Add some comments for ppyoloe (#324)

* Add some comments for ppyoloe

* Update runtime.h

Doxyfile

@@ -982,7 +982,7 @@ FILTER_SOURCE_PATTERNS =
 # (index.html). This can be useful if you have a project on for instance GitHub
 # and want to reuse the introduction page also for the doxygen output.
 
-USE_MDFILE_AS_MAINPAGE =
+USE_MDFILE_AS_MAINPAGE = fastdeploy/README.md
 
 #---------------------------------------------------------------------------
 # Configuration options related to source browsing
@@ -2059,7 +2059,7 @@ ENABLE_PREPROCESSING   = YES
 # The default value is: NO.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-MACRO_EXPANSION        = NO
+MACRO_EXPANSION        = YES
 
 # If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
 # the macro expansion is limited to the macros specified with the PREDEFINED and
@@ -2067,7 +2067,7 @@ MACRO_EXPANSION        = NO
 # The default value is: NO.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-EXPAND_ONLY_PREDEF     = NO
+EXPAND_ONLY_PREDEF     = YES
 
 # If the SEARCH_INCLUDES tag is set to YES, the include files in the
 # INCLUDE_PATH will be searched if a #include is found.
@@ -2099,7 +2099,7 @@ INCLUDE_FILE_PATTERNS  =
 # recursively expanded use the := operator instead of the = operator.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-PREDEFINED             =
+PREDEFINED = protected=private
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The

fastdeploy/README.md

@@ -0,0 +1,27 @@
+# FastDeploy C++ API Summary
+
+## Runtime
+
+FastDeploy Runtime can be used as an inference engine with the same code, we can deploy Paddle/ONNX model on different device by different backends.  
+Currently, FastDeploy supported backends listed as below,
+
+| Backend | Hardware | Support Model Format | Platform |
+| :------ | :------- | :------------------- | :------- |
+| Paddle Inference | CPU/Nvidia GPU | Paddle | Windows(x64)/Linux(x64) |
+| ONNX Runtime | CPU/Nvidia GPU | Paddle/ONNX | Windows(x64)/Linux(x64/aarch64)/Mac(x86/arm64) |
+| TensorRT | Nvidia GPU | Paddle/ONNX | Windows(x64)/Linux(x64)/Jetson |
+| OpenVINO | CPU | Paddle/ONNX | Windows(x64)/Linux(x64)/Mac(x86) |
+
+### Example code
+- [Python examples](./)
+- [C++ examples](./)
+
+### Related APIs
+- [RuntimeOption](./structfastdeploy_1_1RuntimeOption.html)
+- [Runtime](./structfastdeploy_1_1Runtime.html)
+
+## Vision Models
+
+| Task | Model | API | Example |
+| :---- | :---- | :---- | :----- |
+| object detection | PaddleDetection/PPYOLOE | [fastdeploy::vision::detection::PPYOLOE](./classfastdeploy_1_1vision_1_1detection_1_1PPYOLOE.html) | [C++](./)/[Python](./) |

fastdeploy/fastdeploy_model.h

@@ -16,45 +16,82 @@
 
 namespace fastdeploy {
 
+/*! @brief Base model object for all the vision models
+ */
 class FASTDEPLOY_DECL FastDeployModel {
  public:
+  /// Get model's name
   virtual std::string ModelName() const { return "NameUndefined"; }
 
-  virtual bool InitRuntime();
-  virtual bool CreateCpuBackend();
-  virtual bool CreateGpuBackend();
+  /** \brief Inference the model by the runtime. This interface is included in the `Predict()` function, so we don't call `Infer()` directly in most common situation
+  */
   virtual bool Infer(std::vector<FDTensor>& input_tensors,
                      std::vector<FDTensor>* output_tensors);
 
