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"/app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035/labels/train/herenthout_bos_0007_r512_c256.txt", + "low_visual_variance": false, + "recommended_split": "train", + "repeat_index": 0, + "sample_role": "background_candidate", + "sample_slug": "herenthout_bos", + "split": "train", + "tile_index": 7, + "window": { + "col_off": 256, + "height": 512, + "row_off": 512, + "width": 512 + } + }, + { + "background_category": "sparse_building_context", + "image_path": "/app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035/images/train/herenthout_bos_0008_r512_c512.png", + "is_negative": false, + "is_repeated_background_negative": false, + "kept": true, + "label_count": 18, + "label_path": "/app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035/labels/train/herenthout_bos_0008_r512_c512.txt", + "low_visual_variance": false, + "recommended_split": "train", + "repeat_index": 0, + "sample_role": "background_candidate", + "sample_slug": "herenthout_bos", + "split": "train", + "tile_index": 8, + "window": { + "col_off": 512, + "height": 512, + "row_off": 512, + "width": 512 + } + } + ], + "train_tile_count": 180, + "val_tile_count": 18, + "validation_sample_slugs": [ + "arendonk_heide", + "grobbendonk_center", + "vosselaar_center" + ] +} \ No newline at end of file diff --git a/artifacts/evidence/accuracy/model-training/20260810-v74-root-cause/rehearsal-v74.experimental.json b/artifacts/evidence/accuracy/model-training/20260810-v74-root-cause/rehearsal-v74.experimental.json new file mode 100644 index 00000000..f02e4a27 --- /dev/null +++ b/artifacts/evidence/accuracy/model-training/20260810-v74-root-cause/rehearsal-v74.experimental.json @@ -0,0 +1,17 @@ +{ + "dataset_yaml": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/rehearsal-v74/dataset.yaml", + "dataset_yaml_sha256": "2fa0c66ddb7105629b7873e45030e6977da941130cb032c63e6449033ed42409", + "human_review_pending": true, + "promotion_allowed": false, + "protected_v72_used": false, + "rehearsal_summary": "/app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035/yolo_tile_dataset_summary.json", + "rehearsal_summary_sha256": "49b2a07d2105d08356431757b83eafc1498eaf1fb76965b1efe05b776824942a", + "rehearsal_train_tile_count": 180, + "release_claim_allowed": false, + "remediation_repeat": 2, + "remediation_summary": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/yolo512/experimental_dataset_summary.json", + "remediation_summary_sha256": "047874a4f9362c82263ffe11c389a2f65168c449d3f6cbcc77cea709c0ba4c59", + "remediation_unique_train_tile_count": 48, + "schema_version": 1, + "status": "experimental_rehearsal_only" +} diff --git a/docs/API_CONTRACTS.md b/docs/API_CONTRACTS.md index b77c3976..4284d696 100644 --- a/docs/API_CONTRACTS.md +++ b/docs/API_CONTRACTS.md @@ -1712,6 +1712,12 @@ Returns persisted detections for a dataset as a GeoJSON FeatureCollection. Optio Compares persisted detection geometries from an analysis run against persisted `vector_features` from a reference vector dataset. +The map-driven building workflow immediately loads the persisted evidence from +the resulting quality check. For current Flemish building validation it labels +strict matches as GRB-confirmed, unmatched detections as unconfirmed AI-only +proposals and unmatched reference footprints as official GRB buildings missed +by AI. AI-only proposals are never promoted to authoritative building results. + Request: ```json diff --git a/docs/CODEX_EXECUTION_LOG.md b/docs/CODEX_EXECUTION_LOG.md index cd643ca7..2190d024 100644 --- a/docs/CODEX_EXECUTION_LOG.md +++ b/docs/CODEX_EXECUTION_LOG.md @@ -1,3 +1,23 @@ +## 2026-08-10 - V74 root-cause remediation en authority-first uitvoer + +### Opgeleverd + +- De volledige V72 failure gallery gerenderd en AI-assisted geïnspecteerd; + terugkerende foutpositieven omvatten spoorinfrastructuur, voertuigen, bomen + en schaduwen. +- Een hard-negative proposal-filter en twee rehearsal-kandidaten getraind op + de Tower RTX 4080 SUPER en alle drie afgewezen op de onafhankelijke V72-set. +- De experimentele trainer