def initial_pool_generator(config, problem):
good_members_found = 0
attempts = 0
storage = SeedStorage('initial_pool')
while good_members_found < config.POOLSIZE: #40:
path = storage.get_path_by_index(good_members_found + 1)
# if path.exists():
# print('member already exists', path)
# good_members_found += 1
# continue
attempts += 1
print(
f'attempts {attempts} good {good_members_found} looking for {path}'
)
member = problem.generate_random_member()
member.evaluate()
if member.distance_to_boundary <= 0:
continue
member = problem.member_class().from_dict(member.to_dict())
member.config = config
member.problem = problem
#member.clear_evaluation()
member.distance_to_boundary = None
good_members_found += 1
path.write_text(json.dumps(member.to_dict()))
return storage.folder
def main():
storage = SeedStorage('short')
for i in range(1, 100):
member_filepath = storage.get_path_by_index(i)
if not member_filepath.exists():
continue
member = BeamNGMember.from_dict(storage.load_json_by_index(i))
sample_nodes = member.sample_nodes
road_imagery = BeamNGRoadImagery.from_sample_nodes(sample_nodes)
for extension in ['.jpg', '.svg']:
image_filename = member_filepath.with_suffix(extension)
print('saving', image_filename)
road_imagery.save(image_filename)
def initial_pool_generator(config, problem):
good_members_found = 0
attempts = 0
storage = SeedStorage('initial_pool')
i = 0
while good_members_found < config.POOLSIZE:
path = storage.get_path_by_index(good_members_found + 1)
# if path.exists():
# print('member already exists', path)
# good_members_found += 1
# continue
attempts += 1
log.debug(
f'attempts {attempts} good {good_members_found} looking for {path}'
)
max_angle = random.randint(10, 100)
member = problem.generate_random_member(max_angle)
member.evaluate()
if member.distance_to_boundary <= 0:
continue
#member = problem.member_class().from_dict(member.to_dict())
member.config = config
member.problem = problem
#member.clear_evaluation()
#member.distance_to_boundary = None
good_members_found += 1
# path.write_text(json.dumps(member.to_dict()))
path.write_text(
json.dumps({
"control_nodes":
member.control_nodes,
member.simulation.f_params:
member.simulation.params._asdict(),
member.simulation.f_info:
member.simulation.info.__dict__,
member.simulation.f_road:
member.simulation.road.to_dict(),
member.simulation.f_records:
[r._asdict() for r in member.simulation.states]
}))
return storage.folder
class SeedPoolFolder(SeedPool):
def __init__(self, problem: Problem, folder_name):
super().__init__(problem)
self.storage = SeedStorage(folder_name)
self.file_path_list = self.storage.all_files()
assert (len(self.file_path_list)) > 0
self.cache: Dict[str, Member] = {}
def __len__(self):
return len(self.file_path_list)
def __getitem__(self, item) -> Member:
path = self.file_path_list[item]
result: Member = self.cache.get(path, None)
if not result:
result = self.problem.member_class().from_dict(
self.storage.read(path))
self.cache[path] = result
result.problem = self.problem
return result
def initial_population_generator(path, config, problem):
all_roads = [
filename
for filename in glob.glob(str(path) + "\*.json", recursive=True)
]
#all_roads += [filename for filename in glob.glob(path2)]
shuffle(all_roads)
roads = all_roads[:40]
starting_point = choice(roads)
original_set = list()
original_set.append(starting_point)
popsize = config.POPSIZE
i = 0
while i < popsize - 1:
max_dist = 0
for ind in roads:
dist = get_min_distance_from_set(ind, original_set)
if dist > max_dist:
max_dist = dist
best_ind = ind
original_set.append(best_ind)
i += 1
base = config.initial_population_folder
storage = SeedStorage(base)
for index, road in enumerate(original_set):
path = storage.get_path_by_index(index + 1)
dst = path
copy(road, dst)
beamng.resume()
