scripts/process.py

from datasets import load_dataset
import io
from PIL import Image
import json
import os
from tqdm import tqdm

# 创建保存图像的目录
os.makedirs("images", exist_ok=True)

dataset = load_dataset(
    "/mnt/dolphinfs/ssd_pool/docker/user/hadoop-mlm-hl/hadoop-mlm/common/spatial_data/spatial_relation/SAT",
    data_files={
        "train": "SAT_train.parquet",
        "validation": "SAT_val.parquet",
    },
    batch_size=128,
)

def process_dataset(dataset, split_name):
    processed_data = []
    
    for i, example in enumerate(tqdm(dataset[split_name])):
        # 保存图像到本地
        image_paths = []

        # 计算子目录路径（每1000张图像一个目录）
        subdir_num = i // 1000
        subdir_path = os.path.join("images", split_name, f"{subdir_num:03d}")
        os.makedirs(subdir_path, exist_ok=True)

        for j, img in enumerate(example['image_bytes']):
            # 生成唯一的图像文件名
            img_filename = f"{split_name}_{i:06d}_{j}.jpg"
            img_path = os.path.join(subdir_path, img_filename)
            img.save(img_path)
            image_paths.append(img_path)
        
        # 创建新的数据样本
        processed_example = {
            'image': image_paths,
            'question': example['question'],
            'answers': example['answers'],
            'question_type': example['question_type'],
            'correct_answer': example['correct_answer']
        }
        processed_data.append(processed_example)
    
    # 保存为JSON文件
    output_file = f"{split_name}_data.json"
    with open(output_file, 'w', encoding='utf-8') as f:
        json.dump(processed_data, f, ensure_ascii=False, indent=2)
    
    print(f"Saved {len(processed_data)} examples to {output_file}")

# 处理训练集和验证集
process_dataset(dataset, "train")
process_dataset(dataset, "validation")