| |
| """查: 现有结果里到底有没有记录"学习效率"(达标步数曲线)。 |
| lm4 的 JSON 有 efficiency / curves 字段; lm5 有 matrix(逐步行)。""" |
| import glob |
| import json |
| import os |
|
|
| R = "/work/liuyuanjie/headless/results" |
|
|
| print("=" * 80) |
| print("LM4: efficiency / curves 字段的填充情况") |
| print("=" * 80) |
| has_eff, empty_eff = [], [] |
| for p in sorted(glob.glob(f"{R}/*/lm4_wave_results.json")): |
| tag = os.path.basename(os.path.dirname(p)) |
| try: |
| d = json.load(open(p)) |
| except Exception: |
| continue |
| for meth in ("naive", "replay"): |
| v = d.get(meth) |
| if not isinstance(v, dict): |
| continue |
| eff = v.get("efficiency") or {} |
| cur = v.get("curves") or {} |
| if eff or cur: |
| has_eff.append((tag, meth, len(eff), len(cur))) |
| else: |
| empty_eff.append(tag) |
| print(" 有 learning-efficiency 数据的 run:") |
| if has_eff: |
| for t, m, ne, nc in has_eff: |
| print(" %-28s %-7s efficiency=%d 项 curves=%d 项" % (t, m, ne, nc)) |
| else: |
| print(" (无)") |
| print(" 无 efficiency 数据的 run 数: %d (去重 %d)" % |
| (len(empty_eff), len(set(empty_eff)))) |
| if empty_eff: |
| uniq = sorted(set(empty_eff)) |
| print(" " + ", ".join(uniq[:20]) + (" ..." if len(uniq) > 20 else "")) |
|
|
| print() |
| print("=" * 80) |
| print("LM5: matrix 的粒度 (能否算学习效率)") |
| print("=" * 80) |
| for p in sorted(glob.glob(f"{R}/lm5_*/lm5_mm_results.json")): |
| tag = os.path.basename(os.path.dirname(p)) |
| d = json.load(open(p)) |
| M = d.get("matrix") or [] |
| cfg = d.get("config", {}) |
| print(" %-26s backbone=%-7s 步数=%s 矩阵 %dx%d" % |
| (tag, cfg.get("backbone", "?"), cfg.get("text_steps", "?"), |
| len(M), len(M[0]) if M else 0)) |
|
|
| print() |
| print("=" * 80) |
| print("关键: matrix 的行是『学完第 i 个域后』的评估, 不是『训练中每 N 步』的评估,") |
| print(" 所以只能算『域间遗忘』, 算不出『达标步数』这类学习效率。") |
| print("=" * 80) |
|
|
