Modify decoders to handle one relation at a time.

src/icosagon/decode.py

@@ -26,22 +26,20 @@ class DEDICOMDecoder(torch.nn.Module):
                 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, self.keep_prob)
-            inputs_col = dropout(inputs_col, self.keep_prob)
+    def forward(self, inputs_row, inputs_col, relation_index):
+        inputs_row = dropout(inputs_row, self.keep_prob)
+        inputs_col = dropout(inputs_col, self.keep_prob)
 
-            relation = torch.diag(self.local_variation[k])
+        relation = torch.diag(self.local_variation[relation_index])
 
-            product1 = torch.mm(inputs_row, relation)
-            product2 = torch.mm(product1, self.global_interaction)
-            product3 = torch.mm(product2, relation)
-            rec = torch.bmm(product3.view(product3.shape[0], 1, product3.shape[1]),
-                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
-            rec = torch.flatten(rec)
-            outputs.append(self.activation(rec))
-        return outputs
+        product1 = torch.mm(inputs_row, relation)
+        product2 = torch.mm(product1, self.global_interaction)
+        product3 = torch.mm(product2, relation)
+        rec = torch.bmm(product3.view(product3.shape[0], 1, product3.shape[1]),
+            inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+        rec = torch.flatten(rec)
+
+        return self.activation(rec)
 
 
 class DistMultDecoder(torch.nn.Module):
@@ -60,20 +58,18 @@ class DistMultDecoder(torch.nn.Module):
                 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, self.keep_prob)
-            inputs_col = dropout(inputs_col, self.keep_prob)
+    def forward(self, inputs_row, inputs_col, relation_index):
+        inputs_row = dropout(inputs_row, self.keep_prob)
+        inputs_col = dropout(inputs_col, self.keep_prob)
+
+        relation = torch.diag(self.relation[relation_index])
 
-            relation = torch.diag(self.relation[k])
+        intermediate_product = torch.mm(inputs_row, relation)
+        rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
+            inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+        rec = torch.flatten(rec)
 
-            intermediate_product = torch.mm(inputs_row, relation)
-            rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
-                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
-            rec = torch.flatten(rec)
-            outputs.append(self.activation(rec))
-        return outputs
+        return self.activation(rec)
 
 
 class BilinearDecoder(torch.nn.Module):
@@ -92,18 +88,16 @@ class BilinearDecoder(torch.nn.Module):
                 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, self.keep_prob)
-            inputs_col = dropout(inputs_col, self.keep_prob)
+    def forward(self, inputs_row, inputs_col, relation_index):
+        inputs_row = dropout(inputs_row, self.keep_prob)
+        inputs_col = dropout(inputs_col, self.keep_prob)
 
-            intermediate_product = torch.mm(inputs_row, self.relation[k])
-            rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
-                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
-            rec = torch.flatten(rec)
-            outputs.append(self.activation(rec))
-        return outputs
+        intermediate_product = torch.mm(inputs_row, self.relation[relation_index])
+        rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
+            inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+        rec = torch.flatten(rec)
+
+        return self.activation(rec)
 
 
 class InnerProductDecoder(torch.nn.Module):
@@ -118,14 +112,12 @@ class InnerProductDecoder(torch.nn.Module):
         self.activation = activation
 
 
-    def forward(self, inputs_row, inputs_col):
-        outputs = []
-        for k in range(self.num_relation_types):
-            inputs_row = dropout(inputs_row, self.keep_prob)
-            inputs_col = dropout(inputs_col, self.keep_prob)
+    def forward(self, inputs_row, inputs_col, _):
+        inputs_row = dropout(inputs_row, self.keep_prob)
+        inputs_col = dropout(inputs_col, self.keep_prob)
+
+        rec = torch.bmm(inputs_row.view(inputs_row.shape[0], 1, inputs_row.shape[1]),
+            inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+        rec = torch.flatten(rec)
 
-            rec = torch.bmm(inputs_row.view(inputs_row.shape[0], 1, inputs_row.shape[1]),
-                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
-            rec = torch.flatten(rec)
-            outputs.append(self.activation(rec))
-        return outputs
+        return self.activation(rec)

tests/icosagon/test_decode.py

@@ -15,7 +15,7 @@ def test_dedicom_decoder_01():
     dec_2.global_interaction = dec_1.global_interaction
     dec_2.local_variation = dec_1.local_variation
 
-    res_1 = dec_1(repr_, repr_)
+    res_1 = [ dec_1(repr_, repr_, k) for k in range(7) ]
     res_2 = dec_2(repr_, repr_)
 
     assert isinstance(res_1, list)
@@ -35,7 +35,7 @@ def test_dist_mult_decoder_01():
         activation=torch.sigmoid)
     dec_2.relation = dec_1.relation
 
-    res_1 = dec_1(repr_, repr_)
+    res_1 = [ dec_1(repr_, repr_, k) for k in range(7) ]
     res_2 = dec_2(repr_, repr_)
 
     assert isinstance(res_1, list)
@@ -55,7 +55,7 @@ def test_bilinear_decoder_01():
         activation=torch.sigmoid)
     dec_2.relation = dec_1.relation
 
-    res_1 = dec_1(repr_, repr_)
+    res_1 = [ dec_1(repr_, repr_, k) for k in range(7) ]
     res_2 = dec_2(repr_, repr_)
 
     assert isinstance(res_1, list)
@@ -74,7 +74,7 @@ def test_inner_product_decoder_01():
     dec_2 = decagon_pytorch.decode.pairwise.InnerProductDecoder(32, 7, drop_prob=0.,
         activation=torch.sigmoid)
 
-    res_1 = dec_1(repr_, repr_)
+    res_1 = [ dec_1(repr_, repr_, k) for k in range(7) ]
     res_2 = dec_2(repr_, repr_)
 
     assert isinstance(res_1, list)