mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
synced 2025-12-08 20:34:24 +08:00
53 lines
1.2 KiB
Python
53 lines
1.2 KiB
Python
import argparse
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import torch
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from vbench import VBench
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full_info_path = "./vbench/VBench_full_info.json"
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dimensions = [
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"subject_consistency",
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"imaging_quality",
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"background_consistency",
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"motion_smoothness",
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"overall_consistency",
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"human_action",
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"multiple_objects",
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"spatial_relationship",
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"object_class",
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"color",
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"aesthetic_quality",
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"appearance_style",
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"temporal_flickering",
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"scene",
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"temporal_style",
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"dynamic_degree",
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]
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--video_path", required=True, type=str)
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parser.add_argument("--save_path", required=True, type=str)
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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args = parse_args()
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kwargs = {}
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kwargs["imaging_quality_preprocessing_mode"] = "longer" # use VBench/evaluate.py default
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for dimension in dimensions:
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my_VBench = VBench(torch.device("cuda"), full_info_path, args.save_path)
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my_VBench.evaluate(
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videos_path=args.video_path,
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name=dimension,
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local=False,
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read_frame=False,
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dimension_list=[dimension],
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mode="vbench_standard",
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**kwargs,
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)
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