text: import os
  import glob
  import json
  import argparse
  
  from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
  from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint
  from tensorflow.keras.optimizers import Adam
  
  from paz.backend.image import write_image
  from paz.abstract import GeneratingSequence
  from paz.optimization.callbacks import DrawInferences
  from paz.pipelines import AutoEncoderPredictor
  
  from poseur.scenes import SingleView
  
  from pipelines import DomainRandomization
  from model import AutoEncoder
  
  description = 'Training script for learning implicit orientation vector'
  root_path = os.path.join(os.path.expanduser('~'), '.keras/paz/')
  parser = argparse.ArgumentParser(description=description)
  parser.add_argument('-op', '--obj_path', type=str, help='Path of 3D OBJ model',
                      default=os.path.join(
                          root_path,
                          'datasets/ycb/models/035_power_drill/textured.obj'))
  parser.add_argument('-cl', '--class_name', default='035_power_drill', type=str,
                      help='Class name to be added to model save path')
  parser.add_argument('-id', '--images_directory', type=str,
                      help='Path to directory containing background images',
                      default=os.path.join(
                          root_path, 'datasets/voc-backgrounds/'))
  parser.add_argument('-bs', '--batch_size', default=32, type=int,
                      help='Batch size for training')
  parser.add_argument('-lr', '--learning_rate', default=0.001, type=float,
                      help='Initial learning rate for Adam')
  parser.add_argument('-is', '--latent_dimension', default=128, type=int,
                      help='Latent dimension of the auto-encoder')
  parser.add_argument('-ld', '--image_size', default=128, type=int,
                      help='Size of the side of a square image e.g. 64')
  parser.add_argument('-sp', '--stop_patience', default=7, type=int,
                      help='Number of epochs before doing early stopping')
  parser.add_argument('-pp', '--plateau_patience', default=3, type=int,
                      help='Number of epochs before reducing learning rate')
  parser.add_argument('-e', '--max_num_epochs', default=10000, type=int,
                      help='Maximum number of epochs before finishing')
  parser.add_argument('-st', '--steps_per_epoch', default=1000, type=int,
                      help='Steps per epoch')
  parser.add_argument('-sh', '--sphere', default='full',
                      choices=['full', 'half'], type=str,
                      help='Flag for full sphere or top half for rendering')
  parser.add_argument('-ls', '--loss', default='binary_crossentropy', type=str,
                      help='tf.keras loss function name to be used')
  parser.add_argument('-r', '--roll', default=3.14159, type=float,
                      help='Threshold for camera roll in radians')
  parser.add_argument('-t', '--translation', default=0.05, type=float,
                      help='Threshold for translation')
  parser.add_argument('-s', '--shift', default=0.05, type=float,
                      help='Threshold of random shift of camera')
  parser.add_argument('-d', '--depth', nargs='+', type=float,
                      default=[0.3, 0.5],
                      help='Distance from camera to origin in meters')
  parser.add_argument('-fv', '--y_fov', default=3.14159 / 4.0, type=float,
                      help='Field of view angle in radians')
  parser.add_argument('-l', '--light', nargs='+', type=float,
                      default=[.5, 30],
                      help='Light intensity from poseur')
  parser.add_argument('-oc', '--num_occlusions', default=2, type=int,
                      help='Number of occlusions')
  parser.add_argument('-sa', '--save_path',
                      default=os.path.join(
                          os.path.expanduser('~'), '.keras/paz/models'),
                      type=str, help='Path for writing model weights and logs')
  args = parser.parse_args()
  
  
  # setting optimizer and compiling model
  latent_dimension = args.latent_dimension
  model = AutoEncoder((args.image_size, args.image_size, 3), latent_dimension)
  optimizer = Adam(args.learning_rate, amsgrad=True)
  model.compile(optimizer, args.loss, metrics=['mse'])
  model.summary()
  
  # setting scene
  renderer = SingleView(
      args.obj_path, (args.image_size, args.image_size),
      args.y_fov, args.depth, args.sphere, args.roll,
      args.translation, args.shift, args.light)
  
  # creating sequencer
  image_paths = glob.glob(os.path.join(args.images_directory, '*.png'))
  processor = DomainRandomization(
      renderer, args.image_size, image_paths, args.num_occlusions)
  sequence = GeneratingSequence(processor, args.batch_size, args.steps_per_epoch)
  
  # making directory for saving model weights and logs
  model_name = '_'.join([model.name, str(latent_dimension), args.class_name])
  save_path = os.path.join(args.save_path, model_name)
  if not os.path.exists(save_path):
      os.makedirs(save_path)
  
  # setting callbacks
  log = CSVLogger(os.path.join(save_path, '%s.log' % model_name))
  stop = EarlyStopping('loss', patience=args.stop_patience, verbose=1)
  plateau = ReduceLROnPlateau('loss', patience=args.plateau_patience, verbose=1)
  model_path = os.path.join(save_path, '%s_weights.hdf5' % model_name)
  save = ModelCheckpoint(
      model_path, 'loss', verbose=1, save_best_only=True, save_weights_only=True)
  
  # setting drawing callbacks
  images = (sequence.__getitem__(0)[0]['input_image'] * 255).astype('uint8')
  for arg, image in enumerate(images):
      image_name = 'image_%03d.png' % arg
      image_path = os.path.join(save_path, 'original_images/' + image_name)
      write_image(image_path, image)
  inferencer = AutoEncoderPredictor(model)
  draw = DrawInferences(save_path, images, inferencer)
  
  # saving hyper-parameters and model summary as text files
  print(save_path)
  with open(os.path.join(save_path, 'hyperparameters.json'), 'w') as filer:
      json.dump(args.__dict__, filer, indent=4)
  with open(os.path.join(save_path, 'model_summary.txt'), 'w') as filer:
      model.summary(print_fn=lambda x: filer.write(x + '\n'))
  
  # model optimization
  model.fit_generator(
      sequence,
      steps_per_epoch=args.steps_per_epoch,
      epochs=args.max_num_epochs,
      callbacks=[stop, log, save, plateau, draw],
      verbose=1,
      workers=0)
  