#!/usr/bin/env python3 """Report the saved V14 results: pooled EVAL-V3/V4 primary gates, DEV secondary (no model/media).""" # ruff: noqa: E501 -- Chinese scientific report prose import argparse import json from pathlib import Path V11 = Path("outputs/EXP-3D-RGBD-COMPLETION-V11") METHODS = ( "CONSTANT", "REPROJ_NN", "REPROJ_HARMONIC", "C0", "C1", "HFILL8_C0", "HFILL8_C1", "AHFILL8_C0", "AHFILL8_C1", ) NEXT = { "A": "训练场景从 72 个增加到 {n} 个，改善了未观测区域的补全；用新数据训练的 carrier 在可部署组合中也超过了经典几何补洞。数据规模是有效的杠杆，这支持在 GPU 上继续扩大数据与模型。", "B": "两个条件只成立一个，需要分开解读：是补全本身随数据改善、但还不足以胜过经典补洞，还是组合相对经典补洞的优势并非来自更多的数据。", "C": "在 CPU 可及的规模内（训练场景 72→{n}），增加数据没有稳定改善补全，也没有让组合胜过经典几何补洞。", } def read(root, name, default=None): path = Path(root) / (name + ".json") if not path.exists() and default is not None: return default return json.loads(path.read_text()) def stat(value): if value is None or value.get("mean") is None: return "UNDEFINED" lo, hi = value["ci95"] return f"{value['mean']:+.4f}，95% CI [{lo:+.4f}, {hi:+.4f}]，场景 {value['positive']}/{value['tied']}/{value['negative']}" def c0_reproduces_v11(root, contract): """Largest |DEV query AbsRel| difference between V14 C0 and V11 C1, per step.""" diffs = [] for seed in contract["seeds"]: mine = read(root / "checkpoints" / f"C0_{seed}", "dev_curve", []) base = read(V11 / "checkpoints" / f"C1_{seed}", "dev_curve", []) if not mine or len(mine) != len(base): return None diffs += [ abs(a["query_absrel"] - b["query_absrel"]) for a, b in zip(mine, base, strict=True) ] return max(diffs) if diffs else None def run(root, docs=None): root, docs = Path(root), Path(docs) if docs else Path(root) contract = read(root, "training_contract") result = read(root, "eval_results") static = read(root, "static_results") qualification = read(root, "qualification_results") tests, integrity = (read(root, n, {"status": "PENDING"}) for n in ("tests", "integrity")) primary = result["primary"] absolute = primary["absolute_absrel"] contrasts = primary["contrasts"] statuses = result["statuses"] branch = result["INTERPRETATION_BRANCH"] groups = static["groups"] reproduction = c0_reproduces_v11(root, contract) n_train = len(contract["train_sets"]["TRAIN_EXT"]) fields = { "TRAIN72_SCENES": len(contract["train_sets"]["TRAIN72"]), "TRAIN_EXT_SCENES": n_train, "EVAL_SCENES": primary["n_scenes"], "FAR_PIXEL_FRACTION": round(primary["far_pixel_fraction"], 4), **{ f"EVAL_{method}_{region}": round(absolute[method][region]["mean"], 6) for method in METHODS for region in ("ALL", "NEAR", "FAR") if method in absolute and (absolute[method][region] or {}).get("mean") is not None }, **{ f"{name}{suffix}": ( (round(gain["mean"], 6) if suffix == "" else gain["ci95"]) if gain else "UNDEFINED" ) for name, gain in ( (name, contrasts[name]["gain"]) for name in ("DATA_GAIN", "HARMONIC_HYBRID_GAIN", "HFILL8_DATA_GAIN") ) for suffix in ("", "_CI") }, **statuses, **{ f"{cohort}_{name}": ( round(block["contrasts"][name]["gain"]["mean"], 6) if block["contrasts"][name]["gain"] else "UNDEFINED" ) for cohort, block in result["cohorts"].items() for name in ("DATA_GAIN", "HARMONIC_HYBRID_GAIN") }, "C0_MINUS_V11_C1_EVAL_MAX_ABS": result["consistency"]["c0_minus_v11_c1_max_abs"], "C0_V11_QUERIES_COMPARED": result["consistency"]["queries_compared"], "DEV_C0_QUERY_ABSREL": round( groups["C0|direct"]["query_metrics"]["depth_absrel"]["mean"], 6 ), "DEV_C1_QUERY_ABSREL": round( groups["C1|direct"]["query_metrics"]["depth_absrel"]["mean"], 