class_docstr: Args:
      use_gpu (bool): whether use gpu
      threshold (float): threshold to reserve the result for output.
class_docstr_tok: ["Args",":","use_gpu","(","bool",")",":","whether","use","gpu","threshold","(","float",")",":","threshold","to","reserve","the","result","for","output","."]
docstr: Args:
      image (str/np.ndarray): path of image/ np.ndarray read by cv2
      threshold (float): threshold of predicted box' score
      visualization (bool): Whether to save visualization result.
      save_dir (str): save path.
docstr_tok: ["Args",":","image","(","str\/np.ndarray",")",":","path","of","image\/","np.ndarray","read","by","cv2","threshold","(","float",")",":","threshold","of","predicted","box","'","score","visualization","(","bool",")",":","Whether","to","save","visualization","result",".","save_dir","(","str",")",":","save","path","."]
code: def predict(
      self,
      image: Union[str, np.ndarray],
      threshold: float = 0.5,
      visualization: bool = False,
      save_dir: str = "solov2_result",
  ):
      """
      Args:
          image (str/np.ndarray): path of image/ np.ndarray read by cv2
          threshold (float): threshold of predicted box' score
          visualization (bool): Whether to save visualization result.
          save_dir (str): save path.
  
      """
  
      inputs, im_info = self.transform(image)
      np_label, np_score, np_segms = None, None, None
  
      input_names = self.predictor.get_input_names()
      for i in range(len(input_names)):
          input_tensor = self.predictor.get_input_handle(input_names[i])
          input_tensor.copy_from_cpu(inputs[input_names[i]])
  
      self.predictor.run()
      output_names = self.predictor.get_output_names()
      np_label = self.predictor.get_output_handle(
          output_names[1]
      ).copy_to_cpu()
      np_score = self.predictor.get_output_handle(
          output_names[2]
      ).copy_to_cpu()
      np_segms = self.predictor.get_output_handle(
          output_names[3]
      ).copy_to_cpu()
      output = dict(segm=np_segms, label=np_label, score=np_score)
  
      if visualization:
          if not os.path.exists(save_dir):
              os.makedirs(save_dir)
          image = D.visualize_box_mask(
              im=image, results=output, threshold=threshold
          )
          name = str(time.time()) + ".png"
          save_path = os.path.join(save_dir, name)
          image.save(save_path)
      return output
  
imports: import base64
  import os
  import time
  from functools import reduce
  from typing import Union
  import numpy
  import solov2.data_feed
  import solov2.processor
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import serving
label_desc: Use image node_id 2 for calling the DetectorSOLOv2 obj's underlying member method code with example usage: obj.predict(image, threshold, visualization, save_dir) and returns: output