部署系列六基于nndeploy的深度学习图像降噪unet部署

文章目录

- 1.直接在源代码demo中修改
- 2. 如何修改呢？

https://github.com/DeployAI/nndeploy
https://nndeploy-zh.readthedocs.io/zh/latest/introduction/index.html

1.直接在源代码demo中修改

如果你想运行yolo5:
onnxruntime:115ms
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeOnnxRuntime --device_type kDeviceTypeCodeX86:0 --model_type kModelTypeOnnx --is_path --model_value …/…/yolov5s.onnx --input_type kInputTypeImage --input_path …/…/sample.jpg --output_path …/…/sample_output.jpg

openvino:57ms
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeOpenVino --device_type kDeviceTypeCodeX86:0 --model_type kModelTypeOnnx --is_path --model_value …/…/yolov5s.onnx --input_type kInputTypeImage --input_path …/…/sample.jpg --output_path …/…/sample_output.jpg

mnn:78ms
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeMnn --device_type kDeviceTypeCodeX86:0 --model_type kModelTypeMnn --is_path --model_value …/…/yolov5s.mnn --input_type kInputTypeImage --input_path …/…/sample.jpg --output_path …/…/sample_output.jpg

tensorrt: 17ms
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeTensorRt --device_type kDeviceTypeCodeCuda:0 --model_type kModelTypeOnnx --is_path --model_value …/…/yolov5s.onnx --input_type kInputTypeImage --input_path …/…/sample.jpg --output_path …/…/sample_output.jpg

然后我直接修改源码然后通过下面的命令可以运行 unet de model
modify for unet
tensorrt
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeTensorRt --device_type kDeviceTypeCodeCuda:0 --model_type kModelTypeOnnx --is_path --model_value /home/tony/nndeploy/mymodel/scripts/unet8.opt.onnx --input_type kInputTypeImage --input_path …/…/1007_01_06_40_000101.png --output_path …/…/sample_output.jpg

onnxruntime
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeOnnxRuntime --device_type kDeviceTypeCodeX86:0 --model_type kModelTypeOnnx --is_path --model_value /home/tony/nndeploy/mymodel/scripts/unet8.opt.onnx --input_type kInputTypeImage --input_path …/…/1007_01_06_40_000101.png --output_path …/…/sample_output.jpg

openvino:
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeOpenVino --device_type kDeviceTypeCodeX86:0 --model_type kModelTypeOnnx --is_path --model_value /home/tony/nndeploy/mymodel/scripts/unet8.opt.onnx --input_type kInputTypeImage --input_path …/…/1007_01_06_40_000101.png --output_path …/…/sample_output.jpg

MNN:
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeMnn --device_type kDeviceTypeCodeX86:0 --model_type kModelTypeMnn --is_path --model_value /home/tony/nndeploy/mymodel/scripts/unet8.opt.mnn --input_type kInputTypeImage --input_path …/…/1007_01_06_40_000101.png --output_path …/…/sample_output.jpg

TNN:
./install/lib/demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeTnn --device_type kDeviceTypeCodeX86:0 --model_type kModelTypeTnn --is_path --model_value /home/tony/nndeploy/mymodel/scripts/unet8.sim.tnnproto,/home/tony/nndeploy/mymodel/scripts/unet8.sim.tnnmodel --input_type kInputTypeImage --input_path …/…/1007_01_06_40_000101.png --output_path …/…/sample_output.jpg

2. 如何修改呢？

首先修改demo.cc

输入是 h,w,c float32
输出也是h,w,c float32
在这里插入图片描述
完整代码：

#include "flag.h"
#include "nndeploy/base/glic_stl_include.h"
#include "nndeploy/base/time_profiler.h"
#include "nndeploy/dag/node.h"
#include "nndeploy/device/device.h"
#include "nndeploy/model/detect/yolo/yolo.h"

using namespace nndeploy;

cv::Mat drawBox(cv::Mat &cv_mat, model::DetectResult &result) {
  // float w_ratio = float(cv_mat.cols) / float(640);
  // float h_ratio = float(cv_mat.rows) / float(640);
  float w_ratio = float(cv_mat.cols);
  float h_ratio = float(cv_mat.rows);
  const int CNUM = 80;
  cv::RNG rng(0xFFFFFFFF);
  cv::Scalar_<int> randColor[CNUM];
  for (int i = 0; i < CNUM; i++)
    rng.fill(randColor[i], cv::RNG::UNIFORM, 0, 256);
  int i = -1;
  for (auto bbox : result.bboxs_) {
    std::array<float, 4> box;
    box[0] = bbox.bbox_[0];  // 640.0;
    box[2] = bbox.bbox_[2];  // 640.0;
    box[1] = bbox.bbox_[1];  // 640.0;
    box[3] = bbox.bbox_[3];  // 640.0;
    box[0] *= w_ratio;
    box[2] *= w_ratio;
    box[1] *= h_ratio;
    box[3] *= h_ratio;
    int width = box[2] - box[0];
    int height = box[3] - box[1];
    int id = bbox.label_id_;
    NNDEPLOY_LOGE("box[0]:%f, box[1]:%f, width :%d, height :%d\n", box[0],
                  box[1], width, height);
    cv::Point p = cv::Point(box[0], box[1]);
    cv::Rect rect = cv::Rect(box[0], box[1], width, height);
    cv::rectangle(cv_mat, rect, randColor[id]);
    std::string text = " ID:" + std::to_string(id);
    cv::putText(cv_mat, text, p, cv::FONT_HERSHEY_PLAIN, 1, randColor[id]);
  }
  return cv_mat;
}

//
int main(int argc, char *argv[]) {
  gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
  if (demo::FLAGS_usage) {
    demo::showUsage();
    return -1;
  }

