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collect_IL_data.py
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import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--ep-per-env', type=int, default=100, help='number of episodes per environments')
parser.add_argument('--num-procs', type=int, default=4, help='number of processes to run simultaneously')
parser.add_argument('--num-goals', type=int, default=5, help='number of goals per episodes')
parser.add_argument("--gpu", type=str, default="0", help="gpus",)
parser.add_argument('--split', type=str, default="train", choices=['train','val'], help='data split to use')
parser.add_argument('--data-dir', type=str, default="./IL_data_newnewadd", help='directory to save the collected data')
args = parser.parse_args()
import os
os.environ['GLOG_minloglevel'] = "2"
os.environ['MAGNUM_LOG'] = "quiet"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
import glob
import numpy as np
import habitat
import habitat.sims
import habitat.sims.habitat_simulator
import joblib
from configs.default import get_config
from env_utils.task_search_env import MultiSearchEnv
from tqdm import tqdm
CONTENT_PATH = os.path.join(habitat.__path__[0],'../data/datasets/pointnav/gibson/v1/train/content/')
NUM_EPISODE_PER_SPACE = args.ep_per_env
def make_env_fn(config_env, rank):
config_env.defrost()
config_env.SEED = rank * 1121
config_env.freeze()
env = MultiSearchEnv(config=config_env)
env.seed(rank * 1121)
return env
def data_collect(space, config, DATA_DIR):
num_of_envs = args.num_procs
envs = habitat.VectorEnv(
make_env_fn=make_env_fn,
env_fn_args=tuple(
tuple(
zip([config] * num_of_envs, range(num_of_envs))
)
),
auto_reset_done=False
)
num_episodes = int(NUM_EPISODE_PER_SPACE/num_of_envs)
with tqdm(total=num_episodes) as pbar:
for episode in range(num_episodes):
observations = envs.reset()
episodes = envs.current_episodes()
space_dirnames = []
episode_names = []
for idx, ep in enumerate(episodes):
space_name = episodes[0].scene_id.split('/')[-1][:-4]
space_dirnames.append(DATA_DIR)
episode_num = int(ep.episode_id) * num_of_envs + idx
episode_name = '%s_%03d' % (space_name, episode_num)
episode_names.append(episode_name)
datas = [{'rgb': [], 'position': [], 'rotation': [], 'action': [], 'depth': [], 'target_idx': [], 'target_img': None, 'target_pose': None, 'distance': []} for _ in range(num_of_envs)]
step = 0
dones = envs.call(['get_episode_over']*num_of_envs)
paused = [False] * num_of_envs
env_ind_states = np.arange(num_of_envs)
for i in range(num_of_envs):
datas[i]['target_img'] = []
datas[i]['target_pose'] = []
for e in range(len(episodes[i].goals)):
datas[i]['target_img'].append(observations[i]['target_goal'][e])
datas[i]['target_pose'].append(episodes[i].goals[e].position)
past_alive_indices = np.where(np.array(paused) == False)
while (np.array(dones) == 0).any():
best_actions = np.array(envs.call(['get_best_action']*num_of_envs))
curr_goal_indices = envs.call(['get_curr_goal_index']*num_of_envs)
alive_indices = np.where(np.array(paused) == False)
past_obs = observations
best_actions[np.where(best_actions == None)] = 0
best_actions[np.where(envs.call(['get_episode_over']*num_of_envs)) == 1] = 0
outputs = envs.step(best_actions)
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
for i, j in enumerate(past_alive_indices[0]):
