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, policy_cls, env_id, args):
logging.getLogger("tensorflow").setLevel(logging.ERROR)
self.args = args
self.env = gym.make(env_id, **args2envkwargs(args))
self.policy_with_value = policy_cls(self.args)
self.iteration = 0
if self.args.mode == 'training':
self.log_dir = self.args.log_dir + '/evaluator'
else:
self.log_dir = self.args.test_log_dir
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
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.writer = self.tf.summary.create_file_writer(self.log_dir)
self.stats = {}
self.eval_timer = TimerStat()
self.eval_times = 0
def __init__(self, workers, learners, replay_buffers, evaluator, args):
"""Initialize an off-policy async optimizers.
Arguments:
workers (dict): {local worker, remote workers (list)>=0}
learners (list): list of remote learners, len >= 1
replay_buffers (list): list of replay buffers, len >= 1
"""
self.args = args
self.workers = workers
self.local_worker = self.workers['local_worker']
self.learners = learners
self.learner_queue = queue.Queue(LEARNER_QUEUE_MAX_SIZE)
self.replay_buffers = replay_buffers
self.evaluator = evaluator
self.num_sampled_steps = 0
self.iteration = 0
self.num_samples_dropped = 0
self.num_grads_dropped = 0
self.optimizer_steps = 0
self.timers = {
k: TimerStat()
for k in ["sampling_timer", "replay_timer", "learning_timer"]
}
self.stats = {}
self.update_thread = UpdateThread(self.workers, self.evaluator,
self.args, self.stats)
self.update_thread.start()
self.max_weight_sync_delay = self.args.max_weight_sync_delay
self.steps_since_update = {}
self.log_dir = self.args.log_dir
self.model_dir = self.args.model_dir
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
self.sample_tasks = TaskPool()
self._set_workers()
# fill buffer to replay starts
logger.info('start filling the replay')
while not all([
l >= self.args.replay_starts for l in ray.get(
[rb.__len__.remote() for rb in self.replay_buffers])
]):
for worker, objID in list(self.sample_tasks.completed()):
sample_batch, count = ray.get(objID)
random.choice(
self.replay_buffers).add_batch.remote(sample_batch)
self.num_sampled_steps += count
self.sample_tasks.add(worker,
worker.sample_with_count.remote())
logger.info('end filling the replay')
self.replay_tasks = TaskPool()
self._set_buffers()
self.learn_tasks = TaskPool()
self._set_learners()
logger.info('Optimizer initialized')
def __init__(self, policy_cls, env_id, args):
logging.getLogger("tensorflow").setLevel(logging.ERROR)
self.args = args
kwargs = copy.deepcopy(vars(self.args))
if self.args.env_id == 'PathTracking-v0':
self.env = gym.make(self.args.env_id,
num_agent=self.args.num_eval_agent,
num_future_data=self.args.num_future_data)
else:
env = gym.make(self.args.env_id)
self.env = DummyVecEnv(env)
self.policy_with_value = policy_cls(**kwargs)
self.iteration = 0
if self.args.mode == 'training':
self.log_dir = self.args.log_dir + '/evaluator'
else:
self.log_dir = self.args.test_log_dir
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
self.preprocessor = Preprocessor(**kwargs)
self.writer = self.tf.summary.create_file_writer(self.log_dir)
self.stats = {}
self.eval_timer = TimerStat()
self.eval_times = 0
def __init__(self, shared_list, lock, args):
self.args = args
self.shared_list = shared_list
self.lock = lock
self.task = self.args.task
self.model_only_test = self.args.model_only_test
self.step_old = -1
self.acc_timer = TimerStat()
