def parse_options():
sys.setrecursionlimit(40000)
parser = OptionParser()
parser.add_option("-p",
"--path",
dest="train_path",
help="Path to training data.")
parser.add_option("-n",
"--num_rois",
type="int",
dest="num_rois",
help="Number of RoIs to process at once.",
default=32)
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=
"Path to preload weights. (Default = 'vgg16_weights_tf_dim_ordering_tf_kernels.h5'"
)
parser.add_option("--loss_func",
dest="loss_func",
help="Specify loss function to use. (Default = frcnn)",
default='frcnn')
(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'
)
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)
# Specify CNN to use: either 'vgg'
C.network = 'vgg'
return options, C
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:
def train(im_size,
train_mode,
epoch_length=1000,
num_epochs=10,
val_steps=200,
lr_mode='adaptive'):
""" Train the model
Args:
im_size: user input. Input images are resized to this size.
train_mode: 1 to train new model with pre-trained weights on generic dataset.
2 to keep training on the WashingtonOB Race dataset.
epoch_length: number of steps per training epoch
num_epochs: maximum number of training epochs
val_steps: number of validation steps, at the end of each training epoch
lr_mode: if 'adaptive', learning rate varies with eponential decay. If 'constant', it is 1e-5.
Returns:
None - (trained model saved automatically)
"""
C = config.Config()
C.network = 'resnet50'
C.im_size = im_size
C.model_path = f'models/model_frcnn_{C.im_size}.hdf5'
all_imgs, classes_count, class_mapping = get_data(
'data/training/corners_training.txt')
# add background class
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
print(
f'Total number of objects per class (across all dataset): {class_mapping}'
)
config_output_filename = f'models/conf/config_frcnn_{C.im_size}.pickle'
record_path = f'models/logs/frcnn_{C.im_size}.csv' # Where to record data (used to save the losses, classification accuracy and mean average precision)
if lr_mode == 'adaptive':
record_path = f'models/logs/frcnn_{C.im_size}_adap.csv'
pickle.dump(C, open(config_output_filename, 'wb'))
train_imgs = [s for s in all_imgs if s['imageset'] == 'train']
val_imgs = [s for s in all_imgs if s['imageset'] == 'val']
# Shuffle the images with seed 1
random.seed(1)
random.shuffle(train_imgs)
random.shuffle(val_imgs)
print(
f'{len(train_imgs)} and {len(val_imgs)} training and validation samples (before augmentation), respectively.'
)
data_gen_train = regions_proposal_network.get_anchor_gt(
train_imgs, C, nn.get_img_output_length, mode='train')
data_gen_val = regions_proposal_network.get_anchor_gt(
val_imgs, C, nn.get_img_output_length, mode='val')
X, Y, image_data = next(data_gen_train)
print('Original image: height=%d width=%d' %
(image_data['height'], image_data['width']))
print('Resized image: height=%d width=%d' % (X.shape[1], X.shape[2]))
print('Feature map size: height=%d width=%d C.rpn_stride=%d' %
(Y[0].shape[1], Y[0].shape[2], C.rpn_stride))
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)
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))
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)
model_rpn, model_classifier, record_df = load_weights(
im_size, model_rpn, model_classifier, train_mode)
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')
# Training setting
total_epochs = len(record_df)
if len(record_df) == 0:
best_loss = np.Inf
else:
best_loss = np.min(record_df['curr_loss_val'])
print(
f'Resuming training. Already trained for {len(record_df)} epochs.')