# print(i, best_actions[0],best_actions[1],best_actions[2])
# if best_actions[i] == 0 : continue
# try:
# assert episodes[j].start_position == past_obs[i]['episode_id'].start_position
# assert (episodes[j].start_rotation == past_obs[i]['episode_id'].start_rotation).all()
# assert episodes[j].goals[0].position == past_obs[i]['episode_id'].goals[0].position
# except:
# print('where!!!!!!!!!!!!')
datas[j]['rgb'].append(past_obs[i]['panoramic_rgb'])
datas[j]['depth'].append(past_obs[i]['panoramic_depth'])
datas[j]['position'].append(past_obs[i]['position'])
datas[j]['rotation'].append(past_obs[i]['rotation'])
datas[j]['distance'].append(past_obs[i]['distance'])
if j in alive_indices[0]:
datas[j]['action'].append(best_actions[alive_indices[0].tolist().index(j)])
datas[j]['target_idx'].append(curr_goal_indices[alive_indices[0].tolist().index(j)])
try:
if j in alive_indices[0] and dones[alive_indices[0].tolist().index(j)] == 1:
ind = np.where(env_ind_states == j)
envs.pause_at(ind[0][0])
env_ind_states = np.delete(env_ind_states, ind)
paused[j] = True
continue
except:
print('h')
step += 1
past_alive_indices = alive_indices
envs.resume_all()
successes = envs.call(['get_success']*num_of_envs)
for i in range(num_of_envs):
success = successes[i]
if success:
joblib.dump(datas[i], os.path.join(space_dirnames[i], episode_names[i] + '_env{}.dat.gz'.format(i)))
pbar.update(1)
pbar.set_description('Total %05d, %s %03d/%03d data collected' % (len(os.listdir(space_dirnames[0])),
space_name,
len(glob.glob(os.path.join(space_dirnames[0], space_name) + '*')),
NUM_EPISODE_PER_SPACE))
envs.close()
from habitat import make_dataset
from env_utils.make_env_utils import add_panoramic_camera
def main():
split = args.split
DATA_DIR = args.data_dir
if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR)
DATA_DIR = os.path.join(DATA_DIR, split)
if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR)
config = get_config()
habitat_api_path = os.path.dirname(habitat.__file__)#os.path.join(os.path.dirname(habitat.__file__), '../')
config.defrost()
config.RL.SUCCESS_DISTANCE = 0.5
config.TASK_CONFIG.DATASET.SCENES_DIR = os.path.join(habitat_api_path, config.TASK_CONFIG.DATASET.SCENES_DIR)
config.TASK_CONFIG.DATASET.DATA_PATH = os.path.join(habitat_api_path, config.TASK_CONFIG.DATASET.DATA_PATH)
config.TASK_CONFIG.DATASET.SPLIT = split
config.TASK_CONFIG.ENVIRONMENT.ITERATOR_OPTIONS.MAX_SCENE_REPEAT_EPISODES = 300
config.TASK_CONFIG.ENVIRONMENT.NUM_GOALS = 5
config.TASK_CONFIG = add_panoramic_camera(config.TASK_CONFIG)
config.TASK_CONFIG.TASK.MEASUREMENTS = ["GOAL_INDEX"] + config.TASK_CONFIG.TASK.MEASUREMENTS
config.TASK_CONFIG.TASK.GOAL_INDEX = config.TASK_CONFIG.TASK.SPL.clone()
config.TASK_CONFIG.TASK.GOAL_INDEX.TYPE = 'GoalIndex'
config.DIFFICULTY = 'random'
config.noisy_actuation = False
config.freeze()
dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)
print(config.TASK_CONFIG.DATASET)
scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
# test_list = ['Haxtun', 'Eagerville', 'Denmark', 'Hambleton', 'Angiola', 'Roane', 'Superior', 'Oyens', 'Delton',
# 'Roxboro',
# 'Kerrtown', 'Pettigrew', 'Annawan', 'Dunmor', 'Sumas', 'Rosenberg', 'Dryville', 'Rosser', 'Ribera',
# 'Woonsocket', 'Mobridge','Beach',
# 'Bolton','Nicut','Cantwell','Matoaca','Rabbit','Quantico']
# train_list = ['Albertville','Adrian','Ballou','Colebrook','Espanola','Mesic','Nemacolin','plessis','Springhill','Micanopy']#'Spencerville', 'Shelbiana', 'Sawpit', 'Nicut', 'Mosinee', 'Hometown', 'Avonia']
# print(scenes)
for space_id, space in enumerate(scenes):
# if space not in train_list :
# continue
try:
print('=' * 50)
print('SPACE[%03d/%03d] STARTED %s' % (space_id, len(scenes), space))
config.defrost()
config.TASK_CONFIG.DATASET.CONTENT_SCENES = [space]
config.freeze()
data_collect(space, config, DATA_DIR)
except:
raise
print('{} failed may be the space is too large or unexpected error'.format(space))
unexpected_skip.append(space)
print('unexpected_skipped envs : ', unexpected_skip)
if __name__ == "__main__":
main()