[Model] 更新SCRFD模型demo以及文档 (#1108)

* 更新scrfd

* 更新scrfd

examples/vision/facedet/scrfd/rknpu2/README_CN.md

@@ -10,7 +10,7 @@
 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 \
+        --config_path tools/rknpu2/config/scrfd_quantized.yaml \
         --target_platform rk3588
 ```
 

examples/vision/facedet/scrfd/rknpu2/cpp/CMakeLists.txt

@@ -1,36 +1,15 @@
+PROJECT(infer_demo C CXX)
 CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
-project(rknpu_test)
-
-set(CMAKE_CXX_STANDARD 14)
 
 # 指定下载解压后的fastdeploy库路径
-set(FASTDEPLOY_INSTALL_DIR "thirdpartys/fastdeploy-0.7.0")
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
 
-include(${FASTDEPLOY_INSTALL_DIR}/FastDeployConfig.cmake)
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
-include_directories(${FastDeploy_INCLUDE_DIRS})
-add_executable(rknpu_test infer.cc)
-target_link_libraries(rknpu_test
-        ${FastDeploy_LIBS}
-        )
 
-set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
 
-install(TARGETS rknpu_test DESTINATION ./)
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
 
-install(DIRECTORY model DESTINATION ./)
+# 添加FastDeploy库依赖
-install(DIRECTORY images DESTINATION ./)
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
-
-file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
-message("${FASTDEPLOY_LIBS}")
-install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
-
-file(GLOB ONNXRUNTIME_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/onnxruntime/lib/*)
-install(PROGRAMS ${ONNXRUNTIME_LIBS} DESTINATION lib)
-
-install(DIRECTORY ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib DESTINATION ./)
-
-file(GLOB PADDLETOONNX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddle2onnx/lib/*)
-install(PROGRAMS ${PADDLETOONNX_LIBS} DESTINATION lib)
-
-file(GLOB RKNPU2_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/rknpu2_runtime/RK3588/lib/*)
-install(PROGRAMS ${RKNPU2_LIBS} DESTINATION lib)

examples/vision/facedet/scrfd/rknpu2/cpp/README_CN.md

@@ -1,66 +1,37 @@
 [English](README.md) | 简体中文
 # SCRFD C++部署示例
 
-本目录下提供`infer.cc`快速完成SCRFD在NPU加速部署的示例。
+本目录下提供`infer.cc`在RK356X上，快速完成SCRFD在NPU加速部署的示例。
 
 在部署前，需确认以下两个步骤:
 
 1. 软硬件环境满足要求
 2. 根据开发环境，下载预编译部署库或者从头编译FastDeploy仓库
 
-以上步骤请参考[RK2代NPU部署库编译](../../../../../../docs/cn/build_and_install/rknpu2.md)实现
+以上步骤请参考[RK2代NPU文档导航](../../../../../../docs/cn/build_and_install/rknpu2.md)实现
-
-## 生成基本目录文件
-
-该例程由以下几个部分组成
-```text
-.
-├── CMakeLists.txt
-├── build  # 编译文件夹
-├── image  # 存放图片的文件夹
-├── infer.cc
-├── model  # 存放模型文件的文件夹
-└── thirdpartys  # 存放sdk的文件夹
-```
-
-首先需要先生成目录结构
-```bash
-mkdir build
-mkdir images
-mkdir model
-mkdir thirdpartys
-```
 
 ## 编译
 
-### 编译并拷贝SDK到thirdpartys文件夹
-
-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
-wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
-cp test_lite_face_detector_3.jpg ./images
-```
-
-### 编译example
-
 ```bash
+mkdir build
 cd build
-cmake ..
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-aarch64-rk356X-x.x.x.tgz
+tar -xzvf fastdeploy-linux-aarch64-rk356X-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-aarch64-rk356X
 make -j8
-make install
 ```
+
 ## 运行例程
 
