df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
import pytest
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import torch
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from pina import Trainer
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from pina.solver import PINN
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from pina.model import FeedForward
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from pina.problem.zoo import Poisson2DSquareProblem
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from pina.loss import ScalarWeighting
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problem = Poisson2DSquareProblem()
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model = FeedForward(len(problem.input_variables), len(problem.output_variables))
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condition_names = problem.conditions.keys()
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print(problem.conditions.keys())
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@pytest.mark.parametrize("weights",
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[1, 1., dict(zip(condition_names, [1]*len(condition_names)))])
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def test_constructor(weights):
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ScalarWeighting(weights=weights)
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@pytest.mark.parametrize("weights", ['a', [1,2,3]])
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def test_wrong_constructor(weights):
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with pytest.raises(ValueError):
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ScalarWeighting(weights=weights)
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@pytest.mark.parametrize("weights",
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[1, 1., dict(zip(condition_names, [1]*len(condition_names)))])
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def test_aggregate(weights):
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weighting = ScalarWeighting(weights=weights)
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losses = dict(zip(condition_names, [torch.randn(1) for _ in range(len(condition_names))]))
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weighting.aggregate(losses=losses)
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@pytest.mark.parametrize("weights",
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[1, 1., dict(zip(condition_names, [1]*len(condition_names)))])
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def test_train_aggregation(weights):
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weighting = ScalarWeighting(weights=weights)
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problem.discretise_domain(50)
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solver = PINN(
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problem=problem,
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model=model,
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weighting=weighting)
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trainer = Trainer(solver=solver, max_epochs=5, accelerator='cpu')
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trainer.train() |