| import numpy as np |
| import tqdm |
| import os |
|
|
| N_TRAIN_EPISODES = 100 |
| N_VAL_EPISODES = 100 |
|
|
| EPISODE_LENGTH = 10 |
|
|
|
|
| def create_fake_episode(path): |
| episode = [] |
| for step in range(EPISODE_LENGTH): |
| episode.append({ |
| 'image': np.asarray(np.random.rand(64, 64, 3) * 255, dtype=np.uint8), |
| 'wrist_image': np.asarray(np.random.rand(64, 64, 3) * 255, dtype=np.uint8), |
| 'state': np.asarray(np.random.rand(10), dtype=np.float32), |
| 'action': np.asarray(np.random.rand(10), dtype=np.float32), |
| 'language_instruction': 'dummy instruction', |
| }) |
| np.save(path, episode) |
|
|
|
|
| |
| print("Generating train examples...") |
| os.makedirs('data/train', exist_ok=True) |
| for i in tqdm.tqdm(range(N_TRAIN_EPISODES)): |
| create_fake_episode(f'data/train/episode_{i}.npy') |
|
|
| print("Generating val examples...") |
| os.makedirs('data/val', exist_ok=True) |
| for i in tqdm.tqdm(range(N_VAL_EPISODES)): |
| create_fake_episode(f'data/val/episode_{i}.npy') |
|
|
| print('Successfully created example data!') |
|
|
