提交 d8fe85c4 编写于 作者: J jack

add comment in paddlex.h

上级 bcbff526
......@@ -39,14 +39,44 @@
namespace PaddleX {
/*
* @brief
* This class encapsulates all necessary proccess steps of model infering, which
* include image matrix preprocessing, model predicting and results postprocessing.
* The entire process of model infering can be simplified as below:
* 1. preprocess image matrix (resize, padding, ......)
* 2. model infer
* 3. postprocess the results which generated from model infering
*
* @example
* PaddleX::Model cls_model;
* // initialize model configuration
* cls_model.Init(cls_model_dir, use_gpu, use_trt, gpu_id, encryption_key);
* // define a Classification result object
* PaddleX::ClsResult cls_result;
* // get image matrix from image file
* cv::Mat im = cv::imread(image_file_path, 1);
* cls_model.predict(im, &cls_result);
* */
class Model {
public:
/*
* @brief
* This method aims to initialize the model configuration
*
* @param model_dir: the directory which contains model.yml
* @param use_gpu: use gpu or not when infering
* @param use_trt: use Tensor RT or not when infering
* @param gpu_id: the id of gpu when infering with using gpu
* @param key: the key of encryption when using encrypted model
* @param batch_size: batch size of infering
* */
void Init(const std::string& model_dir,
bool use_gpu = false,
bool use_trt = false,
int gpu_id = 0,
std::string key = "",
int batch_size = 1) {
int batch_size = 1) {
create_predictor(model_dir, use_gpu, use_trt, gpu_id, key, batch_size);
}
......@@ -55,33 +85,128 @@ class Model {
bool use_trt = false,
int gpu_id = 0,
std::string key = "",
int batch_size = 1);
int batch_size = 1);
/*
* @brief
* This method aims to load model configurations which include
* transform steps and label list
*
* @param model_dir: the directory which contains model.yml
* @return true if load configuration successfully
* */
bool load_config(const std::string& model_dir);
/*
* @brief
* This method aims to transform single image matrix, the result will be
* returned at second parameter.
*
* @param input_im: single image matrix to be transformed
* @param blob: the raw data of single image matrix after transformed
* @return true if preprocess image matrix successfully
* */
bool preprocess(const cv::Mat& input_im, ImageBlob* blob);
/*
* @brief
* This method aims to transform mutiple image matrixs, the result will be
* returned at second parameter.
*
* @param input_im_batch: a batch of image matrixs to be transformed
* @param blob_blob: raw data of a batch of image matrixs after transformed
* @param thread_num: the number of preprocessing threads,
* each thread run preprocess on single image matrix
* @return true if preprocess a batch of image matrixs successfully
* */
bool preprocess(const std::vector<cv::Mat> &input_im_batch, std::vector<ImageBlob> &blob_batch, int thread_num = 1);
/*
* @brief
* This method aims to execute classification model prediction on single image matrix,
* the result will be returned at second parameter.
*
* @param im: single image matrix to be predicted
* @param result: classification prediction result data after postprocessed
* @return true if predict successfully
* */
bool predict(const cv::Mat& im, ClsResult* result);
/*
* @brief
* This method aims to execute classification model prediction on a batch of image matrixs,
* the result will be returned at second parameter.
*
* @param im: a batch of image matrixs to be predicted
* @param results: a batch of classification prediction result data after postprocessed
* @param thread_num: the number of predicting threads, each thread run prediction
* on single image matrix
* @return true if predict successfully
* */
bool predict(const std::vector<cv::Mat> &im_batch, std::vector<ClsResult> &results, int thread_num = 1);
/*
* @brief
* This method aims to execute detection or instance segmentation model prediction
* on single image matrix, the result will be returned at second parameter.
*
* @param im: single image matrix to be predicted
* @param result: detection or instance segmentation prediction result data after postprocessed
* @return true if predict successfully
* */
bool predict(const cv::Mat& im, DetResult* result);
/*
* @brief
* This method aims to execute detection or instance segmentation model prediction
* on a batch of image matrixs, the result will be returned at second parameter.
*
* @param im: a batch of image matrix to be predicted
* @param result: detection or instance segmentation prediction result data after postprocessed
* @param thread_num: the number of predicting threads, each thread run prediction
* on single image matrix
* @return true if predict successfully
* */
bool predict(const std::vector<cv::Mat> &im_batch, std::vector<DetResult> &result, int thread_num = 1);
/*
* @brief
* This method aims to execute segmentation model prediction on single image matrix,
* the result will be returned at second parameter.
*
* @param im: single image matrix to be predicted
* @param result: segmentation prediction result data after postprocessed
* @return true if predict successfully
* */
bool predict(const cv::Mat& im, SegResult* result);
/*
* @brief
* This method aims to execute segmentation model prediction on a batch of image matrix,
* the result will be returned at second parameter.
*
* @param im: a batch of image matrix to be predicted
* @param result: segmentation prediction result data after postprocessed
* @param thread_num: the number of predicting threads, each thread run prediction
* on single image matrix
* @return true if predict successfully
* */
bool predict(const std::vector<cv::Mat> &im_batch, std::vector<SegResult> &result, int thread_num = 1);
// model type, include 3 type: classifier, detector, segmenter
std::string type;
// model name, such as FasterRCNN, YOLOV3 and so on.
std::string name;
std::map<int, std::string> labels;
// transform(preprocessing) pipeline manager
Transforms transforms_;
// single input preprocessed data
ImageBlob inputs_;
// batch input preprocessed data
std::vector<ImageBlob> inputs_batch_;
// raw data of predicting results
std::vector<float> outputs_;
// a predictor which run the model predicting
std::unique_ptr<paddle::PaddlePredictor> predictor_;
};
} // namespce of PaddleX
......@@ -101,8 +101,6 @@ bool Model::load_config(const std::string& model_dir) {
bool Model::preprocess(const cv::Mat& input_im, ImageBlob* blob) {
cv::Mat im = input_im.clone();
int max_h = im.rows;
int max_w = im.cols;
if (!transforms_.Run(&im, blob)) {
return false;
}
......@@ -113,8 +111,6 @@ bool Model::preprocess(const cv::Mat& input_im, ImageBlob* blob) {
bool Model::preprocess(const std::vector<cv::Mat> &input_im_batch, std::vector<ImageBlob> &blob_batch, int thread_num) {
int batch_size = input_im_batch.size();
bool success = true;
int max_h = -1;
int max_w = -1;
thread_num = std::min(thread_num, batch_size);
#pragma omp parallel for num_threads(thread_num)
for(int i = 0; i < input_im_batch.size(); ++i) {
......
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