入口

if __name__ == '__main__': #主文件入口
  args = parse_args()#解析參數(shù)

  print('Called with args:')
  print(args)

  if args.cfg_file is not None:
    cfg_from_file(args.cfg_file)#加載配置文件并合入到默認(rèn)項(xiàng)
  if args.set_cfgs is not None:
    cfg_from_list(args.set_cfgs)#加載配置列表并合入到默認(rèn)項(xiàng)

  print('Using config:')
  pprint.pprint(cfg)

  np.random.seed(cfg.RNG_SEED)#生成隨機(jī)種子，預(yù)測(cè)隨機(jī)值

  # 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()
  else:
    raise NotImplementedError
    
  train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
            pretrained_model=args.weight,
            max_iters=args.max_iters)

combined_roidb(imdb_names)

def combined_roidb(imdb_names):
  """
  Combine multiple roidbs
  """
  #內(nèi)部函數(shù)
  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