6 ), "DATA_GAIN_DEV": round(static["surface_gain"]["mean"], 6), "DATA_GAIN_DEV_CI": static["surface_gain"]["ci95"], "DATA_DEV_STATUS": qualification["SURFACE_TRAINING_STATUS"], "STATIC_DEV_STATUS": qualification["STATIC_DEV_STATUS"], "C0_REPRODUCES_V11_C1_MAX_DEV_DIFF": reproduction, "INTERPRETATION_BRANCH": branch, "FRESH_QUALIFICATION_INCLUDED": False, "FINAL_HOLDOUT_TOUCHED": False, "DYNAMIC_TTT_NEXT_STAGE_ALLOWED": False, "TEST_TIME_INPUT": "RGB+DEPTH+CAMERA", "TESTS": tests["status"], "FINAL_INTEGRITY": integrity["status"], "REPORT_DIR": str(docs), } lines = [ "# EXP-3D-RGBD-DATA-SCALE-V14", "", f"单因素机制检验：V11 C1 的配方不变，只把训练场景从 TRAIN72 增加到 TRAIN72 + TRAIN-EXT（共 {n_train} 个）。计划在读取任何新训练数据之前写定；C0 按同一配方在 TRAIN72 上重新训练，必须与 V11 C1 逐位相同。主评价在 EVAL-V3 与 EVAL-V4 的合并队列（{primary['n_scenes']} 个场景）上进行；EVAL-FAR 的远处 query 只作描述。所有预测在读取 query 深度之前封存。全程 CPU。", "", f"FAR 为离上下文深度重投影命中点超过 8 像素的像素（占 {primary['far_pixel_fraction']:.1%}）；HFILL8 在其余像素用调和插值补洞，在 FAR 用 carrier；AHFILL8 另把不透明度 < 0.5 的 FAR 像素也交给调和插值（V13）。", "", "| EVAL-V3 + EVAL-V4 AbsRel | ALL | NEAR | FAR |", "|---|---:|---:|---:|", ] for method in METHODS: if method in absolute: cells = [(absolute[method][r] or {}).get("mean") for r in ("ALL", "NEAR", "FAR")] lines.append( f"| {method} | " + " | ".join("—" if c is None else f"{c:.4f}" for c in cells) + " |" ) lines += [ "", "## 判定", "", f"- DATA_GAIN = FAR AbsRel(C0 TRAIN72) − FAR AbsRel(C1 TRAIN72+EXT) = {stat(contrasts['DATA_GAIN']['gain'])}；{statuses['DATA_STATUS']}", f"- HARMONIC_HYBRID_GAIN = AbsRel(REPROJ_HARMONIC) − AbsRel(HFILL8 C1) = {stat(contrasts['HARMONIC_HYBRID_GAIN']['gain'])}；{statuses['HARMONIC_HYBRID_LABEL']}", f"- HFILL8_DATA_GAIN = AbsRel(HFILL8 C0) − AbsRel(HFILL8 C1) = {stat(contrasts['HFILL8_DATA_GAIN']['gain'])}；{statuses['HFILL8_DATA_STATUS']}", f"- 描述性：AHFILL8_DATA_GAIN = {stat(contrasts['AHFILL8_DATA_GAIN']['gain'])}；调和插值 − AHFILL8 C1 = {stat(contrasts['HARMONIC_MINUS_AHFILL8_C1']['gain'])}", f"- DEV（次要）：C0 {fields['DEV_C0_QUERY_ABSREL']:.4f}，C1 {fields['DEV_C1_QUERY_ABSREL']:.4f}，DATA_GAIN_DEV={stat(static['surface_gain'])}，{fields['DATA_DEV_STATUS']}", f"- 完整性：C0 与 V11 C1 的 DEV 曲线最大差 = {reproduction}；C0 与 V11 C1 封存预测的最大差 = {fields['C0_MINUS_V11_C1_EVAL_MAX_ABS']}（{fields['C0_V11_QUERIES_COMPARED']} 个 query）", "", "## 各队列", "", "| 队列 | 场景 | DATA_GAIN | 调和插值 − HFILL8 C1 | 调和插值 − AHFILL8 C1 |", "|---|---:|---|---|---|", ] for cohort, block in result["cohorts"].items(): cells = [ stat(block["contrasts"][name]["gain"]) + f"，{block['contrasts'][name].get('label', 'UNDEFINED')}" for name in ("DATA_GAIN", "HARMONIC_HYBRID_GAIN", "HARMONIC_MINUS_AHFILL8_C1") ] lines.append(f"| {cohort} | {block['n_scenes']} | " + " | ".join(cells) + " |") lines += [ "", f"分支 {branch}：{NEXT[branch].format(n=n_train)}", "", "说明：DEV 的 8 个场景在 V2–V12 中反复使用，本轮只用它选择 checkpoint 并给出描述性结果；EVAL-V3/V4/FAR 是机制队列，不是资格验证；FRESH-V1/V2 未使用；受保护的 final holdout 未打开。", ] summary = ( "RGBD DATA SCALE V14 FINAL\n" + "\n".join( f"{k}={str(v).lower() if isinstance(v, bool) else v}" for k, v in fields.items() ) + "\n" ) (root / "README.md").write_text("\n".join(lines) + "\n") (root / "STATUS.md").write_text("```text\n" + summary + "```\n") (root / "terminal_summary.txt").write_text(summary) print(summary) return fields if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--root", type=Path, required=True) parser.add_argument("--docs", type=Path) args = parser.parse_args() run(args.root, args.docs)