text: import time
  from absl import app, flags, logging
  from absl.flags import FLAGS
  import cv2
  import numpy as np
  import tensorflow as tf
  from yolov3_tf2.models import (
      YoloV3, YoloV3Tiny
  )
  from yolov3_tf2.dataset import transform_images
  
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.util import nest
  
  flags.DEFINE_string('weights', './checkpoints/yolov3.tf',
                      'path to weights file')
  flags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')
  flags.DEFINE_string('output', './serving/yolov3/1', 'path to saved_model')
  flags.DEFINE_string('classes', './data/coco.names', 'path to classes file')
  flags.DEFINE_string('image', './data/girl.png', 'path to input image')
  flags.DEFINE_integer('num_classes', 80, 'number of classes in the model')
  
  
  # TODO: remove this after upstream fix
  # modified from: tensorflow.python.keras.saving.saving_utils.trace_model_call
  def trace_model_call(model):
      inputs = model.inputs
      input_names = model.input_names
  
      input_signature = []
      for input_tensor, input_name in zip(inputs, input_names):
          input_signature.append(tensor_spec.TensorSpec(
              shape=input_tensor.shape, dtype=input_tensor.dtype,
              name=input_name))
  
      @def_function.function(input_signature=input_signature, autograph=False)
      def _wrapped_model(*args):
          inputs = args[0] if len(input_signature) == 1 else list(args)
          outputs_list = nest.flatten(model(inputs=inputs))
          output_names = model.output_names
          return {"{}_{}".format(kv[0], i): kv[1] for i, kv in enumerate(
              zip(output_names, outputs_list))}
  
      return _wrapped_model
  
  
  def main(_argv):
      if FLAGS.tiny:
          yolo = YoloV3Tiny(classes=FLAGS.num_classes)
      else:
          yolo = YoloV3(classes=FLAGS.num_classes)
  
      yolo.load_weights(FLAGS.weights)
      logging.info('weights loaded')
  
      tf.saved_model.save(yolo, FLAGS.output, signatures=trace_model_call(yolo))
      logging.info("model saved to: {}".format(FLAGS.output))
  
      model = tf.saved_model.load(FLAGS.output)
      infer = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
      logging.info(infer.structured_outputs)
  
      class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
      logging.info('classes loaded')
  
      img = tf.image.decode_image(open(FLAGS.image, 'rb').read(), channels=3)
      img = tf.expand_dims(img, 0)
      img = transform_images(img, 416)
  
      t1 = time.time()
      outputs = infer(img)
      boxes, scores, classes, nums = outputs["yolo_nms_0"], outputs[
          "yolo_nms_1_1"], outputs["yolo_nms_2_2"], outputs["yolo_nms_3_3"]
      t2 = time.time()
      logging.info('time: {}'.format(t2 - t1))
  
      logging.info('detections:')
      for i in range(nums[0]):
          logging.info('\t{}, {}, {}'.format(class_names[int(classes[0][i])],
                                             scores[0][i].numpy(),
                                             boxes[0][i].numpy()))
  
  
  if __name__ == '__main__':
      try:
          app.run(main)
      except SystemExit:
          pass
  