add test multisource and cce mdd

Author: antoinedemathelin

tests/test_base.py

@@ -211,4 +211,28 @@ def gens():
     model = BaseAdaptDeep()
     model.fit(dataset, Xt=Xt, validation_data=dataset.batch(10))
     model.predict(tf.data.Dataset.from_tensor_slices(Xs).batch(32))
-    model.evaluate(dataset.batch(32))
+    model.evaluate(dataset.batch(32))
+    
+    
+def _unpack_data_ms(self, data):
+    data_src = data[0]
+    data_tgt = data[1]
+    Xs = data_src[0][0]
+    ys = data_src[1][0]
+    if isinstance(data_tgt, tuple):
+        Xt = data_tgt[0]
+        yt = data_tgt[1]
+        return Xs, Xt, ys, yt
+    else:
+        Xt = data_tgt
+        return Xs, Xt, ys, None
+    
+    
+def test_multisource():
+    np.random.seed(0)
+    model = BaseAdaptDeep()
+    model._unpack_data = _unpack_data_ms.__get__(model)
+    model.fit(Xs, ys, Xt=Xt, domains=np.random.choice(2, len(Xs)))
+    model.predict(Xs)
+    model.evaluate(Xs, ys)
+    assert model.n_sources_ == 2

tests/test_mdd.py

@@ -96,8 +96,4 @@ def test_cce():
     model = MDD(encoder, task, copy=False,
                 loss="categorical_crossentropy", optimizer=Adam(0.01), metrics=["acc"])
     model.fit(Xs, ys_2, Xt, yt_2,
-              epochs=0, batch_size=34, verbose=0)
-    assert np.any(model.task_.get_weights()[0] !=
-                  model.discriminator_.get_weights()[0])
-    assert np.all(model.task_.get_weights()[0] ==
-                  task.get_weights()[0])
+              epochs=10, batch_size=34, verbose=0)