bug fix

Author: Matt Sokoloff

labelbox/data/serialization/labelbox_v1/classification.py

@@ -1,6 +1,7 @@
 from typing import List, Union
 
 from pydantic.main import BaseModel
+from pydantic.schema import schema
 
 from ...annotation_types.annotation import ClassificationAnnotation
 from ...annotation_types.classification import Checklist, ClassificationAnswer, Radio, Text, Dropdown
@@ -15,10 +16,8 @@ class LBV1ClassificationAnswer(LBV1Feature):
 class LBV1Radio(LBV1Feature):
     answer: LBV1ClassificationAnswer
 
-    def to_common(self):
-        return Radio(answer=ClassificationAnswer(
-            feature_schema_id=self.answer.schema_id,
-            name=self.answer.title,
+    def to_common(self) -> Radio:
+        return Radio(answer=ClassificationAnswer(feature_schema_id=self.answer.schema_id, name=self.answer.title,
             extra={
                 'feature_id': self.answer.feature_id,
                 'value': self.answer.value
@@ -27,7 +26,8 @@ def to_common(self):
     @classmethod
     def from_common(cls, radio: Radio, feature_schema_id: Cuid,
                     **extra) -> "LBV1Radio":
-        return cls(schema_id=feature_schema_id,
+        return cls(
+                schema_id=feature_schema_id,
                    answer=LBV1ClassificationAnswer(
                        schema_id=radio.answer.feature_schema_id,
                        title=radio.answer.name,
@@ -39,8 +39,9 @@ def from_common(cls, radio: Radio, feature_schema_id: Cuid,
 class LBV1Checklist(LBV1Feature):
     answers: List[LBV1ClassificationAnswer]
 
-    def to_common(self):
-        return Checklist(answer=[
+    def to_common(self) -> Checklist:
+        return Checklist(
+            answer=[
             ClassificationAnswer(feature_schema_id=answer.schema_id,
                                  name=answer.title,
                                  extra={
@@ -64,6 +65,34 @@ def from_common(cls, checklist: Checklist, feature_schema_id: Cuid,
                    **extra)
 
 
+class LBV1Dropdown(LBV1Feature):
+    answer: List[LBV1ClassificationAnswer]
+    def to_common(self) -> Dropdown:
+        return Dropdown(
+            answer=[
+            ClassificationAnswer(feature_schema_id=answer.schema_id,
+                                 name=answer.title,
+                                 extra={
+                                     'feature_id': answer.feature_id,
+                                     'value': answer.value
+                                 }) for answer in self.answer
+        ])
+
+    @classmethod
+    def from_common(cls, dropdown: Dropdown, feature_schema_id: Cuid,
+                    **extra) -> "LBV1Dropdown":
+        return cls(schema_id = feature_schema_id,
+                   answers=[
+                       LBV1ClassificationAnswer(
+                           schema_id=answer.feature_schema_id,
+                           title=answer.name,
+                           value=answer.extra.get('value'),
+                           feature_id=answer.extra.get('feature_id'))
+                       for answer in dropdown.answer
+                   ],
+                   **extra)
+
+
 class LBV1Text(LBV1Feature):
     answer: str
 
@@ -77,7 +106,7 @@ def from_common(cls, text: Text, feature_schema_id: Cuid,
 
 
 class LBV1Classifications(BaseModel):
-    classifications: List[Union[LBV1Radio, LBV1Checklist, LBV1Text]] = []
+    classifications: List[Union[LBV1Text, LBV1Radio, LBV1Dropdown, LBV1Checklist]] = []
 
     def to_common(self) -> List[ClassificationAnnotation]:
         classifications = [
@@ -112,10 +141,10 @@ def from_common(
     @staticmethod
     def lookup_classification(
         annotation: ClassificationAnnotation
-    ) -> Union[LBV1Text, LBV1Checklist, LBV1Radio]:
+    ) -> Union[LBV1Text, LBV1Checklist, LBV1Radio, LBV1Checklist]:
         return {
             Text: LBV1Text,
-            Dropdown: LBV1Checklist,
+            Dropdown: LBV1Dropdown,
             Checklist: LBV1Checklist,
             Radio: LBV1Radio
         }.get(type(annotation.value))

labelbox/data/serialization/ndjson/label.py

@@ -1,4 +1,5 @@
 from itertools import groupby
+from labelbox.data.annotation_types.classification.classification import Dropdown
 from labelbox.data.annotation_types.metrics import ScalarMetric
 
 from operator import itemgetter
@@ -102,6 +103,9 @@ def _create_non_video_annotations(cls, label: Label):
         ]
         for annotation in non_video_annotations:
             if isinstance(annotation, ClassificationAnnotation):
+                if isinstance(annotation.value, Dropdown):
+                    raise ValueError("Dropdowns are not supported by the NDJson format."
+                                    " Please filter out Dropdown annotations before converting.")
                 yield NDClassification.from_common(annotation, label.data)
             elif isinstance(annotation, ObjectAnnotation):
                 yield NDObject.from_common(annotation, label.data)