[dev] fix bug of cpp deploy when benchmark=True (#3392)

* fix bug when benchmark=True

* split warmup and repeat

deploy/cpp/src/main.cc

@@ -280,12 +280,15 @@ void PredictImage(const std::vector<std::string> all_img_paths,
 
       int item_start_idx = 0;
       for (int i = 0; i < left_image_cnt; i++) {
-        std::cout << all_img_paths.at(idx * batch_size + i) << "result" << std::endl;
+        std::cout << all_img_paths.at(idx * batch_size + i) << " bbox_num " << bbox_num[i] << std::endl;
         if (bbox_num[i] <= 1) {
             continue;
         }
         for (int j = 0; j < bbox_num[i]; j++) {
           PaddleDetection::ObjectResult item = result[item_start_idx + j];
+          if (item.confidence < threshold) {
+            continue;
+          }
           if (item.rect.size() > 6){
             is_rbox = true;
             printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",

deploy/cpp/src/object_detector.cc

@@ -176,18 +176,16 @@ void ObjectDetector::Postprocess(
     bool is_rbox=false) {
   result->clear();
   int start_idx = 0;
-  for (int im_id = 0; im_id < bbox_num.size(); im_id++) {
+  for (int im_id = 0; im_id < mats.size(); im_id++) {
     cv::Mat raw_mat = mats[im_id];
-    for (int j = start_idx; j < start_idx+bbox_num[im_id]; j++) {
     int rh = 1;
     int rw = 1;
     if (config_.arch_ == "Face") {
       rh = raw_mat.rows;
       rw = raw_mat.cols;
     }
-
+    for (int j = start_idx; j < start_idx+bbox_num[im_id]; j++) {
       if (is_rbox) {
-        for (int j = 0; j < bbox_num[im_id]; ++j) {
         // Class id
         int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
         // Confidence score
@@ -200,17 +198,14 @@ void ObjectDetector::Postprocess(
         int y3 = (output_data_[7 + j * 10] * rh);
         int x4 = (output_data_[8 + j * 10] * rw);
         int y4 = (output_data_[9 + j * 10] * rh);
-          if (score > threshold_ && class_id > -1) {
+          
         ObjectResult result_item;
         result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
         result_item.class_id = class_id;
         result_item.confidence = score;
         result->push_back(result_item);
       }
-        }
-      }
       else {
-        for (int j = 0; j < bbox_num[im_id]; ++j) {
         // Class id
         int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
         // Confidence score
@@ -221,7 +216,7 @@ void ObjectDetector::Postprocess(
         int ymax = (output_data_[5 + j * 6] * rh);
         int wd = xmax - xmin;
         int hd = ymax - ymin;
-          if (score > threshold_ && class_id > -1) {
+          
         ObjectResult result_item;
         result_item.rect = {xmin, ymin, xmax, ymax};
         result_item.class_id = class_id;
@@ -229,8 +224,6 @@ void ObjectDetector::Postprocess(
         result->push_back(result_item);
       }
     }
-      }
-    }
     start_idx += bbox_num[im_id];
   }
 }
@@ -265,6 +258,7 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
   }
 
   // Prepare input tensor
+  
   auto input_names = predictor_->GetInputNames();
   for (const auto& tensor_name : input_names) {
     auto in_tensor = predictor_->GetInputHandle(tensor_name);
@@ -281,8 +275,10 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
       in_tensor->CopyFromCpu(scale_factor_all.data());
     }
   }
+  
   auto preprocess_end = std::chrono::steady_clock::now();
   // Run predictor
+  // warmup
   for (int i = 0; i < warmup; i++)
   {
     predictor_->Run();
@@ -290,13 +286,26 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
     auto output_names = predictor_->GetOutputNames();
     auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
     std::vector<int> output_shape = out_tensor->shape();
+    auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
+    std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
     // Calculate output length
     int output_size = 1;
+    for (int j = 0; j < output_shape.size(); ++j) {
+      output_size *= output_shape[j];
+    }
+
     if (output_size < 6) {
       std::cerr << "[WARNING] No object detected." << std::endl;
     }
     output_data_.resize(output_size);
     out_tensor->CopyToCpu(output_data_.data()); 
+
+    int out_bbox_num_size = 1;
+    for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
+      out_bbox_num_size *= out_bbox_num_shape[j];
+    }
+    out_bbox_num_data_.resize(out_bbox_num_size);
+    out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
   }
   
   bool is_rbox = false;
@@ -333,6 +342,7 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
   auto inference_end = std::chrono::steady_clock::now();
   auto postprocess_start = std::chrono::steady_clock::now();
   // Postprocessing result
+  result->clear();
   Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
   bbox_num->clear();
   for (int k=0; k<out_bbox_num_data_.size(); k++) {