0e98abf204
* Update label_tensor.py cpu/gpu * Update test_adaptive_refinment_callbacks.py * Update test_optimizer_callbacks.py
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
from pina.callbacks import SwitchOptimizer
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import torch
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import pytest
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from pina.problem import SpatialProblem
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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 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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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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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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}
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# make the problem
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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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model = FeedForward(len(poisson_problem.input_variables),
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len(poisson_problem.output_variables))
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# make the solver
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solver = PINN(problem=poisson_problem, model=model)
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def test_switch_optimizer_constructor():
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SwitchOptimizer(new_optimizers=torch.optim.Adam,
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new_optimizers_kwargs={'lr': 0.01},
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epoch_switch=10)
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with pytest.raises(ValueError):
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SwitchOptimizer(new_optimizers=[torch.optim.Adam, torch.optim.Adam],
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new_optimizers_kwargs=[{
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'lr': 0.01
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}],
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epoch_switch=10)
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def test_switch_optimizer_routine():
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# make the trainer
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trainer = Trainer(solver=solver,
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callbacks=[
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SwitchOptimizer(new_optimizers=torch.optim.LBFGS,
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new_optimizers_kwargs={'lr': 0.01},
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epoch_switch=3)
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],
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accelerator='cpu',
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max_epochs=5)
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trainer.train()
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