[RKNPU2] Update quantitative model (#879)

* 对RKNPU2后端进行修改，当模型为非量化模型时，不在NPU执行normalize操作，当模型为量化模型时，在NUP上执行normalize操作 * 更新RKNPU2框架，输出数据的数据类型统一返回fp32类型 * 更新scrfd，拆分disable_normalize和disable_permute * 更新scrfd代码，支持量化 * 更新scrfd python example代码 * 更新模型转换代码，支持量化模型 * 更新文档 * 按照要求修改 * 按照要求修改 * 修正模型转换文档 * 更新一下转换脚本
2025-10-05 16:48:03 +08:00 · 2022-12-19 13:58:43 +08:00
parent 383887dede
commit dc13eb7049
18 changed files with 92 additions and 148 deletions
--- a/examples/vision/facedet/scrfd/rknpu2/README.md
+++ b/examples/vision/facedet/scrfd/rknpu2/README.md
@@ -1,67 +1,20 @@
 # SCRFD RKNPU2部署模型

-
- [SCRFD](https://github.com/deepinsight/insightface/tree/17cdeab12a35efcebc2660453a8cbeae96e20950)
-    - （1）[官方库](https://github.com/deepinsight/insightface/)中提供的*.pt通过[导出ONNX模型](#导出ONNX模型)操作后，可进行部署；
-    - （2）开发者基于自己数据训练的SCRFD模型，可按照[导出ONNX模型](#导出ONNX模型)后，完成部署。
-
-## 下载预训练ONNX模型
-
-为了方便开发者的测试，下面提供了SCRFD导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
-| 模型                                                               | 大小    | 精度    |
-|:---------------------------------------------------------------- |:----- |:----- |
-| [SCRFD-500M-kps-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape160x160.onnx) | 2.5MB | - |
-| [SCRFD-500M-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_shape160x160.onnx) | 2.2MB | - |
-| [SCRFD-500M-kps-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape320x320.onnx) | 2.5MB | - |
-| [SCRFD-500M-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_shape320x320.onnx) | 2.2MB | - |
-| [SCRFD-500M-kps-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape640x640.onnx) | 2.5MB | 90.97% |
-| [SCRFD-500M-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_shape640x640.onnx) | 2.2MB | 90.57% |
-| [SCRFD-1G-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_1g_shape160x160.onnx ) | 2.5MB | - |
-| [SCRFD-1G-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_1g_shape320x320.onnx) | 2.5MB | - |
-| [SCRFD-1G-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_1g_shape640x640.onnx) | 2.5MB | 92.38% |
-| [SCRFD-2.5G-kps-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_bnkps_shape160x160.onnx) | 3.2MB | - |
-| [SCRFD-2.5G-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_shape160x160.onnx) | 2.6MB | - |
-| [SCRFD-2.5G-kps-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_bnkps_shape320x320.onnx) | 3.2MB | - |
-| [SCRFD-2.5G-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_shape320x320.onnx) | 2.6MB | - |
-| [SCRFD-2.5G-kps-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_bnkps_shape640x640.onnx) | 3.2MB | 93.8% |
-| [SCRFD-2.5G-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_shape640x640.onnx) | 2.6MB | 93.78% |
-| [SCRFD-10G-kps-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_bnkps_shape320x320.onnx) | 17MB | - |
-| [SCRFD-10G-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_shape320x320.onnx) | 15MB | - |
-| [SCRFD-10G-kps-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_bnkps_shape640x640.onnx) | 17MB | 95.4% |
-| [SCRFD-10G-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_shape640x640.onnx) | 15MB | 95.16% |
-| [SCRFD-10G-kps-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_bnkps_shape1280x1280.onnx) | 17MB | - |
-| [SCRFD-10G-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_shape1280x1280.onnx) | 15MB | - |
-
-## 导出ONNX模型
-
-  ```bash
-  #下载scrfd模型文件
-  e.g. download from  https://onedrive.live.com/?authkey=%21ABbFJx2JMhNjhNA&id=4A83B6B633B029CC%215542&cid=4A83B6B633B029CC
-
-  # 安装官方库配置环境，此版本导出环境为：
-  - 手动配置环境
-    torch==1.8.0
-    mmcv==1.3.5
-    mmdet==2.7.0
-
-  - 通过docker配置
-    docker pull qyjdefdocker/onnx-scrfd-converter:v0.3
-
-  # 导出onnx格式文件
-  - 手动生成
-    python tools/scrfd2onnx.py configs/scrfd/scrfd_500m.py weights/scrfd_500m.pth --shape 640 --input-img face-xxx.jpg
-
-  - docker
-    docker的onnx目录中已有生成好的onnx文件
-
-  ```
+本教程提供SCRFD模型在RKNPU2环境下的部署，模型的详细介绍已经ONNX模型的下载请查看[模型介绍文档](../README.md)。