left_construct_traj = np.load('./map/left_ref.npy')
straight_construct_traj = np.load('./map/straight_ref.npy')
right_construct_traj = np.load('./map/right_ref.npy')
self.ref_path_all = {
'left': left_construct_traj,
'straight': straight_construct_traj,
'right': right_construct_traj,
}
self.ref_path = self.ref_path_all[self.task][0] # todo
def __init__(self, policy_cls, args):
self.args = args
self.batch_size = self.args.replay_batch_size
self.policy_with_value = policy_cls(**vars(self.args))
self.batch_data = {}
self.preprocessor = Preprocessor(self.args.obs_dim,
self.args.obs_ptype,
self.args.rew_ptype,
self.args.obs_scale,
self.args.rew_scale,
self.args.rew_shift,
gamma=self.args.gamma)
self.policy_gradient_timer = TimerStat()
self.q_gradient_timer = TimerStat()
self.target_timer = TimerStat()
self.stats = {}
self.info_for_buffer = {}
self.counter = 0
self.num_batch_reuse = self.args.num_batch_reuse
def __init__(self, policy_cls, args):
self.args = args
self.batch_size = self.args.replay_batch_size
self.policy_with_value = policy_cls(**vars(self.args))
self.batch_data = {}
self.M = self.args.M
self.num_rollout_list_for_policy_update = self.args.num_rollout_list_for_policy_update
self.num_rollout_list_for_q_estimation = self.args.num_rollout_list_for_q_estimation
self.model = NAME2MODELCLS[self.args.env_id](**vars(self.args))
self.preprocessor = Preprocessor(self.args.obs_dim, self.args.obs_ptype, self.args.rew_ptype,
self.args.obs_scale, self.args.rew_scale, self.args.rew_shift,
gamma=self.args.gamma)
self.policy_gradient_timer = TimerStat()
self.q_gradient_timer = TimerStat()
self.stats = {}
self.info_for_buffer = {}
self.counter = 0
self.num_batch_reuse = self.args.num_batch_reuse
def __init__(self, policy_cls, args):
self.args = args
self.sample_num_in_learner = self.args.sample_num_in_learner
self.batch_size = self.args.replay_batch_size
if self.args.env_id == 'PathTracking-v0':
self.env = gym.make(self.args.env_id, num_agent=self.batch_size, num_future_data=self.args.num_future_data)
else:
env = gym.make(self.args.env_id)
self.env = DummyVecEnv(env)
self.policy_with_value = policy_cls(**vars(self.args))
self.batch_data = {}
self.counter = 0
self.num_batch_reuse = self.args.num_batch_reuse
self.preprocessor = Preprocessor(self.args.obs_dim, self.args.obs_ptype, self.args.rew_ptype,
self.args.obs_scale, self.args.rew_scale, self.args.rew_shift,
gamma=self.args.gamma)
self.policy_gradient_timer = TimerStat()
self.q_gradient_timer = TimerStat()
self.target_timer = TimerStat()
self.stats = {}
self.info_for_buffer = {}
def __init__(self, workers, evaluator, args, optimizer_stats):
threading.Thread.__init__(self)
self.args = args
self.workers = workers
self.local_worker = workers['local_worker']
self.evaluator = evaluator
self.optimizer_stats = optimizer_stats
self.inqueue = queue.Queue(maxsize=self.args.grads_queue_size)
self.stopped = False
self.log_dir = self.args.log_dir
self.model_dir = self.args.model_dir
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
self.iteration = 0
self.update_timer = TimerStat()
self.grad_queue_get_timer = TimerStat()
self.grad_apply_timer = TimerStat()
self.writer = tf.summary.create_file_writer(self.log_dir +
'/optimizer')
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, policy_cls, args):
self.args = args
self.sample_num_in_learner = self.args.sample_num_in_learner
self.batch_size = self.args.replay_batch_size
if self.args.env_id == 'PathTracking-v0':
self.env = gym.make(self.args.env_id,
