downstream/ProxyCLIP_TPAMI/robustness_eval/run_robustness_eval.py

#!/usr/bin/env python
"""
批量运行鲁棒性评估脚本

该脚本会遍历所有常见的图像退化类型和严重程度，在Cityscapes数据集上评估模型性能。

使用方法:
    python robustness_eval/run_robustness_eval.py \
        --config configs/eva_declip/cfg_city_scapes_robust.py \
        --work-dir work_logs/robustness_eval \
        --corruptions common \
        --severities 1 2 3 4 5
"""

import os
import sys
import argparse
import subprocess
import json
from pathlib import Path
from imagecorruptions import get_corruption_names

# 添加项目根目录到路径
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))

from robustness_eval.prepare_corrupted_images import (
    corrupt_and_save_images,
    cleanup_tmp_directory
)


def parse_args():
    parser = argparse.ArgumentParser(
        description='批量运行鲁棒性评估')
    parser.add_argument(
        '--config',
        type=str,
        default='configs/eva_declip/cfg_city_scapes_robust.py',
        help='配置文件路径')
    parser.add_argument(
        '--work-dir',
        type=str,
        default='robustness_eval/results',
        help='工作目录，用于保存结果（默认: robustness_eval/results）')
    parser.add_argument(
        '--corruptions',
        type=str,
        choices=['common', 'validation', 'all', 'noise', 'blur', 'weather', 'digital'],
        default='common',
        help='退化类型子集')
    parser.add_argument(
        '--severities',
        type=int,
        nargs='+',
        default=[1, 2, 3, 4, 5],
        help='严重程度级别列表')
    parser.add_argument(
        '--clean-only',
        action='store_true',
        help='只运行干净图像的评估（baseline）')
    parser.add_argument(
        '--skip-clean',
        action='store_true',
        help='跳过干净图像的评估')
    parser.add_argument(
        '--parallel',
        type=int,
        default=1,
        help='并行运行的进程数（1表示串行）')
    parser.add_argument(
        '--resume',
        action='store_true',
        help='从上次中断的地方继续（跳过已完成的评估）')
    return parser.parse_args()


def get_eval_script_path():
    """获取eval.py脚本的路径"""
    return project_root / 'eval.py'


def get_dataset_info_from_config(config_path):
    """从配置文件中读取数据集信息
    
    读取原始数据集的data_root和img_path_prefix，用于图片预处理
    """
    from mmengine.config import Config
    
    # 临时保存并清除CORRUPTED_DATA_ROOT，以获取原始配置
    original_corrupted_root = os.environ.pop('CORRUPTED_DATA_ROOT', None)
    
    try:
        cfg = Config.fromfile(str(config_path))
        
        # 读取data_root（允许为空，保持原配置含义）
        data_root = cfg.get('data_root', '')

        # 获取img_path_prefix；若为绝对路径则直接使用
        data_prefix = cfg.test_dataloader.dataset.get('data_prefix', {})
        img_path_prefix = data_prefix.get('img_path', 'leftImg8bit/val')

        return data_root, img_path_prefix
    finally:
        # 恢复环境变量
        if original_corrupted_root is not None:
            os.environ['CORRUPTED_DATA_ROOT'] = original_corrupted_root


def run_single_eval(config_path, corruption_name, severity, work_dir_base, 
                    source_data_root=None, source_img_prefix=None):
    """运行单次评估
    
    Args:
        config_path: 配置文件路径
        corruption_name: corruption类型名称
        severity: 严重程度
        work_dir_base: 工作目录基础路径
        source_data_root: 源数据集根目录（如果为None，则从配置文件读取）
        source_img_prefix: 源图片相对路径前缀（如果为None，则从配置文件读取）
    """
    # 确保工作目录是绝对路径
    work_dir_base = Path(work_dir_base)
    if not work_dir_base.is_absolute():
        work_dir_base = project_root / work_dir_base
    
