| 43 | |
| 44 | class DataModuleFromConfig(pl.LightningDataModule): |
| 45 | def __init__(self, batch_size, train=None, validation=None, test=None, predict=None, |
| 46 | wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False, |
| 47 | shuffle_val_dataloader=False, train_img=None, |
| 48 | test_max_n_samples=None): |
| 49 | super().__init__() |
| 50 | self.batch_size = batch_size |
| 51 | self.dataset_configs = dict() |
| 52 | self.num_workers = num_workers if num_workers is not None else batch_size * 2 |
| 53 | self.use_worker_init_fn = use_worker_init_fn |
| 54 | if train is not None: |
| 55 | self.dataset_configs["train"] = train |
| 56 | self.train_dataloader = self._train_dataloader |
| 57 | if validation is not None: |
| 58 | self.dataset_configs["validation"] = validation |
| 59 | self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader) |
| 60 | if test is not None: |
| 61 | self.dataset_configs["test"] = test |
| 62 | self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader) |
| 63 | if predict is not None: |
| 64 | self.dataset_configs["predict"] = predict |
| 65 | self.predict_dataloader = self._predict_dataloader |
| 66 | |
| 67 | self.img_loader = None |
| 68 | self.wrap = wrap |
| 69 | self.test_max_n_samples = test_max_n_samples |
| 70 | self.collate_fn = None |
| 71 | |
| 72 | def prepare_data(self): |
| 73 | pass |