wip

Author: Matt Sokoloff

labelbox/data/annotation_types/__init__.py

@@ -29,3 +29,6 @@
 from .collection import LabelGenerator
 
 from .metrics import ScalarMetric
+from .metrics import CustomScalarMetric
+from .metrics import MetricAggregation
+

labelbox/data/annotation_types/label.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+from labelbox.data.annotation_types.metrics.scalar import CustomScalarMetric
 
 from typing import Any, Callable, Dict, List, Union, Optional
 
@@ -21,7 +22,7 @@ class Label(BaseModel):
     data: Union[VideoData, ImageData, TextData]
     annotations: List[Union[ClassificationAnnotation, ObjectAnnotation,
                             VideoObjectAnnotation,
-                            VideoClassificationAnnotation, ScalarMetric]] = []
+                            VideoClassificationAnnotation, ScalarMetric, CustomScalarMetric]] = []
     extra: Dict[str, Any] = {}
 
     def object_annotations(self) -> List[ObjectAnnotation]:

labelbox/data/annotation_types/metrics.py

@@ -388,8 +388,8 @@ def create_from_local_file(cls,
 
     def delete(self) -> None:
         """ Deletes the import job and also any annotations created by this import.
-        
-        Returns: 
+
+        Returns:
             None
         """
         id_param = "bulk_request_id"
@@ -635,7 +635,6 @@ def determinants(parent_cls) -> List[str]:
 
 ###### Classifications ######
 
-
 class NDText(NDBase):
     ontology_type: Literal["text"] = "text"
     answer: str = pydantic.Field(determinant=True)

labelbox/schema/bulk_import_request.py

@@ -1,6 +1,9 @@
+from labelbox.data.annotation_types.metrics.aggregations import MetricAggregation
+from labelbox.data.annotation_types.metrics.scalar import CustomScalarMetric
 from labelbox.data.annotation_types.collection import LabelList
 from labelbox.data.annotation_types import ScalarMetric, Label, ImageData
 
+import pytest
 
 def test_scalar_metric():
     value = 10
@@ -27,3 +30,42 @@ def test_scalar_metric():
     }
     assert label.dict() == expected
     next(LabelList([label])).dict() == expected
+
+
+@pytest.mark.parametrize(
+    'feature_name,subclass_name,aggregation',
+    [
+        ("cat", "orange" , MetricAggregation.ARITHMETIC_MEAN),
+        ("cat", None, MetricAggregation.ARITHMETIC_MEAN),
+        (None, None, MetricAggregation.ARITHMETIC_MEAN),
+        (None, None,  None),
+    ])
+def test_custom_scalar_metric(feature_name, subclass_name, aggregation):
+    value = 0.5
+    metric = CustomScalarMetric(metric_name = "iou", value=value, feature_name=feature_name, subclass_name = subclass_name, aggregation = aggregation)
+    assert metric.value == value
+
+    label = Label(data=ImageData(uid="ckrmd9q8g000009mg6vej7hzg"),
+                  annotations=[metric])
+    expected = {
+        'data': {
+            'external_id': None,
+            'uid': 'ckrmd9q8g000009mg6vej7hzg',
+            'im_bytes': None,
+            'file_path': None,
+            'url': None,
+            'arr': None
+        },
+        'annotations': [{
+            'value': 10.0,
+
+            'extra': {}
+        }],
+        'extra': {},
+        'uid': None
+    }
+    assert label.dict() == expected
+    next(LabelList([label])).dict() == expected
+
+
+