Make generalized_box_iou and box_iou share common code.

Author: datumbox

torchvision/ops/boxes.py

@@ -187,6 +187,21 @@ def box_area(boxes: Tensor) -> Tensor:
 
 # 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[:, :2])  # [N,M,2]
+    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+    union = area1[:, None] + area2 - inter
+
+    return inter, union
+
+
 def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
     """
     Return intersection-over-union (Jaccard index) of boxes.
@@ -200,16 +215,8 @@ def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
     Returns:
         iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2
     """
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
-
-    iou = inter / (area1[:, None] + area2 - inter)
+    inter, union = _box_inter_union(boxes1, boxes2)
+    iou = inter / union
     return iou
 
 
@@ -234,17 +241,7 @@ def generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
     assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
     assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
 
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
-
-    union = area1[:, None] + area2 - inter
-
+    inter, union = _box_inter_union(boxes1, boxes2)
     iou = inter / union
 
     lti = torch.min(boxes1[:, None, :2], boxes2[:, :2])