DataKey: add 'label' as alias of 'class' (kornia#2873)

* DataKey: add 'label' as alias of 'class'

* Move up LABEL

* Simplify expressions

Author: ashnair1

kornia/augmentation/_2d/mix/base.py

@@ -29,7 +29,7 @@ class MixAugmentationBaseV2(_BasicAugmentationBase):
         keepdim: whether to keep the output shape the same as input ``True`` or broadcast it
           to the batch form ``False``.
         data_keys: the input type sequential for applying augmentations.
-            Accepts "input", "image", "mask", "bbox", "bbox_xyxy", "bbox_xywh", "keypoints".
+            Accepts "input", "image", "mask", "bbox", "bbox_xyxy", "bbox_xywh", "keypoints", "class", "label".
     """
 
     def __init__(

kornia/augmentation/_2d/mix/jigsaw.py

@@ -24,7 +24,8 @@ class RandomJigsaw(MixAugmentationBaseV2):
         ensure_perm: to ensure the nonidentical patch permutation generation against
             the original one.
         data_keys: the input type sequential for applying augmentations.
-            Accepts "input", "image", "mask", "bbox", "bbox_xyxy", "bbox_xywh", "keypoints".
+            Accepts "input", "image", "mask", "bbox", "bbox_xyxy", "bbox_xywh", "keypoints",
+            "class", "label".
         p: probability of applying the transformation for the whole batch.
         same_on_batch: apply the same transformation across the batch.
         keepdim: whether to keep the output shape the same as input ``True`` or broadcast it

kornia/augmentation/_2d/mix/mosaic.py

@@ -35,7 +35,8 @@ class RandomMosaic(MixAugmentationBaseV2):
             each output will mix 4 images in a 2x2 grid.
         min_bbox_size: minimum area of bounding boxes. Default to 0.
         data_keys: the input type sequential for applying augmentations.
-            Accepts "input", "image", "mask", "bbox", "bbox_xyxy", "bbox_xywh", "keypoints".
+            Accepts "input", "image", "mask", "bbox", "bbox_xyxy", "bbox_xywh", "keypoints",
+            "class", "label".
         p: probability of applying the transformation for the whole batch.
         keepdim: whether to keep the output shape the same as input ``True`` or broadcast it
             to the batch form ``False``.

kornia/augmentation/container/augment.py

@@ -33,7 +33,7 @@ class AugmentationSequential(TransformMatrixMinIn, ImageSequential):
         *args: a list of kornia augmentation modules.
 
         data_keys: the input type sequential for applying augmentations. Accepts "input", "image", "mask",
-                   "bbox", "bbox_xyxy", "bbox_xywh", "keypoints".
+                   "bbox", "bbox_xyxy", "bbox_xywh", "keypoints", "class", "label".
 
         same_on_batch: apply the same transformation across the batch. If None, it will not overwrite the function-wise
                        settings.
@@ -234,7 +234,7 @@ def __init__(
             self.data_keys = data_keys
 
         if self.data_keys:
-            if not all(in_type in DataKey for in_type in self.data_keys):
+            if any(in_type not in DataKey for in_type in self.data_keys):
                 raise AssertionError(f"`data_keys` must be in {DataKey}. Got {self.data_keys}.")
 
             if self.data_keys[0] != DataKey.INPUT:
@@ -446,9 +446,9 @@ def _preproc_dict_data(
         if self.data_keys is not None:
             raise ValueError("If you are using a dictionary as input, the data_keys should be None.")
 
-        data_keys = self._read_datakeys_from_dict(tuple(data.keys()))
         keys = tuple(data.keys())
-        data_unpacked = tuple(v for v in data.values())
+        data_keys = self._read_datakeys_from_dict(keys)
+        data_unpacked = tuple(data.values())
 
         return keys, data_keys, data_unpacked
 
@@ -467,7 +467,7 @@ def retrieve_key(key: str) -> DataKey:
 
             allowed_dk = " | ".join(f"`{d.name}`" for d in DataKey)
             raise ValueError(
-                f"You input data dictionary keys should starts with some of datakey values: {allowed_dk}. Got `{key}`"
+                f"Your input data dictionary keys should start with some of datakey values: {allowed_dk}. Got `{key}`"
             )
 
         return [DataKey.get(retrieve_key(k)) for k in keys]

kornia/constants.py

@@ -130,6 +130,7 @@ class DataKey(Enum, metaclass=_KORNIA_EnumMeta):
     BBOX_XYXY = 3
     BBOX_XYWH = 4
     KEYPOINTS = 5
+    LABEL = 6
     CLASS = 6
 
     @classmethod

tests/augmentation/test_container.py

@@ -514,10 +514,11 @@ def test_bbox(self, bbox, augmentation, device, dtype):
         for i in range(len(bbox)):
             assert len(bboxes_transformed[i]) == len(bbox[i])
 
-    def test_class(self, device, dtype):
+    @pytest.mark.parametrize("class_data_key", ["class", "label"])
+    def test_class(self, class_data_key, device, dtype):
         img = torch.zeros((5, 1, 5, 5))
         labels = torch.randint(0, 10, size=(5, 1))
-        aug = K.AugmentationSequential(K.RandomCrop((3, 3), pad_if_needed=True), data_keys=["input", "class"])
+        aug = K.AugmentationSequential(K.RandomCrop((3, 3), pad_if_needed=True), data_keys=["input", class_data_key])
 
         _, out_labels = aug(img, labels)
         assert labels is out_labels