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#!/usr/bin/env python3
import torch
import vbench
from vbench import VBench
import argparse
def parse_args():
parser = argparse.ArgumentParser(description='VBench')
parser.add_argument(
"--output_path",
type=str,
default='./evaluation_results/',
help="output path to save the evaluation results",
)
parser.add_argument(
"--full_json_dir",
type=str,
default='./VBench_full_info.json',
help="path to save the json file that contains the prompt and dimension information",
)
parser.add_argument(
"--videos_path",
type=str,
required=True,
help="folder that contains the sampled videos",
)
parser.add_argument(
"--dimension",
type=str,
required=True,
help="evaluation dimensions",
)
parser.add_argument(
"--load_ckpt_from_local",
type=bool,
required=False,
help="whether load checkpoints from local default paths (assuming you have downloaded the checkpoints locally",
)
parser.add_argument(
"--read_frame",
type=bool,
required=False,
help="whether directly read frames, or directly read videos",
)
args = parser.parse_args()
return args
def main():
args = parse_args()
print(f'args: {args}')
device = torch.device("cuda")
my_VBench = VBench(device, args.full_json_dir, args.output_path)
print(f'start evaluation')
my_VBench.evaluate(
videos_path = args.videos_path,
name = args.dimension,
dimension_list = [args.dimension],
local=args.load_ckpt_from_local,
read_frame=args.read_frame,
)
print('done')
if __name__ == "__main__":
main()
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