Revert back SPP and LRN to heritate from Module

Author: fmassa

SpatialPyramidPooling.lua

@@ -1,12 +1,13 @@
-local SpatialPyramidPooling, parent = torch.class('inn.SpatialPyramidPooling', 'nn.Concat')
+local SpatialPyramidPooling, parent = torch.class('inn.SpatialPyramidPooling', 'nn.Module')
 
 -- allows nn.SpatialPyramidPooling({4,4},{3,3}) or {{4,4},{3,3}}
 function SpatialPyramidPooling:__init(...)
-   parent.__init(self, 1)
+   parent.__init(self)
    local pyr = {...}
    self.pyr = torch.type(pyr[1][1]) == 'table' and pyr[1] or pyr
+   self._modules = nn.Concat(1)
    for k,v in ipairs(self.pyr) do
-      parent.add(self, nn.Sequential()
+      self._modules:add(nn.Sequential()
          :add(nn.SpatialAdaptiveMaxPooling(v[1], v[2]))
          :add(nn.View(-1):setNumInputDims(3))
          :add(nn.Contiguous())
@@ -16,7 +17,15 @@ end
 
 function SpatialPyramidPooling:updateOutput(input)
    assert(input:dim() == 4 or input:dim() == 3, 'unsupported dimensionality')
-   self.dimension = input:dim() - 2
-   return parent.updateOutput(self, input)
+   self._modules.dimension = input:dim() - 2
+   self.output = self._modules:updateOutput(input)
+   return self.output
+end
+
+function SpatialPyramidPooling:updateGradInput(input, gradOutput)
+   assert(input:dim() == 4 or input:dim() == 3, 'unsupported dimensionality')
+   self._modules.dimension = input:dim() - 2
+   self.gradInput = self._modules:updateGradInput(input, gradOutput)
+   return self.gradInput
 end
 

SpatialSameResponseNormalization.lua

@@ -1,4 +1,4 @@
-local SpatialSameResponseNormalization, parent = torch.class('inn.SpatialSameResponseNormalization', 'nn.Sequential')
+local SpatialSameResponseNormalization, parent = torch.class('inn.SpatialSameResponseNormalization', 'nn.Module')
 
 function SpatialSameResponseNormalization:__init(size, alpha, beta)
   parent.__init(self)
@@ -21,7 +21,17 @@ function SpatialSameResponseNormalization:__init(size, alpha, beta)
     :add(numerator)
     :add(denominator)
 
-  self:add(divide)
-  self:add(nn.CDivTable())
+  self._modules = nn.Sequential()
+  self._modules:add(divide)
+  self._modules:add(nn.CDivTable())
 end
 
+function SpatialSameResponseNormalization:updateOutput(input)
+  self.output = self._modules:updateOutput(input)
+  return self.output
+end
+
+function SpatialSameResponseNormalization:updateGradInput(input, gradOutput)
+  self.gradInput = self._modules:updateGradInput(input, gradOutput)
+  return self.gradInput
+end