added rba-pinn (#308)
* added rba-pinn * changes to loss logger * tests * doc --------- Co-authored-by: Monthly Tag bot <mtbot@noreply.github.com> Co-authored-by: Nicola Demo <demo.nicola@gmail.com>
This commit is contained in:
Giovanni_Canali
437
tests/test_solvers/test_rba_pinn.py
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437
tests/test_solvers/test_rba_pinn.py
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import torch
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import pytest
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from pina.problem import SpatialProblem, InverseProblem
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from pina.operators import laplacian
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from pina.geometry import CartesianDomain
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from pina import Condition, LabelTensor
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from pina.solvers import RBAPINN as PINN
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from pina.trainer import Trainer
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from pina.model import FeedForward
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from pina.equation.equation import Equation
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from pina.equation.equation_factory import FixedValue
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from pina.loss import LpLoss
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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 InversePoisson(SpatialProblem, InverseProblem):
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'''
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Problem definition for the Poisson equation.
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'''
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output_variables = ['u']
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x_min = -2
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x_max = 2
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y_min = -2
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y_max = 2
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data_input = LabelTensor(torch.rand(10, 2), ['x', 'y'])
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data_output = LabelTensor(torch.rand(10, 1), ['u'])
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spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]})
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# define the ranges for the parameters
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unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]})
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def laplace_equation(input_, output_, params_):
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'''
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Laplace equation with a force term.
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'''
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force_term = torch.exp(
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- 2*(input_.extract(['x']) - params_['mu1'])**2
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- 2*(input_.extract(['y']) - params_['mu2'])**2)
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delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
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return delta_u - force_term
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# define the conditions for the loss (boundary conditions, equation, data)
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conditions = {
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'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max],
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'y': y_max}),
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equation=FixedValue(0.0, components=['u'])),
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'gamma2': Condition(location=CartesianDomain(
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{'x': [x_min, x_max], 'y': y_min
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}),
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equation=FixedValue(0.0, components=['u'])),
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'gamma3': Condition(location=CartesianDomain(
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{'x': x_max, 'y': [y_min, y_max]
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}),
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equation=FixedValue(0.0, components=['u'])),
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'gamma4': Condition(location=CartesianDomain(
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{'x': x_min, 'y': [y_min, y_max]
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}),
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equation=FixedValue(0.0, components=['u'])),
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'D': Condition(location=CartesianDomain(
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{'x': [x_min, x_max], 'y': [y_min, y_max]
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}),
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equation=Equation(laplace_equation)),
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'data': Condition(input_points=data_input.extract(['x', 'y']),
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output_points=data_output)
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}
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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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class myFeature(torch.nn.Module):
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"""
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Feature: sin(x)
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"""
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def __init__(self):
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super(myFeature, self).__init__()
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def forward(self, x):
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t = (torch.sin(x.extract(['x']) * torch.pi) *
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torch.sin(x.extract(['y']) * torch.pi))
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return LabelTensor(t, ['sin(x)sin(y)'])
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# make the problem
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poisson_problem = Poisson()
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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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extra_feats = [myFeature()]
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def test_constructor():
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PINN(problem=poisson_problem, model=model, extra_features=None)
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with pytest.raises(ValueError):
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PINN(problem=poisson_problem, model=model, eta='x')
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PINN(problem=poisson_problem, model=model, gamma='x')
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def test_constructor_extra_feats():
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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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PINN(problem=poisson_problem,
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model=model_extra_feats,
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extra_features=extra_feats)
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def test_train_cpu():
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem = poisson_problem, model=model,
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extra_features=None, loss=LpLoss())
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trainer = Trainer(solver=pinn, max_epochs=1,
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accelerator='cpu', batch_size=20)
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trainer.train()
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def test_train_restore():
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tmpdir = "tests/tmp_restore"
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model,
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extra_features=None,
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loss=LpLoss())
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trainer = Trainer(solver=pinn,
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max_epochs=5,
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accelerator='cpu',
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default_root_dir=tmpdir)
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trainer.train()
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ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu')
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t = ntrainer.train(
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ckpt_path=f'{tmpdir}/lightning_logs/version_0/'
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'checkpoints/epoch=4-step=10.ckpt')
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import shutil
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shutil.rmtree(tmpdir)
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def test_train_load():
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tmpdir = "tests/tmp_load"
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model,
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extra_features=None,
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loss=LpLoss())
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trainer = Trainer(solver=pinn,
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max_epochs=15,
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accelerator='cpu',
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default_root_dir=tmpdir)
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trainer.train()
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new_pinn = PINN.load_from_checkpoint(
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt',
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problem = poisson_problem, model=model)
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test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10)
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assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1)
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assert new_pinn.forward(test_pts).extract(
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['u']).shape == pinn.forward(test_pts).extract(['u']).shape
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torch.testing.assert_close(
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new_pinn.forward(test_pts).extract(['u']),
