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local CMul, parent = torch.class('nn.CMul', 'nn.Module')
function CMul:__init(...)
parent.__init(self)
local arg = {...}
self.size = torch.LongStorage()
local n = #arg
if n == 1 and torch.type(arg[1]) == 'torch.LongStorage' then
self.size:resize(#arg[1]):copy(arg[1])
else
self.size:resize(n)
for i=1,n do
self.size[i] = arg[i]
end
end
self.weight = torch.Tensor(self.size)
self.gradWeight = torch.Tensor(self.size)
self.output:resize(self.size)
self:reset()
end
function CMul:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1./math.sqrt(self.weight:nElement())
end
self.weight:uniform(-stdv,stdv)
end
function CMul:updateOutput(input)
-- lazy-initialize
self._output = self._output or input.new()
self._weight = self._weight or input.new()
self._expand = self._expand or input.new()
self._repeat = self._repeat or input.new()
self.output:resizeAs(input):copy(input)
if input:nElement() == self.weight:nElement() then
self._output:view(self.output, -1)
self._weight:view(self.weight, -1)
self._output:cmul(self._weight)
else
if self.weight:dim() == input:dim() then
self._output:set(self.output)
self._weight:set(self.weight)
else
local batchSize = input:size(1)
self._output:view(self.output, batchSize, -1)
self._weight:view(self.weight, 1, -1)
end
self._expand:expandAs(self._weight, self._output)
if torch.type(input) == 'torch.CudaTensor' then
self._repeat:resizeAs(self._expand):copy(self._expand)
self._output:cmul(self._repeat)
else
self._output:cmul(self._expand)
end
end
return self.output
end
function CMul:updateGradInput(input, gradOutput)
if not self.gradInput then
return
end
self._gradOutput = self._gradOutput or input.new()
self._gradInput = self._gradInput or input.new()
self.gradInput:resizeAs(input):zero()
if self.weight:nElement() == gradOutput:nElement() then
self.gradInput:addcmul(1, self.weight, gradOutput)
else
if self.weight:dim() == input:dim() then
nn.utils.contiguousView(self._gradOutput, gradOutput, gradOutput:size())
nn.utils.contiguousView(self._gradInput, self.gradInput, self.gradInput:size())
self._weight:set(self.weight)
else
local batchSize = input:size(1)
nn.utils.contiguousView(self._gradOutput, gradOutput, batchSize, -1)
nn.utils.contiguousView(self._gradInput, self.gradInput, batchSize, -1)
self._weight:view(self.weight, 1, -1)
end
self._expand:expandAs(self._weight, self._gradOutput)
if torch.type(input) == 'torch.CudaTensor' then
self._repeat:resizeAs(self._expand):copy(self._expand)
self._gradInput:addcmul(1, self._repeat, self._gradOutput)
else
self._gradInput:addcmul(1, self._expand, self._gradOutput)
end
end
return self.gradInput
end
function CMul:accGradParameters(input, gradOutput, scale)
scale = scale or 1
self._input = self._input or input.new()
self._gradWeight = self._gradWeight or input.new()
self._sum = self._sum or input.new()
if self.weight:nElement() == gradOutput:nElement() then
self.gradWeight:addcmul(scale, input, gradOutput)
else
if self.weight:dim() == input:dim() then
nn.utils.contiguousView(self._input, input, input:size())
nn.utils.contiguousView(self._gradOutput, gradOutput, gradOutput:size())
self._gradWeight:set(self.gradWeight)
self._repeat:cmul(self._input, self._gradOutput)
local sumInto = self._sum
local sumFrom = self._repeat
for i=1,self.weight:dim() do
if self.weight:size(i) ~= input:size(i) then
sumInto:sum(sumFrom, i)
sumInto = sumFrom
sumFrom = sumFrom == self._repeat and self._sum or self._repeat
end
end
self._gradWeight:add(scale, sumFrom)
else
local batchSize = input:size(1)
nn.utils.contiguousView(self._input, input, batchSize, -1)
nn.utils.contiguousView(self._gradOutput, gradOutput, batchSize, -1)
self._gradWeight:view(self.gradWeight, 1, -1)
self._repeat:cmul(self._input, self._gradOutput)
self._sum:sum(self._repeat, 1)
self._gradWeight:add(scale, self._sum)
end
end
end
function CMul:type(type, tensorCache)
if type then
self:clearState()
end
return parent.type(self, type, tensorCache)
end
function CMul:clearState()
nn.utils.clear(self, {
'_input',
'_output',
'_weight',
'_gradWeight',
'_expand',
'_repeat',
'_sum',
})
return parent.clearState(self)
end
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