Implement Dataset, Dataloader and DataModule class and fix SupervisedSolver
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FilippoOlivo
committed by
Nicola Demo
Nicola Demo
parent
b9753c34b2
commit
c9304fb9bb
@@ -10,168 +10,6 @@ import torch
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import sys
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# class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
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# """
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# Solver base class. This class inherits is a wrapper of
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# LightningModule class, inheriting all the
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# LightningModule methods.
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# """
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# def __init__(
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# self,
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# models,
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# problem,
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# optimizers,
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# optimizers_kwargs,
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# extra_features=None,
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# ):
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# """
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# :param models: A torch neural network model instance.
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# :type models: 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.optim.Optimizer) optimizer: A list of neural network optimizers to
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# use.
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# :param list(dict) optimizer_kwargs: A list of optimizer constructor keyword args.
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# :param list(torch.nn.Module) extra_features: The additional input
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# features to use as augmented input. If ``None`` no extra features
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# are passed. If it is a list of :class:`torch.nn.Module`, the extra feature
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# list is passed to all models. If it is a list of extra features' lists,
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# each single list of extra feature is passed to a model.
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# """
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# super().__init__()
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# # check consistency of the inputs
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# check_consistency(models, torch.nn.Module)
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# check_consistency(problem, AbstractProblem)
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# check_consistency(optimizers, torch.optim.Optimizer, subclass=True)
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# check_consistency(optimizers_kwargs, dict)
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# # put everything in a list if only one input
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# if not isinstance(models, list):
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# models = [models]
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# if not isinstance(optimizers, list):
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# optimizers = [optimizers]
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# optimizers_kwargs = [optimizers_kwargs]
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# # number of models and optimizers
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# len_model = len(models)
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# len_optimizer = len(optimizers)
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# len_optimizer_kwargs = len(optimizers_kwargs)
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# # check length consistency optimizers
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# if len_model != len_optimizer:
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# raise ValueError(
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# "You must define one optimizer for each model."
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# f"Got {len_model} models, and {len_optimizer}"
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# " optimizers."
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# )
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# # check length consistency optimizers kwargs
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# if len_optimizer_kwargs != len_optimizer:
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# raise ValueError(
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# "You must define one dictionary of keyword"
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# " arguments for each optimizers."
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# f"Got {len_optimizer} optimizers, and"
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# f" {len_optimizer_kwargs} dicitionaries"
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# )
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# # extra features handling
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# if (extra_features is None) or (len(extra_features) == 0):
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# extra_features = [None] * len_model
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# else:
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# # if we only have a list of extra features
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# if not isinstance(extra_features[0], (tuple, list)):
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# extra_features = [extra_features] * len_model
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# else: # if we have a list of list extra features
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# if len(extra_features) != len_model:
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# raise ValueError(
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# "You passed a list of extrafeatures list with len"
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# f"different of models len. Expected {len_model} "
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# f"got {len(extra_features)}. If you want to use "
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# "the same list of extra features for all models, "
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# "just pass a list of extrafeatures and not a list "
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# "of list of extra features."
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# )
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# # assigning model and optimizers
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# self._pina_models = []
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# self._pina_optimizers = []
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# for idx in range(len_model):
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# model_ = Network(
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# model=models[idx],
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# input_variables=problem.input_variables,
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# output_variables=problem.output_variables,
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# extra_features=extra_features[idx],
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# )
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# optim_ = optimizers[idx](
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# model_.parameters(), **optimizers_kwargs[idx]
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# )
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# self._pina_models.append(model_)
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# self._pina_optimizers.append(optim_)
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# # assigning problem
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# self._pina_problem = problem
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# @abstractmethod
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# def forward(self, *args, **kwargs):
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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 models(self):
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# """
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# The torch model."""
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# return self._pina_models
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# @property
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# def optimizers(self):
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# """
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# The torch model."""
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# return self._pina_optimizers
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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._pina_problem
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# def on_train_start(self):
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# """
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# On training epoch start this function is call to do global checks for
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# the different solvers.
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# """
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# # 1. Check the verison for dataloader
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# dataloader = self.trainer.train_dataloader
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# if sys.version_info < (3, 8):
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# dataloader = dataloader.loaders
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# self._dataloader = dataloader
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# return super().on_train_start()
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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
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class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
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"""
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Solver base class. This class inherits is a wrapper of
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@@ -181,10 +19,12 @@ class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
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def __init__(
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self,
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model,
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models,
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problem,
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optimizer,
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scheduler,
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optimizers,
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schedulers,
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extra_features,
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use_lt=True
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):
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"""
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:param model: A torch neural network model instance.
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@@ -197,22 +37,45 @@ class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
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super().__init__()
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# check consistency of the inputs
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check_consistency(model, torch.nn.Module)
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check_consistency(problem, AbstractProblem)
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check_consistency(optimizer, Optimizer)
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check_consistency(scheduler, Scheduler)
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self._check_solver_consistency(problem)
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# put everything in a list if only one input
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if not isinstance(model, list):
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model = [model]
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if not isinstance(scheduler, list):
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scheduler = [scheduler]
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if not isinstance(optimizer, list):
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optimizer = [optimizer]
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#Check consistency of models argument and encapsulate in list
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if not isinstance(models, list):
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check_consistency(models, torch.nn.Module)
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# put everything in a list if only one input
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models = [models]
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else:
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for idx in range(len(models)):
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# Check consistency
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check_consistency(models[idx], torch.nn.Module)
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len_model = len(models)
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# number of models and optimizers
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len_model = len(model)
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len_optimizer = len(optimizer)
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#If use_lt is true add extract operation in input
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if use_lt is True:
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for idx in range(len(models)):
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models[idx] = Network(
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model = models[idx],
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input_variables=problem.input_variables,
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output_variables=problem.output_variables,
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extra_features=extra_features, )
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#Check scheduler consistency + encapsulation
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if not isinstance(schedulers, list):
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check_consistency(schedulers, Scheduler)
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schedulers = [schedulers]
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else:
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for scheduler in schedulers:
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check_consistency(scheduler, Scheduler)
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#Check optimizer consistency + encapsulation
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if not isinstance(optimizers, list):
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check_consistency(optimizers, Optimizer)
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optimizers = [optimizers]
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else:
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for optimizer in optimizers:
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check_consistency(optimizer, Optimizer)
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len_optimizer = len(optimizers)
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# check length consistency optimizers
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if len_model != len_optimizer:
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@@ -223,10 +86,12 @@ class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
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)
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# extra features handling
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self._pina_models = models
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self._pina_optimizers = optimizers
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self._pina_schedulers = schedulers
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self._pina_problem = problem
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self._pina_model = model
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self._pina_optimizer = optimizer
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self._pina_scheduler = scheduler
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@abstractmethod
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def forward(self, *args, **kwargs):
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@@ -244,13 +109,13 @@ class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
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def models(self):
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"""
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The torch model."""
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return self._pina_model
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return self._pina_models
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@property
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def optimizers(self):
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"""
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The torch model."""
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return self._pina_optimizer
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return self._pina_optimizers
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@property
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def problem(self):
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@@ -272,16 +137,10 @@ class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
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return super().on_train_start()
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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
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def _check_solver_consistency(self, problem):
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"""
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TODO
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"""
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for _, condition in problem.conditions.items():
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if not set(self.accepted_condition_types).issubset(condition.condition_type):
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raise ValueError(f'{self.__name__} support only dose not support condition {condition.condition_type}')
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