 ## ONNX模型转换RKNN模型
+
+下面以scrfd_500m_bnkps_shape640x640为例子，快速的转换SCRFD ONNX模型为RKNN量化模型。 以下命令在Ubuntu18.04下执行:
 ```bash
-wget  https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape640x640.onnx
-python tools/rknpu2/export.py --config_path tools/rknpu2/config/RK3588/scrfd.yaml
+wget  https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/scrfd_500m_bnkps_shape640x640.zip
+unzip scrfd_500m_bnkps_shape640x640.zip
+python  /Path/To/FastDeploy/tools/rknpu2/export.py \
+        --config_path tools/rknpu2/config/scrfd.yaml \
+        --target_platform rk3588
 ```

+
+
 ## 详细部署文档

 - [Python部署](python/README.md)
--- a/examples/vision/facedet/scrfd/rknpu2/cpp/README.md
+++ b/examples/vision/facedet/scrfd/rknpu2/cpp/README.md
@@ -17,9 +17,7 @@
 ├── CMakeLists.txt
 ├── build  # 编译文件夹
 ├── image  # 存放图片的文件夹
-├── infer_cpu_npu.cc
-├── infer_cpu_npu.h
-├── main.cc
+├── infer.cc
 ├── model  # 存放模型文件的文件夹
 └── thirdpartys  # 存放sdk的文件夹
 ```
@@ -39,9 +37,8 @@ mkdir thirdpartys
 请参考[RK2代NPU部署库编译](../../../../../../docs/cn/build_and_install/rknpu2.md)仓库编译SDK，编译完成后，将在build目录下生成
 fastdeploy-0.7.0目录，请移动它至thirdpartys目录下.

-### 拷贝模型文件，以及配置文件至model文件夹
-在Paddle动态图模型 -> Paddle静态图模型 -> ONNX模型的过程中，将生成ONNX文件以及对应的yaml配置文件，请将配置文件存放到model文件夹内。
-转换为RKNN后的模型文件也需要拷贝至model。
+### 拷贝模型文件至model文件夹
+请参考[SCRFD模型转换文档](../README.md)转换SCRFD ONNX模型到RKNN模型,再将RKNN模型移动到model文件夹。

 ### 准备测试图片至image文件夹
 ```bash
@@ -61,6 +58,7 @@ make install

 ```bash
 cd ./build/install
+export LD_LIBRARY_PATH=${PWD}/lib:${LD_LIBRARY_PATH}
 ./rknpu_test
 ```
 运行完成可视化结果如下图所示
--- a/examples/vision/facedet/scrfd/rknpu2/cpp/infer.cc
+++ b/examples/vision/facedet/scrfd/rknpu2/cpp/infer.cc
@@ -29,6 +29,7 @@ void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
  tc.End();
  tc.PrintInfo("SCRFD in ONNX");

+  std::cout << res.Str() << std::endl;
  cv::imwrite("infer_onnx.jpg", vis_im);
  std::cout
      << "Visualized result saved in ./infer_onnx.jpg"
@@ -48,7 +49,8 @@ void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }
-  model.DisableNormalizeAndPermute();
+  model.DisableNormalize();
+  model.DisablePermute();

  fastdeploy::TimeCounter tc;
  tc.Start();
@@ -62,6 +64,7 @@ void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
  tc.End();
  tc.PrintInfo("SCRFD in RKNN");

