| |
| """Compute per-model metrics + attributes + real statistics, dump one JSON for the report page.""" |
| import json, math, os |
|
|
| EVAL = "/mnt/data/cvhci_video_understanding/eval" |
|
|
| ATTR = { |
| "Qwen2.5-VL-7B": dict(org="Alibaba", total=7, active=7, moe=False, thinking=False, year=2025), |
| "Qwen3-VL-8B": dict(org="Alibaba", total=8, active=8, moe=False, thinking=False, year=2026), |
| "Qwen3.5-9B": dict(org="Alibaba", total=9, active=9, moe=False, thinking=False, year=2026), |
| "Kimi-VL-A3B-Thinking-2506": dict(org="Moonshot", total=16, active=3, moe=True, thinking=True, year=2025), |
| "GLM-4.1V-9B-Thinking": dict(org="Z.ai", total=9, active=9, moe=False, thinking=True, year=2025), |
| "InternVL3.5-8B": dict(org="OpenGVLab",total=8, active=8, moe=False, thinking=False, year=2025), |
| "ERNIE-4.5-VL-28B-A3B": dict(org="Baidu", total=28, active=3, moe=True, thinking=False, year=2025), |
| "Qwen3-VL-30B-A3B-Thinking": dict(org="Alibaba", total=30, active=3, moe=True, thinking=True, year=2026), |
| "Pixtral-12B-2409": dict(org="Mistral", total=12, active=12, moe=False, thinking=False, year=2024), |
| "InternVL3.5-14B": dict(org="OpenGVLab",total=14, active=14, moe=False, thinking=False, year=2025), |
| "InternVL3.5-20B-A4B": dict(org="OpenGVLab",total=20, active=4, moe=True, thinking=False, year=2026), |
| "Qwen3.5-27B": dict(org="Alibaba", total=27, active=27, moe=False, thinking=False, year=2026), |
| "InternVL3.5-30B-A3B": dict(org="OpenGVLab",total=30, active=3, moe=True, thinking=False, year=2025), |
| "Qwen3.5-35B-A3B": dict(org="Alibaba", total=35, active=3, moe=True, thinking=False, year=2026), |
| "InternVL3.5-38B": dict(org="OpenGVLab",total=38, active=38, moe=False, thinking=False, year=2025), |
| "Qwen3.5-122B-A10B": dict(org="Alibaba", total=122, active=10, moe=True, thinking=False, year=2026), |
| "InternVL3.5-241B-A28B": dict(org="OpenGVLab",total=241, active=28, moe=True, thinking=False, year=2025), |
| "Qwen3-VL-235B-A22B-Instruct": dict(org="Alibaba", total=235, active=22, moe=True, thinking=False, year=2026), |
| "Qwen3-VL-235B-A22B-Thinking": dict(org="Alibaba", total=235, active=22, moe=True, thinking=True, year=2026), |
| "GLM-4.6V": dict(org="Z.ai", total=106, active=None,moe=True, thinking=False, year=2026), |
| "Qwen2.5-VL-72B-Instruct": dict(org="Alibaba", total=72, active=72, moe=False, thinking=False, year=2025), |
| "Gemma-3n-E4B-it": dict(org="Google", total=8, active=4, moe=False, thinking=False, year=2025), |
| "Gemma-4-31B-it": dict(org="Google", total=31, active=31, moe=False, thinking=False, year=2026), |
| "Gemma-4-26B-A4B-it": dict(org="Google", total=26, active=4, moe=True, thinking=False, year=2026), |
| } |
|
|
| MIN_N = 700 |
|
|
|
|
| def load(tag): |
| rows = [] |
| for l in open(f"{EVAL}/results/{tag}.jsonl"): |
| try: |
| r = json.loads(l) |
| except Exception: |
| continue |
| if not r.get("error"): |
| rows.append(r) |
| return list({r["review_id"]: r for r in rows}.values()) |
|
|
|
|
| def main(): |
| models = [] |
| for tag in sorted(ATTR): |
| path = f"{EVAL}/results/{tag}.jsonl" |
| if not os.path.exists(path): |
| continue |
| rows = load(tag) |
| n = len(rows) |
| if n < MIN_N: |
| continue |
| exact = sum(r["exact"] for r in rows) |
