def __init__(self, checkpoint, town_name, carla_version='0.84'):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
# this entire segment is for loading models for ensemble evaluation - take care for the paths and checkpoints
'''
self.weights = [0.25, 0.25, 0.25, 0.25] # simple ensemble
self.model_ids = ['660000', '670000', '1070000', '2640000'] # model checkpoints
self.models_dir = '/is/sg2/aprakash/Projects/carla_autonomous_driving/code/coiltraine/_logs/ensemble'
self._ensemble_model_list = []
for i in range(len(self.model_ids)):
curr_checkpoint = torch.load(self.models_dir+'/resnet34imnet10S1/checkpoints/'+self.model_ids[i]+'.pth')
self._ensemble_model_list.append(CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION))
self._ensemble_model_list[i].load_state_dict(curr_checkpoint['state_dict'])
self._ensemble_model_list[i].cuda().eval()
'''
self.latest_image = None
self.latest_image_tensor = None
# for image corruptions
self.corruption_number = None
self.severity = None
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE: # for evaluating expert
self.control_agent = CommandFollower(town_name)
def __init__(self, checkpoint, town_name, carla_version='0.84'):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
self.latest_image = None
self.latest_image_tensor = None
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE:
self.control_agent = CommandFollower(town_name)
def __init__(self, checkpoint, town_name, carla_version='0.84'):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
# Create model
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
# If we are evaluating squeeze model (so we are using ground truth seg mask),
# also run the autopilot to get its stop intentions
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE or "seg" in g_conf.SENSORS.keys():
self.control_agent = CommandFollower(town_name)
def __init__(self, checkpoint, town_name, carla_version='0.84'):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
# Set ERFnet for segmentation
self.model_erf = ERFNet(20)
self.model_erf = torch.nn.DataParallel(self.model_erf)
self.model_erf.cuda()
print("LOAD ERFNet - validate")
def load_my_state_dict(
model, state_dict
): #custom function to load model when not all dict elements
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
own_state[name].copy_(param)
return model
self.model_erf = load_my_state_dict(
self.model_erf,
torch.load(os.path.join('trained_models/erfnet_pretrained.pth')))
self.model_erf.eval()
print("ERFNet and weights LOADED successfully")
self.latest_image = None
self.latest_image_tensor = None
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE:
self.control_agent = CommandFollower(town_name)
def __init__(self,
checkpoint,
town_name,
carla_version='0.84',
vae_params=None):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
self._vae_params = vae_params
if g_conf.VAE_MODEL_CONFIGURATION != {}:
# adding VAE model
self._VAE_model = CoILModel('VAE', g_conf.VAE_MODEL_CONFIGURATION)
self._VAE_model.cuda()
VAE_checkpoint = torch.load(
os.path.join('_logs', vae_params['vae_folder'],
vae_params['vae_exp'], 'checkpoints',
str(vae_params['vae_checkpoint']) + '.pth'))
print(
"VAE model ", str(vae_params['vae_checkpoint']),
" already loaded from ",
os.path.join('_logs', vae_params['vae_folder'],
vae_params['vae_exp'], 'checkpoints'))
self._VAE_model.load_state_dict(VAE_checkpoint['state_dict'])
self._VAE_model.eval()
self.latest_image = None
self.latest_image_tensor = None
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE:
self.control_agent = CommandFollower(town_name)
class CoILAgent(object):
def __init__(self,
checkpoint,
town_name,
carla_version='0.84',
vae_params=None):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
self._vae_params = vae_params
if g_conf.VAE_MODEL_CONFIGURATION != {}:
# adding VAE model
self._VAE_model = CoILModel('VAE', g_conf.VAE_MODEL_CONFIGURATION)
self._VAE_model.cuda()
VAE_checkpoint = torch.load(
os.path.join('_logs', vae_params['vae_folder'],
vae_params['vae_exp'], 'checkpoints',
str(vae_params['vae_checkpoint']) + '.pth'))
print(
"VAE model ", str(vae_params['vae_checkpoint']),
" already loaded from ",
os.path.join('_logs', vae_params['vae_folder'],
vae_params['vae_exp'], 'checkpoints'))
self._VAE_model.load_state_dict(VAE_checkpoint['state_dict'])
self._VAE_model.eval()
self.latest_image = None
self.latest_image_tensor = None
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE:
self.control_agent = CommandFollower(town_name)
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.
if g_conf.VAE_MODEL_CONFIGURATION != {}:
input_data = self._process_sensors(sensor_data)
_, _, _, z = self._VAE_model(input_data)
model_outputs = self._model.forward_branch(z, norm_speed,
directions_tensor)
else:
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 get_attentions(self, layers=None):
"""
Returns
The activations obtained from the first layers of the latest iteration.