num_agent=self.batch_size,
num_future_data=self.args.num_future_data)
else:
env = gym.make(self.args.env_id)
self.env = DummyVecEnv(env)
self.policy_with_value = policy_cls(**vars(self.args))
self.batch_data = {}
self.counter = 0
self.num_batch_reuse = self.args.num_batch_reuse
self.policy_for_rollout = policy_cls(**vars(self.args))
self.M = self.args.M
self.num_rollout_list_for_policy_update = self.args.num_rollout_list_for_policy_update
self.num_rollout_list_for_q_estimation = self.args.num_rollout_list_for_q_estimation
self.model = NAME2MODELCLS[self.args.env_id](**vars(self.args))
self.preprocessor = Preprocessor(self.args.obs_dim,
self.args.obs_ptype,
self.args.rew_ptype,
self.args.obs_scale,
self.args.rew_scale,
self.args.rew_shift,
gamma=self.args.gamma)
self.policy_gradient_timer = TimerStat()
self.q_gradient_timer = TimerStat()
self.target_timer = TimerStat()
self.stats = {}
self.info_for_buffer = {}
self.ws_old = self.tf.convert_to_tensor([0.] + [
1. / (len(self.num_rollout_list_for_policy_update) - 1)
for _ in range(len(self.num_rollout_list_for_policy_update) - 1)
],
dtype=self.tf.float32)
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()
def __init__(self, policy_cls, args):
self.args = args
self.policy_with_value = policy_cls(self.args)
self.batch_data = {}
self.all_data = {}
self.M = self.args.M
self.num_rollout_list_for_policy_update = self.args.num_rollout_list_for_policy_update
self.model = EnvironmentModel(**args2envkwargs(args))
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.grad_timer = TimerStat()
self.stats = {}
self.info_for_buffer = {}
def __init__(self, shared_list, lock, args):
self.args = args
self.shared_list = shared_list
self.lock = lock
self.task = self.args.task
self.model_only_test = self.args.model_only_test
self.step_old = -1
self.acc_timer = TimerStat()
self._load_xml()
self.red_img = self._read_png('utils/Rendering/red.png')
self.green_img = self._read_png('utils/Rendering/green.png')
self.GL_TEXTURE_RED = glGenTextures(1)
self.GL_TEXTURE_GREEN = glGenTextures(1)
left_construct_traj = np.load('./map/left_ref.npy')
straight_construct_traj = np.load('./map/straight_ref.npy')
right_construct_traj = np.load('./map/right_ref.npy')
self.ref_path_all = {
'left': left_construct_traj,
'straight': straight_construct_traj,
'right': right_construct_traj
}
def __init__(self, worker, learner, replay_buffer, evaluator, args):
self.args = args
self.worker = worker
self.learner = learner
self.replay_buffer = replay_buffer
self.evaluator = evaluator
self.num_sampled_steps = 0
self.iteration = 0
self.timers = {
k: TimerStat()
for k in [
"sampling_timer", "replay_timer", "learning_timer",
"grad_apply_timer"
]
}
self.stats = {}
self.log_dir = self.args.log_dir
self.model_dir = self.args.model_dir
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
self.args.log_interval = 10
self.args.eval_interval = 3000
self.args.save_interval = 3000
# fill buffer to replay starts
logger.info('start filling the replay')
while not len(self.replay_buffer) >= self.args.replay_starts:
sample_batch, count = self.worker.sample_with_count()
self.num_sampled_steps += count
self.replay_buffer.add_batch(sample_batch)
logger.info('end filling the replay')
self.writer = tf.summary.create_file_writer(self.log_dir +
'/optimizer')
logger.info('Optimizer initialized')
self.get_stats()
class UpdateThread(threading.Thread):
"""Background thread that updates the local model from gradient list.
