def create_render_map(model_labels,
model_args_filepaths,
model_params_filepaths,
multi=False,
rand=None,
max_steps=200,
n_vehs=None,
remove_ngsim=False):
render_map = dict()
env_kwargs = dict()
if rand != None:
env_kwargs = dict(random_seed=rand)
if not multi:
env_kwargs = dict(egoid=worst_egoid, start=worst_start)
render_kwargs = dict(camera_rotation=45.,
canvas_height=500,
canvas_width=600)
for i in range(len(model_labels)):
print('\nrunning: {}'.format(model_labels[i]))
# create session
tf.reset_default_graph()
sess = tf.InteractiveSession()
# load args and params
args = hyperparams.load_args(model_args_filepaths[i])
print('\nargs loaded from {}'.format(model_args_filepaths[i]))
if multi:
args.env_multiagent = True
if remove_ngsim:
args.remove_ngsim_veh = True
if n_vehs:
args.n_envs = 100
args.n_vehs = 100
params = hgail.misc.utils.load_params(model_params_filepaths[i])
print('\nparams loaded from {}'.format(model_params_filepaths[i]))
# load env and params
# Raunak adding in an argument for videmaking
# See build_ngsim_env in utils.py for what this does
env, _, _ = utils.build_ngsim_env(args, videoMaking=True)
print("Raunak says: This is the videmaker reporting")
normalized_env = hgail.misc.utils.extract_normalizing_env(env)
if normalized_env is not None:
normalized_env._obs_mean = params['normalzing']['obs_mean']
normalized_env._obs_var = params['normalzing']['obs_var']
# load policy
if 'hgail' in model_labels[i]:
policy = utils.build_hierarchy(args, env)
else:
policy = utils.build_policy(args, env)
# initialize variables
sess.run(tf.global_variables_initializer())
# load params
if 'hgail' in model_labels[i]:
for j, level in enumerate(policy):
level.algo.policy.set_param_values(params[j]['policy'])
policy = policy[0].algo.policy
else:
policy.set_param_values(params['policy'])
# collect imgs
if args.env_multiagent:
imgs = mutliagent_simulate(env,
policy,
max_steps=max_steps,
env_kwargs=env_kwargs,
render_kwargs=render_kwargs)
else:
imgs = simulate(env,
policy,
max_steps=max_steps,
env_kwargs=env_kwargs,
render_kwargs=render_kwargs)
render_map[model_labels[i]] = imgs
return render_map
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='validation settings')
parser.add_argument('--n_proc', type=int, default=1)
parser.add_argument('--exp_dir',
type=str,
default='../../data/experiments/gail/')
parser.add_argument('--params_filename', type=str, default='itr_2000.npz')
parser.add_argument('--n_runs_per_ego_id', type=int, default=1)
parser.add_argument('--use_hgail', type=str2bool, default=False)
parser.add_argument('--use_multiagent', type=str2bool, default=False)
parser.add_argument('--n_multiagent_trajs', type=int, default=10000)
parser.add_argument('--debug', type=str2bool, default=False)
run_args = parser.parse_args()
args_filepath = os.path.join(run_args.exp_dir, 'imitate/log/args.npz')
args = hyperparams.load_args(args_filepath)
if run_args.use_multiagent:
args.env_multiagent = True
if run_args.debug:
collect_fn = single_process_collect_trajectories
else:
collect_fn = parallel_collect_trajectories
filenames = [
"trajdata_i80_trajectories-0400-0415.txt",
"trajdata_i80_trajectories-0500-0515.txt",
"trajdata_i80_trajectories-0515-0530.txt",
"trajdata_i101_trajectories-0805am-0820am.txt",
"trajdata_i101_trajectories-0820am-0835am.txt",
"trajdata_i101_trajectories-0750am-0805am.txt"