| | 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 ExtractedVGG(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 = [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']
|
| | self.std = td['std']
|
| | self.clip_features = td['clip_features']
|
| | self.sync_features = td['sync_features']
|
| | self.text_features = td['text_features']
|
| |
|
| | if local_rank == 0:
|
| | log.info(f'Loaded {len(self)} samples.')
|
| | log.info(f'Loaded mean: {self.mean.shape}.')
|
| | log.info(f'Loaded std: {self.std.shape}.')
|
| | log.info(f'Loaded clip_features: {self.clip_features.shape}.')
|
| | log.info(f'Loaded sync_features: {self.sync_features.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.clip_features.shape[1] == self.data_dim['clip_seq_len'], \
|
| | f'{self.clip_features.shape[1]} != {self.data_dim["clip_seq_len"]}'
|
| | assert self.sync_features.shape[1] == self.data_dim['sync_seq_len'], \
|
| | f'{self.sync_features.shape[1]} != {self.data_dim["sync_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.clip_features.shape[-1] == self.data_dim['clip_dim'], \
|
| | f'{self.clip_features.shape[-1]} != {self.data_dim["clip_dim"]}'
|
| | assert self.sync_features.shape[-1] == self.data_dim['sync_dim'], \
|
| | f'{self.sync_features.shape[-1]} != {self.data_dim["sync_dim"]}'
|
| | assert self.text_features.shape[-1] == self.data_dim['text_dim'], \
|
| | f'{self.text_features.shape[-1]} != {self.data_dim["text_dim"]}'
|
| |
|
| | self.video_exist = torch.tensor(1, 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,
|
| | 'clip_features': self.clip_features,
|
| | 'sync_features': self.sync_features,
|
| | 'text_features': self.text_features,
|
| | })
|
| | return td
|
| |
|
| | def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
|
| | data = {
|
| | 'id': self.df_list[idx]['id'],
|
| | 'a_mean': self.mean[idx],
|
| | 'a_std': self.std[idx],
|
| | 'clip_features': self.clip_features[idx],
|
| | 'sync_features': self.sync_features[idx],
|
| | 'text_features': self.text_features[idx],
|
| | 'caption': self.df_list[idx]['label'],
|
| | 'video_exist': self.video_exist,
|
| | 'text_exist': self.text_exist,
|
| | }
|
| |
|
| | return data
|
| |
|
| | def __len__(self):
|
| | return len(self.ids)
|
| |
|
