Bubbliiiingandhuangkunzhe.hkz
e1e7145c87
Update Wan and Wan-Fun --------- Co-authored-by: huangkunzhe.hkz <huangkunzhe.hkz@alibaba-inc.com>
220 lines
11 KiB
Python
220 lines
11 KiB
Python
import argparse
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import os
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import pandas as pd
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from accelerate import PartialState
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from accelerate.utils import gather_object
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from natsort import index_natsorted
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from tqdm import tqdm
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from torch.utils.data import DataLoader
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import utils.image_evaluator as image_evaluator
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import utils.video_evaluator as video_evaluator
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from utils.filter import filter
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from utils.logger import logger
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from utils.video_dataset import VideoDataset, collate_fn
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def parse_args():
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parser = argparse.ArgumentParser(description="Compute scores of uniform sampled frames from videos.")
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parser.add_argument(
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"--video_metadata_path", type=str, default=None, help="The path to the video dataset metadata (csv/jsonl)."
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)
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parser.add_argument(
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"--video_path_column",
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type=str,
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default="video_path",
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help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
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)
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parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
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parser.add_argument("--caption_column", type=str, default=None, help="The column contains the caption.")
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parser.add_argument(
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"--frame_sample_method",
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type=str,
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choices=["mid", "uniform", "image"],
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default="uniform",
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)
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parser.add_argument("--num_sampled_frames", type=int, default=8, help="The number of sampled frames.")
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parser.add_argument("--metrics", nargs="+", type=str, required=True, help="The evaluation metric(s) for generated images.")
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parser.add_argument("--batch_size", type=int, default=1, help="The batch size for the video dataset.")
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parser.add_argument("--num_workers", type=int, default=1, help="The number of workers for the video dataset.")
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parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
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parser.add_argument("--saved_freq", type=int, default=1, help="The frequency to save the output results.")
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parser.add_argument("--basic_metadata_path", type=str, default=None, help="The path to the basic metadata (csv/jsonl).")
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parser.add_argument("--min_resolution", type=float, default=0, help="The resolution threshold.")
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parser.add_argument("--min_duration", type=float, default=-1, help="The minimum duration.")
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parser.add_argument("--max_duration", type=float, default=-1, help="The maximum duration.")
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parser.add_argument(
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"--text_score_metadata_path", type=str, default=None, help="The path to the video text score metadata (csv/jsonl)."
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)
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parser.add_argument("--min_text_score", type=float, default=0.02, help="The text threshold.")
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parser.add_argument(
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"--motion_score_metadata_path", type=str, default=None, help="The path to the video motion score metadata (csv/jsonl)."
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)
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parser.add_argument("--min_motion_score", type=float, default=2, help="The minimum motion threshold.")
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parser.add_argument("--max_motion_score", type=float, default=999999, help="The maximum motion threshold.")
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parser.add_argument(
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"--semantic_consistency_score_metadata_path",
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nargs="+",
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type=str,
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default=None,
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help="The path to the semantic consistency metadata (csv/jsonl)."
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)
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parser.add_argument(
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"--min_semantic_consistency_score", type=float, default=0.80, help="The semantic consistency score threshold."
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)
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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if args.video_metadata_path.endswith(".csv"):
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video_metadata_df = pd.read_csv(args.video_metadata_path)
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elif args.video_metadata_path.endswith(".jsonl"):
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video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
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else:
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raise ValueError("The video_metadata_path must end with .csv or .jsonl.")
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if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
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raise ValueError("The saved_path must end with .csv or .jsonl.")
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if os.path.exists(args.saved_path):
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if args.saved_path.endswith(".csv"):
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saved_metadata_df = pd.read_csv(args.saved_path)
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elif args.saved_path.endswith(".jsonl"):
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saved_metadata_df = pd.read_json(args.saved_path, lines=True)
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# Filter out the unprocessed video-caption pairs by setting the indicator=True.
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merged_df = video_metadata_df.merge(saved_metadata_df, on=args.video_path_column, how="outer", indicator=True)
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video_metadata_df = merged_df[merged_df["_merge"] == "left_only"]
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# Sorting to guarantee the same result for each process.
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video_metadata_df = video_metadata_df.iloc[index_natsorted(video_metadata_df[args.video_path_column])].reset_index(drop=True)
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if args.caption_column is None:
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video_metadata_df = video_metadata_df[[args.video_path_column]]
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else:
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video_metadata_df = video_metadata_df[[args.video_path_column, args.caption_column + "_x"]]
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video_metadata_df.rename(columns={args.caption_column + "_x": args.caption_column}, inplace=True)
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logger.info(f"Resume from {args.saved_path}: {len(saved_metadata_df)} processed and {len(video_metadata_df)} to be processed.")
