class DriveTrain:
###########################################################################
# data_path = 'path_to_drive_data' e.g. ../data/2017-09-22-10-12-34-56'
def __init__(self, data_path):
model_name = data_path[data_path.rfind('/'):] # get folder name
model_name = model_name.strip('/')
csv_path = data_path + '/' + model_name + '.csv' # use it for csv file name
self.csv_path = csv_path
self.train_generator = None
self.valid_generator = None
self.train_hist = None
self.drive = None
self.config = Config() #model_name)
self.data_path = data_path
#self.model_name = model_name
self.drive = DriveData(self.csv_path)
self.net_model = NetModel(data_path)
self.image_process = ImageProcess()
self.data_aug = DataAugmentation()
###########################################################################
#
def _prepare_data(self):
self.drive.read()
from sklearn.model_selection import train_test_split
samples = list(zip(self.drive.image_names, self.drive.measurements))
self.train_data, self.valid_data = train_test_split(
samples, test_size=self.config.valid_rate)
self.num_train_samples = len(self.train_data)
self.num_valid_samples = len(self.valid_data)
print('Train samples: ', self.num_train_samples)
print('Valid samples: ', self.num_valid_samples)
###########################################################################
#
def _build_model(self, show_summary=True):
def _generator(samples, batch_size=self.config.batch_size):
num_samples = len(samples)
while True: # Loop forever so the generator never terminates
samples = sklearn.utils.shuffle(samples)
for offset in range(0, num_samples, batch_size):
batch_samples = samples[offset:offset + batch_size]
images = []
measurements = []
for image_name, measurement in batch_samples:
image_path = self.data_path + '/' + image_name + \
self.config.fname_ext
image = cv2.imread(image_path)
image = cv2.resize(image, (self.config.image_size[0],
self.config.image_size[1]))
image = self.image_process.process(image)
images.append(image)
steering_angle, throttle = measurement
#if abs(steering_angle) < self.config.jitter_tolerance:
# steering_angle = 0
measurements.append(steering_angle)
#measurements.append(steering_angle*self.config.raw_scale)
###-----------------------Flipping the image-----------------------###
flip_image, flip_steering = self.data_aug.flipping(
image, steering_angle)
images.append(flip_image)
measurements.append(flip_steering)
'''
# add the flipped image of the original
images.append(cv2.flip(image,1))
#measurement = (steering_angle*-1.0, measurement[1])
measurements.append(steering_angle*-1.0)
#measurements.append(steering_angle*self.config.raw_scale*-1.0)
'''
###----------------Changing the brightness of image----------------###
if steering_angle > 0.01 or steering_angle < -0.015:
bright_image = self.data_aug.brightness(image)
images.append(bright_image)
measurements.append(steering_angle)
###-----------------------Shifting the image-----------------------###
shift_image, shift_steering = self.data_aug.shift(
image, steering_angle)
images.append(shift_image)
measurements.append(shift_steering)
X_train = np.array(images)
y_train = np.array(measurements)
if self.config.typeofModel == 4 or self.config.typeofModel == 5:
X_train = np.array(images).reshape(
-1, 1, self.config.image_size[1],
self.config.image_size[0],
self.config.image_size[2])
y_train = np.array(measurements).reshape(-1, 1, 1)
yield sklearn.utils.shuffle(X_train, y_train)
self.train_generator = _generator(self.train_data)
self.valid_generator = _generator(self.valid_data)
if (show_summary):
self.net_model.model.summary()
###########################################################################
#
def _start_training(self):
if (self.train_generator == None):
raise NameError('Generators are not ready.')
