Update solvers (#434)
* Enable DDP training with batch_size=None and add validity check for split sizes * Refactoring SolverInterfaces (#435) * Solver update + weighting * Updating PINN for 0.2 * Modify GAROM + tests * Adding more versatile loggers * Disable compilation when running on Windows * Fix tests --------- Co-authored-by: giovanni <giovanni.canali98@yahoo.it> Co-authored-by: FilippoOlivo <filippo@filippoolivo.com>
This commit is contained in:
Dario Coscia
committed by
Nicola Demo
Nicola Demo
parent
780c4921eb
commit
9cae9a438f
@@ -1,429 +1,145 @@
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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.domain import CartesianDomain
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from pina import Condition, LabelTensor
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from pina.solvers import CompetitivePINN as PINN
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from pina import LabelTensor, Condition
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from pina.solvers import CompetitivePINN as CompPINN
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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 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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from pina.problem.zoo import (
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Poisson2DSquareProblem as Poisson,
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InversePoisson2DSquareProblem as InversePoisson
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)
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from pina.condition import (
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InputOutputPointsCondition,
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InputPointsEquationCondition,
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DomainEquationCondition
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)
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from torch._dynamo.eval_frame import OptimizedModule
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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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# define problems and model
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problem = Poisson()
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problem.discretise_domain(50)
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inverse_problem = InversePoisson()
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inverse_problem.discretise_domain(50)
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model = FeedForward(
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len(problem.input_variables),
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len(problem.output_variables)
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)
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# add input-output condition to test supervised learning
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input_pts = torch.rand(50, len(problem.input_variables))
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input_pts = LabelTensor(input_pts, problem.input_variables)
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output_pts = torch.rand(50, len(problem.output_variables))
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output_pts = LabelTensor(output_pts, problem.output_variables)
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problem.conditions['data'] = Condition(
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input_points=input_pts,
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output_points=output_pts
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)
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@pytest.mark.parametrize("problem", [problem, inverse_problem])
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@pytest.mark.parametrize("discr", [None, model])
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def test_constructor(problem, discr):
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solver = CompPINN(problem=problem, model=model)
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solver = CompPINN(problem=problem, model=model, discriminator=discr)
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assert solver.accepted_conditions_types == (
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InputOutputPointsCondition,
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InputPointsEquationCondition,
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DomainEquationCondition
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)
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@pytest.mark.parametrize("problem", [problem, inverse_problem])
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@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_train(problem, batch_size, compile):
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solver = CompPINN(problem=problem, model=model)
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trainer = Trainer(solver=solver,
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max_epochs=2,
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accelerator='cpu',
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batch_size=batch_size,
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train_size=1.,
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val_size=0.,
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test_size=0.,
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compile=compile)
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trainer.train()
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if trainer.compile:
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assert (all([isinstance(model, OptimizedModule)
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for model in solver.models]))
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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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@pytest.mark.parametrize("problem", [problem, inverse_problem])
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@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_validation(problem, batch_size, compile):
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solver = CompPINN(problem=problem, model=model)
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trainer = Trainer(solver=solver,
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max_epochs=2,
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accelerator='cpu',
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batch_size=batch_size,
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train_size=0.9,
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val_size=0.1,
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test_size=0.,
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compile=compile)
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trainer.train()
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if trainer.compile:
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assert (all([isinstance(model, OptimizedModule)
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for model in solver.models]))
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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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@pytest.mark.parametrize("problem", [problem, inverse_problem])
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@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_test(problem, batch_size, compile):
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solver = CompPINN(problem=problem, model=model)
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trainer = Trainer(solver=solver,
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max_epochs=2,
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accelerator='cpu',
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batch_size=batch_size,
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train_size=0.7,
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val_size=0.2,
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test_size=0.1,
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compile=compile)