    # 创建工作目录
    if corruption_name == 'clean':
        work_dir = work_dir_base / corruption_name
    else:
        work_dir = work_dir_base / corruption_name / f'severity_{severity}'
    work_dir.mkdir(parents=True, exist_ok=True)
    
    # 从配置文件读取数据集信息（如果未提供）
    if source_data_root is None or source_img_prefix is None:
        cfg_data_root, cfg_img_prefix = get_dataset_info_from_config(config_path)
        if source_data_root is None:
            source_data_root = cfg_data_root
        if source_img_prefix is None:
            source_img_prefix = cfg_img_prefix
    
    # 准备corrupt图片到临时目录
    tmp_dir = None
    try:
        # 创建临时目录
        tmp_base = work_dir_base / 'tmp_corrupted_images'
        if corruption_name == 'clean':
            tmp_dir = tmp_base / 'clean'
        else:
            tmp_dir = tmp_base / corruption_name / f'severity_{severity}'
        
        print(f"\n{'='*80}")
        print(f"准备图片: {corruption_name}, severity={severity}")
        print(f"源目录: {source_data_root}")
        print(f"临时目录: {tmp_dir}")
        print(f"{'='*80}\n")
        
        # 预处理图片（应用corruption并保存到tmp目录）
        target_data_root = corrupt_and_save_images(
            source_data_root=source_data_root,
            source_img_prefix=source_img_prefix,
            target_tmp_root=str(tmp_dir),
            corruption_name=corruption_name,
            severity=severity
        )
        
        # 设置环境变量，告诉配置文件使用临时目录
        env = os.environ.copy()
        env['CORRUPTED_DATA_ROOT'] = target_data_root
        env['CORRUPTION_NAME'] = corruption_name
        if corruption_name != 'clean':
            env['SEVERITY'] = str(severity)
        else:
            env['SEVERITY'] = '1'  # clean时severity不重要，但需要设置
        
        # 构建命令
        eval_script = get_eval_script_path()
        cmd = [
            sys.executable,
            str(eval_script),
            '--config', str(config_path),
            '--work-dir', str(work_dir)
        ]
        
        # 运行评估
        print(f"\n{'='*80}")
        print(f"运行评估: {corruption_name}, severity={severity}")
        print(f"工作目录: {work_dir}")
        print(f"使用数据目录: {target_data_root}")
        print(f"{'='*80}\n")
        
        result = subprocess.run(cmd, env=env, cwd=project_root)
        
        if result.returncode == 0:
            print(f"✓ 完成: {corruption_name}, severity={severity}")
            success = True
        else:
            print(f"✗ 失败: {corruption_name}, severity={severity}")
            success = False
        
        return success, work_dir
        
    finally:
        # 清理临时目录
        if tmp_dir and tmp_dir.exists():
            print(f"\n清理临时目录: {tmp_dir}")
            cleanup_tmp_directory(str(tmp_dir))


def load_results(work_dir):
    """从工作目录加载评估结果"""
    results_file = Path(work_dir) / 'results.txt'
    if not results_file.exists():
        return None
    
    results = {}
    with open(results_file, 'r') as f:
        lines = f.readlines()
        for line in lines:
            if ':' in line:
                key, value = line.strip().split(':', 1)
                key = key.strip()
                value = value.strip()
                # 尝试转换为数字
                try:
                    if '.' in value:
                        value = float(value)
                    else:
                        value = int(value)
                except ValueError:
                    pass
                results[key] = value
    
    return results


def check_if_done(work_dir):
    """检查评估是否已完成"""
    results_file = Path(work_dir) / 'results.txt'
    if results_file.exists():
        # 检查是否包含mIoU结果
        with open(results_file, 'r') as f:
            content = f.read()
            if 'mIoU' in content or 'aAcc' in content:
                return True
    return False


def main():
    args = parse_args()
    
    # 获取配置文件路径
    config_path = Path(args.config)
    if not config_path.is_absolute():
        config_path = project_root / config_path
    
    if not config_path.exists():
        print(f"错误: 配置文件不存在: {config_path}")
        return
    
