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273b14c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | """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) # (steps, 88, 2048) -> (steps, 2048)
wrist = z["wrist_image"].astype(np.float32).mean(axis=1)
mask = z["language_mask"].astype(np.float32) # (steps, 200), True for real tokens
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) # one row per policy step
arrays[r["dir"]] = x
np.savez(out, **arrays) # keys are the `dir` column of manifest.csv
print(f"wrote {len(arrays)} episodes to {out}")
if __name__ == "__main__":
main(sys.argv[1], sys.argv[2])
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