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import argparse
import os
import numpy as np
import pandas as pd
import torch
from accelerate import PartialState
from accelerate.utils import gather_object
from natsort import natsorted
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoImageProcessor, AutoModel
from utils.filter import filter
from utils.logger import logger
from utils.video_dataset import VideoDataset, collate_fn
from utils.video_utils import ALL_FRAME_SAMPLE_METHODS
ALL_MODEL_NAME = [
"dinov2-small",
"dinov2-base",
"dinov2-large",
"clip-vit-large-patch14",
"clip-vit-base-patch32",
"clip-vit-large-patch14-336",
]
def init_model(model_name, device):
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name).to(device)
return processor, model
def compute_adjacent_similarity(frame_features):
frame_features /= frame_features.norm(dim=-1, keepdim=True)
roll_frame_features = torch.roll(frame_features, shifts=-1, dims=0)
similarity_matrix = frame_features.squeeze(dim=1).cpu().numpy() @ roll_frame_features.squeeze(dim=1).cpu().numpy().T
return np.diag(similarity_matrix).tolist()[:-1]
def parse_args():
parser = argparse.ArgumentParser(description="Compute the semantic consistency score across frames.")
parser.add_argument(
"--video_metadata_path", type=str, required=True, 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(
"--model_path", type=str, default="openai/clip-vit-large-patch14-336", help="The path to the DINO/CLIP model."
)
parser.add_argument("--frame_sample_method", type=str, choices=ALL_FRAME_SAMPLE_METHODS, default="keyframe+first")
parser.add_argument("--num_sampled_frames", type=int, default=1, help="The number of sampled frames.")
parser.add_argument("--sample_stride", type=int, default=None, help="The stride between two sampled frames.")
parser.add_argument("--batch_size", type=int, default=1, help="The batch size for the video dataset.")
parser.add_argument("--num_workers", type=int, default=1, 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=1, help="The frequency to save the output results.")
parser.add_argument("--basic_metadata_path", type=str, default=None, help="The path to the basic metadata (csv/jsonl).")
parser.add_argument("--min_resolution", type=float, default=0, help="The resolution threshold.")
parser.add_argument("--min_duration", type=float, default=-1, help="The minimum duration.")
parser.add_argument("--max_duration", type=float, default=-1, help="The maximum duration.")
parser.add_argument(
"--aesthetic_score_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
)
parser.add_argument("--min_aesthetic_score", type=float, default=4.0, help="The aesthetic score threshold.")
parser.add_argument(
"--aesthetic_score_siglip_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
)
parser.add_argument("--min_aesthetic_score_siglip", type=float, default=4.0, help="The aesthetic score (SigLIP) threshold.")
parser.add_argument(
"--text_score_metadata_path", type=str, default=None, help="The path to the video text score metadata (csv/jsonl)."
)
parser.add_argument("--min_text_score", type=float, default=0.02, help="The text threshold.")
parser.add_argument(
"--motion_score_metadata_path", type=str, default=None, help="The path to the video motion score metadata (csv/jsonl)."
)
parser.add_argument("--min_motion_score", type=float, default=2, help="The motion threshold.")
parser.add_argument("--max_motion_score", type=float, default=999999, help="The maximum motion threshold.")
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.")
video_path_list = video_metadata_df[args.video_path_column].tolist()
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)
saved_video_path_list = saved_metadata_df[args.video_path_column].tolist()
video_path_list = list(set(video_path_list).difference(set(saved_video_path_list)))
logger.info(f"Resume from {args.saved_path}: {len(saved_video_path_list)} processed and {len(video_path_list)} to be processed.")
video_path_list = filter(
video_path_list,
basic_metadata_path=args.basic_metadata_path,
min_resolution=args.min_resolution,
min_duration=args.min_duration,
max_duration=args.max_duration,
aesthetic_score_metadata_path=args.aesthetic_score_metadata_path,
min_aesthetic_score=args.min_aesthetic_score,
aesthetic_score_siglip_metadata_path=args.aesthetic_score_siglip_metadata_path,
min_aesthetic_score_siglip=args.min_aesthetic_score_siglip,
text_score_metadata_path=args.text_score_metadata_path,
min_text_score=args.min_text_score,
motion_score_metadata_path=args.motion_score_metadata_path,
min_motion_score=args.min_motion_score,
max_motion_score=args.max_motion_score,
video_path_column=args.video_path_column
)
# Sorting to guarantee the same result for each process.
video_path_list = natsorted(video_path_list)
if not any(name in args.model_path for name in ALL_MODEL_NAME):
raise ValueError(f"The model_path should be among the following list: {ALL_MODEL_NAME}.")
