lib/torch/nn/conv2d.rb in torch-rb-0.1.5 vs lib/torch/nn/conv2d.rb in torch-rb-0.1.6

@@ -1,35 +1,27 @@
 module Torch
   module NN
     class Conv2d < ConvNd
-      def initialize(in_channels, out_channels, kernel_size, stride: 1, padding: 0, dilation: 1, groups: 1, bias: true, padding_mode: "zeros")
-        kernel_size = pair(kernel_size)
-        stride = pair(stride)
-        padding = pair(padding)
-        dilation = pair(dilation)
-        super(in_channels, out_channels, kernel_size, stride, padding, dilation, false, pair(0), groups, bias, padding_mode)
+      def initialize(in_channels, out_channels, kernel_size, stride: 1,
+        padding: 0, dilation: 1, groups: 1, bias: true, padding_mode: "zeros")
+
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        super(in_channels, out_channels, kernel_size, stride, padding, dilation, false, _pair(0), groups, bias, padding_mode)
       end
 
       def forward(input)
         if @padding_mode == "circular"
           raise NotImplementedError
         end
-        F.conv2d(input, @weight, @bias, stride: @stride, padding: @padding, dilation: @dilation, groups: @groups)
+        F.conv2d(input, @weight, @bias, @stride, @padding, @dilation, @groups)
       end
 
       # TODO add more parameters
       def extra_inspect
         format("%s, %s, kernel_size: %s, stride: %s", @in_channels, @out_channels, @kernel_size, @stride)
-      end
-
-      private
-
-      def pair(value)
-        if value.is_a?(Array)
-          value
-        else
-          [value] * 2
-        end
       end
     end
   end
 end