convert all bounding boxes to XYXY in dataset wrapper (#7275)

torchvision/datapoints/_dataset_wrapper.py

@@ -230,10 +230,13 @@ def coco_dectection_wrapper_factory(dataset):
         batched_target["image_id"] = image_id
 
         spatial_size = tuple(F.get_spatial_size(image))
-        batched_target["boxes"] = datapoints.BoundingBox(
+        batched_target["boxes"] = F.convert_format_bounding_box(
+            datapoints.BoundingBox(
                 batched_target["bbox"],
                 format=datapoints.BoundingBoxFormat.XYWH,
                 spatial_size=spatial_size,
+            ),
+            new_format=datapoints.BoundingBoxFormat.XYXY,
         )
         batched_target["masks"] = datapoints.Mask(
             torch.stack(
@@ -323,8 +326,13 @@ def celeba_wrapper_factory(dataset):
             target,
             target_types=dataset.target_type,
             type_wrappers={
-                "bbox": lambda item: datapoints.BoundingBox(
-                    item, format=datapoints.BoundingBoxFormat.XYWH, spatial_size=(image.height, image.width)
+                "bbox": lambda item: F.convert_format_bounding_box(
+                    datapoints.BoundingBox(
+                        item,
+                        format=datapoints.BoundingBoxFormat.XYWH,
+                        spatial_size=(image.height, image.width),
+                    ),
+                    new_format=datapoints.BoundingBoxFormat.XYXY,
                 ),
             },
         )
@@ -416,8 +424,11 @@ def widerface_wrapper(dataset):
         image, target = sample
 
         if target is not None:
-            target["bbox"] = datapoints.BoundingBox(
+            target["bbox"] = F.convert_format_bounding_box(
+                datapoints.BoundingBox(
                     target["bbox"], format=datapoints.BoundingBoxFormat.XYWH, spatial_size=(image.height, image.width)
+                ),
+                new_format=datapoints.BoundingBoxFormat.XYXY,
             )
 
         return image, target