#!/usr/bin/env python3 """EXP-3D-RGBD-SEGMENT-DIVERSITY-V21 analysis: P1, P2, the branch and the descriptive contrasts. P1 DIVERSITY_GAIN = FAR AbsRel(G1 at T24000) - FAR AbsRel(G5 at T24000) over the pooled EVAL-V3 + EVAL-V4 scenes, paired by scene, with V11's aggregation, scene bootstrap and the frozen V2 gate. P2 = the INPAINT-V1 label of AbsRel(REPROJ_HARMONIC) - AbsRel(AHFILL8 of G5 at T24000), from the frozen V17 analysis of the G5 rows. Branch A: P1 SUPPORTED and P2 ABOVE; B: exactly one; C: neither. Descriptive: the same at T6000; each descriptive cohort; every arm's frozen V17 analysis; the share of scenes where each arm beats harmonic inpainting on FAR pixels; G1 (GPU) against V17 L (CPU) at T24000. Writes results.json and README.md into the V21 directory. Run from the repository root. """ from __future__ import annotations import hashlib import importlib.util import json from pathlib import Path V21 = Path("outputs/EXP-3D-RGBD-SEGMENT-DIVERSITY-V21") ROOTS = {arm: V21 / arm / "EXP-3D-RGBD-LONG-TRAIN-V17" for arm in ("G1", "G5")} V17 = Path("outputs/EXP-3D-RGBD-LONG-TRAIN-V17") PRIMARY = ("EVAL_V3", "EVAL_V4") DESCRIPTIVE = ("EVAL_FAR", "FRESH_V2") def sha(path): return hashlib.sha256(Path(path).read_bytes()).hexdigest() def read(path): return json.loads(Path(path).read_text()) def frozen_v17_evaluation(): spec = importlib.util.spec_from_file_location( "v21_frozen_v17_eval", Path("scripts/evaluate_rgbd_long_train_eval.py") ) loaded = importlib.util.module_from_spec(spec) spec.loader.exec_module(loaded) return loaded def branch(diversity_status, ahfill8_label): first, second = diversity_status == "SUPPORTED", ahfill8_label == "ABOVE" return "A" if first and second else "B" if first or second else "C" def analyze(rows, cpu_rows, evaluation): v11 = evaluation.host() def far(table, cohorts, label): return v11.scene_values([r for r in table if r["cohort"] in cohorts], label, "absrel_FAR") def contrast(a, b, gate=False): stats = v11.contrast(a, b) out = {"gain": stats} if gate and stats is not None: out["gate"], out["gate_checks"] = v11.gate(stats) return out p1 = contrast(far(rows["G1"], PRIMARY, "T24000"), far(rows["G5"], PRIMARY, "T24000"), True) blocks = { arm: evaluation.block([r for r in table if r["cohort"] in PRIMARY]) for arm, table in rows.items() } p2 = blocks["G5"]["contrasts"].get("HARMONIC_MINUS_AHFILL8_T24000", {}) statuses = { "DIVERSITY_STATUS": p1.get("gate", "UNDEFINED"), "AHFILL8_LABEL": p2.get("label", "UNDEFINED"), } descriptive = { "DIVERSITY_GAIN_T6000": contrast( far(rows["G1"], PRIMARY, "T6000"), far(rows["G5"], PRIMARY, "T6000") ), "cohorts": { name: { step: contrast(far(rows["G1"], (name,), step), far(rows["G5"], (name,), step)) for step in ("T6000", "T24000") } for name in DESCRIPTIVE }, "harmonic_minus_carrier_far_T24000": { arm: block["contrasts"].get("HARMONIC_MINUS_T24000_FAR", {}).get("gain") for arm, block in blocks.items() }, "DEVICE_CHECK_V17_CPU_MINUS_G1_GPU_T24000": contrast( far(cpu_rows, PRIMARY, "T24000"), far(rows["G1"], PRIMARY, "T24000") ), } return { "statuses": statuses, "INTERPRETATION_BRANCH": branch(statuses["DIVERSITY_STATUS"], statuses["AHFILL8_LABEL"]), "P1_DIVERSITY_GAIN_T24000": p1, "P2_HARMONIC_MINUS_AHFILL8_G5_T24000": p2, "descriptive": descriptive, "arms": blocks, } def stat(value): if not value: return "无数据" lo, hi = value["ci95"] return ( f"{value['mean']:+.4f} [{lo:+.4f}, {hi:+.4f}],场景 " f"{value['positive']}/{value['tied']}/{value['negative']}" ) def mean(summary): return f"{summary['mean']:.4f}" if summary else "—" def readme(result, segments): p1, p2 = result["P1_DIVERSITY_GAIN_T24000"], result["P2_HARMONIC_MINUS_AHFILL8_G5_T24000"] d = result["descriptive"] lines = [ "# EXP-3D-RGBD-SEGMENT-DIVERSITY-V21", "", "同一批 TRAIN72 场景里更多样的训练内容(相机轨迹后段的片段)能否让补全更好、更一致。" "计划见 [PLAN_BEFORE_V21_DATA.md](PLAN_BEFORE_V21_DATA.md)。" "两组都在 GPU(4 号卡,关闭 TF32)" f"上用冻结的 V17 代码训练 24000 步;G5 多了 {segments} 个 SEG-V1 片段。GPU 不能逐位复现," "所有比较都在本轮内部进行。", "", f"- **P1(DIVERSITY_GAIN = FAR(G1) − FAR(G5),第 24000 步):** {stat(p1.get('gain'))}," f"{p1.get('gate', '—')}", f"- **P2(调和插值 − AHFILL8(G5,第 24000 步)):** {stat(p2.get('gain'))}," f"{p2.get('label', '—')}", f"- **分支 {result['INTERPRETATION_BRANCH']}**", f"- **第 6000 步的同样对比(描述性):** {stat(d['DIVERSITY_GAIN_T6000']['gain'])}", "", "## 各组的补全曲线(主队列 FAR AbsRel,三个 seed 平均)", "", "| 步数 | G1 | G5 |", "|---:|---:|---:|", ] curves = {arm: block["curve"] for arm, block in result["arms"].items()} for step in sorted(set(curves["G1"]) | set(curves["G5"]), key=int): g1 = curves["G1"].get(step, {}).get("FAR") g5 = curves["G5"].get(step, {}).get("FAR") lines.append(f"| {step} | {mean(g1)} | {mean(g5)} |") lines += [ "", "## 描述性", "", "| 队列 | 第 6000 步 G1 − G5 | 第 24000 步 G1 − G5 |", "|---|---|---|", ] for name, steps in d["cohorts"].items(): lines.append( f"| {name} | {stat(steps['T6000']['gain'])} | {stat(steps['T24000']['gain'])} |" ) lines += [ "", "- 调和插值 − carrier(FAR,第 24000 步):" + ";".join( f"{arm} {stat(v)}" for arm, v in d["harmonic_minus_carrier_far_T24000"].items() ), f"- 设备检查,V17 L(CPU)− G1(GPU),第 24000 步 FAR:" f"{stat(d['DEVICE_CHECK_V17_CPU_MINUS_G1_GPU_T24000']['gain'])}", "", "## 限制", "", "- EVAL-V3/V4 已被多个实验使用,FRESH-V2 是第八次使用,本轮结论只作机制对比;" "受保护的 final holdout 未打开。", "- 片段来自同一批训练场景;结论不推广到新的场景或新的数据集。", ] return "\n".join(lines) + "\n" def main(): freeze = read(V21 / "freeze.json") rows, inputs = {}, {} for arm, root in ROOTS.items(): if sha(root / "preregistration.json") != freeze["arms"][arm]["preregistration_sha256"]: raise PermissionError(f"{arm} lock changed after the V21 freeze") path = root / "raw/eval/query_rows.json" rows[arm], inputs[str(path)] = read(path), sha(path) cpu_path = V17 / "raw/eval/query_rows.json" inputs[str(cpu_path)] = sha(cpu_path) result = analyze(rows, read(cpu_path), frozen_v17_evaluation()) result["input_sha256"] = inputs result["source_sha256"] = {str(Path(__file__).resolve()): sha(Path(__file__))} (V21 / "results.json").write_text(json.dumps(result, indent=2, sort_keys=True) + "\n") (V21 / "README.md").write_text(readme(result, freeze["seg_v1"]["segments"])) print("V21_BRANCH", result["INTERPRETATION_BRANCH"], json.dumps(result["statuses"]), flush=True) if __name__ == "__main__": main()