| import os |
| import subprocess |
| import argparse |
|
|
| CUR_DIR = os.path.dirname(os.path.abspath(__file__)) |
| def register_subparsers(subparser): |
| parser = subparser.add_parser('evaluate', formatter_class=argparse.RawTextHelpFormatter) |
| parser.add_argument( |
| "--ngpus", |
| type=int, |
| default=1, |
| help="Number of GPUs to run evaluation on" |
| ) |
| 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", |
| type=str, |
| 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( |
| "--mode", |
| choices=['custom_input', 'vbench_standard', 'vbench_category'], |
| default='vbench_standard', |
| help="""This flags determine the mode of evaluations, choose one of the following: |
| 1. "custom_input": receive input prompt from either --prompt/--prompt_file flags or the filename |
| 2. "vbench_standard": evaluate on standard prompt suite of VBench |
| 3. "vbench_category": evaluate on specific category |
| """, |
| ) |
| parser.add_argument( |
| "--prompt", |
| type=str, |
| default="None", |
| help="""Specify the input prompt |
| If not specified, filenames will be used as input prompts |
| * Mutually exclusive to --prompt_file. |
| ** This option must be used with --mode=custom_input flag |
| """ |
| ) |
| parser.add_argument( |
| "--prompt_file", |
| type=str, |
| required=False, |
| help="""Specify the path of the file that contains prompt lists |
| If not specified, filenames will be used as input prompts |
| * Mutually exclusive to --prompt. |
| ** This option must be used with --mode=custom_input flag |
| """ |
| ) |
| parser.add_argument( |
| "--category", |
| type=str, |
| required=False, |
| help="""This is for mode=='vbench_category' |
| The category to evaluate on, usage: --category=animal. |
| """, |
| ) |
|
|
| |
| 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 |
| """, |
| ) |
| parser.set_defaults(func=evaluate) |
|
|
| def stringify_cmd(cmd_ls): |
| cmd = "" |
| for string in cmd_ls: |
| cmd += string + " " |
| return cmd |
|
|
| |
| def evaluate(args): |
| cmd = ['python', '-m', 'torch.distributed.run', '--standalone', '--nproc_per_node', str(args.ngpus), f'{CUR_DIR}/../launch/evaluate.py'] |
| args_dict = vars(args) |
| for arg in args_dict: |
| if arg == "ngpus" or (args_dict[arg] == None) or arg == "func": |
| continue |
| if arg in ["videos_path", "prompt", "prompt_file", "output_path", "full_json_dir"]: |
| cmd.append(f"--{arg}=\"{str(args_dict[arg])}\"") |
| continue |
| cmd.append(f'--{arg}') |
| cmd.append(str(args_dict[arg])) |
| |
| subprocess.run(stringify_cmd(cmd), shell=True) |
|
|
|
|
