2 修改配置文件

deploy/configs/PULC/vehicle_attribute/inference_vehicle_attribute.yaml
修改Global下的infer_imgs和inference_model_dir。

Global:
  infer_imgs: "./images/PULC/vehicle_attribute/0002_c002_00030670_0.jpg"
  inference_model_dir: "./models/class_vehicle_attribute_infer"
  batch_size: 1
  use_gpu: True
  enable_mkldnn: True
  cpu_num_threads: 10
  #benchmark: False
  enable_benchmark: False
  use_fp16: False
  ir_optim: True
  use_tensorrt: False
  gpu_mem: 8000
  enable_profile: False

3 编译

工程整体目录结构如下：

G:/paddle/c++
  ├── paddle_inference
G:/paddle
  ├── PaddleClas-release-2.5

3.1 使用CMake生成项目文件

3.2 编译

用Visual Studio 2022打开cpp\build\clas_system.sln，将编译模式设置为Release，点击生成->生成解决方案，在cpp\build\Release文件夹内生成clas_system.exe。

3.3 执行

进入到build/Release目录下，将准备的模型和图片放到clas_system.exe同级目录，build/Release目录结构如下：

Release
├──clas_system.exe                # 可执行文件
├──images         				  # 测试图片
    ├── PULC
        ├── vehicle_attribute
        	├── 0002_c002_00030670_0.jpg
├──configs         				  # 配置文件
    ├── PULC
        ├── vehicle_attribute
        	├── inference_vehicle_attribute.yaml
├──models      					  # 推理用到的模型
    ├── class_vehicle_attribute_infer
    	├── inference.pdmodel          # 预测模型的拓扑结构文件
    	├── inference.pdiparams        # 预测模型的权重文件
    	└── inference.pdiparams.info   # 参数额外信息，一般无需关注
├──*.dll                          # dll文件

3.4 添加后处理程序

3.4.1 postprocess.h

// postprocess.h
#include <iostream>
#include <vector>

namespace PaddleClas {

	class VehicleAttribute {

	public:
		float color_threshold = 0.5;
		float type_threshold = 0.5;
		float direction_threshold = 0.5;

		std::vector<std::string> color_list = { "yellow", "orange", "green", "gray", "red", "blue", "white",
			"golden", "brown", "black" };
		std::vector<std::string> type_list = { "sedan", "suv", "van", "hatchback", "mpv", "pickup", "bus",
			"truck", "estate" };
		std::vector<std::string> direction_list = { "forward", "sideward", "backward" };

		std::string run(std::vector<float>& pred_data);
	};
}

3.4.2 postprocess.cpp

// postprocess.cpp

#include "include/postprocess.h"
#include <string>
namespace PaddleClas {
	std::string VehicleAttribute::run(std::vector<float>& pred_data) {
		int color_num = 10;
		int type_num = 9;
		int direction_num = 3;

		int index_color = std::distance(&pred_data[0], std::max_element(&pred_data[0], &pred_data[0] + 10));//左闭右开
		int index_type = std::distance(&pred_data[0] + 10, std::max_element(&pred_data[0] + 10, &pred_data[0] + 19));
		int index_direction = std::distance(&pred_data[0] + 19, std::max_element(&pred_data[0] + 19, &pred_data[0] + 22));

		std::string color_info, type_info, direction_info;
		if (pred_data[index_color] >= this->color_threshold) {
			color_info = "Color: (" + color_list[index_color] + ", pro: " + std::to_string(pred_data[index_color]) + ")";
		}
		if (pred_data[index_type + 10] >= this->type_threshold) {
			type_info = "Type: (" + type_list[index_type] + ", pro: " + std::to_string(pred_data[index_type + 10]) + ")";
		}
		if (pred_data[index_direction + 19] >= this->direction_threshold) {
			direction_info = "Direction: (" + direction_list[index_direction] + ", pro: " + std::to_string(pred_data[index_direction + 19]) + ")";
		}

		std::string pred_res = color_info + type_info + direction_info;
		pred_res += "pred: ";
		for (int i = 0; i < pred_data.size(); i++) {

			if (i < 10) {
				if (pred_data[i] > color_threshold) {
					pred_res += "1, ";
				}
				else {
					pred_res += "0, ";
				}
			}
			else if (i < 19) {
				if (pred_data[i] > type_threshold) {
					pred_res += "1, ";
				}
				else {
					pred_res += "0, ";
				}
			}
			else {
				if (pred_data[i] > direction_threshold) {
					pred_res += "1, ";
				}
				else {
					pred_res += "0, ";
				}
			}
		}
		return pred_res;
	}
}//namespace

3.4.3 在cls.h中添加函数声明

// Run predictor for vehicle attribute
void Run(cv::Mat& img, std::vector<float>& out_data, std::string &pred_res,
    std::vector<double>& times);

3.4.4 在cls.cpp中添加函数定义

void Classifier::Run(cv::Mat& img, std::vector<float>& out_data, std::string& pred_res,
    std::vector<double>& times){
    cv::Mat srcimg;
    cv::Mat resize_img;
    img.copyTo(srcimg);

    auto preprocess_start = std::chrono::system_clock::now();
    this->resize_op_.Run(img, resize_img, this->resize_size_);

