| | import bisect
|
| |
|
| | import torch
|
| | from torch.utils.data.dataset import Dataset
|
| |
|
| |
|
| |
|
| | class MultiModalDataset(Dataset):
|
| | datasets: list[Dataset]
|
| | cumulative_sizes: list[int]
|
| |
|
| | @staticmethod
|
| | def cumsum(sequence):
|
| | r, s = [], 0
|
| | for e in sequence:
|
| | l = len(e)
|
| | r.append(l + s)
|
| | s += l
|
| | return r
|
| |
|
| | def __init__(self, video_datasets: list[Dataset], audio_datasets: list[Dataset]):
|
| | super().__init__()
|
| | self.video_datasets = list(video_datasets)
|
| | self.audio_datasets = list(audio_datasets)
|
| | self.datasets = self.video_datasets + self.audio_datasets
|
| |
|
| | self.cumulative_sizes = self.cumsum(self.datasets)
|
| |
|
| | def __len__(self):
|
| | return self.cumulative_sizes[-1]
|
| |
|
| | def __getitem__(self, idx):
|
| | if idx < 0:
|
| | if -idx > len(self):
|
| | raise ValueError("absolute value of index should not exceed dataset length")
|
| | idx = len(self) + idx
|
| | dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
|
| | if dataset_idx == 0:
|
| | sample_idx = idx
|
| | else:
|
| | sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
|
| | return self.datasets[dataset_idx][sample_idx]
|
| |
|
| | def compute_latent_stats(self) -> tuple[torch.Tensor, torch.Tensor]:
|
| | return self.video_datasets[0].compute_latent_stats()
|
| |
|
