8b7b61b3bd
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
""" Trainer module. """
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import pytorch_lightning
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from .utils import check_consistency
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from .dataset import SamplePointDataset, SamplePointLoader, DataPointDataset
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from .solvers.solver import SolverInterface
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class Trainer(pytorch_lightning.Trainer):
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def __init__(self, solver, batch_size=None, **kwargs):
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"""
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PINA Trainer class for costumizing every aspect of training via flags.
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:param solver: A pina:class:`SolverInterface` solver for the differential problem.
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:type solver: SolverInterface
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:param batch_size: How many samples per batch to load. If ``batch_size=None`` all
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samples are loaded and data are not batched, defaults to None.
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:type batch_size: int | None
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:Keyword Arguments:
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The additional keyword arguments specify the training setup
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and can be choosen from the `pytorch-lightning
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Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_
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"""
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super().__init__(**kwargs)
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# check inheritance consistency for solver and batch size
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check_consistency(solver, SolverInterface)
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if batch_size is not None:
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check_consistency(batch_size, int)
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self._model = solver
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self.batch_size = batch_size
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# create dataloader
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if solver.problem.have_sampled_points is False:
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raise RuntimeError(f'Input points in {solver.problem.not_sampled_points} '
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'training are None. Please '
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'sample points in your problem by calling '
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'discretise_domain function before train '
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'in the provided locations.')
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self._create_or_update_loader()
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def _create_or_update_loader(self):
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"""
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This method is used here because is resampling is needed
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during training, there is no need to define to touch the
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trainer dataloader, just call the method.
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"""
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devices = self._accelerator_connector._parallel_devices
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if len(devices) > 1:
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raise RuntimeError('Parallel training is not supported yet.')
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device = devices[0]
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dataset_phys = SamplePointDataset(self._model.problem, device)
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dataset_data = DataPointDataset(self._model.problem, device)
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self._loader = SamplePointLoader(
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dataset_phys, dataset_data, batch_size=self.batch_size,
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shuffle=True)
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def train(self, **kwargs):
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"""
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Train the solver method.
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"""
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return super().fit(self._model, train_dataloaders=self._loader, **kwargs)
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@property
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def solver(self):
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"""
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Returning trainer solver.
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"""
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return self._model
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