diff --git a/models.py b/models.py
index de4ad892..f84da507 100755
--- a/models.py
+++ b/models.py
@@ -74,6 +74,7 @@ def create_modules(module_defs, img_size):
             layers = mdef['layers']
             filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])
             routs.extend([i + l if l < 0 else l for l in layers])
+            modules = FeatureConcat(layers=layers)
 
         elif mdef['type'] == 'shortcut':  # nn.Sequential() placeholder for 'shortcut' layer
             layers = mdef['from']
@@ -234,27 +235,12 @@ class Darknet(nn.Module):
 
         for i, (mdef, module) in enumerate(zip(self.module_defs, self.module_list)):
             mtype = mdef['type']
-            if mtype == 'shortcut':  # sum
+            if mtype in ['shortcut', 'route']:  # sum, concat
                 if verbose:
                     l = [i - 1] + module.layers  # layers
                     s = [list(x.shape)] + [list(out[i].shape) for i in module.layers]  # shapes
                     str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)])
-                x = module(x, out)  # WeightedFeatureFusion()
-            elif mtype == 'route':  # concat
-                layers = mdef['layers']
-                if verbose:
-                    l = [i - 1] + layers  # layers
-                    s = [list(x.shape)] + [list(out[i].shape) for i in layers]  # shapes
-                    str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)])
-                if len(layers) == 1:
-                    x = out[layers[0]]
-                else:
-                    try:
-                        x = torch.cat([out[i] for i in layers], 1)
-                    except:  # apply stride 2 for darknet reorg layer
-                        out[layers[1]] = F.interpolate(out[layers[1]], scale_factor=[0.5, 0.5])
-                        x = torch.cat([out[i] for i in layers], 1)
-                    # print(''), [print(out[i].shape) for i in layers], print(x.shape)
+                x = module(x, out)  # WeightedFeatureFusion(), FeatureConcat()
             elif mtype == 'yolo':
                 yolo_out.append(module(x, img_size, out))
             else:  # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc.
diff --git a/utils/layers.py b/utils/layers.py
index 1f19279d..6424fba7 100644
--- a/utils/layers.py
+++ b/utils/layers.py
@@ -3,6 +3,16 @@ import torch.nn.functional as F
 from utils.utils import *
 
 
+class FeatureConcat(nn.Module):
+    def __init__(self, layers):
+        super(FeatureConcat, self).__init__()
+        self.layers = layers  # layer indices
+        self.multiple = len(layers) > 1  # multiple layers flag
+
+    def forward(self, x, outputs):
+        return torch.cat([outputs[i] for i in self.layers], 1) if self.multiple else outputs[self.layers[0]]
+
+
 class WeightedFeatureFusion(nn.Module):  # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
     def __init__(self, layers, weight=False):
         super(WeightedFeatureFusion, self).__init__()