"""
if layers is None:
layers = [0, 1, 2]
if self.latest_image_tensor is None:
raise ValueError(
'No step was ran yet. '
'No image to compute the activations, Try Running ')
all_layers = self._model.get_perception_layers(
self.latest_image_tensor)
cmap = plt.get_cmap('inferno')
attentions = []
for layer in layers:
y = all_layers[layer]
att = torch.abs(y).mean(1)[0].data.cpu().numpy()
att = att / att.max()
att = cmap(att)
att = np.delete(att, 3, 2)
attentions.append(imresize(att, [88, 200]))
return attentions
def _process_sensors(self, sensors):
iteration = 0
for name, size in g_conf.SENSORS.items():
if self._carla_version == '0.9':
sensor = sensors[name][g_conf.IMAGE_CUT[0]:g_conf.IMAGE_CUT[1],
...]
else:
sensor = sensors[name].data[
g_conf.IMAGE_CUT[0]:g_conf.IMAGE_CUT[1], ...]
sensor = scipy.misc.imresize(sensor, (size[1], size[2]))
self.latest_image = sensor
sensor = np.swapaxes(sensor, 0, 1)
sensor = np.transpose(sensor, (2, 1, 0))
sensor = torch.from_numpy(sensor / 255.0).type(
torch.FloatTensor).cuda()
if iteration == 0:
image_input = sensor
else:
image_input = torch.cat((image_input, sensor), 0)
iteration += 1
image_input = image_input.unsqueeze(0)
self.latest_image_tensor = image_input
return image_input
def _process_model_outputs(self, outputs):
"""
A bit of heuristics in the control, to eventually make car faster, for instance.
Returns:
"""
steer, throttle, brake = outputs[0], outputs[1], outputs[2]
if brake < 0.05:
brake = 0.0
if throttle > brake:
brake = 0.0
return steer, throttle, brake
def _process_model_outputs_wp(self, outputs):
"""
A bit of heuristics in the control, to eventually make car faster, for instance.
Returns:
"""
wpa1, wpa2, throttle, brake = outputs[3], outputs[4], outputs[
1], outputs[2]
if brake < 0.2:
brake = 0.0
if throttle > brake:
brake = 0.0
steer = 0.7 * wpa2
if steer > 0:
steer = min(steer, 1)
else:
steer = max(steer, -1)
return steer, throttle, brake
def _get_oracle_prediction(self, measurements, target):
# For the oracle, the current version of sensor data is not really relevant.
control, _, _, _, _ = self.control_agent.run_step(
measurements, [], [], target)
return control.steer, control.throttle, control.brake
class CoILAgent(object):
def __init__(self, checkpoint, town_name, carla_version='0.84'):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
# this entire segment is for loading models for ensemble evaluation - take care for the paths and checkpoints
'''
self.weights = [0.25, 0.25, 0.25, 0.25] # simple ensemble
self.model_ids = ['660000', '670000', '1070000', '2640000'] # model checkpoints
self.models_dir = '/is/sg2/aprakash/Projects/carla_autonomous_driving/code/coiltraine/_logs/ensemble'
self._ensemble_model_list = []
for i in range(len(self.model_ids)):
curr_checkpoint = torch.load(self.models_dir+'/resnet34imnet10S1/checkpoints/'+self.model_ids[i]+'.pth')
self._ensemble_model_list.append(CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION))
self._ensemble_model_list[i].load_state_dict(curr_checkpoint['state_dict'])
self._ensemble_model_list[i].cuda().eval()
'''
self.latest_image = None
self.latest_image_tensor = None
# for image corruptions
self.corruption_number = None
self.severity = None
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE: # for evaluating expert
self.control_agent = CommandFollower(town_name)
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
# define run step for carla 9
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 get_attentions(self, layers=None):
"""
Returns
The activations obtained from the first layers of the latest iteration.
"""
if layers is None:
layers = [0, 1, 2]
if self.latest_image_tensor is None:
raise ValueError('No step was ran yet. '
'No image to compute the activations, Try Running ')
all_layers = self._model.get_perception_layers(self.latest_image_tensor)
cmap = plt.get_cmap('inferno')
attentions = []
for layer in layers:
y = all_layers[layer]
att = torch.abs(y).mean(1)[0].data.cpu().numpy()
att = att / att.max()
att = cmap(att)
att = np.delete(att, 3, 2)
attentions.append(imresize(att, [88, 200]))
# attentions.append(np.array(Image.fromarray(sensor).resize((200, 88))))
return attentions
def _process_sensors(self, sensors):
iteration = 0
for name, size in g_conf.SENSORS.items():
if self._carla_version == '0.9':
sensor = sensors[name][g_conf.IMAGE_CUT[0]:g_conf.IMAGE_CUT[1], ...]
