修改动态推理C++示例

Src/YOLOV5.cpp

@@ -54,15 +54,15 @@ ErrorCode DetectorYOLOV5::Initialize(InitializationParameterOfDetector initializ
     yolov5Parameter.numberOfClasses=(int)netNode["NumberOfClasses"];
     useFP16=(bool)(int)netNode["UseFP16"];
 
-    if(Exists(modelPath)==false)
-    {
-        LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
-        return MODEL_NOT_EXIST;
-    }
-
-    // 加载模型
     if(dynamic)
     {
+        // 加载模型
+        if(Exists(modelPath)==false)
+        {
+            LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
+            return MODEL_NOT_EXIST;
+        }
+        
         migraphx::onnx_options onnx_options;
         onnx_options.map_input_dims["images"]={1,3,800,800};// 
         net = migraphx::parse_onnx(modelPath, onnx_options);
@@ -83,6 +83,12 @@ ErrorCode DetectorYOLOV5::Initialize(InitializationParameterOfDetector initializ
     }
     else
     {
+        // 加载模型
+        if(Exists(modelPath)==false)
+        {
+            LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
+            return MODEL_NOT_EXIST;
+        }
         net = migraphx::parse_onnx(modelPath);
         LOG_INFO(stdout,"succeed to load model: %s\n",GetFileName(modelPath).c_str());
 
@@ -142,11 +148,12 @@ ErrorCode DetectorYOLOV5::Initialize(InitializationParameterOfDetector initializ
         classNames.resize(yolov5Parameter.numberOfClasses);
     }
 
+
     return SUCCESS;
 
 }
 
-ErrorCode DetectorYOLOV5::Detect(const cv::Mat &srcImage, std::vector<ResultOfDetection> &resultsOfDetection, bool dynamic)
+ErrorCode DetectorYOLOV5::Detect(const cv::Mat &srcImage, std::vector<std::size_t> &relInputShape, std::vector<ResultOfDetection> &resultsOfDetection, bool dynamic)
 {
     if(srcImage.empty()||srcImage.type()!=CV_8UC3)
     {
@@ -154,33 +161,16 @@ ErrorCode DetectorYOLOV5::Detect(const cv::Mat &srcImage, std::vector<ResultOfDe
         return IMAGE_ERROR;
     }
 
-    // 数据预处理
+    // 数据预处理并转换为NCHW格式
+    inputSize = cv::Size(relInputShape[3], relInputShape[2]);
     cv::Mat inputBlob;
-    std::vector<std::size_t> relInputShape;
-    int height, width;
-    if(dynamic)
-    {
-        width = srcImage.rows;
-        height = srcImage.cols;
-        relInputShape = {1,3,height,width};
-        cv::dnn::blobFromImage(srcImage,
-                    inputBlob,
-                    1 / 255.0,
-                    cv::Size(width, height),
-                    cv::Scalar(0, 0, 0),
-                    true,
-                    false);
-    }
-    else
-    {
-        cv::dnn::blobFromImage(srcImage,
+    cv::dnn::blobFromImage(srcImage,
                     inputBlob,
                     1 / 255.0,
                     inputSize,
                     cv::Scalar(0, 0, 0),
                     true,
                     false);
-    }
 
     // 创建输入数据
     migraphx::parameter_map inputData;
@@ -193,6 +183,7 @@ ErrorCode DetectorYOLOV5::Detect(const cv::Mat &srcImage, std::vector<ResultOfDe
         inputData[inputName]= migraphx::argument{inputShape, (float*)inputBlob.data};
     }
     
+
     // 推理
     std::vector<migraphx::argument> inferenceResults = net.eval(inputData);
 

Src/YOLOV5.h

@@ -25,7 +25,7 @@ public:
 
     ErrorCode Initialize(InitializationParameterOfDetector initializationParameterOfDetector, bool dynamic);
 
-    ErrorCode Detect(const cv::Mat &srcImage, std::vector<ResultOfDetection> &resultsOfDetection, bool dynamic);
+    ErrorCode Detect(const cv::Mat &srcImage, std::vector<std::size_t> &relInputShape, std::vector<ResultOfDetection> &resultsOfDetection, bool dynamic);
 
 private:
     cv::FileStorage configurationFile;

Src/main.cpp

@@ -65,11 +65,14 @@ void Sample_YOLOV5()
 
     // 读取测试图片
     cv::Mat srcImage = cv::imread("../Resource/Images/DynamicPics/image1.jpg",1);
+
+    // 静态推理固定尺寸
+    std::vector<std::size_t> inputShape={1,3,608,608};
     
     // 推理
     std::vector<migraphxSamples::ResultOfDetection> predictions;
     double time1 = cv::getTickCount();
-    detector.Detect(srcImage, predictions, false);
+    detector.Detect(srcImage,inputShape,predictions,false);
     double time2 = cv::getTickCount();
     double elapsedTime = (time2 - time1)*1000 / cv::getTickFrequency();
     LOG_INFO(stdout, "inference time:%f ms\n", elapsedTime);
@@ -119,7 +122,7 @@ void Sample_YOLOV5_Dynamic()
     cv::glob(folder,imagePathList);
     for (int i = 0; i < imagePathList.size(); ++i)
     {
-        cv::Mat srcImage=cv::imread(imagePathList[i], 1);
+        cv:: Mat srcImage=cv::imread(imagePathList[i], 1);
         srcImages.push_back(srcImage); 
     }
 
@@ -130,13 +133,10 @@ void Sample_YOLOV5_Dynamic()
 
     for (int i = 0; i < srcImages.size(); ++i)
     {
-        // 生成不同尺寸的图像
-        cv::resize(srcImages[i], srcImages[i], cv::Size(inputShapes[i][3], inputShapes[i][2]));
-
         // 推理
         std::vector<migraphxSamples::ResultOfDetection> predictions;
         double time1 = cv::getTickCount();
-        detector.Detect(srcImages[i], predictions, true);
+        detector.Detect(srcImages[i], inputShapes[i], predictions, true);
         double time2 = cv::getTickCount();
         double elapsedTime = (time2 - time1)*1000 / cv::getTickFrequency();
         LOG_INFO(stdout, "inference image%d time:%f ms\n", i, elapsedTime);