Download Dataset/hex_pathfinding_dataset_3000/flatten_annotations.py from bilaljawaid980/GRIP-Benchmark: direct link, hf CLI and curl.
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https://huggingface.co/datasets/bilaljawaid980/GRIP-Benchmark/resolve/main/Dataset/hex_pathfinding_dataset_3000/flatten_annotations.py
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hf download hf://datasets/bilaljawaid980/GRIP-Benchmark/Dataset/hex_pathfinding_dataset_3000/flatten_annotations.py
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curl -L -o flatten_annotations.py https://huggingface.co/datasets/bilaljawaid980/GRIP-Benchmark/resolve/main/Dataset/hex_pathfinding_dataset_3000/flatten_annotations.py
2.02 kB
| import argparse,csv,json | |
| from collections import Counter | |
| from pathlib import Path | |
| TASKS={1:"Image Description",2:"Basic Relational Reasoning",3:"Comparative Reasoning",4:"Compound Reasoning",5:"Extrapolative/Counterfactual Reasoning"};META_KEYS=("difficulty_score","grid_radius","num_hole_tiles","hole_density","shortest_path_length","num_alternate_shortest_paths","seed") | |
| def main(): | |
| p=argparse.ArgumentParser();p.add_argument("annotations",nargs="?",type=Path,default=Path(__file__).resolve().parent/"annotations.jsonl");a=p.parse_args();root=a.annotations.parent;rows=[json.loads(x) for x in a.annotations.read_text().splitlines() if x];public=[];private=[];final=[];counts=Counter() | |
| for row in rows: | |
| meta=json.dumps({k:row.get(k) for k in META_KEYS},separators=(",",":"));image=Path(row["image_path"]).name | |
| for q in row["questions"]: | |
| task=TASKS[q["difficulty_level"]];base={"question_id":q["question_id"],"task":task,"image":image,"prompt":q["question_text"]};public.append(base);private.append({**base,"groundtruth":str(q["ground_truth"]),"answer_format":q["answer_format"]});final.append({"task":task,"image":image,"prompt":q["question_text"],"groundtruth":str(q["ground_truth"]),"metadata":meta});counts[task]+=1 | |
| def write(name,fields,data): | |
| with (root/name).open("w",encoding="utf-8-sig",newline="") as h:w=csv.DictWriter(h,fieldnames=fields,lineterminator="\n");w.writeheader();w.writerows(data) | |
| write("question_set.csv",["question_id","task","image","prompt"],public);write("answer_key.csv",["question_id","task","image","prompt","groundtruth","answer_format"],private);write("dataset_final.csv",["task","image","prompt","groundtruth","metadata"],final) | |
| with (root/"dataset_final.jsonl").open("w",encoding="utf-8",newline="\n") as h: | |
| for row in final:h.write(json.dumps(row,separators=(",",":"))+"\n") | |
| if len(final)!=5*len(rows):raise ValueError("row count") | |
| print(f"Images: {len(rows)}\nRows written: {len(final)}\nTasks: {dict(counts)}\nSanity check: PASS") | |
| if __name__=="__main__":main() | |