1 files changed, 93 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/SpatialAveragePooling.lua b/contrib/lua-torch/nn/SpatialAveragePooling.lua
new file mode 100644
index 000000000..1e7666827
--- /dev/null
+++ b/contrib/lua-torch/nn/SpatialAveragePooling.lua
@@ -0,0 +1,93 @@
+local SpatialAveragePooling, parent = torch.class('nn.SpatialAveragePooling', 'nn.Module')
+
+function SpatialAveragePooling:__init(kW, kH, dW, dH, padW, padH)
+   parent.__init(self)
+
+   self.kW = kW
+   self.kH = kH
+   self.dW = dW or 1
+   self.dH = dH or 1
+   self.padW = padW or 0
+   self.padH = padH or 0
+   self.ceil_mode = false
+   self.count_include_pad = true
+   self.divide = true
+end
+
+function SpatialAveragePooling:ceil()
+   self.ceil_mode = true
+   return self
+end
+
+function SpatialAveragePooling:floor()
+   self.ceil_mode = false
+   return self
+end
+
+function SpatialAveragePooling:setCountIncludePad()
+   self.count_include_pad = true
+   return self
+end
+
+function SpatialAveragePooling:setCountExcludePad()
+   self.count_include_pad = false
+   return self
+end
+
+local function backwardCompatible(self)
+   if self.ceil_mode == nil then
+      self.ceil_mode = false
+      self.count_include_pad = true
+      self.padH = 0
+      self.padW = 0
+   end
+end
+
+function SpatialAveragePooling:updateOutput(input)
+   backwardCompatible(self)
+   input.THNN.SpatialAveragePooling_updateOutput(
+      input:cdata(),
+      self.output:cdata(),
+      self.kW, self.kH,
+      self.dW, self.dH,
+      self.padW, self.padH,
+      self.ceil_mode,
+      self.count_include_pad
+   )
+   -- for backward compatibility with saved models
+   -- which are not supposed to have "divide" field
+   if not self.divide then
+     self.output:mul(self.kW*self.kH)
+   end
+   return self.output
+end
+
+function SpatialAveragePooling:updateGradInput(input, gradOutput)
+   if self.gradInput then
+      input.THNN.SpatialAveragePooling_updateGradInput(
+         input:cdata(),
+         gradOutput:cdata(),
+         self.gradInput:cdata(),
+         self.kW, self.kH,
+         self.dW, self.dH,
+         self.padW, self.padH,
+         self.ceil_mode,
+         self.count_include_pad
+      )
+      -- for backward compatibility
+      if not self.divide then
+         self.gradInput:mul(self.kW*self.kH)
+      end
+      return self.gradInput
+   end
+end
+
+function SpatialAveragePooling:__tostring__()
+   local s = string.format('%s(%dx%d, %d,%d', torch.type(self),
+                            self.kW, self.kH, self.dW, self.dH)
+   if (self.padW or self.padH) and (self.padW ~= 0 or self.padH ~= 0) then
+      s = s .. ', ' .. self.padW .. ','.. self.padH
+   end
+   s = s .. ')'
+   return s
+end