Ethanfel
6bc3fd6443
Pure PyTorch SelVA source for SelvaModelLoader/FeatureExtractor/Sampler nodes. Imports rewritten from selva.* to selva_core.*. mel_converter.py: replaced librosa.filters.mel with pure-numpy implementation to avoid librosa→numba→NumPy version incompatibility in some ComfyUI environments. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
300 lines
12 KiB
Python
300 lines
12 KiB
Python
import logging
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import os
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from pathlib import Path
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from typing import Optional, Union
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import pandas as pd
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import torch
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import torchaudio
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from torch.utils.data.dataset import Dataset
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from torchvision.transforms import v2
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from torio.io import StreamingMediaDecoder
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from tensordict import TensorDict
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from selva_core.data.av_utils import normalize_video_chunk
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from selva_core.utils.dist_utils import local_rank
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log = logging.getLogger()
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_CLIP_SIZE = 384
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_CLIP_FPS = 8.0
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_SYNC_SIZE = 224
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_SYNC_FPS = 25.0
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class VGGSound(Dataset):
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def __init__(
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self,
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root: Union[str, Path],
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*,
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tsv_path: Union[str, Path] = 'sets/vgg3-train.tsv',
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for_generator: bool = True,
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audio_required: bool = False,
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sample_rate: int = 16_000,
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duration_sec: float = 8.0,
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audio_samples: Optional[int] = None,
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normalize_audio: bool = False,
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clip_video_required: bool = False,
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mmap_dir: Union[str, Path] = None,
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tsv_tsynch_path: Union[str, Path] = None,
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mmap_tsync_dir: Union[str, Path] = None,
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data_dim: dict[str, int] = None,
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):
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self.root = Path(root)
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self.audio_required = audio_required
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if audio_required:
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self.normalize_audio = normalize_audio
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if audio_samples is None:
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self.audio_samples = int(sample_rate * duration_sec)
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else:
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self.audio_samples = audio_samples
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effective_duration = audio_samples / sample_rate
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# make sure the duration is close enough, within 15ms
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assert abs(effective_duration - duration_sec) < 0.015, \
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f'audio_samples {audio_samples} does not match duration_sec {duration_sec}'
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self.clip_video_required = clip_video_required
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self.for_generator = for_generator
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videos = sorted(os.listdir(self.root))
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videos = set([Path(v).stem for v in videos]) # remove extensions
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self.labels = {}
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self.videos = []
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missing_videos = []
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# read the tsv for subset information
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df_list = pd.read_csv(tsv_path, sep='\t', dtype={'id': str}).to_dict('records')
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for record in df_list:
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id = record['id']
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label = record['label']
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if id in videos:
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self.labels[id] = label
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self.videos.append(id)
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else:
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missing_videos.append(id)
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if local_rank == 0:
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log.info(f'{len(videos)} videos found in {root}')
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log.info(f'{len(self.videos)} videos found in {tsv_path}')
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log.info(f'{len(missing_videos)} videos missing in {root}')
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self.sample_rate = sample_rate
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self.duration_sec = duration_sec
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if audio_required:
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self.expected_audio_length = self.audio_samples
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self.sync_expected_length = int(_SYNC_FPS * self.duration_sec)
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if clip_video_required:
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self.clip_expected_length = int(_CLIP_FPS * self.duration_sec)
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self.sync_transform = v2.Compose([
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v2.Resize((_SYNC_SIZE, _SYNC_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
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# v2.CenterCrop(_SYNC_SIZE),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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if clip_video_required:
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self.clip_transform = v2.Compose([
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v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
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v2.ToImage(),
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v2.ToDtype(torch.float32, scale=True),
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])
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if audio_required:
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self.resampler = {}
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# mmap
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log.info(f'Loading precomputed mmap from {mmap_dir}')
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mmap_dir = Path(mmap_dir)
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td = TensorDict.load_memmap(mmap_dir)
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log.info(f'Loaded precomputed mmap from {mmap_dir}')
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self.sync_features = td['sync_features']
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if for_generator:
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self.mean = td['mean']
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self.std = td['std']
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self.text_clip_features = td['text_features']
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if clip_video_required:
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self.clip_features = td['clip_features']
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else:
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self.clip_features = None
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self.id2idx_mmap = {d['id']: i for i, d in enumerate(df_list)}
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mmap_tsync_dir = Path(mmap_tsync_dir)
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td_tsync = TensorDict.load_memmap(mmap_tsync_dir)
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log.info(f'Loaded precomputed tsync mmap from {mmap_tsync_dir}')
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self.text_features = td_tsync['text_features']
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self.text_masks = td_tsync['text_masks']
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df_list_tsync = pd.read_csv(tsv_tsynch_path, sep='\t').to_dict('records')
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self.id2idx_mmap_tsync = {d['id']: i for i, d in enumerate(df_list_tsync)}
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if local_rank == 0:
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log.info(f'Loaded {len(self)} samples.')
