Make soft the default clamping_mode, and add a test

Author: NicolasHug

test/common_utils.py

@@ -410,7 +410,7 @@ def make_bounding_boxes(
     canvas_size=DEFAULT_SIZE,
     *,
     format=tv_tensors.BoundingBoxFormat.XYXY,
-    clamping_mode="hard",  # TODOBB
+    clamping_mode="soft",
     num_boxes=1,
     dtype=None,
     device="cpu",

test/test_tv_tensors.py

@@ -406,3 +406,8 @@ def test_return_type_input():
         tv_tensors.set_return_type("typo")
 
     tv_tensors.set_return_type("tensor")
+
+
+def test_box_clamping_mode_default():
+    assert tv_tensors.BoundingBoxes([0, 0, 10, 10], format="XYXY", canvas_size=(100, 100)).clamping_mode == "soft"
+    assert tv_tensors.BoundingBoxes([0, 0, 10, 10, 0], format="XYWHR", canvas_size=(100, 100)).clamping_mode == "soft"

torchvision/transforms/v2/functional/_geometry.py

@@ -522,7 +522,7 @@ def resize_bounding_boxes(
     size: Optional[list[int]],
     max_size: Optional[int] = None,
     format: tv_tensors.BoundingBoxFormat = tv_tensors.BoundingBoxFormat.XYXY,
-    clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB soft
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> tuple[torch.Tensor, tuple[int, int]]:
     # We set the default format as `tv_tensors.BoundingBoxFormat.XYXY`
     # to ensure backward compatibility.
@@ -1108,7 +1108,7 @@ def _affine_bounding_boxes_with_expand(
     shear: list[float],
     center: Optional[list[float]] = None,
     expand: bool = False,
-    clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB soft
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> tuple[torch.Tensor, tuple[int, int]]:
     if bounding_boxes.numel() == 0:
         return bounding_boxes, canvas_size
@@ -1211,7 +1211,7 @@ def affine_bounding_boxes(
     scale: float,
     shear: list[float],
     center: Optional[list[float]] = None,
-    clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB soft
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> torch.Tensor:
     out_box, _ = _affine_bounding_boxes_with_expand(
         bounding_boxes,
@@ -1740,7 +1740,7 @@ def pad_bounding_boxes(
     canvas_size: tuple[int, int],
     padding: list[int],
     padding_mode: str = "constant",
-    clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB soft
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> tuple[torch.Tensor, tuple[int, int]]:
     if padding_mode not in ["constant"]:
         # TODO: add support of other padding modes
@@ -1858,7 +1858,7 @@ def crop_bounding_boxes(
     left: int,
     height: int,
     width: int,
-    clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB soft
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> tuple[torch.Tensor, tuple[int, int]]:
 
     # Crop or implicit pad if left and/or top have negative values:
@@ -2098,7 +2098,7 @@ def perspective_bounding_boxes(
     startpoints: Optional[list[list[int]]],
     endpoints: Optional[list[list[int]]],
     coefficients: Optional[list[float]] = None,
-    clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB soft
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> torch.Tensor:
     if bounding_boxes.numel() == 0:
         return bounding_boxes
@@ -2413,7 +2413,7 @@ def elastic_bounding_boxes(
     format: tv_tensors.BoundingBoxFormat,
     canvas_size: tuple[int, int],
     displacement: torch.Tensor,
-    clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB soft
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> torch.Tensor:
     expected_shape = (1, canvas_size[0], canvas_size[1], 2)
     if not isinstance(displacement, torch.Tensor):

torchvision/tv_tensors/_bounding_boxes.py

@@ -105,7 +105,7 @@ def __new__(
         *,
         format: BoundingBoxFormat | str,
         canvas_size: tuple[int, int],
-        clamping_mode: CLAMPING_MODE_TYPE = "hard",  # TODOBB change default to soft!
+        clamping_mode: CLAMPING_MODE_TYPE = "soft",
         dtype: torch.dtype | None = None,
         device: torch.device | str | int | None = None,
         requires_grad: bool | None = None,