evaluate fresh Flemish remediation training
This commit is contained in:
Jens
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|
||||
"nearest_lineage_raster_path": "/app/storage/operator-data/building-be-v73-fresh-flanders-train-r1/corpus/pairs/v73-kaprijke-field-train-bg/image.tif",
|
||||
"nearest_lineage_sample_slug": "v73-kaprijke-field-train-bg",
|
||||
"passes_minimum_distance": true,
|
||||
"sample_slug": "v72-assenede-field-cal-bg"
|
||||
},
|
||||
{
|
||||
"bbox_epsg4326": [
|
||||
5.378193608487878,
|
||||
49.91683360963306,
|
||||
5.381806468144563,
|
||||
49.91916636935071
|
||||
],
|
||||
"minimum_lineage_distance_m": 98427.49240649818,
|
||||
"nearest_lineage_raster_path": "/app/storage/operator-data/building-be-v73-fresh-flanders-train-r1/corpus/pairs/v73-borgloon-rural-train/image.tif",
|
||||
"nearest_lineage_sample_slug": "v73-borgloon-rural-train",
|
||||
"passes_minimum_distance": true,
|
||||
"sample_slug": "v72-libramont-lowrise-cal"
|
||||
},
|
||||
{
|
||||
"bbox_epsg4326": [
|
||||
5.237179060792866,
|
||||
50.38783580367982,
|
||||
5.2408210177303065,
|
||||
50.39016417441682
|
||||
],
|
||||
"minimum_lineage_distance_m": 46555.585616985845,
|
||||
"nearest_lineage_raster_path": "/app/storage/operator-data/building-be-v73-fresh-flanders-train-r1/corpus/pairs/v73-borgloon-rural-train/image.tif",
|
||||
"nearest_lineage_sample_slug": "v73-borgloon-rural-train",
|
||||
"passes_minimum_distance": true,
|
||||
"sample_slug": "v72-havelange-field-cal-bg"
|
||||
},
|
||||
{
|
||||
"bbox_epsg4326": [
|
||||
4.305179924172925,
|
||||
50.871848393352586,
|
||||
4.308819978598048,
|
||||
50.87415158315595
|
||||
],
|
||||
"minimum_lineage_distance_m": 24551.91275864387,
|
||||
"nearest_lineage_raster_path": "/app/storage/operator-data/building-be-v73-fresh-flanders-train-r1/corpus/pairs/v73-lede-ribbon-train/image.tif",
|
||||
"nearest_lineage_sample_slug": "v73-lede-ribbon-train",
|
||||
"passes_minimum_distance": true,
|
||||
"sample_slug": "v72-ganshoren-lowrise-cal"
|
||||
},
|
||||
{
|
||||
"bbox_epsg4326": [
|
||||
4.349180265292469,
|
||||
50.89084908113465,
|
||||
4.3528196373886185,
|
||||
50.893150895320936
|
||||
],
|
||||
"minimum_lineage_distance_m": 26691.61793409974,
|
||||
"nearest_lineage_raster_path": "/app/storage/operator-data/building-be-v73-fresh-flanders-train-r1/corpus/pairs/v73-lede-ribbon-train/image.tif",
|
||||
"nearest_lineage_sample_slug": "v73-lede-ribbon-train",
|
||||
"passes_minimum_distance": true,
|
||||
"sample_slug": "v72-laeken-park-cal-bg"
|
||||
}
|
||||
],
|
||||
"lineage_sample_count": 12,
|
||||
"lineage_summaries": [
|
||||
{
|
||||
"path": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/yolo512/experimental_dataset_summary.json",
|
||||
"sample_slugs": [
|
||||
"v73-adinkerke-dunes-train-bg",
|
||||
"v73-borgloon-rural-train",
|
||||
"v73-doel-port-train-bg",
|
||||
"v73-houthulst-forest-train-bg",
|
||||
"v73-kaprijke-field-train-bg",
|
||||
"v73-kontich-lowrise-train",
|
||||
"v73-lede-ribbon-train",
|
||||
"v73-peer-field-train-bg",
|
||||
"v73-ronse-industry-train-bg",
|
||||
"v73-schoten-lowrise-train",
|
||||
"v73-tielt-rural-train",
|
||||
"v73-wevelgem-industry-train"
|
||||
],
|
||||
"sha256": "047874a4f9362c82263ffe11c389a2f65168c449d3f6cbcc77cea709c0ba4c59"
|
||||
}
|
||||
],
|
||||
"minimum_distance_m": 2000.0,
|
||||
"missing_lineage_sample_slugs": [],
|
||||
"resolved_lineage_sample_count": 12,
|
||||
"schema_version": 1,
|
||||
"status": "independent"
|
||||
}
|
||||
+19
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"schema_version": 1,
|
||||
"review_type": "ai_assisted_visual_review",
|
||||
"human": false,
|
||||
"reviewed_tile_count": 48,
|
||||
"coverage": "all canonical V73 training tiles",
