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65 lines (59 loc) · 2.01 KB
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Copy pathMax.lua
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65 lines (59 loc) · 2.01 KB
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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()
self._indices = self._indices or
(torch.type(self.output) == 'torch.CudaTensor' and torch.CudaTensor() or torch.LongTensor())
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 = self._output:select(dimension, 1)
else
self.output = 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)
-- torch.max expects a LongTensor as indices, whereas cutorch.max expects a CudaTensor.
if type == 'torch.CudaTensor' then
parent.type(self, type, tensorCache)
else
-- self._indices must be a LongTensor. Setting it to nil temporarily avoids
-- unnecessary memory allocations.
local indices
indices, self._indices = self._indices, nil
parent.type(self, type, tensorCache)
self._indices = indices and indices:long() or nil
end
return self
end