downstream/ProxyCLIP_TPAMI/env_setup/compress_datasets.py

#!/usr/bin/env python3
"""
压缩数据集并上传到 HuggingFace
=====================================================
每个数据集单独压缩为 tar.gz 文件，便于下载和管理
=====================================================
"""

import os
import tarfile
import argparse
from pathlib import Path
from tqdm import tqdm

try:
    from huggingface_hub import HfApi, login, upload_file
except ImportError:
    print("请先安装 huggingface_hub: pip install huggingface_hub")
    exit(1)

# HuggingFace 仓库
DATASET_REPO = "xiaomoguhzz/xiaomogu_pami_dataset"

# 数据集配置
DATASETS = {
    "ade20k": {
        "source": "/mnt/SSD8T/home/wjj/dataset/ADEChallengeData2016",
        "archive_name": "ADEChallengeData2016.tar.gz",
        "description": "ADE20K Dataset (150 classes)"
    },
    "cityscapes": {
        "source": "/mnt/SSD8T/home/wjj/dataset/cityscapes",
        "archive_name": "cityscapes.tar.gz",
        "description": "Cityscapes Dataset (19 classes)"
    },
    "coco_stuff": {
        "source": "/mnt/SSD8T/home/wjj/dataset/standard_coco",
        "archive_name": "coco_stuff_164k.tar.gz",
        "description": "COCO-Stuff 164K Dataset (171 classes)"
    },
    "coco_obj": {
        "source": "/mnt/SSD8T/home/wjj/dataset/coco_obj",
        "archive_name": "coco_object.tar.gz",
        "description": "COCO-Object Dataset (81 classes)"
    },
    "voc2012": {
        "source": "/mnt/SSD8T/home/wjj/dataset/VOCdevkit/VOC2012",
        "archive_name": "VOC2012.tar.gz",
        "description": "PASCAL VOC 2012"
    },
    "voc2010": {
        "source": "/mnt/SSD8T/home/wjj/dataset/VOCdevkit/VOC2010",
        "archive_name": "VOC2010_context.tar.gz",
        "description": "PASCAL Context (VOC2010)"
    },
}


def get_dir_size(path: Path) -> int:
    """获取目录大小"""
    total = 0
    for f in path.rglob('*'):
        if f.is_file():
            total += f.stat().st_size
    return total


def compress_dataset(name: str, config: dict, output_dir: Path, dry_run: bool = False) -> Path:
    """压缩单个数据集"""
    source = Path(config["source"])
    archive_name = config["archive_name"]
    output_path = output_dir / archive_name
    
    print(f"\n{'='*60}")
    print(f"📦 [{name}] {config['description']}")
    print(f"{'='*60}")
    print(f"源路径: {source}")
    print(f"输出文件: {output_path}")
    
    if not source.exists():
        print(f"⚠️ 源路径不存在，跳过")
        return None
    
    # 计算源目录大小
    size_bytes = get_dir_size(source)
    size_gb = size_bytes / (1024**3)
    print(f"源目录大小: {size_gb:.2f} GB")
    
    if dry_run:
        print(f"🔍 [DRY RUN] 将压缩为: {archive_name}")
        return output_path
    
    if output_path.exists():
        existing_size = output_path.stat().st_size / (1024**3)
        print(f"✅ 已存在压缩文件 ({existing_size:.2f} GB)")
        return output_path
    
    # 创建 tar.gz 压缩文件
    print(f"⏳ 正在压缩...")
    
    try:
        with tarfile.open(output_path, "w:gz") as tar:
            # 使用 arcname 保持目录结构
            tar.add(source, arcname=source.name)
        
        compressed_size = output_path.stat().st_size / (1024**3)
        compression_ratio = (1 - compressed_size / size_gb) * 100 if size_gb > 0 else 0
        print(f"✅ 压缩完成: {compressed_size:.2f} GB (压缩率: {compression_ratio:.1f}%)")
        return output_path
    except Exception as e:
        print(f"❌ 压缩失败: {e}")
        return None


def upload_datasets(api: HfApi, archives: dict, dry_run: bool = False):
    """上传压缩的数据集到 HuggingFace"""
    print(f"\n{'='*60}")
    print(f"📤 上传到 HuggingFace: {DATASET_REPO}")
    print(f"{'='*60}")
    
    for name, archive_path in archives.items():
        if archive_path is None or not archive_path.exists():
            continue
        
        size_gb = archive_path.stat().st_size / (1024**3)
        print(f"\n[{name}] {archive_path.name} ({size_gb:.2f} GB)")
        
        if dry_run:
            print(f"  🔍 [DRY RUN] 将上传到: {archive_path.name}")
            continue
        
        try:
            print(f"  ⏳ 正在上传...")
            api.upload_file(
                path_or_fileobj=str(archive_path),
                path_in_repo=archive_path.name,
                repo_id=DATASET_REPO,
                repo_type="dataset",
            )
            print(f"  ✅ 上传完成")
        except Exception as e:
            print(f"  ❌ 上传失败: {e}")


def main():
    parser = argparse.ArgumentParser(description="压缩数据集并上传到 HuggingFace")
    parser.add_argument("--compress-only", action="store_true", help="仅压缩，不上传")
    parser.add_argument("--upload-only", action="store_true", help="仅上传已压缩的文件")
    parser.add_argument("--output-dir", type=str, default="./dataset_archives", help="压缩文件输出目录")
    parser.add_argument("--datasets", type=str, nargs="+", 
                       choices=list(DATASETS.keys()) + ["all"],
                       default=["all"], help="要处理的数据集")
    parser.add_argument("--dry-run", action="store_true", help="预览模式")
    parser.add_argument("--token", type=str, help="HuggingFace token")
    args = parser.parse_args()
    
    # 输出目录
    output_dir = Path(args.output_dir).resolve()
    output_dir.mkdir(parents=True, exist_ok=True)
    
    # 确定要处理的数据集
    if "all" in args.datasets:
        datasets_to_process = DATASETS
    else:
        datasets_to_process = {k: v for k, v in DATASETS.items() if k in args.datasets}
    
    print("="*60)
    print("📊 数据集压缩与上传工具")
    print("="*60)
    print(f"输出目录: {output_dir}")
    print(f"处理数据集: {list(datasets_to_process.keys())}")
    
    if args.dry_run:
        print("\n🔍 [DRY RUN] 预览模式\n")
    
    archives = {}
    
    # 压缩数据集
    if not args.upload_only:
        for name, config in datasets_to_process.items():
            archive_path = compress_dataset(name, config, output_dir, dry_run=args.dry_run)
            archives[name] = archive_path
    else:
        # 仅上传模式，查找已有的压缩文件
        for name, config in datasets_to_process.items():
            archive_path = output_dir / config["archive_name"]
            if archive_path.exists():
                archives[name] = archive_path
            else:
                print(f"⚠️ 未找到: {archive_path}")
    
    # 上传到 HuggingFace
    if not args.compress_only:
        if not args.dry_run:
            print("\n🔐 登录 HuggingFace...")
            if args.token:
                login(token=args.token)
            else:
                login()
            api = HfApi()
        else:
            api = None
        
        upload_datasets(api, archives, dry_run=args.dry_run)
    
    print(f"\n{'='*60}")
    print("✅ 操作完成!")
    print("="*60)


if __name__ == "__main__":
    main()