0.2

examples/problems/stokes.py

@@ -49,11 +49,19 @@ def wall(input_, output_):
         value = 0.0
         return output_.extract(['ux', 'uy']) - value
 
+    domains = {
+        'gamma_top': CartesianDomain({'x': [-2, 2], 'y':  1}),
+        'gamma_bot': CartesianDomain({'x': [-2, 2], 'y': -1}),
+        'gamma_out': CartesianDomain({'x':  2, 'y': [-1, 1]}),
+        'gamma_in':  CartesianDomain({'x': -2, 'y': [-1, 1]}),
+        'D': CartesianDomain({'x': [-2, 2], 'y': [-1, 1]})
+    }
+
     # problem condition statement
     conditions = {
-        'gamma_top': Condition(location=CartesianDomain({'x': [-2, 2], 'y':  1}), equation=Equation(wall)),
-        'gamma_bot': Condition(location=CartesianDomain({'x': [-2, 2], 'y': -1}), equation=Equation(wall)),
-        'gamma_out': Condition(location=CartesianDomain({'x':  2, 'y': [-1, 1]}), equation=Equation(outlet)),
-        'gamma_in':  Condition(location=CartesianDomain({'x': -2, 'y': [-1, 1]}), equation=Equation(inlet)),
-        'D': Condition(location=CartesianDomain({'x': [-2, 2], 'y': [-1, 1]}), equation=SystemEquation([momentum, continuity]))
+        'gamma_top': Condition(domain='gamma_top', equation=Equation(wall)),
+        'gamma_bot': Condition(domain='gamma_bot', equation=Equation(wall)),
+        'gamma_out': Condition(domain='gamma_out', equation=Equation(outlet)),
+        'gamma_in':  Condition(domain='gamma_in', equation=Equation(inlet)),
+        'D': Condition(domain='D', equation=SystemEquation([momentum, continuity]))
     }

examples/run_stokes.py

@@ -17,8 +17,8 @@
 
     # create problem and discretise domain
     stokes_problem = Stokes()
-    stokes_problem.discretise_domain(n=1000, locations=['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out'])
-    stokes_problem.discretise_domain(n=2000, locations=['D'])
+    stokes_problem.discretise_domain(n=1000, domains=['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out'])
+    stokes_problem.discretise_domain(n=2000, domains=['D'])
 
     # make the model
     model = FeedForward(

pina/condition/condition.py

@@ -84,14 +84,15 @@ def __new__(cls, *args, **kwargs):
             return DomainEquationCondition(**kwargs)
         else:
             raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.")
-
+        # TODO: remove, not used anymore
+        '''
         if (
             sorted(kwargs.keys()) != sorted(["input_points", "output_points"])
             and sorted(kwargs.keys()) != sorted(["location", "equation"])
             and sorted(kwargs.keys()) != sorted(["input_points", "equation"])
         ):
             raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.")
-
+        # TODO: remove, not used anymore
         if not self._dictvalue_isinstance(kwargs, "input_points", LabelTensor):
             raise TypeError("`input_points` must be a torch.Tensor.")
         if not self._dictvalue_isinstance(kwargs, "output_points", LabelTensor):
@@ -103,3 +104,4 @@ def __new__(cls, *args, **kwargs):
 
         for key, value in kwargs.items():
             setattr(self, key, value)
+        '''

pina/condition/condition_interface.py

@@ -15,4 +15,7 @@ def residual(self, model):
         :param model: The model to evaluate the condition.
         :return: The residual of the condition.
         """
-        pass
+        pass
+
+    def set_problem(self, problem):
+        self._problem = problem

pina/condition/domain_equation_condition.py

@@ -15,14 +15,20 @@ def __init__(self, domain, equation):
         self.domain = domain
         self.equation = equation
 
+    def residual(self, model):
+        """
+        Compute the residual of the condition.
+        """
+        self.batch_residual(model, self.domain, self.equation)
+
     @staticmethod
     def batch_residual(model, input_pts, equation):
         """
         Compute the residual of the condition for a single batch. Input and
         output points are provided as arguments.
 
