File size: 3,503 Bytes
33c93f7 | 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 | from __future__ import annotations
import json
import random
from dataclasses import dataclass
from pathlib import Path
from PIL import Image
from torch.utils.data import Dataset
@dataclass(frozen=True)
class ImageRecord:
image_id: str
path: Path
def load_manifest(path: str | Path, data_root: str | Path | None = None) -> list[ImageRecord]:
manifest = Path(path)
root = Path(data_root) if data_root else manifest.parent
rows = json.loads(manifest.read_text(encoding="utf-8"))
if not isinstance(rows, list):
raise ValueError(f"{manifest}: expected a JSON list")
records = []
for index, row in enumerate(rows):
if not isinstance(row, dict) or not {"image_id", "image_path"} <= row.keys():
raise ValueError(f"{manifest}: invalid row {index}")
relative = Path(row["image_path"])
candidates = (relative,) if relative.is_absolute() else (
root / relative,
manifest.parent / relative,
manifest.parent.parent / relative,
manifest.parent.parent.parent / relative,
)
resolved = next((candidate for candidate in candidates if candidate.is_file()), None)
if resolved is None:
raise FileNotFoundError(f"Cannot resolve {relative} from {manifest}")
records.append(ImageRecord(str(row["image_id"]), resolved.resolve()))
if len({record.image_id for record in records}) != len(records):
raise ValueError(f"{manifest}: duplicate image IDs")
return records
def pseudo_split(records: list[ImageRecord], seed: int = 42, gallery_size: int = 1000, query_size: int = 200):
if len(records) < gallery_size:
raise ValueError(f"Need at least {gallery_size} records")
shuffled = records.copy()
random.Random(seed).shuffle(shuffled)
gallery = shuffled[:gallery_size]
query_indices = random.Random(seed + 1).sample(range(gallery_size), query_size)
queries = [(gallery[index], seed * 100_000 + order) for order, index in enumerate(query_indices)]
return shuffled[gallery_size:], gallery, queries
class ImageDataset(Dataset):
def __init__(self, records: list[ImageRecord], transform):
self.records, self.transform = records, transform
def __len__(self):
return len(self.records)
def __getitem__(self, index):
record = self.records[index]
with Image.open(record.path) as image:
return record.image_id, self.transform(image.convert("RGB"))
class TrainingDataset(Dataset):
def __init__(self, records: list[ImageRecord], method: str, transforms):
self.records, self.method, self.transforms = records, method, transforms
def __len__(self):
return len(self.records)
def __getitem__(self, index):
with Image.open(self.records[index].path) as source:
image = source.convert("RGB")
clean = self.transforms.clean(image)
sample = {"clean": clean}
if self.method in {"simclr", "joint"}:
sample["view1"] = self.transforms.contrastive(image)
sample["view2"] = self.transforms.contrastive(image)
if self.method in {"autoencoder", "joint"}:
sample["degraded"] = self.transforms.degraded(image)
if self.method in {"rotation", "joint"}:
rotated, label = self.transforms.rotation(image)
sample["rotated"], sample["rotation_label"] = rotated, label
return sample
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