63fd068988
* multiple functions for version 0.0 * lightining update * minor changes * data pinn loss added --------- Co-authored-by: Nicola Demo <demo.nicola@gmail.com> Co-authored-by: Dario Coscia <dariocoscia@cli-10-110-3-125.WIFIeduroamSTUD.units.it> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.station> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Dario Coscia <dariocoscia@192.168.1.38>
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
""" Solver module. """
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from abc import ABCMeta, abstractmethod
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from .model.network import Network
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import lightning.pytorch as pl
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from .utils import check_consistency
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from .problem import AbstractProblem
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class SolverInterface(pl.LightningModule, metaclass=ABCMeta):
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""" Solver base class. """
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def __init__(self, model, problem, extra_features=None):
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"""
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:param model: A torch neural network model instance.
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:type model: torch.nn.Module
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:param problem: A problem definition instance.
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:type problem: AbstractProblem
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:param list(torch.nn.Module) extra_features: the additional input
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features to use as augmented input.
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"""
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super().__init__()
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# check inheritance for pina problem
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check_consistency(problem, AbstractProblem, 'pina problem')
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# assigning class variables (check consistency inside Network class)
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self._pina_model = Network(model=model, extra_features=extra_features)
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self._problem = problem
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@abstractmethod
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def forward(self):
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pass
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@abstractmethod
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def training_step(self):
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pass
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@abstractmethod
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def configure_optimizers(self):
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pass
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@property
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def model(self):
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"""
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The torch model."""
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return self._pina_model
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@property
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def problem(self):
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"""
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The problem formulation."""
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return self._problem
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# @model.setter
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# def model(self, new_model):
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# """
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# Set the torch."""
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# check_consistency(new_model, nn.Module, 'torch model')
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# self._model= new_model
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# @problem.setter
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# def problem(self, problem):
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# """
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# Set the problem formulation."""
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# check_consistency(problem, AbstractProblem, 'pina problem')
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# self._problem = problem |