e0429bb445
* gpinn/basepinn new classes, pinn restructure * codacy fix gpinn/basepinn/pinn * inverse problem fix * Causal PINN (#267) * fix GPU training in inverse problem (#283) * Create a `compute_residual` attribute for `PINNInterface` * Modify dataloading in solvers (#286) * Modify PINNInterface by removing _loss_phys, _loss_data * Adding in PINNInterface a variable to track the current condition during training * Modify GPINN,PINN,CausalPINN to match changes in PINNInterface * Competitive Pinn Addition (#288) * fixing after rebase/ fix loss * fixing final issues --------- Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> * Modify min max formulation to max min for paper consistency * Adding SAPINN solver (#291) * rom solver * fix import --------- Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Anna Ivagnes <75523024+annaivagnes@users.noreply.github.com> Co-authored-by: valc89 <103250118+valc89@users.noreply.github.com> Co-authored-by: Monthly Tag bot <mtbot@noreply.github.com> Co-authored-by: Nicola Demo <demo.nicola@gmail.com>
105 lines
4.2 KiB
Python
105 lines
4.2 KiB
Python
import torch
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import pytest
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from pina.problem import AbstractProblem
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from pina import Condition, LabelTensor
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from pina.solvers import ReducedOrderModelSolver
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from pina.trainer import Trainer
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from pina.model import FeedForward
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from pina.loss import LpLoss
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class NeuralOperatorProblem(AbstractProblem):
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input_variables = ['u_0', 'u_1']
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output_variables = [f'u_{i}' for i in range(100)]
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conditions = {'data' : Condition(input_points=
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LabelTensor(torch.rand(10, 2),
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input_variables),
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output_points=
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LabelTensor(torch.rand(10, 100),
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output_variables))}
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# make the problem + extra feats
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class AE(torch.nn.Module):
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def __init__(self, input_dimensions, rank):
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super().__init__()
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self.encode = FeedForward(input_dimensions, rank, layers=[input_dimensions//4])
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self.decode = FeedForward(rank, input_dimensions, layers=[input_dimensions//4])
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class AE_missing_encode(torch.nn.Module):
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def __init__(self, input_dimensions, rank):
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super().__init__()
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self.encode = FeedForward(input_dimensions, rank, layers=[input_dimensions//4])
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class AE_missing_decode(torch.nn.Module):
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def __init__(self, input_dimensions, rank):
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super().__init__()
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self.decode = FeedForward(rank, input_dimensions, layers=[input_dimensions//4])
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rank = 10
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problem = NeuralOperatorProblem()
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interpolation_net = FeedForward(len(problem.input_variables),
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rank)
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reduction_net = AE(len(problem.output_variables), rank)
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def test_constructor():
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ReducedOrderModelSolver(problem=problem,reduction_network=reduction_net,
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interpolation_network=interpolation_net)
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with pytest.raises(SyntaxError):
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ReducedOrderModelSolver(problem=problem,
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reduction_network=AE_missing_encode(
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len(problem.output_variables), rank),
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interpolation_network=interpolation_net)
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ReducedOrderModelSolver(problem=problem,
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reduction_network=AE_missing_decode(
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len(problem.output_variables), rank),
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interpolation_network=interpolation_net)
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def test_train_cpu():
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solver = ReducedOrderModelSolver(problem = problem,reduction_network=reduction_net,
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interpolation_network=interpolation_net, loss=LpLoss())
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trainer = Trainer(solver=solver, max_epochs=3, accelerator='cpu', batch_size=20)
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trainer.train()
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def test_train_restore():
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tmpdir = "tests/tmp_restore"
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solver = ReducedOrderModelSolver(problem=problem,
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reduction_network=reduction_net,
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interpolation_network=interpolation_net,
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loss=LpLoss())
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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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default_root_dir=tmpdir)
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trainer.train()
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ntrainer = Trainer(solver=solver, max_epochs=15, 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_load():
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tmpdir = "tests/tmp_load"
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solver = ReducedOrderModelSolver(problem=problem,
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reduction_network=reduction_net,
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interpolation_network=interpolation_net,
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loss=LpLoss())
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trainer = Trainer(solver=solver,
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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_solver = ReducedOrderModelSolver.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,reduction_network=reduction_net,
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interpolation_network=interpolation_net)
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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, 100)
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assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape
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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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import shutil
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shutil.rmtree(tmpdir) |