def run_step_carla9(self, observations):
norm_speed = np.linalg.norm(observations['velocity'])/g_conf.SPEED_FACTOR
norm_speed = torch.cuda.FloatTensor([norm_speed]).unsqueeze(0)
directions_tensor = torch.cuda.LongTensor([int(observations['command'])])
# print ('rgb: ', observations['big_cam'].shape)
# print ('velocity: ', observations['velocity'])
# print ('norm velocity: ', np.linalg.norm(observations['velocity']))
# print ('norm_speed: ', norm_speed.shape, norm_speed.item())
# print ('directions_tensor: ', directions_tensor.shape, directions_tensor.item())
model_outputs = self._model.forward_branch(self._process_sensors(observations), norm_speed,
directions_tensor)
steer, throttle, brake = self._process_model_outputs(model_outputs[0])
if self._carla_version == '0.9':
import carla
control = carla.VehicleControl()
else:
control = VehicleControl()
# single model
control.steer = float(steer)
control.throttle = float(throttle)
control.brake = float(brake)
return control
def run_step(self, measurements, sensor_data, directions, target):
"""
Run a step on the benchmark simulation
Args:
measurements: All the float measurements from CARLA ( Just speed is used)
sensor_data: All the sensor data used on this benchmark
directions: The directions, high level commands
target: Final objective. Not used when the agent is predicting all outputs.
Returns:
Controls for the vehicle on the CARLA simulator.
"""
# Get speed and high-level turning command
# Take the forward speed and normalize it for it to go from 0-1
norm_speed = measurements.player_measurements.forward_speed / g_conf.SPEED_FACTOR
norm_speed = torch.cuda.FloatTensor([norm_speed]).unsqueeze(0)
directions_tensor = torch.cuda.LongTensor([directions])
# If we're evaluating squeeze network (so we are using ground truth seg mask)
if "seg" in g_conf.SENSORS.keys():
# Run the autopilot agent to get stop intentions
_, state = self.control_agent.run_step(measurements, [], [], target)
inputs_vec = []
for input_name in g_conf.INTENTIONS:
inputs_vec.append(float(state[input_name]))
intentions = torch.cuda.FloatTensor(inputs_vec).unsqueeze(0)
# Run squeeze network
model_outputs = self._model.forward_branch(self._process_sensors(sensor_data), norm_speed,
directions_tensor, intentions, benchmark=True)
else:
# Run driving model
model_outputs = self._model.forward_branch(self._process_sensors(sensor_data), norm_speed,
directions_tensor, benchmark=True)
steer, throttle, brake = self._process_model_outputs(model_outputs[0])
if self._carla_version == '0.9':
import carla
control = carla.VehicleControl()
else:
control = VehicleControl()
control.steer = float(steer)
control.throttle = float(throttle)
control.brake = float(brake)
# There is the posibility to replace some of the predictions with oracle predictions.
if g_conf.USE_ORACLE:
_, control.throttle, control.brake = self._get_oracle_prediction(
measurements, target)
if self.first_iter:
coil_logger.add_message('Iterating', {"Checkpoint": self.checkpoint['iteration'],
'Agent': str(steer)},
self.checkpoint['iteration'])
self.first_iter = False
return control
def run_step(self, measurements, sensor_data, directions, target):
"""
Run a step on the benchmark simulation
Args:
measurements: All the float measurements from CARLA ( Just speed is used)
sensor_data: All the sensor data used on this benchmark
directions: The directions, high level commands
target: Final objective. Not used when the agent is predicting all outputs.
Returns:
Controls for the vehicle on the CARLA simulator.
