Fix d/c IoU for different batch sizes (#6338)

* Fix bug in calculating cIoU for unequal sizes

* Remove comment

* what the epsilon?

* Fixing DIoU

* Optimization by Francisco.

* Fix the expected values on CompleteBoxIoU

* Apply suggestions from code review
Co-authored-by: Abhijit Deo <72816663+abhi-glitchhg@users.noreply.github.com>

* Adding cartesian product test.

* remove static
Co-authored-by: Vasilis Vryniotis <vvryniotis@fb.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Abhijit Deo <72816663+abhi-glitchhg@users.noreply.github.com>

test/test_ops.py

@@ -1111,14 +1111,6 @@ class TestBoxConvert:
         torch.testing.assert_close(scripted_cxcywh, box_cxcywh)
 
 
-INT_BOXES = [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
-FLOAT_BOXES = [
-    [285.3538, 185.5758, 1193.5110, 851.4551],
-    [285.1472, 188.7374, 1192.4984, 851.0669],
-    [279.2440, 197.9812, 1189.4746, 849.2019],
-]
-
-
 class TestBoxArea:
     def area_check(self, box, expected, atol=1e-4):
         out = ops.box_area(box)
@@ -1152,99 +1144,155 @@ class TestBoxArea:
         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 = [
+    [285.3538, 185.5758, 1193.5110, 851.4551],
+    [285.1472, 188.7374, 1192.4984, 851.0669],
+    [279.2440, 197.9812, 1189.4746, 849.2019],
+]
+
+
+def gen_box(size, dtype=torch.float):
+    xy1 = torch.rand((size, 2), dtype=dtype)
+    xy2 = xy1 + torch.rand((size, 2), dtype=dtype)
+    return torch.cat([xy1, xy2], axis=-1)
+
+
 class TestIouBase:
     @staticmethod
-    def _run_test(target_fn: Callable, test_input: List, dtypes: List[torch.dtype], atol: float, expected: List):
+    def _run_test(target_fn: Callable, actual_box1, actual_box2, dtypes, atol, expected):
         for dtype in dtypes:
-            actual_box = torch.tensor(test_input, dtype=dtype)
+            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_box, actual_box)
+            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, test_input: List):
-        box_tensor = torch.tensor(test_input, dtype=torch.float)
+    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)
+        assert torch.allclose(a, b)
+
 
 class TestBoxIou(TestIouBase):
-    int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
+    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]]
 
     @pytest.mark.parametrize(
-        "test_input, dtypes, atol, expected",
+        "actual_box1, actual_box2, dtypes, atol, expected",
         [
-            pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
-            pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
-            pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
+            pytest.param(INT_BOXES, INT_BOXES2, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float16], 0.002, float_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
         ],
     )
-    def test_iou(self, test_input, dtypes, atol, expected):
-        self._run_test(ops.box_iou, test_input, dtypes, atol, 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)
+
 
 class TestGeneralizedBoxIou(TestIouBase):
-    int_expected = [[1.0, 0.25, -0.7778], [0.25, 1.0, -0.8611], [-0.7778, -0.8611, 1.0]]
+    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]]
 
     @pytest.mark.parametrize(
-        "test_input, dtypes, atol, expected",
+        "actual_box1, actual_box2, dtypes, atol, expected",
         [
-            pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
-            pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
-            pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
+            pytest.param(INT_BOXES, INT_BOXES2, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float16], 0.002, float_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
         ],
     )
-    def test_iou(self, test_input, dtypes, atol, expected):
-        self._run_test(ops.generalized_box_iou, test_input, dtypes, atol, expected)
+    def test_iou(self, actual_box1, actual_box2, dtypes, atol, expected):
+        self._run_test(ops.generalized_box_iou, actual_box1, actual_box2, dtypes, atol, expected)
 
     def test_iou_jit(self):
         self._run_jit_test(ops.generalized_box_iou, INT_BOXES)
 
+    def test_iou_cartesian(self):
+        self._run_cartesian_test(ops.generalized_box_iou)
+
 
 class TestDistanceBoxIoU(TestIouBase):
-    int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
+    int_expected = [
+        [1.0000, 0.1875, -0.4444],
+        [0.1875, 1.0000, -0.5625],
+        [-0.4444, -0.5625, 1.0000],
+        [-0.0781, 0.1875, -0.6267],
+    ]
     float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
 
     @pytest.mark.parametrize(
-        "test_input, dtypes, atol, expected",
+        "actual_box1, actual_box2, dtypes, atol, expected",
         [
-            pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
-            pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
-            pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
+            pytest.param(INT_BOXES, INT_BOXES2, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float16], 0.002, float_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
         ],
     )
-    def test_iou(self, test_input, dtypes, atol, expected):
-        self._run_test(ops.distance_box_iou, test_input, dtypes, atol, expected)
+    def test_iou(self, actual_box1, actual_box2, dtypes, atol, expected):
+        self._run_test(ops.distance_box_iou, actual_box1, actual_box2, dtypes, atol, expected)
 
     def test_iou_jit(self):
         self._run_jit_test(ops.distance_box_iou, INT_BOXES)
 
