removed xy/wh loss reporting

utils/utils.py

@@ -562,20 +562,27 @@ def print_model_biases(model):
               'classification: %.2f+/-%.2f' % (b[:, 5:].mean(), b[:, 5:].std()))
 
 
-def strip_optimizer(f='weights/best.pt'):  # from utils.utils import *; strip_optimizer()
+def strip_optimizer(f='weights/last.pt'):  # from utils.utils import *; strip_optimizer()
     # Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
     x = torch.load(f)
     x['optimizer'] = None
-    # x['training_results'] = None
-    # x['epoch'] = -1
-    # for p in x['model'].values():
-    #     try:
-    #         p.requires_grad = True
-    #     except:
-    #         pass
     torch.save(x, f)
 
 
+def create_backbone(f='weights/last.pt'):  # from utils.utils import *; create_backbone()
+    # create a backbone from a *.pt file
+    x = torch.load(f)
+    x['optimizer'] = None
+    x['training_results'] = None
+    x['epoch'] = -1
+    for p in x['model'].values():
+        try:
+            p.requires_grad = True
+        except:
+            pass
+    torch.save(x, 'weights/backbone.pt')
+
+
 def coco_class_count(path='../coco/labels/train2014/'):
     # Histogram of occurrences per class
     nc = 80  # number classes