|
||||
"decision": "suitable_for_non_promotable_experiment_only",
|
||||
"observations": [
|
||||
"GRB bounding boxes generally cover visible building roofs across all selected contexts.",
|
||||
"Rotated and complex footprints produce intentionally loose axis-aligned detection boxes.",
|
||||
"Sparse and empty field tiles are present; background_candidate does not imply pure empty.",
|
||||
"The first export contained 60 overlapping edge-cover tiles; all were removed before training.",
|
||||
"This review does not satisfy the independent human release gate."
|
||||
],
|
||||
"contact_sheets": [
|
||||
"contact_sheet_001.png",
|
||||
"contact_sheet_002.png"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"schema_version": 1,
|
||||
"purpose": "Fresh train-only Flemish failure-remediation corpus; never calibration or test",
|
||||
"frozen_before_acquisition": true,
|
||||
"revision_note": "r2 replaces the rejected Maaseik border AOI with inland Peer; the provider reported zero Flemish orthophoto coverage for the original rectangle before any sample pair was accepted.",
|
||||
"aois": [
|
||||
{"slug": "v73-kontich-lowrise-train", "region": "flanders", "context": "detached-lowrise", "split": "train", "lon": 4.447, "lat": 51.135, "sample_role": "positive"},
|
||||
{"slug": "v73-schoten-lowrise-train", "region": "flanders", "context": "detached-lowrise", "split": "train", "lon": 4.500, "lat": 51.250, "sample_role": "positive"},
|
||||
{"slug": "v73-lede-ribbon-train", "region": "flanders", "context": "ribbon-development", "split": "train", "lon": 3.985, "lat": 50.966, "sample_role": "positive"},
|
||||
{"slug": "v73-tielt-rural-train", "region": "flanders", "context": "rural-town", "split": "train", "lon": 3.327, "lat": 50.999, "sample_role": "positive"},
|
||||
{"slug": "v73-borgloon-rural-train", "region": "flanders", "context": "rural-lowrise", "split": "train", "lon": 5.343, "lat": 50.805, "sample_role": "positive"},
|
||||
{"slug": "v73-wevelgem-industry-train", "region": "flanders", "context": "industrial", "split": "train", "lon": 3.180, "lat": 50.820, "sample_role": "positive"},
|
||||
{"slug": "v73-kaprijke-field-train-bg", "region": "flanders", "context": "farmland-hard-negative", "split": "train", "lon": 3.620, "lat": 51.220, "sample_role": "background_candidate"},
|
||||
{"slug": "v73-doel-port-train-bg", "region": "flanders", "context": "port-hard-negative", "split": "train", "lon": 4.260, "lat": 51.310, "sample_role": "background_candidate"},
|
||||
{"slug": "v73-adinkerke-dunes-train-bg", "region": "flanders", "context": "dunes-hard-negative", "split": "train", "lon": 2.590, "lat": 51.070, "sample_role": "background_candidate"},
|
||||
{"slug": "v73-houthulst-forest-train-bg", "region": "flanders", "context": "forest-hard-negative", "split": "train", "lon": 2.950, "lat": 50.980, "sample_role": "background_candidate"},
|
||||
{"slug": "v73-ronse-industry-train-bg", "region": "flanders", "context": "industrial-hard-negative", "split": "train", "lon": 3.610, "lat": 50.750, "sample_role": "background_candidate"},
|
||||
{"slug": "v73-peer-field-train-bg", "region": "flanders", "context": "farmland-hard-negative", "split": "train", "lon": 5.450, "lat": 51.120, "sample_role": "background_candidate"}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,60 @@
|
||||
# V73 Vlaamse foutremediatie
|
||||
|
||||
## Uitkomst
|
||||
|
||||
V73 heeft de datadekking aantoonbaar verbreed, maar geen beter productiemodel
|
||||
opgeleverd. Daarom blijft het actieve model ongewijzigd. Dit is een geslaagde
|
||||
fail-closed beslissing: twee nieuw getrainde GPU-kandidaten zijn afgewezen op
|
||||
de vooraf bevroren en ruimtelijk onafhankelijke V72-calibratieportfolio.