"""
def __init__(self, workers, evaluator, args, optimizer_stats):
threading.Thread.__init__(self)
self.args = args
self.workers = workers
self.local_worker = workers['local_worker']
self.evaluator = evaluator
self.optimizer_stats = optimizer_stats
self.inqueue = queue.Queue(maxsize=self.args.grads_queue_size)
self.stopped = False
self.log_dir = self.args.log_dir
self.model_dir = self.args.model_dir
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
self.iteration = 0
self.update_timer = TimerStat()
self.grad_queue_get_timer = TimerStat()
self.grad_apply_timer = TimerStat()
self.writer = tf.summary.create_file_writer(self.log_dir +
'/optimizer')
def run(self):
while not self.stopped:
with self.update_timer:
self.step()
self.update_timer.push_units_processed(1)
def step(self):
self.optimizer_stats.update(
dict(update_queue_size=self.inqueue.qsize(),
update_time=self.update_timer.mean,
update_throughput=self.update_timer.mean_throughput,
grad_queue_get_time=self.grad_queue_get_timer.mean,
grad_apply_timer=self.grad_apply_timer.mean))
# fetch grad
with self.grad_queue_get_timer:
try:
grads, learner_stats = self.inqueue.get(timeout=30)
except Empty:
return
# apply grad
with self.grad_apply_timer:
try:
judge_is_nan(grads)
except ValueError:
grads = [tf.zeros_like(grad) for grad in grads]
logger.info('Grad is nan!, zero it')
self.local_worker.apply_gradients(self.iteration, grads)
# log
if self.iteration % self.args.log_interval == 0:
logger.info('updating {} in total'.format(self.iteration))
logger.info('sampling {} in total'.format(
self.optimizer_stats['num_sampled_steps']))
with self.writer.as_default():
for key, val in learner_stats.items():
if not isinstance(val, list):
tf.summary.scalar(
'optimizer/learner_stats/scalar/{}'.format(key),
val,
step=self.iteration)
else:
assert isinstance(val, list)
for i, v in enumerate(val):
tf.summary.scalar(
'optimizer/learner_stats/list/{}/{}'.format(
key, i),
v,
step=self.iteration)
for key, val in self.optimizer_stats.items():
tf.summary.scalar('optimizer/{}'.format(key),
val,
step=self.iteration)
self.writer.flush()
# evaluate
if self.iteration % self.args.eval_interval == 0:
self.evaluator.set_weights.remote(self.local_worker.get_weights())
if self.args.obs_preprocess_type == 'normalize' or self.args.reward_preprocess_type == 'normalize':
self.evaluator.set_ppc_params.remote(
self.local_worker.get_ppc_params())
self.evaluator.run_evaluation.remote(self.iteration)
# save
if self.iteration % self.args.save_interval == 0:
self.local_worker.save_weights(self.model_dir, self.iteration)
self.workers['remote_workers'][0].save_ppc_params.remote(
self.model_dir)
self.iteration += 1
class Plot():
def __init__(self, shared_list, lock, args):
self.args = args
self.shared_list = shared_list
self.lock = lock
self.task = self.args.task
self.model_only_test = self.args.model_only_test
self.step_old = -1
self.acc_timer = TimerStat()
left_construct_traj = np.load('./map/left_ref.npy')
straight_construct_traj = np.load('./map/straight_ref.npy')
right_construct_traj = np.load('./map/right_ref.npy')
self.ref_path_all = {
'left': left_construct_traj,
'straight': straight_construct_traj,
'right': right_construct_traj,
}
self.ref_path = self.ref_path_all[self.task][0] # todo
def run(self):
extension = 40
light_line_width = 3
dotted_line_style = '--'
solid_line_style = '-'
start_time = 0
v_old = 0.
plt.title("Crossroad")
ax = plt.axes(xlim=(-CROSSROAD_HALF_WIDTH - extension,
CROSSROAD_HALF_WIDTH + extension),
ylim=(-CROSSROAD_D_HEIGHT - extension,
CROSSROAD_U_HEIGHT + extension))
plt.axis("equal")
plt.axis('off')
def is_in_plot_area(x, y, tolerance=5):
if -CROSSROAD_HALF_WIDTH - extension + tolerance < x < CROSSROAD_HALF_WIDTH + extension - tolerance and \
-CROSSROAD_D_HEIGHT - extension + tolerance < y < CROSSROAD_U_HEIGHT + extension - tolerance:
return True
else:
return False
def draw_rotate_rec(x, y, a, l, w, color, linestyle='-'):
RU_x, RU_y, _ = rotate_coordination(l / 2, w / 2, 0, -a)
RD_x, RD_y, _ = rotate_coordination(l / 2, -w / 2, 0, -a)
LU_x, LU_y, _ = rotate_coordination(-l / 2, w / 2, 0, -a)
LD_x, LD_y, _ = rotate_coordination(-l / 2, -w / 2, 0, -a)
ax.plot([RU_x + x, RD_x + x], [RU_y + y, RD_y + y],
color=color,
linestyle=linestyle)
ax.plot([RU_x + x, LU_x + x], [RU_y + y, LU_y + y],
color=color,
linestyle=linestyle)
ax.plot([LD_x + x, RD_x + x], [LD_y + y, RD_y + y],
color=color,
linestyle=linestyle)
ax.plot([LD_x + x, LU_x + x], [LD_y + y, LU_y + y],
color=color,
linestyle=linestyle)
def plot_phi_line(x, y, phi, color):
line_length = 5
x_forw, y_forw = x + line_length * cos(phi * pi / 180.), \
y + line_length * sin(phi * pi / 180.)
