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import argparse
import os
import pandas as pd
from accelerate import PartialState
from accelerate.utils import gather_object
from natsort import index_natsorted
from tqdm import tqdm
from torch.utils.data import DataLoader
import utils.image_evaluator as image_evaluator
import utils.video_evaluator as video_evaluator
from utils.logger import logger
from utils.video_dataset import VideoDataset, collate_fn
def parse_args():
parser = argparse.ArgumentParser(description="Compute scores of uniform sampled frames from videos.")
parser.add_argument(
"--video_metadata_path", type=str, default=None, help="The path to the video dataset metadata (csv/jsonl)."
)
parser.add_argument(
"--video_path_column",
type=str,
default="video_path",
help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
)
parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
parser.add_argument(
"--caption_column",
type=str,
default=None,
help="The column contains the caption.",
)
parser.add_argument(
"--frame_sample_method",
type=str,
choices=["mid", "uniform", "image"],
default="uniform",
)
parser.add_argument(
"--num_sampled_frames",
type=int,
default=8,
help="num_sampled_frames",
)
parser.add_argument("--metrics", nargs="+", type=str, required=True, help="The evaluation metric(s) for generated images.")
parser.add_argument(
"--batch_size",
type=int,
default=10,
required=False,
help="The batch size for the video dataset.",
)
parser.add_argument(
"--num_workers",
type=int,
default=4,
required=False,
help="The number of workers for the video dataset.",
)
parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
parser.add_argument("--saved_freq", type=int, default=1000, help="The frequency to save the output results.")
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.video_metadata_path.endswith(".csv"):
video_metadata_df = pd.read_csv(args.video_metadata_path)
elif args.video_metadata_path.endswith(".jsonl"):
video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
else:
raise ValueError("The video_metadata_path must end with .csv or .jsonl.")
if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
raise ValueError("The saved_path must end with .csv or .jsonl.")
if os.path.exists(args.saved_path):
if args.saved_path.endswith(".csv"):
saved_metadata_df = pd.read_csv(args.saved_path)
elif args.saved_path.endswith(".jsonl"):
saved_metadata_df = pd.read_json(args.saved_path, lines=True)
# Filter out the unprocessed video-caption pairs by setting the indicator=True.
merged_df = video_metadata_df.merge(saved_metadata_df, on="video_path", how="outer", indicator=True)
video_metadata_df = merged_df[merged_df["_merge"] == "left_only"]
# Sorting to guarantee the same result for each process.
video_metadata_df = video_metadata_df.iloc[index_natsorted(video_metadata_df["video_path"])].reset_index(drop=True)
if args.caption_column is None:
video_metadata_df = video_metadata_df[[args.video_path_column]]
else:
video_metadata_df = video_metadata_df[[args.video_path_column, args.caption_column + "_x"]]
video_metadata_df.rename(columns={args.caption_column + "_x": args.caption_column}, inplace=True)
logger.info(f"Resume from {args.saved_path}: {len(saved_metadata_df)} processed and {len(video_metadata_df)} to be processed.")
state = PartialState()
metric_fns = []
for metric in args.metrics:
if hasattr(image_evaluator, metric): # frame-wise
if state.is_main_process:
logger.info("Initializing frame-wise evaluator metrics...")
# Check if the model is downloaded in the main process.
getattr(image_evaluator, metric)(device="cpu")
state.wait_for_everyone()
metric_fns.append(getattr(image_evaluator, metric)(device=state.device))
else: # video-wise
if state.is_main_process:
logger.info("Initializing video-wise evaluator metrics...")
# Check if the model is downloaded in the main process.
getattr(video_evaluator, metric)(device="cpu")
state.wait_for_everyone()
metric_fns.append(getattr(video_evaluator, metric)(device=state.device))
result_dict = {args.video_path_column: [], "sample_frame_idx": []}
for metric in metric_fns:
result_dict[str(metric)] = []
if args.caption_column is not None:
result_dict[args.caption_column] = []
if args.frame_sample_method == "image":
logger.warning("Set args.num_sampled_frames to 1 since args.frame_sample_method is image.")
args.num_sampled_frames = 1
index = len(video_metadata_df) - len(video_metadata_df) % state.num_processes
# Avoid the NCCL timeout in the final gather operation.
logger.info(f"Drop {len(video_metadata_df) % state.num_processes} videos to ensure each process handles the same number of videos.")
video_metadata_df = video_metadata_df.iloc[:index]
logger.info(f"{len(video_metadata_df)} videos are to be processed.")
video_metadata_list = video_metadata_df.to_dict(orient='list')
with state.split_between_processes(video_metadata_list) as splitted_video_metadata:
video_dataset = VideoDataset(
dataset_inputs=splitted_video_metadata,
video_folder=args.video_folder,
text_column=args.caption_column,
sample_method=args.frame_sample_method,
num_sampled_frames=args.num_sampled_frames
)
video_loader = DataLoader(video_dataset, batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=collate_fn)
for idx, batch in enumerate(tqdm(video_loader)):
if len(batch) > 0:
batch_video_path = batch["path"]
result_dict["sample_frame_idx"].extend(batch["sampled_frame_idx"])
batch_frame = batch["sampled_frame"] # [batch_size, num_sampled_frames, H, W, C]
batch_caption = None
if args.caption_column is not None:
batch_caption = batch["text"]
result_dict["caption"].extend(batch_caption)
# Compute the quality.
for i, metric in enumerate(args.metrics):
quality_scores = metric_fns[i](batch_frame, batch_caption)
if isinstance(quality_scores[0], list): # frame-wise
quality_scores = [
[round(score, 5) for score in inner_list]
for inner_list in quality_scores
]
else: # video-wise
quality_scores = [round(score, 5) for score in quality_scores]
result_dict[str(metric_fns[i])].extend(quality_scores)
if args.video_folder == "":
saved_video_path_list = batch_video_path
else:
saved_video_path_list = [os.path.relpath(video_path, args.video_folder) for video_path in batch_video_path]
result_dict[args.video_path_column].extend(saved_video_path_list)
# Save the metadata in the main process every saved_freq.
if (idx != 0) and (idx % args.saved_freq == 0):
state.wait_for_everyone()
gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
if state.is_main_process and len(gathered_result_dict[args.video_path_column]) != 0:
result_df = pd.DataFrame(gathered_result_dict)
if args.saved_path.endswith(".csv"):
header = False if os.path.exists(args.saved_path) else True
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
logger.info(f"Save result to {args.saved_path}.")
for k in result_dict.keys():
result_dict[k] = []
# Wait for all processes to finish and gather the final result.
state.wait_for_everyone()
gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
# Save the metadata in the main process.
if state.is_main_process and len(gathered_result_dict[args.video_path_column]) != 0:
result_df = pd.DataFrame(gathered_result_dict)
if args.saved_path.endswith(".csv"):
header = False if os.path.exists(args.saved_path) else True
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
logger.info(f"Save the final result to {args.saved_path}.")
if __name__ == "__main__":
main()
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