print('Saved models:')
# Check paths from hparams, filter out models whose files exist on disk
filtered_models = {}
for model in hparams['models']:
if os.path.isfile(model):
filtered_models[int(model.split('.')[0].split('_')[-1])] = model
filtered_models = list(dict(sorted(filtered_models.items(), key=lambda kv: kv[0])).values())
for index, model in enumerate(filtered_models):
print(f'{index+1}. {model}')
# Ask user for a model to choose
model = filtered_models[int(input('Choose the model (empty - most recent one): ') or '0') - 1]
print('Starting...')
# Kill Carla processes if there are any and start simulator
start_carla(playing=True)
# Run Carla settings (weather, NPC control) in a separate thread
pause_object = PauseObject()
carla_settings = CarlaEnvSettings(0, [pause_object], car_npcs=hparams['car_npcs'])
carla_settings_thread = Thread(target=carla_settings.update_settings_in_loop, daemon=True)
carla_settings_thread.start()
# Play
print(f'Starting agent ({model})...')
play_agent(model, pause_object, ConsoleStats.print_short)
from sources import get_carla_exec_command, kill_carla_processes, CarlaEnvSettings, CARLA_SETTINGS_STATE
if __name__ == '__main__':
print('Starting...')
# overal start time
start_time = time.time()
# Create required folders
os.makedirs('models', exist_ok=True)
os.makedirs('tmp', exist_ok=True)
os.makedirs('checkpoint', exist_ok=True)
# Kill Carla processes if there are any and start simulator
start_carla()
# Load hparams if they are being saved by trainer
hparams = get_hparams()
if hparams:
# If everything is ok, update start time by previous running time
start_time -= hparams['duration']
# Spawn limited trainer process and get weights' size
print('Calculating weights size...')
weights_size = Value('L', 0)
p = Process(target=check_weights_size,
args=(hparams['model_path'] if hparams else False,
weights_size),
daemon=True)
p.start()