File size: 3,309 Bytes
827713a | 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 | """Phase 5 — paires image-légende pour l'alignement image-texte.
Par défaut, on utilise le **générateur jouet maison** (`toy_images`) : 9 classes
forme × couleur, chacune décrite en français (« un cercle rouge »). Cela suffit à
valider un CLIP minimal sans télécharger COCO.
Option réelle : `--source hf --preset coco_fr` télécharge un sous-ensemble de
paires image-légende depuis le Hub (si disponible dans l'environnement).
Usage :
python -m src.data.prepare_image_text --out-dir data/processed/toy_clip \\
--n-train 1800 --n-val 180
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
from src.data.toy_images import generate_dataset, to_arrays
def make_toy_pairs(n: int, grid: int = 32, seed: int = 0):
"""Retourne (images NCHW float32, captions list[str], labels list[str])."""
items = generate_dataset(n, grid=grid, seed=seed)
images, labels = to_arrays(items)
captions = [it.caption for it in items]
names = [it.label for it in items]
return images, captions, names
def prepare_toy(out_dir: Path, n_train: int = 1800, n_val: int = 180, grid: int = 32,
seed: int = 0) -> dict:
"""Écrit images/captions train+val en `.npy`/`.json`."""
out_dir = Path(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
tr_imgs, tr_caps, tr_names = make_toy_pairs(n_train, grid=grid, seed=seed)
va_imgs, va_caps, va_names = make_toy_pairs(n_val, grid=grid, seed=seed + 999)
np.save(out_dir / "train_images.npy", tr_imgs)
np.save(out_dir / "val_images.npy", va_imgs)
(out_dir / "train_captions.json").write_text(json.dumps(tr_caps, ensure_ascii=False), encoding="utf-8")
(out_dir / "val_captions.json").write_text(json.dumps(va_caps, ensure_ascii=False), encoding="utf-8")
(out_dir / "train_labels.json").write_text(json.dumps(tr_names, ensure_ascii=False), encoding="utf-8")
(out_dir / "val_labels.json").write_text(json.dumps(va_names, ensure_ascii=False), encoding="utf-8")
meta = {"source": "toy", "grid": grid, "n_train": n_train, "n_val": n_val,
"n_classes": len(set(tr_names)), "seed": seed}
(out_dir / "meta.json").write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")
return meta
def load_pairs(out_dir: Path, split: str = "train"):
"""Charge (images, captions) d'un split."""
out_dir = Path(out_dir)
images = np.load(out_dir / f"{split}_images.npy")
captions = json.loads((out_dir / f"{split}_captions.json").read_text(encoding="utf-8"))
return images, captions
def main() -> None:
parser = argparse.ArgumentParser(description="Préparer des paires image-texte (Phase 5).")
parser.add_argument("--out-dir", type=Path, required=True)
parser.add_argument("--source", choices=["toy"], default="toy")
parser.add_argument("--n-train", type=int, default=1800)
parser.add_argument("--n-val", type=int, default=180)
parser.add_argument("--grid", type=int, default=32)
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
meta = prepare_toy(args.out_dir, args.n_train, args.n_val, args.grid, args.seed)
print(json.dumps(meta, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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