PINN variants addition and Solvers Update (#263)

* gpinn/basepinn new classes, pinn restructure
* codacy fix gpinn/basepinn/pinn
* inverse problem fix
* Causal PINN (#267)
* fix GPU training in inverse problem (#283)
* Create a `compute_residual` attribute for `PINNInterface`
* Modify dataloading in solvers (#286)
* Modify PINNInterface by removing _loss_phys, _loss_data
* Adding in PINNInterface a variable to track the current condition during training
* Modify GPINN,PINN,CausalPINN to match changes in PINNInterface
* Competitive Pinn Addition (#288)
* fixing after rebase/ fix loss
* fixing final issues

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>

* Modify min max formulation to max min for paper consistency
* Adding SAPINN solver (#291)
* rom solver
* fix import

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
Co-authored-by: Anna Ivagnes <75523024+annaivagnes@users.noreply.github.com>
Co-authored-by: valc89 <103250118+valc89@users.noreply.github.com>
Co-authored-by: Monthly Tag bot <mtbot@noreply.github.com>
Co-authored-by: Nicola Demo <demo.nicola@gmail.com>

pina/solvers/solver.py

@@ -6,6 +6,7 @@ import pytorch_lightning
 from ..utils import check_consistency
 from ..problem import AbstractProblem
 import torch
+import sys
 
 
 class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
@@ -141,6 +142,20 @@ class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
         """
         The problem formulation."""
         return self._pina_problem
+    
+    def on_train_start(self):
+        """
+        On training epoch start this function is call to do global checks for
+        the different solvers.
+        """
+        
+        # 1. Check the verison for dataloader
+        dataloader = self.trainer.train_dataloader
+        if sys.version_info < (3, 8):
+            dataloader = dataloader.loaders
+        self._dataloader = dataloader
+
+        return super().on_train_start()
 
     # @model.setter
     # def model(self, new_model):