fixing adaptive functions

tests/test_layers/test_adaptive_func.py

@@ -1,48 +0,0 @@
-import torch
-import pytest
-
-from pina.model.layers.adaptive_func import AdaptiveActivationFunction
-
-x = torch.rand(5)
-torchfunc = torch.nn.Tanh()
-
-def test_constructor():
-    # simple
-    AdaptiveActivationFunction(torchfunc)
-
-    # setting values
-    af = AdaptiveActivationFunction(torchfunc, alpha=1., beta=2., gamma=3.)
-    assert af.alpha.requires_grad
-    assert af.beta.requires_grad
-    assert af.gamma.requires_grad
-    assert af.alpha == 1.
-    assert af.beta == 2.
-    assert af.gamma == 3.
-
-    # fixed variables
-    af = AdaptiveActivationFunction(torchfunc, alpha=1., beta=2.,
-                                    gamma=3., fixed=['alpha'])
-    assert af.alpha.requires_grad is False
-    assert af.beta.requires_grad
-    assert af.gamma.requires_grad
-    assert af.alpha == 1.
-    assert af.beta == 2.
-    assert af.gamma == 3.
-
-    with pytest.raises(TypeError):
-        AdaptiveActivationFunction(torchfunc, alpha=1., beta=2.,
-                                    gamma=3., fixed=['delta'])
-
-    with pytest.raises(ValueError):
-        AdaptiveActivationFunction(torchfunc, alpha='s')
-        AdaptiveActivationFunction(torchfunc, alpha=1., fixed='alpha')
-        AdaptiveActivationFunction(torchfunc, alpha=1)
-        
-def test_forward():
-    af = AdaptiveActivationFunction(torchfunc)
-    af(x)
-
-def test_backward():
-    af = AdaptiveActivationFunction(torchfunc)
-    y = af(x)
-    y.mean().backward()