plt.plot([x, x_forw], [y, y_forw], color=color, linewidth=0.5)
while True:
plt.cla()
plt.axis('off')
ax.add_patch(
plt.Rectangle(
(-CROSSROAD_HALF_WIDTH - extension,
-CROSSROAD_D_HEIGHT - extension),
2 * CROSSROAD_HALF_WIDTH + 2 * extension,
CROSSROAD_D_HEIGHT + CROSSROAD_U_HEIGHT + 2 * extension,
edgecolor='black',
facecolor='none'))
# ----------horizon--------------
plt.plot(
[-CROSSROAD_HALF_WIDTH - extension, -CROSSROAD_HALF_WIDTH],
[0, 0],
color='black')
plt.plot([CROSSROAD_HALF_WIDTH + extension, CROSSROAD_HALF_WIDTH],
[0, 0],
color='black')
#
for i in range(1, LANE_NUMBER_LR + 1):
linestyle = dotted_line_style if i < LANE_NUMBER_LR else solid_line_style
plt.plot(
[-CROSSROAD_HALF_WIDTH - extension, -CROSSROAD_HALF_WIDTH],
[i * LANE_WIDTH_LR, i * LANE_WIDTH_LR],
linestyle=linestyle,
color='black')
plt.plot(
[CROSSROAD_HALF_WIDTH + extension, CROSSROAD_HALF_WIDTH],
[i * LANE_WIDTH_LR, i * LANE_WIDTH_LR],
linestyle=linestyle,
color='black')
plt.plot(
[-CROSSROAD_HALF_WIDTH - extension, -CROSSROAD_HALF_WIDTH],
[-i * LANE_WIDTH_LR, -i * LANE_WIDTH_LR],
linestyle=linestyle,
color='black')
plt.plot(
[CROSSROAD_HALF_WIDTH + extension, CROSSROAD_HALF_WIDTH],
[-i * LANE_WIDTH_LR, -i * LANE_WIDTH_LR],
linestyle=linestyle,
color='black')
# ----------vertical----------------
plt.plot([0, 0],
[-CROSSROAD_D_HEIGHT - extension, -CROSSROAD_D_HEIGHT],
color='black')
plt.plot([0, 0],
[CROSSROAD_U_HEIGHT + extension, CROSSROAD_U_HEIGHT],
color='black')
#
for i in range(1, LANE_NUMBER_UD + 1):
linestyle = dotted_line_style if i < LANE_NUMBER_UD else solid_line_style
plt.plot(
[i * LANE_WIDTH_UD, i * LANE_WIDTH_UD],
[-CROSSROAD_D_HEIGHT - extension, -CROSSROAD_D_HEIGHT],
linestyle=linestyle,
color='black')
plt.plot([i * LANE_WIDTH_UD, i * LANE_WIDTH_UD],
[CROSSROAD_U_HEIGHT + extension, CROSSROAD_U_HEIGHT],
linestyle=linestyle,
color='black')
plt.plot(
[-i * LANE_WIDTH_UD, -i * LANE_WIDTH_UD],
[-CROSSROAD_D_HEIGHT - extension, -CROSSROAD_D_HEIGHT],
linestyle=linestyle,
color='black')
plt.plot([-i * LANE_WIDTH_UD, -i * LANE_WIDTH_UD],
[CROSSROAD_U_HEIGHT + extension, CROSSROAD_U_HEIGHT],
linestyle=linestyle,
color='black')
# ----------Oblique--------------
plt.plot([LANE_NUMBER_UD * LANE_WIDTH_UD, CROSSROAD_HALF_WIDTH],
[-CROSSROAD_D_HEIGHT, -LANE_NUMBER_LR * LANE_WIDTH_LR],
color='black')
plt.plot([LANE_NUMBER_UD * LANE_WIDTH_UD, CROSSROAD_HALF_WIDTH],
[CROSSROAD_U_HEIGHT, LANE_NUMBER_LR * LANE_WIDTH_LR],
color='black')
plt.plot([-LANE_NUMBER_UD * LANE_WIDTH_UD, -CROSSROAD_HALF_WIDTH],
[-CROSSROAD_D_HEIGHT, -LANE_NUMBER_LR * LANE_WIDTH_LR],
color='black')
plt.plot([-LANE_NUMBER_UD * LANE_WIDTH_UD, -CROSSROAD_HALF_WIDTH],
[CROSSROAD_U_HEIGHT, LANE_NUMBER_LR * LANE_WIDTH_LR],
color='black')
