im2markup/src/utils/model_utils.lua
 --[[ Adapted from https://github.com/karpathy/char-rnn/blob/master/model/model_utils.lua
--]]
function clone_many_times(net, T)
    local clones = {}

    local params, gradParams
    if net.parameters then
        params, gradParams = net:parameters()
        if params == nil then
            params = {}
        end
    end

    local paramsNoGrad
    if net.parametersNoGrad then
        paramsNoGrad = net:parametersNoGrad()
    end

    local mem = torch.MemoryFile("w"):binary()
    mem:writeObject(net)

    for t = 1, T do
        -- We need to use a new reader for each clone.
        -- We don't want to use the pointers to already read objects.
        local reader = torch.MemoryFile(mem:storage(), "r"):binary()
        local clone = reader:readObject()
        reader:close()

        if net.parameters then
            local cloneParams, cloneGradParams = clone:parameters()
            local cloneParamsNoGrad
            for i = 1, #params do
                cloneParams[i]:set(params[i])
                cloneGradParams[i]:set(gradParams[i])
            end
            if paramsNoGrad then
                cloneParamsNoGrad = clone:parametersNoGrad()
                for i =1,#paramsNoGrad do
                    cloneParamsNoGrad[i]:set(paramsNoGrad[i])
                end
            end
        end

        clones[t] = clone
        collectgarbage()
    end

    mem:close()
    return clones
end

------------------------------------------------------------------------
--[[ LinearNoBias ]]--
-- Subclass of nn.Linear with no bias term
------------------------------------------------------------------------
nn = require 'nn'
local LinearNoBias, Linear = torch.class('nn.LinearNoBias', 'nn.Linear')

function LinearNoBias:__init(inputSize, outputSize)
   nn.Module.__init(self)

   self.weight = torch.Tensor(outputSize, inputSize)
   self.gradWeight = torch.Tensor(outputSize, inputSize)

   self:reset()
end

function LinearNoBias:reset(stdv)
   if stdv then
      stdv = stdv * math.sqrt(3)
   else
      stdv = 1./math.sqrt(self.weight:size(2))
   end
   if nn.oldSeed then
      for i=1,self.weight:size(1) do
         self.weight:select(1, i):apply(function()
            return torch.uniform(-stdv, stdv)
         end)
      end
   else
      self.weight:uniform(-stdv, stdv)
   end

   return self
end

function LinearNoBias:updateOutput(input)
   if input:dim() == 1 then
      self.output:resize(self.weight:size(1))
      self.output:mv(self.weight, input)
   elseif input:dim() == 2 then
      local nframe = input:size(1)
      local nElement = self.output:nElement()
      self.output:resize(nframe, self.weight:size(1))
      if self.output:nElement() ~= nElement then
         self.output:zero()
      end
      if not self.addBuffer or self.addBuffer:nElement() ~= nframe then
         self.addBuffer = input.new(nframe):fill(1)
      end
      self.output:addmm(0, self.output, 1, input, self.weight:t())
   else
      error('input must be vector or matrix')
   end

   return self.output
end

function LinearNoBias:accGradParameters(input, gradOutput, scale)
   scale = scale or 1
   if input:dim() == 1 then
      self.gradWeight:addr(scale, gradOutput, input)
   elseif input:dim() == 2 then
      self.gradWeight:addmm(scale, gradOutput:t(), input)
   end
end
