updates

train.py

@@ -228,7 +228,7 @@ def train(
             pred = model(imgs)
 
             # Compute loss
-            loss, loss_items = compute_loss(pred, targets, model)
+            loss, loss_items = compute_loss(pred, targets, model, giou_loss=False)
             if torch.isnan(loss):
                 print('WARNING: nan loss detected, ending training')
                 return results

utils/utils.py

@@ -270,9 +270,9 @@ def wh_iou(box1, box2):
     return inter_area / union_area  # iou
 
 
-def compute_loss(p, targets, model):  # predictions, targets, model
+def compute_loss(p, targets, model, giou_loss=False):  # predictions, targets, model
     ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
-    lxy, lwh, lcls, lconf, lgiou = ft([0]), ft([0]), ft([0]), ft([0]), ft([0])
+    lxy, lwh, lcls, lconf = ft([0]), ft([0]), ft([0]), ft([0])
     txy, twh, tcls, tbox, indices, anchor_vec = build_targets(model, targets)
     h = model.hyp  # hyperparameters
 
@@ -298,9 +298,11 @@ def compute_loss(p, targets, model):  # predictions, targets, model
             pbox = torch.cat((torch.sigmoid(pi[..., 0:2]), torch.exp(pi[..., 2:4]) * anchor_vec[i]), 1)  # predicted box
             giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)
 
-            # lxy += (k * h['giou']) * (1.0 - giou).mean()  # giou loss
+            if giou_loss:
-            lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy loss
+                lxy += (k * h['giou']) * (1.0 - giou).mean()  # giou loss
-            lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i])  # wh yolo loss
+            else:
+                lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy loss
+                lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i])  # wh yolo loss
             lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i])  # class_conf loss
 
         lconf += (k * h['conf']) * BCE(pi0[..., 4], tconf)  # obj_conf loss