def execute(gpu, exp_batch, exp_alias, dataset_name):
# We set the visible cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('validation', dataset_name)
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"], dataset_name)
print(full_dataset)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=120,
shuffle=False,
num_workers=12,
pin_memory=True)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
model = CoILModel(g_conf.MODEL_NAME)
model.cuda()
model.eval()
criterion = Loss()
latest = get_latest_evaluated_checkpoint()
if latest is None: # When nothing was tested, get latest returns none, we fix that.
latest = 0
latest = 200000
best_loss = 1000.0
best_error = 1000.0
best_loss_iter = 0
best_error_iter = 0
print(dataset.meta_data[0][0])
for k in dataset.meta_data:
k[0] = str(k[0], 'utf-8')
print(dataset.meta_data[0][0])
cpts = glob.glob(
'/home-local/rohitrishabh/coil_20-06/_logs/eccv/experiment_1/checkpoints/*.pth'
)
# while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
for ckpt in cpts:
# if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
# latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
latest = int(ckpt[-10:-4])
# checkpoint = torch.load(os.path.join('_logs', exp_batch, exp_alias
# , 'checkpoints', str(latest) + '.pth'))
checkpoint = torch.load(ckpt)
checkpoint_iteration = checkpoint['iteration']
print("Validation loaded ", checkpoint_iteration)
accumulated_loss = 0.0
accumulated_error = 0.0
iteration_on_checkpoint = 0
for data in data_loader:
input_data, float_data = data
control_position = np.where(
dataset.meta_data[:, 0] == 'control')[0][0]
speed_position = np.where(
dataset.meta_data[:, 0] == 'speed_module')[0][0]
# print (torch.squeeze(input_data['rgb']).shape)
# print (control_position)
# print (speed_position)
# Obs : Maybe we could also check for other branches ??
output = model.forward_branch(
torch.squeeze(input_data['rgb']).cuda(),
float_data[:, speed_position, :].cuda(),
float_data[:, control_position, :].cuda())
for i in range(input_data['rgb'].shape[0]):
coil_logger.write_on_csv(
checkpoint_iteration,
[output[i][0], output[i][1], output[i][2]])
# TODO: Change this a functional standard using the loss functions.
loss = torch.mean(
(output -
dataset.extract_targets(float_data).cuda())**2).data.tolist()
mean_error = torch.mean(
torch.abs(
output -
dataset.extract_targets(float_data).cuda())).data.tolist()
accumulated_error += mean_error
accumulated_loss += loss
error = torch.abs(output -
dataset.extract_targets(float_data).cuda())
# Log a random position
position = random.randint(0, len(float_data) - 1)
#print (output[position].data.tolist())
coil_logger.add_message(
'Iterating in Validation', {
'Checkpoint':
latest,
'Iteration': (str(iteration_on_checkpoint * 120) + '/' +
str(len(dataset))),
'MeanError':
mean_error,
'Loss':
loss,
'Output':
output[position].data.tolist(),
'GroundTruth':
dataset.extract_targets(float_data)
[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(float_data)[position].data.tolist()
}, latest)
iteration_on_checkpoint += 1
checkpoint_average_loss = accumulated_loss / len(dataset)
checkpoint_average_error = accumulated_error / len(dataset)
coil_logger.add_scalar('Loss', checkpoint_average_loss, latest)
coil_logger.add_scalar('Error', checkpoint_average_error, latest)
print('Loss: ', checkpoint_average_loss, "----Error: ",
checkpoint_average_error)
if checkpoint_average_loss < best_loss:
best_loss = checkpoint_average_loss
best_loss_iter = latest
state = {
'state_dict': model.state_dict(),
'best_loss': best_loss,
'best_loss_iter': best_loss_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias,
'best_model_l2' + '.pth'))
if checkpoint_average_error < best_error:
best_error = checkpoint_average_error
best_error_iter = latest
state = {
'state_dict': model.state_dict(),
'best_error': best_error,
