修改paddleocr工程格式

Python/PaddleOCR_infer_migraphx.py

@@ -28,13 +28,10 @@ class NormalizeImage(object):
         if isinstance(img, Image.Image):
             img = np.array(img)
 
-        assert isinstance(img,
-                          np.ndarray), "invalid input 'img' in NormalizeImage"
-        data['image'] = (
-            img.astype('float32') * self.scale - self.mean) / self.std
+        assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage"
+        data['image'] = (img.astype('float32') * self.scale - self.mean) / self.std
         return data
 
-
 class ToCHWImage(object):
     def __init__(self, **kwargs):
         pass
@@ -298,16 +295,8 @@ class det_rec_functions(object):
         self.modelDet = migraphx.parse_onnx(self.det_file, map_input_dims=detInput)
 
         # 获取模型输入/输出节点信息
-        print("det_inputs:")
         inputs_det = self.modelDet.get_inputs()
-        for key,value in inputs_det.items():
-            print("{}:{}".format(key,value))
-
-        print("det_outputs:")
         outputs_det = self.modelDet.get_outputs()
-        for key,value in outputs_det.items():
-            print("{}:{}".format(key,value))
-
         self.inputName = self.modelDet.get_parameter_names()[0]
         self.inputShape = inputs_det[self.inputName].lens()
         print("DB inputName:{0} \nDB inputShape:{1}".format(self.inputName, self.inputShape))
@@ -321,16 +310,8 @@ class det_rec_functions(object):
         self.modelRec = migraphx.parse_onnx(self.rec_file, map_input_dims=recInput)
 
         # 获取模型输入/输出节点信息
-        print("rec_inputs:")
         inputs_rec = self.modelRec.get_inputs()
-        for key,value in inputs_rec.items():
-            print("{}:{}".format(key,value))
-
-        print("rec_outputs:")
         outputs_rec = self.modelRec.get_outputs()
-        for key,value in outputs_rec.items():
-            print("{}:{}".format(key,value))
-
         self.inputName = self.modelRec.get_parameter_names()[0]
         self.inputShape = inputs_rec[self.inputName].lens()
         print("SVTR inputName:{0} \nSVTR inputShape:{1}".format(self.inputName, self.inputShape))
@@ -480,8 +461,7 @@ class det_rec_functions(object):
 
         # migraphx推理
         resultDets = self.modelDet.run({self.modelDet.get_parameter_names()[0]: img_part})
-        # 获取第一个输出节点的数据,migraphx.argument类型
-        resultDet = resultDets[0]
+        resultDet = resultDets[0]       # 获取第一个输出节点的数据,migraphx.argument类型
         outs_part = np.array(resultDet)
 
         post_res_part = self.det_re_process_op(outs_part, shape_part_list)
@@ -523,8 +503,7 @@ class det_rec_functions(object):
         
         # migraphx推理
         results = self.modelRec.run({self.modelRec.get_parameter_names()[0]: img})
-        # 获取第一个输出节点的数据,migraphx.argument类型
-        result = results[0]
+        result = results[0]       # 获取第一个输出节点的数据,migraphx.argument类型
         outs = np.array(result)
 
         result = process_op(outs)
@@ -552,12 +531,9 @@ class det_rec_functions(object):
 if __name__=='__main__':
   
     image = cv2.imread('../Resource/Images/vlpr.jpg')
-    start = time.time()
     ocr_sys = det_rec_functions(image)
     dt_boxes = ocr_sys.get_boxes()
     results = ocr_sys.recognition_img(dt_boxes)
     results_info = results[0][0]
-    
-    print('net forward time: {:.4f}'.format(time.time() - start))
     print("############# OCR Results #############")
     print(results_info)
\ No newline at end of file

Src/PaddleOCR/OcrDB.cpp

@@ -25,7 +25,7 @@ ErrorCode DB::Initialize(InitializationParameterOfDB InitializationParameterOfDB
 {
     // 读取配置文件
     std::string configFilePath=InitializationParameterOfDB.configFilePath;
-    if(Exists(configFilePath)==false)
+    if(!Exists(configFilePath))
     {
         LOG_ERROR(stdout, "no configuration file!\n");
         return CONFIG_FILE_NOT_EXIST;
@@ -47,29 +47,19 @@ ErrorCode DB::Initialize(InitializationParameterOfDB InitializationParameterOfDB
     dbParameter.ScoreMode = (string)netNode["ScoreMode"];
 
