import argparse import json import os import shutil import sys RUN_DIR = os.environ.get("RUN_DIR", "/content") def get_token() -> str: tok = os.environ.get("HF_TOKEN", "").strip() if tok: return tok for p in ( os.path.join(RUN_DIR, "king_hf_token.txt"), "/content/king_hf_token.txt", "/content/hf_token.txt", ): if os.path.isfile(p): tok = open(p, encoding="utf-8-sig").read().strip() if tok: return tok try: from google.colab import userdata tok = str(userdata.get("HF_TOKEN") or "").strip() if tok: return tok except Exception: pass try: from kaggle_secrets import UserSecretsClient tok = str(UserSecretsClient().get_secret("HF_TOKEN") or "").strip() if tok: return tok except Exception: pass raise RuntimeError(f"HF_TOKEN not found (env, {RUN_DIR}/king_hf_token.txt, Colab or Kaggle secret 'HF_TOKEN')") def _push_metadata(rows_by_key: dict, output: str, repo_id: str) -> None: from huggingface_hub import HfApi tmp = os.path.join(RUN_DIR, "metadata_upload.jsonl") with open(tmp, "w", encoding="utf-8") as fh: for k, v in sorted(rows_by_key.items()): fh.write(json.dumps({"file_name": k, "text": v}, ensure_ascii=False) + "\n") HfApi().upload_file( path_or_fileobj=tmp, path_in_repo=output, repo_id=repo_id, repo_type="dataset", commit_message=f"captions progress ({len(rows_by_key)} rows)", ) print(f"[pushed] {len(rows_by_key)} rows -> {repo_id}/{output}", flush=True) def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--repo_id", default="RASHID778/king2-image-dataset") ap.add_argument("--data_dir", default="", help="local dir with indoor/outdoor subfolders + metadata.jsonl; skips snapshot_download") ap.add_argument("--model", default="microsoft/Florence-2-base") ap.add_argument("--task", default="DETAILED_CAPTION") ap.add_argument("--batch", type=int, default=8) ap.add_argument("--max_new_tokens", type=int, default=512) ap.add_argument("--start", type=int, default=0) ap.add_argument("--end", type=int, default=0) ap.add_argument("--output", default="metadata.jsonl") args = ap.parse_args() import torch from huggingface_hub import login, snapshot_download from PIL import Image from transformers import AutoModelForCausalLM, AutoProcessor login(token=get_token()) print("[download] dataset images", flush=True) data_dir = args.data_dir or snapshot_download( args.repo_id, repo_type="dataset", allow_patterns=["indoor/*", "outdoor/*", "metadata.jsonl"], ) if not os.path.isdir(data_dir): raise RuntimeError(f"data_dir missing: {data_dir}") # always pull the newest metadata.jsonl from the repo so we resume from huggingface_hub import hf_hub_download meta_dl = hf_hub_download( repo_id=args.repo_id, filename="metadata.jsonl", repo_type="dataset" ) shutil.copy(meta_dl, os.path.join(data_dir, "metadata.jsonl")) meta_path = os.path.join(data_dir, "metadata.jsonl") rows_by_key: dict[str, str] = {} if os.path.isfile(meta_path): for line in open(meta_path, encoding="utf-8"): row = json.loads(line) rows_by_key[row["file_name"]] = row.get("text", "") images = [] actual = {} for sub in ("indoor", "outdoor"): subdir = os.path.join(data_dir, sub) if not os.path.isdir(subdir): subdir = os.path.join(data_dir, sub.capitalize()) if not os.path.isdir(subdir): continue actual[sub] = subdir for name in sorted(os.listdir(subdir)): images.append(f"{sub}/{name}") total = len(images) end = args.end or total images = images[args.start:end] pending = [im for im in images if not rows_by_key.get(im, "").strip()] print( f"[plan] caption {len(pending)} of {len(images)} (start={args.start}, end={args.end or total})", flush=True, ) images = pending processor = AutoProcessor.from_pretrained(args.model, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( args.model, trust_remote_code=True, torch_dtype=torch.float16 ).to("cuda") print(f"[loaded] {args.model} on cuda, task={args.task}", flush=True) task_prompt = f"<{args.task}>" done = 0 last_upload = 0 for i in range(0, len(images), args.batch): chunk = images[i : i + args.batch] paths = [] for fn in chunk: sub, name = fn.split("/", 1) paths.append(os.path.join(actual.get(sub, os.path.join(data_dir, sub)), name)) pil = [Image.open(p).convert("RGB") for p in paths] inputs = processor(text=[task_prompt] * len(pil), images=pil, return_tensors="pt").to("cuda") with torch.no_grad(): with torch.autocast(device_type="cuda"): generated = model.generate( **inputs, max_new_tokens=args.max_new_tokens, num_beams=3, do_sample=False, ) texts = processor.batch_decode(generated, skip_special_tokens=True) for fn, t in zip(chunk, texts): cap = t.replace(task_prompt, "").strip() rows_by_key[fn] = cap done += 1 if done % (args.batch * 25) == 0 or i + args.batch >= len(images): print(f"[progress] {done}/{len(images)} captioned", flush=True) with open(os.path.join(RUN_DIR, "caption_progress.jsonl"), "w", encoding="utf-8") as fh: for k, v in rows_by_key.items(): fh.write(json.dumps({"file_name": k, "text": v}, ensure_ascii=False) + "\n") if done - last_upload >= 500: last_upload = done _push_metadata(rows_by_key, args.output, args.repo_id) print("[complete] writing metadata.jsonl", flush=True) out_path = os.path.join(RUN_DIR, "metadata.jsonl") lines = [{"file_name": k, "text": v} for k, v in sorted(rows_by_key.items())] with open(out_path, "w", encoding="utf-8") as fh: for row in lines: fh.write(json.dumps(row, ensure_ascii=False) + "\n") sample = [r for r in lines if r["text"]][:3] for s in sample: print(f" {s['file_name']}: {s['text'][:120]}", flush=True) _push_metadata(rows_by_key, args.output, args.repo_id) print(f"[uploaded] {args.output} -> {args.repo_id}", flush=True) if __name__ == "__main__": main()