'best_error_iter': best_error_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias,
'best_model_l1' + '.pth'))
print('Best Loss: ', best_loss, "Checkpoint", best_loss_iter)
print('Best Error: ', best_error, "Checkpoint", best_error_iter)
coil_logger.add_message(
'Iterating in Validation', {
'Summary': {
'Error': checkpoint_average_error,
'Loss': checkpoint_average_loss,
'BestError': best_error,
'BestLoss': best_loss,
'BestLossCheckpoint': best_loss_iter,
'BestErrorCheckpoint': best_error_iter
},
'Checkpoint': latest
})
def execute(gpu, exp_batch, exp_alias, dataset_name, suppress_output):
latest = None
try:
# We set the visible cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join('configs', exp_batch,
exp_alias + '.yaml'))
# The validation dataset is always fully loaded, so we fix a very high number of hours
g_conf.NUMBER_OF_HOURS = 10000
set_type_of_process('validation', dataset_name)
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
if suppress_output:
sys.stdout = open(os.path.join(
'_output_logs', exp_alias + '_' + g_conf.PROCESS_NAME + '_' +
str(os.getpid()) + ".out"),
"a",
buffering=1)
sys.stderr = open(os.path.join(
'_output_logs', exp_alias + '_err_' + g_conf.PROCESS_NAME +
'_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
# Define the dataset. This structure is has the __get_item__ redefined in a way
# that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
dataset_name)
augmenter = Augmenter(None)
# Definition of the dataset to be used. Preload name is just the validation data name
dataset = CoILDataset(full_dataset,
transform=augmenter,
preload_name=dataset_name)
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=g_conf.BATCH_SIZE,
shuffle=False,
num_workers=g_conf.NUMBER_OF_LOADING_WORKERS,
pin_memory=True)
model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
# The window used to keep track of the trainings
l1_window = []
latest = get_latest_evaluated_checkpoint()
if latest is not None: # When latest is noe
l1_window = coil_logger.recover_loss_window(dataset_name, None)
model.cuda()
best_mse = 1000
best_error = 1000
best_mse_iter = 0
best_error_iter = 0
while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(latest) + '.pth'))
checkpoint_iteration = checkpoint['iteration']
print("Validation loaded ", checkpoint_iteration)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
accumulated_mse = 0
accumulated_error = 0
iteration_on_checkpoint = 0
for data in data_loader:
# Compute the forward pass on a batch from the validation dataset
controls = data['directions']
output = model.forward_branch(
torch.squeeze(data['rgb']).cuda(),
dataset.extract_inputs(data).cuda(), controls)
# It could be either waypoints or direct control
if 'waypoint1_angle' in g_conf.TARGETS:
write_waypoints_output(checkpoint_iteration, output)
else:
write_regular_output(checkpoint_iteration, output)
mse = torch.mean(
(output - dataset.extract_targets(data).cuda()
)**2).data.tolist()
mean_error = torch.mean(
torch.abs(output -
dataset.extract_targets(data).cuda())
).data.tolist()
accumulated_error += mean_error
accumulated_mse += mse
error = torch.abs(output -
dataset.extract_targets(data).cuda())
# Log a random position
position = random.randint(0, len(output.data.tolist()) - 1)
coil_logger.add_message(
'Iterating', {
'Checkpoint':
latest,
'Iteration': (str(iteration_on_checkpoint * 120) +
'/' + str(len(dataset))),
'MeanError':
mean_error,
'MSE':
mse,
'Output':
output[position].data.tolist(),
'GroundTruth':
dataset.extract_targets(
data)[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(data)
[position].data.tolist()
}, latest)
iteration_on_checkpoint += 1
print("Iteration %d on Checkpoint %d : Error %f" %
(iteration_on_checkpoint, checkpoint_iteration,
mean_error))
"""
########
Finish a round of validation, write results, wait for the next
########
"""
checkpoint_average_mse = accumulated_mse / (len(data_loader))
