| import logging
|
| from pathlib import Path
|
| from typing import Union
|
|
|
| import pandas as pd
|
| import torch
|
| from tensordict import TensorDict
|
| from torch.utils.data.dataset import Dataset
|
|
|
| from ..utils.dist_utils import local_rank
|
|
|
| log = logging.getLogger()
|
|
|
|
|
| class ExtractedAudio(Dataset):
|
|
|
| def __init__(
|
| self,
|
| tsv_path: Union[str, Path],
|
| *,
|
| premade_mmap_dir: Union[str, Path],
|
| data_dim: dict[str, int],
|
| ):
|
| super().__init__()
|
|
|
| self.data_dim = data_dim
|
| self.df_list = pd.read_csv(tsv_path, sep='\t').to_dict('records')
|
| self.ids = [str(d['id']) for d in self.df_list]
|
|
|
| log.info(f'Loading precomputed mmap from {premade_mmap_dir}')
|
|
|
| premade_mmap_dir = Path(premade_mmap_dir)
|
| td = TensorDict.load_memmap(premade_mmap_dir)
|
| log.info(f'Loaded precomputed mmap from {premade_mmap_dir}')
|
| self.mean = td['mean']
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| self.std = td['std']
|
| self.text_features = td['text_features']
|
|
|
| log.info(f'Loaded {len(self)} samples from {premade_mmap_dir}.')
|
| log.info(f'Loaded mean: {self.mean.shape}.')
|
| log.info(f'Loaded std: {self.std.shape}.')
|
| log.info(f'Loaded text features: {self.text_features.shape}.')
|
|
|
| assert self.mean.shape[1] == self.data_dim['latent_seq_len'], \
|
| f'{self.mean.shape[1]} != {self.data_dim["latent_seq_len"]}'
|
| assert self.std.shape[1] == self.data_dim['latent_seq_len'], \
|
| f'{self.std.shape[1]} != {self.data_dim["latent_seq_len"]}'
|
|
|
| assert self.text_features.shape[1] == self.data_dim['text_seq_len'], \
|
| f'{self.text_features.shape[1]} != {self.data_dim["text_seq_len"]}'
|
| assert self.text_features.shape[-1] == self.data_dim['text_dim'], \
|
| f'{self.text_features.shape[-1]} != {self.data_dim["text_dim"]}'
|
|
|
| self.fake_clip_features = torch.zeros(self.data_dim['clip_seq_len'],
|
| self.data_dim['clip_dim'])
|
| self.fake_sync_features = torch.zeros(self.data_dim['sync_seq_len'],
|
| self.data_dim['sync_dim'])
|
| self.video_exist = torch.tensor(0, dtype=torch.bool)
|
| self.text_exist = torch.tensor(1, dtype=torch.bool)
|
|
|
| def compute_latent_stats(self) -> tuple[torch.Tensor, torch.Tensor]:
|
| latents = self.mean
|
| return latents.mean(dim=(0, 1)), latents.std(dim=(0, 1))
|
|
|
| def get_memory_mapped_tensor(self) -> TensorDict:
|
| td = TensorDict({
|
| 'mean': self.mean,
|
| 'std': self.std,
|
| 'text_features': self.text_features,
|
| })
|
| return td
|
|
|
| def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
|
| data = {
|
| 'id': str(self.df_list[idx]['id']),
|
| 'a_mean': self.mean[idx],
|
| 'a_std': self.std[idx],
|
| 'clip_features': self.fake_clip_features,
|
| 'sync_features': self.fake_sync_features,
|
| 'text_features': self.text_features[idx],
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| 'caption': self.df_list[idx]['caption'],
|
| 'video_exist': self.video_exist,
|
| 'text_exist': self.text_exist,
|
| }
|
| return data
|
|
|
| def __len__(self):
|
| return len(self.ids)
|
|
|
