[Hackthon_4th 177] Support PP-YOLOE-R with BM1684 (#1809)

* first draft

* add robx iou

* add benchmark for ppyoloe_r

* remove trash code

* fix bugs

* add pybind nms rotated option

* add missing head file

* fix bug

* fix bug2

* fix shape bug

---------

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

examples/vision/detection/paddledetection/python/infer_ppyoloe_r.py

@@ -0,0 +1,78 @@
+import cv2
+import os
+
+import fastdeploy as fd
+print(111)
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_dir",
+        default=None,
+        help="Path of PaddleDetection model directory")
+    parser.add_argument(
+        "--image", default=None, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "kunlunxin":
+        option.use_kunlunxin()
+
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+    return option
+
+
+args = parse_arguments()
+
+if args.model_dir is None:
+    model_dir = fd.download_model(name='ppyoloe_crn_l_300e_coco')
+else:
+    model_dir = args.model_dir
+
+model_file = os.path.join(model_dir, "model.pdmodel")
+params_file = os.path.join(model_dir, "model.pdiparams")
+config_file = os.path.join(model_dir, "infer_cfg.yml")
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+print(args)
+model = fd.vision.detection.PPYOLOER(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+print(2222)
+
+# 预测图片检测结果
+if args.image is None:
+    image = fd.utils.get_detection_test_image()
+else:
+    image = args.image
+im = cv2.imread(image)
+result = model.predict(im)
+print(result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")