checkpoint_average_error = accumulated_error / (
len(data_loader))
coil_logger.add_scalar('Loss', checkpoint_average_mse, latest,
True)
coil_logger.add_scalar('Error', checkpoint_average_error,
latest, True)
if checkpoint_average_mse < best_mse:
best_mse = checkpoint_average_mse
best_mse_iter = latest
if checkpoint_average_error < best_error:
best_error = checkpoint_average_error
best_error_iter = latest
coil_logger.add_message(
'Iterating', {
'Summary': {
'Error': checkpoint_average_error,
'Loss': checkpoint_average_mse,
'BestError': best_error,
'BestMSE': best_mse,
'BestMSECheckpoint': best_mse_iter,
'BestErrorCheckpoint': best_error_iter
},
'Checkpoint': latest
}, latest)
l1_window.append(checkpoint_average_error)
coil_logger.write_on_error_csv(dataset_name,
checkpoint_average_error)
# If we are using the finish when validation stops, we check the current
if g_conf.FINISH_ON_VALIDATION_STALE is not None:
if dlib.count_steps_without_decrease(l1_window) > 3 and \
dlib.count_steps_without_decrease_robust(l1_window) > 3:
coil_logger.write_stop(dataset_name, latest)
break
else:
latest = get_latest_evaluated_checkpoint()
time.sleep(1)
coil_logger.add_message('Loading',
{'Message': 'Waiting Checkpoint'})
print("Waiting for the next Validation")
coil_logger.add_message('Finished', {})
except KeyboardInterrupt:
coil_logger.add_message('Error', {'Message': 'Killed By User'})
# We erase the output that was unfinished due to some process stop.
if latest is not None:
coil_logger.erase_csv(latest)
except RuntimeError as e:
if latest is not None:
coil_logger.erase_csv(latest)
coil_logger.add_message('Error', {'Message': str(e)})
except:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Something Happened'})
# We erase the output that was unfinished due to some process stop.
if latest is not None:
coil_logger.erase_csv(latest)
def execute(gpu, exp_batch, exp_alias):
# We set the visible cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join(exp_batch, exp_alias + '.yaml'))
set_type_of_process('validation')
sys.stdout = open(str(os.getpid()) + ".out", "a", buffering=1)
if monitorer.get_status(exp_batch, exp_alias,
g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.DATASET_NAME)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=120,
shuffle=False,
num_workers=12,
pin_memory=True)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
model = CoILModel(g_conf.MODEL_NAME)
model.cuda()
# TODO: The checkpoint will continue, so the logs should restart ??? OR continue were it was
latest = get_latest_evaluated_checkpoint()
if latest is None: # When nothing was tested, get latest returns none, we fix that.
latest = 0
print(dataset.meta_data)
while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(latest) + '.pth'))
checkpoint_iteration = checkpoint['iteration']
print("Validation loaded ", checkpoint_iteration)
for data in data_loader:
input_data, labels = data
control_position = np.where(
dataset.meta_data[:, 0] == 'control')[0][0]
speed_position = np.where(
dataset.meta_data[:, 0] == 'speed_module')[0][0]
print(torch.squeeze(input_data['rgb']).shape)
print(control_position)
print(speed_position)
# Obs : Maybe we could also check for other branches ??
output = model.forward_branch(
torch.squeeze(input_data['rgb']).cuda(),
labels[:, speed_position, :].cuda(),
labels[:, control_position, :].cuda())
# TODO: clean this squeeze and dimension things
for i in range(input_data['rgb'].shape[0]):
coil_logger.write_on_csv(
checkpoint_iteration,
[output[i][0], output[i][1], output[i][2]])
#loss = criterion(output, labels)
#loss.backward()
#optimizer.step()
#shutil.copyfile(filename, 'model_best.pth.tar')
else:
time.sleep(1)
print("Waiting for the next Validation")
def execute(gpu, exp_batch, exp_alias, drive_conditions, params):
"""
Main loop function. Executes driving benchmarks the specified iterations.
