File size: 8,080 Bytes
8e04e6f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | from abc import ABC, abstractmethod
from typing import Callable, Dict, Iterable, List, Literal, Optional
import lightning as L
from loguru import logger
from torch_geometric import transforms as T
from torch_geometric.data import Dataset
from torch_geometric.loader import DataLoader
from models.utils.cluster_utils import ClusterSampler
from models.utils.dense_padding_data_loader import DensePaddingDataLoader
class BaseLightningDataModule(L.LightningDataModule, ABC):
"""Base class for all datamodules"""
def __init__(
self,
batch_padding: bool = True,
sampling_mode: Literal["random", "cluster-random", "cluster-reps"] = "random",
transforms: Optional[List[Callable]] = None,
pre_transforms: Optional[List[Callable]] = None,
pre_filters: Optional[List[Callable]] = None,
batch_size: int = 32,
num_workers: int = 32,
pin_memory: bool = False,
):
"""Initialising the base data module class.
Args:
batch_padding (bool, optional): Whether batches should be padded to a dense representation
with the length being either a pre-specified max length or the maximum length of the
sample in the batch (base PyTorch batch) or whether a sparse representation should be
used (PyG batch). Defaults to True (base PyTorch batch).
sampling_mode (Literal["random", "cluster-random", "cluster-reps"], optional): How the data should be
sampled from the dataset later on:
- "random": Select a random sequence and ignore clusters.
- "cluster-random": Select a random sequence from each cluster. Keep all samples for each cluster.
- "cluster-reps": Select the cluster representative from each cluster. Only keep the representative for each cluster.
Defaults to "random".
transforms (List[Callable]): List of transforms applied to each example.
pre_transforms (List[Callable]): List of transforms applied to each example before processing.
pre_filters (List[Callable]): List of filters applied to each example before processing.
batch_size (int, optional): Batch size used for dataloaders. Defaults to 32.
num_workers (int, optional): Number of workers used for dataloading. Defaults to 32.
pin_memory (bool, optional): Whether memory should be pinned. Defaults to False.
"""
super().__init__()
self.batch_padding = batch_padding
self.sampling_mode = sampling_mode
self.transform = (
self._compose_transforms(transforms) if transforms is not None else None
)
self.pre_transform = (
self._compose_transforms(pre_transforms)
if pre_transforms is not None
else None
)
self.pre_filter = (
self._compose_filters(pre_filters) if pre_filters is not None else None
)
self.batch_size = batch_size
self.num_workers = num_workers
self.pin_memory = pin_memory
self.train_ds = None
self.val_ds = None
self.test_ds = None
self.clusterid_to_seqid_mappings = None # for cluster sampling
def setup(self, stage: Optional[str] = None):
if stage == "fit" or stage is None:
self.train_ds = self.train_dataset()
elif stage == "validation":
self.val_ds = self.val_dataset()
elif stage == "test":
self.test_ds = self.test_dataset()
def _compose_transforms(self, transforms: Iterable[Callable]) -> T.Compose:
try:
return T.Compose(list(transforms.values()))
except Exception:
return T.Compose(transforms)
def _compose_filters(self, filters: Iterable[Callable]) -> T.ComposeFilters:
try:
return T.ComposeFilters(list(filters.values()))
except Exception:
return T.ComposeFilters(filters)
@abstractmethod
def _get_dataset(self, split: str) -> Dataset:
"""Creates a dataset given a split.
Args:
split (str): Split for which to get the dataset, with options "train", "val" or "test"
Returns:
Dataset: Dataset created for the respective split
"""
...
def train_dataset(self) -> Dataset:
return self._get_dataset("train")
def val_dataset(self) -> Dataset:
return self._get_dataset("val")
def test_dataset(self) -> Dataset:
return self._get_dataset("test")
def _get_dataloader(
self,
dataset: Dataset,
shuffle: bool = False,
clusterid_to_seqid_mapping: Dict[str, List[str]] = None,
) -> DataLoader:
"""Returns the dataloader for the corresponding dataset.
Args:
dataset (Dataset): PyG dataset for which the dataloader will be created.
shuffle (bool, optional): Whether the dataloader should be shuffled. Defaults to False. False when cluster_id mapping is given.
clusterid_to_seqid_mapping (Dict[str, List[str]], optional): Maps cluster ids to sequence ids. Defaults to None.
Returns:
DataLoader: Dataloader to be used by model.
"""
if self.sampling_mode is None:
raise ValueError(
"Sampling mode not set, should be one of 'random', 'cluster-random' or 'cluster-reps'"
)
if clusterid_to_seqid_mapping and self.sampling_mode != "random":
sampler = ClusterSampler(
dataset=dataset,
clusterid_to_seqid_mapping=clusterid_to_seqid_mapping,
sampling_mode=self.sampling_mode,
)
shuffle = False
elif self.sampling_mode == "random":
sampler = None
shuffle = shuffle
else:
raise ValueError(
f"Sampling mode is {self.sampling_mode}, but clusterid_to_seqid_mapping is {clusterid_to_seqid_mapping}"
)
dataloader_class = DensePaddingDataLoader if self.batch_padding else DataLoader
return dataloader_class(
dataset,
batch_size=self.batch_size,
sampler=sampler,
shuffle=shuffle,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
drop_last=True,
)
def train_dataloader(self) -> DataLoader:
if self.train_ds is None:
self.train_ds = self.train_dataset()
clusterid_to_seqid_mapping = (
self.clusterid_to_seqid_mappings["train"]
if self.clusterid_to_seqid_mappings
else None
)
shuffle = True
train_dl = self._get_dataloader(
dataset=self.train_ds,
shuffle=shuffle,
clusterid_to_seqid_mapping=clusterid_to_seqid_mapping,
)
return train_dl
def val_dataloader(self) -> DataLoader:
if self.val_ds is None:
self.val_ds = self.val_dataset()
clusterid_to_seqid_mapping = (
self.clusterid_to_seqid_mappings["val"]
if self.clusterid_to_seqid_mappings
else None
)
shuffle = False
logger.info(f"Length of validation set: {len(self.val_ds)}")
val_dl = self._get_dataloader(
dataset=self.val_ds,
shuffle=shuffle,
clusterid_to_seqid_mapping=clusterid_to_seqid_mapping,
)
return val_dl
def test_dataloader(self) -> DataLoader:
if self.test_ds is None:
self.test_ds = self.test_dataset()
clusterid_to_seqid_mapping = (
self.clusterid_to_seqid_mappings["test"]
if self.clusterid_to_seqid_mappings
else None
)
shuffle = False
test_dl = self._get_dataloader(
dataset=self.test_ds,
shuffle=shuffle,
clusterid_to_seqid_mapping=clusterid_to_seqid_mapping,
)
return test_dl |