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from numbers import Number
from pathlib import Path
from typing import Any, Dict, Optional, Sequence, Type
from lightning import LightningDataModule
from sklearn.base import TransformerMixin
from torch.utils.data import Dataset, DataLoader
from deepscreen.data.utils import collate_fn, SafeBatchSampler
from deepscreen.data.utils.dataset import BaseEntityDataset
class EntityDataModule(LightningDataModule):
"""
def prepare_data(self):
# things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
# download data, pre-process, split, save to disk, etc.
def setup(self, stage):
# things to do on every process in DDP
# load data, set variables, etc.
def train_dataloader(self):
# return train dataloader
def val_dataloader(self):
# return validation dataloader
def test_dataloader(self):
# return test dataloader
def teardown(self):
# called on every process in DDP
# clean up after fit or test
"""
def __init__(
self,
dataset: type[BaseEntityDataset],
transformer: type[TransformerMixin],
train: bool,
batch_size: int,
data_dir: str = "data/",
data_file: Optional[str] = None,
train_val_test_split: Optional[Sequence[Number], Sequence[str]] = None,
split: Optional[callable] = None,
num_workers: int = 0,
pin_memory: bool = False,
):
super().__init__()
# data processing
self.split = split
if train:
if all([data_file, split]):
if all(isinstance(split, Number) for split in train_val_test_split):
pass
else:
raise ValueError('`train_val_test_split` must be a sequence of 3 numbers '
'(float for percentages and int for sample numbers) if '
'`data_file` and `split` have been specified.')
elif all(isinstance(split, str) for split in train_val_test_split) and not any([data_file, split]):
self.train_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[0]))
self.val_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[1]))
self.test_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[2]))
else:
raise ValueError('For training (train=True), you must specify either '
'`dataset_name` and `split` with `train_val_test_split` of 3 numbers or '
'solely `train_val_test_split` of 3 data file names.')
else:
if data_file and not any([split, train_val_test_split]):
self.test_data = self.predict_data = dataset(dataset_path=str(Path(data_dir) / data_file))
else:
raise ValueError("For testing/predicting (train=False), you must specify only `data_file` without "
"`train_val_test_split` or `split`")
# this line allows to access init params with 'self.hparams' attribute
# also ensures init params will be stored in ckpt
self.save_hyperparameters(logger=False)
def prepare_data(self):
"""
Download data if needed.
Do not use it to assign state (e.g., self.x = x).
"""
def setup(self, stage: Optional[str] = None, encoding: str = None):
"""
Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
careful not to execute data splitting twice.
"""
# TODO test SafeBatchSampler (which skips samples with any None without introducing variable batch size)
# TODO: find a way to apply transformer.fit_transform only to train and transformer.transform only to val, test
# load and split datasets only if not loaded in initialization
if not any([self.train_data, self.test_data, self.val_data, self.predict_data]):
self.train_data, self.val_data, self.test_data = self.split(
dataset=self.hparams.dataset(data_dir=self.hparams.data_dir,
dataset_name=self.hparams.train_dataset_name),
lengths=self.hparams.train_val_test_split
)
def train_dataloader(self):
return DataLoader(
dataset=self.train_data,
batch_sampler=SafeBatchSampler(
data_source=self.train_data,
batch_size=self.hparams.batch_size,
shuffle=True),
# batch_size=self.hparams.batch_size,
# shuffle=True,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
collate_fn=collate_fn,
persistent_workers=True if self.hparams.num_workers > 0 else False
)
def val_dataloader(self):
return DataLoader(
dataset=self.val_data,
batch_sampler=SafeBatchSampler(
data_source=self.val_data,
batch_size=self.hparams.batch_size,
shuffle=False),
# batch_size=self.hparams.batch_size,
# shuffle=False,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
collate_fn=collate_fn,
persistent_workers=True if self.hparams.num_workers > 0 else False
)
def test_dataloader(self):
return DataLoader(
dataset=self.test_data,
batch_sampler=SafeBatchSampler(
data_source=self.test_data,
batch_size=self.hparams.batch_size,
shuffle=False),
# batch_size=self.hparams.batch_size,
# shuffle=False,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
collate_fn=collate_fn,
persistent_workers=True if self.hparams.num_workers > 0 else False
)
def predict_dataloader(self):
return DataLoader(
dataset=self.predict_data,
batch_sampler=SafeBatchSampler(
data_source=self.predict_data,
batch_size=self.hparams.batch_size,
shuffle=False),
# batch_size=self.hparams.batch_size,
# shuffle=False,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
collate_fn=collate_fn,
persistent_workers=True if self.hparams.num_workers > 0 else False
)
def teardown(self, stage: Optional[str] = None):
"""Clean up after fit or test."""
pass
def state_dict(self):
"""Extra things to save to checkpoint."""
return {}
def load_state_dict(self, state_dict: Dict[str, Any]):
"""Things to do when loading checkpoint."""
pass
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