format

Author: Matt Sokoloff

labelbox/schema/bulk_import_request.py

@@ -788,25 +788,28 @@ class RLEMaskFeatures(BaseModel):
     @validator('counts')
     def validate_counts(cls, counts):
         if not all([count >= 0 for count in counts]):
-            raise ValueError("Found negative value for counts. They should all be zero or positive")
+            raise ValueError(
+                "Found negative value for counts. They should all be zero or positive"
+            )
         return counts
 
     @validator('size')
     def validate_size(cls, size):
         if len(size) != 2:
-            raise ValueError(f"Mask `size` should have two ints representing height and with. Found : {size}")
+            raise ValueError(
+                f"Mask `size` should have two ints representing height and with. Found : {size}"
+            )
         if not all([count > 0 for count in size]):
-            raise ValueError(f"Mask `size` should be a postitive int. Found : {size}" )
+            raise ValueError(
+                f"Mask `size` should be a postitive int. Found : {size}")
         return size
 
 
-
 class PNGMaskFeatures(BaseModel):
     # base64 encoded png bytes
     png: str
 
 
-
 class URIMaskFeatures(BaseModel):
     instanceURI: str
     colorRGB: Union[List[int], Tuple[int, int, int]]
@@ -816,7 +819,8 @@ def validate_color(cls, colorRGB):
         #Does the dtype matter? Can it be a float?
         if not isinstance(colorRGB, (tuple, list)):
             raise ValueError(
-                f"Received color that is not a list or tuple. Found : {colorRGB}")
+                f"Received color that is not a list or tuple. Found : {colorRGB}"
+            )
         elif len(colorRGB) != 3:
             raise ValueError(
                 f"Must provide RGB values for segmentation colors. Found : {colorRGB}"
@@ -827,12 +831,10 @@ def validate_color(cls, colorRGB):
         return colorRGB
 
 
-
-
 class NDMask(NDBaseTool):
     ontology_type: Literal["superpixel"] = "superpixel"
-    mask: Union[URIMaskFeatures, PNGMaskFeatures, RLEMaskFeatures] = pydantic.Field(determinant=True)
-
+    mask: Union[URIMaskFeatures, PNGMaskFeatures,
+                RLEMaskFeatures] = pydantic.Field(determinant=True)
 
 
 #A union with custom construction logic to improve error messages

tests/integration/annotation_import/conftest.py

@@ -231,31 +231,34 @@ def segmentation_inference(prediction_id_mapping):
     del segmentation['tool']
     return segmentation
 
+
 @pytest.fixture
 def segmentation_inference_rle(prediction_id_mapping):
     segmentation = prediction_id_mapping['superpixel'].copy()
-    segmentation.update(
-        {
-            'uuid' : str(uuid.uuid4()),
-            'mask': {
-            'size': [10,10],
-            'counts': [1, 0, 10,100]
-        }})
+    segmentation.update({
+        'uuid': str(uuid.uuid4()),
+        'mask': {
+            'size': [10, 10],
+            'counts': [1, 0, 10, 100]
+        }
+    })
     del segmentation['tool']
     return segmentation
 
+
 @pytest.fixture
 def segmentation_inference_png(prediction_id_mapping):
     segmentation = prediction_id_mapping['superpixel'].copy()
-    segmentation.update(
-        {
-            'uuid' : str(uuid.uuid4()),
-            'mask': {
+    segmentation.update({
+        'uuid': str(uuid.uuid4()),
+        'mask': {
             'png': "somedata",
-        }})
+        }
+    })
     del segmentation['tool']
     return segmentation
 
+
 @pytest.fixture
 def checklist_inference(prediction_id_mapping):
     checklist = prediction_id_mapping['checklist'].copy()
@@ -307,10 +310,12 @@ def model_run_predictions(polygon_inference, rectangle_inference,
 # also used for label imports
 @pytest.fixture
 def object_predictions(polygon_inference, rectangle_inference, line_inference,
-                       entity_inference, segmentation_inference, segmentation_inference_rle, segmentation_inference_png):
+                       entity_inference, segmentation_inference,
+                       segmentation_inference_rle, segmentation_inference_png):
     return [
         polygon_inference, rectangle_inference, line_inference,
-        entity_inference, segmentation_inference, segmentation_inference_rle, segmentation_inference_png
+        entity_inference, segmentation_inference, segmentation_inference_rle,
+        segmentation_inference_png
     ]
 
 

tests/integration/annotation_import/test_ndjson_validation.py

@@ -133,11 +133,11 @@ def test_incorrect_mask(segmentation_inference, configured_project):
     with pytest.raises(MALValidationError):
         _validate_ndjson([seg], configured_project)
 
-    seg['mask'] = {'counts' : [0], 'size' : [0,1]}
+    seg['mask'] = {'counts': [0], 'size': [0, 1]}
     with pytest.raises(MALValidationError):
         _validate_ndjson([seg], configured_project)
 
-    seg['mask'] = {'counts' : [-1], 'size' : [1,1]}
+    seg['mask'] = {'counts': [-1], 'size': [1, 1]}
     with pytest.raises(MALValidationError):
         _validate_ndjson([seg], configured_project)