4d1187898f
* Implementing a callback to switch between optimizers during training * Implementing the R3Refinment for collocation points * Modify trainer -> dataloader is created or updated by calling `_create_or_update_loader` * Adding `add_points` routine to AbstractProblem so that new points can be added without resampling from scratch
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
""" Solver module. """
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import lightning.pytorch as pl
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from .utils import check_consistency
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from .dataset import DummyLoader
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from .solvers.solver import SolverInterface
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class Trainer(pl.Trainer):
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def __init__(self, solver, **kwargs):
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super().__init__(**kwargs)
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# check inheritance consistency for solver
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check_consistency(solver, SolverInterface)
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self._model = solver
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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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# TODO: make a better dataloader for train
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self._create_or_update_loader()
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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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def _create_or_update_loader(self):
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# get accellerator
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device = self._accelerator_connector._accelerator_flag
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self._loader = DummyLoader(self._model.problem.input_pts, device)
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def train(self, **kwargs): # TODO add kwargs and lightining capabilities
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return super().fit(self._model, self._loader, **kwargs)
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