Add support for negative dimension and both batch and non batch inputs for nn.Min, nn.Max and nn.Mean

1 files changed, 19 insertions, 6 deletions

diff --git a/Mean.lua b/Mean.lua
index c8d2b30..14ad0d1 100644
--- a/Mean.lua
+++ b/Mean.lua
@@ -1,27 +1,40 @@
 local Mean, parent = torch.class('nn.Mean', 'nn.Module')
 
-function Mean:__init(dimension)
+function Mean:__init(dimension, nInputDims)
    parent.__init(self)
    dimension = dimension or 1
    self.dimension = dimension
+   -- do not assign default value to nInputDims or it will break backward compatibility
+   self.nInputDims = nInputDims
    self._gradInput = torch.Tensor()
 end
 
+function Mean:_getPositiveDimension(input)
+   local dimension = self.dimension
+   if dimension < 0 then
+      dimension = input:dim() + dimension + 1
+   elseif self.nInputDims and input:dim()==(self.nInputDims+1) then
+      dimension = dimension + 1
+   end
+   return dimension
+end
+
 function Mean:updateOutput(input)
-   self.output:mean(input, self.dimension)
+   local dimension = self:_getPositiveDimension(input)
+   self.output:mean(input, dimension)
    if self.output:nDimension() > 1 then
-      self.output = self.output:select(self.dimension, 1)
+      self.output = self.output:select(dimension, 1)
    end
    return self.output
 end
 
 function Mean:updateGradInput(input, gradOutput)
+   local dimension = self:_getPositiveDimension(input)
    self._gradInput:resizeAs(gradOutput):copy(gradOutput)
-   self._gradInput:mul(1/input:size(self.dimension))
+   self._gradInput:mul(1/input:size(dimension))
 
    if input:nDimension() > 1 then
-      self._gradInput = nn.utils.addSingletonDimension(self._gradInput,
-                                                       self.dimension)
+      self._gradInput = nn.utils.addSingletonDimension(self._gradInput, dimension)
    end
    self.gradInput = self._gradInput:expandAs(input)
    return self.gradInput