Update solvers (#434)
* Enable DDP training with batch_size=None and add validity check for split sizes * Refactoring SolverInterfaces (#435) * Solver update + weighting * Updating PINN for 0.2 * Modify GAROM + tests * Adding more versatile loggers * Disable compilation when running on Windows * Fix tests --------- Co-authored-by: giovanni <giovanni.canali98@yahoo.it> Co-authored-by: FilippoOlivo <filippo@filippoolivo.com>
This commit is contained in:
Dario Coscia
committed by
Nicola Demo
Nicola Demo
parent
780c4921eb
commit
9cae9a438f
@@ -1,105 +1,187 @@
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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.problem import AbstractProblem
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from pina.condition import InputOutputPointsCondition
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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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from pina.problem.zoo import Poisson2DSquareProblem
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from torch._dynamo.eval_frame import OptimizedModule
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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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class LabelTensorProblem(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(
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input_points=LabelTensor(torch.randn(20, 2), ['u_0', 'u_1']),
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output_points=LabelTensor(torch.randn(20, 1), ['u'])),
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}
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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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class TensorProblem(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(
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input_points=torch.randn(20, 2),
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output_points=torch.randn(20, 1))
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}
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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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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(
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input_dimensions, rank, layers=[input_dimensions//4])
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self.decode = FeedForward(
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rank, input_dimensions, layers=[input_dimensions//4])
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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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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(
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input_dimensions, rank, layers=[input_dimensions//4])
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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)
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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(
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rank, input_dimensions, layers=[input_dimensions//4])
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rank = 10
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model = AE(2, 1)
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interpolation_net = FeedForward(2, rank)
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reduction_net = AE(1, rank)
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def test_constructor():
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problem = TensorProblem()
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ReducedOrderModelSolver(problem=problem,
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interpolation_network=interpolation_net,
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reduction_network=reduction_net)
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ReducedOrderModelSolver(problem=LabelTensorProblem(),
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reduction_network=reduction_net,
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interpolation_network=interpolation_net)
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assert ReducedOrderModelSolver.accepted_conditions_types == InputOutputPointsCondition
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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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with pytest.raises(ValueError):
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ReducedOrderModelSolver(problem=Poisson2DSquareProblem(),
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reduction_network=reduction_net,
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interpolation_network=interpolation_net)
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@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
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@pytest.mark.parametrize("use_lt", [True, False])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_train(use_lt, batch_size, compile):
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problem = LabelTensorProblem() if use_lt else TensorProblem()
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solver = ReducedOrderModelSolver(problem=problem,
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reduction_network=reduction_net,
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interpolation_network=interpolation_net, use_lt=use_lt)
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trainer = Trainer(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.,
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test_size=0.,
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val_size=0.,
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compile=compile)
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trainer.train()
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if trainer.compile:
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for v in solver.model.values():
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assert (isinstance(v, OptimizedModule))
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@pytest.mark.parametrize("use_lt", [True, False])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_validation(use_lt, compile):
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problem = LabelTensorProblem() if use_lt else TensorProblem()
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solver = ReducedOrderModelSolver(problem=problem,
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reduction_network=reduction_net,
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interpolation_network=interpolation_net, use_lt=use_lt)
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trainer = Trainer(solver=solver,
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max_epochs=2,
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accelerator='cpu',
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batch_size=None,
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train_size=0.9,
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val_size=0.1,
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test_size=0.,
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compile=compile)
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trainer.train()
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if trainer.compile:
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for v in solver.model.values():
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assert (isinstance(v, OptimizedModule))
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@pytest.mark.parametrize("use_lt", [True, False])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_test(use_lt, compile):
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problem = LabelTensorProblem() if use_lt else TensorProblem()
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solver = ReducedOrderModelSolver(problem=problem,
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reduction_network=reduction_net,
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interpolation_network=interpolation_net, use_lt=use_lt)
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trainer = Trainer(solver=solver,
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max_epochs=2,
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accelerator='cpu',
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batch_size=None,
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train_size=0.8,
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val_size=0.1,
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test_size=0.1,
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compile=compile)
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trainer.train()
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if trainer.compile:
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for v in solver.model.values():
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assert (isinstance(v, OptimizedModule))
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def test_train_load_restore():
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dir = "tests/test_solvers/tmp/"
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problem = LabelTensorProblem()
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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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trainer = Trainer(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=0.9,
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test_size=0.1,
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val_size=0.,
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default_root_dir=dir)
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trainer.train()
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# restore
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ntrainer = Trainer(solver=solver,
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max_epochs=5,
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accelerator='cpu',)
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ntrainer.train(
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ckpt_path=f'{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt')
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# loading
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new_solver = ReducedOrderModelSolver.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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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, 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),
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solver.forward(test_pts))
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# rm directories
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import shutil
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shutil.rmtree('tests/test_solvers/tmp')
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