validation_trend_hold = False
total_epochs += num_epochs
iter_num = 0
loss = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
print('Starting training')
start_time = time.time()
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 = []
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
# use next to extract data since it is a generator
X, Y, img_data = next(data_gen_train)
# Train rpn model and get loss value [_, loss_rpn_cls, loss_rpn_regr]
current_learning_rate = calculate_learning_rate(epoch_num,
mode=lr_mode)
K.set_value(model_rpn.optimizer.lr, current_learning_rate)
loss_rpn = model_rpn.train_on_batch(X, Y)
# Get predicted rpn from rpn model [rpn_cls, rpn_regr]
P_rpn = model_rpn.predict_on_batch(X)
# R: bboxes (shape=(im_size,4))
# Convert rpn layer to roi bboxes
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
overlap_thresh=0.6,
max_boxes=C.max_boxes)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
# X2: bboxes with iou > C.classifier_min_overlap for all gt bboxes in 20 non_max_suppression bboxes
# Y2: corresponding labels and corresponding ground truth bboxes
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 number of positive anchors is larger than 4//2 = 2, randomly choose 2 pos samples
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()
# Randomly choose (num_rois - num_pos) neg samples
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()
# Save all the pos and neg samples in sel_samples
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)
# training_data: [X, X2[:, sel_samples, :]]
# labels: [Y1[:, sel_samples, :], Y2[:, sel_samples, :]]
# X => img_data resized image
# X2[:, sel_samples, :] => num_rois (4 in here) bboxes which contains selected neg and pos
# Y1[:, sel_samples, :] => one hot encode for num_rois bboxes which contains selected neg and pos
# Y2[:, sel_samples, :] => labels and gt bboxes for num_rois bboxes which contains selected neg and pos
K.set_value(model_classifier.optimizer.lr,
current_learning_rate)
loss_class = model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
loss[iter_num, 0] = loss_rpn[1]
loss[iter_num, 1] = loss_rpn[2]
loss[iter_num, 2] = loss_class[1]
loss[iter_num, 3] = loss_class[2]
loss[iter_num, 4] = loss_class[3]
progbar.update(
iter_num + 1,
[('RPN Classifier Loss', loss[iter_num, 0]),
('RPN Regression Loss', loss[iter_num, 1]),
('Detector Classifier Loss', loss[iter_num, 2]),
('Detector Regression Loss', loss[iter_num, 3])])
iter_num += 1
# end of epoch check
if iter_num == epoch_length:
loss_rpn_cls = np.mean(loss[:, 0])
loss_rpn_regr = np.mean(loss[:, 1])
loss_class_cls = np.mean(loss[:, 2])
loss_class_regr = np.mean(loss[:, 3])
class_acc = np.mean(loss[:, 4])
print("Performing validation.")
val_loss = validate(val_steps, data_gen_val, model_rpn, C,
class_mapping, model_classifier)
mean_overlapping_bboxes = float(sum(
rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print(
f'Classifier accuracy for bounding boxes: {class_acc}'
)
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
if val_loss['curr_loss'] <= best_loss:
if C.verbose:
print(
f'Total validation loss decreased from {best_loss} to {val_loss["curr_loss"]}, saving weights.'
)
print('')
best_loss = val_loss['curr_loss']
model_all.save_weights(C.model_path)
validation_trend_hold = False
elif not validation_trend_hold:
if C.verbose:
print(
f'Total validation loss increased for the first time, from {best_loss} to {val_loss["curr_loss"]}. Performing one more epoch to verify trend. Not saving weights for now.'
)
print('')
validation_trend_hold = True
else:
if C.verbose:
print(
f'Total validation loss increased for the second time, from {best_loss} to {val_loss["curr_loss"]}.'
)
print(
f'Terminating training now to prevent over-fitting. Keeping weights from epoch {epoch_num - 1}.'
)
exit()
new_row = {
'mean_overlapping_bboxes':
round(mean_overlapping_bboxes, 3),
'class_acc':
round(class_acc, 3),
'loss_rpn_cls':
round(loss_rpn_cls, 3),
'loss_rpn_regr':
round(loss_rpn_regr, 3),
'loss_class_cls':
round(loss_class_cls, 3),
'loss_class_regr':
round(loss_class_regr, 3),
'curr_loss':
round(curr_loss, 3),
'class_acc_val':
round(val_loss['class_acc'], 3),
'curr_loss_val':
round(val_loss['curr_loss'], 3),
'elapsed_time':
round(time.time() - start_time, 3)
}
start_time = time.time()
record_df = record_df.append(new_row, ignore_index=True)
record_df.to_csv(record_path, index=False)
break
except Exception as e:
print(f'Exception: {e}')
continue
print('Training complete.')
return
def trainModel(options):
import random
import pprint
import sys
import time
import numpy as np
import pickle
import os
from keras import backend as K
from keras.optimizers import Adam, SGD, RMSprop
from keras.layers import Input
from keras.models import Model
from frcnn import config, data_generators
from frcnn import losses as losses
from frcnn import resnet as nn
import frcnn.roi_helpers as roi_helpers
from keras.utils import generic_utils
from frcnn.simple_parser import get_data
from frcnn.simple_parser import load_data
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
configTF = tf.ConfigProto()
configTF.gpu_options.allow_growth = True
sess = tf.Session(config=configTF)
sys.setrecursionlimit(40000)
# Config class stores all relevant settings to be recalled during test/further training
C = config.Config()
C.im_size = options.im_size
C.anchor_box_scales = options.anchor_box_scales
C.anchor_box_ratios = options.anchor_box_ratios
C.num_rois = int(options.num_rois)
C.use_horizontal_flips = bool(options.horizontal_flips)
C.use_vertical_flips = bool(options.vertical_flips)
C.rot_90 = bool(options.rot_90)
C.rpn_max_overlap = options.rpn_max_overlap_threshold
C.model_path = options.output_weight_path
C.balanced_classes = options.balanced_classes
C.rpn_stride = options.rpn_stride
C.cutImage = options.cutImage
# loading old weights to continue training
if options.load_weights:
C.base_net_weights = options.input_weight_path
# get_data() function returns image list along with info on bbox,
# height, width in all_imgs,and class data in classes_count and class_mapping
if options.load_data:
all_imgs, classes_count, class_mapping = load_data(options.name)
else:
all_imgs, classes_count, class_mapping = get_data(
options.train_path, C, options.name, options.num_frames)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
# making assigned value of class the key to index the dictionary
inv_map = {v: k for k, v in class_mapping.iteritems()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
print '\nUsing RPN Stride = %d' % C.rpn_stride
config_output_filename = options.config_filename
with open(config_output_filename, 'w') 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']
print('\nTraining on {} Frames'.format(len(train_imgs)))
# ground truth boxes are obtained
data_gen_train = data_generators.get_anchor_gt(train_imgs,
classes_count,
C,
K.image_dim_ordering(),
mode='train')
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=(C.num_rois, 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:
model_rpn.load_weights(C.base_net_weights, by_name=True)
model_classifier.load_weights(C.base_net_weights, by_name=True)
print('\nLoaded weights from {}'.format(C.base_net_weights))
except:
print('\nNo pretrained weights found in folder...')
print('Proceeding to train from scratch\n\n')
# setting learning rates and optimizers
optimizer = Adam(lr=1e-4)
optimizer_classifier = Adam(lr=1e-4)
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')
# if epoch_length was set as default, a epoch goes over entire trainset images
if options.epoch_length == 'default':
epoch_length = len(train_imgs)
# else it uses the number of images given
else:
epoch_length = int(options.epoch_length)
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.iteritems()}
print('Starting training\n')
prev_pos_samples = []
prev_neg_samples = []
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 iter_num == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
if mean_overlapping_bboxes == 0:
print(
'\n\nRPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.\n'
)
# date_gen_train is a generator defined in data_generators.py
# it reads the the image with the filepath in image list and returns relevant data for training
X, Y, img_data = data_gen_train.next()
# train_on_batch() is the keras function defined in Sequential.
# does a single gradient update on given data (essentially, one epoch)
loss_rpn = model_rpn.train_on_batch(X, Y)
# uses trained model to make prediction
P_rpn = model_rpn.predict_on_batch(X)
# converting RPN prediction to ROI
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 = roi_helpers.calc_iou(R, img_data, C,
class_mapping)
# if no ROI is detected
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:
# Take half of positive samples and half of negative samples for classfier training
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, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
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])),
('rpn_overlap', float(sum(rpn_accuracy_rpn_monitor)) /
len(rpn_accuracy_rpn_monitor))])
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(
'\n---------------------------------------------------------------------------------------'
)
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(
'\nTotal loss decreased from {} to {}'.format(
best_loss, curr_loss))
print(
'---------------------------------------------------------------------------------------\n'
)
best_loss = curr_loss
# saving weights with smallest loss (overwrite)
model_all.save_weights(C.model_path)
else:
if C.verbose:
print('\nLoss did not improve')
print(
'---------------------------------------------------------------------------------------\n'
)
# also saving weights for each epoch
model_all.save_weights(C.model_path[:-5] + '_%03d' %
(epoch_num + 1) +
'_%2.2f' % mean_overlapping_bboxes +
'.hdf5')
break
except Exception as e:
print('\nException: {}\n'.format(e))
continue
print('\n-----------------\nTraining complete!\n')