111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
import torch
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import pytest
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from pina import Condition, LabelTensor, Trainer
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from pina.problem import SpatialProblem
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from pina.operators import laplacian
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from pina.domain import CartesianDomain
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from pina.model import FeedForward
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from pina.solvers import PINNInterface
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from pina.problem.zoo import Poisson2DSquareProblem as Poisson
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# from pina.equation import Equation
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# from pina.equation.equation_factory import FixedValue
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# def laplace_equation(input_, output_):
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# force_term = (torch.sin(input_.extract(['x']) * torch.pi) *
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# torch.sin(input_.extract(['y']) * torch.pi))
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# delta_u = laplacian(output_.extract(['u']), input_)
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# return delta_u - force_term
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# my_laplace = Equation(laplace_equation)
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# in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y'])
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# out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
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# in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y'])
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# out2_ = LabelTensor(torch.rand(60, 1), ['u'])
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# class Poisson(SpatialProblem):
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# output_variables = ['u']
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# spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
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# conditions = {
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# 'gamma1': Condition(
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# location=CartesianDomain({'x': [0, 1], 'y': 1}),
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# equation=FixedValue(0.0)),
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# 'gamma2': Condition(
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# location=CartesianDomain({'x': [0, 1], 'y': 0}),
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# equation=FixedValue(0.0)),
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# 'gamma3': Condition(
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# location=CartesianDomain({'x': 1, 'y': [0, 1]}),
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# equation=FixedValue(0.0)),
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# 'gamma4': Condition(
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# location=CartesianDomain({'x': 0, 'y': [0, 1]}),
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# equation=FixedValue(0.0)),
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# 'D': Condition(
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# input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']),
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# equation=my_laplace),
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# 'data': Condition(
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# input_points=in_,
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# output_points=out_),
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# 'data2': Condition(
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# input_points=in2_,
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# output_points=out2_)
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# }
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# def poisson_sol(self, pts):
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# return -(torch.sin(pts.extract(['x']) * torch.pi) *
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# torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2)
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# truth_solution = poisson_sol
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# from pina import TorchOptimizer
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# class FOOPINN(PINNInterface):
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# def __init__(self, model, problem):
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# super().__init__(models=[model], problem=problem,
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# optimizers=TorchOptimizer(torch.optim.Adam, lr=1e-3),
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# loss=torch.nn.MSELoss())
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# def forward(self, x):
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# return self.models[0](x)
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# def loss_phys(self, samples, equation):
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# residual = self.compute_residual(samples=samples, equation=equation)
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# loss_value = self.loss(
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# torch.zeros_like(residual, requires_grad=True), residual
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# )
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# self.store_log(loss_value=float(loss_value))
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# return loss_value
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# # make the problem
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# poisson_problem = Poisson()
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# poisson_problem.discretise_domain(100)
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# model = FeedForward(len(poisson_problem.input_variables),
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# len(poisson_problem.output_variables))
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# model_extra_feats = FeedForward(
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# len(poisson_problem.input_variables) + 1,
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# len(poisson_problem.output_variables))
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# def test_constructor():
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# with pytest.raises(TypeError):
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# PINNInterface()
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# # a simple pinn built with PINNInterface
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# FOOPINN(model, poisson_problem)
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# def test_train_step():
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# solver = FOOPINN(model, poisson_problem)
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# trainer = Trainer(solver, max_epochs=2, accelerator='cpu')
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# trainer.train()
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# def test_log():
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# solver = FOOPINN(model, poisson_problem)
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# trainer = Trainer(solver, max_epochs=2, accelerator='cpu')
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# trainer.train()
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# # assert the logged metrics are correct
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# logged_metrics = sorted(list(trainer.logged_metrics.keys()))
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# total_metrics = sorted(
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# list([key + '_loss' for key in poisson_problem.conditions.keys()])
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# + ['mean_loss'])
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# assert logged_metrics == total_metrics |