         :param torch.nn.Module model: The model to evaluate the condition.
-        :param torch.Tensor input_points: The input points.
-        :param torch.Tensor output_points: The output points.
+        :param torch.Tensor input_pts: The input points.
+        :param torch.Tensor equation: The output points.
         """
-        return equation.residual(model(input_pts))
+        return equation.residual(input_pts, model(input_pts))

pina/condition/domain_output_condition.py

@@ -40,4 +40,5 @@ def batch_residual(model, input_points, output_points):
         :param torch.Tensor input_points: The input points.
         :param torch.Tensor output_points: The output points.
         """
+
         return output_points - model(input_points)

pina/domain/cartesian.py

@@ -1,4 +1,5 @@
 import torch
+import torch
 
 from .domain_interface import DomainInterface
 from ..label_tensor import LabelTensor

pina/label_tensor.py

@@ -5,7 +5,6 @@
 from torch import Tensor
 
 
-
 # class LabelTensor(torch.Tensor):
 #     """Torch tensor with a label for any column."""
 
@@ -307,13 +306,13 @@
 #             s = "no labels\n"
 #         s += super().__str__()
 #         return s
-
 def issubset(a, b):
     """
     Check if a is a subset of b.
     """
     return set(a).issubset(set(b))
 
+
 class LabelTensor(torch.Tensor):
     """Torch tensor with a label for any column."""
 
@@ -403,6 +402,10 @@ def extract(self, label_to_extract):
         return LabelTensor(new_tensor, label_to_extract)
 
     def __str__(self):
+        """
+        returns a string with the representation of the class
+        """
+
         s = ''
         for key, value in self.labels.items():
             s += f"{key}: {value}\n"
@@ -431,4 +434,32 @@ def requires_grad_(self, mode=True):
 
     @property
     def dtype(self):
-        return super().dtype
+        return super().dtype
+
+
+    def to(self, *args, **kwargs):
+        """
+        Performs Tensor dtype and/or device conversion. For more details, see
+        :meth:`torch.Tensor.to`.
+        """
+        tmp = super().to(*args, **kwargs)
+        new = self.__class__.clone(self)
+        new.data = tmp.data
+        return new
+
+
+    def clone(self, *args, **kwargs):
+        """
+        Clone the LabelTensor. For more details, see
+        :meth:`torch.Tensor.clone`.
+
+        :return: A copy of the tensor.
+        :rtype: LabelTensor
+        """
+        # # used before merging
+        # try:
+        #     out = LabelTensor(super().clone(*args, **kwargs), self.labels)
+        # except:
+        #     out = super().clone(*args, **kwargs)
+        out = LabelTensor(super().clone(*args, **kwargs), self.labels)
+        return out

pina/problem/abstract_problem.py

@@ -20,26 +20,26 @@ class AbstractProblem(metaclass=ABCMeta):
 
     def __init__(self):
 
-
         self._discretized_domains = {}
 
         for name, domain in self.domains.items():
             if isinstance(domain, (torch.Tensor, LabelTensor)):
                 self._discretized_domains[name] = domain
 
         for condition_name in self.conditions:
-            self.conditions[condition_name]._problem = self
+            self.conditions[condition_name].set_problem(self)
+
         # # variable storing all points
-        # self.input_pts = {}
+        self.input_pts = {}
 
         # # varible to check if sampling is done. If no location
         # # element is presented in Condition this variable is set to true
         # self._have_sampled_points = {}
-        # for condition_name in self.conditions:
-        #     self._have_sampled_points[condition_name] = False
+        for condition_name in self.conditions:
+            self._discretized_domains[condition_name] = False
 
