Reduce G/D/CIoU logic operations (#6074)

utils/metrics.py

@@ -222,20 +222,20 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=
     union = w1 * h1 + w2 * h2 - inter + eps
 
     iou = inter / union
-    if GIoU or DIoU or CIoU:
+    if CIoU or DIoU or GIoU:
         cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
         ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
         if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
             c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
             rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
                     (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared
-            if DIoU:
-                return iou - rho2 / c2  # DIoU
-            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
                 v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
                 with torch.no_grad():
                     alpha = v / (v - iou + (1 + eps))
                 return iou - (rho2 / c2 + v * alpha)  # CIoU
+            else:
+                return iou - rho2 / c2  # DIoU
         else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
             c_area = cw * ch + eps  # convex area
             return iou - (c_area - union) / c_area  # GIoU