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"""Base lightning DataModule class."""
from pathlib import Path
from typing import Dict
import pytorch_lightning as pl
from torch.utils.data import DataLoader
def load_and_print_info(data_module_class: type) -> None:
"""Load dataset and print dataset information."""
dataset = data_module_class()
dataset.prepare_data()
dataset.setup()
print(dataset)
class BaseDataModule(pl.LightningDataModule):
"""Base PyTorch Lightning DataModule."""
def __init__(self, batch_size: int = 128, num_workers: int = 0) -> None:
super().__init__()
self.batch_size = batch_size
self.num_workers = num_workers
# Placeholders for subclasses.
self.dims = None
self.output_dims = None
self.mapping = None
@classmethod
def data_dirname(cls) -> Path:
"""Return the path to the base data directory."""
return Path(__file__).resolve().parents[2] / "data"
def config(self) -> Dict:
"""Return important settings of the dataset."""
return {
"input_dim": self.dims,
"output_dims": self.output_dims,
"mapping": self.mapping,
}
def prepare_data(self) -> None:
"""Prepare data for training."""
pass
def setup(self, stage: str = None) -> None:
"""Split into train, val, test, and set dims.
Should assign `torch Dataset` objects to self.data_train, self.data_val, and
optionally self.data_test.
Args:
stage (Any): Variable to set splits.
"""
self.data_train = None
self.data_val = None
self.data_test = None
def train_dataloader(self) -> DataLoader:
"""Retun DataLoader for train data."""
return DataLoader(
self.data_train,
shuffle=True,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=True,
)
def val_dataloader(self) -> DataLoader:
"""Return DataLoader for val data."""
return DataLoader(
self.data_val,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=True,
)
def test_dataloader(self) -> DataLoader:
"""Return DataLoader for val data."""
return DataLoader(
self.data_test,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=True,
)
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