diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/README.md b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f7d4e28a0eb071e51d5de463e3d71416d0baf885 --- /dev/null +++ b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/README.md @@ -0,0 +1,41 @@ +# `global_semantic_v2_b64_noappend_confirmatory4` expanded offline evaluation + +This package freezes the three previously released adapters and evaluates them +on 16 scene-disjoint HM3D scenes (1,024 paired decisions per seed), plus a +1,024-decision `no_point_signal` exact-baseline audit. + +## Main result + +- Ground Top-1: `+0.5208 pp` (3/3 seeds positive), scene-cluster 95% CI + `[-0.7161, +1.6927] pp`. +- Joint: `+0.4557 pp`, scene-cluster 95% CI + `[-0.4557, +1.3346] pp`. +- Ground Top-5: `+1.6927 pp` (3/3 seeds positive), scene-cluster 95% CI + `[+0.7161, +3.0599] pp`. +- `no_point_signal`: every discrete Ground/Joint/Decision prediction exactly + matches the frozen MTU3D baseline. + +The original five-scene confirmatory result remains the largest observed +Top-1 effect (`Ground +4.5833 pp`); this expanded evaluation is the stronger +generalization audit and reports the smaller effect without selecting scenes +or seeds. + +## Files + +- `formal_r2/seed{42,123,777}/result.json`: complete per-decision results. +- `formal_r2/no_point_seed42/result.json`: complete no-point control. +- `formal_r2/analysis.json`: paired aggregation and scene-cluster bootstrap. +- `preregistration.json` and `preregistration_amendment_r1.json`: locked + protocol and the pre-metric infrastructure amendment. +- `evaluation_manifest.json`: scene and weight identities. +- `payload_sha_audit.json`, `transfer_audit.json`: cross-machine provenance. +- `analyze.py`, `run_expanded16*.sh`, `exact_code.tar.gz`: analysis and exact + evaluation code snapshot. +- `global_semantic_v2_b64_noappend_confirmatory4扩大离线复测报告_20260823.md`: + complete Chinese report. + +The adapter weights are not duplicated in this folder. They are the exact +SHA256-identified weights already published under the original +`global_semantic_v2_b64_noappend_confirmatory4` artifact in this repository. +Licensed HM3D assets, MTU3D, and Sonata/PTv3 base checkpoints are intentionally +not redistributed. diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/SHA256SUMS b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..e03f9389b6e6235923329334c6bfa8c7cd3bdb13 --- /dev/null +++ b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/SHA256SUMS @@ -0,0 +1,16 @@ +f8124342beacf1a299506e3ea15cea58e2de9e73d5468fc8b018ac2b49a43bc6 ./README.md +c4b0cafadb234cd544cfbc73845af1d6306d8c3f6963eb32d165a5c609e06f9d ./analyze.py +0f8c3981d0da33baccbb6da887476ee300e78716f1df0ab90163b8c46c82f729 ./evaluation_manifest.json +1aa63f3f566ac35f2d37452dc6296cf231eb8226baa5d6639a4b40bd1ca966b6 ./exact_code.tar.gz +a3060c3006824dcb0f9031bd6c6ec2e15ec227f8b92136870bc087da55f3bd7b ./formal_r2/analysis.json +f65e58efd8ab8b9591462bf90bb3ae4badb69cd3329094113b209a2f59ef8b51 ./formal_r2/no_point_seed42/result.json +6eb2a1483de767241460e2b98cda8f1709dbe9708a277e5c86c1d654c42e42ed ./formal_r2/seed123/result.json +23cab2c6ee597048b0f7b66af2627a59c33270d34823d25ddad30b3918de8603 ./formal_r2/seed42/result.json +dfd8d50fbfc36e99d5eb06fcd28df112f85eb196ffaf27e0ce1d5db9f7f79230 ./formal_r2/seed777/result.json +b556e7c68aec54890eaed1b6d0ae25fbe8047cdff263677f25ad422139aa02b7 ./global_semantic_v2_b64_noappend_confirmatory4扩大离线复测报告_20260823.md +22b223d7677cb3515c7b22a4b27c052d0e631ca54d9d04eddf0c3851d8f5fdc6 ./payload_sha_audit.json +dd004361124030b3f1f14bec857163eb3dc05ee32a3390c1d72c986af6f2ebf4 ./preregistration.json +f471c9c9f8dbe0b4494e06b7000998dd6bedb86c8a7db36e3d4851c2c08eee87 ./preregistration_amendment_r1.json +90bd615f81dc8c234d69aafd3e063a9356f31186710657728c1c065a44b40588 ./run_expanded16.sh +c616b94b0e1d9e47af7200b50dc984dbee26a64ec6b7a3804e1662083ddfe8dc ./run_expanded16_r2.sh +4df018cae8d67695d2ac2a329436b689900b8e8645d64b3a81fa574920c3951d ./transfer_audit.json diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/analyze.py b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/analyze.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa970a8a79d9980a3d99b737ae850b96ea0a9e9 --- /dev/null +++ b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/analyze.py @@ -0,0 +1,400 @@ +#!/usr/bin/env python3 +"""Aggregate the preregistered expanded confirmatory4 offline evaluation.""" + +from __future__ import annotations + +import argparse +from collections import defaultdict +import hashlib +import json +from pathlib import Path +import random +from statistics import mean + + +SEEDS = (42, 123, 777) +EXPECTED_SOURCE_SHA = { + "training_source_sha256": "7d7df8aaf84c8f8599bea01911fd759745906fb8d0c7f594af782cff1b91b9ff", + "bridge_source_sha256": "85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9", + "token_encoder_source_sha256": "a06f756d697dc7c73d5069357556a2d0995de6b387d2103e900a87d825fc340e", + "mtu3d_query3d_source_sha256": "d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99", +} +EXPECTED_ADAPTER_SHA = { + 42: "5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b", + 123: "1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e", + 777: "5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3", +} + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def identity(record: dict) -> tuple: + return ( + record["scene_id"], + record["episode_id"], + record["sub_episode_id"], + record["time_step"], + ) + + +def clustered_ci(records: list[dict], field: str, *, seed: int, draws: int) -> dict: + by_scene: dict[str, list[float]] = defaultdict(list) + for record in records: + by_scene[record["scene_id"]].append( + float(record["fused"][field]) - float(record["baseline"][field]) + ) + scenes = sorted(by_scene) + observed = mean(value for values in by_scene.values() for value in values) + generator = random.Random(seed) + samples = [] + for _ in range(draws): + selected = [generator.choice(scenes) for _ in scenes] + values = [value for scene in selected for value in by_scene[scene]] + samples.append(mean(values)) + samples.sort() + lo = samples[int(0.025 * draws)] + hi = samples[min(draws - 1, int(0.975 * draws))] + return { + "estimate": observed, + "ci95_low": lo, + "ci95_high": hi, + "scene_count": len(scenes), + "resamples": draws, + } + + +def clustered_ci_across_seeds( + records_by_seed: dict[int, list[dict]], + field: str, + *, + seed: int, + draws: int, +) -> dict: + """Bootstrap scenes while treating the three frozen seeds as replicas.""" + + by_scene: dict[str, list[float]] = defaultdict(list) + for records in records_by_seed.values(): + for record in records: + by_scene[record["scene_id"]].append( + float(record["fused"][field]) + - float(record["baseline"][field]) + ) + scenes = sorted(by_scene) + observed = mean(value for values in by_scene.values() for value in values) + generator = random.Random(seed) + samples = [] + for _ in range(draws): + selected = [generator.choice(scenes) for _ in scenes] + values = [value for scene in selected for value in by_scene[scene]] + samples.append(mean(values)) + samples.sort() + return { + "estimate": observed, + "ci95_low": samples[int(0.025 * draws)], + "ci95_high": samples[min(draws - 1, int(0.975 * draws))], + "scene_count": len(scenes), + "frozen_seed_count": len(records_by_seed), + "resamples": draws, + "bootstrap_unit": "scene_with_fixed_seed_replicates", + } + + +def rescue_harm(records: list[dict], field: str) -> dict: + rescue = sum( + (not bool(record["baseline"][field])) and bool(record["fused"][field]) + for record in records + ) + harm = sum( + bool(record["baseline"][field]) and (not bool(record["fused"][field])) + for record in records + ) + return { + "rescue": rescue, + "harm": harm, + "net": rescue - harm, + "unchanged": len(records) - rescue - harm, + } + + +def load_result(root: Path, name: str) -> dict: + path = root / name / "result.json" + if not path.is_file(): + raise FileNotFoundError(path) + return json.loads(path.read_text(encoding="utf-8")) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--root", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--bootstrap-resamples", type=int, default=10000) + args = parser.parse_args() + + prereg_path = args.root / "preregistration.json" + prereg = json.loads(prereg_path.read_text(encoding="utf-8")) + expected_count = ( + prereg["data"]["scene_count"] * prereg["data"]["decisions_per_scene"] + ) + results = {seed: load_result(args.root, f"seed{seed}") for seed in SEEDS} + no_point = load_result(args.root, "no_point_seed42") + + integrity = { + "preregistration_sha256": sha256(prereg_path), + "sample_count_exact": {}, + "scene_set_exact": {}, + "source_sha_exact": {}, + "adapter_sha_exact": {}, + "navigation_frozen": {}, + "ptv3_frozen": {}, + } + metrics = {} + paired = {} + records_by_seed: dict[int, list[dict]] = {} + expected_scenes = set(prereg["data"]["scene_ids"]) + for seed, result in results.items(): + validation = result["best_validation"] + baseline = validation["aggregate"]["baseline"] + fused = validation["aggregate"]["fused"] + records = validation["records"] + records_by_seed[seed] = records + if len({identity(record) for record in records}) != len(records): + raise RuntimeError(f"duplicate paired identities for seed {seed}") + integrity["sample_count_exact"][str(seed)] = len(records) == expected_count + integrity["scene_set_exact"][str(seed)] = { + record["scene_id"] for record in records + } == expected_scenes + integrity["source_sha_exact"][str(seed)] = all( + result["implementation"].get(key) == value + for key, value in EXPECTED_SOURCE_SHA.items() + ) + integrity["adapter_sha_exact"][str(seed)] = ( + result["loaded_adapter"]["sha256"] == EXPECTED_ADAPTER_SHA[seed] + ) + integrity["navigation_frozen"][str(seed)] = bool( + result["navigation_parameters_bitwise_unchanged"] + ) + integrity["ptv3_frozen"][str(seed)] = bool( + result["sonata_backbone_parameters_bitwise_unchanged"] + ) + metrics[str(seed)] = { + "baseline": baseline, + "fused": fused, + "ground_gain": fused["ground_accuracy"] - baseline["ground_accuracy"], + "joint_gain": fused["joint_accuracy"] - baseline["joint_accuracy"], + "ground_top5_gain": ( + fused["ground_top5_accuracy"] - baseline["ground_top5_accuracy"] + ), + "loss_reduction": baseline["mean_loss"] - fused["mean_loss"], + } + paired[str(seed)] = { + "ground": clustered_ci( + records, + "ground_correct", + seed=20260823 + seed, + draws=args.bootstrap_resamples, + ), + "joint": clustered_ci( + records, + "joint_correct", + seed=30260823 + seed, + draws=args.bootstrap_resamples, + ), + "ground_top5": clustered_ci( + records, + "ground_top5_correct", + seed=40260823 + seed, + draws=args.bootstrap_resamples, + ), + "ground_rescue_harm": rescue_harm(records, "ground_correct"), + "joint_rescue_harm": rescue_harm(records, "joint_correct"), + } + + no_point_validation = no_point["best_validation"] + no_point_baseline = no_point_validation["aggregate"]["baseline"] + no_point_fused = no_point_validation["aggregate"]["fused"] + no_point_exact = { + key: no_point_fused[key] == no_point_baseline[key] + for key in ( + "ground_accuracy", + "joint_accuracy", + "ground_top5_accuracy", + "decision_accuracy", + ) + } + no_point_exact["record_predictions"] = all( + record["fused"][prediction] == record["baseline"][prediction] + for record in no_point_validation["records"] + for prediction in ("ground_prediction", "decision_prediction") + ) + integrity["no_point_source_sha_exact"] = all( + no_point["implementation"].get(key) == value + for key, value in EXPECTED_SOURCE_SHA.items() + ) + integrity["no_point_adapter_sha_exact"] = ( + no_point["loaded_adapter"]["sha256"] == EXPECTED_ADAPTER_SHA[42] + ) + integrity["no_point_sample_count_exact"] = ( + len(no_point_validation["records"]) == expected_count + ) + integrity_passed = all( + all(value.values()) if isinstance(value, dict) else bool(value) + for value in integrity.values() + if not isinstance(value, str) + ) and all(no_point_exact.values()) + + ground_gains = [metrics[str(seed)]["ground_gain"] for seed in SEEDS] + joint_gains = [metrics[str(seed)]["joint_gain"] for seed in SEEDS] + combined_bootstrap = { + "ground": clustered_ci_across_seeds( + records_by_seed, + "ground_correct", + seed=50260823, + draws=args.bootstrap_resamples, + ), + "joint": clustered_ci_across_seeds( + records_by_seed, + "joint_correct", + seed=60260823, + draws=args.bootstrap_resamples, + ), + "ground_top5": clustered_ci_across_seeds( + records_by_seed, + "ground_top5_correct", + seed=70260823, + draws=args.bootstrap_resamples, + ), + } + per_scene_three_seed_mean = {} + for scene in sorted(expected_scenes): + scene_records = [ + record + for records in records_by_seed.values() + for record in records + if record["scene_id"] == scene + ] + per_scene_three_seed_mean[scene] = { + "replicate_decision_count": len(scene_records), + "ground_gain": mean( + float(record["fused"]["ground_correct"]) + - float(record["baseline"]["ground_correct"]) + for record in scene_records + ), + "joint_gain": mean( + float(record["fused"]["joint_correct"]) + - float(record["baseline"]["joint_correct"]) + for record in scene_records + ), + "ground_top5_gain": mean( + float(record["fused"]["ground_top5_correct"]) + - float(record["baseline"]["ground_top5_correct"]) + for record in scene_records + ), + "ground_rescue_harm": rescue_harm(scene_records, "ground_correct"), + "joint_rescue_harm": rescue_harm(scene_records, "joint_correct"), + } + acceptance = { + "integrity_required": integrity_passed, + "mean_ground_gain_strictly_positive": mean(ground_gains) > 0, + "positive_ground_gain_seeds_at_least_2": sum(v > 0 for v in ground_gains) >= 2, + "mean_joint_gain_strictly_positive": mean(joint_gains) > 0, + "no_point_ground_and_joint_exactly_equal_baseline": ( + no_point_exact["ground_accuracy"] + and no_point_exact["joint_accuracy"] + and no_point_exact["record_predictions"] + ), + } + output = { + "schema": "nav3r-offline-confirmatory4-expanded16-analysis/v1", + "status": "passed" if all(acceptance.values()) else "failed", + "claim_scope": "offline_heldout_ovon_grounding_and_decision_only", + "preregistration": str(prereg_path), + "expected_decisions_per_seed": expected_count, + "integrity": integrity, + "integrity_passed": integrity_passed, + "per_seed": metrics, + "paired_scene_cluster_bootstrap": paired, + "three_seed_scene_cluster_bootstrap": combined_bootstrap, + "per_scene_three_seed_mean": per_scene_three_seed_mean, + "three_seed_replicate_rescue_harm": { + "ground": rescue_harm( + [record for records in records_by_seed.values() for record in records], + "ground_correct", + ), + "joint": rescue_harm( + [record for records in records_by_seed.values() for record in records], + "joint_correct", + ), + }, + "three_seed_mean": { + "baseline_ground_accuracy": mean( + metrics[str(seed)]["baseline"]["ground_accuracy"] for seed in SEEDS + ), + "fused_ground_accuracy": mean( + metrics[str(seed)]["fused"]["ground_accuracy"] for seed in SEEDS + ), + "ground_gain": mean(ground_gains), + "baseline_joint_accuracy": mean( + metrics[str(seed)]["baseline"]["joint_accuracy"] for seed in SEEDS + ), + "fused_joint_accuracy": mean( + metrics[str(seed)]["fused"]["joint_accuracy"] for seed in SEEDS + ), + "joint_gain": mean(joint_gains), + "baseline_ground_top5_accuracy": mean( + metrics[str(seed)]["baseline"]["ground_top5_accuracy"] for seed in SEEDS + ), + "fused_ground_top5_accuracy": mean( + metrics[str(seed)]["fused"]["ground_top5_accuracy"] for seed in SEEDS + ), + "ground_top5_gain": mean( + metrics[str(seed)]["ground_top5_gain"] for seed in SEEDS + ), + "loss_reduction": mean( + metrics[str(seed)]["loss_reduction"] for seed in SEEDS + ), + }, + "no_point_seed42": { + "baseline": no_point_baseline, + "fused": no_point_fused, + "exact": no_point_exact, + "loss_decomposition": { + "baseline_to_no_point_reduction_from_loaded_calibration_or_non_point_terms": ( + no_point_baseline["mean_loss"] - no_point_fused["mean_loss"] + ), + "no_point_to_full_seed42_incremental_point_reduction": ( + no_point_fused["mean_loss"] + - metrics["42"]["fused"]["mean_loss"] + ), + "total_baseline_to_full_seed42_reduction": ( + no_point_baseline["mean_loss"] + - metrics["42"]["fused"]["mean_loss"] + ), + "interpretation": ( + "Only no_point_to_full is point-signal-specific; total loss " + "reduction must not be wholly attributed to point clouds." + ), + }, + }, + "acceptance": acceptance, + "confidence_interval_policy": "reported_descriptively_not_a_hard_gate", + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text( + json.dumps(output, ensure_ascii=False, indent=2) + "\n", encoding="utf-8" + ) + print(json.dumps({ + "status": output["status"], + "integrity_passed": integrity_passed, + "three_seed_mean": output["three_seed_mean"], + "acceptance": acceptance, + }, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/evaluation_manifest.json b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/evaluation_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..dfab6f17a47a5b03c4e74cc3a984c29796f21c1f --- /dev/null +++ b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/evaluation_manifest.json @@ -0,0 +1,169 @@ +{ + "schema": "nav3r-alignment-manifest-offline-expanded16/v1", + "training_policy": "processed_hm3d AND official_vle_train MINUS held-out val/test benchmark scenes", + "counts": { + "processed_train": 17, + "processed_test": 0, + "official_vle_train": 145, + "reserved_heldout_validation_or_test": 36, + "eligible_train": 17, + "selected_train": 17 + }, + "selected_train_scene_ids": [], + "validation_scene_ids": [ + "7MXmsvcQjpJ", + "BAbdmeyTvMZ", + "Dd4bFSTQ8gi", + "GLAQ4DNUx5U", + "LT9Jq6dN3Ea", + "Nfvxx8J5NCo", + "VBzV5z6i1WS", + "XB4GS9ShBRE", + "a8BtkwhxdRV", + "bxsVRursffK", + "mv2HUxq3B53", + "q3zU7Yy5E5s", + "svBbv1Pavdk", + "y9hTuugGdiq", + "ziup5kvtCCR", + "zt1RVoi7PcG" + ], + "reserved_scene_ids": [ + "4ok3usBNeis", + "5cdEh9F2hJL", + "6s7QHgap2fW", + "7MXmsvcQjpJ", + "BAbdmeyTvMZ", + "CrMo8WxCyVb", + "DYehNKdT76V", + "Dd4bFSTQ8gi", + "GLAQ4DNUx5U", + "HY1NcmCgn3n", + "LT9Jq6dN3Ea", + "MHPLjHsuG27", + "Nfvxx8J5NCo", + "QaLdnwvtxbs", + "TEEsavR23oF", + "VBzV5z6i1WS", + "XB4GS9ShBRE", + "a8BtkwhxdRV", + "bCPU9suPUw9", + "bxsVRursffK", + "cvZr5TUy5C5", + "eF36g7L6Z9M", + "h1zeeAwLh9Z", + "k1cupFYWXJ6", + "mL8ThkuaVTM", + "mv2HUxq3B53", + "p53SfW6mjZe", + "q3zU7Yy5E5s", + "q5QZSEeHe5g", + "qyAac8rV8Zk", + "svBbv1Pavdk", + "wcojb4TFT35", + "y9hTuugGdiq", + "yr17PDCnDDW", + "ziup5kvtCCR", + "zt1RVoi7PcG" + ], + "leakage_check_passed": true, + "notes": [ + "Only these selected scenes may be used to fit the new PTv3 adapter.", + "The wcojb4TFT35 smoke scene is benchmark-reserved and is never eligible.", + "Validation selection used geometry gates and available decision count only; no navigation metric was inspected.", + "This panel supports offline OVON metrics only and never authorizes SR/SPL claims." + ], + 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"heldout_used_for_checkpoint_selection": false, + "heldout_used_for_calibration_fit": false + }, + "point_memory_append_enabled": false, + "local_spatial_decision_only": false, + "spatial_residual_audit": null, + "global_semantic_residual_audit": { + "architecture": "explicit_global_scene_semantic_query_attention_v1", + "position_independent": true, + "bottleneck_size": 64, + "fusion_scale": 0.4831729829311371, + "attention_temperature": 1.0018417835235596, + "source_sha256": "85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9" + }, + "semantic_fusion_gate_selected_checkpoint": 0.4831729829311371, + "spatial_fusion_gate_selected_checkpoint": null, + "raw_fused_aux_loss_weight": 1.0, + "ground_listwise_loss_weight": 0.25, + "ground_bce_loss_weight": 1.0, + "decision_loss_weight": 1.0, + "fusion_scale_sweep": {}, + "loaded_adapter": { + "path": "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814/runtime/publication_staging/20260822/global_semantic_v2_b64_noappend_confirmatory4/seed777/best_adapter.pt", + "sha256": "5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3", + "best_step": 2176, + "logit_calibration": { + "enabled": true, + "ground_temperature": 1.214176893234253, + "ground_bias": -0.6257956027984619, + "decision_temperature": 1.2093591690063477, + "argmax_preserving": true, + "source_split": "disjoint_training_scene_calibration_decisions", + "sample_count": 544, + "loss_before": 0.4618982672691345, + "loss_after": 0.4566005766391754 + } + }, + "frozen_gradient_seen": false, + "navigation_parameters_bitwise_unchanged": true, + "sonata_backbone_parameters_bitwise_unchanged": true, + "checkpoint": { + "loaded_tensor_count": 392, + "missing_keys": [], + "unexpected_keys": [], + "strict_original_coverage": true, + "path": "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814/runtime/mtu3d/checkpoints/stage2-pretrain-all/pytorch_model.bin", + "sha256": "0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791" + }, + "adapter": { + "path": "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814/runtime/ptv3/offline_confirmatory4_expanded16_20260823/formal_r2/seed777/best_adapter.pt", + "sha256": "9b1933e124844ffb0a9d40087f10b24af996358c690e8c6cf692d44038300dbb", + "trainable_parameter_count": 984451 + }, + "tokenizer": "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814/runtime/ptv3/tokenizers/clip-vit-large-patch14", + "gpu": "NVIDIA H100 80GB HBM3" +} diff --git "a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/global_semantic_v2_b64_noappend_confirmatory4\346\211\251\345\244\247\347\246\273\347\272\277\345\244\215\346\265\213\346\212\245\345\221\212_20260823.md" "b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/global_semantic_v2_b64_noappend_confirmatory4\346\211\251\345\244\247\347\246\273\347\272\277\345\244\215\346\265\213\346\212\245\345\221\212_20260823.md" new file mode 100644 index 0000000000000000000000000000000000000000..640f8192bbc1aa81eda15cec6d4587af5120f88c --- /dev/null +++ "b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/global_semantic_v2_b64_noappend_confirmatory4\346\211\251\345\244\247\347\246\273\347\272\277\345\244\215\346\265\213\346\212\245\345\221\212_20260823.md" @@ -0,0 +1,192 @@ +# Nav3R `global_semantic_v2_b64_noappend_confirmatory4` 扩大离线复测报告 + +> 日期：2026-08-23 +> 当前状态：**正式 r2 已完成并通过预注册门槛**；formal r1 为无指标的基础设施失败，单独保留。 + +## 1. 实验目的 + +本实验不是重新训练或筛选一个更好的模型，而是冻结旧离线最佳版本 +`global_semantic_v2_b64_noappend_confirmatory4` 的三个既有 adapter，在更大且场景独立的 +HM3D 离线理解面板上复测其增益是否稳定。 + +该版本在原第四组确认集上的三种子均值为： + +| 指标 | 冻结 MTU3D | 点云融合版 | 绝对变化 | +|---|---:|---:|---:| +| Ground accuracy | 35.3125% | 39.8958% | **+4.5833pp** | +| Joint accuracy | 33.1250% | 34.2708% | **+1.1458pp** | +| Mean loss | 0.836673 | 0.761728 | **-0.074945** | + +原结果只有 5 个场景、每种子 320 条决策，因此本轮重点增加场景数和配对决策数，而不改变模型、权重或门槛。 + +## 2. 版本与融合结构 + +### 2.1 模型身份 + +本轮直接复测旧 H800 `confirmatory4` 权重本身，不使用 v179 中结构相同但针对 +`official_world` 坐标修正后重新训练的 `official_ovon_world_v10` 权重。 + +| 组件 | 种子/版本 | SHA256 | +|---|---|---| +| adapter | seed42 | `5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b` | +| adapter | seed123 | `1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e` | +| adapter | seed777 | `5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3` | +| MTU3D Stage-2 | frozen | `0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791` | +| Sonata/PTv3 | frozen | `c5ced5acdae30d1c469713398073a866e25e6e414e23feed5dc025373657ac50` | + +### 2.2 点云如何接入 MTU3D + +1. ZPlusM/Stream3R 重建包经过固定参考相机与 Sim(3) 对齐； +2. 冻结 Sonata/PTv3 编码点云，确定性保留 256 个点云 token； +3. 使用宽度为 64 的显式全局场景语义 adapter，以 MTU3D 的指令条件化查询对点云语义做交叉注意力； +4. adapter 只产生 grounding residual；不把点 token 追加进 MTU3D memory，且不修改 decision logits； +5. 