| 13 | |
| 14 | |
| 15 | class DataModuleFromConfig(pl.LightningDataModule): |
| 16 | def __init__( |
| 17 | self, |
| 18 | batch_size=8, |
| 19 | num_workers=4, |
| 20 | train=None, |
| 21 | validation=None, |
| 22 | test=None, |
| 23 | **kwargs, |
| 24 | ): |
| 25 | super().__init__() |
| 26 | |
| 27 | self.batch_size = batch_size |
| 28 | self.num_workers = num_workers |
| 29 | |
| 30 | self.dataset_configs = dict() |
| 31 | if train is not None: |
| 32 | self.dataset_configs['train'] = train |
| 33 | if validation is not None: |
| 34 | self.dataset_configs['validation'] = validation |
| 35 | if test is not None: |
| 36 | self.dataset_configs['test'] = test |
| 37 | |
| 38 | def setup(self, stage): |
| 39 | |
| 40 | if stage in ['fit']: |
| 41 | self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) |
| 42 | else: |
| 43 | raise NotImplementedError |
| 44 | |
| 45 | def train_dataloader(self): |
| 46 | |
| 47 | sampler = DistributedSampler(self.datasets['train']) |
| 48 | return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler) |
| 49 | |
| 50 | def val_dataloader(self): |
| 51 | |
| 52 | sampler = DistributedSampler(self.datasets['validation']) |
| 53 | return wds.WebLoader(self.datasets['validation'], batch_size=4, num_workers=self.num_workers, shuffle=False, sampler=sampler) |
| 54 | |
| 55 | def test_dataloader(self): |
| 56 | |
| 57 | return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False) |
| 58 | |
| 59 | |
| 60 | class ObjaverseData(Dataset): |
nothing calls this directly
no outgoing calls
no test coverage detected