else:
sensor = sensors[name].data[g_conf.IMAGE_CUT[0]:g_conf.IMAGE_CUT[1], ...]
sensor = scipy.misc.imresize(sensor, (size[1], size[2])) # depreciated
# sensor = np.array(Image.fromarray(sensor).resize((size[2], size[1]))) # for running corruptions
'''
# corrupt the image here
# print ('out of corruption: ', self.corruption_number, self.severity)
if self.corruption_number is not None and self.severity is not None:
# print ('in corruption: ', self.corruption_number, self.severity)
sensor = corrupt(sensor, corruption_number=self.corruption_number,
severity=self.severity+1)
'''
self.latest_image = sensor
sensor = np.swapaxes(sensor, 0, 1)
sensor = np.transpose(sensor, (2, 1, 0))
sensor = torch.from_numpy(sensor / 255.0).type(torch.FloatTensor).cuda()
if iteration == 0:
image_input = sensor
else:
image_input = torch.cat((image_input, sensor), 0)
iteration += 1
image_input = image_input.unsqueeze(0)
self.latest_image_tensor = image_input
return image_input
def _process_model_outputs(self, outputs):
"""
A bit of heuristics in the control, to eventually make car faster, for instance.
Returns:
"""
steer, throttle, brake = outputs[0].item(), outputs[1].item(), outputs[2].item()
# print ('steer: ', steer, 'throttle: ', throttle, 'brake: ', brake)
# these heuristics are a part of the original benchmark, evaluation doesn't run properly without these
if brake < 0.05:
brake = 0.0
if throttle > brake:
brake = 0.0
# print ('steer after heuristic: ', steer, 'throttle after heuristic: ', throttle, 'brake after heuristic: ', brake)
return steer, throttle, brake
def _process_model_outputs_wp(self, outputs):
"""
A bit of heuristics in the control, to eventually make car faster, for instance.
Returns:
"""
wpa1, wpa2, throttle, brake = outputs[3], outputs[4], outputs[1], outputs[2]
if brake < 0.2:
brake = 0.0
if throttle > brake:
brake = 0.0
steer = 0.7 * wpa2
if steer > 0:
steer = min(steer, 1)
else:
steer = max(steer, -1)
return steer, throttle, brake
def _get_oracle_prediction(self, measurements, sensor_data, target):
# For the oracle, the current version of sensor data is not really relevant.
control, _ = self.control_agent.run_step(measurements, sensor_data, [], target)
return control.steer, control.throttle, control.brake
class CoILAgent(object):
def __init__(self, checkpoint, town_name, carla_version='0.84'):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
# Create model
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
# If we are evaluating squeeze model (so we are using ground truth seg mask),
# also run the autopilot to get its stop intentions
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE or "seg" in g_conf.SENSORS.keys():
self.control_agent = CommandFollower(town_name)
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 _process_sensors(self, sensors):
iteration = 0
sensor_dict = {}
for name, size in g_conf.SENSORS.items():
if self._carla_version == '0.9':
sensor = sensors[name][g_conf.IMAGE_CUT[0]:g_conf.IMAGE_CUT[1], ...]
else:
sensor = sensors[name].data[g_conf.IMAGE_CUT[0]:g_conf.IMAGE_CUT[1], ...]
# Process RGB image or CARLA seg mask
if name == 'rgb':
# Resize image, convert it to [0, 1] BGR image
sensor = scipy.misc.imresize(sensor, (size[1], size[2]))
sensor = sensor[:, :, ::-1]
sensor = np.swapaxes(sensor, 0, 1)
sensor = np.transpose(sensor, (2, 1, 0))
sensor = torch.from_numpy(sensor / 255.0).type(torch.FloatTensor)
elif name == 'seg':
seg = scipy.misc.imresize(sensor, (size[1], size[2]), 'nearest')
# Re-map classes, mapping irrelevant classes to a "nuisance" class
class_map = \
{0: 0, # None
1: 0, # Buildings -> None
2: 0, # Fences -> None
3: 0, # Other -> None
4: 1, # Pedestrians kept
5: 0, # Poles -> None
6: 2, # RoadLines kept
7: 3, # Roads kept
8: 2, # Sidewalks mapped to roadlines (both are boundaries of road)
9 : 0, # Vegetation -> None
10: 4, # Vehicles kept
11: 0, # Walls -> None
12: 5} # TrafficSigns kept (for traffic lights)
new_seg = np.zeros((seg.shape[0], seg.shape[1]))
# Remap classes
for key, value in class_map.items():
new_seg[np.where(seg == key)] = value
# One hot encode seg mask, for now hardcode max of class map values + 1
new_seg = np.eye(6)[new_seg.astype(np.int32)]
new_seg = new_seg.transpose(2, 0, 1)
new_seg = new_seg.astype(np.float)
sensor = torch.from_numpy(new_seg).type(torch.FloatTensor)
sensor = sensor.unsqueeze(0)
sensor_dict[name] = sensor
return sensor_dict
def _process_model_outputs(self, outputs):
"""
A bit of heuristics in the control, to eventually make car faster, for instance.