    # 获取退化类型列表
    if args.clean_only:
        corruption_list = ['clean']
    else:
        corruption_list = get_corruption_names(args.corruptions)
        if not args.skip_clean:
            corruption_list = ['clean'] + corruption_list
    
    # 准备所有评估任务
    tasks = []
    for corruption in corruption_list:
        if corruption == 'clean':
            # 干净图像只需要评估一次
            tasks.append(('clean', 1))  # severity设为1，但实际不会被使用
        else:
            for severity in args.severities:
                tasks.append((corruption, severity))
    
    print(f"\n{'='*80}")
    print(f"鲁棒性评估计划")
    print(f"{'='*80}")
    print(f"配置文件: {config_path}")
    print(f"工作目录: {args.work_dir}")
    print(f"退化类型: {args.corruptions} ({len(corruption_list)} 种)")
    print(f"严重程度: {args.severities}")
    print(f"总任务数: {len(tasks)}")
    print(f"{'='*80}\n")
    
    # 确保工作目录是绝对路径
    work_dir_base = Path(args.work_dir)
    if not work_dir_base.is_absolute():
        work_dir_base = project_root / work_dir_base
    
    # 先加载已有的结果（如果存在），以便合并
    summary_file = work_dir_base / 'results_summary.json'
    results_summary = {}
    if summary_file.exists():
        try:
            with open(summary_file, 'r') as f:
                results_summary = json.load(f)
        except:
            pass
    
    # 运行评估
    completed = 0
    failed = 0
    skipped = 0
    
    for corruption, severity in tasks:
        if corruption == 'clean':
            corruption_name = 'clean'
            severity_display = 'N/A'
            severity_for_result = 0  # 用于结果存储
        else:
            corruption_name = corruption
            severity_display = severity
            severity_for_result = severity
        
        # 确保工作目录是绝对路径
        work_dir_base = Path(args.work_dir)
        if not work_dir_base.is_absolute():
            work_dir_base = project_root / work_dir_base
        
        work_dir = work_dir_base / corruption_name
        if corruption != 'clean':
            work_dir = work_dir / f'severity_{severity}'
        
        # 检查是否需要跳过
        if args.resume and check_if_done(work_dir):
            print(f"⊘ 跳过已完成: {corruption_name}, severity={severity_display}")
            skipped += 1
            # 加载已有结果
            result = load_results(work_dir)
            if result:
                if corruption_name not in results_summary:
                    results_summary[corruption_name] = {}
                results_summary[corruption_name][severity_for_result] = result
            continue
        
        # 运行评估
        success, work_dir_actual = run_single_eval(
            config_path, corruption_name, severity, args.work_dir,
            source_data_root=None, source_img_prefix=None)
        
        if success:
            completed += 1
            # 加载结果
            result = load_results(work_dir_actual)
            if result:
                if corruption_name not in results_summary:
                    results_summary[corruption_name] = {}
                results_summary[corruption_name][severity_for_result] = result
        else:
            failed += 1
    
    # 保存汇总结果
    summary_file = work_dir_base / 'results_summary.json'
    summary_file.parent.mkdir(parents=True, exist_ok=True)
    with open(summary_file, 'w') as f:
        json.dump(results_summary, f, indent=2)
    
    # 打印汇总
    print(f"\n{'='*80}")
    print(f"评估完成汇总")
    print(f"{'='*80}")
    print(f"总任务数: {len(tasks)}")
    print(f"已完成: {completed}")
    print(f"已跳过: {skipped}")
    print(f"失败: {failed}")
    print(f"结果汇总已保存到: {summary_file}")
    print(f"{'='*80}\n")
    
    # 生成Excel文件（解耦：不在这里生成，由用户单独调用）
    # Excel生成已解耦，用户可以在所有任务完成后单独调用save_results_to_excel.py


if __name__ == '__main__':
    main()