 ```bash
-cd ./build/install
+#下载官方转换好的SCRFD模型文件和测试图片
-export LD_LIBRARY_PATH=${PWD}/lib:${LD_LIBRARY_PATH}
+wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/scrfd_500m_bnkps_shape640x640_rknpu2.zip
-./rknpu_test
+unzip scrfd_500m_bnkps_shape640x640_rknpu2.zip
+wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
+./infer_demo scrfd_500m_bnkps_shape640x640_rknpu2/scrfd_500m_bnkps_shape640x640_rk3568_quantized.rknn \
+              test_lite_face_detector_3.jpg \
+              1
 ```
 运行完成可视化结果如下图所示
 

examples/vision/facedet/scrfd/rknpu2/cpp/infer.cc

@@ -1,16 +1,16 @@
 #include <iostream>
 #include <string>
+
 #include "fastdeploy/vision.h"
 
-void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
+void ONNXInfer(const std::string& model_file, const std::string& image_file) {
-  std::string model_file = model_dir + "/scrfd_500m_bnkps_shape640x640.onnx";
   std::string params_file;
   auto option = fastdeploy::RuntimeOption();
   option.UseCpu();
   auto format = fastdeploy::ModelFormat::ONNX;
 
-  auto model = fastdeploy::vision::facedet::SCRFD(
+  auto model = fastdeploy::vision::facedet::SCRFD(model_file, params_file,
-      model_file, params_file, option, format);
+                                                  option, format);
 
   if (!model.Initialized()) {
     std::cerr << "Failed to initialize." << std::endl;
@@ -31,19 +31,17 @@ void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
 
   std::cout << res.Str() << std::endl;
   cv::imwrite("infer_onnx.jpg", vis_im);
-  std::cout
+  std::cout << "Visualized result saved in ./infer_onnx.jpg" << std::endl;
-      << "Visualized result saved in ./infer_onnx.jpg"
-      << std::endl;
 }
 
-void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
+void RKNPU2Infer(const std::string& model_file, const std::string& image_file) {
-  std::string model_file = model_dir + "/scrfd_500m_bnkps_shape640x640_rk3588.rknn";
   std::string params_file;
   auto option = fastdeploy::RuntimeOption();
   option.UseRKNPU2();
   auto format = fastdeploy::ModelFormat::RKNN;
 
-  auto model = fastdeploy::vision::facedet::SCRFD(model_file, params_file, option, format);
+  auto model = fastdeploy::vision::facedet::SCRFD(model_file, params_file,
+                                                  option, format);
 
   if (!model.Initialized()) {
     std::cerr << "Failed to initialize." << std::endl;
@@ -66,21 +64,25 @@ void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
 
   std::cout << res.Str() << std::endl;
   cv::imwrite("infer_rknn.jpg", vis_im);
-  std::cout
+  std::cout << "Visualized result saved in ./infer_rknn.jpg" << std::endl;
-      << "Visualized result saved in ./infer_rknn.jpg"
-      << std::endl;
 }
 
 int main(int argc, char* argv[]) {
-  if (argc < 3) {
+  if (argc < 4) {
     std::cout
-        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
+        << "Usage: infer_demo path/to/model path/to/image run_option, "
-           "e.g ./infer_model ./picodet_model_dir ./test.jpeg"
+           "e.g ./infer_model scrfd_500m_bnkps_shape640x640.onnx ./test.jpeg 0"
+        << std::endl;
+    std::cout << "The data type of run_option is int, "
+                 "0: run with cpu; 1: run with rknpu2."
               << std::endl;
     return -1;
   }
 
-  RKNPU2Infer(argv[1], argv[2]);
+  if (std::atoi(argv[3]) == 0) {
     ONNXInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    RKNPU2Infer(argv[1], argv[2]);
+  }
   return 0;
 }

tools/rknpu2/config/scrfd_quantized.yaml

@@ -11,5 +11,5 @@ std:
 model_path: ./scrfd_500m_bnkps_shape640x640.onnx
 outputs_nodes:
 do_quantization: True
-dataset: "./datasets.txt"
+dataset: "./dataset.txt"
 output_folder: "./"

tools/rknpu2/config/scrfd_unquantized.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: False
+dataset: "./dataset.txt"
+output_folder: "./"

tools/rknpu2/export.py

@@ -37,17 +37,24 @@ if __name__ == "__main__":
     # Config
     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)
+    model.config(
+        mean_values=mean_values,
+        std_values=std_values,
+        target_platform=config.target_platform)
 
     # Load ONNX model
     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_nodes"])
+        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=yaml_config["do_quantization"], dataset=yaml_config["dataset"])
+    ret = model.build(
+        do_quantization=yaml_config["do_quantization"],
+        dataset=yaml_config["dataset"])
     assert ret == 0, "Build model failed!"
 
     # Init Runtime
@@ -60,7 +67,11 @@ if __name__ == "__main__":
 
     model_base_name = os.path.basename(yaml_config["model_path"]).split(".")[0]
     model_device_name = config.target_platform.lower()
-    model_save_name = model_base_name + "_" + model_device_name + ".rknn"
+    if yaml_config["do_quantization"]:
-    ret = model.export_rknn(os.path.join(yaml_config["output_folder"], model_save_name))
+        model_save_name = model_base_name + "_" + model_device_name + "_quantized" + ".rknn"
+    else:
+        model_save_name = model_base_name + "_" + model_device_name + "_unquantized" + ".rknn"
+    ret = model.export_rknn(
+        os.path.join(yaml_config["output_folder"], model_save_name))
     assert ret == 0, "Export rknn model failed!"
     print("Export OK!")