2025-08-26 nightly release (7a936da)

Author: pytorchbot

test/test_ops.py

@@ -1,7 +1,7 @@
 import math
 import os
 from abc import ABC, abstractmethod
-from functools import lru_cache
+from functools import lru_cache, partial
 from itertools import product
 from typing import Callable
 
@@ -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)
@@ -1446,34 +1446,60 @@ def test_bbox_convert_jit(self):
 
 
 class TestBoxArea:
-    def area_check(self, box, expected, atol=1e-4):
-        out = ops.box_area(box)
+    def area_check(self, box, expected, fmt="xyxy", atol=1e-4):
+        out = ops.box_area(box, fmt=fmt)
         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 = torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=dtype)
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_int_boxes(self, dtype, fmt):
+        box_tensor = ops.box_convert(
+            torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=dtype), in_fmt="xyxy", out_fmt=fmt
+        )
         expected = torch.tensor([10000, 0], dtype=torch.int32)
-        self.area_check(box_tensor, expected)
+        self.area_check(box_tensor, expected, fmt)
 
     @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
-    def test_float_boxes(self, dtype):
-        box_tensor = torch.tensor(FLOAT_BOXES, dtype=dtype)
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_float_boxes(self, dtype, fmt):
+        box_tensor = ops.box_convert(torch.tensor(FLOAT_BOXES, dtype=dtype), in_fmt="xyxy", out_fmt=fmt)
         expected = torch.tensor([604723.0806, 600965.4666, 592761.0085], dtype=dtype)
-        self.area_check(box_tensor, expected)
-
-    def test_float16_box(self):
-        box_tensor = 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
+        self.area_check(box_tensor, expected, fmt)
+
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_float16_box(self, fmt):
+        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=fmt,
         )
 
         expected = torch.tensor([3.2170, 3.7108, 18.5071], dtype=torch.float16)
-        self.area_check(box_tensor, expected, atol=0.01)
+        self.area_check(box_tensor, expected, fmt, atol=0.01)
+
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_box_area_jit(self, fmt):
+        box_tensor = ops.box_convert(
+            torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=torch.float), in_fmt="xyxy", out_fmt=fmt
+        )
+        expected = ops.box_area(box_tensor, fmt)
+
+        class BoxArea(torch.nn.Module):
+            # We are using this intermediate class
+            # since torchscript does not support
+            # neither partial nor lambda functions for this test.
+            def __init__(self, fmt):
+                super().__init__()
+                self.area = ops.box_area
+                self.fmt = fmt
 
-    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)
-        scripted_fn = torch.jit.script(ops.box_area)
+            def forward(self, boxes):
+                return self.area(boxes, self.fmt)
+
+        scripted_fn = torch.jit.script(BoxArea(fmt))
         scripted_area = scripted_fn(box_tensor)
         torch.testing.assert_close(scripted_area, expected)
 
@@ -1487,25 +1513,28 @@ def test_box_area_jit(self):
 ]
 
 
-def gen_box(size, dtype=torch.float) -> Tensor:
+def gen_box(size, dtype=torch.float, fmt="xyxy") -> Tensor:
     xy1 = torch.rand((size, 2), dtype=dtype)
     xy2 = xy1 + torch.rand((size, 2), dtype=dtype)
-    return torch.cat([xy1, xy2], axis=-1)
+    return ops.box_convert(torch.cat([xy1, xy2], axis=-1), in_fmt="xyxy", out_fmt=fmt)
 
 
 class TestIouBase:
     @staticmethod
-    def _run_test(target_fn: Callable, actual_box1, actual_box2, dtypes, atol, expected):
+    def _run_test(target_fn: Callable, actual_box1, actual_box2, dtypes, atol, expected, fmt="xyxy"):
         for dtype in dtypes:
-            actual_box1 = torch.tensor(actual_box1, dtype=dtype)
-            actual_box2 = torch.tensor(actual_box2, dtype=dtype)
+            _actual_box1 = ops.box_convert(torch.tensor(actual_box1, dtype=dtype), in_fmt="xyxy", out_fmt=fmt)
+            _actual_box2 = ops.box_convert(torch.tensor(actual_box2, dtype=dtype), in_fmt="xyxy", out_fmt=fmt)
             expected_box = torch.tensor(expected)
-            out = target_fn(actual_box1, actual_box2)
+            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)
+    def _run_jit_test(target_fn: Callable, actual_box: list, fmt="xyxy"):
+        box_tensor = ops.box_convert(torch.tensor(actual_box, dtype=torch.float), in_fmt="xyxy", out_fmt=fmt)
         expected = target_fn(box_tensor, box_tensor)
         scripted_fn = torch.jit.script(target_fn)
         scripted_out = scripted_fn(box_tensor, box_tensor)
@@ -1522,17 +1551,17 @@ def _cartesian_product(boxes1, boxes2, target_fn: Callable):
         return result
 
