text: import argparse
  
  parser = argparse.ArgumentParser()
  
  parser.description='''
  python trans_tools/run_with_lite.py \
  --image_path = /data/CelebAMask-HQ/2.jpg \
  --lite_path = ./graph.tflite \
  --output_path = ./seg.png
  '''
  
  parser.add_argument("--image_path", help="input image file path", type=str)
  parser.add_argument("--lite_path", help="tflite file path", type=str)
  parser.add_argument("--output_path", default = "./seg.png", help="output image path", type=str)
  
  args = parser.parse_args()
  
  
  import tensorflow as tf
  import cv2
  import numpy as np
  
  colormap = np.asarray([
      [0,0,0], [0,0,128], [0,128,0], [0,128,128],[128,0,0], 
      [128,0,128], [128,128,0], [128,128,128], [0,0,64], [0,0,192],
      [0,128,64], [0,128,192], [128, 0, 64], [128,64,0], [128,128,64],
      [128,128,192], [0,192,128], [0,64,128],[0,192,0], [0,64,0],[128,0,192]
  ])
  
  # Load TFLite model and allocate tensors.
  interpreter = tf.lite.Interpreter(model_path=args.lite_path)
  
  interpreter.allocate_tensors()
  
  # Get input and output tensors.
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()
  all_details = interpreter.get_tensor_details()
  
  input_shape = input_details[0]['shape']
  input_data = cv2.imread(args.image_path)
  input_data = input_data[:,:,::-1]
  input_data = [input_data]
  
  interpreter.set_tensor(input_details[0]['index'], input_data)
  
  interpreter.invoke()
  
  output_data = interpreter.get_tensor(output_details[0]['index'])
  cv2.imwrite(args.output_path, colormap[output_data[0,:,:,0]])
  