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local JoinTable, parent = torch.class('nn.JoinTable', 'nn.Module')
function JoinTable:__init(dimension, nInputDims)
parent.__init(self)
self.size = torch.LongStorage()
self.dimension = dimension
self.gradInput = {}
self.nInputDims = nInputDims
end
function JoinTable:_getPositiveDimension(input)
local dimension = self.dimension
if dimension < 0 then
dimension = input[1]:dim() + dimension + 1
elseif self.nInputDims and input[1]:dim()==(self.nInputDims+1) then
dimension = dimension + 1
end
return dimension
end
function JoinTable:updateOutput(input)
local dimension = self:_getPositiveDimension(input)
for i=1,#input do
local currentOutput = input[i]
if i == 1 then
self.size:resize(currentOutput:dim()):copy(currentOutput:size())
else
self.size[dimension] = self.size[dimension]
+ currentOutput:size(dimension)
end
end
self.output:resize(self.size)
local offset = 1
for i=1,#input do
local currentOutput = input[i]
self.output:narrow(dimension, offset,
currentOutput:size(dimension)):copy(currentOutput)
offset = offset + currentOutput:size(dimension)
end
return self.output
end
function JoinTable:updateGradInput(input, gradOutput)
local dimension = self:_getPositiveDimension(input)
for i=1,#input do
if self.gradInput[i] == nil then
self.gradInput[i] = input[i].new()
end
self.gradInput[i]:resizeAs(input[i])
end
-- clear out invalid gradInputs
for i=#input+1, #self.gradInput do
self.gradInput[i] = nil
end
local offset = 1
for i=1,#input do
local currentOutput = input[i]
local currentGradInput = gradOutput:narrow(dimension, offset,
currentOutput:size(dimension))
self.gradInput[i]:copy(currentGradInput)
offset = offset + currentOutput:size(dimension)
end
return self.gradInput
end
function JoinTable:type(type, tensorCache)
self.gradInput = {}
return parent.type(self, type, tensorCache)
end
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