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log.info(f'Loaded sync_features: {self.sync_features.shape}.')
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log.info(f'Loaded text_features: {self.text_features.shape}.')
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log.info(f'Loaded text_masks: {self.text_masks.shape}.')
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if for_generator:
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log.info(f'Loaded mean: {self.mean.shape}.')
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log.info(f'Loaded std: {self.std.shape}.')
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log.info(f'Loaded text_clip_features: {self.text_clip_features.shape}.')
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if clip_video_required:
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log.info(f'Loaded clip_features: {self.clip_features.shape}.')
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assert self.sync_features.shape[1] == data_dim['sync_seq_len'], \
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f'{self.sync_features.shape[1]} != {data_dim["sync_seq_len"]}'
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assert self.text_features.shape[1] <= data_dim['text_flant5_max_seq_len'], \
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f'{self.text_features.shape[1]} > {data_dim["text_flant5_max_seq_len"]}'
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assert self.text_masks.shape[1] <= data_dim['text_flant5_max_seq_len'], \
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f'{self.text_masks.shape[1]} > {data_dim["text_flant5_max_seq_len"]}'
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assert self.sync_features.shape[-1] == data_dim['sync_dim'], \
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f'{self.sync_features.shape[-1]} != {data_dim["sync_dim"]}'
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assert self.text_features.shape[-1] == data_dim['text_flant5_dim'], \
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f'{self.text_features.shape[-1]} != {data_dim["text_flant5_dim"]}'
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if for_generator:
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assert self.mean.shape[1] == data_dim['latent_seq_len'], \
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f'{self.mean.shape[1]} != {data_dim["latent_seq_len"]}'
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assert self.std.shape[1] == data_dim['latent_seq_len'], \
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f'{self.std.shape[1]} != {data_dim["latent_seq_len"]}'
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assert self.text_clip_features.shape[1] == data_dim['text_clip_seq_len'], \
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f'{self.text_clip_features.shape[1]} != {data_dim["text_clip_seq_len"]}'
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assert self.text_clip_features.shape[-1] == data_dim['text_clip_dim'], \
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f'{self.text_clip_features.shape[-1]} != {data_dim["text_clip_dim"]}'
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if clip_video_required:
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assert self.clip_features.shape[1] == data_dim['clip_seq_len'], \
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f'{self.clip_features.shape[1]} != {data_dim["clip_seq_len"]}'
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assert self.clip_features.shape[-1] == data_dim['clip_dim'], \
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f'{self.clip_features.shape[-1]} != {data_dim["clip_dim"]}'
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self.video_exist = torch.tensor(1, dtype=torch.bool)
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self.text_exist = torch.tensor(1, dtype=torch.bool)
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def compute_latent_stats(self) -> tuple[torch.Tensor, torch.Tensor]: # mmap
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latents = self.mean
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return latents.mean(dim=(0, 1)), latents.std(dim=(0, 1))
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def get_memory_mapped_tensor(self) -> TensorDict:
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td = TensorDict({
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'sync_features': self.sync_features,
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'text_features': self.text_features,
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'text_masks': self.text_masks,
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})
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if self.for_generator:
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td['mean'] = self.mean
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td['std'] = self.std
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td['text_clip_features'] = self.text_clip_features
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if self.clip_video_required:
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td['clip_features'] = self.clip_features
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return td
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def sample(self, idx: int) -> dict[str, torch.Tensor]:
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video_id = self.videos[idx]
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if video_id in self.captions and torch.rand(1).item() < self.autoacd_sample_prob:
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label = self.captions[video_id]
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else:
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label = self.labels[video_id]
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reader = StreamingMediaDecoder(self.root / (video_id + '.mp4'))
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reader.add_basic_video_stream(
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frames_per_chunk=int(_SYNC_FPS * self.duration_sec),
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frame_rate=_SYNC_FPS,
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format='rgb24',
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)
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if self.audio_required:
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reader.add_basic_audio_stream(frames_per_chunk=2**30, )
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if self.clip_video_required:
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reader.add_basic_video_stream(
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frames_per_chunk=int(_CLIP_FPS * self.duration_sec),