######################################################################
# callbacks
from keras.callbacks import ModelCheckpoint, EarlyStopping
# checkpoint
callbacks = []
checkpoint = ModelCheckpoint(self.net_model.name + '.h5',
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks.append(checkpoint)
# early stopping
earlystop = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=0,
verbose=1,
mode='min')
callbacks.append(earlystop)
self.train_hist = self.net_model.model.fit_generator(
self.train_generator,
steps_per_epoch=self.num_train_samples // self.config.batch_size,
epochs=self.config.num_epochs,
validation_data=self.valid_generator,
validation_steps=self.num_valid_samples // self.config.batch_size,
verbose=1,
callbacks=callbacks)
###########################################################################
#
def _plot_training_history(self):
print(self.train_hist.history.keys())
### plot the training and validation loss for each epoch
plt.plot(self.train_hist.history['loss'])
plt.plot(self.train_hist.history['val_loss'])
plt.ylabel('mse loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validatation set'], loc='upper right')
plt.show()
###########################################################################
#
def train(self, show_summary=True):
self._prepare_data()
self._build_model(show_summary)
self._start_training()
self.net_model.save()
self._plot_training_history()
class DriveTrain:
###########################################################################
# data_path = 'path_to_drive_data' e.g. ../data/2017-09-22-10-12-34-56/'
def __init__(self, data_path):
if data_path[-1] == '/':
data_path = data_path[:-1]
loc_slash = data_path.rfind('/')
if loc_slash != -1: # there is '/' in the data path
model_name = data_path[loc_slash + 1:] # get folder name
#model_name = model_name.strip('/')
else:
model_name = data_path
csv_path = data_path + '/' + model_name + const.DATA_EXT # use it for csv file name
self.csv_path = csv_path
self.train_generator = None
self.valid_generator = None
self.train_hist = None
self.drive = None
#self.config = Config() #model_name)
self.data_path = data_path
#self.model_name = model_name
self.drive = DriveData(self.csv_path)
self.net_model = NetModel(data_path)
self.image_process = ImageProcess()
self.data_aug = DataAugmentation()
###########################################################################
#
def _prepare_data(self):
self.drive.read()
from sklearn.model_selection import train_test_split
samples = list(zip(self.drive.image_names, self.drive.measurements))
self.train_data, self.valid_data = train_test_split(samples,
test_size=Config.config['validation_rate'])
self.num_train_samples = len(self.train_data)
self.num_valid_samples = len(self.valid_data)
print('Train samples: ', self.num_train_samples)
print('Valid samples: ', self.num_valid_samples)
###########################################################################
#
def _build_model(self, show_summary=True):
def _generator(samples, batch_size=Config.config['batch_size']):
num_samples = len(samples)
while True: # Loop forever so the generator never terminates
if Config.config['lstm'] is False:
samples = sklearn.utils.shuffle(samples)
for offset in range(0, num_samples, batch_size):
batch_samples = samples[offset:offset+batch_size]
images = []
measurements = []
for image_name, measurement in batch_samples:
image_path = self.data_path + '/' + image_name
image = cv2.imread(image_path)
image = cv2.resize(image,
(Config.config['input_image_width'],
Config.config['input_image_height']))
image = self.image_process.process(image)
images.append(image)
steering_angle, throttle = measurement
if abs(steering_angle) < Config.config['steering_angle_jitter_tolerance']:
steering_angle = 0
measurements.append(steering_angle*Config.config['steering_angle_scale'])
if Config.config['data_aug_flip'] is True:
# Flipping the image
flip_image, flip_steering = self.data_aug.flipping(image, steering_angle)
images.append(flip_image)
measurements.append(flip_steering*Config.config['steering_angle_scale'])
if Config.config['data_aug_bright'] is True:
# Changing the brightness of image
if steering_angle > Config.config['steering_angle_jitter_tolerance'] or \
steering_angle < -Config.config['steering_angle_jitter_tolerance']:
bright_image = self.data_aug.brightness(image)
images.append(bright_image)
measurements.append(steering_angle*Config.config['steering_angle_scale'])
if Config.config['data_aug_shift'] is True:
# Shifting the image
shift_image, shift_steering = self.data_aug.shift(image, steering_angle)
images.append(shift_image)
measurements.append(shift_steering*Config.config['steering_angle_scale'])
X_train = np.array(images)
y_train = np.array(measurements)
if Config.config['lstm'] is True:
X_train = np.array(images).reshape(-1, 1,
Config.config['input_image_height'],
Config.config['input_image_width'],
Config.config['input_image_depth'])
y_train = np.array(measurements).reshape(-1, 1, 1)
if Config.config['lstm'] is False:
yield sklearn.utils.shuffle(X_train, y_train)
else:
yield X_train, y_train
self.train_generator = _generator(self.train_data)
self.valid_generator = _generator(self.valid_data)
if (show_summary):
self.net_model.model.summary()
###########################################################################
#
def _start_training(self):
if (self.train_generator == None):
raise NameError('Generators are not ready.')
