| """How pooled/features.npz was made: one (policy steps, 6144) float32 array per episode, pooled from its rollout.npz. |
| |
| python pool_features.py <dataset folder with manifest.csv and <dir>/rollout.npz> features.npz |
| |
| Columns: [0:2048] mean over the base-image tokens, [2048:4096] mean over the wrist-image tokens, |
| [4096:6144] mean over the non-padded language tokens (language_mask), all averaged in float32. |
| """ |
|
|
| import csv |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
|
|
| def pool_rollout(path: Path) -> np.ndarray: |
| """(n_policy, 6144) float32 from one rollout.npz.""" |
| with np.load(path) as z: |
| base = z["base_image"].astype(np.float32).mean(axis=1) |
| wrist = z["wrist_image"].astype(np.float32).mean(axis=1) |
| mask = z["language_mask"].astype(np.float32) |
| language = (z["language"].astype(np.float32) * mask[..., None]).sum(axis=1) / np.maximum(mask.sum(axis=1, keepdims=True), 1.0) |
| return np.concatenate([base, wrist, language], axis=1).astype(np.float32) |
|
|
|
|
| def main(root: str, out: str) -> None: |
| with open(Path(root) / "manifest.csv", newline="") as f: |
| rows = list(csv.DictReader(f)) |
| arrays = {} |
| for r in rows: |
| x = pool_rollout(Path(root) / r["dir"] / "rollout.npz") |
| assert x.shape == (int(r["n_policy"]), 6144), (r["dir"], x.shape) |
| arrays[r["dir"]] = x |
| np.savez(out, **arrays) |
| print(f"wrote {len(arrays)} episodes to {out}") |
|
|
|
|
| if __name__ == "__main__": |
| main(sys.argv[1], sys.argv[2]) |
|
|