+  std::cout << res.Str() << std::endl;
  cv::imwrite("infer_rknn.jpg", vis_im);
  std::cout
      << "Visualized result saved in ./infer_rknn.jpg"
--- a/examples/vision/facedet/scrfd/rknpu2/python/README.md
+++ b/examples/vision/facedet/scrfd/rknpu2/python/README.md
@@ -4,9 +4,14 @@

 - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/rknpu2.md)

-
 本目录下提供`infer.py`快速完成SCRFD在RKNPU上部署的示例。执行如下脚本即可完成

+## 拷贝模型文件
+请参考[SCRFD模型转换文档](../README.md)转换SCRFD ONNX模型到RKNN模型,再将RKNN模型移动到该目录下。
+
+
+## 运行example
+拷贝模型文件后，请输入以下命令，运行RKNPU2 Python example
 ```bash
 # 下载部署示例代码
 git clone https://github.com/PaddlePaddle/FastDeploy.git
@@ -20,10 +25,17 @@ python3 infer.py --model_file ./scrfd_500m_bnkps_shape640x640_rk3588.rknn \
                 --image test_lite_face_detector_3.jpg
 ```

+## 可视化
+运行完成可视化结果如下图所示
+
+<img width="640" src="https://user-images.githubusercontent.com/67993288/184301789-1981d065-208f-4a6b-857c-9a0f9a63e0b1.jpg">
+
+

 ## 注意事项
 RKNPU上对模型的输入要求是使用NHWC格式，且图片归一化操作会在转RKNN模型时，内嵌到模型中，因此我们在使用FastDeploy部署时，
-需要先调用DisableNormalizePermute(C++)或`disable_normalize_permute(Python)，在预处理阶段禁用归一化以及数据格式的转换。
+需要先调用DisablePermute(C++)或`disable_permute(Python)，在预处理阶段禁用归一化以及数据格式的转换。
+
 ## 其它文档

 - [SCRFD 模型介绍](../README.md)
--- a/examples/vision/facedet/scrfd/rknpu2/python/infer.py
+++ b/examples/vision/facedet/scrfd/rknpu2/python/infer.py
@@ -45,7 +45,8 @@ model = fd.vision.facedet.SCRFD(
    runtime_option=runtime_option,
    model_format=fd.ModelFormat.RKNN)

-model.disable_normalize_and_permute()
+model.disable_normalize()
+model.disable_permute()

 # 预测图片分割结果
 im = cv2.imread(args.image)
--- a/fastdeploy/backends/rknpu/rknpu2/rknpu2_backend.cc
+++ b/fastdeploy/backends/rknpu/rknpu2/rknpu2_backend.cc
@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "fastdeploy/backends/rknpu/rknpu2/rknpu2_backend.h"
-#include "fastdeploy/utils/perf.h"
+
 namespace fastdeploy {
 RKNPU2Backend::~RKNPU2Backend() {
  // Release memory uniformly here
@@ -254,12 +254,11 @@ bool RKNPU2Backend::GetModelInputOutputInfos() {
 void RKNPU2Backend::DumpTensorAttr(rknn_tensor_attr& attr) {
  printf("index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], "
         "n_elems=%d, size=%d, fmt=%s, type=%s, "
-         "qnt_type=%s, zp=%d, scale=%f, pass_through=%d",
+         "qnt_type=%s, zp=%d, scale=%f\n",
         attr.index, attr.name, attr.n_dims, attr.dims[0], attr.dims[1],
         attr.dims[2], attr.dims[3], attr.n_elems, attr.size,
         get_format_string(attr.fmt), get_type_string(attr.type),
-         get_qnt_type_string(attr.qnt_type), attr.zp, attr.scale,
-         attr.pass_through);
+         get_qnt_type_string(attr.qnt_type), attr.zp, attr.scale);
 }

