updates

Author: farkob

labelbox/data/annotation_types/feature.py

@@ -10,9 +10,6 @@ class FeatureSchema(BaseModel):
     Class that represents a feature schema.
     Could be a annotation, a subclass, or an option.
     Schema ids might not be known when constructing these objects so both a name and schema id are valid.
-
-    Use `LabelList.assign_feature_schema_ids` or `LabelGenerator.assign_feature_schema_ids`
-    to retroactively add schema ids by looking them up from the names.
     """
     name: Optional[str] = None
     feature_schema_id: Optional[Cuid] = None
@@ -27,8 +24,8 @@ def must_set_one(cls, values):
 
     def dict(self, *args, **kwargs):
         res = super().dict(*args, **kwargs)
-        if res['name'] is None:
+        if 'name' in res and res['name'] is None:
             res.pop('name')
-        if res['feature_schema_id'] is None:
-            res.pop('feature_schema_id')
+        if 'featureSchemaId' in res and res.featureSchemaId is None:
+            res.pop('featureSchemaId')
         return res

labelbox/data/annotation_types/label.py

@@ -1,5 +1,6 @@
 from collections import defaultdict
 from typing import Any, Callable, Dict, List, Union, Optional
+import warnings
 
 from pydantic import BaseModel, validator
 
@@ -136,6 +137,9 @@ def assign_feature_schema_ids(
             ontology_builder: The ontology that matches the feature names assigned to objects in this dataset
         Returns:
             Label. useful for chaining these modifying functions
+
+        Warning: assign_feature_schema_ids is now obsolete, you can
+                 now use names directly without having to lookup schema_ids.
         """
         tool_lookup, classification_lookup = get_feature_schema_lookup(
             ontology_builder)

labelbox/data/serialization/ndjson/base.py

@@ -44,15 +44,13 @@ def validate_name(cls, v, values):
     @validator('schema_id', pre=True, always=True)
     def validate_id(cls, v, values):
         if v is None and 'name' not in values:
-            raise ValueError(
-                "Schema ids or names are not set. Use `LabelGenerator.assign_feature_schema_ids`, `LabelList.assign_feature_schema_ids`, or `Label.assign_feature_schema_ids`."
-            )
+            raise ValueError("Schema id or name are not set. Set either one.")
         return v
 
     def dict(self, *args, **kwargs):
         res = super().dict(*args, **kwargs)
-        if res['name'] is None:
+        if 'name' in res and res['name'] is None:
             res.pop('name')
-        if res['schemaId'] is None:
+        if 'schemaId' in res and res['schemaId'] is None:
             res.pop('schemaId')
         return res

labelbox/data/serialization/ndjson/classification.py

@@ -1,3 +1,4 @@
+from tkinter import N
 from typing import Any, Dict, List, Union, Optional
 
 from pydantic import BaseModel, Field, validator
@@ -29,9 +30,9 @@ def validate_id(cls, v, values):
 
     def dict(self, *args, **kwargs):
         res = super().dict(*args, **kwargs)
-        if res['name'] is None:
+        if 'name' in res and res['name'] is None:
             res.pop('name')
-        if res['schemaId'] is None:
+        if 'schemaId' in res and res['schemaId'] is None:
             res.pop('schemaId')
         return res
 
@@ -153,7 +154,8 @@ class NDRadio(NDAnnotation, NDRadioSubclass, VideoSupported):
     def from_common(cls, radio: Radio, name: str, feature_schema_id: Cuid,
                     extra: Dict[str, Any], data: Union[VideoData, TextData,
                                                        ImageData]) -> "NDRadio":
-        return cls(answer=NDFeature(schema_id=radio.answer.feature_schema_id),
+        return cls(answer=NDFeature(name=radio.answer.name,
+                                    schema_id=radio.answer.feature_schema_id),
                    data_row={'id': data.uid},
                    name=name,
                    schema_id=feature_schema_id,
@@ -172,7 +174,7 @@ def from_common(
             raise TypeError(
                 f"Unable to convert object to MAL format. `{type(annotation.value)}`"
             )
-        return classify_obj.from_common(annotation.value,
+        return classify_obj.from_common(annotation.value, annotation.name,
                                         annotation.feature_schema_id)
 
     @staticmethod

labelbox/data/serialization/ndjson/label.py

@@ -50,7 +50,8 @@ def _generate_annotations(
             for annotation in annotations:
                 if isinstance(annotation, NDSegments):
                     annots.extend(
-                        NDSegments.to_common(annotation, annotation.schema_id))
+                        NDSegments.to_common(annotation, annotation.name,
+                                             annotation.schema_id))
 
                 elif isinstance(annotation, NDObjectType.__args__):
                     annots.append(NDObject.to_common(annotation))
@@ -92,10 +93,12 @@ def _create_video_annotations(
 
         video_annotations = defaultdict(list)
         for annot in label.annotations:
+            # print(dict(annot))
             if isinstance(
                     annot,
                 (VideoClassificationAnnotation, VideoObjectAnnotation)):
-                video_annotations[annot.schema_id or annot.name].append(annot)
+                video_annotations[annot.feature_schema_id or
+                                  annot.name].append(annot)
 
         for annotation_group in video_annotations.values():
             consecutive_frames = cls._get_consecutive_frames(

tests/data/annotation_types/classification/test_classification.py

@@ -43,8 +43,9 @@ def test_subclass():
         classification = ClassificationAnnotation(value=Text(answer=answer))
     classification = ClassificationAnnotation(value=Text(answer=answer),
                                               name=name)
-    assert classification.dict() == {
+    assert classification.dict(by_alias=True) == {
         'name': name,
+        'feature_schema_id': None,
         'extra': {},
         'value': {
             'answer': answer
@@ -92,6 +93,7 @@ def test_radio():
     assert classification.dict() == {
         'answer': {
             'name': answer.name,
+            'feature_schema_id': None,
             'extra': {}
         }
     }
@@ -106,6 +108,7 @@ def test_radio():
         'value': {
             'answer': {
                 'name': answer.name,
+                'feature_schema_id': None,
                 'extra': {}
             }
         }
@@ -127,6 +130,7 @@ def test_checklist():
     assert classification.dict() == {
         'answer': [{
             'name': answer.name,
+            'feature_schema_id': None,
             'extra': {}
         }]
     }
@@ -142,6 +146,7 @@ def test_checklist():
         'value': {
             'answer': [{
                 'name': answer.name,
+                'feature_schema_id': None,
                 'extra': {}
             }]
         },
@@ -160,7 +165,13 @@ def test_dropdown():
     with pytest.raises(ValidationError):
         classification = Dropdown(answer=answer)
     classification = Dropdown(answer=[answer])
-    assert classification.dict() == {'answer': [{'name': '1', 'extra': {}}]}
+    assert classification.dict() == {
+        'answer': [{
+            'name': '1',
+            'feature_schema_id': None,
+            'extra': {}
+        }]
+    }
     classification = ClassificationAnnotation(
         value=Dropdown(answer=[answer]),
         feature_schema_id=feature_schema_id,
@@ -172,6 +183,7 @@ def test_dropdown():
         'value': {
             'answer': [{
                 'name': answer.name,
+                'feature_schema_id': None,
                 'extra': {}
             }]
         }