Merge pull request #2690 from littletomatodonkey/dyg/cp_user_pr

cherry-pick users' pr

deploy/cpp_infer/src/postprocess_op.cpp

@@ -186,18 +186,23 @@ float PostProcessor::PolygonScoreAcc(std::vector<cv::Point> contour,
   cv::Mat mask;
   mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
 
-  cv::Point rook_point[contour.size()];
+
+  cv::Point* rook_point = new cv::Point[contour.size()];
+   
   for (int i = 0; i < contour.size(); ++i) {
     rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin);
   }
   const cv::Point *ppt[1] = {rook_point};
   int npt[] = {int(contour.size())};
+
+
   cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
 
   cv::Mat croppedImg;
-  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
-      .copyTo(croppedImg);
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
   float score = cv::mean(croppedImg, mask)[0];
+
+  delete []rook_point;
   return score;
 }
 

tools/program.py

@@ -199,8 +199,12 @@ def train(config,
         train_reader_cost = 0.0
         batch_sum = 0
         batch_start = time.time()
-        for idx, batch in enumerate(train_dataloader()):
+        max_iter = len(train_dataloader) - 1 if platform.system(
+        ) == "Windows" else len(train_dataloader)
+        for idx, batch in enumerate(train_dataloader):
             train_reader_cost += time.time() - batch_start
+            if idx >= max_iter:
+                break
             lr = optimizer.get_lr()
             images = batch[0]
             if use_srn: