text: from __future__ import division
  import random
  import pprint
  import sys
  import time
  import numpy as np
  from optparse import OptionParser
  import pickle
  
  from keras import backend as K
  from keras.optimizers import Adam, SGD, RMSprop
  from keras.layers import Input
  from keras.models import Model
  from keras_frcnn import config, data_generators
  from keras_frcnn import losses as losses
  import keras_frcnn.roi_helpers as roi_helpers
  from keras.utils import generic_utils
  
  sys.setrecursionlimit(40000)
  
  parser = OptionParser()
  
  parser.add_option("-p", "--path", dest="train_path", help="Path to training data.")
  parser.add_option("-o", "--parser", dest="parser", help="Parser to use. One of simple or pascal_voc",
  				default="pascal_voc")
  parser.add_option("-n", "--num_rois", type="int", dest="num_rois", help="Number of RoIs to process at once.", default=32)
  parser.add_option("--network", dest="network", help="Base network to use. Supports vgg or resnet50.", default='resnet50')
  parser.add_option("--hf", dest="horizontal_flips", help="Augment with horizontal flips in training. (Default=false).", action="store_true", default=False)
  parser.add_option("--vf", dest="vertical_flips", help="Augment with vertical flips in training. (Default=false).", action="store_true", default=False)
  parser.add_option("--rot", "--rot_90", dest="rot_90", help="Augment with 90 degree rotations in training. (Default=false).",
  				  action="store_true", default=False)
  parser.add_option("--num_epochs", type="int", dest="num_epochs", help="Number of epochs.", default=3000)
  parser.add_option("--config_filename", dest="config_filename", help=
  				"Location to store all the metadata related to the training (to be used when testing).",
  				default="config.pickle")
  parser.add_option("--output_weight_path", dest="output_weight_path", help="Output path for weights.", default='./model_frcnn.hdf5')
  parser.add_option("--input_weight_path", dest="input_weight_path", help="Input path for weights. If not specified, will try to load default weights provided by keras.")
  
  (options, args) = parser.parse_args()
  
  if not options.train_path:   # if filename is not given
  	parser.error('Error: path to training data must be specified. Pass --path to command line')
  
  if options.parser == 'pascal_voc':
  	from keras_frcnn.pascal_voc_parser import get_data
  elif options.parser == 'simple':
  	from keras_frcnn.simple_parser import get_data
  else:
  	raise ValueError("Command line option parser must be one of 'pascal_voc' or 'simple'")
  
  # pass the settings from the command line, and persist them in the config object
  C = config.Config()
  
  C.use_horizontal_flips = bool(options.horizontal_flips)
  C.use_vertical_flips = bool(options.vertical_flips)
  C.rot_90 = bool(options.rot_90)
  
  C.model_path = options.output_weight_path
  C.num_rois = int(options.num_rois)
  
  if options.network == 'vgg':
  	C.network = 'vgg'
  	from keras_frcnn import vgg as nn
  elif options.network == 'resnet50':
  	from keras_frcnn import resnet as nn
  	C.network = 'resnet50'
  else:
  	print('Not a valid model')
  	raise ValueError
  
  
  # check if weight path was passed via command line
  if options.input_weight_path:
  	C.base_net_weights = options.input_weight_path
  else:
  	# set the path to weights based on backend and model
  	C.base_net_weights = nn.get_weight_path()
  
  all_imgs, classes_count, class_mapping = get_data(options.train_path)
  
  if 'bg' not in classes_count:
  	classes_count['bg'] = 0
  	class_mapping['bg'] = len(class_mapping)
  
  C.class_mapping = class_mapping
  
  inv_map = {v: k for k, v in class_mapping.items()}
  
  print('Training images per class:')
  pprint.pprint(classes_count)
  print('Num classes (including bg) = {}'.format(len(classes_count)))
  
  config_output_filename = options.config_filename
  
  with open(config_output_filename, 'wb') as config_f:
  	pickle.dump(C,config_f)
  	print('Config has been written to {}, and can be loaded when testing to ensure correct results'.format(config_output_filename))
  
  random.shuffle(all_imgs)
  
  num_imgs = len(all_imgs)
  
  train_imgs = [s for s in all_imgs if s['imageset'] == 'trainval']
  val_imgs = [s for s in all_imgs if s['imageset'] == 'test']
  
  print('Num train samples {}'.format(len(train_imgs)))
  print('Num val samples {}'.format(len(val_imgs)))
  
  
  data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='train')
  data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, nn.get_img_output_length,K.image_dim_ordering(), mode='val')
  
  if K.image_dim_ordering() == 'th':
  	input_shape_img = (3, None, None)
  else:
  	input_shape_img = (None, None, 3)
  
  img_input = Input(shape=input_shape_img)
  roi_input = Input(shape=(None, 4))
  
  # define the base network (resnet here, can be VGG, Inception, etc)
  shared_layers = nn.nn_base(img_input, trainable=True)
  
  # define the RPN, built on the base layers
  num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
  rpn = nn.rpn(shared_layers, num_anchors)
  
  classifier = nn.classifier(shared_layers, roi_input, C.num_rois, nb_classes=len(classes_count), trainable=True)
  
  model_rpn = Model(img_input, rpn[:2])
  model_classifier = Model([img_input, roi_input], classifier)
  