Args:
gpu:
exp_batch:
exp_alias:
drive_conditions:
params:
Returns:
"""
try:
print("Running ", __file__, " On GPU ", gpu, "of experiment name ", exp_alias)
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
exp_set_name, town_name = drive_conditions.split('_')
experiment_suite_module = __import__('drive.suites.' + camelcase_to_snakecase(exp_set_name)
+ '_suite',
fromlist=[exp_set_name])
experiment_suite_module = getattr(experiment_suite_module, exp_set_name)
experiment_set = experiment_suite_module()
set_type_of_process('drive', drive_conditions)
if params['suppress_output']:
sys.stdout = open(os.path.join('_output_logs',
g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a", buffering=1)
sys.stderr = open(os.path.join('_output_logs',
exp_alias + '_err_'+g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a", buffering=1)
coil_logger.add_message('Loading', {'Poses': experiment_set.build_experiments()[0].poses})
if g_conf.USE_ORACLE:
control_filename = 'control_output_auto'
else:
control_filename = 'control_output'
"""
#####
Preparing the output files that will contain the driving summary
#####
"""
experiment_list = experiment_set.build_experiments()
# Get all the uniquely named tasks
task_list = unique([experiment.task_name for experiment in experiment_list ])
# Now actually run the driving_benchmark
latest = get_latest_evaluated_checkpoint(control_filename + '_' + task_list[0])
if latest is None: # When nothing was tested, get latest returns none, we fix that.
latest = 0
# The used tasks are hardcoded, this need to be improved
file_base = os.path.join('_logs', exp_batch, exp_alias,
g_conf.PROCESS_NAME + '_csv', control_filename)
for i in range(len(task_list)):
# Write the header of the summary file used conclusion
# While the checkpoint is not there
write_header_control_summary(file_base, task_list[i])
"""
######
Run a single driving benchmark specified by the checkpoint were validation is stale
######
"""
if g_conf.FINISH_ON_VALIDATION_STALE is not None:
while validation_stale_point(g_conf.FINISH_ON_VALIDATION_STALE) is None:
time.sleep(0.1)
validation_state_iteration = validation_stale_point(g_conf.FINISH_ON_VALIDATION_STALE)
driving_benchmark(validation_state_iteration, gpu, town_name, experiment_set, exp_batch,
exp_alias, params, control_filename, task_list)
else:
"""
#####
Main Loop , Run a benchmark for each specified checkpoint on the "Test Configuration"
#####
"""
while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
# Get the correct checkpoint
# We check it for some task name, all of then are ready at the same time
if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE,
control_filename + '_' + task_list[0]):
latest = get_next_checkpoint(g_conf.TEST_SCHEDULE,
control_filename + '_' + task_list[0])
driving_benchmark(latest, gpu, town_name, experiment_set, exp_batch,
exp_alias, params, control_filename, task_list)
else:
time.sleep(0.1)
coil_logger.add_message('Finished', {})
except KeyboardInterrupt:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Killed By User'})
except:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Something happened'})
def execute(gpu,
exp_batch,
exp_alias,
drive_conditions,
memory_use=0.2,
host='127.0.0.1',
suppress_output=True,
no_screen=False):
try:
print("Running ", __file__, " On GPU ", gpu, "of experiment name ",
exp_alias)
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
merge_with_yaml(os.path.join('configs', exp_batch,
exp_alias + '.yaml'))
print("drive cond", drive_conditions)
exp_set_name, town_name = drive_conditions.split('_')
if g_conf.USE_ORACLE:
control_filename = 'control_output_auto.csv'
else:
control_filename = 'control_output.csv'
if exp_set_name == 'ECCVTrainingSuite':
experiment_set = ECCVTrainingSuite()
set_type_of_process('drive', drive_conditions)
elif exp_set_name == 'ECCVGeneralizationSuite':
experiment_set = ECCVGeneralizationSuite()
set_type_of_process('drive', drive_conditions)
elif exp_set_name == 'TestT1':
experiment_set = TestT1()
set_type_of_process('drive', drive_conditions)
elif exp_set_name == 'TestT2':
experiment_set = TestT2()
set_type_of_process('drive', drive_conditions)
else:
raise ValueError(" Exp Set name is not correspondent to a city")
if suppress_output:
sys.stdout = open(os.path.join(
'_output_logs',
g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
#sys.stderr = open(os.path.join('_output_logs',
# 'err_'+g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
# "a", buffering=1)
carla_process, port = start_carla_simulator(gpu, town_name, no_screen)
coil_logger.add_message(
'Loading', {'Poses': experiment_set.build_experiments()[0].poses})
coil_logger.add_message('Loading',
{'CARLAClient': host + ':' + str(port)})
# Now actually run the driving_benchmark
latest = get_latest_evaluated_checkpoint()
if latest is None: # When nothing was tested, get latest returns none, we fix that.