Returns:
"""
steer, throttle, brake = outputs[0], outputs[1], outputs[2]
if brake < 0.05:
brake = 0.0
if throttle > brake:
brake = 0.0
return steer, throttle, brake
def _get_oracle_prediction(self, measurements, target):
# For the oracle, the current version of sensor data is not really relevant.
control, _ = self.control_agent.run_step(measurements, [], [], target)
return control.steer, control.throttle, control.brake
class CoILAgent(object):
def __init__(self, checkpoint, town_name, carla_version='0.84'):
# Set the carla version that is going to be used by the interface
self._carla_version = carla_version
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
self.latest_image = None
self.latest_image_tensor = None
if g_conf.USE_ORACLE or g_conf.USE_FULL_ORACLE:
self.control_agent = CommandFollower(town_name)
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.
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 get_attentions(self, layers=None):
"""
Returns
The activations obtained from the first layers of the latest iteration.
"""
if layers is None:
layers = [0, 1, 2]
if self.latest_image_tensor is None:
raise ValueError(
'No step was ran yet. '
'No image to compute the activations, Try Running ')
all_layers = self._model.get_perception_layers(
self.latest_image_tensor)
cmap = plt.get_cmap('inferno')
attentions = []
for layer in layers:
y = all_layers[layer]
att = torch.abs(y).mean(1)[0].data.cpu().numpy()
att = att / att.max()
att = cmap(att)
att = np.delete(att, 3, 2)
attentions.append(imresize(att, [150, 200]))
return attentions
def _process_sensors(self, sensors):
colors = [[0, 0, 0], [70, 70, 70], [190, 153, 153], [250, 170, 160],
[220, 20, 60], [153, 153, 153], [157, 234, 50],
[128, 64, 128], [244, 35, 232], [107, 142, 35], [0, 0, 142],
[102, 102, 156], [220, 220, 0]]
def label_to_color_0(e):
return colors[e][0]
def label_to_color_1(e):
return colors[e][1]
def label_to_color_2(e):
return colors[e][2]
iteration = 0
for name, size in g_conf.SENSORS.items():
sensor = sensors[name].data
if (sensor.shape == (600, 800)):
labels = sensor
tmp = np.zeros((600, 800, 3))
f0 = np.vectorize(label_to_color_0)
f1 = np.vectorize(label_to_color_1)
f2 = np.vectorize(label_to_color_2)
tmp[:, :, 0] = f0(sensor)
tmp[:, :, 1] = f1(sensor)
tmp[:, :, 2] = f2(sensor)
sensor = tmp
sensor = scipy.misc.imresize(sensor, (size[1], size[2]))
##### Save sensor
self.latest_image = sensor
sensor = np.swapaxes(sensor, 0, 1)
sensor = np.transpose(sensor, (2, 1, 0))
sensor = torch.from_numpy(sensor / 255.0).type(
torch.FloatTensor).cuda()
if iteration == 0:
image_input = sensor
else:
image_input = torch.cat((image_input, sensor), 0)
iteration += 1
image_input = image_input.unsqueeze(0)
self.latest_image_tensor = image_input
return image_input
def _process_model_outputs(self, outputs):
"""
A bit of heuristics in the control, to eventually make car faster, for instance.
Returns:
"""
steer, throttle, brake = outputs[0], outputs[1], outputs[2]
if brake < 0.05:
brake = 0.0
if throttle > brake:
brake = 0.0
return steer, throttle, brake
def _process_model_outputs_wp(self, outputs):
"""
A bit of heuristics in the control, to eventually make car faster, for instance.
Returns:
"""
wpa1, wpa2, throttle, brake = outputs[3], outputs[4], outputs[
1], outputs[2]
if brake < 0.2:
brake = 0.0
if throttle > brake:
brake = 0.0
steer = 0.7 * wpa2
if steer > 0:
steer = min(steer, 1)
else:
steer = max(steer, -1)
return steer, throttle, brake
def _get_oracle_prediction(self, measurements, target):
# For the oracle, the current version of sensor data is not really relevant.
control, _, _, _, _ = self.control_agent.run_step(
measurements, [], [], target)
return control.steer, control.throttle, control.brake
