| |
| """Prepare ULVR_v2_clean scene_graph parquet for Monet step1 (precompute_teacher_reps). |
| |
| Reads scene_graph(+val) parquet (cols: sample_id, category, source_dataset, question, |
| answer, input_image, intermediate_image_1..3, num_intermediate_steps, messages_json), |
| and produces: |
| <out_root>/train.jsonl (and val.jsonl) -- {"metadata":{dataset_name,sample_id}, "data":[...]} |
| <out_root>/images/input/scene_graph/*.png |
| <out_root>/images/intermediate/scene_graph/*.png |
| Image relative paths are taken verbatim from messages_json so precompute can resolve them |
| against --dataset_root=<out_root>. |
| """ |
| import argparse, glob, json, os |
| import pyarrow.parquet as pq |
|
|
| def run(parquet_glob, out_root, split_name): |
| files=sorted(glob.glob(parquet_glob)) |
| assert files, f"no parquet matched {parquet_glob}" |
| os.makedirs(out_root, exist_ok=True) |
| jsonl_path=os.path.join(out_root, f"{split_name}.jsonl") |
| n=0; nimg=0 |
| IMGCOLS=["input_image","intermediate_image_1","intermediate_image_2","intermediate_image_3"] |
| with open(jsonl_path,"w") as jf: |
| for fp in files: |
| t=pq.read_table(fp, columns=["sample_id","messages_json"]+IMGCOLS).to_pylist() |
| for r in t: |
| mj=json.loads(r["messages_json"]) |
| |
| if "data" in mj and "metadata" in mj: |
| rec=mj |
| else: |
| rec={"metadata":{"dataset_name":"scene_graph","sample_id":r["sample_id"]},"data":mj if isinstance(mj,list) else mj.get("data",mj)} |
| rec["metadata"].setdefault("dataset_name","scene_graph") |
| rec["metadata"]["sample_id"]=r["sample_id"] |
| jf.write(json.dumps(rec,ensure_ascii=False)+"\n"); n+=1 |
| |
| inpath=None; interpaths=[] |
| for msg in rec["data"]: |
| for c in msg.get("content",[]): |
| if c.get("type")=="image": |
| if msg["role"]=="user": inpath=c["image"] |
| elif msg["role"]=="assistant": interpaths.append(c["image"]) |
| def dump(relpath, cell): |
| nonlocal nimg |
| if not relpath or not cell or not isinstance(cell,dict): return |
| b=cell.get("bytes") |
| if not b: return |
| out=os.path.join(out_root, relpath) |
| os.makedirs(os.path.dirname(out), exist_ok=True) |
| if not os.path.exists(out) or os.path.getsize(out)!=len(b): |
| with open(out,"wb") as fp2: fp2.write(b) |
| nimg+=1 |
| dump(inpath, r.get("input_image")) |
| for i,ip in enumerate(interpaths, start=1): |
| dump(ip, r.get(f"intermediate_image_{i}")) |
| print(f"[{split_name}] wrote {n} samples -> {jsonl_path} ; images written={nimg}") |
|
|
| if __name__=="__main__": |
| ap=argparse.ArgumentParser() |
| ap.add_argument("--train-glob", required=True) |
| ap.add_argument("--val-glob", default="") |
| ap.add_argument("--out-root", required=True) |
| a=ap.parse_args() |
| run(a.train_glob, a.out_root, "train") |
| if a.val_glob: run(a.val_glob, a.out_root, "val") |
|
|