######################################################################
# callbacks
from keras.callbacks import ModelCheckpoint, EarlyStopping
# checkpoint
callbacks = []
#weight_filename = self.net_model.name + '_' + const.CONFIG_YAML + '_ckpt'
weight_filename = self.data_path + '_' + const.CONFIG_YAML + '_ckpt'
checkpoint = ModelCheckpoint(weight_filename+'.h5',
monitor='val_loss',
verbose=1, save_best_only=True, mode='min')
callbacks.append(checkpoint)
# early stopping
earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3,
verbose=1, mode='min')
callbacks.append(earlystop)
self.train_hist = self.net_model.model.fit_generator(
self.train_generator,
steps_per_epoch=self.num_train_samples//Config.config['batch_size'],
epochs=Config.config['num_epochs'],
validation_data=self.valid_generator,
validation_steps=self.num_valid_samples//Config.config['batch_size'],
verbose=1, callbacks=callbacks)
###########################################################################
#
def _plot_training_history(self):
print(self.train_hist.history.keys())
### plot the training and validation loss for each epoch
plt.plot(self.train_hist.history['loss'])
plt.plot(self.train_hist.history['val_loss'])
plt.ylabel('mse loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validatation set'], loc='upper right')
plt.show()
###########################################################################
#
def train(self, show_summary=True):
self._prepare_data()
self._build_model(show_summary)
self._start_training()
self.net_model.save()
self._plot_training_history()
Config.summary()
class DriveTrain:
###########################################################################
# data_path = 'path_to_drive_data' e.g. ../data/2017-09-22-10-12-34-56/'
def __init__(self, data_path):
if data_path[-1] == '/':
data_path = data_path[:-1]
loc_slash = data_path.rfind('/')
if loc_slash != -1: # there is '/' in the data path
model_name = data_path[loc_slash + 1:] # get folder name
#model_name = model_name.strip('/')
else:
model_name = data_path
csv_path = data_path + '/' + model_name + const.DATA_EXT # use it for csv file name
self.csv_path = csv_path
self.train_generator = None
self.valid_generator = None
self.train_hist = None
self.data = None
#self.config = Config() #model_name)
self.data_path = data_path
#self.model_name = model_name
self.model_name = data_path + '_' + Config.neural_net_yaml_name \
+ '_N' + str(config['network_type'])
self.model_ckpt_name = self.model_name + '_ckpt'
self.data = DriveData(self.csv_path)
self.net_model = NetModel(data_path)
self.image_process = ImageProcess()
self.data_aug = DataAugmentation()
###########################################################################
#
def _prepare_data(self):
self.data.read()