 TensorInfo RKNPU2Backend::GetInputInfo(int index) {
@@ -310,12 +309,7 @@ bool RKNPU2Backend::Infer(std::vector<FDTensor>& inputs,
      input_attrs_[i].type = input_type;
      input_attrs_[i].size = inputs[0].Nbytes();
      input_attrs_[i].size_with_stride = inputs[0].Nbytes();
-      if(input_attrs_[i].type == RKNN_TENSOR_FLOAT16 ||
-          input_attrs_[i].type == RKNN_TENSOR_FLOAT32){
-        FDINFO << "The input model is not a quantitative model. "
-                  "Close the normalize operation." << std::endl;
-      }
-
+      input_attrs_[i].pass_through = 0;
      input_mems_[i] = rknn_create_mem(ctx, inputs[i].Nbytes());
      if (input_mems_[i] == nullptr) {
        FDERROR << "rknn_create_mem input_mems_ error." << std::endl;
@@ -346,7 +340,6 @@ bool RKNPU2Backend::Infer(std::vector<FDTensor>& inputs,

      // default output type is depend on model, this requires float32 to compute top5
      ret = rknn_set_io_mem(ctx, output_mems_[i], &output_attrs_[i]);
-
      // set output memory and attribute
      if (ret != RKNN_SUCC) {
        FDERROR << "output tensor memory rknn_set_io_mem fail! ret=" << ret
--- a/fastdeploy/vision/facedet/contrib/scrfd.cc
+++ b/fastdeploy/vision/facedet/contrib/scrfd.cc
@@ -141,7 +141,7 @@ bool SCRFD::Preprocess(Mat* mat, FDTensor* output,
                   is_scale_up, stride);

  BGR2RGB::Run(mat);
-  if (!disable_normalize_and_permute_) {
+  if (!disable_normalize_) {
    // Normalize::Run(mat, std::vector<float>(mat->Channels(), 0.0),
    //                std::vector<float>(mat->Channels(), 1.0));
    // Compute `result = mat * alpha + beta` directly by channel
@@ -150,6 +150,9 @@ bool SCRFD::Preprocess(Mat* mat, FDTensor* output,
    std::vector<float> alpha = {1.f / 128.f, 1.f / 128.f, 1.f / 128.f};
    std::vector<float> beta = {-127.5f / 128.f, -127.5f / 128.f, -127.5f / 128.f};
    Convert::Run(mat, alpha, beta);
+  }
+
+  if(!disable_permute_){
    HWC2CHW::Run(mat);
    Cast::Run(mat, "float");
  }
@@ -347,7 +350,6 @@ bool SCRFD::Predict(cv::Mat* im, FaceDetectionResult* result,
                            static_cast<float>(mat.Width())};
  im_info["output_shape"] = {static_cast<float>(mat.Height()),
                             static_cast<float>(mat.Width())};
-
  if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
    FDERROR << "Failed to preprocess input image." << std::endl;
    return false;
@@ -367,8 +369,13 @@ bool SCRFD::Predict(cv::Mat* im, FaceDetectionResult* result,
  }
  return true;
 }
-void SCRFD::DisableNormalizeAndPermute(){
-  disable_normalize_and_permute_ = true;
+
+void SCRFD::DisableNormalize() {
+  disable_normalize_=true;
+}
+
+void SCRFD::DisablePermute() {
+  disable_permute_=true;
 }
 }  // namespace facedet
 }  // namespace vision
--- a/fastdeploy/vision/facedet/contrib/scrfd.h
+++ b/fastdeploy/vision/facedet/contrib/scrfd.h
@@ -90,8 +90,10 @@ class FASTDEPLOY_DECL SCRFD : public FastDeployModel {
  unsigned int num_anchors;

  /// This function will disable normalize and hwc2chw in preprocessing step.
-  void DisableNormalizeAndPermute();
+  void DisableNormalize();

+  /// This function will disable hwc2chw in preprocessing step.
+  void DisablePermute();
 private:
  bool Initialize();

@@ -121,8 +123,10 @@ class FASTDEPLOY_DECL SCRFD : public FastDeployModel {

  std::unordered_map<int, std::vector<SCRFDPoint>> center_points_;

