diff --git a/models.py b/models.py
index 5585c1d2..3e5947f4 100755
--- a/models.py
+++ b/models.py
@@ -254,7 +254,7 @@ class Darknet(nn.Module):
 
     def forward(self, x, var=None):
         img_size = x.shape[-2:]
-        output, layer_outputs = [], []
+        yolo_out, out = [], []
         verbose = False
         if verbose:
             print('0', x.shape)
@@ -265,34 +265,34 @@ class Darknet(nn.Module):
                 x = module(x)
             elif mtype == 'shortcut':  # sum
                 if verbose:
-                    print('shortcut/add %s + %s' % (x.shape, [layer_outputs[i].shape for i in module.layers]))
-                x = module(x, layer_outputs)  # weightedFeatureFusion()
+                    print('shortcut/add %s + %s' % (list(x.shape), [list(out[i].shape) for i in module.layers]))
+                x = module(x, out)  # weightedFeatureFusion()
             elif mtype == 'route':  # concat
                 layers = [int(x) for x in mdef['layers'].split(',')]
                 if verbose:
-                    print('route/concatenate %s' % ([layer_outputs[i].shape for i in layers]))
+                    print('route/concatenate %s + %s' % (list(x.shape), [list(out[i].shape) for i in layers]))
                 if len(layers) == 1:
-                    x = layer_outputs[layers[0]]
+                    x = out[layers[0]]
                 else:
                     try:
-                        x = torch.cat([layer_outputs[i] for i in layers], 1)
+                        x = torch.cat([out[i] for i in layers], 1)
                     except:  # apply stride 2 for darknet reorg layer
-                        layer_outputs[layers[1]] = F.interpolate(layer_outputs[layers[1]], scale_factor=[0.5, 0.5])
-                        x = torch.cat([layer_outputs[i] for i in layers], 1)
-                    # print(''), [print(layer_outputs[i].shape) for i in layers], print(x.shape)
+                        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)
             elif mtype == 'yolo':
-                output.append(module(x, img_size))
-            layer_outputs.append(x if i in self.routs else [])
+                yolo_out.append(module(x, img_size))
+            out.append(x if i in self.routs else [])
             if verbose:
-                print(i, x.shape)
+                print(i, list(x.shape))
 
         if self.training:  # train
-            return output
+            return yolo_out
         elif ONNX_EXPORT:  # export
-            x = [torch.cat(x, 0) for x in zip(*output)]
+            x = [torch.cat(x, 0) for x in zip(*yolo_out)]
             return x[0], torch.cat(x[1:3], 1)  # scores, boxes: 3780x80, 3780x4
         else:  # test
-            io, p = zip(*output)  # inference output, training output
+            io, p = zip(*yolo_out)  # inference output, training output
             return torch.cat(io, 1), p
 
     def fuse(self):