Add box_area_center and box_iou_center functions for cxcywh format with tests

test/test_ops.py

@@ -242,7 +242,7 @@ def _helper_boxes_shape(self, func):
             boxes = torch.tensor([[0, 0, 3, 3]], dtype=a.dtype)
             func(a, boxes, output_size=(2, 2))
 
-        # test boxes as List[Tensor[N, 4]]
+        # test boxes as list[Tensor[N, 4]]
         with pytest.raises(AssertionError):
             a = torch.linspace(1, 8 * 8, 8 * 8).reshape(1, 1, 8, 8)
             boxes = torch.tensor([[0, 0, 3]], dtype=a.dtype)
@@ -1445,9 +1445,9 @@ def test_bbox_convert_jit(self):
         torch.testing.assert_close(scripted_cxcywh, box_cxcywh)
 
 
-class TestBoxArea:
+class TestBoxAreaXYXY:
     def area_check(self, box, expected, atol=1e-4):
-        out = ops.box_area(box)
+        out = ops.box_area(box, fmt="xyxy")
         torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=atol)
 
     @pytest.mark.parametrize("dtype", [torch.int8, torch.int16, torch.int32, torch.int64])
@@ -1472,12 +1472,54 @@ def test_float16_box(self):
 
     def test_box_area_jit(self):
         box_tensor = torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=torch.float)
-        expected = ops.box_area(box_tensor)
+        expected = ops.box_area(box_tensor, fmt="xyxy")
         scripted_fn = torch.jit.script(ops.box_area)
         scripted_area = scripted_fn(box_tensor)
         torch.testing.assert_close(scripted_area, expected)
 
 
+class TestBoxAreaCXCYWH:
+    def area_check(self, box, expected, atol=1e-4):
+        out = ops.box_area(box, fmt="cxcywh")
+        torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=atol)
+
+    @pytest.mark.parametrize("dtype", [torch.int8, torch.int16, torch.int32, torch.int64])
+    def test_int_boxes(self, dtype):
+        box_tensor = ops.box_convert(
+            torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=dtype), in_fmt="xyxy", out_fmt="cxcywh"
+        )
+        expected = torch.tensor([10000, 0], dtype=torch.int32)
+        self.area_check(box_tensor, expected)
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
+    def test_float_boxes(self, dtype):
+        box_tensor = ops.box_convert(torch.tensor(FLOAT_BOXES, dtype=dtype), in_fmt="xyxy", out_fmt="cxcywh")
+        expected = torch.tensor([604723.0806, 600965.4666, 592761.0085], dtype=dtype)
+        self.area_check(box_tensor, expected)
+
+    def test_float16_box(self):
+        box_tensor = ops.box_convert(
+            torch.tensor(
+                [[2.825, 1.8625, 3.90, 4.85], [2.825, 4.875, 19.20, 5.10], [2.925, 1.80, 8.90, 4.90]],
+                dtype=torch.float16,
+            ),
+            in_fmt="xyxy",
+            out_fmt="cxcywh",
+        )
+
+        expected = torch.tensor([3.2170, 3.7108, 18.5071], dtype=torch.float16)
+        self.area_check(box_tensor, expected, atol=0.01)
+
+    def test_box_area_jit(self):
+        box_tensor = ops.box_convert(
+            torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=torch.float), in_fmt="xyxy", out_fmt="cxcywh"
+        )
+        expected = ops.box_area(box_tensor, fmt="cxcywh")
+        scripted_fn = torch.jit.script(ops.box_area)
+        scripted_area = scripted_fn(box_tensor, fmt="cxcywh")
+        torch.testing.assert_close(scripted_area, expected)
+
+
 INT_BOXES = [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300], [0, 0, 25, 25]]
 INT_BOXES2 = [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
 FLOAT_BOXES = [
@@ -1486,29 +1528,37 @@ def test_box_area_jit(self):
     [279.2440, 197.9812, 1189.4746, 849.2019],
 ]
 
+INT_BOXES_CXCYWH = [[50, 50, 100, 100], [25, 25, 50, 50], [250, 250, 100, 100], [10, 10, 20, 20]]
+INT_BOXES2_CXCYWH = [[50, 50, 100, 100], [25, 25, 50, 50], [250, 250, 100, 100]]
+FLOAT_BOXES_CXCYWH = [
+    [739.4324, 518.5154, 908.1572, 665.8793],
+    [738.8228, 519.9021, 907.3512, 662.3295],
+    [734.3593, 523.5916, 910.2306, 651.2207],
+]
+
 
 def gen_box(size, dtype=torch.float) -> Tensor:
     xy1 = torch.rand((size, 2), dtype=dtype)
     xy2 = xy1 + torch.rand((size, 2), dtype=dtype)
     return torch.cat([xy1, xy2], axis=-1)
 
