Datasets:
File size: 10,244 Bytes
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"""Validate a built SmoothStyle repository."""
from __future__ import annotations
import argparse
import hashlib
import json
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Any
from PIL import Image
IMAGE_FIELDS = ("content_file_name", "style_file_name", "target_file_name")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--root", type=Path, default=Path(__file__).resolve().parents[1])
parser.add_argument("--skip-images", action="store_true")
parser.add_argument("--skip-checksums", action="store_true")
parser.add_argument("--huggingface", action="store_true")
return parser.parse_args()
def load_json(path: Path) -> Any:
with path.open("r", encoding="utf-8") as handle:
return json.load(handle)
def load_jsonl(path: Path) -> list[dict[str, Any]]:
rows = []
with path.open("r", encoding="utf-8") as handle:
for line_number, line in enumerate(handle, 1):
try:
value = json.loads(line)
except json.JSONDecodeError as error:
raise ValueError(f"Invalid JSON at {path}:{line_number}: {error}") from error
if not isinstance(value, dict):
raise ValueError(f"Expected object at {path}:{line_number}")
rows.append(value)
return rows
def safe_resolve(split_root: Path, relative_name: str) -> Path:
path = (split_root / relative_name).resolve()
try:
path.relative_to(split_root.resolve())
except ValueError as error:
raise ValueError(f"Image path escapes split directory: {relative_name}") from error
return path
def validate_image(path: Path) -> None:
with Image.open(path) as image:
image.verify()
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def validate_checksums(root: Path) -> int:
checksum_path = root / "metadata" / "checksums.sha256"
entries: list[tuple[str, Path]] = []
with checksum_path.open("r", encoding="utf-8") as handle:
for line_number, line in enumerate(handle, 1):
digest, separator, relative_name = line.rstrip("\n").partition(" ")
if not separator or len(digest) != 64:
raise ValueError(f"Invalid checksum line {line_number}")
path = (root / relative_name).resolve()
try:
path.relative_to(root.resolve())
except ValueError as error:
raise ValueError(f"Checksum path escapes repository: {relative_name}") from error
if not path.is_file():
raise FileNotFoundError(path)
entries.append((digest, path))
def check(entry: tuple[str, Path]) -> None:
expected, path = entry
actual = sha256(path)
if actual != expected:
raise ValueError(f"Checksum mismatch: {path}")
with ThreadPoolExecutor(max_workers=8) as executor:
list(executor.map(check, entries))
return len(entries)
def validate_huggingface(root: Path, expected: dict[str, int]) -> None:
try:
from datasets import load_dataset
except ImportError as error:
raise RuntimeError(
"The optional Hugging Face check requires the 'datasets' package"
) from error
dataset = load_dataset("imagefolder", data_dir=str(root / "data"))
if set(dataset) != set(expected):
raise ValueError(f"Unexpected Hugging Face splits: {sorted(dataset)}")
for split, expected_rows in expected.items():
if len(dataset[split]) != expected_rows:
raise ValueError(
f"Hugging Face row mismatch for {split}: "
f"{len(dataset[split])} != {expected_rows}"
)
expected_features = {
"content",
"content_id",
"generator",
"id",
"pair_id",
"strength",
"strength_id",
"style",
"style_id",
"style_source",
"target",
}
actual_features = set(dataset[split].features)
if actual_features != expected_features:
raise ValueError(
f"Unexpected Hugging Face features for {split}: "
f"{sorted(actual_features)}"
)
for image_feature in ("content", "style", "target"):
if dataset[split].features[image_feature].__class__.__name__ != "Image":
raise ValueError(
f"{split}.{image_feature} was not inferred as an Image feature"
)
def main() -> None:
args = parse_args()
root = args.root.resolve()
stats = load_json(root / "metadata" / "dataset_stats.json")
split_manifest = load_json(root / "metadata" / "split_manifest.json")
all_images: set[Path] = set()
split_pairs: dict[str, set[str]] = {}
split_contents: dict[str, set[str]] = {}
