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import bisect
import torch
from torch.utils.data.dataset import Dataset
# modified from https://pytorch.org/docs/stable/_modules/torch/utils/data/dataset.html#ConcatDataset
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()
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