Default empty task name

luxonis_ml/data/datasets/annotation.py

@@ -506,7 +506,7 @@ def validate_path(cls, path: FilePath) -> FilePath:
 class DatasetRecord(BaseModelExtraForbid):
     files: Dict[str, FilePath]
     annotation: Optional[Detection] = None
-    task: str = "detection"
+    task: str = ""
 
     @property
     def file(self) -> FilePath:
@@ -564,7 +564,8 @@ def check_valid_identifier(name: str, *, label: str) -> None:
     Albumentations requires that the names of the targets
     passed as `additional_targets` are valid Python identifiers.
     """
-    if not name.replace("-", "_").isidentifier():
+    name = name.replace("-", "_")
+    if name and not name.isidentifier():
         raise ValueError(
             f"{label} can only contain alphanumeric characters, "
             "underscores, and dashes. Additionaly, the first character "

luxonis_ml/data/datasets/luxonis_dataset.py

@@ -728,10 +728,10 @@ def delete_dataset(self, *, delete_remote: bool = False) -> None:
         """
         if not self.is_remote:
             shutil.rmtree(self.path)
-            logger.info(f"Deleted dataset {self.dataset_name}")
+            logger.info(f"Deleted dataset '{self.dataset_name}'")
 
         if self.is_remote and delete_remote:
-            logger.info(f"Deleting dataset {self.dataset_name} from cloud")
+            logger.info(f"Deleting dataset '{self.dataset_name}' from cloud")
             assert self.path
             assert self.dataset_name
             assert self.local_path
@@ -828,6 +828,7 @@ def _add_process_batch(
     def add(
         self, generator: DatasetIterator, batch_size: int = 1_000_000
     ) -> Self:
+        logger.info(f"Adding data to dataset '{self.dataset_name}'...")
         index = self._get_file_index(sync_from_cloud=True)
         new_index = {"uuid": [], "file": [], "original_filepath": []}
         processed_uuids = set()

luxonis_ml/data/loaders/luxonis_loader.py

@@ -143,8 +143,6 @@ def __init__(
 
         self.class_mappings: Dict[str, Dict[str, int]] = {}
         for task in self.df["task_name"].unique():
-            if not task:
-                continue
             class_mapping = {
                 class_: i
                 for i, class_ in enumerate(

luxonis_ml/data/parsers/base_parser.py

@@ -240,7 +240,7 @@ def _add_task(self, generator: DatasetIterator) -> DatasetIterator:
         @return: Generator function with added task
         """
 
-        task_name = self.task_name or self.dataset_type.value
+        task_name = self.task_name or ""
         for item in generator:
             if isinstance(item, dict):
                 item["task"] = task_name

luxonis_ml/data/utils/visualizations.py

@@ -329,21 +329,22 @@ def create_mask(
 
     for task, arr in task_type_iterator(labels, "segmentation"):
         task_name = get_task_name(task)
-        images[task_name] = create_mask(
+        image_name = task_name if task_name and not blend_all else "labels"
+        images[image_name] = create_mask(
             image, arr, task_name, is_instance=False
         )
 
     for task, arr in task_type_iterator(labels, "instance_segmentation"):
         task_name = get_task_name(task)
-        image_name = task_name if not blend_all else "labels"
+        image_name = task_name if task_name and not blend_all else "labels"
         curr_image = images.get(image_name, image.copy())
         images[image_name] = create_mask(
             curr_image, arr, task_name, is_instance=True
         )
 
     for task, arr in task_type_iterator(labels, "boundingbox"):
         task_name = get_task_name(task)
-        image_name = task_name if not blend_all else "labels"
+        image_name = task_name if task_name and not blend_all else "labels"
         curr_image = images.get(image_name, image.copy())
 
         draw_function = cv2.rectangle
@@ -374,7 +375,7 @@ def create_mask(
 
     for task, arr in task_type_iterator(labels, "keypoints"):
         task_name = get_task_name(task)
-        image_name = task_name if not blend_all else "labels"
+        image_name = task_name if task_name and not blend_all else "labels"
         curr_image = images.get(image_name, image.copy())
 
         task_classes = class_names[task_name]

tests/test_data/test_dataset.py

@@ -53,6 +53,7 @@ def test_dataset(
             bucket_storage=bucket_storage,
             delete_existing=True,
             delete_remote=True,
+            task_name="coco",
         )
         parser.parse()
         dataset = LuxonisDataset(dataset_name, bucket_storage=bucket_storage)
@@ -173,6 +174,7 @@ def test_loader_iterator(storage_url: str, tempdir: Path):
         save_dir=tempdir,
         dataset_type=DatasetType.COCO,
         delete_existing=True,
+        task_name="coco",
     ).parse()
     loader = LuxonisLoader(dataset)
 
@@ -417,12 +419,12 @@ def generator():
     compare_loader_output(
         loader,
         {
-            "detection/classification",
-            "detection/boundingbox",
-            "detection/driver/boundingbox",
-            "detection/driver/keypoints",
-            "detection/license_plate/boundingbox",
-            "detection/license_plate/metadata/text",
+            "/classification",
+            "/boundingbox",
+            "/driver/boundingbox",
+            "/driver/keypoints",
+            "/license_plate/boundingbox",
+            "/license_plate/metadata/text",
         },
     )
 
@@ -482,10 +484,10 @@ def generator():
     compare_loader_output(
         loader,
         {
-            "detection/classification",
-            "detection/boundingbox",
-            "detection/keypoints",
-            "detection/segmentation",
+            "/classification",
+            "/boundingbox",
+            "/keypoints",
+            "/segmentation",
         },
     )
 
@@ -533,6 +535,8 @@ def generator1():
 
     df_cloned = cloned_dataset._load_df_offline()
     df_original = dataset._load_df_offline()
+    assert df_cloned is not None
+    assert df_original is not None
     assert df_cloned.equals(df_original)
 
 
@@ -620,4 +624,6 @@ def generator2():
     df_cloned_merged = dataset1.merge_with(
         dataset2, inplace=True
     )._load_df_offline()
+    assert df_merged is not None
+    assert df_cloned_merged is not None
     assert df_merged.equals(df_cloned_merged)