docstr: Detect images
  
  Args:
      imgpath(str): Image path .
      filelist(str): Path to get label name.
      visualization(bool): Whether to save result image.
      save_path(str) : Path to save detected images.
  
  Returns:
      boxes(np.ndarray): Predict box information.
      scores(np.ndarray): Predict score.
      labels(np.ndarray): Predict labels.
code: def predict(
      self,
      imgpath: str,
      filelist: str,
      visualization: bool = True,
      save_path: str = "result",
  ):
      """
      Detect images
  
      Args:
          imgpath(str): Image path .
          filelist(str): Path to get label name.
          visualization(bool): Whether to save result image.
          save_path(str) : Path to save detected images.
  
      Returns:
          boxes(np.ndarray): Predict box information.
          scores(np.ndarray): Predict score.
          labels(np.ndarray): Predict labels.
      """
      self.eval()
      with paddle.no_grad():
          boxes = []
          scores = []
          self.downsample = 32
          im = self.transform(imgpath)
          h, w, c = utils.img_shape(imgpath)
          im_shape = paddle.to_tensor(
              np.array([[h, w]]).astype("int32")
          )
          label_names = utils.get_label_infos(filelist)
          img_data = paddle.to_tensor(np.array([im]).astype("float32"))
  
          outputs = self(img_data)
  
          for i, out in enumerate(outputs):
              anchor_mask = self.anchor_masks[i]
              mask_anchors = []
              for m in anchor_mask:
                  mask_anchors.append((self.anchors[2 * m]))
                  mask_anchors.append(self.anchors[2 * m + 1])
  
              box, score = F.yolo_box(
                  x=out,
                  img_size=im_shape,
                  anchors=mask_anchors,
                  class_num=self.class_num,
                  conf_thresh=self.valid_thresh,
                  downsample_ratio=self.downsample,
                  name="yolo_box" + str(i),
              )
  
              boxes.append(box)
              scores.append(paddle.transpose(score, perm=[0, 2, 1]))
              self.downsample //= 2
  
          yolo_boxes = paddle.concat(boxes, axis=1)
          yolo_scores = paddle.concat(scores, axis=2)
  
          pred = F.multiclass_nms(
              bboxes=yolo_boxes,
              scores=yolo_scores,
              score_threshold=self.valid_thresh,
              nms_top_k=self.nms_topk,
              keep_top_k=self.nms_posk,
              nms_threshold=self.nms_thresh,
              background_label=-1,
          )
  
          bboxes = pred.numpy()
          labels = bboxes[:, 0].astype("int32")
          scores = bboxes[:, 1].astype("float32")
          boxes = bboxes[:, 2:].astype("float32")
  
          if visualization:
              if not os.path.exists(save_path):
                  os.mkdir(save_path)
              utils.draw_boxes_on_image(
                  imgpath,
                  boxes,
                  scores,
                  labels,
                  label_names,
                  0.5,
                  save_path,
              )
  
          return boxes, scores, labels
  
imports: import time
  import os
  import base64
  import argparse
  from typing import List, Union, Tuple
  from collections import OrderedDict
  import cv2
  import paddle
  import numpy
  import paddle.nn
  import paddle.nn.functional
  from PIL import Image
  import paddlehub.vision.transforms
  import paddlehub.vision.functional
  from paddlehub.vision import utils
  from paddlehub.module.module import serving, RunModule, runnable
  from paddlehub.utils.utils import base64_to_cv2, cv2_to_base64, Version
label_desc: Use image node_id 3 for calling the Yolov3Module obj's underlying member method code with example usage: obj.predict(imgpath, filelist, visualization, save_path) and returns: boxes,  scores,  labels