     @staticmethod
-    def _run_cartesian_test(target_fn: Callable):
-        boxes1 = gen_box(5)
-        boxes2 = gen_box(7)
+    def _run_cartesian_test(target_fn: Callable, fmt: str = "xyxy"):
+        boxes1 = gen_box(5, fmt=fmt)
+        boxes2 = gen_box(7, fmt=fmt)
         a = TestIouBase._cartesian_product(boxes1, boxes2, target_fn)
         b = target_fn(boxes1, boxes2)
         torch.testing.assert_close(a, b)
 
     @staticmethod
-    def _run_batch_test(target_fn: Callable):
-        boxes1 = torch.stack([gen_box(5) for _ in range(3)], dim=0)
-        boxes2 = torch.stack([gen_box(5) for _ in range(3)], dim=0)
+    def _run_batch_test(target_fn: Callable, fmt: str = "xyxy"):
+        boxes1 = torch.stack([gen_box(5, fmt=fmt) for _ in range(3)], dim=0)
+        boxes2 = torch.stack([gen_box(5, fmt=fmt) for _ in range(3)], dim=0)
         native: Tensor = target_fn(boxes1, boxes2)
         iterative: Tensor = torch.stack([target_fn(*pairs) for pairs in zip(boxes1, boxes2)], dim=0)
         torch.testing.assert_close(native, iterative)
@@ -1550,17 +1579,33 @@ class TestBoxIou(TestIouBase):
             pytest.param(FLOAT_BOXES, FLOAT_BOXES, [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)
-
-    def test_iou_cartesian(self):
-        self._run_cartesian_test(ops.box_iou)
-
-    def test_iou_batch(self):
-        self._run_batch_test(ops.box_iou)
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_iou(self, actual_box1, actual_box2, dtypes, atol, expected, fmt):
+        self._run_test(partial(ops.box_iou, fmt=fmt), actual_box1, actual_box2, dtypes, atol, expected, fmt)
+
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_iou_jit(self, fmt):
+        class IoUJit(torch.nn.Module):
+            # We are using this intermediate class
+            # since torchscript does not support
+            # neither partial nor lambda functions for this test.
+            def __init__(self, fmt):
+                super().__init__()
+                self.iou = ops.box_iou
+                self.fmt = fmt
+
+            def forward(self, boxes1, boxes2):
+                return self.iou(boxes1, boxes2, fmt=self.fmt)
+
+        self._run_jit_test(IoUJit(fmt=fmt), INT_BOXES, fmt)
+
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_iou_cartesian(self, fmt):
+        self._run_cartesian_test(partial(ops.box_iou, fmt=fmt))
+
+    @pytest.mark.parametrize("fmt", ["xyxy", "xywh", "cxcywh"])
+    def test_iou_batch(self, fmt):
+        self._run_batch_test(partial(ops.box_iou, fmt=fmt))
 
 
 class TestGeneralizedBoxIou(TestIouBase):

test/test_transforms_v2.py

@@ -7016,6 +7016,29 @@ def test_parallelogram_to_bounding_boxes(input_size, device):
     actual = _parallelogram_to_bounding_boxes(parallelogram)
     torch.testing.assert_close(actual, expected)
 
+    # Test the transformation of a simple parallelogram.
+    #              1
+    #    1-2      /   2
+    #   / /  ->  /   /
+    # 4-3       4   /
+    #              3
+    #
+    #          1
+    # 1-2       \ 2
+    #   \ \  ->  \  \
+    #    4-3       4 \
+    #                 3
+    parallelogram = torch.tensor(
+        [[0, 4, 3, 1, 5, 1, 2, 4], [0, 1, 2, 1, 5, 4, 3, 4]],
+        dtype=torch.float32,
+    )
+    expected = torch.tensor(
+        [[0, 4, 4, 0, 5, 1, 1, 5], [0, 1, 1, 0, 5, 4, 4, 5]],
+        dtype=torch.float32,
+    )
+    actual = _parallelogram_to_bounding_boxes(parallelogram)
+    torch.testing.assert_close(actual, expected)
+
 
 @pytest.mark.parametrize("image_type", (PIL.Image, torch.Tensor, tv_tensors.Image))
 @pytest.mark.parametrize("data_augmentation", ("hflip", "lsj", "multiscale", "ssd", "ssdlite"))

torchvision/ops/boxes.py

@@ -270,33 +270,68 @@ 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)
+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)
 
-    lt = torch.max(boxes1[..., None, :2], boxes2[..., None, :, :2])  # [...,N,M,2]
-    rb = torch.min(boxes1[..., None, 2:], boxes2[..., None, :, 2:])  # [...,N,M,2]
+    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]
     inter = wh[..., 0] * wh[..., 1]  # [N,M]
@@ -306,24 +341,31 @@ def _box_inter_union(boxes1: Tensor, boxes2: Tensor) -> tuple[Tensor, Tensor]:
     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