| 163 | |
| 164 | class DataModuleFromConfig(pl.LightningDataModule): |
| 165 | def __init__(self, batch_size, train=None, validation=None, test=None, predict=None, |
| 166 | wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False, |
| 167 | shuffle_val_dataloader=False): |
| 168 | super().__init__() |
| 169 | self.batch_size = batch_size |
| 170 | self.dataset_configs = dict() |
| 171 | self.num_workers = num_workers if num_workers is not None else batch_size * 2 |
| 172 | self.use_worker_init_fn = use_worker_init_fn |
| 173 | if train is not None: |
| 174 | self.dataset_configs["train"] = train |
| 175 | self.train_dataloader = self._train_dataloader |
| 176 | if validation is not None: |
| 177 | self.dataset_configs["validation"] = validation |
| 178 | self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader) |
| 179 | if test is not None: |
| 180 | self.dataset_configs["test"] = test |
| 181 | self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader) |
| 182 | if predict is not None: |
| 183 | self.dataset_configs["predict"] = predict |
| 184 | self.predict_dataloader = self._predict_dataloader |
| 185 | self.wrap = wrap |
| 186 | |
| 187 | def prepare_data(self): |
| 188 | for data_cfg in self.dataset_configs.values(): |