'--log_name',
metavar='T',
default='test',
help='The name of the log file to be created by the scripts')
argparser.add_argument(
'--avoid-stopping',
default=True,
action='store_false',
help=' Uses the speed prediction branch to avoid unwanted agent stops')
argparser.add_argument(
'--continue-experiment',
action='store_true',
help='If you want to continue the experiment with the given log name')
args = argparser.parse_args()
log_level = logging.DEBUG if args.debug else logging.INFO
logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)
logging.info('listening to server %s:%s', args.host, args.port)
agent = ImitationLearning(args.city_name, args.avoid_stopping)
if args.test_name == 'CORL2017':
corl = CoRL2017(args.city_name)
else:
corl = trainingData(args.city_name)
# Now actually run the driving_benchmark
run_driving_benchmark(agent, corl, args.city_name, args.log_name,
args.continue_experiment, args.host, args.port)
import h5py
import numpy as np
from agents.imitation.imitation_learning import ImitationLearning
learner = ImitationLearning("Town01", False)
data = h5py.File("/localdata2/yasasaa/dataset1.h5")
images = list(data["images"])
outputs = list(data["outputs"])
speeds = list(data["speeds"])
cmds = [2.] * len(images)
learner.train_model(images, speeds, cmds, outputs, epochs=500)
def main():
argparser = argparse.ArgumentParser(
description='CARLA Manual Control Client')
argparser.add_argument('-v',
'--verbose',
action='store_true',
dest='debug',
help='print debug information')
argparser.add_argument('--host',
metavar='H',
default='localhost',
help='IP of the host server (default: localhost)')
argparser.add_argument('-p',
'--port',
metavar='P',
default=2000,
type=int,
help='TCP port to listen to (default: 2000)')
argparser.add_argument('-a',
'--autopilot',
action='store_true',
help='enable autopilot')
argparser.add_argument('-l',
'--lidar',
action='store_true',
help='enable Lidar')
argparser.add_argument(
'-q',
'--quality-level',
choices=['Low', 'Epic'],
type=lambda s: s.title(),
default='Epic',
help=
'graphics quality level, a lower level makes the simulation run considerably faster.'
)
argparser.add_argument(
'-m',
'--map-name',
metavar='M',
default=None,
help='plot the map of the current city (needs to match active map in '
'server, options: Town01 or Town02)')
argparser.add_argument(
'--avoid-stopping',
default=True,
action='store_false',
help=
' Uses the speed prediction branch to avoid unwanted NN agent stops')
args = argparser.parse_args()
log_level = logging.DEBUG if args.debug else logging.INFO
logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)
logging.info('listening to server %s:%s', args.host, args.port)
print(__doc__)
while True:
try:
with make_carla_client(args.host, args.port) as client:
client.load_settings(CarlaSettings())
client.start_episode(0)
NNagent = ImitationLearning(args.map_name, args.avoid_stopping)
game = CarlaGame(client, NNagent, args)
game.execute()
break
except TCPConnectionError as error:
logging.error(error)
time.sleep(1)
'(needs to match active town in server, options: Town01 or Town02)')
argparser.add_argument(
'-n',
'--log_name',
metavar='T',
default='test',
help='The name of the log file to be created by the scripts')
args = argparser.parse_args()
log_level = logging.DEBUG if args.debug else logging.INFO
logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)
logging.info('listening to server %s:%s', args.host, args.port)
agent = ImitationLearning(args.city_name)
while True:
try:
with make_carla_client(args.host, args.port) as client:
corl = CoRL2017(args.city_name, args.log_name)
results = corl.benchmark_agent(agent, client)
corl.plot_summary_test()
corl.plot_summary_train()
break
except TCPConnectionError as error:
logging.error(error)