|
||||
|
||||
## Nieuwe dekking
|
||||
|
||||
- twaalf nieuwe Vlaamse train-only AOI's;
|
||||
- woonwijken, lintbebouwing, landelijke kernen, industrie, haven, bos, duinen
|
||||
en landbouwcontext;
|
||||
- officiële orthofoto en GRB als primaire gebouwreferentie;
|
||||
- 48 canonieke niet-overlappende tegels met 1.710 labels;
|
||||
- drie lege tegels en meerdere moeilijke, dun bebouwde contexten;
|
||||
- minimale afstand tot een V72-calibratie-AOI: 9.203 meter, tegenover de gate
|
||||
van 2.000 meter.
|
||||
|
||||
De oorspronkelijke Maaseik-grensselectie werd door de provider terecht
|
||||
geweigerd wegens nuldekking binnen de Vlaamse orthofotozone. De gecheckpointte
|
||||
acquisitie is hervat met een vooraf geregistreerde inlandse vervanging in Peer.
|
||||
|
||||
## Visuele controle
|
||||
|
||||
Alle 48 uiteindelijke tegels zijn via twee contact sheets gecontroleerd. De
|
||||
eerste export maakte een bestaande edge-coverzwakte zichtbaar: rasters die iets
|
||||
groter dan 1024 pixels waren, leverden vrijwel identieke tegels op de laatste
|
||||
pixeloffset. Zestig zulke overlappende tegels zijn vóór training verwijderd en
|
||||
de nieuwe experimentele exporter bevat hiervoor een regressiegate.
|
||||
|
||||
De controle is AI-assisted en dus expliciet geen vervanging voor de vereiste
|
||||
onafhankelijke menselijke labelreview. Het corpus en de modellen blijven
|
||||
daardoor niet-promoveerbaar.
|
||||
|
||||
## GPU-resultaten
|
||||
|
||||
| Model | Precision | Recall | mAP50 | mAP50-95 | Besluit |
|
||||
|---|---:|---:|---:|---:|---|
|
||||
| Actief | 0,1577 | 0,1178 | 0,0568 | 0,0218 | behouden |
|
||||
| V73 agressief, 20 epochs | 0,0124 | 0,2557 | 0,0189 | 0,0038 | afgewezen |
|
||||
| V73 gecontroleerd, 10 epochs | 0,0179 | 0,3247 | 0,0222 | 0,0057 | afgewezen |
|
||||
|
||||
Beide kandidaten verhogen recall maar produceren veel te veel foutpositieven.
|
||||
Een drempel- of gemiddelde-scoretruc rechtvaardigt hier geen promotie: precision
|
||||
en beide mAP-maten regresseren ernstig.
