File size: 3,541 Bytes
5767e72 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import torch
import os
from vbench2_beta_i2v import VBenchI2V
from datetime import datetime
import argparse
def parse_args():
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
parser = argparse.ArgumentParser(description='VBenchI2V')
parser.add_argument(
"--output_path",
type=str,
default='./evaluation_i2v_results/',
help="output path to save the evaluation results",
)
parser.add_argument(
"--full_json_dir",
type=str,
default=f'{CUR_DIR}/vbench2_beta_i2v/vbench2_i2v_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(
"--ratio",
type=str,
default=None,
help="specify the target ratio",
)
parser.add_argument(
"--custom_image_folder",
type=str,
default=None,
help="the path for customized images",
)
parser.add_argument(
"--mode",
choices=['custom_input', 'vbench_standard'],
default='vbench_standard',
help="""This flags determine the mode of evaluations, choose one of the following:
1. "custom_input": receive reference images from --custom_image_folder flag
2. "vbench_standard": evaluate on standard prompt suite of VBench++
""",
)
parser.add_argument(
"--imaging_quality_preprocessing_mode",
type=str,
required=False,
default='longer',
help="""This is for setting preprocessing in imaging_quality
1. 'shorter': if the shorter side is more than 512, the image is resized so that the shorter side is 512.
2. 'longer': if the longer side is more than 512, the image is resized so that the longer side is 512.
3. 'shorter_centercrop': if the shorter side is more than 512, the image is resized so that the shorter side is 512.
Then the center 512 x 512 after resized is used for evaluation.
4. 'None': no preprocessing
""",
)
args = parser.parse_args()
return args
def main():
args = parse_args()
print(f'args: {args}')
kwargs = {
'imaging_quality_preprocessing_mode': args.imaging_quality_preprocessing_mode
}
device = torch.device("cuda")
my_VBench = VBenchI2V(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,
resolution = args.ratio,
custom_image_folder = args.custom_image_folder,
mode=args.mode,
**kwargs
)
print('done')
if __name__ == "__main__":
main()
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