"""EXP-3D-RGBD-COMPLETION-SELECTOR-ARKIT-V1: scoring and the preregistered analysis on ARKIT_SELECT (80 fresh scans). score: every prediction must be sealed and unchanged before query depth is read: the arms seal (REPROJ_HARMONIC, NEAR, depth and opacity of G1 and GA for three seeds), the DepthSplat CTX seals of JO1-JO3 (cap 3.0) and the selector seal (context depth spread S per episode). Per query with at least one context hit: H3 REPROJ_HARMONIC GAN, G1N per seed depth / opacity where opacity >= 0.5, else H3; mean over the three seeds JO where at least two JO seeds cover (alpha >= 0.5) the mean ray depth of the covering seeds, else H3 HGAN (H3 + GAN) / 2 SEL_X X where the episode's S >= 1.2, else H3 (X = GAN, G1N, JO) DSEL_GAN GAN where the query's median |log(GAN / H3)| outside NEAR >= 0.18, else H3 AbsRel on FARH (valid pixels outside the 8-px NEAR). Scene value = mean over roles of the mean over queries. analyze: V1's paired scene bootstrap (10,000 draws, seed 20260928) and V2 gate (ties count as non-worse). Run through cv_host_run.py from the repository root (CUDA hidden): python sel_score.py ROOT score | analyze """ import hashlib import json import sys import time from collections import defaultdict from pathlib import Path import numpy as np sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "EXP-3D-RGBD-DEPTHSPLAT-JEPA-TTT-V1" / "code")) from score_analyze import contrast, write # noqa: E402 COHORT, ALPHA_MIN, OPACITY_MIN, THRESHOLD, DISAGREE = "ARKIT_SELECT", 0.5, 0.5, 1.2, 0.18 JO_SEEDS = ("JO1", "JO2", "JO3") def sha(path): return hashlib.sha256(Path(path).read_bytes()).hexdigest() def load(item): if sha(item["path"]) != item["sha256"]: raise PermissionError(f"sealed prediction changed: {item['path']}") return dict(np.load(item["path"])) def ds_seal(root, name, keys): preds = {} for part in sorted((root / f"ds/{COHORT}" / name).glob("ds_seal_*of*.json")): seal = json.loads(part.read_text()) if seal["query_depth_read"] is not False or seal["limit"] or seal["cohort"] != COHORT or seal["scale_max"] != 3.0: raise PermissionError(f"{part}: not a complete sealed run") preds.update(seal["predictions"]) if set(preds) != set(keys): raise ValueError(f"{name}: sealed queries differ from the frame-role lock") return preds def ray_cosine(record, qid): from mcss.measurement_planning import geometry as g from mcss.measurement_planning.information import TeacherAccess teacher = TeacherAccess(record, [qid], (128, 160), Path.cwd(), []) return np.asarray(g.ray_cosine(teacher.query_camera(qid)), dtype=np.float64) def normalized(p, arm, seeds, fallback): return np.mean([np.where(p[f"OPACITY_{arm}_{s}"] >= OPACITY_MIN, p[f"{arm}_{s}"] / np.maximum(p[f"OPACITY_{arm}_{s}"].astype(np.float64), 1e-6), fallback) for s in seeds], axis=0) def score(root): lock = json.loads((root / "frame_role_lock.json").read_text())["cohorts"][COHORT] if sha(lock["manifest"]) != lock["manifest_sha256"]: raise PermissionError("manifest changed since the frame-role lock") prereg = json.loads((root / "preregistration.json").read_text()) if sha(root / "selector_seal.json") != prereg["root_files"]["selector_seal.json"]: raise PermissionError("selector seal changed since the lock") selector = json.loads((root / "selector_seal.json").read_text())["episodes"] records = {r["scene_id"]: r for r in json.loads(Path(lock["manifest"]).read_text())["scenes"]} keys = [f"{e['scene_id']}/{e['role']}/{q}" for e in lock["episodes"] for q in e["queries"]] arms = json.loads((root / f"raw/eval/{COHORT}/prediction_seal.json").read_text()) if arms["query_depth_read"] is not False or any(f"{COHORT}/{k}" not in arms["predictions"] for k in keys): raise PermissionError("arms seal incomplete or not certified") jo = {s: ds_seal(root, s, keys) for s in JO_SEEDS} rows, access, started = [], [], time.time() for e in lock["episodes"]: record = records[e["scene_id"]] frames = {f["frame_id"]: f for f in record["frames"]} use = selector[f"{e['scene_id']}/{e['role']}"]["use_learned"] for qid in e["queries"]: key = f"{e['scene_id']}/{e['role']}/{qid}" p = load(arms["predictions"][f"{COHORT}/{key}"]) near = p["near"].astype(bool) if not near.any(): rows.append({"scene_id": e["scene_id"], "role": e["role"], "query": qid, "zero_hit": True, "values": {}}) continue h3 = p["REPROJ_HARMONIC"] gan, g1n = normalized(p, "GA", arms["seeds"], h3), normalized(p, "G1", arms["seeds"], h3) cosine = ray_cosine(record, qid) arrays = [load(jo[s][key]) for s in JO_SEEDS] covers = np.array([a["CTX_alpha"].astype(np.float64) >= ALPHA_MIN for a in arrays]) ray = np.array([a["CTX_depth_z"].astype(np.float64) / np.maximum(a["CTX_alpha"].astype(np.float64), 1e-12) for a in arrays]) / cosine count = covers.sum(axis=0) jo_pred = np.where(count >= 2, np.where(covers, ray, 0.0).sum(axis=0) / np.maximum(count, 1), h3) far = ~near disagree = (float(np.median(np.abs(np.log(np.maximum(gan[far], 1e-6) / h3[far])))) if far.any() else None) readouts = {"H3": h3, "GAN": gan, "G1N": g1n, "JO": jo_pred, "HGAN": (h3 + gan) / 2, "SEL_GAN": gan if use else h3, "SEL_G1N": g1n if use else h3, "SEL_JO": jo_pred if use else h3, "DSEL_GAN": gan if disagree is not None and disagree >= DISAGREE else h3} gt = np.load(frames[qid]["depth"]).squeeze().astype(np.float64) access.append({"key": key, "path": frames[qid]["depth"]}) farh = np.isfinite(gt) & (gt > 0) & far values = {f"{n}|FARH": (float(np.mean(np.abs(x[farh] - gt[farh]) / gt[farh])) if farh.any() else None) for n, x in readouts.items()} rows.append({"scene_id": e["scene_id"], "role": e["role"], "query": qid, "zero_hit": False, "use_learned": use, "spread": selector[f"{e['scene_id']}/{e['role']}"]["spread"], "disagree": disagree, "values": values}) grouped = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) for row in rows: for name, v in row["values"].items(): if v is not None and np.isfinite(v): grouped[name][row["scene_id"]][row["role"]].append(v) table = {n: {s: float(np.mean([np.mean(v) for v in r.values()])) for s, r in sc.items()} for n, sc in grouped.items()} write(root / "scores/query_rows.json", rows) write(root / "scores/scene_table.json", table) write(root / "scores/score_info.json", {"queries": len(rows), "zero_hit_queries": sum(r["zero_hit"] for r in rows), "query_depth_reads": access, "seconds": round(time.time() - started, 1)}) print("SCORED", len(rows), "queries;", sum(r["zero_hit"] for r in rows), "without a context hit") PRIMARY = {"P1_SELECTED_CARRIER_VS_HARMONIC_FARH": ("H3|FARH", "SEL_GAN|FARH")} SECONDARY = {"S1_SELECTED_HYPERSIM_CARRIER_FARH": ("H3|FARH", "SEL_G1N|FARH"), "S2_SELECTED_DEPTHSPLAT_JEPA_FARH": ("H3|FARH", "SEL_JO|FARH"), "S3_CARRIER_UNSELECTED_FARH": ("H3|FARH", "GAN|FARH"), "S4_HYPERSIM_CARRIER_UNSELECTED_FARH": ("H3|FARH", "G1N|FARH"), "S5_DEPTHSPLAT_JEPA_UNSELECTED_FARH": ("H3|FARH", "JO|FARH"), "S6_COST_OF_SELECTION_FARH": ("GAN|FARH", "SEL_GAN|FARH"), "S7_CARRIER_ENSEMBLE_FARH": ("H3|FARH", "HGAN|FARH"), "S8_DISAGREEMENT_SELECTOR_FARH": ("H3|FARH", "DSEL_GAN|FARH")} def rank(x): return np.argsort(np.argsort(x, kind="stable"), kind="stable").astype(float) def analyze(root): table = json.loads((root / "scores/scene_table.json").read_text()) primary = {k: contrast(table, a, b) for k, (a, b) in PRIMARY.items()} secondary = {k: contrast(table, a, b) for k, (a, b) in SECONDARY.items()} status = primary["P1_SELECTED_CARRIER_VS_HARMONIC_FARH"]["status"] branch = {"SUPPORTED": "SELECTOR_CONSISTENT", "PARTIAL": "SELECTOR_PARTIAL", "HARMFUL": "SELECTOR_HARMFUL"}.get(status, "SELECTOR_NOT_ESTABLISHED") rows = [r for r in json.loads((root / "scores/query_rows.json").read_text()) if not r["zero_hit"]] episodes = {(r["scene_id"], r["role"]): r["use_learned"] for r in rows} gain = defaultdict(lambda: defaultdict(list)) spread = defaultdict(lambda: defaultdict(list)) for r in rows: if r["values"]["GAN|FARH"] is not None: gain[r["scene_id"]][r["role"]].append(r["values"]["H3|FARH"] - r["values"]["GAN|FARH"]) spread[r["scene_id"]][r["role"]].append(r["spread"]) s_ids = sorted(gain) g = np.array([np.mean([np.mean(v) for v in gain[s].values()]) for s in s_ids]) x = np.array([np.mean([np.mean(v) for v in spread[s].values()]) for s in s_ids]) descriptive = {"episodes_with_hit_queries": len(episodes), "episodes_selected": int(sum(episodes.values())), "spearman_scene_spread_vs_gain_GAN": float(np.corrcoef(rank(x), rank(g))[0, 1])} means = {k: {"mean": float(np.mean(list(v.values()))), "scenes": len(v)} for k, v in sorted(table.items())} write(root / "results/ARKIT_SELECT_results.json", {"cohort": COHORT, "branch": branch, "primary": primary, "secondary": secondary, "means": means, "descriptive": descriptive}) print("ANALYZED", branch) for name, s in {**primary, **secondary}.items(): print(f" {name}: {s['mean']:+.4f} [{s['ci95'][0]:+.4f}, {s['ci95'][1]:+.4f}] " f"{s['positive']}/{s['tied']}/{s['negative']} median {s['median']:+.4f} ({s['scenes']} scenes) {s['status']}") for k, v in means.items(): print(f" {k:16s} {v['mean']:.4f} ({v['scenes']})") print(" descriptive:", json.dumps(descriptive)) if __name__ == "__main__": root, stage = Path(sys.argv[1]).resolve(), sys.argv[2] {"score": score, "analyze": analyze}[stage](root)