make levelset unittests cover all algs

tests/nn/levelset.py

@@ -11,19 +11,20 @@ class Levelset1dsuper(unittest.TestCase):
         from topologylayer.nn import LevelSetLayer1D
 
         # superlevel set
-        layer = LevelSetLayer1D(size=3, sublevel=False)
+        for alg in ['hom', 'cohom']:
+            layer = LevelSetLayer1D(size=3, sublevel=False, alg=alg)
             y = torch.tensor([1,0,1], dtype=torch.float).requires_grad_(True)
 
             dgms, issub = layer(y)
             self.assertEqual(
                 issub,
                 False,
-            "Expected superlevel set layer")
+                "Expected superlevel set layer. alg=" + alg)
             self.assertEqual(
                 torch.all(torch.eq(remove_infinite_bars(remove_zero_bars(dgms[0]), issub),
                             torch.tensor([[1., 0.]]))),
                 True,
-            "unexpected barcode")
+                "unexpected barcode. alg=" + alg)
 
             p = torch.sum(remove_infinite_bars(remove_zero_bars(dgms[0]), issub)[0])
             p.backward()
@@ -31,7 +32,7 @@ class Levelset1dsuper(unittest.TestCase):
             self.assertEqual(
                 y.grad[1].item(),
                 1.0,
-            "unexpected gradient")
+                "unexpected gradient. alg=" + alg)
 
 
 class Levelset1dsub(unittest.TestCase):
@@ -39,19 +40,20 @@ class Levelset1dsub(unittest.TestCase):
         from topologylayer.nn import LevelSetLayer1D
 
         # sublevel set
-        layer = LevelSetLayer1D(size=3, sublevel=True)
+        for alg in ['hom', 'cohom']:
+            layer = LevelSetLayer1D(size=3, sublevel=True, alg=alg)
             y = torch.tensor([1,0,1], dtype=torch.float).requires_grad_(True)
 
             dgms, issub = layer(y)
             self.assertEqual(
                 issub,
                 True,
-            "Expected sublevel set layer")
+                "Expected sublevel set layer. alg=" + alg)
             self.assertEqual(
                 torch.all(torch.eq(remove_zero_bars(dgms[0]),
                             torch.tensor([[0., np.inf]]))),
                 True,
-            "unexpected barcode")
+                "unexpected barcode. alg=" + alg)
 
         # p = torch.sum(dgms[0][0])
         # p.backward()
@@ -67,24 +69,25 @@ class Levelset2dsuper(unittest.TestCase):
         from topologylayer.nn import LevelSetLayer2D
 
         # sublevel set
-        layer = LevelSetLayer2D(size=(3,3), maxdim=1, sublevel=False)
+        for alg in ['hom', 'cohom']:
+            layer = LevelSetLayer2D(size=(3,3), maxdim=1, sublevel=False, alg=alg)
             x = torch.tensor([[2, 1, 1],[1, 0.5, 1],[1, 1, 1]], dtype=torch.float).requires_grad_(True)
 
             dgms, issub = layer(x)
             self.assertEqual(
                 issub,
                 False,
-            "Expected superlevel set layer")
+                "Expected superlevel set layer. alg=" + alg)
             self.assertEqual(
                 torch.all(torch.eq(remove_zero_bars(dgms[0]),
                             torch.tensor([[2., -np.inf]]))),
                 True,
-            "unexpected 0-dim barcode")
+                "unexpected 0-dim barcode. alg=" + alg)
             self.assertEqual(
                 torch.all(torch.eq(remove_zero_bars(dgms[1]),
                             torch.tensor([[1., 0.5]]))),
                 True,
-            "unexpected 1-dim barcode")
+                "unexpected 1-dim barcode. alg=" + alg)
 
             d1 = remove_zero_bars(dgms[1])
             p = d1[0,0] - d1[0,1]
@@ -93,4 +96,4 @@ class Levelset2dsuper(unittest.TestCase):
             self.assertEqual(
                 x.grad[1,1].item(),
                 -1.0,
-            "unexpected gradient")
+                "unexpected gradient. alg=" + alg)