break
# one step is 0.05 seconds (5/100)
beamng.step(steps)
try:
start()
finally:
beamng.close()
if __name__ == '__main__':
dataset_size = 10
road_storage = SeedStorage('training_set')
simulation_attempts = 5
for idx in range(1, dataset_size + 1):
path = road_storage.get_path_by_index(idx)
road_generator = RoadGenerator(num_control_nodes=30, seg_length=20).generate()
with open(path, 'w') as f:
f.write(json.dumps(road_generator.to_dict()))
street_1 = DecalRoad('street_1').add_4d_points(road_generator.sample_nodes)
for attempt in range(simulation_attempts):
try:
try:
if self.config.beamng_close_at_iteration:
self._close()
else:
if self.brewer:
self.brewer.beamng.stop_scenario()
except Exception as ex:
log.debug('end_iteration() failed:')
traceback.print_exception(type(ex), ex, ex.__traceback__)
def _close(self):
if self.brewer:
try:
self.brewer.beamng.close()
except Exception as ex:
log.debug('beamng.close() failed:')
traceback.print_exception(type(ex), ex, ex.__traceback__)
self.brewer = None
if __name__ == '__main__':
config = BeamNGConfig()
inst = BeamNGNvidiaOob(config)
while True:
seed_storage = SeedStorage('basic5')
for i in range(1, 11):
member = BeamNGMember.from_dict(seed_storage.load_json_by_index(i))
member.clear_evaluation()
inst.evaluate([member])
log.info(member)
config_silly.keras_model_file = 'self-driving-car-4600.h5'
config_smart.beamng_close_at_iteration = True
problem_silliest = BeamNGProblem(config_silliest,
SmartArchive(config_silly.ARCHIVE_THRESHOLD))
problem_silly = BeamNGProblem(config_silly,
SmartArchive(config_silly.ARCHIVE_THRESHOLD))
problem_smart = BeamNGProblem(config_smart,
SmartArchive(config_smart.ARCHIVE_THRESHOLD))
# problem = BeamNGProblem(config, SmartArchive(config.ARCHIVE_THRESHOLD))
if __name__ == '__main__':
good_members_found = 0
attempts = 0
storage = SeedStorage('seeds_5_silly2')
while good_members_found < 12:
path = storage.get_path_by_index(good_members_found + 1)
if path.exists():
print('member already exists', path)
good_members_found += 1
continue
attempts += 1
print(
f'attempts {attempts} good {good_members_found} looking for {path}'
)
member = problem_silliest.generate_random_member()
member.evaluate()
if member.distance_to_boundary <= 0:
def initial_population_generator(path, config, problem):
all_roads = [
filename
for filename in glob.glob(str(path) + "\*.json", recursive=True)
]
type = config.Feature_Combination
shuffle(all_roads)
roads = all_roads
original_set = list()
individuals = []
popsize = config.POPSIZE
for road in roads:
with open(road) as json_file:
data = json.load(json_file)
sample_nodes = data["road"]["nodes"]
for node in sample_nodes:
node[2] = -28.0
sample_nodes = np.array(sample_nodes)
records = data["records"]
bbox_size = (-250.0, 0.0, 250.0, 500.0)
road_bbox = RoadBoundingBox(bbox_size)
member = BeamNGMember(data["control_nodes"],
[tuple(t) for t in sample_nodes], 20, road_bbox)
member.config = config
member.problem = problem
simulation_id = time.strftime('%Y-%m-%d--%H-%M-%S', time.localtime())
sim_name = member.config.simulation_name.replace(
'$(id)', simulation_id)
simulation_data = SimulationData(sim_name)
states = []
for record in records:
state = VehicleState(timer=record["timer"],
pos=record["pos"],
dir=record["dir"],
vel=record["vel"],
steering=record["steering"],
steering_input=record["steering_input"],
brake=record["brake"],
brake_input=record["brake_input"],
throttle=record["throttle"],
throttle_input=record["throttle_input"],
wheelspeed=record["wheelspeed"],
vel_kmh=record["vel_kmh"])
sim_data_record = SimulationDataRecord(
**state._asdict(),
is_oob=record["is_oob"],
oob_counter=record["oob_counter"],
max_oob_percentage=record["max_oob_percentage"],
oob_distance=record["oob_distance"])
states.append(sim_data_record)
