Adaptive Functions (#272)

* adaptive function improvement

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>

tests/test_layers/test_adaptive_func.py

@@ -0,0 +1,48 @@
+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()