state = PartialState()
if state.is_main_process:
# Check if the model is downloaded in the main process.
processor, model = init_model(args.model_path, "cpu")
state.wait_for_everyone()
processor, model = init_model(args.model_path, state.device)
index = len(video_path_list) - len(video_path_list) % state.num_processes
# Avoid the NCCL timeout in the final gather operation.
logger.warning(
f"Drop the last {len(video_path_list) % state.num_processes} videos "
"to ensure each process handles the same number of videos."
)
video_path_list = video_path_list[:index]
logger.info(f"{len(video_path_list)} videos are to be processed.")
result_dict = {
args.video_path_column: [],
"similarity_cross_frame": [],
"similarity_mean": [],
"sample_frame_idx": [],
}
with state.split_between_processes(video_path_list) as splitted_video_path_list:
video_dataset = VideoDataset(
dataset_inputs={args.video_path_column: splitted_video_path_list},
video_folder=args.video_folder,
video_path_column=args.video_path_column,
sample_method=args.frame_sample_method,
num_sampled_frames=args.num_sampled_frames,
sample_stride=args.sample_stride,
)
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_frame = []
batch_sampled_frame_idx = []
# At least two frames are required to calculate cross-frame semantic consistency.
for path, frame, frame_idx in zip(batch["path"], batch["sampled_frame"], batch["sampled_frame_idx"]):
if len(frame) > 1:
batch_video_path.append(path)
batch_frame.append(frame)
batch_sampled_frame_idx.append(frame_idx)
else:
logger.warning(f"Skip {path} because it only has {len(frame)} frames.")
frame_num_list = [len(video_frames) for video_frames in batch_frame]
# [B, T, H, W, C] => [(B * T), H, W, C]
reshaped_batch_frame = [frame for video_frames in batch_frame for frame in video_frames]
with torch.no_grad():
inputs = processor(images=reshaped_batch_frame, return_tensors="pt").to(state.device)
if "dino" in args.model_path.lower():
frame_features = model(**inputs).last_hidden_state.mean(dim=1)
else: # CLIP
frame_features = model.get_image_features(**inputs)
# Each video may have a different number of sampled frames.
# Map the flattened frame features back to their original shape.
batch_frame_features = torch.split(frame_features, frame_num_list)
batch_simi_cross_frame = [compute_adjacent_similarity(frame_features) for frame_features in batch_frame_features]
batch_similarity_mean = [
sum(simi_cross_frame) / len(simi_cross_frame) for simi_cross_frame in batch_simi_cross_frame
]
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)
result_dict["similarity_cross_frame"].extend(batch_simi_cross_frame)
result_dict["similarity_mean"].extend(batch_similarity_mean)
result_dict["sample_frame_idx"].extend(batch_sampled_frame_idx)
# Save the metadata in the main process every saved_freq.
if (idx % args.saved_freq) == 0 or idx == len(video_loader) - 1:
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)
# Append is not supported (oss).
if args.saved_path.endswith(".csv"):
if os.path.exists(args.saved_path):
saved_df = pd.read_csv(args.saved_path)
result_df = pd.concat([saved_df, result_df], ignore_index=True)
result_df.to_csv(args.saved_path, index=False)
elif args.saved_path.endswith(".jsonl"):
if os.path.exists(args.saved_path):
saved_df = pd.read_json(args.saved_path, orient="records", lines=True)
result_df = pd.concat([saved_df, result_df], ignore_index=True)
result_df.to_json(args.saved_path, orient="records", lines=True, force_ascii=False)
logger.info(f"Save result to {args.saved_path}.")
for k in result_dict.keys():
result_dict[k] = []
if __name__ == "__main__":
main()
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