 
-class TestIouBase:
+class TestIouXYXYBase:
     @staticmethod
     def _run_test(target_fn: Callable, actual_box1, actual_box2, dtypes, atol, expected):
         for dtype in dtypes:
             actual_box1 = torch.tensor(actual_box1, dtype=dtype)
             actual_box2 = torch.tensor(actual_box2, dtype=dtype)
             expected_box = torch.tensor(expected)
-            out = target_fn(actual_box1, actual_box2)
+            out = target_fn(actual_box1, actual_box2, fmt="xyxy")
             torch.testing.assert_close(out, expected_box, rtol=0.0, check_dtype=False, atol=atol)
 
     @staticmethod
     def _run_jit_test(target_fn: Callable, actual_box: list):
         box_tensor = torch.tensor(actual_box, dtype=torch.float)
-        expected = target_fn(box_tensor, box_tensor)
+        expected = target_fn(box_tensor, box_tensor, fmt="xyxy")
         scripted_fn = torch.jit.script(target_fn)
-        scripted_out = scripted_fn(box_tensor, box_tensor)
+        scripted_out = scripted_fn(box_tensor, box_tensor, fmt="xyxy")
         torch.testing.assert_close(scripted_out, expected)
 
     @staticmethod
@@ -1518,15 +1568,15 @@ def _cartesian_product(boxes1, boxes2, target_fn: Callable):
         result = torch.zeros((N, M))
         for i in range(N):
             for j in range(M):
-                result[i, j] = target_fn(boxes1[i].unsqueeze(0), boxes2[j].unsqueeze(0))
+                result[i, j] = target_fn(boxes1[i].unsqueeze(0), boxes2[j].unsqueeze(0), fmt="xyxy")
         return result
 
     @staticmethod
     def _run_cartesian_test(target_fn: Callable):
         boxes1 = gen_box(5)
         boxes2 = gen_box(7)
-        a = TestIouBase._cartesian_product(boxes1, boxes2, target_fn)
-        b = target_fn(boxes1, boxes2)
+        a = TestIouXYXYBase._cartesian_product(boxes1, boxes2, target_fn)
+        b = target_fn(boxes1, boxes2, fmt="xyxy")
         torch.testing.assert_close(a, b)
 
     @staticmethod
@@ -1538,7 +1588,7 @@ def _run_batch_test(target_fn: Callable):
         torch.testing.assert_close(native, iterative)
 
 
-class TestBoxIou(TestIouBase):
+class TestBoxIouXYXY(TestIouXYXYBase):
     int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0625, 0.25, 0.0]]
     float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
 
@@ -1563,6 +1613,104 @@ def test_iou_batch(self):
         self._run_batch_test(ops.box_iou)
 
