Add multi-label support for label-studio integration

fiftyone/utils/labelstudio.py

@@ -100,6 +100,7 @@ def supported_media_types(self):
     def supported_label_types(self):
         return [
             "classification",
+            "classifications",
             "detection",
             "detections",
             "instance",
@@ -416,7 +417,14 @@ def _import_annotations(self, tasks, task_map):
             # add to dict
             sample_id = task_map[t["id"]]
             # we save and pass both id and the name of the label field
-            results[sample_id] = {l.id: (ln, l) for (ln, l) in labels}
+            results[sample_id] = {}
+            for ln, l in labels:
+                if isinstance(l, fol.Classifications):
+                    for classification_obj in l.classifications:
+                        label_id = classification_obj.id
+                        results[sample_id][label_id] = (ln, classification_obj)
+                else:
+                    results[sample_id][l.id] = (ln, l)
 
         return results
 
@@ -891,7 +899,9 @@ def _from_choices(result):
         return fol.Classification(label=label_values[0])
 
     # multi-label classification
-    return [fol.Classification(label=l) for l in label_values]
+    return fol.Classifications(
+        classifications=[fol.Classification(label=l) for l in label_values]
+    )
 
 
 def _from_rectanglelabels(result):

tests/intensive/labelstudio_tests.py

@@ -265,10 +265,12 @@ def label_mappings():
                 "from_name": "choice",
                 "type": "choices",
             },
-            "fiftyone": [
-                fo.Classification(label="Airbus"),
-                fo.Classification(label="Boeing"),
-            ],
+            "fiftyone": fo.Classifications(
+                classifications=[
+                    fo.Classification(label="Airbus"),
+                    fo.Classification(label="Boeing"),
+                ]
+            ),
         },
         {
             "labelstudio": {
@@ -477,7 +479,6 @@ def test_import_labels(label_mappings):
     for case in label_mappings:
         label = fouls.import_label_studio_annotation(case["labelstudio"])[1]
         expected = case["fiftyone"]
-
         if isinstance(expected, (list, tuple)):
             for pair in zip(label, expected):
                 _assert_labels_equal(*pair)
@@ -673,6 +674,8 @@ def _assert_labels_equal(converted, expected):
             _assert_labels_equal(*pair)
     elif expected._cls == "Regression":
         assert expected.value == converted.value
+    elif expected._cls == "Classifications":
+        assert all(cls_obj.label for cls_obj in expected.classifications)
     else:
         raise NotImplementedError()