# put velocities regardless we use them or not for simplicity.
samples = list(
zip(self.data.image_names, self.data.velocities,
self.data.measurements))
if config['lstm'] is True:
self.train_data, self.valid_data = self._prepare_lstm_data(samples)
else:
self.train_data, self.valid_data = train_test_split(
samples, test_size=config['validation_rate'])
self.num_train_samples = len(self.train_data)
self.num_valid_samples = len(self.valid_data)
print('Train samples: ', self.num_train_samples)
print('Valid samples: ', self.num_valid_samples)
###########################################################################
# group the samples by the number of timesteps
def _prepare_lstm_data(self, samples):
num_samples = len(samples)
# get the last index number
steps = 1
last_index = (num_samples - config['lstm_timestep']) // steps
image_names = []
velocities = []
measurements = []
for i in range(0, last_index, steps):
sub_samples = samples[i:i + config['lstm_timestep']]
# print('num_batch_sample : ',len(batch_samples))
sub_image_names = []
sub_velocities = []
sub_measurements = []
for image_name, velocity, measurement in sub_samples:
sub_image_names.append(image_name)
sub_velocities.append(velocity)
sub_measurements.append(measurement)
image_names.append(sub_image_names)
velocities.append(sub_velocities)
measurements.append(sub_measurements)
samples = list(zip(image_names, velocities, measurements))
return train_test_split(samples,
test_size=config['validation_rate'],
shuffle=False)
###########################################################################
#
def _build_model(self, show_summary=True):
def _data_augmentation(image, steering_angle):
if config['data_aug_flip'] is True:
# Flipping the image
return True, self.data_aug.flipping(image, steering_angle)
if config['data_aug_bright'] is True:
# Changing the brightness of image
if steering_angle > config['steering_angle_jitter_tolerance'] or \
steering_angle < -config['steering_angle_jitter_tolerance']:
image = self.data_aug.brightness(image)
return True, image, steering_angle
if config['data_aug_shift'] is True:
# Shifting the image
return True, self.data_aug.shift(image, steering_angle)
return False, image, steering_angle
def _prepare_batch_samples(batch_samples):
images = []
velocities = []
measurements = []
for image_name, velocity, measurement in batch_samples:
image_path = self.data_path + '/' + image_name
image = cv2.imread(image_path)
# if collected data is not cropped then crop here
# otherwise do not crop.
if Config.data_collection['crop'] is not True:
image = image[
Config.data_collection['image_crop_y1']:Config.
data_collection['image_crop_y2'],
Config.data_collection['image_crop_x1']:Config.
data_collection['image_crop_x2']]
image = cv2.resize(image, (config['input_image_width'],
config['input_image_height']))
image = self.image_process.process(image)
images.append(image)
velocities.append(velocity)
# if no brake data in collected data, brake values are dummy
steering_angle, throttle, brake = measurement
if abs(steering_angle
) < config['steering_angle_jitter_tolerance']:
steering_angle = 0
if config['num_outputs'] == 2:
measurements.append(
(steering_angle * config['steering_angle_scale'],
throttle))
else:
measurements.append(steering_angle *
config['steering_angle_scale'])
# data augmentation
append, image, steering_angle = _data_augmentation(
image, steering_angle)
if append is True:
images.append(image)
velocities.append(velocity)
if config['num_outputs'] == 2:
measurements.append(
(steering_angle * config['steering_angle_scale'],
throttle))
else:
measurements.append(steering_angle *
config['steering_angle_scale'])
return images, velocities, measurements
def _prepare_lstm_batch_samples(batch_samples):
images = []
velocities = []
measurements = []
for i in range(0, config['batch_size']):
images_timestep = []
velocities_timestep = []
measurements_timestep = []
for j in range(0, config['lstm_timestep']):
image_name = batch_samples[i][0][j]
image_path = self.data_path + '/' + image_name
image = cv2.imread(image_path)
# if collected data is not cropped then crop here
# otherwise do not crop.
if Config.data_collection['crop'] is not True:
image = image[
Config.data_collection['image_crop_y1']:Config.
data_collection['image_crop_y2'],
Config.data_collection['image_crop_x1']:Config.
data_collection['image_crop_x2']]
image = cv2.resize(image, (config['input_image_width'],
config['input_image_height']))
image = self.image_process.process(image)
images_timestep.append(image)
velocity = batch_samples[i][1][j]
velocities_timestep.append(velocity)
if j is config['lstm_timestep'] - 1:
measurement = batch_samples[i][2][j]
# if no brake data in collected data, brake values are dummy
steering_angle, throttle, brake = measurement
if abs(steering_angle
) < config['steering_angle_jitter_tolerance']:
steering_angle = 0
if config['num_outputs'] == 2:
measurements_timestep.append(
(steering_angle *
config['steering_angle_scale'], throttle))
else:
measurements_timestep.append(
steering_angle *
config['steering_angle_scale'])