|
||||
|
||||
## Conclusie en resterende grens
|
||||
|
||||
De huidige detector is nog niet nauwkeurig genoeg om landelijke, regionale of
|
||||
"100% correcte" gebouwclaims te dragen. V73 voorkomt wel dat een slechter model
|
||||
in productie komt en levert nieuwe, herleidbare foutdekking voor een latere
|
||||
training. Voor een echte volgende kwaliteitsstap zijn minimaal een grotere
|
||||
onafhankelijk menselijk beoordeelde trainset, meer pure-backgroundtegels en
|
||||
governed PICC/UrbIS-contracten nodig. Tot dan moeten resultaten als
|
||||
modelvoorstellen met bronvergelijking en onzekerheid worden gepresenteerd.
|
||||
|
||||
Alle ruwe manifests, matrices, hashes en contact sheets staan onder
|
||||
`artifacts/evidence/accuracy/model-training/20260810-v73-flanders-remediation/`.
|
||||
@@ -0,0 +1,201 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Export an isolated train-only YOLO shard without making a release claim.
|
||||
|
||||
This path exists for evidence-generating model experiments when source contracts
|
||||
are eligible but accepted human label review is still pending. It deliberately
|
||||
cannot create a training release and must never be used for model promotion.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
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 export_operator_yolo_tile_dataset import ( # noqa: E402
|
||||
ensure_dependencies,
|
||||
ensure_yolo_directories,
|
||||
export_sample_tiles,
|
||||
file_sha256,
|
||||
write_dataset_yaml,
|
||||
)
|
||||
from training_dataset_eligibility import ( # noqa: E402
|
||||
TrainingEligibilityError,
|
||||
assert_frozen_manifest_training_eligible,
|
||||
)
|
||||
|
||||
EXPERIMENTAL_ROOT = Path("/app/storage/training/experimental")
|
||||
|
||||
|
||||
def _inside(path: Path, root: Path) -> bool:
|
||||
try:
|
||||
path.resolve().relative_to(root.resolve())
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
|
||||
def validate_experimental_request(manifest_path: Path, output_dir: Path) -> dict:
|
||||
if not _inside(output_dir, EXPERIMENTAL_ROOT):
|
||||
raise ValueError(f"output must remain below {EXPERIMENTAL_ROOT}")
|
||||
if (manifest_path.parent / "NO_TRAINING.json").exists():
|
||||
raise ValueError("source corpus explicitly prohibits training")
|
||||
manifest = json.loads(manifest_path.read_text(encoding="utf-8-sig"))
|
||||
if manifest.get("purpose") != "training_corpus":
|
||||
raise ValueError("source manifest is not a training corpus")
|
||||
samples = manifest.get("samples") or []
|
||||
if not samples:
|
||||
raise ValueError("source manifest contains no samples")
|
||||
invalid_splits = sorted(
|
||||
{
|
||||
str(sample.get("split") or "").strip().lower()
|
||||
for sample in samples
|
||||
if str(sample.get("split") or "").strip().lower() != "train"
|
||||
}
|
||||
)
|
||||
if invalid_splits:
|
||||
raise ValueError(f"experimental shard accepts train samples only: {invalid_splits}")
|
||||
return manifest
|
||||
|
||||
