Fixed acceptable and non-acceptable types for Cutmix/Mixup

test/test_prototype_transforms.py

@@ -3,7 +3,13 @@
 import pytest
 import torch
 from common_utils import assert_equal
-from test_prototype_transforms_functional import make_images, make_bounding_boxes, make_one_hot_labels
+from test_prototype_transforms_functional import (
+    make_images,
+    make_bounding_boxes,
+    make_one_hot_labels,
+    make_segmentation_masks,
+    make_label,
+)
 from torchvision.prototype import transforms, features
 from torchvision.transforms.functional import to_pil_image, pil_to_tensor
 
@@ -102,6 +108,20 @@ def test_common(self, transform, input):
     def test_mixup_cutmix(self, transform, input):
         transform(input)
 
+    @pytest.mark.parametrize("transform", [transforms.RandomMixup(alpha=1.0), transforms.RandomCutmix(alpha=1.0)])
+    def test_mixup_cutmix_assertions(self, transform):
+        for bbox in make_bounding_boxes():
+            with pytest.raises(TypeError, match="does not support"):
+                transform(bbox)
+            break
+        for mask in make_segmentation_masks():
+            with pytest.raises(TypeError, match="does not support"):
+                transform(mask)
+            break
+        label = make_label()
+        with pytest.raises(TypeError, match="does not support"):
+            transform(label)
+
     @parametrize(
         [
             (

torchvision/prototype/transforms/_augment.py

@@ -9,7 +9,7 @@
 from torchvision.prototype.transforms import Transform, functional as F
 
 from ._transform import _RandomApplyTransform
-from ._utils import query_image, get_image_dimensions, has_all
+from ._utils import query_image, get_image_dimensions, has_any
 
 
 class RandomErasing(_RandomApplyTransform):
@@ -106,8 +106,10 @@ def __init__(self, *, alpha: float) -> None:
 
     def forward(self, *inpts: Any) -> Any:
         sample = inpts if len(inpts) > 1 else inpts[0]
-        if not has_all(sample, features.Image, features.OneHotLabel):
-            raise TypeError(f"{type(self).__name__}() is only defined for Image's *and* OneHotLabel's.")
+        if has_any(sample, features.BoundingBox, features.SegmentationMask, features.Label):
+            raise TypeError(
+                f"{type(self).__name__}() does not support bounding boxes, segmentation masks and plain labels."
+            )
         return super().forward(sample)
 
     def _mixup_onehotlabel(self, inpt: features.OneHotLabel, lam: float) -> features.OneHotLabel: