Add test_decagon_layer_04().

src/decagon_pytorch/layer.py

@@ -21,7 +21,7 @@
 
 
 import torch
-from .convolve import SparseDropoutGraphConvActivation
+from .convolve import DropoutGraphConvActivation
 from .data import Data
 from typing import List, \
     Union, \
@@ -125,7 +125,7 @@ class DecagonLayer(Layer):
 
         for (nt_row, nt_col), relation_types in self.data.relation_types.items():
             for rel in relation_types:
-                conv = SparseDropoutGraphConvActivation(self.input_dim[nt_col],
+                conv = DropoutGraphConvActivation(self.input_dim[nt_col],
                     self.output_dim[nt_row], rel.adjacency_matrix,
                     self.keep_prob, self.rel_activation)
                 self.next_layer_repr[nt_row].append((conv, nt_col))
@@ -133,7 +133,7 @@ class DecagonLayer(Layer):
                 if nt_row == nt_col:
                     continue
 
-                conv = SparseDropoutGraphConvActivation(self.input_dim[nt_row],
+                conv = DropoutGraphConvActivation(self.input_dim[nt_row],
                     self.output_dim[nt_col], rel.adjacency_matrix.transpose(0, 1),
                     self.keep_prob, self.rel_activation)
                 self.next_layer_repr[nt_col].append((conv, nt_row))
@@ -149,6 +149,7 @@ class DecagonLayer(Layer):
                         self.next_layer_repr[i]
             ]
             next_layer_repr[i] = sum(next_layer_repr[i])
+            next_layer_repr[i] = torch.nn.functional.normalize(next_layer_repr[i], p=2, dim=1)
 
         print('next_layer_repr:', next_layer_repr)
         # next_layer_repr = list(map(sum, next_layer_repr))

tests/decagon_pytorch/test_layer.py

@@ -4,7 +4,9 @@ from decagon_pytorch.layer import InputLayer, \
 from decagon_pytorch.data import Data
 import torch
 import pytest
-from decagon_pytorch.convolve import SparseDropoutGraphConvActivation
+from decagon_pytorch.convolve import SparseDropoutGraphConvActivation, \
+    SparseMultiDGCA, \
+    DropoutGraphConvActivation
 
 
 def _some_data():
@@ -136,9 +138,9 @@ def test_decagon_layer_03():
     assert len(d_layer.next_layer_repr) == 2
     assert len(d_layer.next_layer_repr[0]) == 2
     assert len(d_layer.next_layer_repr[1]) == 4
-    assert all(map(lambda a: isinstance(a[0], SparseDropoutGraphConvActivation),
+    assert all(map(lambda a: isinstance(a[0], DropoutGraphConvActivation),
         d_layer.next_layer_repr[0]))
-    assert all(map(lambda a: isinstance(a[0], SparseDropoutGraphConvActivation),
+    assert all(map(lambda a: isinstance(a[0], DropoutGraphConvActivation),
         d_layer.next_layer_repr[1]))
     assert all(map(lambda a: a[0].output_dim == 32,
         d_layer.next_layer_repr[0]))
@@ -147,7 +149,38 @@ def test_decagon_layer_03():
 
 
 def test_decagon_layer_04():
-    d = _some_data_with_interactions()
+    # check if it is equivalent to MultiDGCA, as it should be
+
+    d = Data()
+    d.add_node_type('Dummy', 100)
+    d.add_relation_type('Dummy Relation', 0, 0,
+        torch.rand((100, 100), dtype=torch.float32).round().to_sparse())
+
     in_layer = OneHotInputLayer(d)
-    d_layer = DecagonLayer(d, in_layer, output_dim=32)
-    _ = d_layer()
+
+    multi_dgca = SparseMultiDGCA([10], 32,
+        [r.adjacency_matrix for r in d.relation_types[0, 0]],
+        keep_prob=1., activation=lambda x: x)
+
+    d_layer = DecagonLayer(d, in_layer, output_dim=32,
+        keep_prob=1., rel_activation=lambda x: x,
+        layer_activation=lambda x: x)
+
+    assert isinstance(d_layer.next_layer_repr[0][0][0],
+        DropoutGraphConvActivation)
+
+    weight = d_layer.next_layer_repr[0][0][0].graph_conv.weight
+    assert isinstance(weight, torch.Tensor)
+
+    assert len(multi_dgca.sparse_dgca) == 1
+    assert isinstance(multi_dgca.sparse_dgca[0], SparseDropoutGraphConvActivation)
+
+    multi_dgca.sparse_dgca[0].sparse_graph_conv.weight = weight
+
+    out_d_layer = d_layer()
+    out_multi_dgca = multi_dgca(in_layer())
+
+    assert isinstance(out_d_layer, list)
+    assert len(out_d_layer) == 1
+
+    assert torch.all(out_d_layer[0] == out_multi_dgca)