     // 加载模型
-    if(Exists(modelPath)==false)
+    if(!Exists(modelPath))
     {
         LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
         return MODEL_NOT_EXIST;
     }
     migraphx::onnx_options onnx_options;
-    onnx_options.map_input_dims["x"]={1,3,2496,2496}; // 设置最大shape
+    onnx_options.map_input_dims["x"] = {1,3,2496,2496}; // 设置最大shape
     net = migraphx::parse_onnx(modelPath, onnx_options);
     LOG_INFO(stdout,"succeed to load model: %s\n",GetFileName(modelPath).c_str());
 
     // 获取模型输入/输出节点信息
-    std::cout<<"DB_inputs:"<<std::endl;
     std::unordered_map<std::string, migraphx::shape> inputs=net.get_inputs();
-    for(auto i:inputs)
-    {
-        std::cout<<i.first<<":"<<i.second<<std::endl;
-    }
-    std::cout<<"DB_outputs:"<<std::endl;
     std::unordered_map<std::string, migraphx::shape> outputs=net.get_outputs();
-    for(auto i:outputs)
-    {
-        std::cout<<i.first<<":"<<i.second<<std::endl;
-    }
     inputName=inputs.begin()->first;
     inputShape=inputs.begin()->second;
     int N=inputShape.lens()[0];
@@ -161,6 +151,7 @@ ErrorCode DB::Infer(const cv::Mat &img, std::vector<cv::Mat> &imgList)
 
     // 推理
     std::vector<migraphx::argument> inferenceResults = net.eval(inputData);
+    
     // 获取推理结果
     migraphx::argument result = inferenceResults[0]; 
 

Src/PaddleOCR/OcrSVTR.cpp

@@ -13,16 +13,14 @@ SVTR::SVTR()
 
 SVTR::~SVTR()
 {
-
     configurationFile.release();
-    
 }
 
 ErrorCode SVTR::Initialize(InitializationParameterOfSVTR InitializationParameterOfSVTR)
 {
     // 读取配置文件
     std::string configFilePath=InitializationParameterOfSVTR.configFilePath;
-    if(Exists(configFilePath)==false)
+    if(!Exists(configFilePath))
     {
         LOG_ERROR(stdout, "no configuration file!\n");
         return CONFIG_FILE_NOT_EXIST;
@@ -40,7 +38,7 @@ ErrorCode SVTR::Initialize(InitializationParameterOfSVTR InitializationParameter
     std::string dictPath = (std::string)netNode["DictPath"];
 
      // 加载模型
-    if(Exists(modelPath)==false)
+    if(!Exists(modelPath))
     {
         LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
         return MODEL_NOT_EXIST;
@@ -51,19 +49,8 @@ ErrorCode SVTR::Initialize(InitializationParameterOfSVTR InitializationParameter
     LOG_INFO(stdout,"succeed to load model: %s\n",GetFileName(modelPath).c_str());
 
     // 获取模型输入/输出节点信息
-    std::cout<<"SVTR_inputs:"<<std::endl;
     std::unordered_map<std::string, migraphx::shape> inputs=net.get_inputs();
-    for(auto i:inputs)
-    {
-        std::cout<<i.first<<":"<<i.second<<std::endl;
-    }
-    std::cout<<"DSVTR_outputs:"<<std::endl;
     std::unordered_map<std::string, migraphx::shape> outputs=net.get_outputs();
-    for(auto i:outputs)
-    {
-        std::cout<<i.first<<":"<<i.second<<std::endl;
-    }
-
     inputName=inputs.begin()->first;
     inputShape=inputs.begin()->second;
     int N=inputShape.lens()[0];

Src/PaddleOCR/VLPR.cpp

@@ -10,9 +10,7 @@ VLPR::VLPR()
 
 VLPR::~VLPR()
 {
-
     configurationFile.release();
-    
 }
 
 ErrorCode VLPR::Initialize(InitializationParameterOfDB initParamOfDB, InitializationParameterOfSVTR initParamOfSVTR)

Src/main.cpp

@@ -20,11 +20,7 @@ int main()
     // 推理
     std::vector<std::string> recTexts;
     std::vector<float> recTextScores;
-    double time1 = cv::getTickCount();
     vlpr.Infer(Image, recTexts, recTextScores); 
-    double time2 = cv::getTickCount();
-    double elapsedTime = (time2 - time1)*1000 / cv::getTickFrequency();
-    LOG_INFO(stdout, "inference time:%f ms\n", elapsedTime);
 
     // 打印结果
     for (int i = 0; i < recTexts.size(); i++)