  // 检测模型的有向无环图graph名称，例如:
  // NNDEPLOY_YOLOV5/NNDEPLOY_YOLOV6/NNDEPLOY_YOLOV8
  std::string name = demo::getName();
  // 推理后端类型，例如:
  // kInferenceTypeOpenVino / kInferenceTypeTensorRt / kInferenceTypeOnnxRuntime
  base::InferenceType inference_type = demo::getInferenceType();
  // 推理设备类型，例如:
  // kDeviceTypeCodeX86:0/kDeviceTypeCodeCuda:0/...
  base::DeviceType device_type = demo::getDeviceType();
  // 模型类型，例如:
  // kModelTypeOnnx/kModelTypeMnn/...
  base::ModelType model_type = demo::getModelType();
  // 模型是否是路径
  bool is_path = demo::isPath();
  // 模型路径或者模型字符串
  std::vector<std::string> model_value = demo::getModelValue();
  // 有向无环图graph的输入边packert
  dag::Edge input("detect_in");
  // 有向无环图graph的输出边packert
  dag::Edge output("detect_out");
  // 创建检测模型有向无环图graph
  dag::Graph *graph =
      dag::createGraph(name, inference_type, device_type, &input, &output,
                       model_type, is_path, model_value);
  if (graph == nullptr) {
    NNDEPLOY_LOGE("graph is nullptr");
    return -1;
  }

  // 初始化有向无环图graph
  NNDEPLOY_TIME_POINT_START("graph->init()");
  base::Status status = graph->init();
  if (status != base::kStatusCodeOk) {
    NNDEPLOY_LOGE("graph init failed");
    return -1;
  }
  NNDEPLOY_TIME_POINT_END("graph->init()");

  // 有向无环图graph的输入图片路径
  std::string input_path = demo::getInputPath();
  // opencv读图
  cv::Mat input_mat = cv::imread(input_path);
  int img_h = input_mat.rows;
  int img_w = input_mat.cols;
  input_mat.convertTo(input_mat, CV_32FC3, 1.0/255);
  // 将图片写入有向无环图graph输入边
  input.set(input_mat);
  // 定义有向无环图graph的输出结果
  cv::Mat result(img_h, img_w, CV_32FC3);
  //model::DetectResult result;
  // 将输出结果写入有向无环图graph输出边
  output.set(result);

  // 有向无环图Graphz运行
  NNDEPLOY_TIME_POINT_START("graph->run()");
  status = graph->run();
  if (status != base::kStatusCodeOk) {
    NNDEPLOY_LOGE("graph run failed");
    return -1;
  }
  NNDEPLOY_TIME_POINT_END("graph->run()");

  //drawBox(input_mat, result);
  std::string ouput_path = demo::getOutputPath();
  result.convertTo(result, CV_8UC3, 255);
  //cv::imwrite("ret.png", output);
  cv::imwrite(ouput_path, result);

  // 有向无环图graphz反初始化
  NNDEPLOY_TIME_POINT_START("graph->deinit()");
  status = graph->deinit();
  if (status != base::kStatusCodeOk) {
    NNDEPLOY_LOGE("graph deinit failed");
    return -1;
  }
  NNDEPLOY_TIME_POINT_END("graph->deinit()");

  NNDEPLOY_TIME_PROFILER_PRINT("detetct time profiler");

  // 有向无环图graphz销毁
  delete graph;

  NNDEPLOY_LOGE("hello world!\n");

  return 0;
}

然后在
dag::Graph* createYoloV5Graph(const std::string& name,
base::InferenceType inference_type,
base::DeviceType device_type, dag::Edge* input,
dag::Edge* output, base::ModelType model_type,
bool is_path,
std::vectorstd::string model_value)
中修改前后处理函数即可。

前处理，infer , 后处理是一个 graph , 也就是demo中完整的图。
demo中的input和output是完整的图的输入输出。

然后前处理，infer, 后处理内部也有自己的input和output，不要搞混淆了。

比如模型infer输出是c,h,w, float32的结果，后处理 input是 c,h,w float32 的数据，output转换为 h,w,c float32的数据（对应上面的cv::Mat result(img_h, img_w, CV_32FC3);）

base::Status YoloPostProcess::runV5V6() {
  YoloPostParam* param = (YoloPostParam*)param_.get();
  float score_threshold = param->score_threshold_;
  int num_classes = param->num_classes_;

  device::Tensor* tensor = inputs_[0]->getTensor();
  //很多代码都是冗余的，这一步只要获得 output的tensor的指针，然后进行c,h,w -> h, w, c即可。
  float* data = (float*)tensor->getPtr();
  int batch = tensor->getBatch();
  int channel = tensor->getChannel();
  int height = tensor->getHeight();
  int width = tensor->getWidth();
NNDEPLOY_LOGE("batch:%d, channel:%d, height:%d, width:%d. (%f,%f,%f))\n", batch,  channel, height, width, data[0], data[1], data[2]);
cv::Mat* dst = outputs_[0]->getCvMat();
NNDEPLOY_LOGE("mat  channel:%d, height:%d, width:%d.\n", dst->channels(),   dst->rows, dst->cols);
    
    auto* img_data = (float*)dst->data;
    for (int h = 0; h < height; h++)
    {
        for (int w = 0; w < width; w++)
        {
            for (int c = 0; c < 3; c++)
            {
                int in_index = h * width * 3 + w * 3 + c;
                int out_index = c * width * height + h * width + w;
                // if (w < 10)
                //   if(h < 10)
                //     printf("%.2f,", data[out_index]);
                img_data[in_index] = data[out_index];
            }
            
        }
          // if(h < 10)
          //   printf("\n");
    }
    


  return base::kStatusCodeOk;
}