# ----------------------ref_path--------------------
color = ['blue', 'coral', 'cyan', 'green']
for index, path in enumerate(self.ref_path_all[self.task]):
if index == self.shared_list[12]:
ax.plot(path[0], path[1], color=color[index], alpha=1.0)
else:
ax.plot(path[0], path[1], color=color[index], alpha=0.3)
# plot_ref = ['left', 'straight', 'right'] # 'left','straight','right', 'left_ref','straight_ref','right_ref'
# for ref in plot_ref:
# ref_path = self.ref_path_all[ref][0]
# ax.plot(ref_path[0], ref_path[1])
# ax.plot(self.ref_path[0], self.ref_path[1], color='g') # todo:
state_other = self.shared_list[4].copy()
# plot cars
for veh in state_other:
veh_x = veh['x']
veh_y = veh['y']
veh_phi = veh['phi']
veh_l = L
veh_w = W
if is_in_plot_area(veh_x, veh_y):
plot_phi_line(veh_x, veh_y, veh_phi, 'black')
draw_rotate_rec(veh_x, veh_y, veh_phi, veh_l, veh_w,
'black')
interested_vehs = self.shared_list[10].copy()
for i in range(int(len(interested_vehs) / 4)): # TODO:
veh_x = interested_vehs[4 * i + 0]
veh_y = interested_vehs[4 * i + 1]
# plt.text(veh_x, veh_y, i, fontsize=12)
veh_phi = interested_vehs[4 * i + 3]
veh_l = L
veh_w = W
if is_in_plot_area(veh_x, veh_y):
plot_phi_line(veh_x, veh_y, veh_phi, 'black')
draw_rotate_rec(veh_x,
veh_y,
veh_phi,
veh_l,
veh_w,
'b',
linestyle='--')
v_light = int(self.shared_list[13]) # todo
if v_light == 0:
v_color, h_color = 'black', 'black' # 'green', 'red'
elif v_light == 1:
v_color, h_color = 'black', 'black'
elif v_light == 2:
v_color, h_color = 'black', 'black'
else:
v_color, h_color = 'black', 'black'
plt.plot([(LANE_NUMBER_UD - 1) * LANE_WIDTH_UD,
LANE_NUMBER_UD * LANE_WIDTH_UD],
[-CROSSROAD_D_HEIGHT, -CROSSROAD_D_HEIGHT],
color=v_color,
linewidth=light_line_width)
plt.plot([
-LANE_NUMBER_UD * LANE_WIDTH_UD,
-(LANE_NUMBER_UD - 1) * LANE_WIDTH_UD
], [CROSSROAD_U_HEIGHT, CROSSROAD_U_HEIGHT],
color=v_color,
linewidth=light_line_width)
plt.plot([0, (LANE_NUMBER_UD - 1) * LANE_WIDTH_UD],
[-CROSSROAD_D_HEIGHT, -CROSSROAD_D_HEIGHT],
color=v_color,
linewidth=light_line_width)
plt.plot([0, -(LANE_NUMBER_UD - 1) * LANE_WIDTH_UD],
[CROSSROAD_U_HEIGHT, CROSSROAD_U_HEIGHT],
color=v_color,
linewidth=light_line_width)
state_ego = self.shared_list[9].copy()
ego_v = state_ego['VehicleSPeedAct']
ego_steer = state_ego['SteerAngleAct']
ego_gear = state_ego['AutoGear']
ego_gps_v = state_ego['GpsSpeed']
ego_north_v = state_ego['NorthVelocity']
ego_east_v = state_ego['EastVelocity']
ego_yaw_rate = state_ego['YawRate']
ego_long_acc = state_ego['LongitudinalAcc']
ego_lat_acc = state_ego['LateralAcc']
ego_throttle = state_ego['Throttle']
ego_brk = state_ego['BrkOn']
ego_x = state_ego['GaussX']
ego_y = state_ego['GaussY']
ego_longitude = state_ego['Longitude']
