fix mobile_v2 single output and neck hard coding

pysot/models/backbone/mobile_v2.py

@@ -128,6 +128,8 @@ class MobileNetV2(nn.Sequential):
             outputs.append(x)
         p0, p1, p2, p3, p4 = [outputs[i] for i in [1, 2, 3, 5, 7]]
         out = [outputs[i] for i in self.used_layers]
+        if len(out) == 1:
+            return out[0]
         return out
 
 

pysot/models/neck/neck.py

@@ -9,32 +9,37 @@ import torch.nn as nn
 
 
 class AdjustLayer(nn.Module):
-    def __init__(self, in_channels, out_channels):
+    def __init__(self, in_channels, out_channels, center_size=7):
         super(AdjustLayer, self).__init__()
         self.downsample = nn.Sequential(
             nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
             nn.BatchNorm2d(out_channels),
             )
+        self.center_size = center_size
 
     def forward(self, x):
         x = self.downsample(x)
         if x.size(3) < 20:
-            l = 4
-            r = l + 7
+            l = (x.size(3) - self.center_size) // 2
+            r = l + self.center_size
             x = x[:, :, l:r, l:r]
         return x
 
 
 class AdjustAllLayer(nn.Module):
-    def __init__(self, in_channels, out_channels):
+    def __init__(self, in_channels, out_channels, center_size=7):
         super(AdjustAllLayer, self).__init__()
         self.num = len(out_channels)
         if self.num == 1:
-            self.downsample = AdjustLayer(in_channels[0], out_channels[0])
+            self.downsample = AdjustLayer(in_channels[0],
+                                          out_channels[0],
+                                          center_size)
         else:
             for i in range(self.num):
                 self.add_module('downsample'+str(i+2),
-                                AdjustLayer(in_channels[i], out_channels[i]))
+                                AdjustLayer(in_channels[i],
+                                            out_channels[i],
+                                            center_size))
 
     def forward(self, features):
         if self.num == 1: