diff --git a/models.py b/models.py
index 6d36f1ef..d77dd410 100755
--- a/models.py
+++ b/models.py
@@ -14,9 +14,10 @@ def create_modules(module_defs):
     hyperparams = module_defs.pop(0)
     output_filters = [int(hyperparams['channels'])]
     module_list = nn.ModuleList()
+    routs = []  # list of layers which rout to deeper layes
     yolo_index = -1
 
-    for mdef in module_defs:
+    for i, mdef in enumerate(module_defs):
         modules = nn.Sequential()
 
         if mdef['type'] == 'convolutional':
@@ -53,11 +54,14 @@ def create_modules(module_defs):
         elif mdef['type'] == 'route':  # nn.Sequential() placeholder for 'route' layer
             layers = [int(x) for x in mdef['layers'].split(',')]
             filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
+            routs.extend([l if l > 0 else l + i for l in layers])
             # if mdef[i+1]['type'] == 'reorg3d':
             #     modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest')  # reorg3d
 
         elif mdef['type'] == 'shortcut':  # nn.Sequential() placeholder for 'shortcut' layer
             filters = output_filters[int(mdef['from'])]
+            layer = int(mdef['from'])
+            routs.extend([i + layer if layer < 0 else layer])
 
         elif mdef['type'] == 'reorg3d':  # yolov3-spp-pan-scale
             # torch.Size([16, 128, 104, 104])
@@ -80,7 +84,7 @@ def create_modules(module_defs):
         module_list.append(modules)
         output_filters.append(filters)
 
-    return hyperparams, module_list
+    return module_list, routs
 
 
 class Swish(nn.Module):
@@ -171,9 +175,8 @@ class Darknet(nn.Module):
         super(Darknet, self).__init__()
 
         self.module_defs = parse_model_cfg(cfg)
-        self.module_defs[0]['cfg'] = cfg
         self.module_defs[0]['height'] = img_size
-        self.hyperparams, self.module_list = create_modules(self.module_defs)
+        self.module_list, self.routs = create_modules(self.module_defs)
         self.yolo_layers = get_yolo_layers(self)
 
         # Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
@@ -201,11 +204,11 @@ class Darknet(nn.Module):
                         x = torch.cat([layer_outputs[i] for i in layer_i], 1)
                     # print(''), [print(layer_outputs[i].shape) for i in layer_i], print(x.shape)
             elif mtype == 'shortcut':
-                x = layer_outputs[-1] + layer_outputs[int(mdef['from'])	]
+                x = x + layer_outputs[int(mdef['from'])]
             elif mtype == 'yolo':
                 x = module(x, img_size)
                 output.append(x)
-            layer_outputs.append(x)
+            layer_outputs.append(x if i in self.routs else [])
 
         if self.training:
             return output