1 files changed, 66 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/Min.lua b/contrib/lua-torch/nn/Min.lua
new file mode 100644
index 000000000..3a3e4a802
--- /dev/null
+++ b/contrib/lua-torch/nn/Min.lua
@@ -0,0 +1,66 @@
+local Min, parent = torch.class('nn.Min', 'nn.Module')
+
+function Min:__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
+end
+
+function Min:_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 Min:_lazyInit()
+   self._output = self._output or self.output.new()
+   if not self._indices then
+      if torch.typename(self.output):find('torch%.Cuda.*Tensor') then
+         self._indices = torch.CudaLongTensor and torch.CudaLongTensor() or torch.CudaTensor()
+      else
+         self._indices = torch.LongTensor()
+      end
+   end
+end
+
+function Min:updateOutput(input)
+   self:_lazyInit()
+   local dimension = self:_getPositiveDimension(input)
+   torch.min(self._output, self._indices, input, dimension)
+   if input:dim() > 1 then
+     self.output:set(self._output:select(dimension, 1))
+   else
+     self.output:set(self._output)
+   end
+   return self.output
+end
+
+function Min:updateGradInput(input, gradOutput)
+   self:_lazyInit()
+   local dimension = self:_getPositiveDimension(input)
+   local gradOutputView
+   if input:dim() > 1 then
+     gradOutputView = nn.utils.addSingletonDimension(gradOutput, dimension)
+   else
+     gradOutputView = gradOutput
+   end
+   self.gradInput:resizeAs(input):zero():scatter(dimension, self._indices, gradOutputView)
+   return self.gradInput
+end
+
+function Min:type(type, tensorCache)
+    self._indices = nil
+    parent.type(self, type, tensorCache)
+    return self
+end
+
+function Min:clearState()
+   nn.utils.clear(self, '_indices', '_output')
+   return parent.clearState(self)
+end