| import argparse |
| import torch |
| import os, sys |
| from datetime import datetime |
|
|
| from competition_utils import transform_to_videos |
|
|
| dir_path = os.path.dirname(os.path.realpath(__file__)) |
| parent_dir_path = os.path.abspath(os.path.join(dir_path, os.pardir)) |
| sys.path.append(parent_dir_path) |
|
|
| from competitions import VBenchCompetition |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument( |
| "--submission_path", |
| type=str, |
| required=False, |
| help="folder that contains short videos or long videos" |
| ) |
| parser.add_argument( |
| "--frame_rate", |
| type=int, |
| required=False, |
| help="frame rate of generated videos" |
| ) |
| parser.add_argument( |
| "--video_path", |
| type=str, |
| required=False, |
| help="folder that contains the sampled videos" |
| ) |
| parser.add_argument( |
| "--output_path", |
| type=str, |
| default="./evaluate_results", |
| help="output path that save evaluation results" |
| ) |
| parser.add_argument( |
| "--dimension", |
| nargs='+', |
| required=True, |
| help="list of evaluation dimensions, usage: --dimension <dim_1> <dim_2>", |
| ) |
| parser.add_argument( |
| "--prompt_file", |
| type=str, |
| required=True, |
| default="./short_prompt_list.txt", |
| help="Specify the path of the file that contains prompt lists" |
| ) |
| |
| args = parser.parse_args() |
| return args |
|
|
|
|
| def main(): |
| args = parse_args() |
| print(f"args: {args}") |
| |
| |
| if not args.video_path: |
| |
| assert args.submission_path is not None and args.frame_rate is not None, "You need to provide the submission_path\ |
| and the frame rate for generating the video." |
| args.video_path = os.path.join(args.output_path, "evaluated_videos") |
| transform_to_videos(args.submission_path, args.video_path, args.frame_rate) |
| |
| device = torch.device("cuda") |
| myvbench = VBenchCompetition(device, None, args.output_path) |
| |
| print(f'start evaluation') |
| current_time = datetime.now().strftime('%Y-%m-%d-%H:%M:%S') |
| |
| kwargs = { |
| 'imaging_quality_preprocessing_mode': 'longer' |
| } |
| |
|
|
| with open(args.prompt_file, "r") as f: |
| prompts = [line.strip() for line in f.readlines()] |
|
|
| |
| if "short_prompt_list" in args.prompt_file: |
| myvbench.evaluate( |
| videos_path = args.video_path, |
| name = f'results_short_{current_time}', |
| prompt_list=prompts, |
| dimension_list = args.dimension, |
| **kwargs |
| ) |
| |
| elif "long_prompt_list" in args.prompt_file: |
| |
| kwargs['sb_clip2clip_feat_extractor'] = 'dino' |
| kwargs['bg_clip2clip_feat_extractor'] = 'clip' |
| kwargs['clip_length_config'] = "clip_length_mix.yaml" |
| kwargs['w_inclip'] = 1.0 |
| kwargs['w_clip2clip'] = 0.0 |
| kwargs['use_semantic_splitting'] = True |
| kwargs['slow_fast_eval_config'] = "configs/slow_fast_params.yaml" |
| kwargs['dev_flag'] = False |
| kwargs['sb_mapping_file_path'] = "configs/subject_mapping_table.yaml" |
| kwargs['bg_mapping_file_path'] = "configs/background_mapping_table.yaml" |
| |
| myvbench.evaluate_long( |
| videos_path = args.video_path, |
| name = f'results_long_{current_time}', |
| prompt_list=prompts, |
| dimension_list = args.dimension, |
| **kwargs |
| ) |
| print("done") |
| |
|
|
| if __name__ == "__main__": |
| main() |
| |