|
||||
def is_canonical_train_window(window: dict, tile_size: int) -> bool:
|
||||
return bool(
|
||||
int(window["row_off"]) % tile_size == 0
|
||||
and int(window["col_off"]) % tile_size == 0
|
||||
and int(window["height"]) == tile_size
|
||||
and int(window["width"]) == tile_size
|
||||
)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--manifest-path", type=Path, required=True)
|
||||
parser.add_argument("--output-dir", type=Path, required=True)
|
||||
parser.add_argument("--tile-size", type=int, default=512)
|
||||
parser.add_argument("--stride", type=int, default=512)
|
||||
parser.add_argument("--negative-keep-ratio", type=float, default=1.0)
|
||||
parser.add_argument("--min-label-px", type=float, default=4.0)
|
||||
parser.add_argument("--min-label-visible-ratio", type=float, default=0.25)
|
||||
parser.add_argument("--force", action="store_true")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> int:
|
||||
args = parse_args()
|
||||
try:
|
||||
manifest = validate_experimental_request(args.manifest_path, args.output_dir)
|
||||
assert_frozen_manifest_training_eligible(args.manifest_path, verify_live=True)
|
||||
except (ValueError, TrainingEligibilityError) as exc:
|
||||
raise SystemExit(str(exc)) from exc
|
||||
ensure_dependencies()
|
||||
if args.force and args.output_dir.exists():
|
||||
shutil.rmtree(args.output_dir)
|
||||
if args.output_dir.exists() and any(args.output_dir.iterdir()):
|
||||
raise SystemExit("experimental output directory already exists and is not empty; use --force")
|
||||
ensure_yolo_directories(args.output_dir)
|
||||
tiles: list[dict] = []
|
||||
for sample in manifest["samples"]:
|
||||
tiles.extend(
|
||||
export_sample_tiles(
|
||||
sample=sample,
|
||||
manifest_path=args.manifest_path,
|
||||
output_dir=args.output_dir,
|
||||
val_slugs=set(),
|
||||
tile_size=args.tile_size,
|
||||
stride=args.stride,
|
||||
negative_keep_ratio=args.negative_keep_ratio,
|
||||
min_label_px=args.min_label_px,
|
||||
min_label_visible_ratio=args.min_label_visible_ratio,
|
||||
background_negative_repeat=1,
|
||||
drop_low_variance_negatives=False,
|
||||
blank_range_threshold=3,
|
||||
reference_source="grb",
|
||||
reference_layer="buildings",
|
||||
)
|
||||
)
|
||||
kept: list[dict] = []
|
||||
excluded_edge_cover: list[dict] = []
|
||||
for tile in tiles:
|
||||
if not tile["kept"]:
|
||||
continue
|
||||
if is_canonical_train_window(tile["window"], args.tile_size):
|
||||
kept.append(tile)
|
||||
continue
|
||||
excluded_edge_cover.append(
|
||||
{
|
||||
"sample_slug": tile["sample_slug"],
|
||||
"tile_index": tile["tile_index"],
|
||||
"window": tile["window"],
|
||||
"reason": "overlapping_edge_cover_tile",
|
||||
}
|
||||
)
|
||||
Path(tile["image_path"]).unlink(missing_ok=True)
|
||||
Path(tile["label_path"]).unlink(missing_ok=True)
|
||||
if not kept:
|
||||
raise SystemExit("experimental export produced no tiles")
|
||||
dataset_yaml = write_dataset_yaml(args.output_dir, "building")
