Datasets:
File size: 4,019 Bytes
7cb8e31 | 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 | #!/usr/bin/env python3
"""Run a model on a FireWorldBench split and compute the six metrics.
Usage (from the repo root):
python scripts/run_eval.py --split main_synthetic
python scripts/run_eval.py --split mmodalfire_c06 --group-by physical
Requires the OpenAI-compatible endpoint env vars:
OPENAI_BASE_URL, OPENAI_API_KEY, OPENAI_MODEL
"""
import argparse
import json
import os
import subprocess
import sys
from pathlib import Path
SPLITS = {
"main_synthetic": ("main_synthetic/full_test_A", "main_synthetic"),
"mmodalfire_c06": ("mmodalfire_c06/full_test_A", "mmodalfire_c06"),
}
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--split", choices=tuple(SPLITS), required=True)
parser.add_argument("--group-by", choices=("task", "physical", "fire"), default="physical",
help="report cells by physical axis P1-P5 (default), fire axis T1-T3, "
"or fine-grained task (36)")
parser.add_argument("--provider", default="openai")
parser.add_argument("--model", default=None, help="default: $OPENAI_MODEL")
parser.add_argument("--max-requests", type=int, default=None,
help="run only the first N questions (for a quick test)")
parser.add_argument("--track", choices=("S", "I"), default=None,
help="run only text track S or vision track I. Text-only models MUST use --track S "
"(I-track items carry images and will fail on a text model); vision models may use --track I.")
parser.add_argument("--data-dir", default=None, help="default: ./data")
parser.add_argument("--out-dir", default="results")
args = parser.parse_args()
root = Path(__file__).resolve().parent.parent
data_dir = Path(args.data_dir) if args.data_dir else root / "data"
split, source = SPLITS[args.split]
questions = data_dir / split / "questions.jsonl"
gold = data_dir / split / "gold.jsonl"
if not questions.exists():
sys.exit(f"Data not found at {questions}\nRun: cd scripts && bash download.sh")
if not gold.exists():
sys.exit(f"Gold not found at {gold}")
model = args.model or os.environ.get("OPENAI_MODEL")
if not model:
sys.exit("Missing model. Set OPENAI_MODEL or pass --model.")
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
preds = out_dir / f"{args.split}.jsonl"
result = out_dir / f"{args.split}-{args.group_by}.json"
# Step 1: run inference (resumable; never reads Gold)
cmd = [sys.executable, str(root / "scripts" / "run_api.py"),
"--provider", args.provider,
"--questions", str(questions),
"--source-root", str(data_dir / source),
"--output", str(preds),
"--model", model]
if args.max_requests:
cmd += ["--max-requests", str(args.max_requests)]
if args.track:
cmd += ["--track", args.track]
if args.track == "I":
print("[track I] vision track: ensure your model endpoint supports images.", flush=True)
elif args.track == "S":
print("[track S] text track: running text-only items.", flush=True)
else:
print("[track all] running S+I together. Text-only model? pass --track S (I items carry images).", flush=True)
print(">", " ".join(cmd), flush=True)
subprocess.run(cmd, check=True)
# Step 2: compute the six metrics
cmd = [sys.executable, str(root / "scripts" / "score_six_metrics.py"),
"--gold", str(gold), "--predictions", str(preds),
"--output", str(result), "--group-by", args.group_by]
print(">", " ".join(cmd), flush=True)
subprocess.run(cmd, check=True)
print(f"\nDone. Six metrics written to {result}")
print(json.dumps(json.load(open(result))["overall"], ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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