split_styles: dict[str, set[str]] = {}
expected_hf_rows: dict[str, int] = {}
for split in ("train", "test"):
split_root = root / "data" / split
rows = load_jsonl(split_root / "metadata.jsonl")
expected_rows = stats["splits"][split]["examples"]
if len(rows) != expected_rows:
raise ValueError(f"{split} row count {len(rows)} != {expected_rows}")
expected_hf_rows[split] = expected_rows
ids: set[str] = set()
pair_strengths: set[tuple[str, int]] = set()
pairs: set[str] = set()
contents: set[str] = set()
styles: set[str] = set()
referenced: set[Path] = set()
for row in rows:
required = {
"id",
"pair_id",
"content_id",
"style_id",
"strength_id",
"strength",
*IMAGE_FIELDS,
}
missing = required - set(row)
if missing:
raise ValueError(f"Missing fields in {split}: {sorted(missing)}")
if row["id"] in ids:
raise ValueError(f"Duplicate sample ID: {row['id']}")
ids.add(row["id"])
strength_id = row["strength_id"]
if strength_id not in range(1, 11):
raise ValueError(f"Invalid strength ID: {strength_id}")
if abs(row["strength"] - strength_id / 10.0) > 1e-12:
raise ValueError(f"Invalid scalar strength: {row}")
pair_strength = (row["pair_id"], strength_id)
if pair_strength in pair_strengths:
raise ValueError(f"Duplicate pair/strength: {pair_strength}")
pair_strengths.add(pair_strength)
expected_pair = f"{row['content_id']}_{row['style_id']}"
if row["pair_id"] != expected_pair:
raise ValueError(f"Pair ID mismatch: {row}")
pairs.add(row["pair_id"])
contents.add(row["content_id"])
styles.add(row["style_id"])
for field in IMAGE_FIELDS:
path = safe_resolve(split_root, row[field])
if not path.is_file():
raise FileNotFoundError(path)
referenced.add(path)
all_images.add(path)
for pair in pairs:
strengths = {
strength for candidate, strength in pair_strengths if candidate == pair
}
if strengths != set(range(1, 11)):
raise ValueError(f"Incomplete target strengths for {split}/{pair}")
actual_images = {path.resolve() for path in split_root.rglob("*.jpg")}
if actual_images != referenced:
missing_from_index = sorted(actual_images - referenced)
missing_from_disk = sorted(referenced - actual_images)
raise ValueError(
f"Image/index mismatch for {split}: "
f"unindexed={missing_from_index[:5]}, absent={missing_from_disk[:5]}"
)
split_pairs[split] = pairs
split_contents[split] = contents
split_styles[split] = styles
if split_pairs["train"] & split_pairs["test"]:
raise ValueError("Train/test pair overlap detected")
if split_contents["train"] & split_contents["test"]:
raise ValueError("Train/test content overlap detected")
manifest_train = set(split_manifest["train_pair_ids"])
manifest_test = set(split_manifest["test_pair_ids"])
if manifest_train != split_pairs["train"] or manifest_test != split_pairs["test"]:
raise ValueError("Split manifest does not match metadata rows")
expected_style_overlap = stats["totals"]["style_ids_shared_between_splits"]
actual_style_overlap = len(split_styles["train"] & split_styles["test"])
if actual_style_overlap != expected_style_overlap:
raise ValueError(
f"Style overlap mismatch: {actual_style_overlap} != {expected_style_overlap}"
)
provenance = load_jsonl(root / "metadata" / "sources.jsonl")
provenance_assets = {root / row["asset_id"] for row in provenance}
if {path.resolve() for path in provenance_assets} != all_images:
raise ValueError("Provenance index does not cover exactly the published images")
if not args.skip_images:
with ThreadPoolExecutor(max_workers=8) as executor:
list(executor.map(validate_image, sorted(all_images)))
checksum_count = 0
if not args.skip_checksums:
checksum_count = validate_checksums(root)
if args.huggingface:
validate_huggingface(root, expected_hf_rows)
result = {
"status": "ok",
"examples": sum(expected_hf_rows.values()),
"images": len(all_images),
"style_ids_shared_between_splits": actual_style_overlap,
"checksums_verified": checksum_count,
"image_decoding_verified": not args.skip_images,
"huggingface_loader_verified": args.huggingface,
}
print(json.dumps(result, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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