ego_latitude = state_ego['Latitude']
ego_phi = state_ego['Heading']
ego_l = L
ego_w = W
if not self.model_only_test:
real_action_x = state_ego['model_x_in_real_action']
real_action_y = state_ego['model_y_in_real_action']
real_action_phi = state_ego['model_phi_in_real_action']
plot_phi_line(real_action_x, real_action_y, real_action_phi,
'blue')
draw_rotate_rec(real_action_x, real_action_y, real_action_phi,
ego_l, ego_w, 'blue')
model_action_x = state_ego['model_x_in_model_action']
model_action_y = state_ego['model_y_in_model_action']
model_action_phi = state_ego['model_phi_in_model_action']
plot_phi_line(model_action_x, model_action_y, model_action_phi,
'coral')
draw_rotate_rec(model_action_x, model_action_y,
model_action_phi, ego_l, ego_w, 'coral')
ego_l = L
ego_w = W
plot_phi_line(ego_x, ego_y, ego_phi, 'red')
draw_rotate_rec(ego_x, ego_y, ego_phi, ego_l, ego_w, 'red')
# model_x = state_ego['model_x']
# model_y = state_ego['model_y']
# model_phi = state_ego['model_phi']
# draw_rotate_rec(model_x, model_y, model_phi, ego_l, ego_w, 'blue')
time1 = time.time()
delta_time = time1 - start_time
acc_actual = (ego_v - v_old) / delta_time
self.acc_timer.push(acc_actual)
start_time = time.time()
v_old = ego_v
indexs, points = find_closest_point(self.ref_path,
np.array([ego_x], np.float32),
np.array([ego_y], np.float32))
path_x, path_y, path_phi = points[0][0], points[1][0], points[2][0]
plt.plot(path_x, path_y, 'g.')
delta_x, delta_y, delta_phi = ego_x - path_x, ego_y - path_y, ego_phi - path_phi
# plot txt
text_x, text_y_start = -110, 60
ge = iter(range(0, 1000, 4))
plt.text(text_x, text_y_start - next(ge),
'ego_GaussX: {:.2f}m'.format(ego_x))
plt.text(text_x, text_y_start - next(ge),
'ego_GaussY: {:.2f}m'.format(ego_y))
plt.text(text_x, text_y_start - next(ge),
'gps_v: {:.2f}m/s'.format(ego_gps_v))
plt.text(text_x, text_y_start - next(ge),
'north_v: {:.2f}m/s'.format(ego_north_v))
plt.text(text_x, text_y_start - next(ge),
'east_v: {:.2f}m/s'.format(ego_east_v))
plt.text(text_x, text_y_start - next(ge),
'yaw_rate: {:.2f}rad/s'.format(ego_yaw_rate))
plt.text(text_x, text_y_start - next(ge),
r'longitude: ${:.2f}\degree$'.format(ego_longitude))
plt.text(text_x, text_y_start - next(ge),
r'latitude: ${:.2f}\degree$'.format(ego_latitude))
plt.text(text_x, text_y_start - next(ge),
r'long_acc: ${:.2f}m/s^2$'.format(ego_long_acc))
plt.text(text_x, text_y_start - next(ge),
r'lat_acc: ${:.2f}m/s^2$'.format(ego_lat_acc))
plt.text(text_x, text_y_start - next(ge),
'path_x: {:.2f}m'.format(path_x))
plt.text(text_x, text_y_start - next(ge),
'path_y: {:.2f}m'.format(path_y))
plt.text(text_x, text_y_start - next(ge),
'delta_x: {:.2f}m'.format(delta_x))
plt.text(text_x, text_y_start - next(ge),
