replace Upsample layer with interpolate function (pytorch#424)

fast_neural_style/neural_style/transformer_net.py

@@ -86,16 +86,14 @@ class UpsampleConvLayer(torch.nn.Module):
     def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
         super(UpsampleConvLayer, self).__init__()
         self.upsample = upsample
-        if upsample:
-            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
         reflection_padding = kernel_size // 2
         self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
         self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
 
     def forward(self, x):
         x_in = x
         if self.upsample:
-            x_in = self.upsample_layer(x_in)
+            x_in = torch.nn.functional.interpolate(x_in, mode='nearest', scale_factor=self.upsample)
         out = self.reflection_pad(x_in)
         out = self.conv2d(out)
         return out