"""
# Take the forward speed and normalize it for it to go from 0-1
norm_speed = measurements.player_measurements.forward_speed / g_conf.SPEED_FACTOR
norm_speed = torch.cuda.FloatTensor([norm_speed]).unsqueeze(0)
directions_tensor = torch.cuda.LongTensor([directions])
# Compute the forward pass processing the sensors got from CARLA.
rgbs = self._process_sensors(sensor_data)
with torch.no_grad():
outputs = self.model_erf(rgbs)
labels = outputs.max(1)[1].byte().cpu().data
seg_road = (labels == 0)
seg_not_road = (labels != 0)
seg = torch.stack((seg_road, seg_not_road), 1).float()
model_outputs = self._model.forward_branch(seg.cuda(), norm_speed,
directions_tensor)
# model_outputs = self._model.forward_branch(self._process_sensors(sensor_data), norm_speed,
# directions_tensor)
steer, throttle, brake = self._process_model_outputs(model_outputs[0])
if self._carla_version == '0.9':
import carla
control = carla.VehicleControl()
else:
control = VehicleControl()
control.steer = float(steer)
control.throttle = float(throttle)
control.brake = float(brake)
# There is the posibility to replace some of the predictions with oracle predictions.
if g_conf.USE_ORACLE:
_, control.throttle, control.brake = self._get_oracle_prediction(
measurements, target)
if self.first_iter:
coil_logger.add_message('Iterating', {
"Checkpoint": self.checkpoint['iteration'],
'Agent': str(steer)
}, self.checkpoint['iteration'])
self.first_iter = False
return control
def run_step(self, measurements, sensor_data, directions, target):
# We basically ignore all the parameters.
for event in pygame.event.get():
if event.type == pygame.QUIT:
return VehicleControl()
return self._get_keyboard_control(pygame.key.get_pressed())
def _get_keyboard_control(self, keys):
"""
Return a VehicleControl message based on the pressed keys.
Return None
if a new episode was requested.
"""
control = VehicleControl()
if keys[K_LEFT] or keys[K_a]:
control.steer = -1.0
if keys[K_RIGHT] or keys[K_d]:
control.steer = 1.0
if keys[K_UP] or keys[K_w]:
control.throttle = 1.0
if keys[K_DOWN] or keys[K_s]:
control.brake = 1.0
if keys[K_SPACE]:
control.hand_brake = True
if keys[K_q]:
self._is_on_reverse = not self._is_on_reverse
control.reverse = self._is_on_reverse
return control
def get_control(self, wp_angle, wp_angle_speed, speed_factor, current_speed):
control = VehicleControl()
current_speed = max(current_speed, 0)
steer = self.params['steer_gain'] * wp_angle
if steer > 0:
control.steer = min(steer, 1)
else:
control.steer = max(steer, -1)
# Don't go0 to fast around corners
if math.fabs(wp_angle_speed) < 0.1: # 0.04 works better
target_speed_adjusted = self.params['target_speed'] * speed_factor
elif math.fabs(wp_angle_speed) < 0.5:
target_speed_adjusted = 20 * speed_factor # 10 works better
else:
target_speed_adjusted = 15 * speed_factor # 10 works better
self.pid.target = target_speed_adjusted
pid_gain = self.pid(feedback=current_speed)
print ('Target: ', self.pid.target, 'Error: ', self.pid.error, 'Gain: ', pid_gain)
print ('Target Speed: ', target_speed_adjusted, 'Current Speed: ', current_speed, 'Speed Factor: ',
speed_factor)
throttle = min(max(self.params['default_throttle'] - 1.3 * pid_gain, 0),
self.params['throttle_max'])
if pid_gain > 0.5:
brake = min(0.35 * pid_gain * self.params['brake_strength'], 1)
else:
brake = 0
control.throttle = max(throttle, 0) # Prevent N by putting at least 0.01
control.brake = brake
print ('Throttle: ', control.throttle, 'Brake: ', control.brake, 'Steering Angle: ', control.steer)
# with open('/is/sg2/aprakash/Documents/debug', 'a+') as log:
# log.write('Target: %f Error: %f Gain: %f \n'%(self.pid.target, self.pid.error, pid_gain))
# log.write('Target Speed: %f Current Speed: %f Speed Factor: %f \n'%(target_speed_adjusted, current_speed, speed_factor))
# log.write('Throttle: %f Brake: %f Steering Angle: %f \n'%(control.throttle, control.brake, control.steer))
# log.close()
return control
def get_control(self, wp_angle, wp_angle_speed, speed_factor,
current_speed):
control = VehicleControl()
current_speed = max(current_speed, 0)
