From 0772ebf7c9f2cf253b51299215db68a44c1c3671 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Tue, 19 Feb 2019 22:19:59 +0100
Subject: [PATCH] xy and wh losses respectively merged

---
 utils/utils.py | 25 ++++++++++---------------
 1 file changed, 10 insertions(+), 15 deletions(-)

diff --git a/utils/utils.py b/utils/utils.py
index e6c1a33d..3a0074b0 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -231,18 +231,16 @@ def build_targets(target, anchor_wh, nA, nC, nG):
             continue
         t = target[b]
 
-        # Convert to position relative to box
-        gx, gy, gw, gh = t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG
+        gxy, gwh = t[:, 1:3] * nG, t[:, 3:5] * nG
 
         # Get grid box indices and prevent overflows (i.e. 13.01 on 13 anchors)
-        gi = torch.clamp(gx.long(), min=0, max=nG - 1)
-        gj = torch.clamp(gy.long(), min=0, max=nG - 1)
+        gi, gj = torch.clamp(gxy.long(), min=0, max=nG - 1).t()
 
         # iou of targets-anchors (using wh only)
-        box1 = t[:, 3:5] * nG
+        box1 = gwh
         box2 = anchor_wh.unsqueeze(1)
         inter_area = torch.min(box1, box2).prod(2)
-        iou = inter_area / (gw * gh + box2.prod(2) - inter_area + 1e-16)
+        iou = inter_area / (box1.prod(1) + box2.prod(2) - inter_area + 1e-16)
 
         # Select best iou_pred and anchor
         iou_best, a = iou.max(0)  # best anchor [0-2] for each target
@@ -269,17 +267,14 @@ def build_targets(target, anchor_wh, nA, nC, nG):
             if iou_best < 0.10:
                 continue
 
-        tc, gx, gy, gwh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3:5] * nG
+        tc, gxy, gwh = t[:, 0].long(), t[:, 1:3] * nG, t[:, 3:5] * nG
 
-        # Coordinates
-        txy[b, a, gj, gi, 0] = gx - gi.float()
-        txy[b, a, gj, gi, 1] = gy - gj.float()
+        # XY coordinates
+        txy[b, a, gj, gi] = gxy - gxy.floor()
 
-        # Width and height (yolo method)
-        twh[b, a, gj, gi] = torch.log(gwh / anchor_wh[a])
-
-        # Width and height (power method)
-        # twh[b, a, gj, gi] = torch.sqrt(gwh / anchor_wh[a]) / 2
+        # Width and height
+        twh[b, a, gj, gi] = torch.log(gwh / anchor_wh[a])  # yolo method
+        # twh[b, a, gj, gi] = torch.sqrt(gwh / anchor_wh[a]) / 2 # power method
 
         # One-hot encoding of label
         tcls[b, a, gj, gi, tc] = 1