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video_path_list = video_metadata_df[args.video_path_column].tolist()
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video_path_list = filter(
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video_path_list,
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basic_metadata_path=args.basic_metadata_path,
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min_resolution=args.min_resolution,
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min_duration=args.min_duration,
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max_duration=args.max_duration,
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text_score_metadata_path=args.text_score_metadata_path,
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min_text_score=args.min_text_score,
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motion_score_metadata_path=args.motion_score_metadata_path,
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min_motion_score=args.min_motion_score,
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max_motion_score=args.max_motion_score,
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semantic_consistency_score_metadata_path=args.semantic_consistency_score_metadata_path,
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min_semantic_consistency_score=args.min_semantic_consistency_score,
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video_path_column=args.video_path_column
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)
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video_metadata_df = video_metadata_df[video_metadata_df[args.video_path_column].isin(video_path_list)]
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state = PartialState()
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metric_fns = []
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for metric in args.metrics:
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if hasattr(image_evaluator, metric): # frame-wise
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if state.is_main_process:
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logger.info("Initializing frame-wise evaluator metrics...")
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# Check if the model is downloaded in the main process.
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getattr(image_evaluator, metric)(device="cpu")
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state.wait_for_everyone()
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metric_fns.append(getattr(image_evaluator, metric)(device=state.device))
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else: # video-wise
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if state.is_main_process:
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logger.info("Initializing video-wise evaluator metrics...")
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# Check if the model is downloaded in the main process.
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getattr(video_evaluator, metric)(device="cpu")
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state.wait_for_everyone()
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metric_fns.append(getattr(video_evaluator, metric)(device=state.device))
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result_dict = {args.video_path_column: [], "sample_frame_idx": []}
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for metric in metric_fns:
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result_dict[str(metric)] = []
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if args.caption_column is not None:
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result_dict[args.caption_column] = []
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if args.frame_sample_method == "image":
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logger.warning("Set args.num_sampled_frames to 1 since args.frame_sample_method is image.")
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args.num_sampled_frames = 1
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index = len(video_metadata_df) - len(video_metadata_df) % state.num_processes
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# Avoid the NCCL timeout in the final gather operation.
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logger.info(
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f"Drop the last {len(video_metadata_df) % state.num_processes} videos "
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"to ensure each process handles the same number of videos."
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)
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video_metadata_df = video_metadata_df.iloc[:index]
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logger.info(f"{len(video_metadata_df)} videos are to be processed.")
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video_metadata_list = video_metadata_df.to_dict(orient='list')
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with state.split_between_processes(video_metadata_list) as splitted_video_metadata:
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video_dataset = VideoDataset(
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dataset_inputs=splitted_video_metadata,
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video_folder=args.video_folder,
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video_path_column=args.video_path_column,
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text_column=args.caption_column,
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sample_method=args.frame_sample_method,
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num_sampled_frames=args.num_sampled_frames
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)
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video_loader = DataLoader(video_dataset, batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=collate_fn)
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for idx, batch in enumerate(tqdm(video_loader)):
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if len(batch) > 0:
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batch_video_path = batch["path"]
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result_dict["sample_frame_idx"].extend(batch["sampled_frame_idx"])
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batch_frame = batch["sampled_frame"] # [batch_size, num_sampled_frames, H, W, C]
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batch_caption = None
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if args.caption_column is not None:
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batch_caption = batch["text"]
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result_dict["caption"].extend(batch_caption)
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# Compute the quality.
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for i, metric in enumerate(args.metrics):
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quality_scores = metric_fns[i](batch_frame, batch_caption)
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if isinstance(quality_scores[0], list): # frame-wise
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quality_scores = [
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[round(score, 5) for score in inner_list]
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for inner_list in quality_scores
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]
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else: # video-wise
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quality_scores = [round(score, 5) for score in quality_scores]
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result_dict[str(metric_fns[i])].extend(quality_scores)
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if args.video_folder == "":
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saved_video_path_list = batch_video_path
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else:
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saved_video_path_list = [os.path.relpath(video_path, args.video_folder) for video_path in batch_video_path]
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result_dict[args.video_path_column].extend(saved_video_path_list)
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# Save the metadata in the main process every saved_freq.
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if (idx % args.saved_freq) == 0 or idx == len(video_loader) - 1:
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state.wait_for_everyone()
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gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
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if state.is_main_process and len(gathered_result_dict[args.video_path_column]) != 0:
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result_df = pd.DataFrame(gathered_result_dict)
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# Append is not supported (oss).
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if args.saved_path.endswith(".csv"):
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if os.path.exists(args.saved_path):
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saved_df = pd.read_csv(args.saved_path)
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result_df = pd.concat([saved_df, result_df], ignore_index=True)
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result_df.to_csv(args.saved_path, index=False)
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elif args.saved_path.endswith(".jsonl"):
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if os.path.exists(args.saved_path):
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saved_df = pd.read_json(args.saved_path, orient="records", lines=True)
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result_df = pd.concat([saved_df, result_df], ignore_index=True)
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result_df.to_json(args.saved_path, orient="records", lines=True, force_ascii=False)
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logger.info(f"Save result to {args.saved_path}.")
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for k in result_dict.keys():
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result_dict[k] = []
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if __name__ == "__main__":
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main()
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