+    def test_iou_cartesian(self):
+        self._run_cartesian_test(ops.distance_box_iou)
+
 
 class TestCompleteBoxIou(TestIouBase):
-    int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
+    int_expected = [
+        [1.0000, 0.1875, -0.4444],
+        [0.1875, 1.0000, -0.5625],
+        [-0.4444, -0.5625, 1.0000],
+        [-0.0781, 0.1875, -0.6267],
+    ]
     float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
 
     @pytest.mark.parametrize(
-        "test_input, dtypes, atol, expected",
+        "actual_box1, actual_box2, dtypes, atol, expected",
         [
-            pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
-            pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
-            pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
+            pytest.param(INT_BOXES, INT_BOXES2, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float16], 0.002, float_expected),
+            pytest.param(FLOAT_BOXES, FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
         ],
     )
-    def test_iou(self, test_input, dtypes, atol, expected):
-        self._run_test(ops.complete_box_iou, test_input, dtypes, atol, expected)
+    def test_iou(self, actual_box1, actual_box2, dtypes, atol, expected):
+        self._run_test(ops.complete_box_iou, actual_box1, actual_box2, dtypes, atol, expected)
 
     def test_iou_jit(self):
         self._run_jit_test(ops.complete_box_iou, INT_BOXES)
 
+    def test_iou_cartesian(self):
+        self._run_cartesian_test(ops.complete_box_iou)
+
 
 def get_boxes(dtype, device):
     box1 = torch.tensor([-1, -1, 1, 1], dtype=dtype, device=device)

torchvision/ops/boxes.py

@@ -325,13 +325,13 @@ def complete_box_iou(boxes1: Tensor, boxes2: Tensor, eps: float = 1e-7) -> Tenso
 
     diou, iou = _box_diou_iou(boxes1, boxes2, eps)
 
-    w_pred = boxes1[:, 2] - boxes1[:, 0]
-    h_pred = boxes1[:, 3] - boxes1[:, 1]
+    w_pred = boxes1[:, None, 2] - boxes1[:, None, 0]
+    h_pred = boxes1[:, None, 3] - boxes1[:, None, 1]
 
     w_gt = boxes2[:, 2] - boxes2[:, 0]
     h_gt = boxes2[:, 3] - boxes2[:, 1]
 
-    v = (4 / (torch.pi**2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
+    v = (4 / (torch.pi**2)) * torch.pow(torch.atan(w_pred / h_pred) - torch.atan(w_gt / h_gt), 2)
     with torch.no_grad():
         alpha = v / (1 - iou + v + eps)
     return diou - alpha * v
@@ -358,7 +358,7 @@ def distance_box_iou(boxes1: Tensor, boxes2: Tensor, eps: float = 1e-7) -> Tenso
 
     boxes1 = _upcast(boxes1)
     boxes2 = _upcast(boxes2)
-    diou, _ = _box_diou_iou(boxes1, boxes2)
+    diou, _ = _box_diou_iou(boxes1, boxes2, eps=eps)
     return diou
 
 
@@ -375,7 +375,9 @@ def _box_diou_iou(boxes1: Tensor, boxes2: Tensor, eps: float = 1e-7) -> Tuple[Te
     x_g = (boxes2[:, 0] + boxes2[:, 2]) / 2
     y_g = (boxes2[:, 1] + boxes2[:, 3]) / 2
     # The distance between boxes' centers squared.
-    centers_distance_squared = (_upcast(x_p - x_g) ** 2) + (_upcast(y_p - y_g) ** 2)
+    centers_distance_squared = (_upcast((x_p[:, None] - x_g[None, :])) ** 2) + (
+        _upcast((y_p[:, None] - y_g[None, :])) ** 2
+    )
     # The distance IoU is the IoU penalized by a normalized
     # distance between boxes' centers squared.
     return iou - (centers_distance_squared / diagonal_distance_squared), iou

torchvision/ops/ciou_loss.py

@@ -14,8 +14,8 @@ def complete_box_iou_loss(
 
     """
     Gradient-friendly IoU loss with an additional penalty that is non-zero when the
-    boxes do not overlap overlap area, This loss function considers important geometrical
-    factors such as  overlap area, normalized central point distance and aspect ratio.
+    boxes do not overlap. This loss function considers important geometrical
+    factors such as overlap area, normalized central point distance and aspect ratio.
     This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.
 
     Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
@@ -35,7 +35,7 @@ def complete_box_iou_loss(
         Tensor: Loss tensor with the reduction option applied.
 
     Reference:
-        Zhaohui Zheng et. al: Complete Intersection over Union Loss:
+        Zhaohui Zheng et al.: Complete Intersection over Union Loss:
         https://arxiv.org/abs/1911.08287
 
     """