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"""Data collection script."""
import os
import hydra
import numpy as np
import random
from cliport import tasks
from cliport.dataset import RavensDataset
from cliport.environments.environment import Environment
import IPython
import random
@hydra.main(config_path='./cfg', config_name='data')
def main(cfg):
# Initialize environment and task.
env = Environment(
cfg['assets_root'],
disp=cfg['disp'],
shared_memory=cfg['shared_memory'],
hz=480,
record_cfg=cfg['record']
)
task = tasks.names[cfg['task']]()
task.mode = cfg['mode']
record = cfg['record']['save_video']
save_data = cfg['save_data']
# Initialize scripted oracle agent and dataset.
agent = task.oracle(env)
data_path = os.path.join(cfg['data_dir'], "{}-{}".format(cfg['task'], task.mode))
dataset = RavensDataset(data_path, cfg, n_demos=0, augment=False)
print(f"Saving to: {data_path}")
print(f"Mode: {task.mode}")
# Train seeds are even and val/test seeds are odd. Test seeds are offset by 10000
seed = dataset.max_seed
max_eps = 3 * cfg['n']
if seed < 0:
if task.mode == 'train':
seed = -2
elif task.mode == 'val': # NOTE: beware of increasing val set to >100
seed = -1
elif task.mode == 'test':
seed = -1 + 10000
else:
raise Exception("Invalid mode. Valid options: train, val, test")
if 'regenerate_data' in cfg:
dataset.n_episodes = 0
curr_run_eps = 0
# Collect training data from oracle demonstrations.
while dataset.n_episodes < cfg['n'] and curr_run_eps < max_eps:
# for epi_idx in range(cfg['n']):
episode, total_reward = [], 0
seed += 2
# Set seeds.
np.random.seed(seed)
random.seed(seed)
print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, cfg['n'], seed))
try:
curr_run_eps += 1 # make sure exits the loop
env.set_task(task)
obs = env.reset()
info = env.info
reward = 0
# Unlikely, but a safety check to prevent leaks.
if task.mode == 'val' and seed > (-1 + 10000):
raise Exception("!!! Seeds for val set will overlap with the test set !!!")
# Start video recording (NOTE: super slow)
if record:
env.start_rec(f'{dataset.n_episodes+1:06d}')
# Rollout expert policy
for _ in range(task.max_steps):
act = agent.act(obs, info)
episode.append((obs, act, reward, info))
lang_goal = info['lang_goal']
obs, reward, done, info = env.step(act)
total_reward += reward
print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}')
if done:
break
if record:
env.end_rec()
except Exception as e:
from pygments import highlight
from pygments.lexers import PythonLexer
from pygments.formatters import TerminalFormatter
import traceback
to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter())
print(to_print)
if record:
env.end_rec()
continue
episode.append((obs, None, reward, info))
# Only save completed demonstrations.
if save_data and total_reward > 0.99:
dataset.add(seed, episode)
if hasattr(env, 'blender_recorder'):
print("blender pickle saved to ", '{}/blender_demo_{}.pkl'.format(data_path, dataset.n_episodes))
env.blender_recorder.save('{}/blender_demo_{}.pkl'.format(data_path, dataset.n_episodes))
if __name__ == '__main__':
main()
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