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e994c16 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | """Dataset loading and validation helpers."""
from __future__ import annotations
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterable
from config import DATA_DIR, REPO_ROOT
from data.schemas import ALLOWED_LABELS, clean_defects, label_counts
TRAINING_JSONL = DATA_DIR / "training_data.jsonl"
FDS_SCANS_DIR = (
DATA_DIR
/ "raw"
/ "FilmDamageSimulator"
/ "FilmDamageSimulator"
/ "scans"
)
@dataclass(frozen=True)
class DatasetIssue:
image: str
message: str
def load_jsonl(path: str | Path = TRAINING_JSONL) -> list[dict[str, Any]]:
path = Path(path)
rows: list[dict[str, Any]] = []
if not path.exists():
return rows
with path.open("r", encoding="utf-8") as f:
for line_no, line in enumerate(f, start=1):
line = line.strip()
if not line:
continue
try:
rows.append(json.loads(line))
except json.JSONDecodeError as exc:
raise ValueError(f"{path}:{line_no}: invalid JSONL row: {exc}") from exc
return rows
def resolve_image_path(entry: dict[str, Any], scans_dir: Path = FDS_SCANS_DIR) -> Path:
image = str(entry.get("image", ""))
path = Path(image)
if path.is_absolute():
return path
candidate = scans_dir / image
if candidate.exists():
return candidate
return REPO_ROOT / image
def validate_entries(
entries: Iterable[dict[str, Any]],
*,
require_images: bool = False,
scans_dir: Path = FDS_SCANS_DIR,
) -> list[DatasetIssue]:
issues: list[DatasetIssue] = []
for entry in entries:
image = str(entry.get("image", ""))
if not image:
issues.append(DatasetIssue(image="(missing)", message="missing image field"))
if require_images and image and not resolve_image_path(entry, scans_dir).exists():
issues.append(DatasetIssue(image=image, message="image file does not exist"))
annotations = entry.get("annotations", [])
cleaned, dropped = clean_defects(annotations)
if dropped:
issues.append(
DatasetIssue(
image=image or "(missing)",
message=f"{dropped} invalid annotations",
)
)
for defect in cleaned:
if defect["label"] not in ALLOWED_LABELS:
issues.append(
DatasetIssue(
image=image or "(missing)",
message=f"unknown label {defect['label']}",
)
)
return issues
def dataset_summary(entries: Iterable[dict[str, Any]]) -> dict[str, Any]:
entries_list = list(entries)
all_defects: list[dict[str, Any]] = []
dropped = 0
sources: dict[str, int] = {}
for entry in entries_list:
source = str(entry.get("source", "unknown"))
sources[source] = sources.get(source, 0) + 1
cleaned, bad = clean_defects(entry.get("annotations", []))
all_defects.extend(cleaned)
dropped += bad
counts = label_counts(all_defects)
return {
"images": len(entries_list),
"defects": len(all_defects),
"dropped_annotations": dropped,
"label_counts": counts,
"sources": dict(sorted(sources.items())),
}
def load_training_summary(path: str | Path = TRAINING_JSONL) -> dict[str, Any]:
entries = load_jsonl(path)
summary = dataset_summary(entries)
summary["issues"] = [
issue.__dict__ for issue in validate_entries(entries, require_images=False)
]
return summary
__all__ = [
"DatasetIssue",
"FDS_SCANS_DIR",
"TRAINING_JSONL",
"dataset_summary",
"load_jsonl",
"load_training_summary",
"resolve_image_path",
"validate_entries",
]
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