Implementation of DataLoader and DataModule (#383)
Refactoring for 0.2 * Data module, data loader and dataset * Refactor LabelTensor * Refactor solvers Co-authored-by: dario-coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
committed by
Nicola Demo
Nicola Demo
parent
dd43c8304c
commit
a27bd35443
@@ -1,17 +1,71 @@
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"""
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This module provide basic data management functionalities
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"""
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import logging
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from lightning.pytorch import LightningDataModule
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import math
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import torch
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import logging
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from pytorch_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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from ..label_tensor import LabelTensor
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from torch.utils.data import DataLoader, BatchSampler, SequentialSampler, \
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RandomSampler
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from torch.utils.data.distributed import DistributedSampler
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from .dataset import PinaDatasetFactory
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class Collator:
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def __init__(self, max_conditions_lengths, ):
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self.max_conditions_lengths = max_conditions_lengths
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self.callable_function = self._collate_custom_dataloader if \
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max_conditions_lengths is None else (
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self._collate_standard_dataloader)
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@staticmethod
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def _collate_custom_dataloader(batch):
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return batch[0]
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def _collate_standard_dataloader(self, batch):
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"""
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Function used to collate the batch
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"""
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batch_dict = {}
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if isinstance(batch, dict):
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return batch
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conditions_names = batch[0].keys()
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# Condition names
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for condition_name in conditions_names:
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single_cond_dict = {}
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condition_args = batch[0][condition_name].keys()
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for arg in condition_args:
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data_list = [batch[idx][condition_name][arg] for idx in range(
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min(len(batch),
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self.max_conditions_lengths[condition_name]))]
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if isinstance(data_list[0], LabelTensor):
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single_cond_dict[arg] = LabelTensor.stack(data_list)
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elif isinstance(data_list[0], torch.Tensor):
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single_cond_dict[arg] = torch.stack(data_list)
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else:
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raise NotImplementedError(
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f"Data type {type(data_list[0])} not supported")
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batch_dict[condition_name] = single_cond_dict
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return batch_dict
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def __call__(self, batch):
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return self.callable_function(batch)
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class PinaBatchSampler(BatchSampler):
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def __init__(self, dataset, batch_size, shuffle, sampler=None):
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if sampler is None:
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if (torch.distributed.is_available() and
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torch.distributed.is_initialized()):
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rank = torch.distributed.get_rank()
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world_size = torch.distributed.get_world_size()
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sampler = DistributedSampler(dataset, shuffle=shuffle,
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rank=rank, num_replicas=world_size)
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else:
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if shuffle:
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sampler = RandomSampler(dataset)
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else:
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sampler = SequentialSampler(dataset)
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super().__init__(sampler=sampler, batch_size=batch_size,
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drop_last=False)
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class PinaDataModule(LightningDataModule):
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"""
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@@ -20,160 +74,218 @@ class PinaDataModule(LightningDataModule):
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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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collector,
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train_size=.7,
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test_size=.1,
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val_size=.2,
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test_size=.2,
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val_size=.1,
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predict_size=0.,
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batch_size=None,
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shuffle=True,
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datasets=None):
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repeat=False,
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automatic_batching=False
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):
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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 Collector collector: PINA problem
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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 val_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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logging.debug('Start initialization of Pina DataModule')
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logging.info('Start initialization of Pina DataModule')
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super().__init__()
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self.problem = problem
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self.device = device
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self.dataset_classes = [
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SupervisedDataset, UnsupervisedDataset, SamplePointDataset
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]
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if datasets is None:
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self.datasets = None
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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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self.loader_functions = {}
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self.default_batching = automatic_batching
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self.batch_size = batch_size
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self.condition_names = problem.collector.conditions_name
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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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self.loader_functions['train_dataloader'] = lambda: PinaDataLoader(
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self.splits['train'], self.batch_size, self.condition_names)
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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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self.loader_functions['test_dataloader'] = lambda: PinaDataLoader(
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self.splits['test'], self.batch_size, self.condition_names)
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if val_size > 0:
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self.split_length.append(val_size)
