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local Max, parent = torch.class('nn.Max', 'nn.Module')
function Max:__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 Max:_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 Max:_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 Max:updateOutput(input)
self:_lazyInit()
local dimension = self:_getPositiveDimension(input)
torch.max(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 Max: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 Max:type(type, tensorCache)
self._indices = nil
parent.type(self, type, tensorCache)
return self
end
function Max:clearState()
nn.utils.clear(self, '_indices', '_output')
return parent.clearState(self)
end
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