import logging import os from pathlib import Path from typing import Optional, Union import pandas as pd import torch import torchaudio from torch.utils.data.dataset import Dataset from torchvision.transforms import v2 from torio.io import StreamingMediaDecoder from torchvision.utils import save_image log = logging.getLogger() _CLIP_SIZE = 384 _CLIP_FPS = 8.0 _SYNC_SIZE = 224 _SYNC_FPS = 25.0 class VGGSound(Dataset): def __init__( self, root: Union[str, Path], *, tsv_path: Union[str, Path] = 'dataset/vggsound/split_txt/train_caption.csv', sample_rate: int = 44_100, duration_sec: float = 9.0, audio_samples: Optional[int] = 397312, normalize_audio: bool = False, start_row: Optional[int] = None, end_row: Optional[int] = None, save_dir: str = 'data/vggsound/video_latents_text/train' ): self.root = Path(root) self.normalize_audio = normalize_audio if audio_samples is None: self.audio_samples = int(sample_rate * duration_sec) else: self.audio_samples = audio_samples effective_duration = audio_samples / sample_rate # make sure the duration is close enough, within 15ms assert abs(effective_duration - duration_sec) < 0.015, \ f'audio_samples {audio_samples} does not match duration_sec {duration_sec}' videos = sorted(os.listdir(self.root)) videos = set([Path(v).stem for v in videos]) # remove extensions # videos = [] self.labels = [] self.videos = [] missing_videos = [] # read the tsv for subset information df_list = pd.read_csv(tsv_path, sep=',', dtype={'id': str}).to_dict('records') # 控制处理的行范围 if start_row is not None and end_row is not None: df_list = df_list[start_row:end_row] for record in df_list: id = record['id'] if os.path.exists(f'{save_dir}/{id}.pth'): continue label = record['caption'] if id in videos: # self.labels.append(label) self.labels[id] = label self.videos.append(id) else: missing_videos.append(id) log.info(f'{len(videos)} videos found in {root}') log.info(f'{len(self.videos)} videos found in {tsv_path}') log.info(f'{len(missing_videos)} videos missing in {root}') self.sample_rate = sample_rate self.duration_sec = duration_sec self.expected_audio_length = self.audio_samples self.clip_expected_length = int(_CLIP_FPS * self.duration_sec) self.sync_expected_length = int(_SYNC_FPS * self.duration_sec) self.clip_transform = v2.Compose([ v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC), v2.ToImage(), v2.ToDtype(torch.float32, scale=True), ]) self.sync_transform = v2.Compose([ v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC), v2.CenterCrop(_SYNC_SIZE), v2.ToImage(), v2.ToDtype(torch.float32, scale=True), v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) self.resampler = {} def sample(self, idx: int) -> dict[str, torch.Tensor]: video_id = self.videos[idx] label = self.labels[idx] reader = StreamingMediaDecoder(self.root / (video_id + '.mp4')) reader.add_basic_video_stream( frames_per_chunk=int(_CLIP_FPS * self.duration_sec), frame_rate=_CLIP_FPS, format='rgb24', ) reader.add_basic_video_stream( frames_per_chunk=int(_SYNC_FPS * self.duration_sec), frame_rate=_SYNC_FPS, format='rgb24', ) reader.add_basic_audio_stream(frames_per_chunk=2**30,) reader.fill_buffer() data_chunk = reader.pop_chunks() clip_chunk = data_chunk[0] sync_chunk = data_chunk[1] audio_chunk = data_chunk[2] if len(audio_chunk.shape) != 2: raise RuntimeError(f'error audio shape {video_id}') if clip_chunk is None: raise RuntimeError(f'CLIP video returned None {video_id}') # if clip_chunk.shape[0] < self.clip_expected_length: # raise RuntimeError( # f'CLIP video too short {video_id}, expected {self.clip_expected_length}, got {clip_chunk.shape[0]}' # ) if sync_chunk is None: raise RuntimeError(f'Sync video returned None {video_id}') # if sync_chunk.shape[0] < self.sync_expected_length: # raise RuntimeError( # f'Sync video too short {video_id}, expected {self.sync_expected_length}, got {sync_chunk.shape[0]}' # ) # import ipdb # ipdb.set_trace() # process audio sample_rate = int(reader.get_out_stream_info(2).sample_rate) audio_chunk = audio_chunk.transpose(0, 1) abs_max = audio_chunk[0].abs().max() # audio_chunk = audio_chunk.mean(dim=0) # mono # if self.normalize_audio: # abs_max = audio_chunk.abs().max() # audio_chunk = audio_chunk / abs_max * 0.95 if abs_max <= 1e-6: if audio_chunk.shape[0] > 1 and audio_chunk[1].abs().max() > 1e-6: audio_chunk = audio_chunk[1:2] else: raise RuntimeError(f'Audio is silent {video_id}') # if abs_max <= 1e-6: # raise RuntimeError(f'Audio is silent {video_id}') # ensure the stereo audio if audio_chunk.shape[0] < 2: audio_chunk = audio_chunk.repeat(2, 1) # resample if sample_rate == self.sample_rate: audio_chunk = audio_chunk else: if sample_rate not in self.resampler: # https://pytorch.org/audio/stable/tutorials/audio_resampling_tutorial.html#kaiser-best self.resampler[sample_rate] = torchaudio.transforms.Resample( sample_rate, self.sample_rate, lowpass_filter_width=64, rolloff=0.9475937167399596, resampling_method='sinc_interp_kaiser', beta=14.769656459379492, ) audio_chunk = self.resampler[sample_rate](audio_chunk) if audio_chunk.shape[1] < self.expected_audio_length: # zero-padding audio padding_length = self.expected_audio_length - audio_chunk.shape[1] # 创建 padding 张量，大小为 [batch_size, padding_length]，值为0 padding = torch.zeros(audio_chunk.shape[0], padding_length) # 将原始音频和 padding 沿第 1 维度拼接在一起 audio_chunk = torch.cat((audio_chunk, padding), dim=1) # raise RuntimeError(f'Audio too short {video_id}') audio_chunk = audio_chunk[:,:self.expected_audio_length] # truncate the video clip_chunk = clip_chunk[:self.clip_expected_length] # import ipdb # ipdb.set_trace() if clip_chunk.shape[0] != self.clip_expected_length: current_length = clip_chunk.shape[0] padding_needed = self.clip_expected_length - current_length # Check that padding needed is no more than 2 assert padding_needed < 4, f'Padding no more than 2 frames allowed, but {padding_needed} needed' # If assertion passes, proceed with padding if padding_needed > 0: last_frame = clip_chunk[-1] log.info(last_frame.shape) # Repeat the last frame to reach the expected length padding = last_frame.repeat(padding_needed, 1, 1, 1) clip_chunk = torch.cat((clip_chunk, padding), dim=0) # raise RuntimeError(f'CLIP video wrong length {video_id}, ' # f'expected {self.clip_expected_length}, ' # f'got {clip_chunk.shape[0]}') # save_image(clip_chunk[0] / 255.0,'ori.png') clip_chunk = self.clip_transform(clip_chunk) # temp_img = clip_chunk[0].permute(1, 2, 0) * 255 # save_image(clip_chunk[0],'scale.png') sync_chunk = sync_chunk[:self.sync_expected_length] if sync_chunk.shape[0] != self.sync_expected_length: # padding using the last frame, but no more than 2 current_length = sync_chunk.shape[0] last_frame = sync_chunk[-1] # 重复最后一帧以进行填充 padding = last_frame.repeat(self.sync_expected_length - current_length, 1, 1, 1) assert self.sync_expected_length - current_length < 12, f'sync can pad no more than 2 while {self.sync_expected_length - current_length}' sync_chunk = torch.cat((sync_chunk, padding), dim=0) # raise RuntimeError(f'Sync video wrong length {video_id}, ' # f'expected {self.sync_expected_length}, ' # f'got {sync_chunk.shape[0]}') sync_chunk = self.sync_transform(sync_chunk) assert audio_chunk.shape[1] == self.expected_audio_length and clip_chunk.shape[0] == self.clip_expected_length \ and sync_chunk.shape[0] == self.sync_expected_length, 'error processed data shape' data = { 'id': video_id, 'caption': label, 'audio': audio_chunk, 'clip_video': clip_chunk, 'sync_video': sync_chunk, } return data def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: try: return self.sample(idx) except Exception as e: log.error(f'Error loading video {self.videos[idx]}: {e}') return None def __len__(self): return len(self.labels) # dataset = VGGSound( # root="data/vggsound/video/test", # tsv_path="data/vggsound/split_txt/temp.csv", # sample_rate=44100, # duration_sec=9.0, # audio_samples=397312, # start_row=0, # end_row=None, # save_dir="data/vggsound/video_latents_text/test" # ) # dataset[0]