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frame_rate=_CLIP_FPS,
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format='rgb24',
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)
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reader.fill_buffer()
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data_chunk = reader.pop_chunks()
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sync_chunk = data_chunk[0]
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if sync_chunk is None:
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raise RuntimeError(f'Sync video returned None {video_id}')
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sync_chunk = normalize_video_chunk(sync_chunk, self.sync_expected_length,
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n_tolerance_frame=3, desc=video_id)
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sync_chunk = self.sync_transform(sync_chunk)
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if self.audio_required:
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audio_chunk = data_chunk[1]
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if self.clip_video_required:
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clip_chunk = data_chunk[2 if self.audio_required else 1]
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if clip_chunk is None:
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raise RuntimeError(f'CLIP video returned None {video_id}')
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clip_chunk = normalize_video_chunk(clip_chunk, self.clip_expected_length,
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n_tolerance_frame=1, desc=video_id)
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clip_chunk = self.clip_transform(clip_chunk)
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# process audio
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if self.audio_required:
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sample_rate = int(reader.get_out_stream_info(1).sample_rate)
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audio_chunk = audio_chunk.transpose(0, 1)
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audio_chunk = audio_chunk.mean(dim=0) # mono
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if self.normalize_audio:
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abs_max = audio_chunk.abs().max()
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audio_chunk = audio_chunk * (0.95 / abs_max)
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if abs_max <= 1e-6:
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raise RuntimeError(f'Audio is silent {video_id}')
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# resample
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if sample_rate == self.sample_rate:
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audio_chunk = audio_chunk
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else:
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if sample_rate not in self.resampler:
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# https://pytorch.org/audio/stable/tutorials/audio_resampling_tutorial.html#kaiser-best
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self.resampler[sample_rate] = torchaudio.transforms.Resample(
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sample_rate,
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self.sample_rate,
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lowpass_filter_width=64,
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rolloff=0.9475937167399596,
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resampling_method='sinc_interp_kaiser',
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beta=14.769656459379492,
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)
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audio_chunk = self.resampler[sample_rate](audio_chunk)
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if audio_chunk.shape[0] < self.expected_audio_length:
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raise RuntimeError(f'Audio too short {video_id}')
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audio_chunk = audio_chunk[:self.expected_audio_length]
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data = {
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'id': video_id,
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'caption': label,
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'sync_video': sync_chunk,
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'sync_f_vid_orig': self.sync_features[self.id2idx_mmap[video_id]],
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'text_features': self.text_features[self.id2idx_mmap_tsync[video_id]],
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'text_masks': self.text_masks[self.id2idx_mmap_tsync[video_id]],
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'video_exist': self.video_exist,
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'text_exist': self.text_exist,
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}
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if self.for_generator:
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data['a_mean'] = self.mean[self.id2idx_mmap[video_id]]
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data['a_std'] = self.std[self.id2idx_mmap[video_id]]
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data['text_clip_features'] = self.text_clip_features[self.id2idx_mmap[video_id]]
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if self.audio_required:
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data['audio'] = audio_chunk
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if self.clip_video_required:
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data['clip_video'] = clip_chunk
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data['clip_features'] = self.clip_features[self.id2idx_mmap[video_id]],
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return data
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def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
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try:
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return self.sample(idx)
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except Exception as e:
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log.error(f'Error loading video {self.videos[idx]}: {e}')
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return None
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def __len__(self):
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return len(self.labels)
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