'delta_y: {:.2f}m'.format(delta_y))
plt.text(text_x, text_y_start - next(ge),
r'ego_phi: ${:.2f}\degree$'.format(ego_phi))
plt.text(text_x, text_y_start - next(ge),
r'path_phi: ${:.2f}\degree$'.format(path_phi))
plt.text(text_x, text_y_start - next(ge),
r'delta_phi: ${:.2f}\degree$'.format(delta_phi))
decision = self.shared_list[8].copy()
decision_steer = decision['SteerAngleAim']
decision_torque = decision['Torque']
decision_brkacc = decision['Deceleration']
decision_Dec_flag = decision['Dec_flag']
decision_Tor_flag = decision['Tor_flag']
front_wheel_deg = decision['front_wheel_deg']
acc = decision['a_x']
text_x, text_y_start = 70, 60
ge = iter(range(0, 1000, 4))
# done info
# plt.text(text_x, text_y_start - next(ge), 'done info: {}'.format(self.done_type))
plt.text(text_x, text_y_start - next(ge), 'CAN')
plt.text(text_x, text_y_start - next(ge),
'speed_act: {:.2f}m/s'.format(ego_v))
plt.text(text_x, text_y_start - next(ge),
r'steering_act: ${:.2f}\degree$'.format(ego_steer))
plt.text(text_x, text_y_start - next(ge),
'throttle: {:.2f}'.format(ego_throttle))
plt.text(text_x, text_y_start - next(ge),
'brake_on: {:.2f}'.format(ego_brk))
plt.text(text_x, text_y_start - next(ge),
'gear: {:.2f}'.format(ego_gear))
plt.text(text_x, text_y_start - next(ge), ' ')
plt.text(text_x, text_y_start - next(ge), 'Decision')
plt.text(
text_x, text_y_start - next(ge),
r'steer_aim_decision: ${:.2f}\degree$'.format(decision_steer))
plt.text(text_x, text_y_start - next(ge),
'torque_decision: {:.2f}Nm'.format(decision_torque))
plt.text(text_x, text_y_start - next(ge),
'torque_flag: {}'.format(decision_Tor_flag))
plt.text(
text_x, text_y_start - next(ge),
r'brake_acc_decision: {:.2f}$m/s^2$'.format(decision_brkacc))
plt.text(text_x, text_y_start - next(ge),
'deceleration_flag: {}'.format(decision_Dec_flag))
plt.text(text_x, text_y_start - next(ge), ' ')
plt.text(text_x, text_y_start - next(ge), 'Net out')
plt.text(text_x, text_y_start - next(ge),
'front_wheel_deg: {:.2f}'.format(front_wheel_deg))
plt.text(text_x, text_y_start - next(ge),
r'acc: {:.2f}$m/s^2$'.format(acc))
plt.text(text_x, text_y_start - next(ge),
r'acc_actual: {:.2f}$m/s^2$'.format(self.acc_timer.mean))
# plot text of trajectroy
text_x, text_y_start = -40, -70
ge = iter(range(0, 1000, 6))
traj_return_value = self.shared_list[11]
for i, value in enumerate(traj_return_value):
if i == self.shared_list[12]:
plt.text(
text_x,
text_y_start - next(ge),
'Path reward={:.4f}, Collision risk={:.4f}'.format(
value[0], value[1]),
fontsize=14,
color=color[i],
fontstyle='italic')
else:
plt.text(
text_x,
text_y_start - next(ge),
'Path reward={:.4f}, Collision risk={:.4f}'.format(
value[0], value[1]),
fontsize=10,
color=color[i],
fontstyle='italic')
plt.pause(0.00001)