text: from __future__ import division
  import random
  import pprint
  import sys
  import time
  import numpy as np
  from optparse import OptionParser
  import os
  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 frcnn_losses
  from keras_frcnn import tf_pascal_voc_parser
  import keras_frcnn.roi_helpers as roi_helpers
  from keras.utils import generic_utils
  
  from vvc.tunner import config as tunner_config
  
  sys.setrecursionlimit(40000)
  
  class FRCNNTunner(object):
  	
  	def __init__(self):
  		pass
  		
  def save_config(c, filename):
  	
  	with open(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(filename))
  	
  
  def train_frcnn(model_name, trainable_nn_base, ):
  
  	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", 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", dest="num_epochs", help="Number of epochs.", default=2000)
  	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()
  	
  	model_folder = os.path.join(tunner_config.MODELS_FOLDER, model_name)
  	
  	options.config_filename = os.path.join(model_folder, 'config.pickle')
  	options.input_weight_path = tunner_config.RESNET50_H5
  	options.train_path = tunner_config.TRAIN_PATH
  	options.num_epochs = 4
  	options.output_weight_path = os.path.join(model_folder, 'model.hdf5')
  	options.parser = 'tf_pascal_voc'
  	
  	os.makedirs(model_folder, exist_ok=True)
  	
  	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
  	elif options.parser == 'tf_pascal_voc':
  		get_data = tf_pascal_voc_parser.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)))
  	
  	
  	save_config(C, options.config_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=trainable_nn_base)
  	
  	# 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=[frcnn_losses.rpn_loss_cls(num_anchors), frcnn_losses.rpn_loss_regr(num_anchors)])
  	model_classifier.compile(optimizer=optimizer_classifier, loss=[frcnn_losses.class_loss_cls, frcnn_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 = 1000
  	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]
  	
  				iter_num += 1
  	
  				if iter_num % 100 == 0:
  					progbar.update(iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),
  										  ('detector_cls', np.mean(losses[:iter_num, 2])), ('detector_regr', np.mean(losses[:iter_num, 3]))])
  	
  				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.')
  	
  if __name__ == '__main__':
  	train_frcnn(model_name='frcnn-resnet50-tunned', trainable_nn_base=False)
  	train_frcnn(model_name='frcnn-resnet50-transfer', trainable_nn_base=True)
  	
  	