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Introduce add_points method in AbstractProblem, removed unused comments in Collector class and add the test for add_points and codacy corrections

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FilippoOlivo
2025-02-06 17:29:37 +01:00
committed by Nicola Demo
parent 004cbc00c0
commit f578b2ed12
4 changed files with 56 additions and 109 deletions
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37
tests/test_collector.py
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@@ -10,6 +10,7 @@ from pina.equation.equation_factory import FixedValue
from pina.operators import laplacian
from pina.collector import Collector
# def test_supervised_tensor_collector():
# class SupervisedProblem(AbstractProblem):
# output_variables = None
@@ -37,6 +38,7 @@ def test_pinn_collector():
my_laplace = Equation(laplace_equation)
in_ = LabelTensor(torch.tensor([[0., 1.]], requires_grad=True), ['x', 'y'])
out_ = LabelTensor(torch.tensor([[0.]], requires_grad=True), ['u'])
class Poisson(SpatialProblem):
output_variables = ['u']
spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
@@ -78,7 +80,8 @@ def test_pinn_collector():
def poisson_sol(self, pts):
return -(torch.sin(pts.extract(['x']) * torch.pi) *
torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2)
torch.sin(pts.extract(['y']) * torch.pi)) / (
2 * torch.pi ** 2)
truth_solution = poisson_sol
@@ -91,30 +94,34 @@ def test_pinn_collector():
collector.store_fixed_data()
collector.store_sample_domains()
for k,v in problem.conditions.items():
for k, v in problem.conditions.items():
if isinstance(v, InputOutputPointsCondition):
assert list(collector.data_collections[k].keys()) == ['input_points', 'output_points']
assert list(collector.data_collections[k].keys()) == [
'input_points', 'output_points']
for k,v in problem.conditions.items():
for k, v in problem.conditions.items():
if isinstance(v, DomainEquationCondition):
assert list(collector.data_collections[k].keys()) == ['input_points', 'equation']
assert list(collector.data_collections[k].keys()) == [
'input_points', 'equation']
def test_supervised_graph_collector():
pos = torch.rand((100,3))
x = [torch.rand((100,3)) for _ in range(10)]
pos = torch.rand((100, 3))
x = [torch.rand((100, 3)) for _ in range(10)]
graph_list_1 = RadiusGraph(pos=pos, x=x, build_edge_attr=True, r=.4)
out_1 = torch.rand((10,100,3))
pos = torch.rand((50,3))
x = [torch.rand((50,3)) for _ in range(10)]
out_1 = torch.rand((10, 100, 3))
pos = torch.rand((50, 3))
x = [torch.rand((50, 3)) for _ in range(10)]
graph_list_2 = RadiusGraph(pos=pos, x=x, build_edge_attr=True, r=.4)
out_2 = torch.rand((10,50,3))
out_2 = torch.rand((10, 50, 3))
class SupervisedProblem(AbstractProblem):
output_variables = None
conditions = {
'data1' : Condition(input_points=graph_list_1,
output_points=out_1),
'data2' : Condition(input_points=graph_list_2,
output_points=out_2),
'data1': Condition(input_points=graph_list_1,
output_points=out_1),
'data2': Condition(input_points=graph_list_2,
output_points=out_2),
}
problem = SupervisedProblem()