#!/usr/bin/env python3 """Report the saved V15 results: pooled EVAL-V3/V4 primary gates, 2x2, DEV (no model/media).""" # ruff: noqa: E501 -- Chinese scientific report prose import argparse import json from pathlib import Path V14 = Path("outputs/EXP-3D-RGBD-DATA-SCALE-V14") METHODS = ( "CONSTANT", "REPROJ_NN", "REPROJ_HARMONIC", "W32_T72", "W64_T72", "C0", "C1", "HFILL8_W32_T72", "HFILL8_W64_T72", "HFILL8_C0", "HFILL8_C1", "AHFILL8_C0", "AHFILL8_C1", "C0_LAST", "C1_LAST", "HFILL8_C0_LAST", "HFILL8_C1_LAST", ) NEXT = { "A": "在 127 个训练场景上，宽度 64 改善了未观测区域的补全，更宽的 carrier 在可部署组合中也超过了经典几何补洞：补全需要同时扩大模型与数据，这支持在 GPU 上继续扩大两者。", "B": "两个条件只成立一个，需要分开解读：是补全本身随宽度改善、但还不足以胜过经典补洞，还是组合的优势并非来自更宽的 carrier。", "C": "在 127 个训练场景上，宽度 64 也没有稳定改善补全，组合仍不能胜过经典几何补洞：在 CPU 可及的宽度与数据范围内，这个架构的补全没有扩展。", } 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 mean(absolute, method, region): value = (absolute.get(method, {}).get(region) or {}).get("mean") return "—" if value is None else f"{value:.4f}" def c0_reproduces_v14(root, contract): """Largest |DEV query AbsRel| difference between V15 C0 and V14 C1, per step.""" diffs = [] for seed in contract["seeds"]: mine = read(root / "checkpoints" / f"C0_{seed}", "dev_curve", []) base = read(V14 / "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_v14(root, contract) names = ( "WIDTH_AT_SCALE_GAIN", "HARMONIC_HYBRID_GAIN", "HFILL8_WIDTH_GAIN", "WIDTH_AT_T72_GAIN", "DATA_AT_W32_GAIN", "DATA_AT_W64_GAIN", "INTERACTION", "LAST_WIDTH_GAIN", "HARMONIC_MINUS_HFILL8_C1_LAST", "HARMONIC_MINUS_AHFILL8_C1", ) fields = { "TRAIN_SCENES": len(contract["train_sets"]["TRAIN_EXT"]), "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 names) 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 ("WIDTH_AT_SCALE_GAIN", "HARMONIC_HYBRID_GAIN") }, "C0_MINUS_V14_C1_EVAL_MAX_ABS": result["consistency"]["c0_minus_v14_c1_max_abs"], "C0_V14_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 ), "WIDTH_GAIN_DEV": round(static["surface_gain"]["mean"], 6), "WIDTH_GAIN_DEV_CI": static["surface_gain"]["ci95"], "WIDTH_DEV_STATUS": qualification["SURFACE_TRAINING_STATUS"], "STATIC_DEV_STATUS": qualification["STATIC_DEV_STATUS"], "C0_REPRODUCES_V14_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-WIDTH-DATA-V15", "", f"宽度 × 数据的检验：在 V14 C1 的配方上（V11 C1 配方，TRAIN72 + TRAIN-EXT 共 {fields['TRAIN_SCENES']} 个训练场景），把 carrier 宽度从 32 加到 64（250,542 个参数）。计划在任何 V15 训练之前写定；C0（宽度 32）必须与 V14 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 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）。", "", "## 宽度 × 数据（FAR AbsRel，合并队列，描述性）", "", "| | 72 个训练场景 | 127 个训练场景 |", "|---|---:|---:|", f"| 宽度 32 | {mean(absolute, 'W32_T72', 'FAR')}（V11 C1） | {mean(absolute, 'C0', 'FAR')}（C0 = V14 C1） |", f"| 宽度 64 | {mean(absolute, 'W64_T72', 'FAR')}（V12 C1） | {mean(absolute, 'C1', 'FAR')}（C1） |", "", f"交互作用 [FAR(32,72) − FAR(64,72)] − [FAR(32,127) − FAR(64,127)] = {stat(contrasts['INTERACTION']['gain'])}（< 0 表示更多数据时宽度更有用）。", "", "| EVAL-V3 + EVAL-V4 AbsRel | ALL | NEAR | FAR |", "|---|---:|---:|---:|", ] for method in METHODS: if method in absolute: lines.append( f"| {method} | " + " | ".join(mean(absolute, method, r) for r in ("ALL", "NEAR", "FAR")) + " |" ) lines += [ "", "## 判定", "", f"- WIDTH_AT_SCALE_GAIN = FAR AbsRel(C0 宽度 32) − FAR AbsRel(C1 宽度 64) = {stat(contrasts['WIDTH_AT_SCALE_GAIN']['gain'])}；{statuses['WIDTH_AT_SCALE_STATUS']}", f"- HARMONIC_HYBRID_GAIN = AbsRel(REPROJ_HARMONIC) − AbsRel(HFILL8 C1) = {stat(contrasts['HARMONIC_HYBRID_GAIN']['gain'])}；{statuses['HARMONIC_HYBRID_LABEL']}", f"- HFILL8_WIDTH_GAIN = AbsRel(HFILL8 C0) − AbsRel(HFILL8 C1) = {stat(contrasts['HFILL8_WIDTH_GAIN']['gain'])}；{statuses['HFILL8_WIDTH_STATUS']}", f"- 描述性：72 个场景时宽度的作用 {stat(contrasts['WIDTH_AT_T72_GAIN']['gain'])}；宽度 32 时数据的作用 {stat(contrasts['DATA_AT_W32_GAIN']['gain'])}；宽度 64 时数据的作用 {stat(contrasts['DATA_AT_W64_GAIN']['gain'])}", f"- 描述性（第 6000 步）：宽度的作用 {stat(contrasts['LAST_WIDTH_GAIN']['gain'])}；调和插值 − HFILL8 C1 = {stat(contrasts['HARMONIC_MINUS_HFILL8_C1_LAST']['gain'])}", f"- 描述性：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}，WIDTH_GAIN_DEV={stat(static['surface_gain'])}，{fields['WIDTH_DEV_STATUS']}", f"- 完整性：C0 与 V14 C1 的 DEV 曲线最大差 = {reproduction}；C0 与 V14 C1 封存预测的最大差 = {fields['C0_MINUS_V14_C1_EVAL_MAX_ABS']}（{fields['C0_V14_QUERIES_COMPARED']} 个 query）", "", "## 各队列", "", "| 队列 | 场景 | WIDTH_AT_SCALE_GAIN | 调和插值 − HFILL8 C1 |", "|---|---:|---|---|", ] for cohort, block in result["cohorts"].items(): cells = [ stat(block["contrasts"][name]["gain"]) + f"，{block['contrasts'][name].get('label', 'UNDEFINED')}" for name in ("WIDTH_AT_SCALE_GAIN", "HARMONIC_HYBRID_GAIN") ] lines.append(f"| {cohort} | {block['n_scenes']} | " + " | ".join(cells) + " |") lines += [ "", f"分支 {branch}：{NEXT[branch]}", "", "说明：DEV 的 8 个场景在 V2–V14 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 WIDTH DATA V15 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)