File size: 4,644 Bytes
0160d0b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
将基于 gen_sft_and_val_data.py 生成的 val JSONL 还原为与原始 OCR-VQA Arrow 相同的列类型与 schema metadata。
输入 JSONL 的 images 字段元素形如：
{"base64": "...", "metadata": {...}} 或 {"error": "..."}
本脚本恢复为原始的 list<struct<bytes: binary, path: string>>，其中 path 无法从 JSONL 恢复，统一置为 None。
"""
import os
import json
import base64
from typing import Any, Dict, List
import pyarrow as pa
import pyarrow.ipc as ipc
from tqdm import tqdm
# 与现有脚本保持一致的默认目录
DATASET_DIR = "/mnt/moonfs/kimiv-ksyun/xulin/datasets/OCR-VQA/ocr_vqa_clean_dataset/validation"
INPUT_JSONL = "/mnt/moonfs/kimiv-ksyun/xulin/abs/data/ocr_vqa_val_512.jsonl"
OUTPUT_ARROW = "/mnt/moonfs/kimiv-ksyun/xulin/abs/data/ocr_vqa_val_512_restored.arrow"
def load_reference_schema(dataset_dir: str) -> pa.Schema:
"""从原始验证集的任意一个 Arrow 分片加载 schema（含 metadata）。"""
arrow_files = sorted(
[f for f in os.listdir(dataset_dir) if f.startswith("data-") and f.endswith(".arrow")]
)
if not arrow_files:
raise RuntimeError("未找到任何 data-*.arrow 文件用于参考 schema")
fpath = os.path.join(dataset_dir, arrow_files[0])
with open(fpath, "rb") as f:
reader = ipc.RecordBatchStreamReader(f)
table = reader.read_all()
return table.schema
def record_from_json(rec: Dict[str, Any]) -> Dict[str, Any]:
"""将 JSONL 的一条记录转为 Python 原生对象，列名与原始 Arrow 对齐。
- images: list[ {"bytes": bytes, "path": Optional[str]} ]
- problem: Optional[str]
- answer: Optional[str]
"""
# 恢复 images
images_py: List[Dict[str, Any]] = []
raw_images = rec.get("images")
if isinstance(raw_images, list):
for it in raw_images:
if isinstance(it, dict) and "base64" in it:
try:
img_bytes = base64.b64decode(it["base64"]) if it["base64"] else b""
except Exception:
img_bytes = b""
images_py.append({"bytes": img_bytes, "path": None})
elif isinstance(it, dict) and "bytes" in it:
# 罕见：如果 JSONL 中直接保留了 bytes（不太可能）
images_py.append({"bytes": it.get("bytes") or b"", "path": it.get("path")})
else:
# 出错或不支持格式，跳过或置空
images_py.append({"bytes": b"", "path": None})
else:
images_py = None # 与 Arrow 的可空语义对齐
return {
"images": images_py,
"problem": rec.get("problem"),
"answer": rec.get("answer"),
}
def build_table_from_records(records: List[Dict[str, Any]], ref_schema: pa.Schema) -> pa.Table:
"""根据参考 schema 构建表，并复制 schema metadata。"""
# 直接使用参考 schema 的各列类型，确保完全一致
images_type = ref_schema.field("images").type
problem_type = ref_schema.field("problem").type
answer_type = ref_schema.field("answer").type
images_col = pa.array([r.get("images") for r in records], type=images_type)
problem_col = pa.array([r.get("problem") for r in records], type=problem_type)
answer_col = pa.array([r.get("answer") for r in records], type=answer_type)
table = pa.Table.from_arrays([images_col, problem_col, answer_col], names=["images", "problem", "answer"])
table = table.replace_schema_metadata(ref_schema.metadata)
return table
def write_arrow_stream(table: pa.Table, out_path: str) -> None:
os.makedirs(os.path.dirname(out_path), exist_ok=True)
with open(out_path, "wb") as f:
with ipc.RecordBatchStreamWriter(f, table.schema) as writer:
writer.write_table(table)
def main():
ref_schema = load_reference_schema(DATASET_DIR)
records: List[Dict[str, Any]] = []
with open(INPUT_JSONL, "r", encoding="utf-8") as f:
for line in tqdm(f, desc="读取 JSONL"):
if not line.strip():
continue
rec_json = json.loads(line)
records.append(record_from_json(rec_json))
table = build_table_from_records(records, ref_schema)
write_arrow_stream(table, OUTPUT_ARROW)
# 读回校验
with open(OUTPUT_ARROW, "rb") as f:
back = ipc.RecordBatchStreamReader(f).read_all()
print("写出条数:", back.num_rows)
print("schema 一致:", back.schema == ref_schema)
print(back.schema)
print("输出:", OUTPUT_ARROW)
if __name__ == "__main__":
main()
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