def __init__(self, init_n_ego_dict): # init_n_ego_dict is used to init traffic (mainly) and ego dynamics
self.TASK2MODEL = dict(left=LoadPolicy('../utils/models/left/experiment-2021-03-15-16-39-00', 180000),
straight=LoadPolicy('../utils/models/straight/experiment-2021-03-15-19-16-13', 175000),
right=LoadPolicy('../utils/models/right/experiment-2021-03-15-21-02-51', 195000),)
self.n_ego_instance = {}
self.n_ego_dynamics = {}
self.n_ego_select_index = {}
for egoID, ego_dict in init_n_ego_dict.items():
self.n_ego_instance[egoID] = CrossroadEnd2end(training_task=NAME2TASK[egoID[:2]],
mode='testing',
multi_display=True)
self.mpp = MultiPathGenerator()
self.virtual_model = dict(left=EnvironmentModel(training_task='left', mode='selecting'),
straight=EnvironmentModel(training_task='straight', mode='selecting'),
right=EnvironmentModel(training_task='right', mode='selecting'))
# ------------------build graph for tf.function in advance-----------------------
for task in ['left', 'straight', 'right']:
env = CrossroadEnd2end(training_task=task, mode='testing', multi_display=False)
for i in range(3):
obs = env.reset()
obs = tf.convert_to_tensor(obs[np.newaxis, :], dtype=tf.float32)
self.is_safe(obs, i, task)
obs = env.reset()
obs_with_specific_shape = np.tile(obs, (3, 1))
self.TASK2MODEL[task].run_batch(obs_with_specific_shape)
self.TASK2MODEL[task].obj_value_batch(obs_with_specific_shape)
env.close()
# ------------------build graph for tf.function in advance-----------------------
self.reset(init_n_ego_dict)
def __init__(self, task, train_exp_dir, ite, logdir=None):
self.task = task
self.policy = LoadPolicy('../utils/models/{}/{}'.format(task, train_exp_dir), ite)
self.env = CrossroadEnd2end(training_task=self.task, mode='testing')
self.model = EnvironmentModel(self.task, mode='selecting')
self.recorder = Recorder()
self.episode_counter = -1
self.step_counter = -1
self.obs = None
self.stg = MultiPathGenerator()
self.step_timer = TimerStat()
self.ss_timer = TimerStat()
self.logdir = logdir
if self.logdir is not None:
config = dict(task=task, train_exp_dir=train_exp_dir, ite=ite)
with open(self.logdir + '/config.json', 'w', encoding='utf-8') as f:
json.dump(config, f, ensure_ascii=False, indent=4)
self.fig = plt.figure(figsize=(8, 8))
plt.ion()
self.hist_posi = []
self.old_index = 0
self.path_list = self.stg.generate_path(self.task)
# ------------------build graph for tf.function in advance-----------------------
for i in range(3):
obs = self.env.reset()
obs = tf.convert_to_tensor(obs[np.newaxis, :], dtype=tf.float32)
self.is_safe(obs, i)
obs = self.env.reset()
obs_with_specific_shape = np.tile(obs, (3, 1))
self.policy.run_batch(obs_with_specific_shape)
self.policy.obj_value_batch(obs_with_specific_shape)
# ------------------build graph for tf.function in advance-----------------------
self.reset()
def __init__(
self, init_n_ego_dict
): # init_n_ego_dict is used to init traffic (mainly) and ego dynamics
self.TASK2MODEL = dict(
left=LoadPolicy('../utils/models/left', 100000),
straight=LoadPolicy('../utils/models/straight', 95000),
right=LoadPolicy('../utils/models/right', 145000),
)
self.n_ego_instance = {}
self.n_ego_dynamics = {}
self.n_ego_select_index = {}
for egoID, ego_dict in init_n_ego_dict.items():
self.n_ego_instance[egoID] = CrossroadEnd2end(