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self.split_names.append('val')
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self.loader_functions['val_dataloader'] = lambda: PinaDataLoader(
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self.splits['val'], self.batch_size, self.condition_names)
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if predict_size > 0:
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self.split_length.append(predict_size)
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self.split_names.append('predict')
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self.loader_functions['predict_dataloader'] = lambda: PinaDataLoader(
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self.splits['predict'], self.batch_size, self.condition_names)
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self.splits = {k: {} for k in self.split_names}
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self.shuffle = shuffle
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self.repeat = repeat
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for k, v in self.loader_functions.items():
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setattr(self, k, v)
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def prepare_data(self):
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if self.datasets is None:
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self._create_datasets()
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# Begin Data splitting
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splits_dict = {}
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if train_size > 0:
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splits_dict['train'] = train_size
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self.train_dataset = None
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else:
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self.train_dataloader = super().train_dataloader
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if test_size > 0:
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splits_dict['test'] = test_size
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self.test_dataset = None
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else:
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self.test_dataloader = super().test_dataloader
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if val_size > 0:
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splits_dict['val'] = val_size
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self.val_dataset = None
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else:
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self.val_dataloader = super().val_dataloader
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if predict_size > 0:
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splits_dict['predict'] = predict_size
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self.predict_dataset = None
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else:
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self.predict_dataloader = super().predict_dataloader
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self.collector_splits = self._create_splits(collector, splits_dict)
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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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logging.debug('Start setup of Pina DataModule obj')
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if self.datasets is None:
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self._create_datasets()
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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[self.split_names[i]][
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dataset.data_type] = splits[i]
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self.train_dataset = PinaDatasetFactory(
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self.collector_splits['train'],
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max_conditions_lengths=self.find_max_conditions_lengths(
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'train'))
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if 'val' in self.collector_splits.keys():
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self.val_dataset = PinaDatasetFactory(
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self.collector_splits['val'],
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max_conditions_lengths=self.find_max_conditions_lengths(
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'val')
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)
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elif stage == 'test':
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raise NotImplementedError("Testing pipeline not implemented yet")
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self.test_dataset = PinaDatasetFactory(
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self.collector_splits['test'],
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max_conditions_lengths=self.find_max_conditions_lengths(
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'test')
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)
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elif stage == 'predict':
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self.predict_dataset = PinaDatasetFactory(
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self.collector_splits['predict'],
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max_conditions_lengths=self.find_max_conditions_lengths(
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'predict')
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)
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else:
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raise ValueError("stage must be either 'fit' or 'test'")
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raise ValueError(
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"stage must be either 'fit' or 'test' or 'predict'."
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)
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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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len_dataset = len(dataset)
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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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def _split_condition(condition_dict, splits_dict):
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len_condition = len(condition_dict['input_points'])
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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)
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else:
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indices = torch.randperm(sum(lengths))
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dataset.apply_shuffle(indices)
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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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lengths = [
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int(math.floor(len_condition * length)) for length in
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splits_dict.values()
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]
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def _create_datasets(self):
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remainder = len_condition - sum(lengths)
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for i in range(remainder):
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lengths[i % len(lengths)] += 1
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splits_dict = {k: v for k, v in zip(splits_dict.keys(), lengths)
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}
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to_return_dict = {}
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offset = 0
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for stage, stage_len in splits_dict.items():
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to_return_dict[stage] = {k: v[offset:offset + stage_len]
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for k, v in condition_dict.items() if
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k != 'equation'
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# Equations are NEVER dataloaded
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}
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offset += stage_len
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return to_return_dict
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def _create_splits(self, collector, splits_dict):
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"""
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Create the dataset objects putting data
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Create the dataset objects putting data
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"""
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logging.debug('Dataset creation in PinaDataModule obj')
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collector = self.problem.collector
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batching_dim = self.problem.batching_dimension
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datasets_slots = [i.__slots__ for i in self.dataset_classes]
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self.datasets = [