bindt nu expliciet AdamW, zodat Ultralytics niet + stilzwijgend de gevraagde learning rate vervangt via `optimizer=auto`. +- De kaartwerkstroom laadt het GRB-QA-bewijs automatisch en onderscheidt + GRB-bevestigde, AI-only en door AI gemiste gebouwobjecten. + +### Grens + +- Het productiecheckpoint bleef ongewijzigd. Er is geen landelijke of + 100%-accuratesseclaim gedaan en de visuele review blijft AI-assisted. +- Evidence: `artifacts/evidence/accuracy/model-training/20260810-v74-root-cause/`. + ## 2026-07-29 - V31 closed-loop iteration 3 assessment and semantic correction - Iteration 3 stopped deterministically after epoch 21 with epoch 3 retained as diff --git a/docs/TODO.md b/docs/TODO.md index b6860198..9f7917d3 100644 --- a/docs/TODO.md +++ b/docs/TODO.md @@ -1,5 +1,15 @@ # GeoIntel TODO +## V74 authority-first gebouwresultaten (2026-08-10) + +- [x] Voer een V72-foutanalyse, hard-negative proposal-filter en rehearsal + training uit; wijs alle regresserende kandidaten af en behoud het actieve + checkpoint. +- [x] Laad na gebouwdetectie automatisch het bewaarde GRB-QA-bewijs en splits + de kaartuitvoer in GRB-bevestigd, AI-only en door AI gemist. +- [ ] Laat een onafhankelijke menselijke reviewer de bevroren contact sheets + en labels ondertekenen voordat een kandidaat ooit promoveerbaar wordt. + ## PyTorch-modelprogramma Uitvoeringsbord: `docs/PYTORCH_TRAINING_ROADMAP_BELGIUM.md`. diff --git a/docs/accuracy-program/14-v74-root-cause-and-authority-first-results.md b/docs/accuracy-program/14-v74-root-cause-and-authority-first-results.md new file mode 100644 index 00000000..c3115546 --- /dev/null +++ b/docs/accuracy-program/14-v74-root-cause-and-authority-first-results.md @@ -0,0 +1,71 @@ +# V74 foutoorzaak en authority-first resultaten + +## Uitkomst + +V74 heeft geen nieuw productiemodel opgeleverd. De actieve checkpoint blijft +ongewijzigd omdat zowel de experimentele proposal-filter als twee +rehearsal-kandidaten op de bevroren V72-calibratie slechter presteren. GeoIntel +presenteert de automatische gebouwanalyse daarom niet langer als één uniforme +resultaatlaag: de GRB-vergelijking maakt voortaan zichtbaar welke AI-kandidaten +officieel bevestigd zijn, welke alleen modelvoorstellen zijn en welke +GRB-gebouwen door AI gemist zijn. + +## Visuele foutanalyse + +De volledige V72-portfolio van 24 onafhankelijke tegels is met het actieve model +op confidence 0,30 gerenderd. De contact sheet toont terugkerende foutpositieven +op spoorlijnen, voertuigen, bomen, schaduwen en andere contrastrijke structuren. +Daarnaast ontbreken veel gebouwen. Dit ondersteunt de conclusie dat extra +epochs op alleen de nieuwe Vlaamse shard de hoofdoorzaak niet oplossen. + +De review is AI-assisted en niet menselijk. Het bewijs mag dus geen menselijke +acceptatie of promotiegate vervangen. + +## Afgewezen proposal-filter + +Een experimentele ResNet18-filter werd op afzonderlijke V73-AOI's getraind om +building proposals van moeilijke negatieve patches te onderscheiden. De interne +V73-validatie behaalde F1 0,8068, maar die score generaliseerde niet naar V72. +De beste globale V72-combinatie behaalde precision 0,2161, recall 0,2241 en F1 +0,2200. Het actieve model zonder filter behaalt op confidence 0,30 F1 0,2504. +De filter kan precision verhogen bij een vaste detectordrempel, maar halveert +dan de recall. Hij blijft daarom experimenteel en wordt niet geladen in +productie. + +## Afgewezen rehearsal-training + +De trainingsset combineert 180 historische trainingstegels met de 48 nieuwe +V73-tegels, waarbij V73 tweemaal voorkomt. V72 is alleen evaluatie en komt niet +in de fitdata voor. + +| Model | Precision | Recall | mAP50 | mAP50-95 | Besluit | +|---|---:|---:|---:|---:|---| +| Actief | 0,1577 | 0,1178 | 0,0568 | 0,0218 | behouden | +| Rehearsal, optimizer auto, 10 epochs | 0,0710 | 0,0776 | 0,0262 | 0,0098 | afgewezen | +| Rehearsal, AdamW 0,0001, 6 epochs | 0,0839 | 0,1034 | 0,0349 | 0,0136 | afgewezen | + +De eerste run bracht bovendien een reproduceerbaarheidsfout aan het licht: +Ultralytics `optimizer=auto` negeerde de gevraagde learning rate en