(self, dataset, args, device)
| 126 | |
| 127 | class Loader: |
| 128 | def __init__(self, dataset, args, device): |
| 129 | self.device = device |
| 130 | split_indices = list(range(len(dataset))) |
| 131 | self.sampler = torch.utils.data.sampler.SubsetRandomSampler(split_indices) |
| 132 | self.loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, sampler=self.sampler, |
| 133 | num_workers=args.train_num_workers, pin_memory=True, |
| 134 | collate_fn=collate_events) |
| 135 | |
| 136 | def __iter__(self): |
| 137 | for data in self.loader: |