simulation_data.params = SimulationParams(
beamng_steps=data["params"]["beamng_steps"],
delay_msec=int(data["params"]["delay_msec"]))
simulation_data.road = DecalRoad.from_dict(data["road"])
simulation_data.info = SimulationInfo()
simulation_data.info.start_time = data["info"]["start_time"]
simulation_data.info.end_time = data["info"]["end_time"]
simulation_data.info.elapsed_time = data["info"]["elapsed_time"]
simulation_data.info.success = data["info"]["success"]
simulation_data.info.computer_name = data["info"]["computer_name"]
simulation_data.info.id = data["info"]["id"]
simulation_data.states = states
if len(states) > 0:
member.distance_to_boundary = simulation_data.min_oob_distance()
member.simulation = simulation_data
individual: BeamNGIndividual = BeamNGIndividual(member, config)
#individual.evaluate()
b = tuple()
feature_dimensions = generate_feature_dimension(type)
for ft in feature_dimensions:
i = feature_simulator(ft.feature_simulator, individual)
b = b + (i, )
individuals.append([b, road, individual])
starting_point = choice(individuals)
original_set.append(starting_point)
i = 0
best_ind = individuals[0]
while i < popsize - 1:
max_dist = 0
for ind in individuals:
dist = get_min_distance_from_set(ind, original_set)
if dist > max_dist:
max_dist = dist
best_ind = ind
original_set.append(best_ind)
i += 1
base = config.initial_population_folder
storage = SeedStorage(base)
for index, road in enumerate(original_set):
dst = storage.get_path_by_index(index + 1)
ind = road[2]
#copy(road[1], dst)
with open(road[1]) as ff:
json_file = json.load(ff)
with open(dst, 'w') as f:
f.write(
json.dumps({
"control_nodes":
json_file["control_nodes"],
ind.m.simulation.f_params:
ind.m.simulation.params._asdict(),
ind.m.simulation.f_info:
ind.m.simulation.info.__dict__,
ind.m.simulation.f_road:
ind.m.simulation.road.to_dict(),
ind.m.simulation.f_records:
[r._asdict() for r in ind.m.simulation.states]
}))
config_silly.keras_model_file = 'self-driving-car-4600.h5'
config_silly.beamng_close_at_iteration = True
config_smart = BeamNGConfig()
config_smart.keras_model_file = 'self-driving-car-185-2020.h5'
config_smart.beamng_close_at_iteration = True
problem_silly = BeamNGProblem(config_silly, SmartArchive(config_silly.ARCHIVE_THRESHOLD))
problem_smart = BeamNGProblem(config_smart, SmartArchive(config_smart.ARCHIVE_THRESHOLD))
# problem = BeamNGProblem(config, SmartArchive(config.ARCHIVE_THRESHOLD))
if __name__ == '__main__':
good_members_found = 0
attempts = 0
storage = SeedStorage('prova_roads')
while good_members_found < 100:
path = storage.get_path_by_index(good_members_found + 1)
if path.exists():
print('member already exists', path)
good_members_found += 1
continue
attempts += 1
print(f'attempts {attempts} good {good_members_found} looking for {path}')
member = problem_silly.generate_random_member()
member.evaluate()
if member.distance_to_boundary <= 0:
continue
member_smart = problem_smart.member_class().from_dict(member.to_dict())
member_smart.config = config_smart
import json
from core.config import Config
from core.folder_storage import SeedStorage
from self_driving.road_generator import RoadGenerator
if __name__ == "__main__":
config = Config()
seed_storage = SeedStorage(config.seed_folder)
for i in range(1, 4):
path = seed_storage.get_path_by_index(i)
if path.exists():
print('file ', path, 'already exists')
else:
obj = RoadGenerator(
num_control_nodes=config.num_control_nodes,
seg_length=config.SEG_LENGTH).generate()
print('saving', path)
path.write_text(json.dumps(obj.to_dict()))
def __init__(self, problem: Problem, folder_name):
super().__init__(problem)
self.storage = SeedStorage(folder_name)
self.file_path_list = self.storage.all_files()
assert (len(self.file_path_list)) > 0
self.cache: Dict[str, Member] = {}