text: # --------------------------------------------------------
  # Tensorflow Faster R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Zheqi He, Xinlei Chen, based on code from Ross Girshick
  # --------------------------------------------------------
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import _init_paths
  from model.train_val import get_training_roidb, train_net
  from model.config import cfg, cfg_from_file, cfg_from_list, get_output_dir, get_output_tb_dir
  from datasets.factory import get_imdb
  import datasets.imdb
  import argparse
  import pprint
  import numpy as np
  import sys
  
  import tensorflow as tf
  from nets.vgg16 import vgg16
  from nets.resnet_v1 import resnetv1
  from nets.mobilenet_v1 import mobilenetv1
  from nets.zfnet import zfnet
  
  def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default=None, type=str)
    parser.add_argument('--weight', dest='weight',
                        help='initialize with pretrained model weights',
                        type=str)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to train on',
                        default='voc_2007_trainval', type=str)
    parser.add_argument('--imdbval', dest='imdbval_name',
                        help='dataset to validate on',
                        default='voc_2007_test', type=str)
    parser.add_argument('--iters', dest='max_iters',
                        help='number of iterations to train',
                        default=70000, type=int)
    parser.add_argument('--tag', dest='tag',
                        help='tag of the model',
                        default=None, type=str)
    parser.add_argument('--net', dest='net',
                        help='vgg16, res50, res101, res152, mobile',
                        default='res50', type=str)
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)
  
    if len(sys.argv) == 1:
      parser.print_help()
      sys.exit(1)
  
    args = parser.parse_args()
    return args
  
  
  def combined_roidb(imdb_names):
    """
    Combine multiple roidbs
    """
  
    def get_roidb(imdb_name):
      imdb = get_imdb(imdb_name)
      print('Loaded dataset `{:s}` for training'.format(imdb.name))
      imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
      print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD))
      roidb = get_training_roidb(imdb)
      return roidb
  
    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
    roidb = roidbs[0]
    if len(roidbs) > 1:
      for r in roidbs[1:]:
        roidb.extend(r)
      tmp = get_imdb(imdb_names.split('+')[1])
      imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
    else:
      imdb = get_imdb(imdb_names)
    return imdb, roidb
  
  
  if __name__ == '__main__':
    args = parse_args()
  
    print('Called with args:')
    print(args)
  
    if args.cfg_file is not None:
      cfg_from_file(args.cfg_file)
    if args.set_cfgs is not None:
      cfg_from_list(args.set_cfgs)
  
    print('Using config:')
    pprint.pprint(cfg)
  
    np.random.seed(cfg.RNG_SEED)
  
    # train set
    imdb, roidb = combined_roidb(args.imdb_name)
    print('{:d} roidb entries'.format(len(roidb)))
  
    # output directory where the models are saved
    output_dir = get_output_dir(imdb, args.tag)
    print('Output will be saved to `{:s}`'.format(output_dir))
  
    # tensorboard directory where the summaries are saved during training
    tb_dir = get_output_tb_dir(imdb, args.tag)
    print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
  
    # also add the validation set, but with no flipping images
    orgflip = cfg.TRAIN.USE_FLIPPED
    cfg.TRAIN.USE_FLIPPED = False
    _, valroidb = combined_roidb(args.imdbval_name)
    print('{:d} validation roidb entries'.format(len(valroidb)))
    cfg.TRAIN.USE_FLIPPED = orgflip
  
    # load network
    if args.net == 'vgg16':
      net = vgg16()
    elif args.net == 'res50':
      net = resnetv1(num_layers=50)
    elif args.net == 'res101':
      net = resnetv1(num_layers=101)
    elif args.net == 'res152':
      net = resnetv1(num_layers=152)
    elif args.net == 'mobile':
      net = mobilenetv1()
    elif args.net == 'zfnet':
      net = zfnet()
    else:
      raise NotImplementedError
      
    train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
              pretrained_model=args.weight,
              max_iters=args.max_iters)
  