def __init__(self):
np.random.seed(0)
self._save_dir = os.path.join(params['exp_dir'], params['exp_name'])
yamls = [
fname for fname in os.listdir(self._save_dir)
if '.yaml' in fname and '~' not in fname
]
assert (len(yamls) == 1)
yaml_path = os.path.join(self._save_dir, yamls[0])
load_params(yaml_path)
params['yaml_path'] = yaml_path
### turn off noise
pred_dagger_params = params['prediction']['dagger']
pred_dagger_params['control_noise']['type'] = 'zero'
pred_dagger_params['epsilon_greedy'] = None
ProbcollBebop2d.__init__(self, read_only=True)
cond_params = copy.deepcopy(pred_dagger_params['conditions'])
cond_params['repeats'] = 100
self._conditions = Conditions(cond_params=cond_params)
self._cost_probcoll_init = None
def main():
args = config.load_params()
if args.seed > 0:
seed = args.seed
seed_torch(seed)
else:
seed = random.randint(1, 999999999)
seed_torch(seed)
config_file = 'program/configs/{}.json'.format(args.config_file)
hp, template_config, dataset_config_set, model_config_set, criterion_config_set, \
optimizer_config, trainer_config = config.load_configs(config_file, args.config_file)
hp.seed = seed
_trainer = trainer.trainer(hp,
template_config,
dataset_config_set,
model_config_set,
criterion_config_set,
optimizer_config,
trainer_config,
resume = args.resume,
)
_trainer()
def __init__(self, on_replay=False, parent_exp_dir=None):
self.on_replay = on_replay
self._save_dir = os.path.join(params['exp_dir'], params['exp_name'])
yamls = [
fname for fname in os.listdir(self._save_dir)
if '.yaml' in fname and '~' not in fname
]
yaml_path = os.path.join(self._save_dir, yamls[0])
load_params(yaml_path)
params['yaml_path'] = yaml_path
if self.on_replay:
self._save_dir = os.path.join(self._save_dir, 'replay')
self._logger = get_logger(self.__class__.__name__, 'info')
image_msg = image_time_msg[1]
cmd_vel_msg = cmd_vel_time_msg[1]
### image
im = AgentBebop2d.process_image(image_msg, cvb)
sample.set_O(im.ravel(), t=t, sub_obs='camera')
### collision
sample.set_O([0], t=t, sub_obs='collision')
### linearvel
sample.set_X([cmd_vel_msg.linear.x, cmd_vel_msg.linear.y], t=t)
sample.set_U([cmd_vel_msg.linear.x, cmd_vel_msg.linear.y], t=t)
sample.set_O([int(is_coll)], t=-1, sub_obs='collision')
# sample.set_O([int(np.random.random() > 0.5)], t=-1, sub_obs='collision') # tmp
assert(sample.isfinite())
return sample
if __name__ == '__main__':
rospy.init_node('rosbag_to_sample', anonymous=True)
yaml_path = os.path.join(FOLDER, 'params_bebop2d.yaml')
load_params(yaml_path)
samples = [rosbag_to_sample(os.path.join(FOLDER, f))
for f in sorted(os.listdir(FOLDER)) if os.path.splitext(f)[1] == '.bag']
samples = filter(lambda x: x is not None, samples)
Sample.save(os.path.join(FOLDER, 'samples.npz'), samples)