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dataset(device=self.device) for dataset in self.dataset_classes
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]
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logging.debug('Filling datasets in PinaDataModule obj')
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for name, data in collector.data_collections.items():
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keys = list(data.keys())
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idx = [
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key for key, val in collector.conditions_name.items()
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if val == name
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]
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for i, slot in enumerate(datasets_slots):
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if slot == keys:
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self.datasets[i].add_points(data, idx[0], batching_dim)
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# ----------- Auxiliary function ------------
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def _apply_shuffle(condition_dict, len_data):
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idx = torch.randperm(len_data)
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for k, v in condition_dict.items():
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if k == 'equation':
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continue
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datasets = []
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for dataset in self.datasets:
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if not dataset.empty:
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dataset.initialize()
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datasets.append(dataset)
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self.datasets = datasets
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if isinstance(v, list):
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condition_dict[k] = [v[i] for i in idx]
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elif isinstance(v, LabelTensor):
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condition_dict[k] = LabelTensor(v.tensor[idx],
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v.labels)
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elif isinstance(v, torch.Tensor):
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condition_dict[k] = v[idx]
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else:
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raise ValueError(f"Data type {type(v)} not supported")
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# ----------- End auxiliary function ------------
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logging.debug('Dataset creation in PinaDataModule obj')
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split_names = list(splits_dict.keys())
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dataset_dict = {name: {} for name in split_names}
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for condition_name, condition_dict in collector.data_collections.items():
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len_data = len(condition_dict['input_points'])
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if self.shuffle:
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_apply_shuffle(condition_dict, len_data)
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for key, data in self._split_condition(condition_dict,
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splits_dict).items():
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dataset_dict[key].update({condition_name: data})
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return dataset_dict
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def find_max_conditions_lengths(self, split):
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max_conditions_lengths = {}
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for k, v in self.collector_splits[split].items():
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if self.batch_size is None:
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max_conditions_lengths[k] = len(v['input_points'])
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elif self.repeat:
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max_conditions_lengths[k] = self.batch_size
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else:
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max_conditions_lengths[k] = min(len(v['input_points']),
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self.batch_size)
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return max_conditions_lengths
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def val_dataloader(self):
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"""
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Create the validation dataloader
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"""
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batch_size = self.batch_size if self.batch_size is not None else len(
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self.val_dataset)
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# Use default batching in torch DataLoader (good is batch size is small)
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if self.default_batching:
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collate = Collator(self.find_max_conditions_lengths('val'))
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return DataLoader(self.val_dataset, self.batch_size,
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collate_fn=collate)
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collate = Collator(None)
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# Use custom batching (good if batch size is large)
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sampler = PinaBatchSampler(self.val_dataset, batch_size, shuffle=False)
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return DataLoader(self.val_dataset, sampler=sampler,
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collate_fn=collate)
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def train_dataloader(self):
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"""
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Create the training dataloader
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"""
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# Use default batching in torch DataLoader (good is batch size is small)
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if self.default_batching:
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collate = Collator(self.find_max_conditions_lengths('train'))
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return DataLoader(self.train_dataset, self.batch_size,
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collate_fn=collate)
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collate = Collator(None)
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# Use custom batching (good if batch size is large)
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batch_size = self.batch_size if self.batch_size is not None else len(
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self.train_dataset)
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sampler = PinaBatchSampler(self.train_dataset, batch_size,
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shuffle=False)
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return DataLoader(self.train_dataset, sampler=sampler,
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collate_fn=collate)
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def test_dataloader(self):
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"""
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Create the testing dataloader
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"""
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raise NotImplementedError("Test dataloader not implemented")
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def predict_dataloader(self):
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"""
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Create the prediction dataloader
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"""
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raise NotImplementedError("Predict dataloader not implemented")
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def transfer_batch_to_device(self, batch, device, dataloader_idx):
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"""
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Transfer the batch to the device. This method is called in the
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training loop and is used to transfer the batch to the device.
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"""
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batch = [
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(k, super(LightningDataModule, self).transfer_batch_to_device(v,
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device,
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dataloader_idx))
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for k, v in batch.items()
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]
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return batch
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