koos AdamW +0,002. De wrapper bindt daarom nu expliciet `optimizer=AdamW` en legt dit vast +in het trainingsmanifest. De gecorrigeerde low-learning-rate-run bleef eveneens +onder de actieve baseline. + +## Productcorrectheid + +Voor actuele gebouwvalidatie in Vlaanderen is GRB de gezagsbron binnen de +beschikbare editie en dekking. De kaartwerkstroom laadt na detectie automatisch +het bewaarde QA-bewijs en toont afzonderlijk: + +- GRB-bevestigde AI-kandidaten; +- AI-only, onbevestigde onderzoeksvoorstellen; +- officiële GRB-gebouwen die het model miste. + +AI-only voorstellen worden niet als officiële gebouwen voorgesteld. Dit maakt +de productuitvoer aantoonbaar correcter zonder de zwakke modelscore te verbergen +of een niet-bewezen nauwkeurigheidsclaim te maken. + +## Evidence + +Alle ruwe contact sheets, foutmanifesten, filtermatrices, trainingssamenvattingen +en modelhashes staan onder +`artifacts/evidence/accuracy/model-training/20260810-v74-root-cause/`. diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index 227afa61..4047c342 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -580,6 +580,7 @@ function WorkbenchApp({ username, accessMode, loggingOut, onLogout }: WorkbenchA prepareAndRunDetection: (datasetId) => prepareAndRunDetection(datasetId, 'yolo-configured'), compareDetectionRunWithReference: (analysisRunId, referenceDatasetId, iouThreshold) => compareDetectionRunWithReference(analysisRunId, referenceDatasetId, false, iouThreshold), + loadQualityEvidence: (qualityCheckId) => loadQualityEvidenceGeoJson(qualityCheckId), onAnalysisReady: () => { setMapContentMode('analysis') setMapLayerVisible(true) diff --git a/frontend/src/components/map/MapWorkspace.tsx b/frontend/src/components/map/MapWorkspace.tsx index e695fa29..975875ba 100644 --- a/frontend/src/components/map/MapWorkspace.tsx +++ b/frontend/src/components/map/MapWorkspace.tsx @@ -2759,13 +2759,17 @@ export function MapWorkspace({ {orthophotoAnalysisQuality ? ( <>
-
Kandidaten{orthophotoAnalysisDetectionCount?.toLocaleString('nl-BE') ?? 'n.v.t.'}
-
Strikte matches{orthophotoAnalysisQuality.matches.toLocaleString('nl-BE')}
-
Precision{formatPercentage(orthophotoAnalysisQuality.precision)}
-
Herkenningsgraad{formatPercentage(orthophotoAnalysisQuality.recall)}
+
AI-kandidaten{orthophotoAnalysisDetectionCount?.toLocaleString('nl-BE') ?? 'n.v.t.'}
+
GRB-bevestigd{orthophotoAnalysisQuality.matches.toLocaleString('nl-BE')}
+
AI-only, onbevestigd{orthophotoAnalysisQuality.false_positives.toLocaleString('nl-BE')}
+
GRB, gemist door AI{orthophotoAnalysisQuality.false_negatives.toLocaleString('nl-BE')}
+
AI-precision{formatPercentage(orthophotoAnalysisQuality.precision)}
+
AI-herkenningsgraad{formatPercentage(orthophotoAnalysisQuality.recall)}
F1{formatPercentage(orthophotoAnalysisQuality.f1_score)}
-
Fout / gemist{orthophotoAnalysisQuality.false_positives.toLocaleString('nl-BE')} / {orthophotoAnalysisQuality.false_negatives.toLocaleString('nl-BE')}
+

+ GRB is hier de gezagsbron voor geregistreerde gebouwen. AI-only vormen zijn onderzoeksvoorstellen en worden niet als officieel resultaat voorgesteld. De bewijslaag op de kaart toont elke categorie afzonderlijk. +

{orthophotoAnalysisQuality.box_to_footprint_diagnostics ? (

Rechthoekcontrole: {orthophotoAnalysisQuality.box_to_footprint_diagnostics.envelope_matches.toLocaleString('nl-BE')} matches, diff --git a/frontend/src/hooks/useMapOrthophotoAnalysis.ts b/frontend/src/hooks/useMapOrthophotoAnalysis.ts index 3b4a6d83..65198c21 100644 --- a/frontend/src/hooks/useMapOrthophotoAnalysis.ts +++ b/frontend/src/hooks/useMapOrthophotoAnalysis.ts @@ -32,6 +32,7 @@ interface MapOrthophotoAnalysisOptions { referenceDatasetId: string, iouThreshold: number, ) => Promise + loadQualityEvidence: (qualityCheckId: string) => Promise onAnalysisReady: () => void } @@ -79,6 +80,7 @@ export function useMapOrthophotoAnalysis({ loadProjectData, prepareAndRunDetection, compareDetectionRunWithReference, + loadQualityEvidence, onAnalysisReady, }: MapOrthophotoAnalysisOptions) { const [stage, setStage] = useState('idle') @@ -180,9 +182,12 @@ export function useMapOrthophotoAnalysis({ MAP_BUILDING_QA_IOU_THRESHOLD, ) setLastQuality(quality) + if (quality) { + await loadQualityEvidence(quality.quality_check_id) + } setStatus( quality - ? `Analyse klaar: ${detection.detection_count.toLocaleString('nl-BE')} kandidaten, ${quality.matches.toLocaleString('nl-BE')} gekoppeld aan GRB. Precision ${formatQualityPercentage(quality.precision)}, recall ${formatQualityPercentage(quality.recall)}.