df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
import torch
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import pytest
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from pina.adaptive_function import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
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AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
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AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
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AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
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adaptive_function = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
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AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
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AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
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AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
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x = torch.rand(10, requires_grad=True)
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@pytest.mark.parametrize("Func", adaptive_function)
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def test_constructor(Func):
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if Func.__name__ == 'AdaptiveExp':
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# simple
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Func()
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# setting values
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af = Func(alpha=1., beta=2.)
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assert af.alpha.requires_grad
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assert af.beta.requires_grad
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assert af.alpha == 1.
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assert af.beta == 2.
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else:
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# simple
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Func()
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# setting values
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af = Func(alpha=1., beta=2., gamma=3.)
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assert af.alpha.requires_grad
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assert af.beta.requires_grad
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assert af.gamma.requires_grad
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assert af.alpha == 1.
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assert af.beta == 2.
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assert af.gamma == 3.
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# fixed variables
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af = Func(alpha=1., beta=2., fixed=['alpha'])
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assert af.alpha.requires_grad is False
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assert af.beta.requires_grad
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assert af.alpha == 1.
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assert af.beta == 2.
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with pytest.raises(TypeError):
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Func(alpha=1., beta=2., fixed=['delta'])
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with pytest.raises(ValueError):
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Func(alpha='s')
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Func(alpha=1)
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@pytest.mark.parametrize("Func", adaptive_function)
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def test_forward(Func):
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af = Func()
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af(x)
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@pytest.mark.parametrize("Func", adaptive_function)
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def test_backward(Func):
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af = Func()
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y = af(x)
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y.mean().backward() |