Explicitely store a distance value that is reused (#4341)

* Explicitely store a distance value that is reused 

I don't see a reason to calculate the value twice for each distance. 

Knowing that this code is going to be called at every epoch and that probably there is not a compiler that optimizes this (+ also code clarity), I think is best to store the value for x and y.

* Update torchvision/models/detection/_utils.py

Co-authored-by: Nicolas Hug <[email protected]>

* removing spaces

Co-authored-by: Nicolas Hug <[email protected]>
Co-authored-by: Vasilis Vryniotis <[email protected]>

Author: 3 people

torchvision/models/detection/_utils.py

@@ -216,10 +216,14 @@ def decode_single(self, rel_codes, boxes):
         pred_w = torch.exp(dw) * widths[:, None]
         pred_h = torch.exp(dh) * heights[:, None]
 
-        pred_boxes1 = pred_ctr_x - torch.tensor(0.5, dtype=pred_ctr_x.dtype, device=pred_w.device) * pred_w
-        pred_boxes2 = pred_ctr_y - torch.tensor(0.5, dtype=pred_ctr_y.dtype, device=pred_h.device) * pred_h
-        pred_boxes3 = pred_ctr_x + torch.tensor(0.5, dtype=pred_ctr_x.dtype, device=pred_w.device) * pred_w
-        pred_boxes4 = pred_ctr_y + torch.tensor(0.5, dtype=pred_ctr_y.dtype, device=pred_h.device) * pred_h
+        # Distance from center to box's corner.
+        c_to_c_h = torch.tensor(0.5, dtype=pred_ctr_y.dtype, device=pred_h.device) * pred_h
+        c_to_c_w = torch.tensor(0.5, dtype=pred_ctr_x.dtype, device=pred_w.device) * pred_w
+
+        pred_boxes1 = pred_ctr_x - c_to_c_w
+        pred_boxes2 = pred_ctr_y - c_to_c_h
+        pred_boxes3 = pred_ctr_x + c_to_c_w
+        pred_boxes4 = pred_ctr_y + c_to_c_h
         pred_boxes = torch.stack((pred_boxes1, pred_boxes2, pred_boxes3, pred_boxes4), dim=2).flatten(1)
         return pred_boxes