1bb3c125ac
* Refactoring solvers * Simplify logic compile * Improve and update doc * Create SupervisedSolverInterface * Specialize SupervisedSolver and ReducedOrderModelSolver * Create EnsembleSolverInterface + EnsembleSupervisedSolver * Create tests ensemble solvers * formatter * codacy * fix issues + speedup test
151 lines
4.4 KiB
Python
151 lines
4.4 KiB
Python
import pytest
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import torch
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from pina import LabelTensor, Condition
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from pina.model import FeedForward
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from pina.trainer import Trainer
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from pina.solver import PINN
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from pina.condition import (
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InputTargetCondition,
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InputEquationCondition,
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DomainEquationCondition,
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)
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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 torch._dynamo.eval_frame import OptimizedModule
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# define problems
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problem = Poisson()
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problem.discretise_domain(10)
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inverse_problem = InversePoisson()
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inverse_problem.discretise_domain(10)
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# reduce the number of data points to speed up testing
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data_condition = inverse_problem.conditions["data"]
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data_condition.input = data_condition.input[:10]
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data_condition.target = data_condition.target[:10]
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# add input-output condition to test supervised learning
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input_pts = torch.rand(10, len(problem.input_variables))
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input_pts = LabelTensor(input_pts, problem.input_variables)
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output_pts = torch.rand(10, len(problem.output_variables))
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output_pts = LabelTensor(output_pts, problem.output_variables)
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problem.conditions["data"] = Condition(input=input_pts, target=output_pts)
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# define model
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model = FeedForward(len(problem.input_variables), len(problem.output_variables))
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@pytest.mark.parametrize("problem", [problem, inverse_problem])
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def test_constructor(problem):
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solver = PINN(problem=problem, model=model)
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assert solver.accepted_conditions_types == (
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InputTargetCondition,
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InputEquationCondition,
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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 = PINN(model=model, problem=problem)
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trainer = Trainer(
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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=1.0,
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val_size=0.0,
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test_size=0.0,
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compile=compile,
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)
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trainer.train()
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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 = PINN(model=model, problem=problem)
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trainer = Trainer(
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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=0.0,
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compile=compile,
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)
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trainer.train()
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if trainer.compile:
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assert isinstance(solver.model, OptimizedModule)
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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 = PINN(model=model, problem=problem)
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trainer = Trainer(
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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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)
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trainer.test()
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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_solver/tmp"
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problem = problem
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solver = PINN(model=model, problem=problem)
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trainer = Trainer(
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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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)
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trainer.train()
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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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)
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# loading
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new_solver = PINN.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,
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model=model,
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)
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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 == solver.forward(test_pts).shape
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torch.testing.assert_close(
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new_solver.forward(test_pts), solver.forward(test_pts)
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)
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# rm directories
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import shutil
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shutil.rmtree("tests/test_solver/tmp")
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