run_eval.py

#!/usr/bin/env python
"""
VBench I2V 评测脚本

用法:
    python run_eval.py --model_name my_model --resolution 16-9
"""

import os
import argparse

# 设置模型缓存路径
os.environ['VBENCH_CACHE_DIR'] = './vbench2_beta_i2v/pretrained_models'
os.environ['HF_HOME'] = './vbench2_beta_i2v/pretrained_models/huggingface'
os.environ['TORCH_HOME'] = './vbench2_beta_i2v/pretrained_models/torch'

from vbench2_beta_i2v import VBenchI2V


# 全维度
ALL_DIMENSIONS = [
    "i2v_subject",
    "i2v_background",
    "camera_motion",
    "subject_consistency",
    "background_consistency",
    "aesthetic_quality",
    "imaging_quality",
    "motion_smoothness",
    "dynamic_degree",
]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="VBench I2V 评测")
    parser.add_argument("--model_name", type=str, required=True, help="模型名称")
    parser.add_argument("--resolution", type=str, default="16-9",
                        choices=["1-1", "8-5", "7-4", "16-9"], help="分辨率")
    parser.add_argument("--videos_path", type=str, default=None,
                        help="视频路径，默认 ./sampled_videos/{model_name}")
    parser.add_argument("--output_path", type=str, default=None,
                        help="结果路径，默认 ./evaluation_results/{model_name}")
    args = parser.parse_args()

    # 视频路径：默认使用采样输出路径
    videos_path = args.videos_path or f"./sampled_videos/{args.model_name}"
    # 结果路径：默认 ./evaluation_results/{model_name}/
    output_path = args.output_path or f"./evaluation_results/{args.model_name}"
    full_info_path = "./vbench2_beta_i2v/vbench2_i2v_full_info.json"

    print("=" * 60)
    print("VBench I2V 评测")
    print("=" * 60)
    print(f"模型: {args.model_name}")
    print(f"视频路径: {videos_path}")
    print(f"分辨率: {args.resolution}")
    print(f"结果路径: {output_path}")
    print(f"评测维度: {len(ALL_DIMENSIONS)} 个")
    print("=" * 60)

    os.makedirs(output_path, exist_ok=True)

    vbench = VBenchI2V("cuda", full_info_path, output_path)
    vbench.evaluate(
        videos_path=videos_path,
        name=args.model_name,
        dimension_list=ALL_DIMENSIONS,
        resolution=args.resolution,
    )

    print(f"\n评测完成! 结果: {output_path}/")