  # this is a model that holds both the RPN and the classifier, used to load/save weights for the models
  model_all = Model([img_input, roi_input], rpn[:2] + classifier)
  
  try:
  	print('loading weights from {}'.format(C.base_net_weights))
  	model_rpn.load_weights(C.base_net_weights, by_name=True)
  	model_classifier.load_weights(C.base_net_weights, by_name=True)
  except:
  	print('Could not load pretrained model weights. Weights can be found in the keras application folder \
  		https://github.com/fchollet/keras/tree/master/keras/applications')
  
  optimizer = Adam(lr=1e-5)
  optimizer_classifier = Adam(lr=1e-5)
  model_rpn.compile(optimizer=optimizer, loss=[losses.rpn_loss_cls(num_anchors), losses.rpn_loss_regr(num_anchors)])
  model_classifier.compile(optimizer=optimizer_classifier, loss=[losses.class_loss_cls, losses.class_loss_regr(len(classes_count)-1)], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
  model_all.compile(optimizer='sgd', loss='mae')
  
  epoch_length = 64
  num_epochs = int(options.num_epochs)
  iter_num = 0
  
  losses = np.zeros((epoch_length, 5))
  rpn_accuracy_rpn_monitor = []
  rpn_accuracy_for_epoch = []
  start_time = time.time()
  
  best_loss = np.Inf
  
  class_mapping_inv = {v: k for k, v in class_mapping.items()}
  print('Starting training')
  
  vis = True
  
  for epoch_num in range(num_epochs):
  
  	progbar = generic_utils.Progbar(epoch_length)
  	print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
  
  	while True:
  		try:
  
  			if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
  				mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
  				rpn_accuracy_rpn_monitor = []
  				print('Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(mean_overlapping_bboxes, epoch_length))
  				if mean_overlapping_bboxes == 0:
  					print('RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
  
  			X, Y, img_data = next(data_gen_train)
  
  			loss_rpn = model_rpn.train_on_batch(X, Y)
  
  			P_rpn = model_rpn.predict_on_batch(X)
  
  			R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)
  			# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
  			X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, C, class_mapping)
  
  			if X2 is None:
  				rpn_accuracy_rpn_monitor.append(0)
  				rpn_accuracy_for_epoch.append(0)
  				continue
  
  			neg_samples = np.where(Y1[0, :, -1] == 1)
  			pos_samples = np.where(Y1[0, :, -1] == 0)
  
  			if len(neg_samples) > 0:
  				neg_samples = neg_samples[0]
  			else:
  				neg_samples = []
  
  			if len(pos_samples) > 0:
  				pos_samples = pos_samples[0]
  			else:
  				pos_samples = []
  			
  			rpn_accuracy_rpn_monitor.append(len(pos_samples))
  			rpn_accuracy_for_epoch.append((len(pos_samples)))
  
  			if C.num_rois > 1:
  				if len(pos_samples) < C.num_rois//2:
  					selected_pos_samples = pos_samples.tolist()
  				else:
  					selected_pos_samples = np.random.choice(pos_samples, C.num_rois//2, replace=False).tolist()
  				try:
  					selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()
  				except:
  					selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()
  
  				sel_samples = selected_pos_samples + selected_neg_samples
  			else:
  				# in the extreme case where num_rois = 1, we pick a random pos or neg sample
  				selected_pos_samples = pos_samples.tolist()
  				selected_neg_samples = neg_samples.tolist()
  				if np.random.randint(0, 2):
  					sel_samples = random.choice(neg_samples)
  				else:
  					sel_samples = random.choice(pos_samples)
  
  			loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
  
  			losses[iter_num, 0] = loss_rpn[1]
  			losses[iter_num, 1] = loss_rpn[2]
  
  			losses[iter_num, 2] = loss_class[1]
  			losses[iter_num, 3] = loss_class[2]
  			losses[iter_num, 4] = loss_class[3]
  
  			progbar.update(iter_num+1, [('rpn_cls', losses[iter_num, 0]), ('rpn_regr', losses[iter_num, 1]),
  									  ('detector_cls', losses[iter_num, 2]), ('detector_regr', losses[iter_num, 3])])
  
  			iter_num += 1
  			
  			if iter_num == epoch_length:
  				loss_rpn_cls = np.mean(losses[:, 0])
  				loss_rpn_regr = np.mean(losses[:, 1])
  				loss_class_cls = np.mean(losses[:, 2])
  				loss_class_regr = np.mean(losses[:, 3])
  				class_acc = np.mean(losses[:, 4])
  
  				mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
  				rpn_accuracy_for_epoch = []
  
  				if C.verbose:
  					print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
  					print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
  					print('Loss RPN classifier: {}'.format(loss_rpn_cls))
  					print('Loss RPN regression: {}'.format(loss_rpn_regr))
  					print('Loss Detector classifier: {}'.format(loss_class_cls))
  					print('Loss Detector regression: {}'.format(loss_class_regr))
  					print('Elapsed time: {}'.format(time.time() - start_time))
  
  				curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
  				iter_num = 0
  				start_time = time.time()
  
  				if curr_loss < best_loss:
  					if C.verbose:
  						print('Total loss decreased from {} to {}, saving weights'.format(best_loss,curr_loss))
  					best_loss = curr_loss
  					model_all.save_weights(C.model_path)
  
  				break
  
  		except Exception as e:
  			print('Exception: {}'.format(e))
  			continue
  
  print('Training complete, exiting.')
  