cddb191fe4
* adaptive function improvement --------- Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
import torch
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import pytest
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from pina.model.layers.adaptive_func import AdaptiveActivationFunction
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x = torch.rand(5)
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torchfunc = torch.nn.Tanh()
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def test_constructor():
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# simple
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AdaptiveActivationFunction(torchfunc)
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# setting values
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af = AdaptiveActivationFunction(torchfunc, 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 = AdaptiveActivationFunction(torchfunc, alpha=1., beta=2.,
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gamma=3., 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.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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with pytest.raises(TypeError):
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AdaptiveActivationFunction(torchfunc, alpha=1., beta=2.,
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gamma=3., fixed=['delta'])
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with pytest.raises(ValueError):
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AdaptiveActivationFunction(torchfunc, alpha='s')
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AdaptiveActivationFunction(torchfunc, alpha=1., fixed='alpha')
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AdaptiveActivationFunction(torchfunc, alpha=1)
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def test_forward():
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af = AdaptiveActivationFunction(torchfunc)
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af(x)
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def test_backward():
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af = AdaptiveActivationFunction(torchfunc)
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y = af(x)
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y.mean().backward() |