` + ? `Controle klaar: ${quality.matches.toLocaleString('nl-BE')} door GRB bevestigde AI-kandidaten, ${quality.false_positives.toLocaleString('nl-BE')} onbevestigde AI-kandidaten en ${quality.false_negatives.toLocaleString('nl-BE')} officiële GRB-gebouwen gemist door AI. AI-precision ${formatQualityPercentage(quality.precision)}, herkenningsgraad ${formatQualityPercentage(quality.recall)}.` : `Analyse klaar: ${detection.detection_count.toLocaleString('nl-BE')} kandidaten; kwaliteitscontrole kon niet afronden.`, ) } else { diff --git a/scripts/compose_experimental_yolo_rehearsal_dataset.py b/scripts/compose_experimental_yolo_rehearsal_dataset.py new file mode 100644 index 00000000..1fbdcc98 --- /dev/null +++ b/scripts/compose_experimental_yolo_rehearsal_dataset.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +"""Compose legacy rehearsal and fresh remediation tiles for an experiment.""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +SCRIPT_DIR = Path(__file__).resolve().parent +if str(SCRIPT_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPT_DIR)) + +from export_experimental_yolo_train_shard import EXPERIMENTAL_ROOT, file_sha256 # noqa: E402 + + +def _inside(path: Path, root: Path) -> bool: + try: + path.resolve().relative_to(root.resolve()) + return True + except ValueError: + return False + + +def validate_sources(rehearsal_summary: Path, remediation_summary: Path, output_dir: Path) -> tuple[dict, dict]: + if not _inside(output_dir, EXPERIMENTAL_ROOT): + raise ValueError(f"output must remain below {EXPERIMENTAL_ROOT}") + rehearsal = json.loads(rehearsal_summary.read_text(encoding="utf-8")) + remediation = json.loads(remediation_summary.read_text(encoding="utf-8")) + marker_path = remediation_summary.parent / "EXPERIMENTAL_ONLY.json" + if not marker_path.is_file(): + raise ValueError("fresh remediation source has no EXPERIMENTAL_ONLY marker") + marker = json.loads(marker_path.read_text(encoding="utf-8")) + if marker.get("promotion_allowed") is not False: + raise ValueError("fresh remediation marker does not prohibit promotion") + if remediation.get("source_manifest_sha256") != marker.get("source_manifest_sha256"): + raise ValueError("fresh remediation marker/summary binding mismatch") + rehearsal_tiles = [tile for tile in rehearsal.get("tiles") or [] if tile.get("kept", True)] + remediation_tiles = [tile for tile in remediation.get("tiles") or [] if tile.get("kept", True)] + if not rehearsal_tiles or not remediation_tiles: + raise ValueError("both rehearsal and remediation summaries require retained tiles") + if any(tile.get("split") not in {"train", "val"} for tile in rehearsal_tiles): + raise ValueError("rehearsal source contains a protected or unsupported split") + if any(tile.get("split") != "train" for tile in remediation_tiles): + raise ValueError("fresh remediation source must be train-only") + return rehearsal, remediation + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--rehearsal-summary", type=Path, required=True) + parser.add_argument("--remediation-summary", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--remediation-repeat", type=int, default=2) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + if args.remediation_repeat < 1 or args.remediation_repeat > 4: + raise SystemExit("remediation-repeat must be between 1 and 4") + if args.output_dir.exists(): + raise SystemExit(f"refusing to overwrite rehearsal dataset: {args.output_dir}") + try: + rehearsal, remediation = validate_sources( + args.rehearsal_summary, args.remediation_summary, args.output_dir + ) + except (OSError, ValueError, json.JSONDecodeError) as exc: + raise SystemExit(str(exc)) from exc + rehearsal_yaml = Path(str(rehearsal["dataset_yaml"])) + remediation_yaml = Path(str(remediation["dataset_yaml"])) + rehearsal_root = rehearsal_yaml.parent + remediation_root = remediation_yaml.parent + for directory in ( + rehearsal_root / "images" / "train", + rehearsal_root / "images" / "val", + remediation_root / "images" / "train", + ): + if not directory.is_dir(): + raise SystemExit(f"rehearsal dataset directory is missing: {directory}") + args.output_dir.mkdir(parents=True) + train_sources = [str(rehearsal_root / "images" / "train")] + [ + str(remediation_root / "images" / "train") + ] * args.remediation_repeat + yaml_path = args.output_dir / "dataset.yaml" + yaml_path.write_text( + "\n".join( + [ + "path: /", + "train:", + *[f" - {path}" for path in train_sources], + f"val: {rehearsal_root / 'images' / 'val'}", + "names:", + " 0: building", + "", + ] + ), + encoding="utf-8", + ) + summary = { + "schema_version": 1, + "status": "experimental_rehearsal_only", + "promotion_allowed": False, + "release_claim_allowed": False, + "human_review_pending": True, + "dataset_yaml": str(yaml_path), + "dataset_yaml_sha256": file_sha256(yaml_path), + "rehearsal_summary": str(args.rehearsal_summary), + "rehearsal_summary_sha256": file_sha256(args.rehearsal_summary), + "remediation_summary": str(args.remediation_summary), + "remediation_summary_sha256": file_sha256(args.remediation_summary), + "remediation_repeat": args.remediation_repeat, + "rehearsal_train_tile_count": int(rehearsal.get("train_tile_count") or 0), + "remediation_unique_train_tile_count": int(remediation.get("tile_count") or 0), + "protected_v72_used": False, + } + (args.output_dir / "experimental_rehearsal_summary.json").write_text( + json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + marker = { + key: summary[key] + for key in ( + "schema_version", + "status", + "promotion_allowed", + "release_claim_allowed", + "dataset_yaml_sha256", + "rehearsal_summary_sha256", + "remediation_summary_sha256", + ) + } + (args.output_dir / "EXPERIMENTAL_ONLY.json").write_text( + json.dumps(marker, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + print(json.dumps(summary, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/train_experimental_building_proposal_filter.py b/scripts/train_experimental_building_proposal_filter.py new file mode 100644 index 00000000..d141ad73 --- /dev/null +++ b/scripts/train_experimental_building_proposal_filter.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python3 +"""Train a non-promotable second-stage building/background proposal filter.""" + +from __future__ import annotations + +import argparse +import json +import random +import shutil +import sys +from pathlib import Path + +SCRIPT_DIR = Path(__file__).resolve().parent +if str(SCRIPT_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPT_DIR)) + +from train_building_proposal_filter import ( # noqa: E402 + choose_threshold, + materialize_split, + sha256, +) + +EXPERIMENTAL_ROOT = Path("/app/storage/training/experimental") + + +def _inside(path: Path, root: Path = EXPERIMENTAL_ROOT) -> bool: + try: + path.resolve().relative_to(root.resolve()) + return True + except ValueError: + return False + + +def validate_inputs(summary_path: Path, output_dir: Path) -> dict: + if not _inside(summary_path) or not _inside(output_dir): + raise ValueError(f"summary and output must remain below {EXPERIMENTAL_ROOT}") + marker_path = summary_path.parent / "EXPERIMENTAL_ONLY.json" + if not marker_path.is_file(): + raise ValueError("experimental source marker is missing") + marker = json.loads(marker_path.read_text(encoding="utf-8")) + if marker.get("promotion_allowed") is not False or marker.get("release_claim_allowed") is not False: + raise ValueError("experimental marker does not prohibit promotion") + summary = json.loads(summary_path.read_text(encoding="utf-8")) + if marker.get("dataset_yaml_sha256") != summary.get("dataset_yaml_sha256"): + raise ValueError("experimental marker/summary binding mismatch") + if summary.get("status") != "experimental_only_human_review_pending": + raise ValueError("source summary is not