Typo: bouding -> bounding (#8386)

Author: NicolasHug

gallery/transforms/plot_transforms_e2e.py

@@ -93,7 +93,7 @@
 # ----------
 #
 # Let's now define our pre-processing transforms. All the transforms know how
-# to handle images, bouding boxes and masks when relevant.
+# to handle images, bounding boxes and masks when relevant.
 #
 # Transforms are typically passed as the ``transforms`` parameter of the
 # dataset so that they can leverage multi-processing from the

test/test_transforms_v2.py

@@ -5930,7 +5930,7 @@ def test_errors_functional(self):
         with pytest.raises(ValueError, match="canvas_size must be None when bounding_boxes is a tv_tensors"):
             F.sanitize_bounding_boxes(good_bbox, format="XYXY", canvas_size=None)
 
-        with pytest.raises(ValueError, match="bouding_boxes must be a tv_tensors.BoundingBoxes instance or a"):
+        with pytest.raises(ValueError, match="bounding_boxes must be a tv_tensors.BoundingBoxes instance or a"):
             F.sanitize_bounding_boxes(good_bbox.tolist())
 
 

torchvision/transforms/v2/functional/_misc.py

@@ -328,12 +328,12 @@ def sanitize_bounding_boxes(
         bounding_boxes = bounding_boxes[valid]
     else:
         if not isinstance(bounding_boxes, tv_tensors.BoundingBoxes):
-            raise ValueError("bouding_boxes must be a tv_tensors.BoundingBoxes instance or a pure tensor.")
+            raise ValueError("bounding_boxes must be a tv_tensors.BoundingBoxes instance or a pure tensor.")
         if format is not None or canvas_size is not None:
             raise ValueError(
                 "format and canvas_size must be None when bounding_boxes is a tv_tensors.BoundingBoxes instance. "
                 f"Got format={format} and canvas_size={canvas_size}. "
-                "Leave those to None or pass bouding_boxes as a pure tensor."
+                "Leave those to None or pass bounding_boxes as a pure tensor."
             )
         valid = _get_sanitize_bounding_boxes_mask(
             bounding_boxes, format=bounding_boxes.format, canvas_size=bounding_boxes.canvas_size, min_size=min_size