def train(tubs,
new_model_path,
base_model_path=None,
train_split=.08,
batch_size=64):
"""
use the specified data in tub_names to train an artifical neural network
saves the output trained model as model_name
"""
X_keys = ['cam/image_array']
y_keys = ['user/angle', 'user/throttle']
new_model_path = os.path.expanduser(new_model_path)
kl = KerasLinear()
if base_model_path is not None:
base_model_path = os.path.expanduser(base_model_path)
kl.load(base_model_path)
print('tubs')
tubgroup = TubGroup(tubs)
train_gen, val_gen = tubgroup.get_train_val_gen(X_keys,
y_keys,
batch_size=batch_size,
train_frac=train_split)
total_records = len(tubgroup.df)
total_train = int(total_records * train_split)
total_val = total_records - total_train
print('train: %d, validation: %d' % (total_train, total_val))
steps_per_epoch = total_train // batch_size
print('steps_per_epoch', steps_per_epoch)
kl.train(train_gen,
val_gen,
saved_model_path=new_model_path,
steps=steps_per_epoch,
train_split=train_split)
def train(cfg, tub_names, new_model_path, base_model_path=None):
"""
use the specified data in tub_names to train an artifical neural network
saves the output trained model as model_name
"""
X_keys = ['cam/image_array']
y_keys = ['user/angle', 'user/throttle']
new_model_path = os.path.expanduser(new_model_path)
kl = KerasLinear()
if base_model_path is not None:
base_model_path = os.path.expanduser(base_model_path)
kl.load(base_model_path)
print('tub_names', tub_names)
if not tub_names:
tub_names = os.path.join(cfg.DATA_PATH, '*')
tubgroup = TubGroup(tub_names)
train_gen, val_gen = tubgroup.get_train_val_gen(
X_keys,
y_keys,
batch_size=cfg.BATCH_SIZE,
train_frac=cfg.TRAIN_TEST_SPLIT)
total_records = len(tubgroup.df)
total_train = int(total_records * cfg.TRAIN_TEST_SPLIT)
total_val = total_records - total_train
print('train: %d, validation: %d' % (total_train, total_val))
steps_per_epoch = total_train // cfg.BATCH_SIZE
print('steps_per_epoch', steps_per_epoch)
kl.train(train_gen,
val_gen,
saved_model_path=new_model_path,
steps=steps_per_epoch,
train_split=cfg.TRAIN_TEST_SPLIT)
def drive(cfg, model_path=None, use_chaos=False):
"""
"""
V = dk.vehicle.Vehicle()
clock = Timestamp()
V.add(clock, outputs=['timestamp'])
cam = PiCamera(resolution=cfg.CAMERA_RESOLUTION)
V.add(cam, outputs=['cam/image_array'], threaded=True)
ctr = LocalWebController(use_chaos=use_chaos)
V.add(ctr,
inputs=['cam/image_array'],
outputs=['user/angle', 'user/throttle', 'user/mode', 'recording'],
threaded=True)
# See if we should even run the pilot module.
# This is only needed because the part run_condition only accepts boolean
pilot_condition_part = MakeRunConditionBoolean()
V.add(pilot_condition_part, inputs=['user/mode'], outputs=['run_pilot'])
# Run the pilot if the mode is not user.
kl = KerasLinear()
if model_path:
kl.load(model_path)
V.add(kl,
inputs=['cam/image_array'],
outputs=['pilot/angle', 'pilot/throttle'],
run_condition='run_pilot')
state_controller = StateController()
V.add(state_controller,
inputs=[
'user/mode', 'user/angle', 'user/throttle', 'pilot/angle',
'pilot/throttle'
],
outputs=['angle', 'throttle'])
steering_controller = PCA9685(cfg.STEERING_CHANNEL)
steering = PWMSteering(controller=steering_controller,
left_pulse=cfg.STEERING_LEFT_PWM,
right_pulse=cfg.STEERING_RIGHT_PWM)
throttle_controller = PCA9685(cfg.THROTTLE_CHANNEL)
throttle = PWMThrottle(controller=throttle_controller,
max_pulse=cfg.THROTTLE_FORWARD_PWM,
zero_pulse=cfg.THROTTLE_STOPPED_PWM,
min_pulse=cfg.THROTTLE_REVERSE_PWM)
V.add(steering, inputs=['angle'])
V.add(throttle, inputs=['throttle'])
# add tub to save data
inputs = [
'cam/image_array', 'user/angle', 'user/throttle', 'user/mode',
'timestamp'
]
types = ['image_array', 'float', 'float', 'str', 'str']
# single tub
tub = TubWriter(path=cfg.TUB_PATH, inputs=inputs, types=types)
V.add(tub, inputs=inputs, run_condition='recording')
# run the vehicle
V.start(rate_hz=cfg.DRIVE_LOOP_HZ)
def drive(cfg, model_path=None, use_chaos=False):
"""
"""
V = dk.vehicle.Vehicle()
clock = Timestamp()
V.add(clock, outputs=['timestamp'])
cam = PiCamera(resolution=cfg.CAMERA_RESOLUTION)
V.add(cam, outputs=['cam/image_array'], threaded=True)
"""
ctr = LocalWebController(use_chaos=use_chaos)
V.add(ctr,
inputs=['cam/image_array'],
outputs=['user/angle', 'user/throttle', 'user/mode', 'recording'],
threaded=True)
"""
# then replace your current controller with...
ctl = BluetoothGameController()
V.add(ctl,
inputs=['cam/image_array'],
outputs=['user/angle', 'user/throttle', 'user/mode', 'recording'],
threaded=True)
# See if we should even run the pilot module.
# This is only needed because the part run_condition only accepts boolean
pilot_condition_part = MakeRunConditionBoolean()
V.add(pilot_condition_part, inputs=['user/mode'], outputs=['run_pilot'])
# Run the pilot if the mode is not user.
kl = KerasLinear()
if model_path:
kl.load(model_path)
V.add(kl,
inputs=['cam/image_array'],
outputs=['pilot/angle', 'pilot/throttle'],
run_condition='run_pilot')
state_controller = StateController()
V.add(state_controller,
inputs=[
'user/mode', 'user/angle', 'user/throttle', 'pilot/angle',
'pilot/throttle'
],
outputs=['angle', 'throttle'])
sombrero = Sombrero(steering_channel=cfg.STEERING_CHANNEL,
steering_left_pwm=cfg.STEERING_LEFT_PWM,
steering_right_pwm=cfg.STEERING_RIGHT_PWM,
throttle_channel=cfg.THROTTLE_CHANNEL,
throttle_forward_pwm=cfg.THROTTLE_FORWARD_PWM,
throttle_stop_pwm=cfg.THROTTLE_STOPPED_PWM,
throttle_reverse_pwm=cfg.THROTTLE_REVERSE_PWM)
V.add(sombrero, inputs=['angle', 'throttle'])
# add tub to save data
inputs = [
'cam/image_array', 'user/angle', 'user/throttle', 'user/mode',
'timestamp'
]
types = ['image_array', 'float', 'float', 'str', 'str']
# single tub
tub = TubWriter(path=cfg.TUB_PATH, inputs=inputs, types=types)
V.add(tub, inputs=inputs, run_condition='recording')
# run the vehicle
V.start(rate_hz=cfg.DRIVE_LOOP_HZ, max_loop_count=cfg.MAX_LOOPS)
def test_linear():
kl = KerasLinear()
assert kl.model is not None
def test_linear_with_model():
kc = KerasLinear(default_linear())
assert kc.model is not None