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import torch
import os
from vbench import VBench
from datetime import datetime
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
def register_subparsers(subparser):
parser = subparser.add_parser('evaluate')
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=f'{CUR_DIR}/../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",
nargs='+',
required=True,
help="list of evaluation dimensions, usage: --dimension <dim_1> <dim_2>",
)
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",
)
parser.add_argument(
"--custom_input",
action="store_true",
required=False,
help="whether use custom input prompt or vbench prompt"
)
parser.set_defaults(func=evaluate)
def evaluate(args):
print(f'args: {args}')
device = torch.device("cuda")
my_VBench = VBench(device, args.full_json_dir, args.output_path)
print(f'start evaluation')
current_time = datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
my_VBench.evaluate(
videos_path = args.videos_path,
name = f'results_{current_time}',
dimension_list = args.dimension,
local=args.load_ckpt_from_local,
read_frame=args.read_frame,
custom_prompt=args.custom_input,
)
print('done')
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