File size: 3,510 Bytes
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 | #!/usr/bin/env python3
"""Validate generated proxy videos and write mechanics/visual evaluator manifests."""
from __future__ import annotations
import argparse
import csv
import hashlib
import json
import os
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
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 main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--selection", type=Path, default=ROOT / "evaluation/proxy100/selection.json")
parser.add_argument("--run-name", required=True)
parser.add_argument("--checkpoint", type=Path, required=True)
parser.add_argument("--generated-root", type=Path, default=ROOT / "evaluation/proxy100/generated")
args = parser.parse_args()
selection = json.loads(args.selection.resolve().read_text(encoding="utf-8"))
output_dir = (args.generated_root / args.run_name).resolve()
mechanics_rows = []
visual_rows = []
missing = []
for sample in selection["samples"]:
prediction = output_dir / sample["output_name"]
if not prediction.is_file() or prediction.stat().st_size == 0:
missing.append(str(prediction))
continue
relative_prediction = prediction.relative_to(args.selection.resolve().parent).as_posix()
mechanics_rows.append({"id": sample["id"], "prediction": relative_prediction, "label": sample["label"]})
if sample.get("reference_video"):
reference = ROOT / sample["reference_video"]
visual_rows.append(
{
"id": sample["id"],
"prediction": relative_prediction,
"reference": os.path.relpath(reference, args.selection.resolve().parent),
"label": sample["label"],
}
)
if missing:
raise FileNotFoundError(f"Missing {len(missing)} generated videos; first: {missing[0]}")
mechanics_path = args.selection.resolve().parent / f"manifest_{args.run_name}_mechanics.csv"
with mechanics_path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=("id", "prediction", "label"))
writer.writeheader(); writer.writerows(mechanics_rows)
visual_path = args.selection.resolve().parent / f"manifest_{args.run_name}_visual.csv"
with visual_path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=("id", "prediction", "reference", "label"))
writer.writeheader(); writer.writerows(visual_rows)
provenance = {
"run_name": args.run_name,
"selection": str(args.selection.resolve().relative_to(ROOT)),
"checkpoint": str(args.checkpoint.resolve().relative_to(ROOT)),
"checkpoint_sha256": sha256_file(args.checkpoint.resolve()),
"mechanics_samples": len(mechanics_rows),
"visual_samples": len(visual_rows),
"mechanics_manifest": str(mechanics_path.relative_to(ROOT)),
"visual_manifest": str(visual_path.relative_to(ROOT)),
}
(args.selection.resolve().parent / f"provenance_{args.run_name}.json").write_text(
json.dumps(provenance, indent=2) + "\n", encoding="utf-8"
)
print(json.dumps(provenance, indent=2))
if __name__ == "__main__":
main()
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