import torch import os from ivebench import VEBench from datetime import datetime import argparse def parse_args(): parser = argparse.ArgumentParser(description='IVEBench - Video Editing Benchmark', formatter_class=argparse.RawTextHelpFormatter) parser.add_argument( "--output_path", type=str, default='', help="Output path to save the evaluation results", ) parser.add_argument( "--source_videos_path", type=str, default='', help="Folder that contains the source video frames", ) parser.add_argument( "--target_videos_path", type=str, default='', help="Folder that contains the edited video frames", ) parser.add_argument( "--info_json_path", type=str, default='', help="Path to the JSON file containing video information and prompts", ) parser.add_argument( "--metric", nargs='+', default=None, help="List of evaluation metrics, usage: --metric ", ) parser.add_argument( "--name", type=str, default="ivebench_eval", help="Name prefix for output files", ) args = parser.parse_args() return args def main(): args = parse_args() print(f'Arguments: {args}') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") my_VEBench = VEBench(device, args.output_path) print(f'Starting IVEBench evaluation on device: {device}') current_time = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') eval_name = f'{args.name}_{current_time}' my_VEBench.evaluate( source_videos_path=args.source_videos_path, target_videos_path=args.target_videos_path, info_json_path=args.info_json_path, name=eval_name, metric_list=args.metric ) print('Evaluation completed successfully!') if __name__ == "__main__": main()