a27bd35443
Refactoring for 0.2 * Data module, data loader and dataset * Refactor LabelTensor * Refactor solvers Co-authored-by: dario-coscia <dariocos99@gmail.com>
144 lines
4.0 KiB
Python
144 lines
4.0 KiB
Python
import torch
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import pytest
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from pina.problem import AbstractProblem, SpatialProblem
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from pina import Condition, LabelTensor
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from pina.solvers import SupervisedSolver
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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.operators import laplacian
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from pina.domain import CartesianDomain
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from pina.trainer import Trainer
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in_ = LabelTensor(torch.tensor([[0., 1.]]), ['u_0', 'u_1'])
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out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
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class NeuralOperatorProblem(AbstractProblem):
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input_variables = ['u_0', 'u_1']
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output_variables = ['u']
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conditions = {
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'data': Condition(input_points=in_, output_points=out_),
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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(['u_0']) * torch.pi) *
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torch.sin(x.extract(['u_1']) * torch.pi))
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return LabelTensor(t, ['sin(x)sin(y)'])
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problem = NeuralOperatorProblem()
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extra_feats = [myFeature()]
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model = FeedForward(len(problem.input_variables), len(problem.output_variables))
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model_extra_feats = FeedForward(
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len(problem.input_variables) + 1, len(problem.output_variables))
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def test_constructor():
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SupervisedSolver(problem=problem, model=model)
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test_constructor()
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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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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':
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Condition(domain=CartesianDomain({
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'x': [0, 1],
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'y': 1
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}),
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equation=FixedValue(0.0)),
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'gamma2':
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Condition(domain=CartesianDomain({
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'x': [0, 1],
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'y': 0
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}),
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equation=FixedValue(0.0)),
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'gamma3':
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Condition(domain=CartesianDomain({
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'x': 1,
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'y': [0, 1]
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}),
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equation=FixedValue(0.0)),
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'gamma4':
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Condition(domain=CartesianDomain({
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'x': 0,
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'y': [0, 1]
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}),
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equation=FixedValue(0.0)),
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'D':
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Condition(domain=CartesianDomain({
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'x': [0, 1],
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'y': [0, 1]
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}),
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equation=my_laplace),
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'data':
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Condition(input_points=in_, output_points=out_)
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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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def test_wrong_constructor():
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poisson_problem = Poisson()
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with pytest.raises(ValueError):
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SupervisedSolver(problem=poisson_problem, model=model)
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def test_train_cpu():
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solver = SupervisedSolver(problem=problem, model=model)
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trainer = Trainer(solver=solver,
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max_epochs=200,
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accelerator='gpu',
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batch_size=5,
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train_size=1,
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test_size=0.,
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val_size=0.)
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trainer.train()
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test_train_cpu()
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def test_extra_features_constructor():
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SupervisedSolver(problem=problem,
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model=model_extra_feats,
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extra_features=extra_feats)
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def test_extra_features_train_cpu():
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solver = SupervisedSolver(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=solver,
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max_epochs=200,
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accelerator='gpu',
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batch_size=5)
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trainer.train()
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