from navrep.tools.commonargs import parse_common_args
from navrep.envs.e2eenv import E2EIANEnv
from navrep.scripts.cross_test_navreptrain_in_ianenv import run_test_episodes
from navrep.scripts.test_e2e import E2ECPolicy
if __name__ == '__main__':
args, _ = parse_common_args()
if args.n is None:
args.n = 1000
collect_trajectories = False
env = E2EIANEnv(silent=True, collect_trajectories=collect_trajectories)
policy = E2ECPolicy()
S = run_test_episodes(env, policy, render=args.render, num_episodes=args.n)
DIR = os.path.expanduser("~/navrep/eval/crosstest")
if args.dry_run:
DIR = "/tmp/navrep/eval/crosstest"
make_dir_if_not_exists(DIR)
if collect_trajectories:
NAME = "e2enavreptrain_in_ianenv_{}.pckl".format(len(S))
PATH = os.path.join(DIR, NAME)
S.to_pickle(PATH)
else:
NAME = "e2enavreptrain_in_ianenv_{}.csv".format(len(S))
PATH = os.path.join(DIR, NAME)
S.to_csv(PATH)
print("{} written.".format(PATH))
model_path = os.path.expanduser(
"~/navrep/models/M/navreptrainrnn{}.json".format(VAE_TYPE))
vae_model_path = os.path.expanduser(
"~/navrep/models/V/navreptrainvae{}.json".format(VAE_TYPE))
if common_args.dry_run:
log_path = log_path.replace(os.path.expanduser("~/navrep"),
"/tmp/navrep")
log_hyperparams_path = log_hyperparams_path.replace(
os.path.expanduser("~/navrep"), "/tmp/navrep")
model_path = model_path.replace(os.path.expanduser("~/navrep"),
"/tmp/navrep")
model_hyperparams_path = model_hyperparams_path.replace(
os.path.expanduser("~/navrep"), "/tmp/navrep")
make_dir_if_not_exists(os.path.dirname(model_path))
make_dir_if_not_exists(os.path.dirname(log_path))
# load preprocessed data
files = []
for dirpath, dirnames, filenames in os.walk(dataset_folder):
for filename in [f for f in filenames if f.endswith(".npz")]:
files.append(os.path.join(dirpath, filename))
all_data = []
for path in files:
arrays = np.load(path)
all_data.append([
arrays["mus"],
arrays["logvars"],
arrays["robotstates"],
arrays["actions"],
for d in dones_ss:
d[-1] = True
dones = np.concatenate(dones_ss, axis=0)
obs = rings_def["lidar_to_rings"](scans).astype(
float) / rings_def["rings_to_bool"]
mus = []
logvars = []
batch_size = 100
for i in range(0, len(obs), batch_size):
mu, logvar = vae.encode_mu_logvar(obs[i:i + batch_size, :, :, :])
z = mu + np.exp(logvar / 2.0) * np.random.randn(*logvar.shape)
mus.extend(mu)
logvars.extend(logvar)
mus = np.array(mus).astype(np.float32)
logvars = np.array(logvars).astype(np.float32)
savepath = path.replace(V_dataset_folder,
M_dataset_folder).replace("scans", "mus_logvars")
make_dir_if_not_exists(os.path.dirname(savepath))
np.savez_compressed(
savepath,
mus=mus,
logvars=logvars,
robotstates=robotstates,
rewards=rewards,
actions=actions,
dones=dones,
)
print("z saved to {}".format(savepath))
data_regen = "navreptrain"
log_path = os.path.expanduser(
"~/navrep/logs/W/navreptrain_vaelstm_train_log_{}.csv".format(
START_TIME))
checkpoint_path = os.path.expanduser(
"~/navrep/models/W/navreptrainvaelstm")
else:
raise NotImplementedError(args.environment)
if args.dry_run:
log_path = log_path.replace(os.path.expanduser("~/navrep"),
"/tmp/navrep")
checkpoint_path = checkpoint_path.replace(
os.path.expanduser("~/navrep"), "/tmp/navrep")
make_dir_if_not_exists(os.path.dirname(checkpoint_path))
make_dir_if_not_exists(os.path.dirname(log_path))
make_dir_if_not_exists(os.path.expanduser("~/tmp_navrep"))
# make deterministic
set_seed(42)
# set up logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger = logging.getLogger(__name__)
mconf = VAELSTMConfig(_Z, _H)
def generate_vae_dataset(
env,
n_sequences,
episode_length=1000,
subset_index=0,
n_subsets=1,
render=True,
policy=RandomMomentumPolicy(),
archive_dir=os.path.expanduser("~/navrep/datasets/V/ian")):
"""
if n_subsets is None, the whole set of sequences is generated (n_sequences)
if n_subsets is a number > 1, this function only generates a portion of the sequences
"""
indices = np.arange(n_sequences)
if n_subsets > 1: # when multiprocessing
indices = np.array_split(indices, n_subsets)[subset_index]
for n in indices:
scans = []
robotstates = []
actions = []
rewards = []
dones = []
policy.reset()
obs = env.reset()
for i in range(episode_length):
# step
action = policy.predict(obs, env)
if isinstance(action, Suicide):
obs = env.reset()
rew = 0
action = np.array([0, 0, 0])
done = True
else:
obs, rew, done, _ = env.step(action)
scans.append(obs[0])
robotstates.append(obs[1])
actions.append(action)
rewards.append(rew)
dones.append(done)
if render:
env.render()
if done:
policy.reset()
obs = env.reset()
print("{} - {} {}".format(n, i, "done" if done else " "),
end="\r")
dones[-1] = True
scans = np.array(scans)
robotstates = np.array(robotstates)
actions = np.array(actions)
rewards = np.array(rewards)
dones = np.array(dones)
data = dict(scans=scans,
robotstates=robotstates,
actions=actions,
rewards=rewards,
dones=dones)
if archive_dir is not None:
make_dir_if_not_exists(archive_dir)
archive_path = os.path.join(
archive_dir,
"{:03}_scans_robotstates_actions_rewards_dones.npz".format(n))
np.savez_compressed(archive_path, **data)
print(archive_path, "written.")
env.close()
return data