-  // for recording the switch of normalize and hwc2chw
-  bool disable_normalize_and_permute_ = false;
+  // for recording the switch of normalize
+  bool disable_normalize_ = false;
+  // for recording the switch of hwc2chw
+  bool disable_permute_ = false;
 };
 }  // namespace facedet
 }  // namespace vision
--- a/fastdeploy/vision/facedet/contrib/scrfd_pybind.cc
+++ b/fastdeploy/vision/facedet/contrib/scrfd_pybind.cc
@@ -28,7 +28,8 @@ void BindSCRFD(pybind11::module& m) {
             self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
             return res;
           })
-      .def("disable_normalize_and_permute",&vision::facedet::SCRFD::DisableNormalizeAndPermute)
+      .def("disable_normalize",&vision::facedet::SCRFD::DisableNormalize)
+      .def("disable_permute",&vision::facedet::SCRFD::DisablePermute)
      .def_readwrite("size", &vision::facedet::SCRFD::size)
      .def_readwrite("padding_value", &vision::facedet::SCRFD::padding_value)
      .def_readwrite("is_mini_pad", &vision::facedet::SCRFD::is_mini_pad)
--- a/python/fastdeploy/vision/facedet/contrib/scrfd.py
+++ b/python/fastdeploy/vision/facedet/contrib/scrfd.py
@@ -51,11 +51,17 @@ class SCRFD(FastDeployModel):
        return self._model.predict(input_image, conf_threshold,
                                   nms_iou_threshold)

-    def disable_normalize_and_permute(self):
+    def disable_normalize(self):
        """
-        This function will disable normalize and hwc2chw in preprocessing step.
+        This function will disable normalize in preprocessing step.
        """
-        self._model.disable_normalize_and_permute()
+        self._model.disable_normalize()
+
+    def disable_permute(self):
+        """
+        This function will disable hwc2chw in preprocessing step.
+        """
+        self._model.disable_permute()

    # 一些跟SCRFD模型有关的属性封装
    # 多数是预处理相关，可通过修改如model.size = [640, 640]改变预处理时resize的大小（前提是模型支持）
--- a/tools/rknpu2/config/RK3568/Portrait_PP_HumanSegV2_Lite_256x144_infer.yaml
+++ b/tools/rknpu2/config/RK3568/Portrait_PP_HumanSegV2_Lite_256x144_infer.yaml
@@ -1,7 +0,0 @@
-model_path: ./Portrait_PP_HumanSegV2_Lite_256x144_infer/Portrait_PP_HumanSegV2_Lite_256x144_infer.onnx
-output_folder: ./Portrait_PP_HumanSegV2_Lite_256x144_infer
-target_platform: RK3568
-normalize:
-  mean: [[0.5,0.5,0.5]]
-  std: [[0.5,0.5,0.5]]
-outputs: None
--- a/tools/rknpu2/config/RK3568/picodet_s_416_coco_lcnet.yaml
+++ b/tools/rknpu2/config/RK3568/picodet_s_416_coco_lcnet.yaml
@@ -1,5 +0,0 @@
-model_path: ./picodet_s_416_coco_lcnet/picodet_s_416_coco_lcnet.onnx
-output_folder: ./picodet_s_416_coco_lcnet
-target_platform: RK3568
-normalize: None
-outputs: ['tmp_17','p2o.Concat.9']
--- a/tools/rknpu2/config/RK3568/scrfd.yaml
+++ b/tools/rknpu2/config/RK3568/scrfd.yaml
@@ -1,7 +0,0 @@
-model_path: ./scrfd_500m_bnkps_shape640x640.onnx
-output_folder: ./
-target_platform: RK3568
-normalize:
-  mean: [[0.5,0.5,0.5]]
-  std: [[0.5,0.5,0.5]]
-outputs: None
--- a/tools/rknpu2/config/RK3588/Portrait_PP_HumanSegV2_Lite_256x144_infer.yaml
+++ b/tools/rknpu2/config/RK3588/Portrait_PP_HumanSegV2_Lite_256x144_infer.yaml
@@ -1,7 +0,0 @@
-model_path: ./Portrait_PP_HumanSegV2_Lite_256x144_infer/Portrait_PP_HumanSegV2_Lite_256x144_infer.onnx
-output_folder: ./Portrait_PP_HumanSegV2_Lite_256x144_infer
-target_platform: RK3588
-normalize:
-  mean: [[0.5,0.5,0.5]]
-  std: [[0.5,0.5,0.5]]
-outputs: None
--- a/tools/rknpu2/config/RK3588/picodet_s_416_coco_lcnet.yaml
+++ b/tools/rknpu2/config/RK3588/picodet_s_416_coco_lcnet.yaml
@@ -1,5 +0,0 @@
-model_path: ./picodet_s_416_coco_lcnet/picodet_s_416_coco_lcnet.onnx
-output_folder: ./picodet_s_416_coco_lcnet
-target_platform: RK3588
-normalize: None
-outputs: ['tmp_16','p2o.Concat.9']
--- a/tools/rknpu2/config/RK3588/scrfd.yaml
+++ b/tools/rknpu2/config/RK3588/scrfd.yaml
@@ -1,7 +0,0 @@
-model_path: ./scrfd_500m_bnkps_shape640x640.onnx
-output_folder: ./
-target_platform: RK3588
-normalize:
-  mean: [[0.5,0.5,0.5]]
-  std: [[0.5,0.5,0.5]]
-outputs: None
--- a/tools/rknpu2/config/scrfd.yaml
+++ b/tools/rknpu2/config/scrfd.yaml
@@ -0,0 +1,15 @@
+mean:
+  -
+    - 128.5
+    - 128.5
+    - 128.5
+std:
+  -
+    - 128.5
+    - 128.5
+    - 128.5
+model_path: ./scrfd_500m_bnkps_shape640x640.onnx
+outputs_nodes:
+do_quantization: True
+dataset: "./datasets.txt"
+output_folder: "./"
--- a/tools/rknpu2/export.py
+++ b/tools/rknpu2/export.py
@@ -21,6 +21,7 @@ def get_config():
    parser = argparse.ArgumentParser()
    parser.add_argument("--verbose", default=True, help="rknntoolkit verbose")
    parser.add_argument("--config_path")
+    parser.add_argument("--target_platform")
    args = parser.parse_args()
    return args

