Fix class_inclusion_list in DetectionDataset

src/super_gradients/training/datasets/data_formats/__init__.py

@@ -1,6 +1,6 @@
 from .format_converter import ConcatenatedTensorFormatConverter
 from .output_adapters import DetectionOutputAdapter
-from .formats import ConcatenatedTensorFormat, BoundingBoxesTensorSliceItem, TensorSliceItem
+from .formats import ConcatenatedTensorFormat, BoundingBoxesTensorSliceItem, TensorSliceItem, LabelTensorSliceItem
 from .bbox_formats import (
     CXCYWHCoordinateFormat,
     NormalizedCXCYWHCoordinateFormat,
@@ -21,6 +21,7 @@
     "NormalizedXYWHCoordinateFormat",
     "NormalizedXYXYCoordinateFormat",
     "TensorSliceItem",
+    "LabelTensorSliceItem",
     "XYWHCoordinateFormat",
     "XYXYCoordinateFormat",
     "YXYXCoordinateFormat",

src/super_gradients/training/datasets/data_formats/default_formats.py

@@ -1,5 +1,5 @@
 from super_gradients.common.object_names import ConcatenatedTensorFormats
-from super_gradients.training.datasets.data_formats.formats import ConcatenatedTensorFormat, BoundingBoxesTensorSliceItem, TensorSliceItem
+from super_gradients.training.datasets.data_formats.formats import ConcatenatedTensorFormat, BoundingBoxesTensorSliceItem, LabelTensorSliceItem
 from super_gradients.training.datasets.data_formats.bbox_formats import (
     XYXYCoordinateFormat,
     XYWHCoordinateFormat,
@@ -12,72 +12,72 @@
 XYXY_LABEL = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=XYXYCoordinateFormat()),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 XYWH_LABEL = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=XYWHCoordinateFormat()),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 CXCYWH_LABEL = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=CXCYWHCoordinateFormat()),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 LABEL_XYXY = ConcatenatedTensorFormat(
     layout=(
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
         BoundingBoxesTensorSliceItem(name="bboxes", format=XYXYCoordinateFormat()),
     )
 )
 LABEL_XYWH = ConcatenatedTensorFormat(
     layout=(
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
         BoundingBoxesTensorSliceItem(name="bboxes", format=XYWHCoordinateFormat()),
     )
 )
 LABEL_CXCYWH = ConcatenatedTensorFormat(
     layout=(
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
         BoundingBoxesTensorSliceItem(name="bboxes", format=CXCYWHCoordinateFormat()),
     )
 )
 NORMALIZED_XYXY_LABEL = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=NormalizedXYXYCoordinateFormat()),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 NORMALIZED_XYWH_LABEL = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=NormalizedXYWHCoordinateFormat()),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 NORMALIZED_CXCYWH_LABEL = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=NormalizedCXCYWHCoordinateFormat()),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 LABEL_NORMALIZED_XYXY = ConcatenatedTensorFormat(
     layout=(
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
         BoundingBoxesTensorSliceItem(name="bboxes", format=NormalizedXYXYCoordinateFormat()),
     )
 )
 LABEL_NORMALIZED_XYWH = ConcatenatedTensorFormat(
     layout=(
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
         BoundingBoxesTensorSliceItem(name="bboxes", format=NormalizedXYWHCoordinateFormat()),
     )
 )
 LABEL_NORMALIZED_CXCYWH = ConcatenatedTensorFormat(
     layout=(
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
         BoundingBoxesTensorSliceItem(name="bboxes", format=NormalizedCXCYWHCoordinateFormat()),
     )
 )

src/super_gradients/training/datasets/data_formats/formats.py

@@ -34,6 +34,13 @@ def __repr__(self):
         return f"name={self.name} length={self.length} format={self.format}"
 
 
+class LabelTensorSliceItem(TensorSliceItem):
+    NAME = "labels"
+
+    def __init__(self):
+        super().__init__(name=self.NAME, length=1)
+
+
 class ConcatenatedTensorFormat(DetectionOutputFormat):
     """
     Define the output format that return a single tensor of shape [N,M] (N - number of detections,

src/super_gradients/training/datasets/detection_datasets/detection_dataset.py

@@ -16,14 +16,14 @@
 from super_gradients.common.object_names import Datasets, Processings
 from super_gradients.common.registry.registry import register_dataset
 from super_gradients.common.decorators.factory_decorator import resolve_param
-from super_gradients.training.utils.detection_utils import get_cls_posx_in_target
+from super_gradients.training.utils.detection_utils import get_class_index_in_target
 from super_gradients.common.abstractions.abstract_logger import get_logger
 from super_gradients.training.transforms.transforms import DetectionTransform, DetectionTargetsFormatTransform, DetectionTargetsFormat
 from super_gradients.common.exceptions.dataset_exceptions import EmptyDatasetException, DatasetValidationException
 from super_gradients.common.factories.list_factory import ListFactory
 from super_gradients.common.factories.transforms_factory import TransformsFactory
 from super_gradients.training.datasets.data_formats.default_formats import XYXY_LABEL
-from super_gradients.training.datasets.data_formats.formats import ConcatenatedTensorFormat
+from super_gradients.training.datasets.data_formats.formats import ConcatenatedTensorFormat, LabelTensorSliceItem
 from super_gradients.training.utils.utils import ensure_is_tuple_of_two
 
 logger = get_logger(__name__)
@@ -298,26 +298,26 @@ def _sub_class_annotation(self, annotation: dict) -> Union[dict, None]:
         :param annotation: Dict representing the annotation of a specific image
         :return:           Subclassed annotation if non-empty after subclassing, otherwise None
         """
-        cls_posx = get_cls_posx_in_target(self.original_target_format)
+        class_index = _get_class_index_in_target(target_format=self.original_target_format)
         for field in self.target_fields:
-            annotation[field] = self._sub_class_target(targets=annotation[field], cls_posx=cls_posx)
+            annotation[field] = self._sub_class_target(targets=annotation[field], class_index=class_index)
         return annotation
 
-    def _sub_class_target(self, targets: np.ndarray, cls_posx: int) -> np.ndarray:
+    def _sub_class_target(self, targets: np.ndarray, class_index: int) -> np.ndarray:
         """Sublass targets of a specific image.
 
         :param targets:     Target array to subclass of shape [n_targets, 5], 5 representing a bbox
-        :param cls_posx:    Position of the class id in a bbox
+        :param class_index:    Position of the class id in a bbox
                                 ex: 0 if bbox of format label_xyxy | -1 if bbox of format xyxy_label
         :return:            Subclassed target
         """
         targets_kept = []
         for target in targets:
-            cls_id = int(target[cls_posx])
+            cls_id = int(target[class_index])
             cls_name = self.all_classes_list[cls_id]
             if cls_name in self.class_inclusion_list:
                 # Replace the target cls_id in self.all_classes_list by cls_id in self.class_inclusion_list
-                target[cls_posx] = self.class_inclusion_list.index(cls_name)
+                target[class_index] = self.class_inclusion_list.index(cls_name)
                 targets_kept.append(target)
 
         return np.array(targets_kept) if len(targets_kept) > 0 else np.zeros((0, 5), dtype=np.float32)
@@ -568,5 +568,16 @@ def get_dataset_preprocessing_params(self):
         return params
 
 
-# TODO
-# - Integration Test
+def _get_class_index_in_target(target_format: DetectionTargetsFormat) -> int:
+    """Get the index of the class in the target format.
+    :param target_format: format of the target. E.g. XYXY_LABEL, LABEL_NORMALIZED_XYXY, ect...
+    :return: index of the class in the target format. E.g. XYXY_LABEL -> 4, LABEL_NORMALIZED_XYXY -> 0, ect....
+    """
+    if isinstance(target_format, ConcatenatedTensorFormat):
+        return target_format.indexes[LabelTensorSliceItem.NAME][0]
+    elif isinstance(target_format, DetectionTargetsFormat):
+        return get_class_index_in_target(target_format)
+    else:
+        raise NotImplementedError(
+            f"{target_format} is not supported. Supported formats are: {ConcatenatedTensorFormat.__name__}, {DetectionTargetsFormat.__name__}"
+        )

src/super_gradients/training/utils/detection_utils.py

@@ -39,7 +39,7 @@ class DetectionTargetsFormat(Enum):
     NORMALIZED_CXCYWH_LABEL = "NORMALIZED_CXCYWH_LABEL"
 
 
-def get_cls_posx_in_target(target_format: DetectionTargetsFormat) -> int:
+def get_class_index_in_target(target_format: DetectionTargetsFormat) -> int:
     """Get the label of a given target
     :param target_format:   Representation of the target (ex: LABEL_XYXY)
     :return:                Position of the class id in a bbox

tests/unit_tests/detection_output_adapter_test.py

@@ -17,6 +17,7 @@
     YXYXCoordinateFormat,
     NormalizedCXCYWHCoordinateFormat,
     DetectionOutputAdapter,
+    LabelTensorSliceItem,
 )
 
 from super_gradients.training.datasets.data_formats.bbox_formats.normalized_cxcywh import xyxy_to_normalized_cxcywh
@@ -25,22 +26,22 @@
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=NormalizedXYWHCoordinateFormat()),
         TensorSliceItem(length=1, name="scores"),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 
 CXCYWH_SCORES_LABELS = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=CXCYWHCoordinateFormat()),
         TensorSliceItem(length=1, name="scores"),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
     )
 )
 
