def __init__(self,
city_name,
avoid_stopping,
memory_fraction=0.25,
image_cut=[115, 510]):
#Agent.__init__(self)
dir_path = os.path.dirname(__file__)
#self.dropout_vec = [1.0] * 8 + [0.7] * 2 + [0.5] * 2 + [0.5] * 1 + [0.5, 1.] *6
self.dropout_vec = [1.0] * 8 + [0.7] * 2 + [0.5] * 2 + [0.5] * 1 + [
0.5, 1.
] * 6
self._image_size = (88, 200, 3)
self._avoid_stopping = True
self._image_cut = image_cut
tf.reset_default_graph()
config_gpu = tf.ConfigProto(allow_soft_placement=True)
# GPU to be selected, just take zero , select GPU with CUDA_VISIBLE_DEVICES
config_gpu.gpu_options.visible_device_list = '0'
config_gpu.gpu_options.per_process_gpu_memory_fraction = memory_fraction
self._sess = tf.Session(config=config_gpu)
self._models_path = "/home/pankaj/Trainer_module/CARLAILtrainer/models/" #"D:/outbox/changed_old_trainer/trainer5/models/"#dir_path + '/model/'
self._sess.run(tf.global_variables_initializer())
#self.load_model()
with tf.device('/gpu:0'):
saver = tf.train.import_meta_graph(self._models_path +
'model.ckpt.meta')
self._graph = tf.get_default_graph()
self._input_images = self._graph.get_tensor_by_name('input_image:0')
self._input_speed = self._graph.get_tensor_by_name('input_speed:0')
self._dout = self._graph.get_tensor_by_name('dropout:0')
self._follow_lane = self._graph.get_tensor_by_name(
'Network/Branch_0/fc_8:0')
self._left = self._graph.get_tensor_by_name('Network/Branch_1/fc_11:0')
self._right = self._graph.get_tensor_by_name(
'Network/Branch_2/fc_14:0')
self._straight = self._graph.get_tensor_by_name(
'Network/Branch_3/fc_17:0')
self._speed = self._graph.get_tensor_by_name(
'Network/Branch_4/fc_20:0')
self._intent = self._graph.get_tensor_by_name(
'Network/Branch_5/fc_23:0')
with tf.device('/gpu:0'):
saver.restore(self._sess, self._models_path + 'model.ckpt')
self._curr_dir = 0
self.count = 0
self._enable_manual_control = False
self._is_on_reverse = False
#pygame.init()
#self._display = pygame.display.set_mode(
# (WINDOW_WIDTH, WINDOW_HEIGHT),
# pygame.HWSURFACE | pygame.DOUBLEBUF)
self.command_follower = CommandFollower(city_name)
self.traffic_light_infraction = False
def make_controlling_agent(args, town_name):
"""
Make the controlling agent object depending on what was selected.
Options:
Forward Agent: Trivial agent that just accelerate forward.
Human Agent: Agent controlled by a human driver, currently only by keyboard.
Command Follower: A* planner followed by a PID controller (used as expert for data collection)
"""
if args.controlling_agent == "ForwardAgent":
return ForwardAgent()
elif args.controlling_agent == "HumanAgent":
return HumanAgent()
elif args.controlling_agent == "CommandFollower":
return CommandFollower(town_name)
else:
raise ValueError("Selected Agent Does not exist")
def make_controlling_agent(args, town_name):
""" Make the controlling agent object depending on what was selected.
Right now we have the following options:
Forward Agent: A trivial agent that just accelerate forward.
Human Agent: An agent controlled by a human driver, currently only by keyboard.
"""
if args.controlling_agent == "ForwardAgent":
return ForwardAgent()
elif args.controlling_agent == "HumanAgent":
# TDNextPR: Add parameters such as joysticks to the human agent.
return HumanAgent()
elif args.controlling_agent == "CommandFollower":
return CommandFollower(town_name)
elif args.controlling_agent == 'LaneFollower':
return LaneFollower(town_name)
else:
raise ValueError("Selected Agent Does not exist")