@@ -34,30 +35,19 @@ if __name__ == "__main__":
    model = RKNN(config.verbose)

    # Config
-    if yaml_config["normalize"] == "None":
-        model.config(target_platform=yaml_config["target_platform"])
-    else:
-        mean_values = [[256 * mean for mean in mean_ls]
-                       for mean_ls in yaml_config["normalize"]["mean"]]
-        std_values = [[256 * std for std in std_ls]
-                      for std_ls in yaml_config["normalize"]["std"]]
-        model.config(
-            mean_values=mean_values,
-            std_values=std_values,
-            target_platform=yaml_config["target_platform"])
+    mean_values = yaml_config["mean"]
+    std_values = yaml_config["std"]
+    model.config(mean_values=mean_values, std_values=std_values, target_platform=config.target_platform)

    # Load ONNX model
-    print(type(yaml_config["outputs"]))
-    print("yaml_config[\"outputs\"] = ", yaml_config["outputs"])
-    if yaml_config["outputs"] == "None":
+    if yaml_config["outputs_nodes"] is None:
        ret = model.load_onnx(model=yaml_config["model_path"])
    else:
-        ret = model.load_onnx(
-            model=yaml_config["model_path"], outputs=yaml_config["outputs"])
+        ret = model.load_onnx(model=yaml_config["model_path"], outputs=yaml_config["outputs_nodes"])
    assert ret == 0, "Load model failed!"

    # Build model
-    ret = model.build(do_quantization=None)
+    ret = model.build(do_quantization=yaml_config["do_quantization"], dataset=yaml_config["dataset"])
    assert ret == 0, "Build model failed!"

    # Init Runtime
@@ -69,9 +59,8 @@ if __name__ == "__main__":
        os.mkdir(yaml_config["output_folder"])

    model_base_name = os.path.basename(yaml_config["model_path"]).split(".")[0]
-    model_device_name = yaml_config["target_platform"].lower()
+    model_device_name = config.target_platform.lower()
    model_save_name = model_base_name + "_" + model_device_name + ".rknn"
-    ret = model.export_rknn(
-        os.path.join(yaml_config["output_folder"], model_save_name))
+    ret = model.export_rknn(os.path.join(yaml_config["output_folder"], model_save_name))
    assert ret == 0, "Export rknn model failed!"
    print("Export OK!")