text: from __future__ import print_function
  import argparse
  import logging
  
  import mxnet as mx
  
  from rcnn.config import config, default, generate_config
  from rcnn.tools.train_rpn import train_rpn
  from rcnn.tools.test_rpn import test_rpn
  from rcnn.tools.train_rcnn import train_rcnn
  from rcnn.utils.combine_model import combine_model
  
  
  def alternate_train(args, ctx, pretrained, epoch,
                      rpn_epoch, rpn_lr, rpn_lr_step,
                      rcnn_epoch, rcnn_lr, rcnn_lr_step):
      # set up logger
      logging.basicConfig()
      logger = logging.getLogger()
      logger.setLevel(logging.INFO)
  
      # basic config
      begin_epoch = 0
      config.TRAIN.BG_THRESH_LO = 0.0
  
      logging.info('########## TRAIN RPN WITH IMAGENET INIT')
      train_rpn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path,
                args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume,
                ctx, pretrained, epoch, 'model/rpn1', begin_epoch, rpn_epoch,
                train_shared=False, lr=rpn_lr, lr_step=rpn_lr_step)
  
      logging.info('########## GENERATE RPN DETECTION')
      image_sets = [iset for iset in args.image_set.split('+')]
      for image_set in image_sets:
          test_rpn(args.network, args.dataset, image_set, args.root_path, args.dataset_path,
                   ctx[0], 'model/rpn1', rpn_epoch,
                   vis=False, shuffle=False, thresh=0)
  
      logging.info('########## TRAIN RCNN WITH IMAGENET INIT AND RPN DETECTION')
      train_rcnn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path,
                 args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume,
                 ctx, pretrained, epoch, 'model/rcnn1', begin_epoch, rcnn_epoch,
                 train_shared=False, lr=rcnn_lr, lr_step=rcnn_lr_step, proposal='rpn')
  
      logging.info('########## TRAIN RPN WITH RCNN INIT')
      train_rpn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path,
                args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume,
                ctx, 'model/rcnn1', rcnn_epoch, 'model/rpn2', begin_epoch, rpn_epoch,
                train_shared=True, lr=rpn_lr, lr_step=rpn_lr_step)
  
      logging.info('########## GENERATE RPN DETECTION')
      image_sets = [iset for iset in args.image_set.split('+')]
      for image_set in image_sets:
          test_rpn(args.network, args.dataset, image_set, args.root_path, args.dataset_path,
                   ctx[0], 'model/rpn2', rpn_epoch,
                   vis=False, shuffle=False, thresh=0)
  
      logger.info('########## COMBINE RPN2 WITH RCNN1')
      combine_model('model/rpn2', rpn_epoch, 'model/rcnn1', rcnn_epoch, 'model/rcnn2', 0)
  
      logger.info('########## TRAIN RCNN WITH RPN INIT AND DETECTION')
      train_rcnn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path,
                 args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume,
                 ctx, 'model/rcnn2', 0, 'model/rcnn2', begin_epoch, rcnn_epoch,
                 train_shared=True, lr=rcnn_lr, lr_step=rcnn_lr_step, proposal='rpn')
  
      logger.info('########## COMBINE RPN2 WITH RCNN2')
      combine_model('model/rpn2', rpn_epoch, 'model/rcnn2', rcnn_epoch, 'model/final', 0)
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Train Faster R-CNN Network')
      # general
      parser.add_argument('--network', help='network name', default=default.network, type=str)
      parser.add_argument('--dataset', help='dataset name', default=default.dataset, type=str)
      args, rest = parser.parse_known_args()
      generate_config(args.network, args.dataset)
      parser.add_argument('--image_set', help='image_set name', default=default.image_set, type=str)
      parser.add_argument('--root_path', help='output data folder', default=default.root_path, type=str)
      parser.add_argument('--dataset_path', help='dataset path', default=default.dataset_path, type=str)
      # training
      parser.add_argument('--frequent', help='frequency of logging', default=default.frequent, type=int)
      parser.add_argument('--kvstore', help='the kv-store type', default=default.kvstore, type=str)
      parser.add_argument('--work_load_list', help='work load for different devices', default=None, type=list)
      parser.add_argument('--no_flip', help='disable flip images', action='store_true')
      parser.add_argument('--no_shuffle', help='disable random shuffle', action='store_true')
      parser.add_argument('--resume', help='continue training', action='store_true')
      # alternate
      parser.add_argument('--gpus', help='GPU device to train with', default='0', type=str)
      parser.add_argument('--pretrained', help='pretrained model prefix', default=default.pretrained, type=str)
      parser.add_argument('--pretrained_epoch', help='pretrained model epoch', default=default.pretrained_epoch, type=int)
      parser.add_argument('--rpn_epoch', help='end epoch of rpn training', default=default.rpn_epoch, type=int)
      parser.add_argument('--rpn_lr', help='base learning rate', default=default.rpn_lr, type=float)
      parser.add_argument('--rpn_lr_step', help='learning rate steps (in epoch)', default=default.rpn_lr_step, type=str)
      parser.add_argument('--rcnn_epoch', help='end epoch of rcnn training', default=default.rcnn_epoch, type=int)
      parser.add_argument('--rcnn_lr', help='base learning rate', default=default.rcnn_lr, type=float)
      parser.add_argument('--rcnn_lr_step', help='learning rate steps (in epoch)', default=default.rcnn_lr_step, type=str)
      args = parser.parse_args()
      return args
  
  
  def main():
      args = parse_args()
      print('Called with argument:', args)
      ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')]
      alternate_train(args, ctx, args.pretrained, args.pretrained_epoch,
                      args.rpn_epoch, args.rpn_lr, args.rpn_lr_step,
                      args.rcnn_epoch, args.rcnn_lr, args.rcnn_lr_step)
  
  if __name__ == '__main__':
      main()
  