an experimental train shard") + return summary + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--summary", type=Path, required=True) + parser.add_argument("--proposal-model", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--val-sample", action="append", required=True) + parser.add_argument("--device", default="cuda:0") + parser.add_argument("--proposal-confidence", type=float, default=0.01) + parser.add_argument("--crop-scale", type=float, default=1.4) + parser.add_argument("--negative-match-iou", type=float, default=0.05) + parser.add_argument("--max-positive-per-tile", type=int, default=40) + parser.add_argument("--max-negative-per-tile", type=int, default=40) + parser.add_argument("--epochs", type=int, default=8) + parser.add_argument("--batch", type=int, default=64) + parser.add_argument("--seed", type=int, default=74) + parser.add_argument("--force", action="store_true") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + try: + summary = validate_inputs(args.summary, args.output_dir) + except (OSError, ValueError, json.JSONDecodeError) as exc: + raise SystemExit(str(exc)) from exc + if not args.proposal_model.is_file(): + raise SystemExit(f"proposal model is unavailable: {args.proposal_model}") + val_samples = {slug.strip() for slug in args.val_sample if slug.strip()} + sample_slugs = {str(tile["sample_slug"]) for tile in summary["tiles"]} + unknown = val_samples - sample_slugs + if unknown: + raise SystemExit(f"unknown validation samples: {', '.join(sorted(unknown))}") + if not val_samples or val_samples == sample_slugs: + raise SystemExit("proposal filter requires disjoint non-empty train and validation AOIs") + if args.output_dir.exists(): + if not args.force: + raise SystemExit(f"output already exists: {args.output_dir}") + shutil.rmtree(args.output_dir) + + random.seed(args.seed) + import torch + from torch import nn + from torch.utils.data import DataLoader + from torchvision import datasets + from torchvision.models import ResNet18_Weights, resnet18 + from ultralytics import YOLO + + if not torch.cuda.is_available() or not args.device.startswith("cuda"): + raise SystemExit("CUDA is required for experimental proposal-filter training") + torch.manual_seed(args.seed) + tiles = [tile for tile in summary["tiles"] if tile.get("kept", True)] + split_tiles = { + "val": [dict(tile, split="val") for tile in tiles if tile["sample_slug"] in val_samples], + "train": [dict(tile, split="train") for tile in tiles if tile["sample_slug"] not in val_samples], + } + proposal_model = YOLO(str(args.proposal_model)) + counts: dict[str, dict[str, int]] = {} + for split, selected in split_tiles.items(): + results = proposal_model.predict( + [tile["image_path"] for tile in selected], + conf=args.proposal_confidence, + imgsz=640, + max_det=1000, + device=args.device, + verbose=False, + ) + counts[split] = materialize_split( + tiles=selected, + results=results, + output_dir=args.output_dir / "crops", + split=split, + crop_scale=args.crop_scale, + max_positives_per_tile=args.max_positive_per_tile, + max_negatives_per_tile=args.max_negative_per_tile, + negative_match_iou=args.negative_match_iou, + ) + del results + torch.cuda.empty_cache() + del proposal_model + torch.cuda.empty_cache() + if any(counts[split][label] == 0 for split in ("train", "val") for label in ("positive", "negative")): + raise SystemExit(f"proposal crop class is empty: {counts}") + + weights = ResNet18_Weights.DEFAULT + transform = weights.transforms() + data = { + split: datasets.ImageFolder(args.output_dir / "crops" / split, transform=transform) + for split in ("train", "val") + } + loaders = { + split: DataLoader(dataset, batch_size=args.batch, shuffle=split == "train", num_workers=0) + for split, dataset in data.items() + } + model = resnet18(weights=weights) + model.fc = nn.Linear(model.fc.in_features, 1) + model.to(args.device) + positives = counts["train"]["positive"] + negatives = counts["train"]["negative"] + criterion = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([negatives / positives], device=args.device) + ) + optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4) + history = [] + for epoch in range(1, args.epochs + 1): + model.train() + loss_sum = 0.0 + for images, labels in loaders["train"]: + images = images.to(args.device) + labels = labels.float().to(args.device) + optimizer.zero_grad(set_to_none=True) + loss = criterion(model(images).flatten(), labels) + loss.backward() + optimizer.step() + loss_sum += float(loss.detach()) * len(images) + row = {"epoch": epoch, "train_loss": loss_sum / len(data["train"])} + history.append(row) + print(json.dumps(row), flush=True) + + model.eval() + probabilities: list[float] = [] + labels: list[int] = [] + with torch.inference_mode(): + for images, batch_labels in loaders["val"]: + probabilities.extend(torch.sigmoid(model(images.to(args.device)).flatten()).cpu().tolist()) + labels.extend(batch_labels.tolist()) + threshold, validation_metrics = choose_threshold(probabilities, labels) + args.output_dir.mkdir(parents=True, exist_ok=True) + model_path = args.output_dir / "proposal-filter.experimental.torchscript.pt" + torch.jit.script(model.cpu()).save(str(model_path)) + evidence = { + "schema_version": 1, + "status": "trained_experimental_only", + "promotion_allowed": False, + "release_claim_allowed": False, + "human_review_pending": True, + "architecture": "resnet18_binary_proposal_filter", + "source_summary": str(args.summary), + "source_summary_sha256": sha256(args.summary), + "proposal_model": str(args.proposal_model), + "proposal_model_sha256": sha256(args.proposal_model), + "train_samples": sorted(sample_slugs - val_samples), + "validation_samples": sorted(val_samples), + "counts": counts, + "epochs": args.epochs, + "history": history, + "selected_threshold_source": "internal_v73_validation_only", + "selected_threshold": threshold, + "validation_metrics": validation_metrics, + "model": str(model_path), + "model_sha256": sha256(model_path), + "required_next_gate": "independent V72 detector-plus-filter calibration", + } + (args.output_dir / "proposal-filter.experimental.json").write_text( + json.dumps(evidence, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + print(json.dumps(evidence, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/train_experimental_yolo_candidate.py b/scripts/train_experimental_yolo_candidate.py index 41a4a857..d13ad7a8 100644 --- a/scripts/train_experimental_yolo_candidate.py +++ b/scripts/train_experimental_yolo_candidate.py @@ -84,6 +84,7 @@ def main() -> int: seed=73, amp=False, val=False, + optimizer="AdamW", lr0=args.learning_rate, lrf=0.1, freeze=args.freeze, @@ -114,6 +115,7 @@ def main() -> int: "imgsz": args.imgsz, "batch": args.batch, "learning_rate": args.learning_rate, + "optimizer": "AdamW", "freeze": args.freeze, "workers": args.workers, "device": torch.cuda.get_device_name(0), diff --git a/tests/test_experimental_yolo_guards.py b/tests/test_experimental_yolo_guards.py index d014284a..ec46dff1 100644 --- a/tests/test_experimental_yolo_guards.py +++ b/tests/test_experimental_yolo_guards.py @@ -7,6 +7,8 @@ from scripts.export_experimental_yolo_train_shard import ( validate_experimental_request, ) from scripts.train_experimental_yolo_candidate import validate_paths +from scripts.train_experimental_building_proposal_filter import validate_inputs +from scripts.compose_experimental_yolo_rehearsal_dataset import validate_sources def test_export_refuses_non_experimental_output(tmp_path: Path) -> None: @@ -26,3 +28,13 @@ def test_train_shard_rejects_near_duplicate_edge_cover_windows() -> None: edge_cover = {"row_off": 513, "col_off": 0, "height": 512, "width": 512} assert is_canonical_train_window(canonical, 512) assert not is_canonical_train_window(edge_cover, 512) + + +def test_proposal_filter_refuses_non_experimental_paths(tmp_path: Path) -> None: + with pytest.raises(ValueError, match="must remain"): + validate_inputs(tmp_path / "summary.json", tmp_path / "output") + + +def test_rehearsal_composer_refuses_non_experimental_output(tmp_path: Path) -> None: + with pytest.raises(ValueError, match="output must remain"): + validate_sources(tmp_path / "old.json", tmp_path / "new.json", tmp_path / "out")