code: def __init__(
      self,
      model_pb_path,
      label_path,
      prob_threshold=0.4,
      iou_threshold=0.3,
  ):
      self.classes = list(
          map(lambda x: x.strip(), open(label_path, "r").readlines())
      )
      self.num_classes = len(self.classes)
      self.prob_threshold = prob_threshold
      self.iou_threshold = iou_threshold
      self.mean = np.array(
          [103.53, 116.28, 123.675], dtype=np.float32
      ).reshape(1, 1, 3)
      self.std = np.array(
          [57.375, 57.12, 58.395], dtype=np.float32
      ).reshape(1, 1, 3)
      so = ort.SessionOptions()
      so.log_severity_level = 3
      self.net = ort.InferenceSession(model_pb_path, so)
      inputs_name = [a.name for a in self.net.get_inputs()]
      inputs_shape = {
          k: v.shape for k, v in zip(inputs_name, self.net.get_inputs())
      }
      self.input_shape = inputs_shape["image"][2:]
  
file_path: PaddleDetection/deploy/third_engine/demo_onnxruntime/infer_demo.py
imports: import cv2
  import numpy
  import argparse
  import onnxruntime
  from pathlib import Path
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new PicoDet object  with example: obj = PicoDet(model_pb_path, label_path, prob_threshold, iou_threshold)