remove clamp_keypoints from transforms

test/test_transforms_v2.py

@@ -631,7 +631,7 @@ def affine_rotated_bounding_boxes(bounding_boxes):
     )
 
 
-def reference_affine_keypoints_helper(keypoints, *, affine_matrix, new_canvas_size=None, clamp=True):
+def reference_affine_keypoints_helper(keypoints, *, affine_matrix, new_canvas_size=None, cast=True):
     canvas_size = new_canvas_size or keypoints.canvas_size
 
     def affine_keypoints(keypoints):
@@ -650,10 +650,7 @@ def affine_keypoints(keypoints):
                 float(transformed_points[0, 1]),
             ]
         )
-
-        if clamp:
-            output = F.clamp_keypoints(output, canvas_size=canvas_size)
-        else:
+        if not cast:
             dtype = output.dtype
 
         return output.to(dtype=dtype, device=device)
@@ -2293,10 +2290,10 @@ def _reference_rotate_keypoints(self, keypoints, *, angle, expand, center):
             keypoints,
             affine_matrix=affine_matrix,
             new_canvas_size=new_canvas_size,
-            clamp=False,
+            cast=False,
         )
 
-        return F.clamp_keypoints(self._recenter_keypoints_after_expand(output, recenter_xy=recenter_xy)).to(keypoints)
+        return self._recenter_keypoints_after_expand(output, recenter_xy=recenter_xy).to(keypoints)
 
     @pytest.mark.parametrize("angle", _CORRECTNESS_AFFINE_KWARGS["angle"])
     @pytest.mark.parametrize("expand", [False, True])
@@ -5360,11 +5357,7 @@ def perspective_keypoints(keypoints):
                 ]
             )
 
-            # It is important to clamp before casting, especially for CXCYWH format, dtype=int64
-            return F.clamp_keypoints(
-                output,
-                canvas_size=canvas_size,
-            ).to(dtype=dtype, device=device)
+            return output.to(dtype=dtype, device=device)
 
         return tv_tensors.KeyPoints(
             torch.cat([perspective_keypoints(k) for k in keypoints.reshape(-1, 2).unbind()], dim=0).reshape(

torchvision/transforms/v2/functional/_geometry.py

@@ -24,7 +24,7 @@
 
 from torchvision.utils import _log_api_usage_once
 
-from ._meta import _get_size_image_pil, clamp_bounding_boxes, clamp_keypoints, convert_bounding_box_format
+from ._meta import _get_size_image_pil, clamp_bounding_boxes, convert_bounding_box_format
 
 from ._utils import _FillTypeJIT, _get_kernel, _register_five_ten_crop_kernel_internal, _register_kernel_internal
 
@@ -71,7 +71,7 @@ def horizontal_flip_keypoints(keypoints: torch.Tensor, canvas_size: tuple[int, i
     shape = keypoints.shape
     keypoints = keypoints.clone().reshape(-1, 2)
     keypoints[..., 0] = keypoints[..., 0].sub_(canvas_size[1] - 1).neg_()
-    return clamp_keypoints(keypoints.reshape(shape), canvas_size=canvas_size)
+    return keypoints.reshape(shape)
 
 
 @_register_kernel_internal(horizontal_flip, tv_tensors.KeyPoints, tv_tensor_wrapper=False)
@@ -159,7 +159,7 @@ def vertical_flip_keypoints(keypoints: torch.Tensor, canvas_size: tuple[int, int
     shape = keypoints.shape
     keypoints = keypoints.clone().reshape(-1, 2)
     keypoints[..., 1] = keypoints[..., 1].sub_(canvas_size[0] - 1).neg_()
-    return clamp_keypoints(keypoints.reshape(shape), canvas_size=canvas_size)
+    return keypoints.reshape(shape)
 
 
 def vertical_flip_bounding_boxes(
@@ -1026,7 +1026,7 @@ def _affine_keypoints_with_expand(
         new_width, new_height = _compute_affine_output_size(affine_vector, width, height)
         canvas_size = (new_height, new_width)
 
-    out_keypoints = clamp_keypoints(transformed_points, canvas_size=canvas_size).reshape(original_shape)
+    out_keypoints = transformed_points.reshape(original_shape)
     out_keypoints = out_keypoints.to(original_dtype)
 
     return out_keypoints, canvas_size
@@ -1695,7 +1695,7 @@ def pad_keypoints(
     left, right, top, bottom = _parse_pad_padding(padding)
     pad = torch.tensor([left, top], dtype=keypoints.dtype, device=keypoints.device)
     canvas_size = (canvas_size[0] + top + bottom, canvas_size[1] + left + right)
-    return clamp_keypoints(keypoints + pad, canvas_size), canvas_size
+    return keypoints + pad, canvas_size
 
 
 @_register_kernel_internal(pad, tv_tensors.KeyPoints, tv_tensor_wrapper=False)
@@ -1817,7 +1817,7 @@ def crop_keypoints(
     keypoints = keypoints - torch.tensor([left, top], dtype=keypoints.dtype, device=keypoints.device)
     canvas_size = (height, width)
 
-    return clamp_keypoints(keypoints, canvas_size=canvas_size), canvas_size
+    return keypoints, canvas_size
 
 
 @_register_kernel_internal(crop, tv_tensors.KeyPoints, tv_tensor_wrapper=False)
@@ -2047,7 +2047,7 @@ def perspective_keypoints(
     numer_points = torch.matmul(points, theta1.T)
     denom_points = torch.matmul(points, theta2.T)
     transformed_points = numer_points.div_(denom_points)
-    return clamp_keypoints(transformed_points.to(keypoints.dtype), canvas_size).reshape(original_shape)
+    return transformed_points.to(keypoints.dtype).reshape(original_shape)
 
 
 @_register_kernel_internal(perspective, tv_tensors.KeyPoints, tv_tensor_wrapper=False)
@@ -2376,7 +2376,7 @@ def elastic_keypoints(
     t_size = torch.tensor(canvas_size[::-1], device=displacement.device, dtype=displacement.dtype)
     transformed_points = inv_grid[0, index_y, index_x, :].add_(1).mul_(0.5 * t_size).sub_(0.5)
 
-    return clamp_keypoints(transformed_points.to(keypoints.dtype), canvas_size=canvas_size).reshape(original_shape)
+    return transformed_points.to(keypoints.dtype).reshape(original_shape)
 
 
 @_register_kernel_internal(elastic, tv_tensors.KeyPoints, tv_tensor_wrapper=False)