   RuntimeOption runtime_option;
+  /** \brief Model's valid cpu backends. This member defined all the cpu backends have successfully tested for the model
+   */
   std::vector<Backend> valid_cpu_backends = {Backend::ORT};
+  /** Model's valid gpu backends. This member defined all the gpu backends have successfully tested for the model
+   */
   std::vector<Backend> valid_gpu_backends = {Backend::ORT};
-  std::vector<Backend> valid_external_backends;
-  bool initialized = false;
+  /// Get number of inputs for this model
   virtual int NumInputsOfRuntime() { return runtime_->NumInputs(); }
+  /// Get number of outputs for this model
   virtual int NumOutputsOfRuntime() { return runtime_->NumOutputs(); }
+  /// Get input information for this model
   virtual TensorInfo InputInfoOfRuntime(int index) {
     return runtime_->GetInputInfo(index);
   }
+  /// Get output information for this model
   virtual TensorInfo OutputInfoOfRuntime(int index) {
     return runtime_->GetOutputInfo(index);
   }
+  /// Check if the model is initialized successfully
   virtual bool Initialized() const {
     return runtime_initialized_ && initialized;
   }
 
+  /** \brief This is a debug interface, used to record the time of backend runtime
+   *
+   * example code @code
+   * auto model = fastdeploy::vision::PPYOLOE("model.pdmodel", "model.pdiparams", "infer_cfg.yml");
+   * if (!model.Initialized()) {
+   *   std::cerr << "Failed to initialize." << std::endl;
+   *   return -1;
+   * }
+   * model.EnableRecordTimeOfRuntime();
+   * cv::Mat im = cv::imread("test.jpg");
+   * for (auto i = 0; i < 1000; ++i) {
+   *   fastdeploy::vision::DetectionResult result;
+   *   model.Predict(&im, &result);
+   * }
+   * model.PrintStatisInfoOfRuntime();
+   * @endcode After called the `PrintStatisInfoOfRuntime()`, the statistical information of runtime will be printed in the console
+   */
   virtual void EnableRecordTimeOfRuntime() {
     time_of_runtime_.clear();
     std::vector<double>().swap(time_of_runtime_);
     enable_record_time_of_runtime_ = true;
   }
 
+  /** \brief Disable to record the time of backend runtime, see `EnableRecordTimeOfRuntime()` for more detail
+  */
   virtual void DisableRecordTimeOfRuntime() {
     enable_record_time_of_runtime_ = false;
   }
 
+  /** \brief Print the statistic information of runtime in the console, see function `EnableRecordTimeOfRuntime()` for more detail
+  */
   virtual std::map<std::string, float> PrintStatisInfoOfRuntime();
 
+ protected:
+  virtual bool InitRuntime();
+  virtual bool CreateCpuBackend();
+  virtual bool CreateGpuBackend();
+  bool initialized = false;
+  std::vector<Backend> valid_external_backends;
+
  private:
   std::unique_ptr<Runtime> runtime_;
   bool runtime_initialized_ = false;

fastdeploy/runtime.h

@@ -54,7 +54,7 @@ FASTDEPLOY_DECL std::ostream& operator<<(std::ostream& out,
                                          const ModelFormat& format);
 
 /*! Paddle Lite power mode for mobile device. */
-enum FASTDEPLOY_DECL LitePowerMode {
+enum LitePowerMode {
   LITE_POWER_HIGH = 0,  ///< Use Lite Backend with high power mode
   LITE_POWER_LOW = 1,  ///< Use Lite Backend with low power mode
   LITE_POWER_FULL = 2,  ///< Use Lite Backend with full power mode

fastdeploy/vision/AddModel.md

@@ -1,3 +0,0 @@
-# 如何添加一个模型
-
-本文档以[yolov5](https://github.com/ultralytics/yolov5)为例，说明如何添加新的模型支持。

fastdeploy/vision/common/result.h

@@ -33,51 +33,81 @@ struct FASTDEPLOY_DECL BaseResult {
   ResultType type = ResultType::UNKNOWN_RESULT;
 };
 
+/*! @brief Classify result structure for all the image classify models
+ */
 struct FASTDEPLOY_DECL ClassifyResult : public BaseResult {
+  /// Classify result for an image
   std::vector<int32_t> label_ids;
+  /// The confidence for each classify result
   std::vector<float> scores;
   ResultType type = ResultType::CLASSIFY;
 
+  /// Clear result
   void Clear();
+
+  /// Debug function, convert the result to string to print
   std::string Str();
 };
 
