173 lines
6.0 KiB
Python
173 lines
6.0 KiB
Python
"""
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This module provide basic data management functionalities
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"""
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import math
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import torch
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from lightning import LightningDataModule
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from .sample_dataset import SamplePointDataset
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from .supervised_dataset import SupervisedDataset
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from .unsupervised_dataset import UnsupervisedDataset
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from .pina_dataloader import PinaDataLoader
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from .pina_subset import PinaSubset
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class PinaDataModule(LightningDataModule):
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"""
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This class extend LightningDataModule, allowing proper creation and
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management of different types of Datasets defined in PINA
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"""
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def __init__(self,
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problem,
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device,
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train_size=.7,
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test_size=.2,
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eval_size=.1,
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batch_size=None,
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shuffle=True,
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datasets = None):
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"""
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Initialize the object, creating dataset based on input problem
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:param AbstractProblem problem: PINA problem
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:param device: Device used for training and testing
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:param train_size: number/percentage of elements in train split
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:param test_size: number/percentage of elements in test split
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:param eval_size: number/percentage of elements in evaluation split
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:param batch_size: batch size used for training
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:param datasets: list of datasets objects
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"""
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super().__init__()
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dataset_classes = [SupervisedDataset, UnsupervisedDataset, SamplePointDataset]
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if datasets is None:
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self.datasets = [DatasetClass(problem, device) for DatasetClass in dataset_classes]
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else:
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self.datasets = datasets
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self.split_length = []
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self.split_names = []
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if train_size > 0:
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self.split_names.append('train')
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self.split_length.append(train_size)
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if test_size > 0:
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self.split_length.append(test_size)
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self.split_names.append('test')
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if eval_size > 0:
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self.split_length.append(eval_size)
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self.split_names.append('eval')
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self.batch_size = batch_size
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self.condition_names = None
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self.splits = {k: {} for k in self.split_names}
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self.shuffle = shuffle
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def setup(self, stage=None):
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"""
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Perform the splitting of the dataset
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"""
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self.extract_conditions()
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if stage == 'fit' or stage is None:
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for dataset in self.datasets:
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if len(dataset) > 0:
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splits = self.dataset_split(dataset,
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self.split_length,
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shuffle=self.shuffle)
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for i in range(len(self.split_length)):
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self.splits[
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self.split_names[i]][dataset.data_type] = splits[i]
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elif stage == 'test':
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raise NotImplementedError("Testing pipeline not implemented yet")
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else:
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raise ValueError("stage must be either 'fit' or 'test'")
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def extract_conditions(self):
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"""
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Extract conditions from dataset and update condition indices
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"""
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# Extract number of conditions
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n_conditions = 0
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for dataset in self.datasets:
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if n_conditions != 0:
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dataset.condition_names = {
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key + n_conditions: value
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for key, value in dataset.condition_names.items()
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}
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n_conditions += len(dataset.condition_names)
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self.condition_names = {
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key: value
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for dataset in self.datasets
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for key, value in dataset.condition_names.items()
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}
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def train_dataloader(self):
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"""
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Return the training dataloader for the dataset
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:return: data loader
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:rtype: PinaDataLoader
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"""
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return PinaDataLoader(self.splits['train'], self.batch_size,
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self.condition_names)
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def test_dataloader(self):
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"""
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Return the testing dataloader for the dataset
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:return: data loader
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:rtype: PinaDataLoader
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"""
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return PinaDataLoader(self.splits['test'], self.batch_size,
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self.condition_names)
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def eval_dataloader(self):
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"""
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Return the evaluation dataloader for the dataset
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:return: data loader
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:rtype: PinaDataLoader
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"""
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return PinaDataLoader(self.splits['eval'], self.batch_size,
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self.condition_names)
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@staticmethod
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def dataset_split(dataset, lengths, seed=None, shuffle=True):
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"""
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Perform the splitting of the dataset
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:param dataset: dataset object we wanted to split
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:param lengths: lengths of elements in dataset
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:param seed: random seed
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:param shuffle: shuffle dataset
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:return: split dataset
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:rtype: PinaSubset
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"""
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if sum(lengths) - 1 < 1e-3:
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lengths = [
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int(math.floor(len(dataset) * length)) for length in lengths
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]
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remainder = len(dataset) - sum(lengths)
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for i in range(remainder):
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lengths[i % len(lengths)] += 1
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elif sum(lengths) - 1 >= 1e-3:
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raise ValueError(f"Sum of lengths is {sum(lengths)} less than 1")
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if sum(lengths) != len(dataset):
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raise ValueError("Sum of lengths is not equal to dataset length")
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if shuffle:
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if seed is not None:
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generator = torch.Generator()
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generator.manual_seed(seed)
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indices = torch.randperm(sum(lengths), generator=generator).tolist()
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else:
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indices = torch.arange(sum(lengths)).tolist()
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else:
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indices = torch.arange(0, sum(lengths), 1, dtype=torch.uint8).tolist()
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offsets = [
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sum(lengths[:i]) if i > 0 else 0 for i in range(len(lengths))
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]
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return [
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PinaSubset(dataset, indices[offset:offset + length])
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for offset, length in zip(offsets, lengths)
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]
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