Add BilinearDecoder and test.

src/decagon_pytorch/decode.py

@@ -64,3 +64,31 @@ class DistMultDecoder(torch.nn.Module):
             rec = torch.mm(intermediate_product, torch.transpose(inputs_col, 0, 1))
             outputs.append(self.activation(rec))
         return outputs
+
+
+class BilinearDecoder(torch.nn.Module):
+    """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
+    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
+        activation=torch.sigmoid, **kwargs):
+
+        super().__init__(**kwargs)
+        self.input_dim = input_dim
+        self.num_relation_types = num_relation_types
+        self.drop_prob = drop_prob
+        self.activation = activation
+
+        self.relation = [
+            init_glorot(input_dim, input_dim) \
+                for _ in range(num_relation_types)
+        ]
+
+    def forward(self, inputs_row, inputs_col):
+        outputs = []
+        for k in range(self.num_relation_types):
+            inputs_row = dropout(inputs_row, 1.-self.drop_prob)
+            inputs_col = dropout(inputs_col, 1.-self.drop_prob)
+
+            intermediate_product = torch.mm(inputs_row, self.relation[k])
+            rec = torch.mm(intermediate_product, torch.transpose(inputs_col, 0, 1))
+            outputs.append(self.activation(rec))
+        return outputs

tests/decagon_pytorch/test_decode.py

@@ -56,3 +56,16 @@ def test_dist_mult_decoder():
             tf.convert_to_tensor(distmult_torch.relation[i].detach().numpy())
 
     _common(distmult_torch, distmult_tf)
+
+
+def test_bilinear_decoder():
+    bilinear_torch = decagon_pytorch.decode.BilinearDecoder(input_dim=10,
+        num_relation_types=7)
+    bilinear_tf = decagon.deep.layers.BilinearDecoder(input_dim=10, num_types=7,
+        edge_type=(0, 0))
+
+    for i in range(bilinear_tf.num_types):
+        bilinear_tf.vars['relation_%d' % i] = \
+            tf.convert_to_tensor(bilinear_torch.relation[i].detach().numpy())
+
+    _common(bilinear_torch, bilinear_tf)