+/*! Mask structure, used in DetectionResult for instance segmentation models
+ */
 struct FASTDEPLOY_DECL Mask : public BaseResult {
+  /// Mask data buffer
   std::vector<int32_t> data;
+  /// Shape of mask
   std::vector<int64_t> shape;  // (H,W) ...
   ResultType type = ResultType::MASK;
 
+  /// clear mask
   void Clear();
 
+  /// Return a mutable pointer of the mask data buffer
   void* Data() { return data.data(); }
 
+  /// Return a pointer of the mask data buffer for read only
   const void* Data() const { return data.data(); }
 
+  /// Reserve size for mask data buffer
   void Reserve(int size);
 
+  /// Resize the mask data buffer
   void Resize(int size);
 
+  /// Debug function, convert the result to string to print
   std::string Str();
 };
 
+
+/*! @brief Detection result structure for all the object detection models and instance segmentation models
+ */
 struct FASTDEPLOY_DECL DetectionResult : public BaseResult {
-  // box: xmin, ymin, xmax, ymax
+  /** \brief All the detected object boxes for an input image, the size of `boxes` is the number of detected objects, and the element of `boxes` is a array of 4 float values, means [xmin, ymin, xmax, ymax]
+   */
   std::vector<std::array<float, 4>> boxes;
+  /** \brief The confidence for all the detected objects
+   */
   std::vector<float> scores;
+  /// The classify label for all the detected objects
   std::vector<int32_t> label_ids;
+  /** \brief For instance segmentation model, `masks` is the predict mask for all the deteced objects
+   */
   std::vector<Mask> masks;
+  //// Shows if the DetectionResult has mask
   bool contain_masks = false;
+
   ResultType type = ResultType::DETECTION;
 
   DetectionResult() {}
   DetectionResult(const DetectionResult& res);
 
+  /// Clear detection result
   void Clear();
 
   void Reserve(int size);
 
   void Resize(int size);
 
+  /// Debug function, convert the result to string to print
   std::string Str();
 };
 

fastdeploy/vision/detection/ppdet/ppyoloe.h

@@ -21,30 +21,50 @@
 
 namespace fastdeploy {
 namespace vision {
+/** \brief All object detection model APIs are defined inside this namespace
+ *
+ */
 namespace detection {
 
+/*! @brief PPYOLOE model object used when to load a PPYOLOE model exported by PaddleDetection
+ */
 class FASTDEPLOY_DECL PPYOLOE : public FastDeployModel {
  public:
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ppyoloe/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] config_file Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
+   * \param[in] model_format Model format of the loaded model, default is Paddle format
+   */
   PPYOLOE(const std::string& model_file, const std::string& params_file,
           const std::string& config_file,
           const RuntimeOption& custom_option = RuntimeOption(),
           const ModelFormat& model_format = ModelFormat::PADDLE);
 
+  /// Get model's name
   virtual std::string ModelName() const { return "PaddleDetection/PPYOLOE"; }
 
-  virtual bool Initialize();
-
-  virtual bool BuildPreprocessPipelineFromConfig();
-
-  virtual bool Preprocess(Mat* mat, std::vector<FDTensor>* outputs);
-
-  virtual bool Postprocess(std::vector<FDTensor>& infer_result,
-                           DetectionResult* result);
-
+  /** \brief Predict the detection result for an input image
+   *
+   * \param[in] im The input image data, comes from cv::imread()
+   * \param[in] result The output detection result will be writen to this structure
+   * \return true if the prediction successed, otherwise false
+   */
   virtual bool Predict(cv::Mat* im, DetectionResult* result);
 
  protected:
   PPYOLOE() {}
+  virtual bool Initialize();
+  /// Build the preprocess pipeline from the loaded model
+  virtual bool BuildPreprocessPipelineFromConfig();
+  /// Preprocess an input image, and set the preprocessed results to `outputs`
+  virtual bool Preprocess(Mat* mat, std::vector<FDTensor>* outputs);
+
+  /// Postprocess the inferenced results, and set the final result to `result`
+  virtual bool Postprocess(std::vector<FDTensor>& infer_result,
+                           DetectionResult* result);
 
   std::vector<std::shared_ptr<Processor>> processors_;
   std::string config_file_;