Pass function handles to add derived classes to object classification

model_server/base/roiset.py

@@ -318,7 +318,7 @@ class RoiSet(object):
                 assert der.shape == mono_data[0].shape
                 mono_data.append(der.data)
             # combine channels
-            input_acc = PatchStack(np.concatenate([mono_data], axis=3))
+            input_acc = PatchStack(np.concatenate(mono_data, axis=raw_acc._ga('C')))
 
         else:
             input_acc = raw_acc

tests/test_roiset.py

@@ -208,15 +208,24 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
         return roiset
 
     def test_classify_by_with_derived_channel(self):
+        class ModelWithDerivedInputs(DummyInstanceSegmentationModel):
+            def infer(self, img, mask):
+                return PatchStack(super().infer(img, mask).data * img.chroma)
+
         roiset = self._make_roi_set()
         roiset.classify_by(
-            'dummy_class',
+            'multiple_input_model',
             [0, 1],
-            DummyInstanceSegmentationModel(),
-            derived_channel_functions={'der1': lambda acc: PatchStack(2*acc.data)}
+            ModelWithDerivedInputs(),
+            derived_channel_functions={
+                'der1': lambda acc: PatchStack(2 * acc.data),
+                'der2': lambda acc: PatchStack(0.5 * acc.data)
+            }
         )
-        self.assertTrue(all(roiset.get_df()['classify_by_dummy_class'].unique() == [1]))
-        self.assertTrue(all(np.unique(roiset.object_class_maps['dummy_class'].data) == [0, 1]))
+        self.assertTrue(all(roiset.get_df()['classify_by_multiple_input_model'].unique() == [3]))
+        print(roiset.get_df().columns)
+        print(roiset.get_df()['classify_by_multiple_input_model'])
+        self.assertTrue(all(np.unique(roiset.object_class_maps['multiple_input_model'].data) == [0, 3]))
         return roiset
 
     def test_export_object_classes(self):