zero-target training bug fix (#609)

utils/general.py

@@ -496,8 +496,7 @@ def compute_loss(p, targets, model):  # predictions, targets, model
     s = 3 / np  # output count scaling
     lbox *= h['giou'] * s
     lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
-    if model.nc > 1:
-        lcls *= h['cls'] * s
+    lcls *= h['cls'] * s
     bs = tobj.shape[0]  # batch size
 
     loss = lbox + lobj + lcls
@@ -524,7 +523,7 @@ def build_targets(p, targets, model):
         gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
 
         # Match targets to anchors
-        t, offsets = targets * gain, 0
+        t = targets * gain
         if nt:
             # Matches
             r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
@@ -540,6 +539,9 @@ def build_targets(p, targets, model):
             j = torch.stack((torch.ones_like(j), j, k, l, m))
             t = t.repeat((5, 1, 1))[j]
             offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
+        else:
+            t = targets[0]
+            offsets = 0
 
         # Define
         b, c = t[:, :2].long().T  # image, class