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4ec41f2 | 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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | #!/usr/bin/env python3
"""Build a reproducible 100-sample StatePlay in-sample proxy benchmark."""
from __future__ import annotations
import argparse
import csv
import hashlib
import json
import random
from collections import Counter
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
LABELS = ("normal", "macro_success", "macro_fail", "result_win", "result_lose")
def sha256_bytes(data: bytes) -> str:
return hashlib.sha256(data).hexdigest()
def sha256_file(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(16 * 1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def repo_path(path: Path) -> str:
return path.resolve().relative_to(ROOT).as_posix()
def normalize_label(value: str) -> str:
label = value.strip().lower().replace("-", "_").replace(" ", "_")
if label not in LABELS:
raise ValueError(f"Unsupported distribution label: {value!r}")
return label
def bundled_examples(examples_manifest: Path) -> list[dict[str, Any]]:
result = []
with examples_manifest.open(newline="", encoding="utf-8-sig") as handle:
rows = list(csv.DictReader(handle))
for row in rows:
input_dir = examples_manifest.parent / row["input_dir"]
metadata_path = input_dir / "metadata.json"
metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
prompt_path = input_dir / "prompt.txt"
actions_path = input_dir / "actions.parquet"
image_path = input_dir / "first_frame.png"
prompt = prompt_path.read_text(encoding="utf-8").strip()
result.append(
{
"source_kind": "bundled_external",
"source_id": f"example_{row['id'].zfill(2)}",
"label": normalize_label(metadata["distribution"]),
"input_image": repo_path(image_path),
"source_video": None,
"reference_video": None,
"actions": repo_path(actions_path),
"prompt": prompt,
"identity": {
"input_image_sha256": sha256_file(image_path),
"actions_sha256": sha256_file(actions_path),
"prompt_sha256": sha256_bytes(prompt.encode("utf-8")),
"metadata_sha256": sha256_file(metadata_path),
},
}
)
return result
def training_examples(
metadata_path: Path,
existing: list[dict[str, Any]],
per_class: int,
seed: int,
) -> list[dict[str, Any]]:
with metadata_path.open(newline="", encoding="utf-8-sig") as handle:
rows = list(csv.DictReader(handle))
by_label: dict[str, list[dict[str, str]]] = {label: [] for label in LABELS}
for row in rows:
by_label[normalize_label(row["distribution"])].append(row)
existing_counts = Counter(item["label"] for item in existing)
rng = random.Random(seed)
selected: list[dict[str, Any]] = []
data_root = metadata_path.parent
for label in LABELS:
needed = per_class - existing_counts[label]
if needed < 0:
raise ValueError(f"Bundled examples exceed target count for {label}: {existing_counts[label]} > {per_class}")
candidates = sorted(by_label[label], key=lambda row: row["video"])
if len(candidates) < needed:
raise ValueError(f"Not enough {label} candidates: need {needed}, found {len(candidates)}")
for row in rng.sample(candidates, needed):
video_path = data_root / row["video"]
actions_path = data_root / row["action"]
clip_id = video_path.parent.name
prompt = row["prompt"].strip()
selected.append(
{
"source_kind": "training_clip",
"source_id": clip_id,
"label": label,
"input_image": None,
"source_video": repo_path(video_path),
"reference_video": repo_path(video_path),
"actions": repo_path(actions_path),
"prompt": prompt,
"identity": {
"video_sha256": sha256_file(video_path),
"video_size": video_path.stat().st_size,
"actions_sha256": sha256_file(actions_path),
"prompt_sha256": sha256_bytes(prompt.encode("utf-8")),
"metadata_video": row["video"],
"metadata_action": row["action"],
},
}
)
return selected
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--metadata", type=Path, default=ROOT / "data/SF3/train/clips_5s/metadata_state_polish.csv")
parser.add_argument("--examples-manifest", type=Path, default=ROOT / "examples/manifest.csv")
parser.add_argument("--output-dir", type=Path, default=ROOT / "evaluation/proxy100")
parser.add_argument("--seed", type=int, default=260726754)
parser.add_argument("--per-class", type=int, default=20)
args = parser.parse_args()
bundled = bundled_examples(args.examples_manifest.resolve())
training = training_examples(args.metadata.resolve(), bundled, args.per_class, args.seed)
samples = bundled + training
samples.sort(key=lambda item: (LABELS.index(item["label"]), item["source_kind"], item["source_id"]))
for index, sample in enumerate(samples, start=1):
sample["id"] = f"proxy_{index:03d}"
sample["output_name"] = f"proxy_{index:03d}_{sample['label']}_{sample['source_id']}.mp4"
counts = Counter(item["label"] for item in samples)
expected_total = args.per_class * len(LABELS)
if len(samples) != expected_total or any(counts[label] != args.per_class for label in LABELS):
raise RuntimeError(f"Invalid selection: total={len(samples)}, counts={dict(counts)}")
output_dir = args.output_dir.resolve()
output_dir.mkdir(parents=True, exist_ok=True)
manifest = {
"benchmark_name": "stateplay_proxy100_mixed_v1",
"warning": "Mixed proxy benchmark: 92 in-sample training clips plus 8 bundled external examples; not the unpublished 100-clip held-out paper test set.",
"selection": {
"seed": args.seed,
"labels": list(LABELS),
"per_class": args.per_class,
"total": len(samples),
"bundled_external": len(bundled),
"training_clips": len(training),
"metadata": repo_path(args.metadata.resolve()),
"metadata_sha256": sha256_file(args.metadata.resolve()),
},
"samples": samples,
}
json_path = output_dir / "selection.json"
json_path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
columns = ("id", "source_kind", "source_id", "label", "input_image", "source_video", "reference_video", "actions", "output_name")
with (output_dir / "selection.csv").open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=columns)
writer.writeheader()
for sample in samples:
writer.writerow({key: sample.get(key) or "" for key in columns})
print(json.dumps({"manifest": str(json_path), "counts": dict(counts), "sources": dict(Counter(x["source_kind"] for x in samples))}, indent=2))
if __name__ == "__main__":
main()
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