[fbsync] Fix rotated box format conversion from XYXYXYXY to XYWHR (#9019)

Reviewed By: AntoineSimoulin

Differential Revision: D79175029

fbshipit-source-id: 210cb6ae76c1793cd61728d9cdf1897e137c992e

Author: NicolasHug

test/test_ops.py

@@ -1384,6 +1384,10 @@ def test_bbox_cxcywhr_to_xyxyxyxy(self):
         box_xyxyxyxy = ops.box_convert(box_tensor, in_fmt="cxcywhr", out_fmt="xyxyxyxy")
         torch.testing.assert_close(box_xyxyxyxy, exp_xyxyxyxy)
 
+        # Reverse conversion
+        box_cxcywhr = ops.box_convert(box_xyxyxyxy, in_fmt="xyxyxyxy", out_fmt="cxcywhr")
+        torch.testing.assert_close(box_cxcywhr, box_tensor)
+
     def test_bbox_xywhr_to_xyxyxyxy(self):
         box_tensor = torch.tensor([[4, 5, 4, 2, 90]], dtype=torch.float)
         exp_xyxyxyxy = torch.tensor([[4, 5, 4, 1, 6, 1, 6, 5]], dtype=torch.float)
@@ -1392,6 +1396,10 @@ def test_bbox_xywhr_to_xyxyxyxy(self):
         box_xyxyxyxy = ops.box_convert(box_tensor, in_fmt="xywhr", out_fmt="xyxyxyxy")
         torch.testing.assert_close(box_xyxyxyxy, exp_xyxyxyxy)
 
+        # Reverse conversion
+        box_xywhr = ops.box_convert(box_xyxyxyxy, in_fmt="xyxyxyxy", out_fmt="xywhr")
+        torch.testing.assert_close(box_xywhr, box_tensor)
+
     @pytest.mark.parametrize("inv_infmt", ["xwyh", "cxwyh", "xwyhr", "cxwyhr", "xxxxyyyy"])
     @pytest.mark.parametrize("inv_outfmt", ["xwcx", "xhwcy", "xwcxr", "xhwcyr", "xyxyxxyy"])
     def test_bbox_invalid(self, inv_infmt, inv_outfmt):

torchvision/ops/_box_convert.py

@@ -178,10 +178,9 @@ def _box_xyxyxyxy_to_xywhr(boxes: Tensor) -> Tensor:
     x1, y1, x3, y3, x2, y2, x4, y4 = boxes.unbind(-1)
     r_rad = torch.atan2(y1 - y3, x3 - x1)
     r = r_rad * 180 / torch.pi
-    cos, sin = torch.cos(r_rad), torch.sin(r_rad)
 
-    w = (x2 - x1) * cos + (y1 - y2) * sin
-    h = (x2 - x1) * sin + (y2 - y1) * cos
+    w = ((x3 - x1) ** 2 + (y1 - y3) ** 2).sqrt()
+    h = ((x3 - x2) ** 2 + (y3 - y2) ** 2).sqrt()
 
     boxes = torch.stack((x1, y1, w, h, r), dim=-1)
 

torchvision/transforms/v2/functional/_meta.py

@@ -252,13 +252,13 @@ def _xyxyxyxy_to_xywhr(xyxyxyxy: torch.Tensor, inplace: bool) -> torch.Tensor:
         xyxyxyxy = xyxyxyxy.float()
 
     r_rad = torch.atan2(xyxyxyxy[..., 1].sub(xyxyxyxy[..., 3]), xyxyxyxy[..., 2].sub(xyxyxyxy[..., 0]))
-    cos, sin = r_rad.cos(), r_rad.sin()
-    # x1, y1, x3, y3, (x2 - x1), (y2 - y1) x4, y4
-    xyxyxyxy[..., 4:6].sub_(xyxyxyxy[..., :2])
-    # (x2 - x1) * cos + (y1 - y2) * sin = w
-    xyxyxyxy[..., 2] = xyxyxyxy[..., 4].mul(cos).sub(xyxyxyxy[..., 5].mul(sin))
-    # (x2 - x1) * sin + (y2 - y1) * cos = h
-    xyxyxyxy[..., 3] = xyxyxyxy[..., 5].mul(cos).add(xyxyxyxy[..., 4].mul(sin))
+    # x1, y1, (x3 - x1), (y3 - y1), (x2 - x3), (y2 - y3) x4, y4
+    xyxyxyxy[..., 4:6].sub_(xyxyxyxy[..., 2:4])
+    xyxyxyxy[..., 2:4].sub_(xyxyxyxy[..., :2])
+    # sqrt((x3 - x1) ** 2 + (y1 - y3) ** 2) = w
+    xyxyxyxy[..., 2] = xyxyxyxy[..., 2].pow(2).add(xyxyxyxy[..., 3].pow(2)).sqrt()
+    # sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2) = h
+    xyxyxyxy[..., 3] = xyxyxyxy[..., 4].pow(2).add(xyxyxyxy[..., 5].pow(2)).sqrt()
     xyxyxyxy[..., 4] = r_rad.div_(torch.pi).mul_(180.0)
     return xyxyxyxy[..., :5].to(dtype)