# --------------------------------------------------------
# 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 random import shuffle
import random

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, imdb

  roidb, imdb = get_roidb(imdb_names)
  return imdb, roidb


if __name__ == '__main__':
  VAL_PERCENT = 0.1

  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)
  random.seed(cfg.RNG_SEED)
  
  # train set
  imdb, roidb = combined_roidb(args.imdb_name)
  shuffle(roidb)
  thresh = int(len(roidb) * (1. - VAL_PERCENT))
  trainroidb = roidb[:thresh]
  valroidb = roidb[thresh:]
  val_im = [r["image"] for r in valroidb]
  # filter val images from train (might be the flipped version)
  trainroidb = [r for r in trainroidb if r["image"] not in val_im]

  print('{:d} train roidb entries'.format(len(trainroidb)))

  # 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
  print('{:d} validation roidb entries'.format(len(valroidb)))
  cfg.TRAIN.USE_FLIPPED = orgflip


  net = resnetv1(imdb.nof_ent_classes, imdb.nof_rel_classes, num_layers=101)
 

  train_net(net, imdb, trainroidb, valroidb, output_dir, tb_dir,
            pretrained_model=args.weight,
            max_iters=args.max_iters)