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trainer.test()
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if trainer.compile:
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assert (all([isinstance(model, OptimizedModule)
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for model in solver.models]))
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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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@pytest.mark.parametrize("problem", [problem, inverse_problem])
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def test_train_load_restore(problem):
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dir = "tests/test_solvers/tmp"
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problem = problem
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solver = CompPINN(problem=problem, model=model)
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trainer = Trainer(solver=solver,
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max_epochs=5,
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accelerator='cpu',
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batch_size=None,
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train_size=0.7,
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val_size=0.2,
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test_size=0.1,
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default_root_dir=dir)
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trainer.train()
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# return delta_u - force_term
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# restore
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new_trainer = Trainer(solver=solver, max_epochs=5, accelerator='cpu')
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new_trainer.train(
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ckpt_path=f'{dir}/lightning_logs/version_0/checkpoints/' +
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'epoch=4-step=5.ckpt')
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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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# loading
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new_solver = CompPINN.load_from_checkpoint(
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f'{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt',
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problem=problem, model=model)
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test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables)
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assert new_solver.forward(test_pts).shape == (20, 1)
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assert new_solver.forward(test_pts).shape == (
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solver.forward(test_pts).shape
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)
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torch.testing.assert_close(
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new_solver.forward(test_pts),
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solver.forward(test_pts))
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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)
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# PINN(problem=poisson_problem, model=model, discriminator = model)
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# def test_constructor_extra_feats():
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# with pytest.raises(TypeError):
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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, 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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# poisson_problem.discretise_domain(100)
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# solver = PINN(problem = poisson_problem, model=model, 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 poisson_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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# 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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# 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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# 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, 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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# # 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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# 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']))
|
||||
# import shutil
|
||||
# shutil.rmtree(tmpdir)
|
||||
|
||||
# # # TODO fix asap. Basically sampling few variables
|
||||
# # # works only if both variables are in a range.
|
||||
# # # if one is fixed and the other not, this will
|
||||
# # # not work. This test also needs to be fixed and
|
||||
# # # insert in test problem not in test pinn.
|
||||
# # def test_train_cpu_sampling_few_vars():
|
||||
# # poisson_problem = Poisson()
|
||||
# # boundaries = ['gamma1', 'gamma2', 'gamma3']
|
||||
# # n = 10
|
||||
# # poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# # poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x'])
|
||||
# # poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y'])
|
||||
# # pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
|
||||
# # trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
|
||||
# # trainer.train()
|
||||
|
||||
|
||||
# # TODO, fix GitHub actions to run also on GPU
|
||||
# # def test_train_gpu():
|
||||
# # poisson_problem = Poisson()
|
||||
# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
||||
# # n = 10
|
||||
# # poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# # pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
|
||||
# # trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})
|
||||
# # trainer.train()
|
||||
|
||||
# # def test_train_gpu(): #TODO fix ASAP
|
||||
# # poisson_problem = Poisson()
|
||||
# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
||||
# # n = 10
|
||||
# # poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# # poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu
|
||||
# # pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
|
||||
# # trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})
|
||||
# # trainer.train()
|
||||
|
||||
# # def test_train_2():
|
||||
# # 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)
|
||||
# # 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
|
||||
|
||||
|
||||
# # def test_train_extra_feats():
|
||||
# # pinn = PINN(problem, model_extra_feat, [myFeature()])
|
||||
# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
||||
# # n = 10
|
||||
# # pinn.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# # pinn.discretise_domain(n, 'grid', locations=['D'])
|
||||
# # pinn.train(5)
|
||||
|
||||
|
||||
# # def test_train_2_extra_feats():
|
||||
# # 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_extra_feat, [myFeature()])
|
||||
# # 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
|
||||
|
||||
|
||||
# # def test_train_with_optimizer_kwargs():
|
||||
# # 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, optimizer_kwargs={'lr' : 0.3})
|
||||
# # 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
|
||||
|
||||
|
||||
# # def test_train_with_lr_scheduler():
|
||||
# # 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,
|
||||
# # lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR,
|
||||
# # lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False}
|
||||
# # )
|
||||
# # 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
|
||||
|
||||
|
||||
# # # def test_train_batch():
|
||||
# # # pinn = PINN(problem, model, batch_size=6)
|
||||
# # # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
||||
# # # n = 10
|
||||
# # # pinn.discretise_domain(n, 'grid', locations=boundaries)
|
||||
# # # pinn.discretise_domain(n, 'grid', locations=['D'])
|
||||
# # # pinn.train(5)
|
||||
|
||||
|
||||
# # # def test_train_batch_2():
|
||||
# # # 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)
|
||||
|
||||
# rm directories
|
||||
import shutil
|
||||
shutil.rmtree('tests/test_solvers/tmp')
|
||||
Reference in New Issue
Block a user