a27bd35443
Refactoring for 0.2 * Data module, data loader and dataset * Refactor LabelTensor * Refactor solvers Co-authored-by: dario-coscia <dariocos99@gmail.com>
260 lines
9.4 KiB
Python
260 lines
9.4 KiB
Python
import torch
|
|
from pina.problem import SpatialProblem, InverseProblem
|
|
from pina.operators import laplacian
|
|
from pina.domain import CartesianDomain
|
|
from pina import Condition, LabelTensor
|
|
from pina.solvers import PINN
|
|
from pina.trainer import Trainer
|
|
from pina.model import FeedForward
|
|
from pina.equation.equation import Equation
|
|
from pina.equation.equation_factory import FixedValue
|
|
from pina.loss import LpLoss
|
|
|
|
|
|
def laplace_equation(input_, output_):
|
|
force_term = (torch.sin(input_.extract(['x']) * torch.pi) *
|
|
torch.sin(input_.extract(['y']) * torch.pi))
|
|
delta_u = laplacian(output_.extract(['u']), input_)
|
|
return delta_u - force_term
|
|
|
|
|
|
my_laplace = Equation(laplace_equation)
|
|
in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y'])
|
|
out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
|
|
in2_ = LabelTensor(torch.rand(60, 2), ['x', 'y'])
|
|
out2_ = LabelTensor(torch.rand(60, 1), ['u'])
|
|
|
|
|
|
class InversePoisson(SpatialProblem, InverseProblem):
|
|
'''
|
|
Problem definition for the Poisson equation.
|
|
'''
|
|
output_variables = ['u']
|
|
x_min = -2
|
|
x_max = 2
|
|
y_min = -2
|
|
y_max = 2
|
|
data_input = LabelTensor(torch.rand(10, 2), ['x', 'y'])
|
|
data_output = LabelTensor(torch.rand(10, 1), ['u'])
|
|
spatial_domain = CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]})
|
|
# define the ranges for the parameters
|
|
unknown_parameter_domain = CartesianDomain({'mu1': [-1, 1], 'mu2': [-1, 1]})
|
|
|
|
def laplace_equation(input_, output_, params_):
|
|
'''
|
|
Laplace equation with a force term.
|
|
'''
|
|
force_term = torch.exp(
|
|
- 2*(input_.extract(['x']) - params_['mu1'])**2
|
|
- 2*(input_.extract(['y']) - params_['mu2'])**2)
|
|
delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
|
|
|
|
return delta_u - force_term
|
|
|
|
# define the conditions for the loss (boundary conditions, equation, data)
|
|
conditions = {
|
|
'gamma1': Condition(domain=CartesianDomain({'x': [x_min, x_max],
|
|
'y': y_max}),
|
|
equation=FixedValue(0.0, components=['u'])),
|
|
'gamma2': Condition(domain=CartesianDomain(
|
|
{'x': [x_min, x_max], 'y': y_min
|
|
}),
|
|
equation=FixedValue(0.0, components=['u'])),
|
|
'gamma3': Condition(domain=CartesianDomain(
|
|
{'x': x_max, 'y': [y_min, y_max]
|
|
}),
|
|
equation=FixedValue(0.0, components=['u'])),
|
|
'gamma4': Condition(domain=CartesianDomain(
|
|
{'x': x_min, 'y': [y_min, y_max]
|
|
}),
|
|
equation=FixedValue(0.0, components=['u'])),
|
|
'D': Condition(domain=CartesianDomain(
|
|
{'x': [x_min, x_max], 'y': [y_min, y_max]
|
|
}),
|
|
equation=Equation(laplace_equation)),
|
|
'data': Condition(input_points=data_input.extract(['x', 'y']),
|
|
output_points=data_output)
|
|
}
|
|
|
|
|
|
class Poisson(SpatialProblem):
|
|
output_variables = ['u']
|
|
spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
|
|
|
|
conditions = {
|
|
'gamma1': Condition(
|
|
domain=CartesianDomain({'x': [0, 1], 'y': 1}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma2': Condition(
|
|
domain=CartesianDomain({'x': [0, 1], 'y': 0}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma3': Condition(
|
|
domain=CartesianDomain({'x': 1, 'y': [0, 1]}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma4': Condition(
|
|
domain=CartesianDomain({'x': 0, 'y': [0, 1]}),
|
|
equation=FixedValue(0.0)),
|
|
'D': Condition(
|
|
input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']),
|
|
equation=my_laplace),
|
|
'data': Condition(
|
|
input_points=in_,
|
|
output_points=out_),
|
|
'data2': Condition(
|
|
input_points=in2_,
|
|
output_points=out2_)
|
|
}
|
|
|
|
def poisson_sol(self, pts):
|
|
return -(torch.sin(pts.extract(['x']) * torch.pi) *
|
|
torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2)
|
|
|
|
truth_solution = poisson_sol
|
|
|
|
class myFeature(torch.nn.Module):
