Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

tests/test_adaptive_function.py

@@ -0,0 +1,62 @@
+import torch
+import pytest
+
+from pina.adaptive_function import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+                            AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
+                            AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
+                            AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
+
+
+adaptive_function = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+                      AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
+                      AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
+                      AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
+x = torch.rand(10, requires_grad=True)
+
+@pytest.mark.parametrize("Func", adaptive_function)
+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_function)      
+def test_forward(Func):
+    af = Func()
+    af(x)
+
+@pytest.mark.parametrize("Func", adaptive_function)
+def test_backward(Func):
+    af = Func()
+    y = af(x)
+    y.mean().backward()