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pinn.forward(test_pts).extract(['u']))
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import shutil
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shutil.rmtree(tmpdir)
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def test_train_inverse_problem_cpu():
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poisson_problem = InversePoisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D']
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n = 100
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poisson_problem.discretise_domain(n, 'random', locations=boundaries)
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pinn = PINN(problem = poisson_problem, model=model,
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extra_features=None, loss=LpLoss())
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trainer = Trainer(solver=pinn, max_epochs=1,
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accelerator='cpu', batch_size=20)
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trainer.train()
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# # TODO does not currently work
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# def test_train_inverse_problem_restore():
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# tmpdir = "tests/tmp_restore_inv"
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# poisson_problem = InversePoisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D']
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# n = 100
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# poisson_problem.discretise_domain(n, 'random', locations=boundaries)
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# pinn = PINN(problem=poisson_problem,
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# model=model,
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# extra_features=None,
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# loss=LpLoss())
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# trainer = Trainer(solver=pinn,
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# max_epochs=5,
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# accelerator='cpu',
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# default_root_dir=tmpdir)
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# trainer.train()
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# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu')
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# t = ntrainer.train(
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# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=10.ckpt')
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# import shutil
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# shutil.rmtree(tmpdir)
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def test_train_inverse_problem_load():
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tmpdir = "tests/tmp_load_inv"
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poisson_problem = InversePoisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D']
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n = 100
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poisson_problem.discretise_domain(n, 'random', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model,
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extra_features=None,
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loss=LpLoss())
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trainer = Trainer(solver=pinn,
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max_epochs=15,
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accelerator='cpu',
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default_root_dir=tmpdir)
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trainer.train()
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new_pinn = PINN.load_from_checkpoint(
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt',
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problem = poisson_problem, model=model)
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test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10)
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assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1)
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assert new_pinn.forward(test_pts).extract(
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['u']).shape == pinn.forward(test_pts).extract(['u']).shape
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torch.testing.assert_close(
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new_pinn.forward(test_pts).extract(['u']),
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pinn.forward(test_pts).extract(['u']))
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import shutil
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shutil.rmtree(tmpdir)
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# # TODO fix asap. Basically sampling few variables
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# # works only if both variables are in a range.
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# # if one is fixed and the other not, this will
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# # not work. This test also needs to be fixed and
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# # insert in test problem not in test pinn.
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# def test_train_cpu_sampling_few_vars():
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# poisson_problem = Poisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3']
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# n = 10
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# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x'])
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# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y'])
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# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
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# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
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# trainer.train()
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def test_train_extra_feats_cpu():
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model_extra_feats,
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extra_features=extra_feats)
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trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu')
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trainer.train()
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# TODO, fix GitHub actions to run also on GPU
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# def test_train_gpu():
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# poisson_problem = Poisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
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# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})
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# trainer.train()
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# def test_train_gpu(): #TODO fix ASAP
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# poisson_problem = Poisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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# poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu
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# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
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# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})
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# trainer.train()
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# def test_train_2():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(problem, model)
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# def test_train_extra_feats():
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# pinn = PINN(problem, model_extra_feat, [myFeature()])
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(5)
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# def test_train_2_extra_feats():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(problem, model_extra_feat, [myFeature()])
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# def test_train_with_optimizer_kwargs():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3})
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# def test_train_with_lr_scheduler():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(
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# problem,
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# model,
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# lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR,
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# lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False}
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# )
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# # def test_train_batch():
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# # pinn = PINN(problem, model, batch_size=6)
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# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# # n = 10
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# # pinn.discretise_domain(n, 'grid', locations=boundaries)
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# # pinn.discretise_domain(n, 'grid', locations=['D'])
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# # pinn.train(5)
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# # def test_train_batch_2():
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# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
||||
# # n = 10
|
||||
# # expected_keys = [[], list(range(0, 50, 3))]
|
||||
# # param = [0, 3]
|
||||
# # for i, truth_key in zip(param, expected_keys):
|
||||
# # pinn = PINN(problem, model, batch_size=6)
|
||||
# # pinn.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# # pinn.discretise_domain(n, 'grid', locations=['D'])
|
||||
# # pinn.train(50, save_loss=i)
|
||||
# # assert list(pinn.history_loss.keys()) == truth_key
|
||||
|
||||
|
||||
# if torch.cuda.is_available():
|
||||
|
||||
# # def test_gpu_train():
|
||||
# # pinn = PINN(problem, model, batch_size=20, device='cuda')
|
||||
# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
||||
# # n = 100
|
||||
# # pinn.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# # pinn.discretise_domain(n, 'grid', locations=['D'])
|
||||
# # pinn.train(5)
|
||||
|
||||
# def test_gpu_train_nobatch():
|
||||
# pinn = PINN(problem, model, batch_size=None, device='cuda')
|
||||
# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
||||
# n = 100
|
||||
# pinn.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# pinn.discretise_domain(n, 'grid', locations=['D'])
|
||||
# pinn.train(5)
|
||||
|
||||
Reference in New Issue
Block a user