 
+class TestIouCXCYWHBase:
+    @staticmethod
+    def _run_test(target_fn: Callable, actual_box1, actual_box2, dtypes, atol, expected):
+        for dtype in dtypes:
+            actual_box1 = torch.tensor(actual_box1, dtype=dtype)
+            actual_box2 = torch.tensor(actual_box2, dtype=dtype)
+            expected_box = torch.tensor(expected)
+            out = target_fn(actual_box1, actual_box2, fmt="cxcywh")
+            torch.testing.assert_close(out, expected_box, rtol=0.0, check_dtype=False, atol=atol)
+
+    @staticmethod
+    def _run_jit_test(target_fn: Callable, actual_box: list):
+        box_tensor = torch.tensor(actual_box, dtype=torch.float)
+        expected = target_fn(box_tensor, box_tensor, fmt="cxcywh")
+        scripted_fn = torch.jit.script(target_fn)
+        scripted_out = scripted_fn(box_tensor, box_tensor, fmt="cxcywh")
+        torch.testing.assert_close(scripted_out, expected)
+
+    @staticmethod
+    def _cartesian_product(boxes1, boxes2, target_fn: Callable):
+        N = boxes1.size(0)
+        M = boxes2.size(0)
+        result = torch.zeros((N, M))
+        for i in range(N):
+            for j in range(M):
+                result[i, j] = target_fn(boxes1[i].unsqueeze(0), boxes2[j].unsqueeze(0), fmt="cxcywh")
+        return result
+
+    @staticmethod
+    def _run_cartesian_test(target_fn: Callable):
+        boxes1 = ops.box_convert(gen_box(5), in_fmt="xyxy", out_fmt="cxcywh")
+        boxes2 = ops.box_convert(gen_box(7), in_fmt="xyxy", out_fmt="cxcywh")
+        a = TestIouCXCYWHBase._cartesian_product(boxes1, boxes2, target_fn)
+        b = target_fn(boxes1, boxes2, fmt="cxcywh")
+        torch.testing.assert_close(a, b)
+
+
+class TestBoxIouCXCYWH(TestIouCXCYWHBase):
+    int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0], [0.04, 0.16, 0.0]]
+    float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
+
+    @pytest.mark.parametrize(
+        "actual_box1, actual_box2, dtypes, atol, expected",
+        [
+            pytest.param(
+                INT_BOXES_CXCYWH, INT_BOXES2_CXCYWH, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected
+            ),
+            pytest.param(FLOAT_BOXES_CXCYWH, FLOAT_BOXES_CXCYWH, [torch.float16], 0.002, float_expected),
+            pytest.param(FLOAT_BOXES_CXCYWH, FLOAT_BOXES_CXCYWH, [torch.float32, torch.float64], 1e-3, float_expected),
+        ],
+    )
+    def test_iou(self, actual_box1, actual_box2, dtypes, atol, expected):
+        self._run_test(ops.box_iou, actual_box1, actual_box2, dtypes, atol, expected)
+
+    def test_iou_jit(self):
+        self._run_jit_test(ops.box_iou, INT_BOXES_CXCYWH)
+
+    def test_iou_cartesian(self):
+        self._run_cartesian_test(ops.box_iou)
+
+
+class TestIouBase:
+    @staticmethod
+    def _run_test(target_fn: Callable, actual_box1, actual_box2, dtypes, atol, expected):
+        for dtype in dtypes:
+            actual_box1 = torch.tensor(actual_box1, dtype=dtype)
+            actual_box2 = torch.tensor(actual_box2, dtype=dtype)
+            expected_box = torch.tensor(expected)
+            out = target_fn(actual_box1, actual_box2)
+            torch.testing.assert_close(out, expected_box, rtol=0.0, check_dtype=False, atol=atol)
+
+    @staticmethod
+    def _run_jit_test(target_fn: Callable, actual_box: list):
+        box_tensor = torch.tensor(actual_box, dtype=torch.float)
+        expected = target_fn(box_tensor, box_tensor)
+        scripted_fn = torch.jit.script(target_fn)
+        scripted_out = scripted_fn(box_tensor, box_tensor)
+        torch.testing.assert_close(scripted_out, expected)
+
+    @staticmethod
+    def _cartesian_product(boxes1, boxes2, target_fn: Callable):
+        N = boxes1.size(0)
+        M = boxes2.size(0)
+        result = torch.zeros((N, M))
+        for i in range(N):
+            for j in range(M):
+                result[i, j] = target_fn(boxes1[i].unsqueeze(0), boxes2[j].unsqueeze(0))
+        return result
+
+    @staticmethod
+    def _run_cartesian_test(target_fn: Callable):
+        boxes1 = gen_box(5)
+        boxes2 = gen_box(7)
+        a = TestIouBase._cartesian_product(boxes1, boxes2, target_fn)
+        b = target_fn(boxes1, boxes2)
+        torch.testing.assert_close(a, b)
+
+
 class TestGeneralizedBoxIou(TestIouBase):
     int_expected = [[1.0, 0.25, -0.7778], [0.25, 1.0, -0.8611], [-0.7778, -0.8611, 1.0], [0.0625, 0.25, -0.8819]]
     float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]

torchvision/ops/boxes.py

@@ -270,60 +270,102 @@ def box_convert(boxes: Tensor, in_fmt: str, out_fmt: str) -> Tensor:
     return boxes
 
 
-def box_area(boxes: Tensor) -> Tensor:
+def box_area(boxes: Tensor, fmt: str = "xyxy") -> Tensor:
     """
-    Computes the area of a set of bounding boxes, which are specified by their
-    (x1, y1, x2, y2) coordinates.
+    Computes the area of a set of bounding boxes from a given format.
 
     Args:
-        boxes (Tensor[..., 4]): boxes for which the area will be computed. They
-            are expected to be in (x1, y1, x2, y2) format with
-            ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
+        boxes (Tensor[..., 4]): boxes for which the area will be computed.
+        fmt (str): Format of the input boxes.
+            Default is "xyxy" to preserve backward compatibility.
+            Supported formats are "xyxy", "xywh", and "cxcywh".
 
