| 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) |
|
|
