Switch to Keras v3 format for nn_ensemble (with legacy h5 support)

annif/backend/nn_ensemble.py

@@ -97,7 +97,8 @@ class NNEnsembleBackend(backend.AnnifLearningBackend, ensemble.BaseEnsembleBacke
 
     name = "nn_ensemble"
 
-    MODEL_FILE = "nn-model.h5"
+    MODEL_FILE = "nn-model.keras"
+    MODEL_FILE_FALLBACK = "nn-model.h5"
     LMDB_FILE = "nn-train.mdb"
 
     DEFAULT_PARAMETERS = {
@@ -122,11 +123,18 @@ def initialize(self, parallel: bool = False) -> None:
             return
         model_filename = os.path.join(self.datadir, self.MODEL_FILE)
         if not os.path.exists(model_filename):
-            raise NotInitializedException(
-                "model file {} not found".format(model_filename),
-                backend_id=self.backend_id,
+            model_filename_fallback = os.path.join(
+                self.datadir, self.MODEL_FILE_FALLBACK
             )
+            if os.path.exists(model_filename_fallback):
+                model_filename = model_filename_fallback
+            else:
+                raise NotInitializedException(
+                    "model file {} not found".format(model_filename),
+                    backend_id=self.backend_id,
+                )
         self.debug("loading Keras model from {}".format(model_filename))
+        print("loading Keras model from {}".format(model_filename))
         self._model = load_model(
             model_filename, custom_objects={"MeanLayer": MeanLayer}
         )

tests/test_backend_nn_ensemble.py

@@ -105,11 +105,11 @@ def test_nn_ensemble_train_and_learn(registry, tmpdir):
     assert nn_ensemble._model.optimizer.learning_rate.value() == 0.001
 
     datadir = py.path.local(project.datadir)
-    assert datadir.join("nn-model.h5").exists()
-    assert datadir.join("nn-model.h5").size() > 0
+    assert datadir.join("nn-model.keras").exists()
+    assert datadir.join("nn-model.keras").size() > 0
 
     # test online learning
-    modelfile = datadir.join("nn-model.h5")
+    modelfile = datadir.join("nn-model.keras")
 
     old_size = modelfile.size()
     old_mtime = modelfile.mtime()
@@ -129,7 +129,7 @@ def test_nn_ensemble_train_cached(registry):
     datadir = py.path.local(project.datadir)
     assert datadir.join("nn-train.mdb").exists()
 
-    datadir.join("nn-model.h5").remove()
+    datadir.join("nn-model.keras").remove()
 
     nn_ensemble_type = annif.backend.get_backend("nn_ensemble")
     nn_ensemble = nn_ensemble_type(
@@ -140,8 +140,8 @@ def test_nn_ensemble_train_cached(registry):
 
     nn_ensemble.train("cached")
 
-    assert datadir.join("nn-model.h5").exists()
-    assert datadir.join("nn-model.h5").size() > 0
+    assert datadir.join("nn-model.keras").exists()
+    assert datadir.join("nn-model.keras").size() > 0
 
 
 def test_nn_ensemble_train_and_learn_params(registry, tmpdir, capfd):
@@ -254,3 +254,44 @@ def test_nn_ensemble_suggest(app_project):
 
     assert nn_ensemble._model is not None
     assert len(results) > 0
+
+
+def test_nn_ensemble_h5_fallback(registry, app_project, monkeypatch):
+    nn_ensemble_type = annif.backend.get_backend("nn_ensemble")
+    nn_ensemble = nn_ensemble_type(
+        backend_id="nn_ensemble",
+        config_params={"sources": "dummy-en"},
+        project=app_project,
+    )
+
+    datadir = py.path.local(app_project.datadir)
+
+    # train model from cached data and save it in legacy HDF5 format
+    with monkeypatch.context() as m:
+        m.setattr(nn_ensemble, "MODEL_FILE", "nn-model.h5")
+        nn_ensemble.train("cached")
+
+    # remove any existing .keras model
+    datadir.join("nn-model.keras").remove()
+
+    # check that the trained model was saved in a HDF5 file
+    assert datadir.join("nn-model.h5").exists()
+    assert datadir.join("nn-model.h5").size() > 0
+
+    # delete the model, so it has to be loaded again
+    nn_ensemble._model = None
+
+    # now try using suggest, which forces loading it from .h5 file
+    results = nn_ensemble.suggest(
+        [
+            """Arkeologiaa sanotaan joskus myös
+        muinaistutkimukseksi tai muinaistieteeksi. Se on humanistinen
+        tiede tai oikeammin joukko tieteitä, jotka tutkivat ihmisen
+        menneisyyttä. Tutkimusta tehdään analysoimalla muinaisjäännöksiä
+        eli niitä jälkiä, joita ihmisten toiminta on jättänyt maaperään
+        tai vesistöjen pohjaan."""
+        ]
+    )[0]
+
+    assert nn_ensemble._model is not None
+    assert len(results) > 0