1 files changed, 80 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/SpatialDilatedConvolution.lua b/contrib/lua-torch/nn/SpatialDilatedConvolution.lua
new file mode 100644
index 000000000..a0590c7e9
--- /dev/null
+++ b/contrib/lua-torch/nn/SpatialDilatedConvolution.lua
@@ -0,0 +1,80 @@
+local THNN = require 'nn.THNN'
+local SpatialDilatedConvolution, parent = torch.class('nn.SpatialDilatedConvolution', 'nn.SpatialConvolution')
+
+function SpatialDilatedConvolution:__init(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, dilationW, dilationH)
+   parent.__init(self, nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
+
+   self.dilationW = dilationW or 1
+   self.dilationH = dilationH or 1
+end
+
+function SpatialDilatedConvolution:updateOutput(input)
+   self.finput = self.finput or self.weight.new()
+   self.fgradInput = self.fgradInput or self.weight.new()
+   input.THNN.SpatialDilatedConvolution_updateOutput(
+      input:cdata(),
+      self.output:cdata(),
+      self.weight:cdata(),
+      THNN.optionalTensor(self.bias),
+      self.finput:cdata(),
+      self.fgradInput:cdata(),
+      self.kW, self.kH,
+      self.dW, self.dH,
+      self.padW, self.padH,
+      self.dilationW, self.dilationH
+   )
+   return self.output
+end
+
+function SpatialDilatedConvolution:updateGradInput(input, gradOutput)
+   if self.gradInput then
+      self.fgradInput = self.fgradInput or self.weight.new()
+      input.THNN.SpatialDilatedConvolution_updateGradInput(
+         input:cdata(),
+         gradOutput:cdata(),
+         self.gradInput:cdata(),
+         self.weight:cdata(),
+         self.finput:cdata(),
+         self.kW, self.kH,
+         self.dW, self.dH,
+         self.padW, self.padH,
+         self.dilationW, self.dilationH
+      )
+      return self.gradInput
+   end
+end
+
+function SpatialDilatedConvolution:accGradParameters(input, gradOutput, scale)
+   scale = scale or 1
+   self.fgradInput = self.fgradInput or self.weight.new()
+   input.THNN.SpatialDilatedConvolution_accGradParameters(
+      input:cdata(),
+      gradOutput:cdata(),
+      self.gradWeight:cdata(),
+      THNN.optionalTensor(self.gradBias),
+      self.finput:cdata(),
+      self.fgradInput:cdata(),
+      self.kW, self.kH,
+      self.dW, self.dH,
+      self.padW, self.padH,
+      self.dilationW, self.dilationH,
+      scale
+   )
+end
+
+function SpatialDilatedConvolution:__tostring__()
+   local s = string.format('%s(%d -> %d, %dx%d', torch.type(self),
+         self.nInputPlane, self.nOutputPlane, self.kW, self.kH)
+   if self.dW ~= 1 or self.dH ~= 1 or self.padW ~= 0 or self.padH ~= 0 then
+     s = s .. string.format(', %d,%d', self.dW, self.dH)
+   end
+   if (self.padW or self.padH) and (self.padW ~= 0 or self.padH ~= 0) then
+     s = s .. ', ' .. self.padW .. ',' .. self.padH
+   end
+   s = s .. ', ' .. self.dilationW .. ',' .. self.dilationH
+   if self.bias then
+      return s .. ')'
+   else
+      return s .. ') without bias'
+   end
+end