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local Replicate, parent = torch.class('nn.Replicate','nn.Module')
function Replicate:__init(nf, dim, ndim)
parent.__init(self)
self.nfeatures = nf
self.dim = dim or 1
self.ndim = ndim
assert(self.dim > 0, "Can only replicate across positive integer dimensions.")
end
function Replicate:updateOutput(input)
self.dim = self.dim or 1 --backwards compatible
assert(
self.dim <= input:dim()+1,
"Not enough input dimensions to replicate along dimension " ..
tostring(self.dim) .. ".")
local batchOffset = self.ndim and input:dim() > self.ndim and 1 or 0
local rdim = self.dim + batchOffset
local sz = torch.LongStorage(input:dim()+1)
sz[rdim] = self.nfeatures
for i = 1,input:dim() do
local offset = 0
if i >= rdim then
offset = 1
end
sz[i+offset] = input:size(i)
end
local st = torch.LongStorage(input:dim()+1)
st[rdim] = 0
for i = 1,input:dim() do
local offset = 0
if i >= rdim then
offset = 1
end
st[i+offset] = input:stride(i)
end
self.output:set(input:storage(),input:storageOffset(),sz,st)
return self.output
end
function Replicate:updateGradInput(input, gradOutput)
self.gradInput:resizeAs(input):zero()
local batchOffset = self.ndim and input:dim() > self.ndim and 1 or 0
local rdim = self.dim + batchOffset
local sz = torch.LongStorage(input:dim()+1)
sz[rdim] = 1
for i = 1,input:dim() do
local offset = 0
if i >= rdim then
offset = 1
end
sz[i+offset] = input:size(i)
end
local gradInput = self.gradInput:view(sz)
gradInput:sum(gradOutput, rdim)
return self.gradInput
end
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