 CXCYWH_LABELS_SCORES_DISTANCE_ATTR = ConcatenatedTensorFormat(
     layout=(
         BoundingBoxesTensorSliceItem(name="bboxes", format=CXCYWHCoordinateFormat()),
-        TensorSliceItem(length=1, name="labels"),
+        LabelTensorSliceItem(),
         TensorSliceItem(length=1, name="scores"),
         TensorSliceItem(length=1, name="distance"),
         TensorSliceItem(length=4, name="attributes"),

tests/unit_tests/detection_sub_classing_test.py

@@ -1,13 +1,17 @@
+from super_gradients.common.exceptions.dataset_exceptions import EmptyDatasetException, DatasetValidationException
+
 import unittest
 import numpy as np
+from typing import Union
 
 from super_gradients.training.datasets import DetectionDataset
 from super_gradients.training.utils.detection_utils import DetectionTargetsFormat
-from super_gradients.common.exceptions.dataset_exceptions import EmptyDatasetException, DatasetValidationException
+from super_gradients.training.datasets.data_formats.formats import ConcatenatedTensorFormat
+from super_gradients.training.datasets.data_formats.default_formats import XYXY_LABEL
 
 
 class DummyDetectionDataset(DetectionDataset):
-    def __init__(self, input_dim, *args, **kwargs):
+    def __init__(self, input_dim, target_format: Union[DetectionTargetsFormat, ConcatenatedTensorFormat], *args, **kwargs):
         """Dummy Dataset testing subclassing, designed with no annotation that includes class_2."""
 
         self.dummy_targets = [
@@ -17,7 +21,7 @@ def __init__(self, input_dim, *args, **kwargs):
 
         self.image_size = input_dim
         kwargs["all_classes_list"] = ["class_0", "class_1", "class_2"]
-        kwargs["original_target_format"] = DetectionTargetsFormat.XYXY_LABEL
+        kwargs["original_target_format"] = target_format
         super().__init__(data_dir="", input_dim=input_dim, *args, **kwargs)
 
     def _setup_data_source(self):
@@ -53,28 +57,45 @@ def setUp(self) -> None:
     def test_subclass_keep_empty(self):
         """Check that subclassing only keeps annotations of wanted class"""
         for config in self.config_keep_empty_annotation:
-            test_dataset = DummyDetectionDataset(input_dim=(640, 512), ignore_empty_annotations=False, class_inclusion_list=config["class_inclusion_list"])
+            test_dataset = DummyDetectionDataset(
+                input_dim=(640, 512), ignore_empty_annotations=False, class_inclusion_list=config["class_inclusion_list"], target_format=XYXY_LABEL
+            )
             n_targets_after_subclass = _count_targets_after_subclass_per_index(test_dataset)
             self.assertListEqual(config["expected_n_targets_after_subclass"], n_targets_after_subclass)
 
     def test_subclass_drop_empty(self):
         """Check that empty annotations are not indexed (i.e. ignored) when ignore_empty_annotations=True"""
         for config in self.config_ignore_empty_annotation:
-            test_dataset = DummyDetectionDataset(input_dim=(640, 512), ignore_empty_annotations=True, class_inclusion_list=config["class_inclusion_list"])
+            test_dataset = DummyDetectionDataset(
+                input_dim=(640, 512), ignore_empty_annotations=True, class_inclusion_list=config["class_inclusion_list"], target_format=XYXY_LABEL
+            )
             n_targets_after_subclass = _count_targets_after_subclass_per_index(test_dataset)
             self.assertListEqual(config["expected_n_targets_after_subclass"], n_targets_after_subclass)
 
         # Check last case when class_2, which should raise EmptyDatasetException because not a single image has
         # a target in class_inclusion_list
         with self.assertRaises(EmptyDatasetException):
-            DummyDetectionDataset(input_dim=(640, 512), ignore_empty_annotations=True, class_inclusion_list=["class_2"])
+            DummyDetectionDataset(input_dim=(640, 512), ignore_empty_annotations=True, class_inclusion_list=["class_2"], target_format=XYXY_LABEL)
 
     def test_wrong_subclass(self):
         """Check that ValueError is raised when class_inclusion_list includes a class that does not exist."""
         with self.assertRaises(DatasetValidationException):
-            DummyDetectionDataset(input_dim=(640, 512), class_inclusion_list=["non_existing_class"])
+            DummyDetectionDataset(input_dim=(640, 512), class_inclusion_list=["non_existing_class"], target_format=XYXY_LABEL)
         with self.assertRaises(DatasetValidationException):
-            DummyDetectionDataset(input_dim=(640, 512), class_inclusion_list=["class_0", "non_existing_class"])
+            DummyDetectionDataset(input_dim=(640, 512), class_inclusion_list=["class_0", "non_existing_class"], target_format=XYXY_LABEL)
+
+    def test_legacy_detection_targets_format(self):
+        """Check that ValueError is raised when class_inclusion_list includes a class that does not exist."""
+
+        for config in self.config_keep_empty_annotation:
+            test_dataset = DummyDetectionDataset(
+                input_dim=(640, 512),
+                ignore_empty_annotations=False,
+                class_inclusion_list=config["class_inclusion_list"],
+                target_format=DetectionTargetsFormat.XYXY_LABEL,
+            )
+            n_targets_after_subclass = _count_targets_after_subclass_per_index(test_dataset)
+            self.assertListEqual(config["expected_n_targets_after_subclass"], n_targets_after_subclass)
 
 
 def _count_targets_after_subclass_per_index(test_dataset: DummyDetectionDataset):