    //this->resize_op_.Run(img, resize_img, this->resize_short_size_);

    //this->crop_op_.Run(resize_img, this->crop_size_);

    this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
    std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
    this->permute_op_.Run(&resize_img, input.data());

    auto input_names = this->predictor_->GetInputNames();
    auto input_t = this->predictor_->GetInputHandle(input_names[0]);
    input_t->Reshape({ 1, 3, resize_img.rows, resize_img.cols });
    auto preprocess_end = std::chrono::system_clock::now();

    auto infer_start = std::chrono::system_clock::now();
    input_t->CopyFromCpu(input.data());
    this->predictor_->Run();

    auto output_names = this->predictor_->GetOutputNames();
    auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
	std::vector<int> output_shape = output_t->shape();
	int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
		std::multiplies<int>());

	out_data.resize(out_num);
	output_t->CopyToCpu(out_data.data());
	auto infer_end = std::chrono::system_clock::now();

	auto postprocess_start = std::chrono::system_clock::now();
	pred_res = this->vehicle_attribute_op.run(out_data);
	auto postprocess_end = std::chrono::system_clock::now();

	std::chrono::duration<float> preprocess_diff =
		preprocess_end - preprocess_start;
	times[0] = double(preprocess_diff.count() * 1000);
	std::chrono::duration<float> inference_diff = infer_end - infer_start;
    double inference_cost_time = double(inference_diff.count() * 1000);
    times[1] = inference_cost_time;
    std::chrono::duration<float> postprocess_diff =
        postprocess_end - postprocess_start;
    times[2] = double(postprocess_diff.count() * 1000);
}

3.4.5 在main.cpp中调用

EFINE_string(config,
"./configs/PULC/vehicle_attribute/inference_vehicle_attribute.yaml", "Path of yaml file");
DEFINE_string(c,
"", "Path of yaml file");

int main(int argc, char** argv) {

    google::ParseCommandLineFlags(&argc, &argv, true);
    std::string yaml_path = "";
    if (FLAGS_config == "" && FLAGS_c == "") {
        std::cerr << "[ERROR] usage: " << std::endl
            << argv[0] << " -c $yaml_path" << std::endl
            << "or:" << std::endl
            << argv[0] << " -config $yaml_path" << std::endl;
        exit(1);
    }
    else if (FLAGS_config != "") {
        yaml_path = FLAGS_config;
    }
    else {
        yaml_path = FLAGS_c;
    }
    ClsConfig config(yaml_path);
    config.PrintConfigInfo();

    std::string path(config.infer_imgs);

    std::vector <std::string> img_files_list;
    if (cv::utils::fs::isDirectory(path)) {
        std::vector <cv::String> filenames;
        cv::glob(path, filenames);
        for (auto f : filenames) {
            img_files_list.push_back(f);
        }
    }
    else {
        img_files_list.push_back(path);
    }

    std::cout << "img_file_list length: " << img_files_list.size() << std::endl;

    Classifier classifier(config);

    std::vector<double> cls_times = { 0, 0, 0 };
    std::vector<double> cls_times_total = { 0, 0, 0 };
    double infer_time;
    std::vector<float> out_data;
    std::string result;
    int warmup_iter = 5;
    bool label_output_equal_flag = true;
    for (int idx = 0; idx < img_files_list.size(); ++idx) {
        std::string img_path = img_files_list[idx];
        cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
        if (!srcimg.data) {
            std::cerr << "[ERROR] image read failed! image path: " << img_path
                << "\n";
            exit(-1);
        }

        cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
        classifier.Run(srcimg, out_data, result, cls_times);

        std::cout << "Current image path: " << img_path << std::endl;
        infer_time = cls_times[0] + cls_times[1] + cls_times[2];
        std::cout << "Current total inferen time cost: " << infer_time << " ms."
            << std::endl;
        std::cout << "Current inferen result: " << result << " ."
            << std::endl;
        if (idx >= warmup_iter) {
            for (int i = 0; i < cls_times.size(); ++i)
                cls_times_total[i] += cls_times[i];
        }
    }
    if (img_files_list.size() > warmup_iter) {

        infer_time = cls_times_total[0] + cls_times_total[1] + cls_times_total[2];
        std::cout << "average time cost in all: "
            << infer_time / (img_files_list.size() - warmup_iter) << " ms."
            << std::endl;
    }

    std::string presion = "fp32";
    if (config.use_fp16)
        presion = "fp16";
    if (config.benchmark) {
        AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
            config.use_mkldnn, config.cpu_threads, 1,
            "1, 3, 224, 224", presion, cls_times_total,
            img_files_list.size());
        autolog.report();
    }
    return 0;
}

4 模型预测

4.1 测试结果

图 4.1 输入图像

图4.2 预测结果

4.2 与python预测结果对比

python deploy\python\predict_cls.py -c .\deploy\configs\PULC\vehicle_attribute\inference_vehicle_attribute.yaml -o Global.pretrained_model=output/PPLCNet_x1_0/best_model