| from typing import Optional |
|
|
| import torchdata.datapipes.iter |
| import webdataset as wds |
| from omegaconf import DictConfig |
| from pytorch_lightning import LightningDataModule |
|
|
| try: |
| from sdata import create_dataset, create_dummy_dataset, create_loader |
| except ImportError as e: |
| print("#" * 100) |
| print("Datasets not yet available") |
| print("to enable, we need to add stable-datasets as a submodule") |
| print("please use ``git submodule update --init --recursive``") |
| print("and do ``pip install -e stable-datasets/`` from the root of this repo") |
| print("#" * 100) |
| exit(1) |
|
|
|
|
| class StableDataModuleFromConfig(LightningDataModule): |
| def __init__( |
| self, |
| train: DictConfig, |
| validation: Optional[DictConfig] = None, |
| test: Optional[DictConfig] = None, |
| skip_val_loader: bool = False, |
| dummy: bool = False, |
| ): |
| super().__init__() |
| self.train_config = train |
| assert ( |
| "datapipeline" in self.train_config and "loader" in self.train_config |
| ), "train config requires the fields `datapipeline` and `loader`" |
|
|
| self.val_config = validation |
| if not skip_val_loader: |
| if self.val_config is not None: |
| assert ( |
| "datapipeline" in self.val_config and "loader" in self.val_config |
| ), "validation config requires the fields `datapipeline` and `loader`" |
| else: |
| print( |
| "Warning: No Validation datapipeline defined, using that one from training" |
| ) |
| self.val_config = train |
|
|
| self.test_config = test |
| if self.test_config is not None: |
| assert ( |
| "datapipeline" in self.test_config and "loader" in self.test_config |
| ), "test config requires the fields `datapipeline` and `loader`" |
|
|
| self.dummy = dummy |
| if self.dummy: |
| print("#" * 100) |
| print("USING DUMMY DATASET: HOPE YOU'RE DEBUGGING ;)") |
| print("#" * 100) |
|
|
| def setup(self, stage: str) -> None: |
| print("Preparing datasets") |
| if self.dummy: |
| data_fn = create_dummy_dataset |
| else: |
| data_fn = create_dataset |
|
|
| self.train_datapipeline = data_fn(**self.train_config.datapipeline) |
| if self.val_config: |
| self.val_datapipeline = data_fn(**self.val_config.datapipeline) |
| if self.test_config: |
| self.test_datapipeline = data_fn(**self.test_config.datapipeline) |
|
|
| def train_dataloader(self) -> torchdata.datapipes.iter.IterDataPipe: |
| loader = create_loader(self.train_datapipeline, **self.train_config.loader) |
| return loader |
|
|
| def val_dataloader(self) -> wds.DataPipeline: |
| return create_loader(self.val_datapipeline, **self.val_config.loader) |
|
|
| def test_dataloader(self) -> wds.DataPipeline: |
| return create_loader(self.test_datapipeline, **self.test_config.loader) |
|
|