# data augmentation?
"""
append, image, steering_angle = _data_augmentation(image, steering_angle)
if append is True:
images_timestep.append(image)
measurements_timestep.append(steering_angle*config['steering_angle_scale'])
"""
images.append(images_timestep)
velocities.append(velocities_timestep)
measurements.append(measurements_timestep)
return images, velocities, measurements
def _generator(samples, batch_size=config['batch_size']):
num_samples = len(samples)
while True: # Loop forever so the generator never terminates
if config['lstm'] is True:
for offset in range(0, (num_samples // batch_size) *
batch_size, batch_size):
batch_samples = samples[offset:offset + batch_size]
images, velocities, measurements = _prepare_lstm_batch_samples(
batch_samples)
X_train = np.array(images)
y_train = np.array(measurements)
if config['num_inputs'] == 2:
X_train_vel = np.array(velocities).reshape(
-1, config['lstm_timestep'], 1)
X_train = [X_train, X_train_vel]
if config['num_outputs'] == 2:
y_train = np.stack(measurements).reshape(
-1, config['num_outputs'])
yield X_train, y_train
else:
samples = sklearn.utils.shuffle(samples)
for offset in range(0, num_samples, batch_size):
batch_samples = samples[offset:offset + batch_size]
images, velocities, measurements = _prepare_batch_samples(
batch_samples)
X_train = np.array(images).reshape(
-1, config['input_image_height'],
config['input_image_width'],
config['input_image_depth'])
y_train = np.array(measurements)
y_train = y_train.reshape(-1, 1)
if config['num_inputs'] == 2:
X_train_vel = np.array(velocities).reshape(-1, 1)
X_train = [X_train, X_train_vel]
yield X_train, y_train
self.train_generator = _generator(self.train_data)
self.valid_generator = _generator(self.valid_data)
if (show_summary):
self.net_model.model.summary()
###########################################################################
#
def _start_training(self):
if (self.train_generator == None):
raise NameError('Generators are not ready.')
######################################################################
# callbacks
from keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard
# checkpoint
callbacks = []
#weight_filename = self.data_path + '_' + Config.config_yaml_name \
# + '_N' + str(config['network_type']) + '_ckpt'
checkpoint = ModelCheckpoint(self.model_ckpt_name +
'.{epoch:02d}-{val_loss:.2f}.h5',
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks.append(checkpoint)
# early stopping
patience = config['early_stopping_patience']
earlystop = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=patience,
verbose=1,
mode='min')
callbacks.append(earlystop)
# tensor board
logdir = config['tensorboard_log_dir'] + datetime.now().strftime(
"%Y%m%d-%H%M%S")
tensorboard = TensorBoard(log_dir=logdir)
callbacks.append(tensorboard)
self.train_hist = self.net_model.model.fit_generator(
self.train_generator,
steps_per_epoch=self.num_train_samples // config['batch_size'],
epochs=config['num_epochs'],
validation_data=self.valid_generator,
validation_steps=self.num_valid_samples // config['batch_size'],
verbose=1,
callbacks=callbacks,
use_multiprocessing=True)
###########################################################################
#
def _plot_training_history(self):
print(self.train_hist.history.keys())
plt.figure() # new figure window
### plot the training and validation loss for each epoch
plt.plot(self.train_hist.history['loss'][1:])
plt.plot(self.train_hist.history['val_loss'][1:])
#plt.title('Mean Squared Error Loss')
plt.ylabel('mse loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validatation set'], loc='upper right')
plt.tight_layout()
#plt.show()
plt.savefig(self.model_name + '_model.png', dpi=150)
plt.savefig(self.model_name + '_model.pdf', dpi=150)
###########################################################################
#
def train(self, show_summary=True):
self._prepare_data()
self._build_model(show_summary)
self._start_training()
self.net_model.save(self.model_name)
self._plot_training_history()
Config.summary()