training_task=NAME2TASK[egoID[:2]], display=True)
self.mpp = MultiPathGenerator()
self.virtual_model = dict(left=EnvironmentModel(training_task='left',
mode='selecting'),
straight=EnvironmentModel(
training_task='straight',
mode='selecting'),
right=EnvironmentModel(training_task='right',
mode='selecting'))
self.reset(init_n_ego_dict)
def __init__(self, exp_dir, iter):
model_dir = exp_dir + '/models'
parser = argparse.ArgumentParser()
params = json.loads(open(exp_dir + '/config.json').read())
for key, val in params.items():
parser.add_argument("-" + key, default=val)
self.args = parser.parse_args()
env = CrossroadEnd2end(training_task=self.args.env_kwargs_training_task,
num_future_data=self.args.env_kwargs_num_future_data)
self.policy = Policy4Toyota(self.args)
self.policy.load_weights(model_dir, iter)
self.preprocessor = Preprocessor((self.args.obs_dim,), self.args.obs_preprocess_type, self.args.reward_preprocess_type,
self.args.obs_scale, self.args.reward_scale, self.args.reward_shift,
gamma=self.args.gamma)
# self.preprocessor.load_params(load_dir)
init_obs = env.reset()
self.run_batch(init_obs[np.newaxis, :])
self.obj_value_batch(init_obs[np.newaxis, :])
def __init__(self, task):
self.task = task
if self.task == 'left':
self.policy = LoadPolicy('G:\\env_build\\utils\\models\\left', 100000)
elif self.task == 'right':
self.policy = LoadPolicy('G:\\env_build\\utils\\models\\right', 145000)
elif self.task == 'straight':
self.policy = LoadPolicy('G:\\env_build\\utils\\models\\straight', 95000)
self.horizon = 25
self.num_future_data = 0
self.env = CrossroadEnd2end(training_task=self.task, num_future_data=self.num_future_data)
self.model = EnvironmentModel(self.task)
self.obs = self.env.reset()
self.stg = StaticTrajectoryGenerator_origin(mode='static_traj')
self.data2plot = []
self.mpc_cal_timer = TimerStat()
self.adp_cal_timer = TimerStat()
self.recorder = Recorder()
def __init__(self, task, logdir=None):
self.task = task
if self.task == 'left':
self.policy = LoadPolicy('../utils/models/left', 100000)
elif self.task == 'right':
self.policy = LoadPolicy('../utils/models/right', 145000)
elif self.task == 'straight':
self.policy = LoadPolicy('../utils/models/straight', 95000)
self.env = CrossroadEnd2end(training_task=self.task)
self.model = EnvironmentModel(self.task, mode='selecting')
self.recorder = Recorder()
self.episode_counter = -1
self.step_counter = -1
self.obs = None
self.stg = None
self.step_timer = TimerStat()
self.ss_timer = TimerStat()
self.logdir = logdir
self.fig = plt.figure(figsize=(8, 8))
plt.ion()
self.hist_posi = []
self.reset()
def __init__(self, task, train_exp_dir, ite, logdir=None):
self.task = task
self.policy = LoadPolicy(
'../utils/models/{}/{}'.format(task, train_exp_dir), ite)
self.env = CrossroadEnd2end(training_task=self.task)
self.model = EnvironmentModel(self.task, mode='selecting')
self.recorder = Recorder()
self.episode_counter = -1
self.step_counter = -1
self.obs = None
self.stg = None
self.step_timer = TimerStat()
self.ss_timer = TimerStat()
self.logdir = logdir
if self.logdir is not None:
config = dict(task=task, train_exp_dir=train_exp_dir, ite=ite)
with open(self.logdir + '/config.json', 'w',
encoding='utf-8') as f:
json.dump(config, f, ensure_ascii=False, indent=4)
self.fig = plt.figure(figsize=(8, 8))
plt.ion()
self.hist_posi = []
self.reset()