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.utils.data_utils import get_file

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=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()

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)

# download weights if there is not available

KERAS_MODEL_BASEURL = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/'

weights_path = get_file(C.base_net_weights,

KERAS_MODEL_BASEURL + C.base_net_weights,

cache_subdir='models')

try:

print('loading weights {}'.format(C.base_net_weights))

model_rpn.load_weights(weights_path, by_name=True)

model_classifier.load_weights(weights_path, 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 = 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

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.')