Remove flat: scripts/embed_figures.py

scripts/embed_figures.py

@@ -1,107 +0,0 @@
-"""Compute CLIP embeddings for all extracted patent figures.
-
-Uses open_clip ViT-L/14 (best open-source CLIP) to embed each TIF.
-Saves embeddings as parquet with figure_id index for fast lookup.
-
-Usage:
-    python scripts/eval/embed_figures.py \
-        --enriched data/enriched/enriched_2022.parquet \
-        --images /tmp/patent_sample/2022 \
-        --out data/embeddings/embeddings_2022.parquet \
-        --batch 64
-"""
-
-import argparse
-from pathlib import Path
-
-import numpy as np
-import open_clip
-import pandas as pd
-import torch
-from PIL import Image
-from tqdm import tqdm
-
-
-def find_image(images_dir: Path, image_filename: str) -> Path | None:
-    parts = image_filename.split("-D0")
-    if len(parts) < 2:
-        return None
-    p = images_dir / parts[0] / image_filename
-    return p if p.exists() else None
-
-
-def embed_batch(model, preprocess, paths: list[Path], device: str) -> np.ndarray:
-    imgs = []
-    for p in paths:
-        try:
-            img = Image.open(p).convert("RGB")
-            imgs.append(preprocess(img))
-        except Exception:
-            imgs.append(torch.zeros(3, 224, 224))
-    batch = torch.stack(imgs).to(device)
-    with torch.no_grad():
-        feats = model.encode_image(batch)
-        feats = feats / feats.norm(dim=-1, keepdim=True)
-    return feats.cpu().numpy()
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--enriched", default="data/enriched/enriched_2022.parquet")
-    parser.add_argument("--images", default="/tmp/patent_sample/2022")
-    parser.add_argument("--out", default="data/embeddings/embeddings_2022.parquet")
-    parser.add_argument("--batch", type=int, default=64)
-    parser.add_argument("--model", default="ViT-L-14")
-    parser.add_argument("--pretrained", default="openai")
-    args = parser.parse_args()
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print(f"Device: {device}")
-
-    print(f"Loading {args.model} ({args.pretrained})...")
-    model, _, preprocess = open_clip.create_model_and_transforms(
-        args.model, pretrained=args.pretrained
-    )
-    model = model.to(device).eval()
-
-    df = pd.read_parquet(args.enriched)
-    images_dir = Path(args.images)
-
-    # Resolve image paths
-    df["_img_path"] = df["image_filename"].apply(
-        lambda fn: find_image(images_dir, fn)
-    )
-    found = df["_img_path"].notna().sum()
-    print(f"Images resolved: {found:,} / {len(df):,}")
-    df = df[df["_img_path"].notna()].reset_index(drop=True)
-
-    # Embed in batches
-    all_ids, all_vecs = [], []
-    batch_size = args.batch
-    paths = df["_img_path"].tolist()
-    fig_ids = df["figure_id"].tolist()
-
-    for i in tqdm(range(0, len(paths), batch_size), desc="Embedding"):
-        batch_paths = paths[i : i + batch_size]
-        batch_ids = fig_ids[i : i + batch_size]
-        vecs = embed_batch(model, preprocess, batch_paths, device)
-        all_ids.extend(batch_ids)
-        all_vecs.append(vecs)
-
-    all_vecs = np.vstack(all_vecs)
-    print(f"Embedding matrix: {all_vecs.shape}")
-
-    Path(args.out).parent.mkdir(parents=True, exist_ok=True)
-    out_df = pd.DataFrame({"figure_id": all_ids})
-    # Store each embedding dimension as a column — efficient for FAISS loading
-    out_df["embedding"] = list(all_vecs)
-    out_df.to_parquet(args.out, index=False)
-    print(f"Saved → {args.out}")
-
-    # Quick sanity check
-    print(f"Sample figure: {all_ids[0]}")
-    print(f"Embedding norm: {np.linalg.norm(all_vecs[0]):.4f} (should be 1.0)")
-
-
-if __name__ == "__main__":
-    main()