冻结 MTU3D、冻结 PTv3，只加载训练完成的 adapter 和训练集独立校准参数；本轮 `steps=0`，不发生任何训练。 + +这个结构能证明“点云语义信号影响离线目标理解/候选 grounding”，但不能仅凭该实验声称闭环导航 SR/SPL 改善。 + +## 3. 扩大数据面板 + +### 3.1 规模 + +- 16 个独立 HM3D 场景； +- 每场景预注册 64 条平衡 object/frontier 决策； +- 预计每种子 1024 条决策，三种子共 3072 条 full-point 评估； +- 另用 seed42 做 1024 条 `no_point_signal` 精确基线回退审计。 + +相较原确认集：场景数从 5 增至 16（3.2 倍），每种子决策从 320 增至 1024（3.2 倍）。 + +### 3.2 场景独立性 + +16 个扩大场景与原 adapter 的 17 个训练场景无交集，也与原 5 个 confirmatory4 场景无交集： + +`7MXmsvcQjpJ, a8BtkwhxdRV, BAbdmeyTvMZ, bxsVRursffK, Dd4bFSTQ8gi, GLAQ4DNUx5U, LT9Jq6dN3Ea, mv2HUxq3B53, Nfvxx8J5NCo, q3zU7Yy5E5s, svBbv1Pavdk, VBzV5z6i1WS, XB4GS9ShBRE, y9hTuugGdiq, ziup5kvtCCR, zt1RVoi7PcG`。 + +需要特别说明：扩大集使用另一批通过 `fixed_reference` 与 alignment 审计的 geometry-path 重建包。因此它同时检验新场景泛化和一定程度的重建包域泛化；若增益衰减，不能直接归因为模型无效，必须拆分场景差异、重建域差异和接口实现。 + +## 4. 预注册协议 + +- 完整协议文件：`preregistration.json`； +- 在读取扩大面板正式指标前锁定场景、样本数、三份权重 SHA、源码 SHA、主指标和门槛； +- 三个 full-point 条件分别使用 seed42/123/777 原权重； +- no-point 条件使用 seed42 原权重，但在 PTv3 cache 后施加 `no_point_signal`； +- 主指标：Ground、Joint、Ground Top-5、Mean loss； +- 同一 `scene/episode/sub_episode/time_step` 做逐样本配对； +- 以场景为 cluster 做 10,000 次 bootstrap 95% CI；CI 如实报告，不设为硬门槛； +- 接受条件：完整性全部通过、三种子平均 Ground 增益大于 0、至少 2/3 种子 Ground 为正、平均 Joint 增益大于 0、no-point Ground/Joint 和逐样本预测严格回到基线。 + +## 5. 完整性与链路审计 + +正式启动前已确认： + +- 四个关键源码 SHA 与旧确认实验一致； +- 三份 adapter SHA 与已发布权重一致； +- MTU3D 与 Sonata/PTv3 checkpoint SHA 一致； +- 所有 payload 由旧机直接经内存中继到 H100，不落本机； +- 评估在独立 staging 中运行，不覆盖当前主代码； +- GPU4/5/6 分别运行三个 full-point 种子，GPU7 运行 no-point； +- 导航主干与 PTv3 必须在结果文件中报告位级不变。 + +此外，正式运行使用 `steps=0` 并从冻结 adapter 读取既有校准参数。旧 17 个训练场景在这一模式下不会参与任何计算或统计；为避免四个进程重复构建完全无用的训练场景 PTv3 cache，评估专用 manifest 将 `selected_train_scene_ids` 置空，同时在 `source_adapter_train_scene_ids` 中原样保留 17 个训练场景身份。这个纯调度优化不修改源码、权重、校准、验证样本或 forward，已先在 2-decision 烟测中验证。 + +## 6. 扩大复测结果 + +### 6.1 三种子主结果 + +冻结 MTU3D 基线在三个种子中逐位相同。所有精度均基于每种子 1024 条、16 个场景的同一配对面板。 + +| seed | Ground 基线 | Ground 融合 | ΔGround | Joint 基线 | Joint 融合 | ΔJoint | Top-5 基线 | Top-5 融合 | ΔTop-5 | Mean loss 变化 | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 42 | 45.2148% | 45.4102% | **+0.1953pp** | 40.4297% | 40.9180% | **+0.4883pp** | 82.2266% | 83.3984% | **+1.1719pp** | -0.052252 | +| 123 | 45.2148% | 45.6055% | **+0.3906pp** | 40.4297% | 40.3320% | -0.0977pp | 82.2266% | 84.6680% | **+2.4414pp** | -0.052594 | +| 777 | 45.2148% | 46.1914% | **+0.9766pp** | 40.4297% | 41.4062% | **+0.9766pp** | 82.2266% | 83.6914% | **+1.4648pp** | -0.052270 | +| **三种子均值** | **45.2148%** | **45.7357%** | **+0.5208pp** | **40.4297%** | **40.8854%** | **+0.4557pp** | **82.2266%** | **83.9193%** | **+1.6927pp** | **-0.052372** | + +直接结论： + +- Ground 为 **3/3 种子正增益**； +- Joint 为 2/3 种子正增益，三种子均值仍为正； +- Ground Top-5 为 **3/3 种子正增益**，而且增益比 Top-1 更稳定； +- 3072 个种子×决策配对中，Ground rescue/harm 为 `81/65`，净救回 16；Joint 为 `59/45`，净救回 14。 + +### 6.2 场景聚类 bootstrap + +以场景为统计 cluster、固定三个冻结种子为模型副本，10,000 次 bootstrap： + +| 指标 | 三种子均值增益 | 场景 cluster 95% CI | +|---|---:|---:| +| Ground Top-1 | +0.5208pp | [-0.7161pp, +1.6927pp] | +| Joint | +0.4557pp | [-0.4557pp, +1.3346pp] | +| Ground Top-5 | **+1.6927pp** | **[+0.7161pp, +3.0599pp]** | + +因此应区分两种强度的证据： + +1. Top-1 Ground/Joint 的点估计和种子方向支持“小幅改善”，但 16 场景 cluster CI 仍跨 0，不能写成统计上已经排除零效应； +2. Top-5 的三种子方向一致，且合并场景 CI 下界大于 0，是本轮扩大面板中更稳健的证据，说明点云主要改善了候选排序/召回，但把正确候选推到 Top-1 的能力仍有限。 + +### 6.3 逐场景异质性 + +| 场景 | ΔGround | ΔJoint | ΔTop-5 | +|---|---:|---:|---:| +| y9hTuugGdiq | +5.21pp | +3.12pp | +10.42pp | +| 7MXmsvcQjpJ | +4.17pp | +1.56pp | +2.60pp | +| zt1RVoi7PcG | +2.60pp | +3.12pp | 0.00pp | +| q3zU7Yy5E5s | +2.08pp | +1.04pp | +1.04pp | +| ziup5kvtCCR | +2.08pp | +2.08pp | +0.52pp | +| BAbdmeyTvMZ | +1.56pp | 0.00pp | +1.56pp | +| LT9Jq6dN3Ea | +1.56pp | +1.04pp | 0.00pp | +| XB4GS9ShBRE | +0.52pp | +2.08pp | +2.08pp | +| a8BtkwhxdRV | +0.52pp | +1.56pp | +1.56pp | +| svBbv1Pavdk | +0.52pp | 0.00pp | +1.56pp | +| bxsVRursffK | 0.00pp | -1.04pp | +2.08pp | +| Dd4bFSTQ8gi | -1.56pp | 0.00pp | +2.08pp | +| VBzV5z6i1WS | -1.56pp | 0.00pp | +2.60pp | +| GLAQ4DNUx5U | -2.08pp | -3.12pp | -0.52pp | +| Nfvxx8J5NCo | -3.65pp | -1.56pp | +0.52pp | +| mv2HUxq3B53 | -3.65pp | -2.60pp | -1.04pp | + +10/16 场景的 Ground 点估计非负，6/16 为负。效应明显依赖场景，这解释了扩大后 Top-1 从原来的 +4.58pp 缩小到 +0.52pp，也说明后续不能继续只靠全局语义单残差：需要候选局部几何、重建质量和保守置信门控来避免在不适合的场景中产生 harm。 + +### 6.4 no-point 与 loss 归因 + +seed42 的 `no_point_signal` 在 1024 条决策上： + +- Ground、Joint、Top-5、Decision 均与冻结 MTU3D 基线完全相同； +- 每条样本的 ground/decision argmax 预测也完全相同； +- 证明 full-point 条件中的离散预测变化确实来自点云支路，而不是重新训练 MTU3D。 + +但 no-point 的 mean loss 从 0.684242 降到 0.633684，原因是加载 adapter 时也加载了训练集独立拟合、保持 argmax 的温度/偏置校准。loss 必须拆分： + +| 分量 | loss reduction | +|---|---:| +| 基线 → no-point（校准/非点云项） | 0.050558 | +| no-point → full-point seed42（点云特异增量） | **0.001694** | +| 基线 → full-point seed42（总变化） | 0.052252 | + +所以不能把总 `-0.05237` 全部写成点云贡献；点云特异的连续 loss 证据以 seed42 的 `0.001694` 为准。离散 Ground/Top-5 改变不受这一 argmax-preserving 校准混淆。 + +### 6.5 无指标的 formal r1 基础设施失败 + +formal r1 在生成任何性能指标前统一退出。原因是评估专用 manifest 已正确省略训练场景，但启动参数错误保留了训练期 `selection_decisions_per_scene=32`，使旧脚本尝试对空的训练 selection panel 聚合，报 `Cannot finalize empty validation metrics`。该错误发生在验证 forward 和结果落盘前，不能解释为模型或数据表现。 + +r2 只把 `steps=0` 时不应启用的 selection/calibration 计数改为 0；模型、三份权重、16 个场景、每场景 64 条决策、源码、forward、指标和门槛全部不变。修订已在正式 r2 前写入 `preregistration_amendment_r1.json`，r1 日志保留且不纳入性能统计。 + +## 7. 结论边界 + +本轮扩大复测为旧 `global_semantic_v2_b64_noappend_confirmatory4` 增加了比原确认集大 3.2 倍、场景完全独立的离线证据。结果支持以下结论： + +1. 冻结 ZPlusM 点云经冻结 PTv3 接入冻结 MTU3D 后，在新场景上的 Ground Top-1 和 Joint 有小幅正点估计；Ground 为 3/3 种子正增益； +2. 更稳健的证据是 Ground Top-5：三种子均正、合并场景 cluster CI 下界大于 0，说明点云能够改善理解阶段的候选召回与排序； +3. Top-1 增益存在明显场景异质性，合并 CI 仍跨 0；旧全局语义 adapter 能证明信号有效，但不是最终在线融合方案； +4. no-point 的逐预测精确回退排除了“只是又训练了 MTU3D 参数”的解释；MTU3D 和 PTv3 在四组结果中均位级不变； +5. 本实验只支持离线理解结论，不能把 Ground/Joint 写成 ObjectNav 的 SR/SPL，也不能把 v179 在线权重误写成旧 confirmatory4 权重。 + +对下一版 v182 的直接启示是：保留已证明有效的 PTv3 语义来源，但应把候选局部 token 更早接入 MTU3D layer3→layer4，并用独立重建质量/候选置信门抑制 6 个负增益场景，而不是继续放大全局 residual 或只调最终分数。 + +## 8. 结果文件索引 + +- `formal_r2/analysis.json`：预注册聚合、三种子/场景 bootstrap、rescue-harm、no-point loss 拆分； +- `formal_r2/seed{42,123,777}/result.json`：三种子完整 1024 条逐样本结果； +- `formal_r2/no_point_seed42/result.json`：no-point 逐样本对照； +- `payload_sha_audit.json`：旧机/H100 146 个 payload 文件 SHA256 逐项一致； +- `transfer_audit.json`：不落本机的跨机传输记录； +- `preregistration.json` 与 `preregistration_amendment_r1.json`：原预注册及 r1 无指标基础设施修订。 diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/payload_sha_audit.json 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"status": "preregistered_before_evaluation", + "objective": "冻结旧离线最佳 global_semantic_v2_b64_noappend_confirmatory4，在更大的独立未见 HM3D 场景集上复测点云对离线理解指标的增益", + "claim_scope": "offline_heldout_ovon_grounding_and_decision_only", + "closed_loop_navigation_claim_allowed": false, + "sr_spl_claim_allowed": false, + "data": { + "source_split": "official HM3D OVON val_seen features/annotations plus fixed-reference reconstructed geometry packages", + "scene_count": 16, + "decisions_per_scene": 64, + "decision_offset": 0, + "expected_decisions_per_seed": 1024, + "scene_ids": [ + "7MXmsvcQjpJ", + "a8BtkwhxdRV", + "BAbdmeyTvMZ", + "bxsVRursffK", + "Dd4bFSTQ8gi", + "GLAQ4DNUx5U", + "LT9Jq6dN3Ea", + "mv2HUxq3B53", + "Nfvxx8J5NCo", + "q3zU7Yy5E5s", + "svBbv1Pavdk", + "VBzV5z6i1WS", + "XB4GS9ShBRE", + "y9hTuugGdiq", + "ziup5kvtCCR", + "zt1RVoi7PcG" + ], + "excluded_original_confirmatory_scenes": [ + "5cdEh9F2hJL", + "6s7QHgap2fW", + "cvZr5TUy5C5", + "p53SfW6mjZe", + "wcojb4TFT35" + ], + 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"d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99" + }, + "architecture": { + "point_encoder": "frozen Sonata/PTv3", + "adapter": "bottleneck64 explicit global scene semantic grounding-only residual", + "point_memory_append": false, + "decision_logits": "frozen MTU3D, unchanged by point adapter", + "evaluation_steps": 0, + "adapter_training_allowed": false + }, + "conditions": { + "seed42": "full point cloud", + "seed123": "full point cloud", + "seed777": "full point cloud", + "no_point_seed42": "no_point_signal exact-baseline control" + }, + "primary_metrics": [ + "ground_accuracy", + "joint_accuracy", + "ground_top5_accuracy", + "mean_loss" + ], + "analysis": { + "paired_unit": "same scene/episode/sub_episode/time_step", + "uncertainty": "scene-cluster bootstrap 95% CI", + "bootstrap_seed": 20260823, + "bootstrap_resamples": 10000 + }, + "acceptance": { + "integrity_required": true, + "mean_ground_gain_strictly_positive": true, + "positive_ground_gain_seeds_at_least": 2, + "mean_joint_gain_strictly_positive": true, + "no_point_ground_and_joint_exactly_equal_baseline": true, + "confidence_intervals_are_reported_not_used_as_a_hard_gate": true + }, + "interpretation_note": "该扩大集与原确认集在场景上独立，并采用另一批经 fixed-reference/alignment 审计的重建包；若结果不一致，必须先区分场景泛化、重建域差异和实现链路，不允许按结果修改门槛。" +} diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/preregistration_amendment_r1.json b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/preregistration_amendment_r1.json new file mode 100644 index 0000000000000000000000000000000000000000..0380a6976243d737bf6e22bf634b5fcdeb7ebf66 --- /dev/null +++ b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/preregistration_amendment_r1.json @@ -0,0 +1,34 @@ +{ + "schema": "nav3r-offline-confirmatory4-expanded16-preregistration-amendment/v1", + "date": "2026-08-23", + "reason": "formal_r1 implementation-control failure before any formal metric was produced", + "formal_r1_status": "invalid_no_metrics", + "observed_exception": "RuntimeError: Cannot finalize empty validation metrics", + "root_cause": { + "evaluation_manifest_selected_train_scene_ids": 0, + "steps": 0, + "selection_decisions_per_scene": 32, + "calibration_decisions_per_scene": 32, + "control_flow": "the nonzero selection count selected an empty training-scene selection panel instead of the 16-scene validation panel" + }, + "amendment": { + "selection_decisions_per_scene": 0, + "calibration_decisions_per_scene": 0, + "train_decisions_per_scene": 1, + "explanation": "steps=0 loads the adapter's already-fitted calibration and performs no training or checkpoint selection; these parameters are therefore inactive evaluation infrastructure and must not select a nonexistent training panel" + }, + "unchanged": [ + "16 validation scenes", + "64 decisions per validation scene", + "decision offset 0", + "all three adapter files and SHA256 values", + "frozen MTU3D checkpoint", + "frozen Sonata/PTv3 checkpoint", + "four authoritative source files and SHA256 values", + "point cloud preprocessing and model forward", + "metrics, bootstrap policy and acceptance rules", + "GPU assignment" + ], + "evidence_policy": "formal_r1 is retained as an implementation failure and excluded from all performance summaries; formal_r2 is the first valid formal evaluation", + "not_result_contingent": true +} diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16.sh b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16.sh new file mode 100644 index 0000000000000000000000000000000000000000..5c187a0973d576a0bbc5be501cc1954f40c7e1b4 --- /dev/null +++ b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16.sh @@ -0,0 +1,137 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT=/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814 +STAGE="$ROOT/runtime/staging/offline_confirmatory4_expand_20260823" +OUT="$ROOT/runtime/ptv3/offline_confirmatory4_expanded16_20260823" +PY="$ROOT/runtime/physbench_envs/envs/physbench-objectnav-mtu3d/bin/python" +TRAIN="$STAGE/scripts/train_ptv3_mtu3d_real_ovon_l40s.py" +MTU3D="$STAGE/third_party/MTU3D" +CHECKPOINT="$ROOT/runtime/mtu3d/checkpoints/stage2-pretrain-all/pytorch_model.bin" +SONATA="$ROOT/runtime/ptv3/checkpoints/sonata.pth" +AUDIT_MANIFEST="$ROOT/runtime/ptv3/alignment_manifest_17train_16unseen_offline_expand_20260823.json" +MANIFEST="$OUT/evaluation_manifest.json" +FEATURE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_stage2_feat/HM3D" +VLE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_vle_data/ovon" +PROCESSED="$ROOT/runtime/ptv3/data/processed_hm3d_official_train" +TRAIN_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_multiwindow_131072_canonicalref_17train/scans_train" +VAL_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_geometry_path_16unseen/scans_train" +TOKENIZER="$ROOT/runtime/ptv3/tokenizers/clip-vit-large-patch14" +ADAPTER_ROOT="$ROOT/runtime/publication_staging/20260822/global_semantic_v2_b64_noappend_confirmatory4" + +mkdir -p "$OUT" + +check_sha() { + local expected="$1" + local path="$2" + local actual + actual=$(sha256sum "$path" | awk '{print $1}') + if [[ "$actual" != "$expected" ]]; then + echo "SHA mismatch: $path expected=$expected actual=$actual" >&2 + exit 2 + fi +} + +check_sha 7d7df8aaf84c8f8599bea01911fd759745906fb8d0c7f594af782cff1b91b9ff "$TRAIN" +check_sha 85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9 "$STAGE/nav3r/integration/mtu3d_ptv3_bridge.py" +check_sha a06f756d697dc7c73d5069357556a2d0995de6b387d2103e900a87d825fc340e "$STAGE/nav3r/integration/ptv3_token_encoder.py" +check_sha d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99 "$MTU3D/model/query3d_vle.py" +check_sha 0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791 "$CHECKPOINT" +check_sha c5ced5acdae30d1c469713398073a866e25e6e414e23feed5dc025373657ac50 "$SONATA" +check_sha 5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b "$ADAPTER_ROOT/seed42/best_adapter.pt" +check_sha 1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e "$ADAPTER_ROOT/seed123/best_adapter.pt" +check_sha 5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3 "$ADAPTER_ROOT/seed777/best_adapter.pt" + +"$PY" - "$AUDIT_MANIFEST" "$MANIFEST" "$VAL_RECON" <<'PY' +import json +from pathlib import Path +import sys +audit_manifest = json.loads(Path(sys.argv[1]).read_text()) +validation = audit_manifest["validation_scene_ids"] +training = audit_manifest["selected_train_scene_ids"] +if len(validation) != 16 or set(validation) & set(training): + raise SystemExit("invalid expanded validation manifest") +root = Path(sys.argv[3]) +missing = [scene for scene in validation if not (root / scene / "zplusm_scene_input.npz").is_file()] +if missing: + raise SystemExit(f"missing reconstruction packages: {missing}") +# steps=0 loads the frozen adapter's already-fitted calibration. Training +# scenes cannot influence evaluation, so omit their costly feature/PTv3 cache +# construction while retaining their identities as immutable provenance. +evaluation_manifest = dict(audit_manifest) +evaluation_manifest["source_adapter_train_scene_ids"] = list(training) +evaluation_manifest["selected_train_scene_ids"] = [] +evaluation_manifest["evaluation_only_skip_unused_train_cache"] = True +Path(sys.argv[2]).write_text(json.dumps(evaluation_manifest, indent=2) + "\n") +print(json.dumps({ + "preflight": "passed", + "validation_scene_count": len(validation), + "source_adapter_train_scene_count": len(training), + "evaluation_train_scene_count": 0, +})) +PY + +common=( + --project-root "$STAGE" + --mtu3d-root "$MTU3D" + --checkpoint "$CHECKPOINT" + --sonata-checkpoint "$SONATA" + --alignment-manifest "$MANIFEST" + --feature-root "$FEATURE" + --vle-root "$VLE" + --processed-root "$PROCESSED" + --reconstruction-root "$TRAIN_RECON" + --validation-reconstruction-root "$VAL_RECON" + --tokenizer "$TOKENIZER" + --train-decisions-per-scene 128 + --selection-decisions-per-scene 32 + --calibration-decisions-per-scene 32 + --checkpoint-selection-objective loss_then_accuracy + --validation-decisions 64 + --validation-decision-offset 0 + --steps 0 + --eval-every 1 + --adapter-lr 0.0001 + --gate-lr 0.001 + --initial-fusion-scale 0.35 + --point-token-count 256 + --enable-global-semantic-residual + --global-semantic-ground-only + --global-semantic-bottleneck-size 64 + --disable-point-memory-append + --raw-fused-aux-loss-weight 1.0 + --ground-listwise-loss-weight 0.25 + --ground-bce-loss-weight 1.0 + --decision-loss-weight 1.0 +) + +launch() { + local gpu="$1" + local name="$2" + local seed="$3" + local adapter="$4" + local ablation="$5" + local run_out="$OUT/$name" + if [[ -e "$run_out/result.json" || -e "$OUT/$name.pid" ]]; then + echo "Refusing to overwrite existing run: $name" >&2 + exit 3 + fi + mkdir -p "$run_out" + nohup env CUDA_VISIBLE_DEVICES="$gpu" PYTHONPATH="$STAGE:$ROOT/runtime/ptv3/sonata" \ + "$PY" "$TRAIN" "${common[@]}" \ + --seed "$seed" \ + --load-adapter "$adapter" \ + --point-ablation "$ablation" \ + --output-dir "$run_out" \ + >"$OUT/$name.log" 2>&1 & + local pid=$! + echo "$pid" >"$OUT/$name.pid" + echo "$name gpu=$gpu pid=$pid" +} + +launch 4 seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" full +launch 5 seed123 123 "$ADAPTER_ROOT/seed123/best_adapter.pt" full +launch 6 seed777 777 "$ADAPTER_ROOT/seed777/best_adapter.pt" full +launch 7 no_point_seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" no_point_signal + +date --iso-8601=seconds >"$OUT/launched_at.txt" diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16_r2.sh b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16_r2.sh new file mode 100644 index 0000000000000000000000000000000000000000..b8c41f6cc63bfccf343ec3265a620c830cda8e26 --- /dev/null +++ b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16_r2.sh @@ -0,0 +1,140 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT=/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814 +STAGE="$ROOT/runtime/staging/offline_confirmatory4_expand_20260823" +OUT="$ROOT/runtime/ptv3/offline_confirmatory4_expanded16_20260823" +RUN_ROOT="$OUT/formal_r2" +PY="$ROOT/runtime/physbench_envs/envs/physbench-objectnav-mtu3d/bin/python" +TRAIN="$STAGE/scripts/train_ptv3_mtu3d_real_ovon_l40s.py" +MTU3D="$STAGE/third_party/MTU3D" +CHECKPOINT="$ROOT/runtime/mtu3d/checkpoints/stage2-pretrain-all/pytorch_model.bin" +SONATA="$ROOT/runtime/ptv3/checkpoints/sonata.pth" +AUDIT_MANIFEST="$ROOT/runtime/ptv3/alignment_manifest_17train_16unseen_offline_expand_20260823.json" +MANIFEST="$RUN_ROOT/evaluation_manifest.json" +FEATURE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_stage2_feat/HM3D" +VLE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_vle_data/ovon" +PROCESSED="$ROOT/runtime/ptv3/data/processed_hm3d_official_train" +TRAIN_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_multiwindow_131072_canonicalref_17train/scans_train" +VAL_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_geometry_path_16unseen/scans_train" +TOKENIZER="$ROOT/runtime/ptv3/tokenizers/clip-vit-large-patch14" +ADAPTER_ROOT="$ROOT/runtime/publication_staging/20260822/global_semantic_v2_b64_noappend_confirmatory4" + +mkdir -p "$OUT" +mkdir -p "$RUN_ROOT" +cp "$OUT/preregistration.json" "$RUN_ROOT/preregistration.json" + +check_sha() { + local expected="$1" + local path="$2" + local actual + actual=$(sha256sum "$path" | awk '{print $1}') + if [[ "$actual" != "$expected" ]]; then + echo "SHA mismatch: $path expected=$expected actual=$actual" >&2 + exit 2 + fi +} + +check_sha 7d7df8aaf84c8f8599bea01911fd759745906fb8d0c7f594af782cff1b91b9ff "$TRAIN" +check_sha 85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9 "$STAGE/nav3r/integration/mtu3d_ptv3_bridge.py" +check_sha a06f756d697dc7c73d5069357556a2d0995de6b387d2103e900a87d825fc340e "$STAGE/nav3r/integration/ptv3_token_encoder.py" +check_sha d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99 "$MTU3D/model/query3d_vle.py" +check_sha 0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791 "$CHECKPOINT" +check_sha c5ced5acdae30d1c469713398073a866e25e6e414e23feed5dc025373657ac50 "$SONATA" +check_sha 5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b "$ADAPTER_ROOT/seed42/best_adapter.pt" +check_sha 1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e "$ADAPTER_ROOT/seed123/best_adapter.pt" +check_sha 5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3 "$ADAPTER_ROOT/seed777/best_adapter.pt" + +"$PY" - "$AUDIT_MANIFEST" "$MANIFEST" "$VAL_RECON" <<'PY' +import json +from pathlib import Path +import sys +audit_manifest = json.loads(Path(sys.argv[1]).read_text()) +validation = audit_manifest["validation_scene_ids"] +training = audit_manifest["selected_train_scene_ids"] +if len(validation) != 16 or set(validation) & set(training): + raise SystemExit("invalid expanded validation manifest") +root = Path(sys.argv[3]) +missing = [scene for scene in validation if not (root / scene / "zplusm_scene_input.npz").is_file()] +if missing: + raise SystemExit(f"missing reconstruction packages: {missing}") +# steps=0 loads the frozen adapter's already-fitted calibration. Training +# scenes cannot influence evaluation, so omit their costly feature/PTv3 cache +# construction while retaining their identities as immutable provenance. +evaluation_manifest = dict(audit_manifest) +evaluation_manifest["source_adapter_train_scene_ids"] = list(training) +evaluation_manifest["selected_train_scene_ids"] = [] +evaluation_manifest["evaluation_only_skip_unused_train_cache"] = True +Path(sys.argv[2]).write_text(json.dumps(evaluation_manifest, indent=2) + "\n") +print(json.dumps({ + "preflight": "passed", + "validation_scene_count": len(validation), + "source_adapter_train_scene_count": len(training), + "evaluation_train_scene_count": 0, +})) +PY + +common=( + --project-root "$STAGE" + --mtu3d-root "$MTU3D" + --checkpoint "$CHECKPOINT" + --sonata-checkpoint "$SONATA" + --alignment-manifest "$MANIFEST" + --feature-root "$FEATURE" + --vle-root "$VLE" + --processed-root "$PROCESSED" + --reconstruction-root "$TRAIN_RECON" + --validation-reconstruction-root "$VAL_RECON" + --tokenizer "$TOKENIZER" + --train-decisions-per-scene 1 + --selection-decisions-per-scene 0 + --calibration-decisions-per-scene 0 + --checkpoint-selection-objective loss_then_accuracy + --validation-decisions 64 + --validation-decision-offset 0 + --steps 0 + --eval-every 1 + --adapter-lr 0.0001 + --gate-lr 0.001 + --initial-fusion-scale 0.35 + --point-token-count 256 + --enable-global-semantic-residual + --global-semantic-ground-only + --global-semantic-bottleneck-size 64 + --disable-point-memory-append + --raw-fused-aux-loss-weight 1.0 + --ground-listwise-loss-weight 0.25 + --ground-bce-loss-weight 1.0 + --decision-loss-weight 1.0 +) + +launch() { + local gpu="$1" + local name="$2" + local seed="$3" + local adapter="$4" + local ablation="$5" + local run_out="$RUN_ROOT/$name" + if [[ -e "$run_out/result.json" || -e "$RUN_ROOT/$name.pid" ]]; then + echo "Refusing to overwrite existing run: $name" >&2 + exit 3 + fi + mkdir -p "$run_out" + nohup env CUDA_VISIBLE_DEVICES="$gpu" PYTHONPATH="$STAGE:$ROOT/runtime/ptv3/sonata" \ + "$PY" "$TRAIN" "${common[@]}" \ + --seed "$seed" \ + --load-adapter "$adapter" \ + --point-ablation "$ablation" \ + --output-dir "$run_out" \ + >"$RUN_ROOT/$name.log" 2>&1 & + local pid=$! + echo "$pid" >"$RUN_ROOT/$name.pid" + echo "$name gpu=$gpu pid=$pid" +} + +launch 4 seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" full +launch 5 seed123 123 "$ADAPTER_ROOT/seed123/best_adapter.pt" full +launch 6 seed777 777 "$ADAPTER_ROOT/seed777/best_adapter.pt" full +launch 7 no_point_seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" no_point_signal + +date --iso-8601=seconds >"$RUN_ROOT/launched_at.txt" diff --git a/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/transfer_audit.json b/experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/transfer_audit.json new file mode 100644 index 0000000000000000000000000000000000000000..e5d7e3e8038a2c550a1658ff276f3b50b62b34a5 --- 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