steer = self.params['steer_gain'] * wp_angle
if steer > 0:
control.steer = min(steer, 1)
else:
control.steer = max(steer, -1)
# Don't go too fast around corners
if math.fabs(wp_angle_speed) < 0.1:
target_speed_adjusted = self.params['target_speed'] * speed_factor
elif math.fabs(wp_angle_speed) < 0.5:
target_speed_adjusted = 20 * speed_factor
else:
target_speed_adjusted = 15 * speed_factor
self.pid.target = target_speed_adjusted
pid_gain = self.pid(feedback=current_speed)
print('Target: ', self.pid.target, 'Error: ', self.pid.error, 'Gain: ',
pid_gain)
print(
f'Target Speed: {target_speed_adjusted} Current Speed: {current_speed} Speed Factor: {speed_factor}'
)
throttle = min(
max(self.params['default_throttle'] - 1.3 * pid_gain, 0),
self.params['throttle_max'])
if pid_gain > 0.5:
brake = min(0.35 * pid_gain * self.params['brake_strength'], 1)
else:
brake = 0
control.throttle = max(throttle,
0) # Prevent N by putting at least 0.01
control.brake = brake
print(
f'Throttle: {control.throttle} Brake: {control.brake} Steering Angle: {control.steer}'
)
return control
def __init__(self, world, start_in_autopilot):
self._autopilot_enabled = start_in_autopilot
self._control = VehicleControl()
self._command_cache = 2.0
world.hud.notification("Press 'H' or '?' for help.", seconds=4.0)
def run_step(self, measurements, sensor_data, directions, target, **kwargs):
"""
Run a step on the benchmark simulation
Args:
measurements: All the float measurements from CARLA ( Just speed is used)
sensor_data: All the sensor data used on this benchmark
directions: The directions, high level commands
target: Final objective. Not used when the agent is predicting all outputs.
Returns:
Controls for the vehicle on the CARLA simulator.
"""
# only required if using corruptions module
# self.corruption_number = kwargs.get('corruption_number', None)
# self.severity = kwargs.get('severity', None)
# Take the forward speed and normalize it for it to go from 0-1
norm_speed = measurements.player_measurements.forward_speed / g_conf.SPEED_FACTOR
norm_speed = torch.cuda.FloatTensor([norm_speed]).unsqueeze(0)
directions_tensor = torch.cuda.LongTensor([directions])
# Compute the forward pass processing the sensors got from CARLA.
model_outputs = self._model.forward_branch(self._process_sensors(sensor_data), norm_speed,
directions_tensor)
# run forward pass using felipe model
# model_outputs_felipe = self._model_felipe.forward_branch(self._process_sensors(sensor_data), norm_speed,
# directions_tensor)
# model_outputs[0] = torch.FloatTensor([(model_outputs[0][i].item()+model_outputs_felipe[0][i].item())/2.0 for i in range(3)]).cuda()
steer, throttle, brake = self._process_model_outputs(model_outputs[0])
# steer_f, throttle_f, brake_f = self._process_model_outputs(model_outputs_felipe[0])
# ensemble
'''
steer_c = []
throttle_c = []
brake_c = []
for i in range(len(self.model_ids)):
mo = self._ensemble_model_list[i].forward_branch(self._process_sensors(sensor_data), norm_speed,
directions_tensor)
s, t, b = self._process_model_outputs(mo[0])
steer_c.append(s)
throttle_c.append(t)
brake_c.append(b)
'''
if self._carla_version == '0.9':
import carla
control = carla.VehicleControl()
else:
control = VehicleControl()
# single model
control.steer = float(steer)
control.throttle = float(throttle)
control.brake = float(brake)
# ensemble
# control.steer = float(np.average(steer_c, weights=self.weights))
# control.throttle = float(np.average(throttle_c, weights=self.weights))
# control.brake = float(np.average(brake_c, weights=self.weights))
# There is the posibility to replace some of the predictions with oracle predictions.
if g_conf.USE_ORACLE:
control.steer, control.throttle, control.brake = self._get_oracle_prediction(
measurements, sensor_data, target)
if self.first_iter:
coil_logger.add_message('Iterating', {"Checkpoint": self.checkpoint['iteration'],
'Agent': str(control.steer)},
self.checkpoint['iteration'])
self.first_iter = False
return control
def run_step(self, measurements, sensor_data, directions, target):
control = VehicleControl()
control.throttle = 0.9
return control
