text: #!/usr/bin/env python
  #-*- coding:utf-8 _*-
  """
  @author:liruihui
  @file: train.py
  @time: 2019/09/17
  @contact: ruihuili.lee@gmail.com
  @github: https://liruihui.github.io/
  @description:
  """
  import os
  import pprint
  pp = pprint.PrettyPrinter()
  from datetime import datetime
  
  from Generation.model_test import Model
  from Generation.config import opts
  
  if __name__ == '__main__':
  
  
      #opts.pretrain_model_G = "Chair_G.pth"
      opts.pretrain_model_G = "airplane_G.pth"
      #opts.pretrain_model_G = "animal-pose_G.pth"
      #opts.pretrain_model_G = "bottle_G.pth"
      #opts.pretrain_model_G = "guitar_G.pth"
      #opts.log_dir = "trained_models"
  
      model = Model(opts)
  
      model.draw_correspondense() # draw the correspondense between sphere and shape
      #model.draw_shape_intepolate() # shape inteporlate
      #model.draw_part_shape_inte()  # shape inteporlate vs part-wise shape inteporlate
      #model.draw_part_shape_inte_detail() # shape inteporlate vs multi-path part-wise shape inteporlate
  
      #model.draw_part_edit() # random change the noise on selected region
      #model.draw_part_flip() # negative the noise vector along x,y,z zxis
      #model.draw_edit_inte() # combine for part edit & part/shape interpolate
  
      #model.draw_part_exchange() # exchange the noise vector of two regions of two shape
  