|
||||
# Ultralytics requires a val key while training. Its score is explicitly
|
||||
# invalid for model selection; independent V72 evaluation is mandatory.
|
||||
dataset_yaml.write_text(
|
||||
dataset_yaml.read_text(encoding="utf-8").replace("val: images/val", "val: images/train"),
|
||||
encoding="utf-8",
|
||||
)
|
||||
asset_hash = hashlib.sha256()
|
||||
for tile in sorted(kept, key=lambda item: str(item["image_path"])):
|
||||
asset_hash.update(file_sha256(Path(tile["image_path"])).encode())
|
||||
asset_hash.update(file_sha256(Path(tile["label_path"])).encode())
|
||||
summary = {
|
||||
"schema_version": 1,
|
||||
"status": "experimental_only_human_review_pending",
|
||||
"promotion_allowed": False,
|
||||
"release_claim_allowed": False,
|
||||
"internal_validation_valid_for_selection": False,
|
||||
"required_independent_evaluation": "frozen V72 calibration portfolio",
|
||||
"source_manifest": str(args.manifest_path),
|
||||
"source_manifest_sha256": file_sha256(args.manifest_path),
|
||||
"dataset_yaml": str(dataset_yaml),
|
||||
"dataset_yaml_sha256": file_sha256(dataset_yaml),
|
||||
"tile_asset_chain_sha256": asset_hash.hexdigest(),
|
||||
"sample_count": len(manifest["samples"]),
|
||||
"tile_count": len(kept),
|
||||
"positive_tile_count": sum(not tile["is_negative"] for tile in kept),
|
||||
"negative_tile_count": sum(tile["is_negative"] for tile in kept),
|
||||
"excluded_overlapping_edge_cover_tile_count": len(excluded_edge_cover),
|
||||
"excluded_overlapping_edge_cover_tiles": excluded_edge_cover,
|
||||
"label_count": sum(int(tile["label_count"]) for tile in kept),
|
||||
"tile_size": args.tile_size,
|
||||
"stride": args.stride,
|
||||
"min_label_px": args.min_label_px,
|
||||
"min_label_visible_ratio": args.min_label_visible_ratio,
|
||||
"tiles": kept,
|
||||
}
|
||||
summary_path = args.output_dir / "experimental_dataset_summary.json"
|
||||
summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True), encoding="utf-8")
|
||||
marker = {
|
||||
key: summary[key]
|
||||
for key in (
|
||||
"schema_version",
|
||||
"status",
|
||||
"promotion_allowed",
|
||||
"release_claim_allowed",
|
||||
"source_manifest_sha256",
|
||||
"dataset_yaml_sha256",
|
||||
"tile_asset_chain_sha256",
|
||||
)
|
||||
}
|
||||
(args.output_dir / "EXPERIMENTAL_ONLY.json").write_text(
|
||||
json.dumps(marker, indent=2, sort_keys=True) + "\n", encoding="utf-8"
|
||||
)
|
||||
print(json.dumps({key: value for key, value in summary.items() if key != "tiles"}, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,130 @@
|
||||
#!/usr/bin/env python3
|
||||
"""CUDA-train an isolated, non-promotable YOLO candidate."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
EXPERIMENTAL_ROOT = Path("/app/storage/training/experimental")
|
||||
|
||||
|
||||
def sha256(path: Path) -> str:
|
||||
digest = hashlib.sha256()
|
||||
with path.open("rb") as handle:
|
||||
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
|
||||
digest.update(chunk)
|
||||
return digest.hexdigest()
|
||||
|
||||
|
||||
def _inside(path: Path, root: Path) -> bool:
|
||||
try:
|
||||
path.resolve().relative_to(root.resolve())
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
|
||||
def validate_paths(dataset_dir: Path, output_dir: Path) -> dict:
|
||||
if not _inside(dataset_dir, EXPERIMENTAL_ROOT) or not _inside(output_dir, EXPERIMENTAL_ROOT):
|
||||
raise ValueError(f"dataset and output must remain below {EXPERIMENTAL_ROOT}")
|
||||
marker_path = dataset_dir / "EXPERIMENTAL_ONLY.json"
|
||||
yaml_path = dataset_dir / "dataset.yaml"
|
||||
if not marker_path.is_file() or not yaml_path.is_file():
|
||||
raise ValueError("experimental marker or dataset.yaml 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 and release claims")
|
||||
if marker.get("dataset_yaml_sha256") != sha256(yaml_path):
|
||||
raise ValueError("experimental dataset.yaml checksum mismatch")
|
||||
return marker
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--dataset-dir", type=Path, required=True)
|
