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import torch
from torch.utils.data import Dataset
from typing import List
import os
from mm_datautils import process_video_frames
from transformers import BaseImageProcessor
from concurrent.futures import ThreadPoolExecutor, as_completed
class EnTubeDataset(Dataset):
def __init__(
self,
folder_paths: List[str],
image_processors: List[BaseImageProcessor],
device: str
) -> None:
self.file_paths = []
self.image_processors = image_processors
self.device = device
for folder_path in folder_paths:
file_names = os.listdir(folder_path)
for file_name in file_names:
file_path = os.path.join(folder_path, file_name)
self.file_paths.append(file_path)
# with ThreadPoolExecutor(max_workers=get_optimal_workers()) as executor:
# futures = []
# for folder_path in folder_paths:
# print(f'@tcm: In EnTubeDataset.__init__(): folder_path={folder_path}')
# file_names = os.listdir(folder_path)
# for file_name in file_names:
# file_path = os.path.join(folder_path, file_name)
# print(f'@tcm: In EnTubeDataset.__init__(): file_path={file_path}')
# future = executor.submit(process_video_frames, file_path, image_processor, device)
# futures.append(future)
# for future in as_completed(futures):
# result = future.result()
# if result is not None:
# video, image_size = result
# self.videos.append(video)
# self.image_sizes.append(image_size)
def __len__(self):
return len(self.file_paths)
def __getitem__(self, idx):
print(f'@tcm: In EnTubeDataset.__getitem__(): idx={idx}')
video, image_size = process_video_frames(self.file_paths[idx], self.image_processors, self.device)
return video, image_size
def collate_fn(batch):
"""
batch: list of samples from EnTubeDataset.__getitem__()
"""
assert isinstance(batch, list)
assert isinstance(batch[0], tuple)
image_sizes = batch[0][1]
batch_videos = [video for video, _ in batch]
batch_videos = [list(videos) for videos in zip(*batch_videos)]
return batch_videos, image_sizes
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