File size: 3,170 Bytes
f907e43 | 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 | import json
import os
import shutil
from typing import Any
from concurrent.futures import ThreadPoolExecutor, as_completed
from tqdm import tqdm
def read_json(path: str) -> Any:
with open(path, "r", encoding="utf-8") as f:
return json.load(f)
def write_json(path: str, data: Any):
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=4)
def copy_one(src: str, dst: str):
if not os.path.exists(src):
raise FileNotFoundError(src)
os.makedirs(os.path.dirname(dst), exist_ok=True)
# 如果目标已经存在，可以跳过，适合断点续跑
if os.path.exists(dst):
return
shutil.copy2(src, dst)
def parallel_copy(copy_tasks, desc: str, colour: str = "green", max_workers: int = 32):
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = [
executor.submit(copy_one, src, dst)
for src, dst in copy_tasks
]
for future in tqdm(as_completed(futures), total=len(futures), desc=desc, colour=colour):
future.result()
EditScore_source_dir = "/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/EditScore-Reward-Data"
EditScore_dst_dir = "/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/ESData/EditScore-Reward-Data"
EditReward_source_dir = "/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/EditReward-Data/Images"
EditReward_dst_dir = "/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/ESData/EditReward-Data"
editscore_data = read_json(
"/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/EditScore-Reward-Data/filter_metadata.json"
)
editreward_data = read_json(
"/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/EditReward-Data/unique_matadata_type.json"
)
# EditScore copy tasks
editscore_tasks = []
for item in editscore_data:
for img in item["images"]:
src = os.path.join(EditScore_source_dir, img)
dst = os.path.join(EditScore_dst_dir, img)
editscore_tasks.append((src, dst))
parallel_copy(editscore_tasks, desc="Copy EditScore", colour="green", max_workers=32)
write_json(
"/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/ESData/EditScore-Reward-Data/data.json",
editscore_data,
)
# EditReward filter + copy tasks
filtered_editreward_data = []
editreward_tasks = []
for item in editreward_data:
item = dict(item)
item.pop("status", None)
if item.get("confidence", 0) < 0.9:
continue
item.pop("confidence", None)
for img in item["images"]:
src = os.path.join(EditReward_source_dir, img)
dst = os.path.join(EditReward_dst_dir, img)
editreward_tasks.append((src, dst))
filtered_editreward_data.append(item)
print(len(filtered_editreward_data))
parallel_copy(editreward_tasks, desc="Copy EditReward", colour="red", max_workers=48)
write_json(
"/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/ESData/EditReward-Data/data.json",
filtered_editreward_data,
) |