         # # put in self.input_pts all the points that we don't need to sample
-        # self._span_condition_points()
+        self._span_condition_points()
 
     def __deepcopy__(self, memo):
         """
@@ -125,7 +125,7 @@ def _span_condition_points(self):
             if hasattr(condition, "input_points"):
                 samples = condition.input_points
                 self.input_pts[condition_name] = samples
-                self._have_sampled_points[condition_name] = True
+                self._discretized_domains[condition_name] = True
             if hasattr(self, "unknown_parameter_domain"):
                 # initialize the unknown parameters of the inverse problem given
                 # the domain the user gives
@@ -141,7 +141,7 @@ def _span_condition_points(self):
                     )
 
     def discretise_domain(
-        self, n, mode="random", variables="all", locations="all"
+        self, n, mode="random", variables="all", domains="all"
     ):
         """
         Generate a set of points to span the `Location` of all the conditions of
@@ -193,24 +193,24 @@ def discretise_domain(
             )
 
         # check consistency location
-        if locations == "all":
-            locations = [condition for condition in self.conditions]
+        if domains == "all":
+            domains = [condition for condition in self.conditions]
         else:
-            check_consistency(locations, str)
-
-        if sorted(locations) != sorted(self.conditions):
+            check_consistency(domains, str)
+        print(domains)
+        if sorted(domains) != sorted(self.conditions):
             TypeError(
                 f"Wrong locations for sampling. Location ",
                 f"should be in {self.conditions}.",
             )
 
         # sampling
-        for location in locations:
-            condition = self.conditions[location]
+        for d in domains:
+            condition = self.conditions[d]
 
             # we try to check if we have already sampled
             try:
-                already_sampled = [self.input_pts[location]]
+                already_sampled = [self.input_pts[d]]
             # if we have not sampled, a key error is thrown
             except KeyError:
                 already_sampled = []
@@ -219,22 +219,23 @@ def discretise_domain(
             # but we want to sample again we set already_sampled
             # to an empty list since we need to sample again, and
             # self._have_sampled_points to False.
-            if self._have_sampled_points[location]:
+            if self._discretized_domains[d]:
                 already_sampled = []
-                self._have_sampled_points[location] = False
-
+                self._discretized_domains[d] = False
+            print(condition.domain)
+            print(d)
             # build samples
             samples = [
-                condition.location.sample(n=n, mode=mode, variables=variables)
+                self.domains[d].sample(n=n, mode=mode, variables=variables)
             ] + already_sampled
             pts = merge_tensors(samples)
-            self.input_pts[location] = pts
+            self.input_pts[d] = pts
 
             # the condition is sampled if input_pts contains all labels
-            if sorted(self.input_pts[location].labels) == sorted(
+            if sorted(self.input_pts[d].labels) == sorted(
                 self.input_variables
             ):
-                self._have_sampled_points[location] = True
+                self._have_sampled_points[d] = True
 
     def add_points(self, new_points):
         """

pina/solvers/supervised.py

@@ -134,8 +134,6 @@ def training_step(self, batch, batch_idx):
             condition = self.problem.conditions[condition_name]
             pts = batch.input
             out = batch.output
-            print(out)
-            print(pts)
 
             if condition_name not in self.problem.conditions:
                 raise RuntimeError("Something wrong happened.")

tests/test_solvers/test_supervised_solver.py

@@ -5,7 +5,7 @@
 from pina.solvers import SupervisedSolver
 from pina.trainer import Trainer
 from pina.model import FeedForward
-from pina.loss.loss_interface import LpLoss
+from pina.loss import LpLoss
 
 
 class NeuralOperatorProblem(AbstractProblem):
@@ -94,11 +94,9 @@ def forward(self, data, edge_index):
         return x
 
 def test_graph():
-
     solver = AutoSolver(problem = problem, model=GraphModel(2, 1), loss=LpLoss())
     trainer = Trainer(solver=solver, max_epochs=30, accelerator='cpu', batch_size=20)
     trainer.train()
-    assert False
 
 
 def test_train_cpu():
@@ -107,6 +105,7 @@ def test_train_cpu():
     trainer.train()
 
 
+
 # def test_train_restore():
 #     tmpdir = "tests/tmp_restore"
 #     solver = SupervisedSolver(problem=problem,
@@ -153,4 +152,4 @@ def test_train_cpu():
 #                 model=model_extra_feats,
 #                 extra_features=extra_feats)
 #     trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu')
-#     trainer.train()
+#     trainer.train()