278 lines
9.7 KiB
Python
278 lines
9.7 KiB
Python
import torch
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import pytest
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from pina.problem import TimeDependentProblem, InverseProblem, SpatialProblem
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from pina.operators import grad
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from pina.domain import CartesianDomain
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from pina import Condition, LabelTensor
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from pina.solvers import CausalPINN
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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.loss_interface import LpLoss
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class FooProblem(SpatialProblem):
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'''
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Foo problem formulation.
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'''
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output_variables = ['u']
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conditions = {}
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spatial_domain = None
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class InverseDiffusionReactionSystem(TimeDependentProblem, SpatialProblem, InverseProblem):
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def diffusionreaction(input_, output_, params_):
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x = input_.extract('x')
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t = input_.extract('t')
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u_t = grad(output_, input_, d='t')
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u_x = grad(output_, input_, d='x')
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u_xx = grad(u_x, input_, d='x')
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r = torch.exp(-t) * (1.5 * torch.sin(2*x) + (8/3)*torch.sin(3*x) +
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(15/4)*torch.sin(4*x) + (63/8)*torch.sin(8*x))
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return u_t - params_['mu']*u_xx - r
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def _solution(self, pts):
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t = pts.extract('t')
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x = pts.extract('x')
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return torch.exp(-t) * (torch.sin(x) + (1/2)*torch.sin(2*x) +
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(1/3)*torch.sin(3*x) + (1/4)*torch.sin(4*x) +
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(1/8)*torch.sin(8*x))
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# assign output/ spatial and temporal variables
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output_variables = ['u']
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spatial_domain = CartesianDomain({'x': [-torch.pi, torch.pi]})
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temporal_domain = CartesianDomain({'t': [0, 1]})
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unknown_parameter_domain = CartesianDomain({'mu': [-1, 1]})
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# problem condition statement
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conditions = {
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'D': Condition(location=CartesianDomain({'x': [-torch.pi, torch.pi],
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't': [0, 1]}),
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equation=Equation(diffusionreaction)),
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'data' : Condition(input_points=LabelTensor(torch.tensor([[0., 0.]]), ['x', 't']),
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output_points=LabelTensor(torch.tensor([[0.]]), ['u'])),
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}
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class DiffusionReactionSystem(TimeDependentProblem, SpatialProblem):
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def diffusionreaction(input_, output_):
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x = input_.extract('x')
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t = input_.extract('t')
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u_t = grad(output_, input_, d='t')
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u_x = grad(output_, input_, d='x')
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u_xx = grad(u_x, input_, d='x')
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r = torch.exp(-t) * (1.5 * torch.sin(2*x) + (8/3)*torch.sin(3*x) +
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(15/4)*torch.sin(4*x) + (63/8)*torch.sin(8*x))
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return u_t - u_xx - r
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def _solution(self, pts):
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t = pts.extract('t')
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x = pts.extract('x')
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return torch.exp(-t) * (torch.sin(x) + (1/2)*torch.sin(2*x) +
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(1/3)*torch.sin(3*x) + (1/4)*torch.sin(4*x) +
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(1/8)*torch.sin(8*x))
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# assign output/ spatial and temporal variables
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output_variables = ['u']
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spatial_domain = CartesianDomain({'x': [-torch.pi, torch.pi]})
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temporal_domain = CartesianDomain({'t': [0, 1]})
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# problem condition statement
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conditions = {
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'D': Condition(location=CartesianDomain({'x': [-torch.pi, torch.pi],
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't': [0, 1]}),
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equation=Equation(diffusionreaction)),
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}
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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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return LabelTensor(t, ['sin(x)'])
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# make the problem
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problem = DiffusionReactionSystem()
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model = FeedForward(len(problem.input_variables),
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len(problem.output_variables))
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model_extra_feats = FeedForward(
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len(problem.input_variables) + 1,
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len(problem.output_variables))
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extra_feats = [myFeature()]
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def test_constructor():
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CausalPINN(problem=problem, model=model, extra_features=None)
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with pytest.raises(ValueError):
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CausalPINN(FooProblem(), model=model, extra_features=None)
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def test_constructor_extra_feats():
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model_extra_feats = FeedForward(
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len(problem.input_variables) + 1,
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len(problem.output_variables))
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CausalPINN(problem=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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problem = DiffusionReactionSystem()
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boundaries = ['D']
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n = 10
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problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = CausalPINN(problem = problem,
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model=model, 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_log():
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problem.discretise_domain(100)
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solver = CausalPINN(problem = problem,
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model=model, extra_features=None, loss=LpLoss())
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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 problem.conditions.keys()])
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+ ['mean_loss'])
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assert logged_metrics == total_metrics
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def test_train_restore():
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tmpdir = "tests/tmp_restore"
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problem = DiffusionReactionSystem()
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boundaries = ['D']
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n = 10
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problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = CausalPINN(problem=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=5.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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problem = DiffusionReactionSystem()
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boundaries = ['D']
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n = 10
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problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = CausalPINN(problem=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 = CausalPINN.load_from_checkpoint(
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt',
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problem = problem, model=model)
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test_pts = CartesianDomain({'x': [0, 1], 't': [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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problem = InverseDiffusionReactionSystem()
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boundaries = ['D']
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n = 100
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problem.discretise_domain(n, 'random', locations=boundaries)
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pinn = CausalPINN(problem = problem,
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model=model, 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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# problem = InverseDiffusionReactionSystem()
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# boundaries = ['D']
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# n = 100
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# problem.discretise_domain(n, 'random', locations=boundaries)
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# pinn = CausalPINN(problem=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=5.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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problem = InverseDiffusionReactionSystem()
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boundaries = ['D']
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n = 100
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problem.discretise_domain(n, 'random', locations=boundaries)
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pinn = CausalPINN(problem=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 = CausalPINN.load_from_checkpoint(
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt',
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problem = problem, model=model)
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test_pts = CartesianDomain({'x': [0, 1], 't': [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_extra_feats_cpu():
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problem = DiffusionReactionSystem()
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boundaries = ['D']
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n = 10
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problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = CausalPINN(problem=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() |