add test_decode_with_add_reversed_relations

tests/taskmodules/test_pointer_network_for_end2end_re.py

@@ -522,6 +522,57 @@ def test_encode_with_add_reversed_relations_already_exists(caplog):
     }
 
 
+def test_decode_with_add_reversed_relations():
+    doc = ExampleDocument(text="This is a dummy text about nothing. Trust me.")
+    doc.entities.append(LabeledSpan(start=10, end=20, label="content"))
+    doc.entities.append(LabeledSpan(start=27, end=34, label="topic"))
+    doc.relations.append(
+        BinaryRelation(head=doc.entities[0], tail=doc.entities[1], label="is_about")
+    )
+
+    taskmodule = PointerNetworkTaskModuleForEnd2EndRE(
+        tokenizer_name_or_path="facebook/bart-base",
+        relation_layer_name="relations",
+        annotation_field_mapping={
+            "entities": "labeled_spans",
+            "relations": "binary_relations",
+        },
+        add_reversed_relations=True,
+    )
+    taskmodule.prepare(documents=[doc])
+
+    task_encodings = taskmodule.encode(doc, encode_target=True)
+    assert len(task_encodings) == 1
+    decoded_annotations, statistics = taskmodule.decode_annotations(task_encodings[0].targets)
+    assert decoded_annotations == {
+        "entities": [
+            LabeledSpan(start=4, end=6, label="content", score=1.0),
+            LabeledSpan(start=7, end=8, label="topic", score=1.0),
+        ],
+        "relations": [
+            BinaryRelation(
+                head=LabeledSpan(start=4, end=6, label="content", score=1.0),
+                tail=LabeledSpan(start=7, end=8, label="topic", score=1.0),
+                label="is_about",
+                score=1.0,
+            ),
+            BinaryRelation(
+                head=LabeledSpan(start=7, end=8, label="topic", score=1.0),
+                tail=LabeledSpan(start=4, end=6, label="content", score=1.0),
+                label="is_about_reversed",
+                score=1.0,
+            ),
+        ],
+    }
+
+    task_outputs = [task_encoding.targets for task_encoding in task_encodings]
+    docs_with_predictions = taskmodule.decode(task_encodings, task_outputs)
+    assert len(docs_with_predictions) == 1
+    doc_with_predictions: ExampleDocument = docs_with_predictions[0]
+    assert list(doc_with_predictions.entities.predictions) == list(doc_with_predictions.entities)
+    assert list(doc_with_predictions.relations.predictions) == list(doc_with_predictions.relations)
+
+
 @pytest.fixture()
 def target_encoding(taskmodule, task_encoding_without_target):
     return taskmodule.encode_target(task_encoding_without_target)