class_docstr: set config of preprocess, postprocess and visualize
  Args:
      model_dir (str): root path of model.yml
class_docstr_tok: ["set","config","of","preprocess",",","postprocess","and","visualize","Args",":","model_dir","(","str",")",":","root","path","of","model.yml"]
code: def __init__(self, model_dir):
      # parsing Yaml config for Preprocess
      deploy_file = os.path.join(model_dir, "infer_cfg.yml")
      with open(deploy_file) as f:
          yml_conf = yaml.safe_load(f)
      self.check_model(yml_conf)
      self.arch = yml_conf["arch"]
      self.archcls = KEYPOINT_SUPPORT_MODELS[yml_conf["arch"]]
      self.preprocess_infos = yml_conf["Preprocess"]
      self.min_subgraph_size = yml_conf["min_subgraph_size"]
      self.labels = yml_conf["label_list"]
      self.tagmap = False
      self.use_dynamic_shape = yml_conf["use_dynamic_shape"]
      if "keypoint_bottomup" == self.archcls:
          self.tagmap = True
      self.print_config()
  
imports: import math
  import os
  import cv2
  import numpy
  import yaml
  from .infer import Detector
  from .keypoint_postprocess import HRNetPostProcess
  from .keypoint_preprocess import expand_crop
  from .visualize import visualize_pose
label_desc: Use image node_id 1 to create a new PredictConfig_KeyPoint object  with example: obj = PredictConfig_KeyPoint(model_dir)