||||
parser.add_argument("--base-model", type=Path, required=True)
|
||||
parser.add_argument("--output-dir", type=Path, required=True)
|
||||
parser.add_argument("--run-name", default="v73-flanders-remediation")
|
||||
parser.add_argument("--epochs", type=int, default=20)
|
||||
parser.add_argument("--imgsz", type=int, default=640)
|
||||
parser.add_argument("--batch", type=int, default=8)
|
||||
parser.add_argument("--learning-rate", type=float, default=0.0001)
|
||||
parser.add_argument("--freeze", type=int, default=10)
|
||||
parser.add_argument("--workers", type=int, default=0)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> int:
|
||||
args = parse_args()
|
||||
validate_paths(args.dataset_dir, args.output_dir)
|
||||
if not args.base_model.is_file():
|
||||
raise SystemExit(f"base model missing: {args.base_model}")
|
||||
import torch
|
||||
from ultralytics import YOLO
|
||||
|
||||
if not torch.cuda.is_available():
|
||||
raise SystemExit("CUDA is required for GeoIntel experimental training")
|
||||
model = YOLO(str(args.base_model))
|
||||
result = model.train(
|
||||
data=str(args.dataset_dir / "dataset.yaml"),
|
||||
epochs=args.epochs,
|
||||
imgsz=args.imgsz,
|
||||
batch=args.batch,
|
||||
workers=args.workers,
|
||||
device=0,
|
||||
project=str(args.output_dir),
|
||||
name=args.run_name,
|
||||
exist_ok=False,
|
||||
pretrained=True,
|
||||
deterministic=True,
|
||||
seed=73,
|
||||
amp=False,
|
||||
val=False,
|
||||
lr0=args.learning_rate,
|
||||
lrf=0.1,
|
||||
freeze=args.freeze,
|
||||
mosaic=0.0,
|
||||
translate=0.05,
|
||||
scale=0.1,
|
||||
plots=False,
|
||||
verbose=True,
|
||||
)
|
||||
run_dir = Path(result.save_dir)
|
||||
best = run_dir / "weights" / "best.pt"
|
||||
if not best.is_file():
|
||||
best = run_dir / "weights" / "last.pt"
|
||||
candidate = run_dir / "candidate.experimental.pt"
|
||||
shutil.copy2(best, candidate)
|
||||
summary = {
|
||||
"schema_version": 1,
|
||||
"status": "trained_experimental_only",
|
||||
"promotion_allowed": False,
|
||||
"release_claim_allowed": False,
|
||||
"human_review_pending": True,
|
||||
"dataset_yaml_sha256": sha256(args.dataset_dir / "dataset.yaml"),
|
||||
"base_model": str(args.base_model),
|
||||
"base_model_sha256": sha256(args.base_model),
|
||||
"candidate_model": str(candidate),
|
||||
"candidate_model_sha256": sha256(candidate),
|
||||
"epochs": args.epochs,
|
||||
"imgsz": args.imgsz,
|
||||
"batch": args.batch,
|
||||
"learning_rate": args.learning_rate,
|
||||
"freeze": args.freeze,
|
||||
"workers": args.workers,
|
||||
"device": torch.cuda.get_device_name(0),
|
||||
"required_next_gate": "independent frozen V72 calibration evaluation",
|
||||
}
|
||||
(run_dir / "experimental_training_summary.json").write_text(
|
||||
json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8"
|
||||
)
|
||||
print(json.dumps(summary, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,28 @@
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from scripts.export_experimental_yolo_train_shard import (
|
||||
is_canonical_train_window,
|
||||
validate_experimental_request,
|
||||
)
|
||||
from scripts.train_experimental_yolo_candidate import validate_paths
|
||||
|
||||
|
||||
def test_export_refuses_non_experimental_output(tmp_path: Path) -> None:
|
||||
manifest = tmp_path / "manifest.json"
|
||||
manifest.write_text("{}", encoding="utf-8")
|
||||
with pytest.raises(ValueError, match="output must remain"):
|
||||
validate_experimental_request(manifest, tmp_path / "output")
|
||||
|
||||
|
||||
def test_training_refuses_paths_outside_experimental_root(tmp_path: Path) -> None:
|
||||
with pytest.raises(ValueError, match="must remain"):
|
||||
validate_paths(tmp_path / "dataset", tmp_path / "output")
|
||||
|
||||
|
||||
def test_train_shard_rejects_near_duplicate_edge_cover_windows() -> None:
|
||||
canonical = {"row_off": 512, "col_off": 0, "height": 512, "width": 512}
|
||||
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)
|
||||
Reference in New Issue
Block a user