format

pytorch · Jul 31, 2023 · 2b2d10a · 2b2d10a
1 parent 124db1f
commit 2b2d10a
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Showing 3 changed files with 9 additions and 3 deletions.
diff --git a/torchvision/prototype/transforms/_geometry.py b/torchvision/prototype/transforms/_geometry.py
@@ -115,7 +115,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
             elif isinstance(inpt, datapoints.BoundingBoxes):
                 inpt = datapoints.BoundingBoxes.wrap_like(
                     inpt,
-                    F.clamp_bounding_boxes(inpt[params["is_valid"]], format=inpt.format, spatial_size=inpt.spatial_size),
+                    F.clamp_bounding_boxes(
+                        inpt[params["is_valid"]], format=inpt.format, spatial_size=inpt.spatial_size
+                    ),
                 )
 
         if params["needs_pad"]:

diff --git a/torchvision/transforms/v2/functional/_geometry.py b/torchvision/transforms/v2/functional/_geometry.py
@@ -1826,7 +1826,9 @@ def center_crop_bounding_boxes(
 ) -> Tuple[torch.Tensor, Tuple[int, int]]:
     crop_height, crop_width = _center_crop_parse_output_size(output_size)
     crop_top, crop_left = _center_crop_compute_crop_anchor(crop_height, crop_width, *spatial_size)
-    return crop_bounding_boxes(bounding_boxes, format, top=crop_top, left=crop_left, height=crop_height, width=crop_width)
+    return crop_bounding_boxes(
+        bounding_boxes, format, top=crop_top, left=crop_left, height=crop_height, width=crop_width
+    )
 
 
 def center_crop_mask(mask: torch.Tensor, output_size: List[int]) -> torch.Tensor:

diff --git a/torchvision/transforms/v2/functional/_meta.py b/torchvision/transforms/v2/functional/_meta.py
@@ -272,7 +272,9 @@ def clamp_bounding_boxes(
     elif isinstance(inpt, datapoints.BoundingBoxes):
         if format is not None or spatial_size is not None:
             raise ValueError("For bounding box datapoint inputs, `format` and `spatial_size` must not be passed.")
-        output = _clamp_bounding_boxes(inpt.as_subclass(torch.Tensor), format=inpt.format, spatial_size=inpt.spatial_size)
+        output = _clamp_bounding_boxes(
+            inpt.as_subclass(torch.Tensor), format=inpt.format, spatial_size=inpt.spatial_size
+        )
         return datapoints.BoundingBoxes.wrap_like(inpt, output)
     else:
         raise TypeError(