latest = 0
csv_outfile = open(
os.path.join('_logs', exp_batch, exp_alias,
g_conf.PROCESS_NAME + '_csv', control_filename),
'w')
csv_outfile.write(
"%s,%s,%s,%s,%s,%s,%s,%s\n" %
('step', 'episodes_completion', 'intersection_offroad',
'intersection_otherlane', 'collision_pedestrians',
'collision_vehicles', 'episodes_fully_completed',
'driven_kilometers'))
csv_outfile.close()
# Write the header of the summary file used conclusion
# While the checkpoint is not there
while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
try:
# Get the correct checkpoint
if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias,
'checkpoints',
str(latest) + '.pth'))
coil_agent = CoILAgent(checkpoint, town_name)
coil_logger.add_message('Iterating',
{"Checkpoint": latest}, latest)
run_driving_benchmark(
coil_agent, experiment_set, town_name,
exp_batch + '_' + exp_alias + '_' + str(latest) +
'_drive_' + control_filename[:-4], True, host, port)
path = exp_batch + '_' + exp_alias + '_' + str(latest) \
+ '_' + g_conf.PROCESS_NAME.split('_')[0] + '_' + control_filename[:-4] \
+ '_' + g_conf.PROCESS_NAME.split('_')[1] + '_' + g_conf.PROCESS_NAME.split('_')[2]
print(path)
print("Finished")
benchmark_json_path = os.path.join(get_latest_path(path),
'metrics.json')
with open(benchmark_json_path, 'r') as f:
benchmark_dict = json.loads(f.read())
averaged_dict = compute_average_std(
[benchmark_dict], experiment_set.weathers,
len(experiment_set.build_experiments()))
print(averaged_dict)
csv_outfile = open(
os.path.join('_logs', exp_batch, exp_alias,
g_conf.PROCESS_NAME + '_csv',
control_filename), 'a')
csv_outfile.write(
"%d,%f,%f,%f,%f,%f,%f,%f\n" %
(latest, averaged_dict['episodes_completion'],
averaged_dict['intersection_offroad'],
averaged_dict['intersection_otherlane'],
averaged_dict['collision_pedestrians'],
averaged_dict['collision_vehicles'],
averaged_dict['episodes_fully_completed'],
averaged_dict['driven_kilometers']))
csv_outfile.close()
# TODO: When you add the message you need to check if the experiment continues properly
# TODO: WRITE AN EFICIENT PARAMETRIZED OUTPUT SUMMARY FOR TEST.
else:
time.sleep(0.1)
except TCPConnectionError as error:
logging.error(error)
time.sleep(1)
carla_process.kill()
coil_logger.add_message('Error',
{'Message': 'TCP serious Error'})
exit(1)
except KeyboardInterrupt:
carla_process.kill()
coil_logger.add_message('Error', {'Message': 'Killed By User'})
exit(1)
except:
traceback.print_exc()
carla_process.kill()
coil_logger.add_message('Error',
{'Message': 'Something Happened'})
exit(1)
coil_logger.add_message('Finished', {})
except KeyboardInterrupt:
traceback.print_exc()
carla_process.kill()
coil_logger.add_message('Error', {'Message': 'Killed By User'})
except:
traceback.print_exc()
carla_process.kill()
coil_logger.add_message('Error', {'Message': 'Something happened'})
carla_process.kill()