     Returns:
-        Tensor[N]: the area for each box
+        Tensor[N]: Tensor containing the area for each box.
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(box_area)
+    allowed_fmts = (
+        "xyxy",
+        "xywh",
+        "cxcywh",
+    )
+    if fmt not in allowed_fmts:
+        raise ValueError(f"Unsupported Bounding Box area for given format {fmt}")
     boxes = _upcast(boxes)
-    return (boxes[..., 2] - boxes[..., 0]) * (boxes[..., 3] - boxes[..., 1])
+    if fmt == "xyxy":
+        area = (boxes[..., 2] - boxes[..., 0]) * (boxes[..., 3] - boxes[..., 1])
+    else:
+        # For formats with width and height, area = width * height
+        # Supported: cxcywh, xywh
+        area = boxes[..., 2] * boxes[..., 3]
+
+    return area
 
 
 # implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
 # with slight modifications
-def _box_inter_union(boxes1: Tensor, boxes2: Tensor) -> tuple[Tensor, Tensor]:
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    lt = torch.max(boxes1[..., None, :2], boxes2[..., None, :, :2])  # [...,N,M,2]
-    rb = torch.min(boxes1[..., None, 2:], boxes2[..., None, :, 2:])  # [...,N,M,2]
+def _box_inter_union(boxes1: Tensor, boxes2: Tensor, fmt: str = "xyxy") -> tuple[Tensor, Tensor]:
+    area1 = box_area(boxes1, fmt=fmt)
+    area2 = box_area(boxes2, fmt=fmt)
 
-    wh = _upcast(rb - lt).clamp(min=0)  # [N,M,2]
-    inter = wh[..., 0] * wh[..., 1]  # [N,M]
+    allowed_fmts = (
+        "xyxy",
+        "xywh",
+        "cxcywh",
+    )
+    if fmt not in allowed_fmts:
+        raise ValueError(f"Unsupported Box IoU Calculation for given fmt {fmt}.")
+
+    if fmt == "xyxy":
+        lt = torch.max(boxes1[..., None, :2], boxes2[..., None, :, :2])  # [...,N,M,2]
+        rb = torch.min(boxes1[..., None, 2:], boxes2[..., None, :, 2:])  # [...,N,M,2]
+    elif fmt == "xywh":
+        lt = torch.max(boxes1[..., None, :2], boxes2[..., None, :, :2])  # [...,N,M,2]
+        rb = torch.min(
+            boxes1[..., None, :2] + boxes1[..., None, 2:], boxes2[..., None, :, :2] + boxes2[..., None, :, 2:]
+        )  # [N,M,2]
+    else:  # fmt == "cxcywh":
+        lt = torch.max(
+            boxes1[..., None, :2] - boxes1[..., None, 2:] / 2, boxes2[..., None, :, :2] - boxes2[..., None, :, 2:] / 2
+        )  # [N,M,2]
+        rb = torch.min(
+            boxes1[..., None, :2] + boxes1[..., None, 2:] / 2, boxes2[..., None, :, :2] + boxes2[..., None, :, 2:] / 2
+        )  # [N,M,2]
+
+    wh = _upcast(rb - lt).clamp(min=0)  # [N,M,2]	    wh = _upcast(rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[..., 0] * wh[..., 1]  # [N,M]	    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
 
     union = area1[..., None] + area2[..., None, :] - inter
 
     return inter, union
 
 
-def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
+def box_iou(boxes1: Tensor, boxes2: Tensor, fmt: str = "xyxy") -> Tensor:
     """
-    Return intersection-over-union (Jaccard index) between two sets of boxes.
-
-    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
-    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
+    Return intersection-over-union (Jaccard index) between two sets of boxes from a given format.
 
     Args:
         boxes1 (Tensor[..., N, 4]): first set of boxes
         boxes2 (Tensor[..., M, 4]): second set of boxes
+        fmt (str): Format of the input boxes.
+            Default is "xyxy" to preserve backward compatibility.
+            Supported formats are "xyxy", "xywh", and "cxcywh".
 
     Returns:
         Tensor[..., N, M]: the NxM matrix containing the pairwise IoU values for every element
         in boxes1 and boxes2
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(box_iou)
-    inter, union = _box_inter_union(boxes1, boxes2)
+    allowed_fmts = (
+        "xyxy",
+        "xywh",
+        "cxcywh",
+    )
+    if fmt not in allowed_fmts:
+        raise ValueError(f"Unsupported Box IoU Calculation for given format {fmt}.")
+    inter, union = _box_inter_union(boxes1, boxes2, fmt=fmt)
     iou = inter / union
     return iou