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Copy pathConcat.lua
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157 lines (140 loc) · 4.24 KB
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local Concat, parent = torch.class('nn.Concat', 'nn.Module')
function Concat:__init(dimension)
parent.__init(self)
self.modules = {}
self.size = torch.LongStorage()
self.dimension = dimension
end
function Concat:add(module)
table.insert(self.modules, module)
return self
end
function Concat:get(index)
return self.modules[index]
end
function Concat:updateOutput(input)
local outs = {}
for i=1,#self.modules do
local currentOutput = self.modules[i]:updateOutput(input)
outs[i] = currentOutput
if i == 1 then
self.size:resize(currentOutput:dim()):copy(currentOutput:size())
else
self.size[self.dimension] = self.size[self.dimension] + currentOutput:size(self.dimension)
end
end
self.output:resize(self.size)
local offset = 1
for i,module in ipairs(self.modules) do
local currentOutput = outs[i]
self.output:narrow(self.dimension, offset, currentOutput:size(self.dimension)):copy(currentOutput)
offset = offset + currentOutput:size(self.dimension)
end
return self.output
end
function Concat:updateGradInput(input, gradOutput)
self.gradInput:resizeAs(input)
local offset = 1
for i,module in ipairs(self.modules) do
local currentOutput = module.output
local currentGradInput = module:updateGradInput(input, gradOutput:narrow(self.dimension, offset, currentOutput:size(self.dimension)))
if currentGradInput then -- if the module does not produce a gradInput (for example first layer), then ignore it and move on.
if i==1 then
self.gradInput:copy(currentGradInput)
else
self.gradInput:add(currentGradInput)
end
end
offset = offset + currentOutput:size(self.dimension)
end
return self.gradInput
end
function Concat:accGradParameters(input, gradOutput, scale)
scale = scale or 1
local offset = 1
for i,module in ipairs(self.modules) do
local currentOutput = module.output
module:accGradParameters(
input,
gradOutput:narrow(self.dimension, offset, currentOutput:size(self.dimension)),
scale)
offset = offset + currentOutput:size(self.dimension)
end
end
function Concat:accUpdateGradParameters(input, gradOutput, lr)
local offset = 1
for i,module in ipairs(self.modules) do
local currentOutput = module.output
module:accUpdateGradParameters(
input,
gradOutput:narrow(self.dimension, offset, currentOutput:size(self.dimension)),
lr)
offset = offset + currentOutput:size(self.dimension)
end
end
function Concat:zeroGradParameters()
for _,module in ipairs(self.modules) do
module:zeroGradParameters()
end
end
function Concat:updateParameters(learningRate)
for _,module in ipairs(self.modules) do
module:updateParameters(learningRate)
end
end
function Concat:training()
for i=1,#self.modules do
self.modules[i]:training()
end
end
function Concat:evaluate()
for i=1,#self.modules do
self.modules[i]:evaluate()
end
end
function Concat:share(mlp,...)
for i=1,#self.modules do
self.modules[i]:share(mlp.modules[i],...);
end
end
function Concat:parameters()
local function tinsert(to, from)
if type(from) == 'table' then
for i=1,#from do
tinsert(to,from[i])
end
else
table.insert(to,from)
end
end
local w = {}
local gw = {}
for i=1,#self.modules do
local mw,mgw = self.modules[i]:parameters()
if mw then
tinsert(w,mw)
tinsert(gw,mgw)
end
end
return w,gw
end
function Concat:__tostring__()
local tab = ' '
local line = '\n'
local next = ' |`-> '
local ext = ' | '
local extlast = ' '
local last = ' ... -> '
local str = torch.type(self)
str = str .. ' {' .. line .. tab .. 'input'
for i=1,#self.modules do
if i == self.modules then
str = str .. line .. tab .. next .. '(' .. i .. '): ' .. tostring(self.modules[i]):gsub(line, line .. tab .. extlast)
else
str = str .. line .. tab .. next .. '(' .. i .. '): ' .. tostring(self.modules[i]):gsub(line, line .. tab .. ext)
end
end
str = str .. line .. tab .. last .. 'output'
str = str .. line .. '}'
return str
end