fixing adaptive functions

tests/test_adaptive_functions.py

@@ -0,0 +1,62 @@
+import torch
+import pytest
+
+from pina.adaptive_functions import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+                            AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
+                            AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
+                            AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
+
+
+adaptive_functions = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+                      AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
+                      AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
+                      AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
+x = torch.rand(10, requires_grad=True)
+
+@pytest.mark.parametrize("Func", adaptive_functions)
+def test_constructor(Func):
+    if Func.__name__ == 'AdaptiveExp':
+        # simple
+        Func()
+        # setting values
+        af = Func(alpha=1., beta=2.)
+        assert af.alpha.requires_grad
+        assert af.beta.requires_grad
+        assert af.alpha == 1.
+        assert af.beta == 2.
+    else:
+        # simple
+        Func()
+        # setting values
+        af = Func(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 = Func(alpha=1., beta=2., fixed=['alpha'])
+    assert af.alpha.requires_grad is False
+    assert af.beta.requires_grad
+    assert af.alpha == 1.
+    assert af.beta == 2.
+
+    with pytest.raises(TypeError):
+        Func(alpha=1., beta=2., fixed=['delta'])
+
+    with pytest.raises(ValueError):
+        Func(alpha='s')
+        Func(alpha=1)
+
+@pytest.mark.parametrize("Func", adaptive_functions)      
+def test_forward(Func):
+    af = Func()
+    af(x)
+
+@pytest.mark.parametrize("Func", adaptive_functions)
+def test_backward(Func):
+    af = Func()
+    y = af(x)
+    y.mean().backward()

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()