prepare_frame_cache_old.py

from time_r1.utils.qwen_vl_utils_prepare import floor_by_factor, FRAME_FACTOR, smart_resize, ceil_by_factor
import decord
import torch
import os
import tqdm
import glob
import multiprocessing
from torchvision import io, transforms
from torchvision.transforms import InterpolationMode
from functools import partial
from time_r1.utils.io import load_jsonl
import debugpy
# try:
#     # 5678 is the default attach port in the VS Code debug configurations. Unless a host and port are specified, host defaults to 127.0.0.1
#     debugpy.listen(("localhost", 9511))
#     print("Waiting for debugger attach")
#     debugpy.wait_for_client()
# except Exception as e:
#     pass



FPS_MIN_FRAMES = 4
FPS_MAX_FRAMES = 120  # 建议根据你的显存情况调整，60-80 是常用范围
FRAME_FACTOR = 2
def get_video_tensor(video_path, target_fps=2, image_factor = 28, min_pixels = 28 * 28 * 4, max_pixels = 28 * 28 * 128):
    """
    将视频以固定帧率提前抽帧、解码保存为tensor，用于后续训练
    """
    vr = decord.VideoReader(video_path)
    total_frames, video_fps = len(vr), vr.get_avg_fps()


    min_nframes = ceil_by_factor(FPS_MIN_FRAMES, FRAME_FACTOR) # ：ceil_by_factor 是为了保证帧数是 FRAME_FACTOR 的倍数
    max_frames = floor_by_factor(min(FPS_MAX_FRAMES, total_frames), FRAME_FACTOR)
    nframes = total_frames / video_fps * target_fps # 理论帧数
    if nframes > total_frames:
        print(f"smart_nframes: nframes[{nframes}] > total_frames[{total_frames}]")

    nframes = min(min(max(nframes, min_nframes), max_frames), total_frames)
    nframes = floor_by_factor(nframes, FRAME_FACTOR)
 
    frame_idx = torch.linspace(0, total_frames - 1, nframes).round().long().tolist() # 均匀采样
    frame_tensor = vr.get_batch(frame_idx).asnumpy()
    frame_tensor = torch.tensor(frame_tensor).permute(0, 3, 1, 2)  # Convert to TCHW format
    sample_fps = nframes / max(total_frames, 1e-6) * video_fps
    height, width = frame_tensor.shape[2], frame_tensor.shape[3]
    resized_height, resized_width = smart_resize(
        height,
        width,
        factor=image_factor,
        min_pixels=min_pixels,
        max_pixels=max_pixels,
    )
    frame_tensor = transforms.functional.resize(
        frame_tensor,
        [resized_height, resized_width],
        interpolation=InterpolationMode.BICUBIC,
        antialias=True,
    )
    frame_cache = {
        "frame_tensor": frame_tensor,
        "fps": sample_fps,
    }
    return frame_cache


def process_single_video(video_path, target_fps=1, image_factor = 28, min_pixels = 28 * 28 * 128, max_pixels = 28 * 28 * 256):
    save_dir = '/data/shuimu.chen/LongVideoBench/video_cache_300p_120'
    os.makedirs(save_dir, exist_ok=True)
    video_filename = os.path.basename(video_path)
    save_path = os.path.join(save_dir, video_filename + '.frame_cache')
    """Helper function to process and save frame cache for a single video."""
    print(f"Processing {video_path}...")
    try:
        frame_cache = get_video_tensor(video_path, target_fps, image_factor, min_pixels, max_pixels)
        torch.save(frame_cache, save_path)
        print(f"Successfully saved frame cache for {video_path}")
    except Exception as e:
        print(f"Error processing {video_path}: {e}")


# def process_single_video(video_path, target_fps=1, image_factor = 28, min_pixels = 28 * 28 * 128, max_pixels = 28 * 28 * 256):
#     save_path = '/data/shuimu.chen/LongVideoBench/video_cache_3p_60'
#     """Helper function to process and save frame cache for a single video."""
#     print(f"Processing {video_path}...")
#     try:
#         frame_cache = get_video_tensor(video_path, target_fps, image_factor, min_pixels, max_pixels)
#         torch.save(frame_cache, video_path + ".frame_cache")
#         print(f"Successfully saved frame cache for {video_path}")
#     except Exception as e:
#         print(f"Error processing {video_path}: {e}")


def prepare_frame_cache(video_root, dataset_path=None, num_workers=8, target_fps=1, overwrite=False, image_factor = 28, min_pixels = 28 * 28 * 128, max_pixels = 28 * 28 * 256):
    if dataset_path is not None:
        # 修改这里：直接使用json.load而不是load_jsonl
        import json
        with open(dataset_path, 'r', encoding='utf-8') as f:
            video_data = json.load(f)  # 这是JSON数组
        
        # 提取video_path字段，并去重（同一个视频可能有多条记录）
        video_paths = set()  # 使用set去重
        for v in video_data:
            if "video_path" in v:
                video_paths.add(v["video_path"])
            elif "video" in v:
                # 如果有video字段，可能需要拼接路径
                video_path = os.path.join(video_root, v["video"])
                video_paths.add(video_path)
        
        video_list = list(video_paths)
    else:
        video_list = glob.glob(os.path.join(video_root, "*.mp4"))
    if not video_list:
        print(f"No MP4 videos found in {video_root}")
        return
    # remove videos that already have frame cache
    if not overwrite:
        print("skipping videos that already have frame cache")
        num_total = len(video_list)
        video_list = [v for v in video_list if not os.path.exists(v + ".frame_cache")]
        num_skipped = num_total - len(video_list)
        print(f"skipped {num_skipped} videos")

    if num_workers is None:
        num_workers = multiprocessing.cpu_count()  # Default to using all available CPU cores
    
    print(f"Found {len(video_list)} videos. Starting processing with {num_workers} workers...")

    # Use a multiprocessing Pool to process videos in parallel
    with multiprocessing.Pool(processes=num_workers) as pool:
        # Using tqdm with pool.imap_unordered for progress bar and efficient iteration
        # We wrap process_single_video if it needs more arguments or if we want to handle results
        # For this case, process_single_video only takes video_path
        func = partial(process_single_video, target_fps=target_fps, image_factor = image_factor, min_pixels = min_pixels, max_pixels = max_pixels)
        list(tqdm.tqdm(pool.imap_unordered(func, video_list), total=len(video_list)))

    print("All videos processed.")


if __name__ == "__main__":
    import fire
    fire.Fire(prepare_frame_cache)