| overlap = sum(r["overlap"] for r in rows) / n |
| wellformed = sum(1 for r in rows if r["n_pred"] == r["n_select"]) / n |
| p = exact / n |
| se = (p * (1 - p) / n) ** 0.5 |
| by_unit = {} |
| for r in rows: |
| by_unit.setdefault(r["unit"], []).append(r["exact"]) |
| by_unit = {u: sum(v) / len(v) for u, v in by_unit.items()} |
| a = ATTR[tag] |
| models.append({ |
| "model": tag, "org": a["org"], "total_b": a["total"], "active_b": a["active"], |
| "moe": a["moe"], "thinking": a["thinking"], "year": a["year"], |
| "n": n, "exact": round(100 * p, 2), "exact_lo": round(100 * max(0, p - 1.96 * se), 2), |
| "exact_hi": round(100 * min(1, p + 1.96 * se), 2), |
| "overlap": round(100 * overlap, 2), "wellformed": round(100 * wellformed, 2), |
| "by_unit": {u: round(100 * v, 1) for u, v in by_unit.items()}, |
| }) |
|
|
| |
| pts = [(math.log10(m["active_b"]), m["exact"]) for m in models if m["active_b"]] |
| def pearson(pts): |
| n = len(pts); mx = sum(x for x, _ in pts) / n; my = sum(y for _, y in pts) / n |
| cov = sum((x - mx) * (y - my) for x, y in pts) |
| sx = (sum((x - mx) ** 2 for x, _ in pts)) ** 0.5 |
| sy = (sum((y - my) ** 2 for _, y in pts)) ** 0.5 |
| return cov / (sx * sy) if sx and sy else 0.0 |
| r_active = pearson(pts) |
| pts_total = [(math.log10(m["total_b"]), m["exact"]) for m in models] |
| r_total = pearson(pts_total) |
|
|
| moe_pts = [m["exact"] for m in models if m["moe"]] |
| dense_pts = [m["exact"] for m in models if not m["moe"]] |
| think_pts = [m["exact"] for m in models if m["thinking"]] |
| nonthink_pts = [m["exact"] for m in models if not m["thinking"]] |
|
|
| org_avg = {} |
| for m in models: |
| org_avg.setdefault(m["org"], []).append(m["exact"]) |
| org_avg = {o: round(sum(v) / len(v), 1) for o, v in sorted(org_avg.items(), key=lambda kv: -sum(kv[1]) / len(kv[1]))} |
|
|
| |
| pair = {m["model"]: m["exact"] for m in models if "235B-A22B" in m["model"]} |
|
|
| out = { |
| "models": sorted(models, key=lambda m: -m["exact"]), |
| "stats": { |
| "n_models": len(models), |
| "r_active_params": round(r_active, 3), |
| "r_total_params": round(r_total, 3), |
| "moe_mean": round(sum(moe_pts) / len(moe_pts), 1), "moe_n": len(moe_pts), |
| "dense_mean": round(sum(dense_pts) / len(dense_pts), 1), "dense_n": len(dense_pts), |
| "thinking_mean": round(sum(think_pts) / len(think_pts), 1), "thinking_n": len(think_pts), |
| "nonthinking_mean": round(sum(nonthink_pts) / len(nonthink_pts), 1), "nonthinking_n": len(nonthink_pts), |
| "org_avg": org_avg, |
| "instruct_vs_thinking_235B": pair, |
| }, |
| } |
| json.dump(out, open(f"{EVAL}/analysis.json", "w"), ensure_ascii=False, indent=1) |
| print(f"models={len(models)} r(log active_params, exact)={r_active:.3f} r(log total_params, exact)={r_total:.3f}") |
| print(f"MoE mean={out['stats']['moe_mean']}% (n={out['stats']['moe_n']}) Dense mean={out['stats']['dense_mean']}% (n={out['stats']['dense_n']})") |
| print(f"Thinking mean={out['stats']['thinking_mean']}% (n={out['stats']['thinking_n']}) Non-thinking mean={out['stats']['nonthinking_mean']}% (n={out['stats']['nonthinking_n']})") |
| print("按厂商均分:", out['stats']['org_avg']) |
| print("235B Instruct vs Thinking:", pair) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
