236 lines
12 KiB
Python
236 lines
12 KiB
Python
import argparse
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import os
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import numpy as np
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import pandas as pd
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import torch
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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 natsorted
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from transformers import AutoImageProcessor, AutoModel
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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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from utils.video_utils import ALL_FRAME_SAMPLE_METHODS
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ALL_MODEL_NAME = [
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"dinov2-small",
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"dinov2-base",
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"dinov2-large",
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"clip-vit-large-patch14",
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"clip-vit-base-patch32",
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"clip-vit-large-patch14-336",
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]
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def init_model(model_name, device):
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processor = AutoImageProcessor.from_pretrained(model_name)
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model = AutoModel.from_pretrained(model_name).to(device)
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return processor, model
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def compute_adjacent_similarity(frame_features):
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frame_features /= frame_features.norm(dim=-1, keepdim=True)
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roll_frame_features = torch.roll(frame_features, shifts=-1, dims=0)
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similarity_matrix = frame_features.squeeze(dim=1).cpu().numpy() @ roll_frame_features.squeeze(dim=1).cpu().numpy().T
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return np.diag(similarity_matrix).tolist()[:-1]
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def parse_args():
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parser = argparse.ArgumentParser(description="Compute the semantic consistency score across frames.")
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parser.add_argument(
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"--video_metadata_path", type=str, required=True, 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(
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"--model_path", type=str, default="openai/clip-vit-large-patch14-336", help="The path to the DINO/CLIP model."
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)
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parser.add_argument("--frame_sample_method", type=str, choices=ALL_FRAME_SAMPLE_METHODS, default="keyframe+first")
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parser.add_argument("--num_sampled_frames", type=int, default=1, help="The number of sampled frames.")
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parser.add_argument("--sample_stride", type=int, default=None, help="The stride between two sampled frames.")
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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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"--aesthetic_score_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
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)
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parser.add_argument("--min_aesthetic_score", type=float, default=4.0, help="The aesthetic score threshold.")
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parser.add_argument(
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"--aesthetic_score_siglip_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
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)
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parser.add_argument("--min_aesthetic_score_siglip", type=float, default=4.0, help="The aesthetic score (SigLIP) threshold.")
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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 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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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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video_path_list = video_metadata_df[args.video_path_column].tolist()
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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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saved_video_path_list = saved_metadata_df[args.video_path_column].tolist()
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video_path_list = list(set(video_path_list).difference(set(saved_video_path_list)))
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logger.info(f"Resume from {args.saved_path}: {len(saved_video_path_list)} processed and {len(video_path_list)} to be processed.")
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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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aesthetic_score_metadata_path=args.aesthetic_score_metadata_path,
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min_aesthetic_score=args.min_aesthetic_score,
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aesthetic_score_siglip_metadata_path=args.aesthetic_score_siglip_metadata_path,
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min_aesthetic_score_siglip=args.min_aesthetic_score_siglip,
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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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video_path_column=args.video_path_column
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)
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# Sorting to guarantee the same result for each process.
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video_path_list = natsorted(video_path_list)
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if not any(name in args.model_path for name in ALL_MODEL_NAME):
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raise ValueError(f"The model_path should be among the following list: {ALL_MODEL_NAME}.")
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state = PartialState()
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if state.is_main_process:
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# Check if the model is downloaded in the main process.
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processor, model = init_model(args.model_path, "cpu")
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state.wait_for_everyone()
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processor, model = init_model(args.model_path, state.device)
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index = len(video_path_list) - len(video_path_list) % state.num_processes
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# Avoid the NCCL timeout in the final gather operation.
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logger.warning(
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f"Drop the last {len(video_path_list) % 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_path_list = video_path_list[:index]
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logger.info(f"{len(video_path_list)} videos are to be processed.")
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result_dict = {
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args.video_path_column: [],
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"similarity_cross_frame": [],
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"similarity_mean": [],
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"sample_frame_idx": [],
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}
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with state.split_between_processes(video_path_list) as splitted_video_path_list:
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video_dataset = VideoDataset(
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dataset_inputs={args.video_path_column: splitted_video_path_list},
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video_folder=args.video_folder,
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video_path_column=args.video_path_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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sample_stride=args.sample_stride,
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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 = []
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batch_frame = []
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batch_sampled_frame_idx = []
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# At least two frames are required to calculate cross-frame semantic consistency.
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for path, frame, frame_idx in zip(batch["path"], batch["sampled_frame"], batch["sampled_frame_idx"]):
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if len(frame) > 1:
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batch_video_path.append(path)
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batch_frame.append(frame)
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batch_sampled_frame_idx.append(frame_idx)
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else:
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logger.warning(f"Skip {path} because it only has {len(frame)} frames.")
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frame_num_list = [len(video_frames) for video_frames in batch_frame]
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# [B, T, H, W, C] => [(B * T), H, W, C]
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reshaped_batch_frame = [frame for video_frames in batch_frame for frame in video_frames]
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with torch.no_grad():
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inputs = processor(images=reshaped_batch_frame, return_tensors="pt").to(state.device)
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if "dino" in args.model_path.lower():
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frame_features = model(**inputs).last_hidden_state.mean(dim=1)
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else: # CLIP
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frame_features = model.get_image_features(**inputs)
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# Each video may have a different number of sampled frames.
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# Map the flattened frame features back to their original shape.
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batch_frame_features = torch.split(frame_features, frame_num_list)
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batch_simi_cross_frame = [compute_adjacent_similarity(frame_features) for frame_features in batch_frame_features]
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batch_similarity_mean = [
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sum(simi_cross_frame) / len(simi_cross_frame) for simi_cross_frame in batch_simi_cross_frame
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]
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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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result_dict["similarity_cross_frame"].extend(batch_simi_cross_frame)
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result_dict["similarity_mean"].extend(batch_similarity_mean)
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result_dict["sample_frame_idx"].extend(batch_sampled_frame_idx)
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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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