101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
import os
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from pathlib import Path
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from typing import Optional
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from func_timeout import FunctionTimedOut, func_timeout
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from PIL import Image
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from torch.utils.data import DataLoader, Dataset
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from .logger import logger
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from .video_utils import extract_frames
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ALL_VIDEO_EXT = set([".mp4", ".webm", ".mkv", ".avi", ".flv", ".mov", ".ts"])
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VIDEO_READER_TIMEOUT = 300
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def collate_fn(batch):
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batch = list(filter(lambda x: x is not None, batch))
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if len(batch) != 0:
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return {k: [item[k] for item in batch] for k in batch[0].keys()}
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return {}
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class VideoDataset(Dataset):
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def __init__(
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self,
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dataset_inputs: dict[str, list[str]],
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video_folder: Optional[str] = None,
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video_path_column: str = "video_path",
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text_column: Optional[str] = None,
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sample_method: str = "mid",
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num_sampled_frames: int = 1,
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sample_stride: Optional[int] = None
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):
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length = len(dataset_inputs[list(dataset_inputs.keys())[0]])
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if not all(len(v) == length for v in dataset_inputs.values()):
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raise ValueError("All values in the dataset_inputs must have the same length.")
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self.video_path_column = video_path_column
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self.video_folder = video_folder
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self.video_path_list = dataset_inputs[video_path_column]
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if self.video_folder is not None:
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self.video_path_list = [os.path.join(self.video_folder, video_path) for video_path in self.video_path_list]
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self.text_column = text_column
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self.text_list = dataset_inputs[self.text_column] if self.text_column is not None else None
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self.sample_method = sample_method
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self.num_sampled_frames = num_sampled_frames
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self.sample_stride = sample_stride
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def __getitem__(self, index):
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video_path = self.video_path_list[index]
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if self.sample_method == "image":
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try:
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sampled_frame_idx_list = None
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with open(video_path, "rb") as f:
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sampled_frame_list = [Image.open(f).convert("RGB")]
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except Exception as e:
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logger.warning(f"Failed to extract frames from video {video_path}. Error is {e}.")
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return None
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else:
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# It is a trick to deal with decord hanging when reading some abnormal videos.
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try:
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sample_args = (video_path, self.sample_method, self.num_sampled_frames, self.sample_stride)
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sampled_frame_idx_list, sampled_frame_list = func_timeout(
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VIDEO_READER_TIMEOUT, extract_frames, args=sample_args
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)
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except FunctionTimedOut:
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logger.warning(f"Read {video_path} timeout.")
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return None
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except Exception as e:
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logger.warning(f"Failed to extract frames from video {video_path}. Error is {e}.")
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return None
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item = {
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"path": video_path,
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"sampled_frame_idx": sampled_frame_idx_list,
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"sampled_frame": sampled_frame_list,
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}
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if self.text_list is not None:
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item["text"] = self.text_list[index]
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return item
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def __len__(self):
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return len(self.video_path_list)
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if __name__ == "__main__":
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video_folder = Path("your_video_folder")
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video_path_list = []
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for ext in ALL_VIDEO_EXT:
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video_path_list += [str(file.relative_to(video_folder)) for file in video_folder.glob(f"*.{ext}")]
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video_dataset = VideoDataset(dataset_inputs={"video_path": video_path_list})
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video_dataloader = DataLoader(
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video_dataset, batch_size=16, num_workers=16, collate_fn=collate_fn
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)
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for idx, batch in enumerate(video_dataloader):
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if len(batch) != 0:
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print(batch["video_path"], batch["sampled_frame_idx"], len(batch["video_path"])) |