|
|
"""
|
|
Feature: sin(x)
|
|
"""
|
|
|
|
def __init__(self):
|
|
super(myFeature, self).__init__()
|
|
|
|
def forward(self, x):
|
|
t = (torch.sin(x.extract(['x']) * torch.pi) *
|
|
torch.sin(x.extract(['y']) * torch.pi))
|
|
return LabelTensor(t, ['sin(x)sin(y)'])
|
|
|
|
|
|
# make the problem
|
|
poisson_problem = Poisson()
|
|
model = FeedForward(len(poisson_problem.input_variables),
|
|
len(poisson_problem.output_variables))
|
|
model_extra_feats = FeedForward(
|
|
len(poisson_problem.input_variables) + 1,
|
|
len(poisson_problem.output_variables))
|
|
extra_feats = [myFeature()]
|
|
|
|
|
|
def test_constructor():
|
|
PINN(problem=poisson_problem, model=model, extra_features=None)
|
|
|
|
|
|
def test_constructor_extra_feats():
|
|
model_extra_feats = FeedForward(
|
|
len(poisson_problem.input_variables) + 1,
|
|
len(poisson_problem.output_variables))
|
|
PINN(problem=poisson_problem,
|
|
model=model_extra_feats,
|
|
extra_features=extra_feats)
|
|
|
|
|
|
def test_train_cpu():
|
|
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, max_epochs=1,
|
|
accelerator='cpu', batch_size=20, val_size=0., train_size=1., test_size=0.)
|
|
|
|
def test_train_load():
|
|
tmpdir = "tests/tmp_load"
|
|
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,
|
|
max_epochs=15,
|
|
accelerator='cpu',
|
|
default_root_dir=tmpdir)
|
|
trainer.train()
|
|
new_pinn = PINN.load_from_checkpoint(
|
|
f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt',
|
|
problem = poisson_problem, model=model)
|
|
test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10)
|
|
assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1)
|
|
assert new_pinn.forward(test_pts).extract(
|
|
['u']).shape == pinn.forward(test_pts).extract(['u']).shape
|
|
torch.testing.assert_close(
|
|
new_pinn.forward(test_pts).extract(['u']),
|
|
pinn.forward(test_pts).extract(['u']))
|
|
import shutil
|
|
shutil.rmtree(tmpdir)
|
|
|
|
def test_train_restore():
|
|
tmpdir = "tests/tmp_restore"
|
|
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,
|
|
max_epochs=5,
|
|
accelerator='cpu',
|
|
default_root_dir=tmpdir)
|
|
trainer.train()
|
|
ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu')
|
|
t = ntrainer.train(
|
|
ckpt_path=f'{tmpdir}/lightning_logs/version_0/'
|
|
'checkpoints/epoch=4-step=5.ckpt')
|
|
import shutil
|
|
shutil.rmtree(tmpdir)
|
|
|
|
def test_train_inverse_problem_cpu():
|
|
poisson_problem = InversePoisson()
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D']
|
|
n = 100
|
|
poisson_problem.discretise_domain(n, 'random', locations=boundaries,
|
|
variables=['x', 'y'])
|
|
pinn = PINN(problem = poisson_problem, model=model,
|
|
extra_features=None, loss=LpLoss())
|
|
trainer = Trainer(solver=pinn, max_epochs=1,
|
|
accelerator='cpu', batch_size=20)
|
|
trainer.train()
|
|
|
|
def test_train_extra_feats_cpu():
|
|
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_feats,
|
|
extra_features=extra_feats)
|
|
trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu')
|
|
trainer.train()
|
|
|
|
def test_train_inverse_problem_load():
|
|
tmpdir = "tests/tmp_load_inv"
|
|
poisson_problem = InversePoisson()
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D']
|
|
n = 100
|
|
poisson_problem.discretise_domain(n, 'random', locations=boundaries)
|
|
pinn = PINN(problem=poisson_problem,
|
|
model=model,
|
|
extra_features=None,
|
|
loss=LpLoss())
|
|
trainer = Trainer(solver=pinn,
|
|
max_epochs=15,
|
|
accelerator='cpu',
|
|
default_root_dir=tmpdir)
|
|
trainer.train()
|
|
new_pinn = PINN.load_from_checkpoint(
|
|
f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt',
|
|
problem = poisson_problem, model=model)
|
|
test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10)
|
|
assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1)
|
|
assert new_pinn.forward(test_pts).extract(
|
|
['u']).shape == pinn.forward(test_pts).extract(['u']).shape
|
|
torch.testing.assert_close(
|
|
new_pinn.forward(test_pts).extract(['u']),
|
|
pinn.forward(test_pts).extract(['u']))
|
|
import shutil
|
|
shutil.rmtree(tmpdir) |