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local SpatialFovea, parent = torch.class('nn.SpatialFovea', 'nn.Module')
local help_desc =
[[From a given image, generates a pyramid of scales, and process each scale
with the given list of preprocessors and processors.
The result of each module/scale is then
upsampled to produce a homogenous list of 3D feature maps (4D tensor).
The pipeline is the following:
input -> pyramid{ratios} -> preProcessors -> padding -> processors -> [alignment] -> output
There are two operating modes: focused [training], and global [inference].
In inference mode,
the entire input is processed, and an alignment step is performed at the end of
the pipeline, to be fed directly to a SpatialLinear module.
In sampling mode, the fovea is first focused on a particular (x,y) point, and no
alignment is performed at the end, as all scales should produce a 1x1 result.
To focus the fovea, simply call fovea:focus(x,y,winSize) before doing a forward.
A call to fovea:focus(nil) makes it unfocus (go back to global mode). ]]
function SpatialFovea:__init(...)
parent.__init(self)
-- check args
xlua.unpack_class(
self,
{...},
'nn.SpatialFovea',
help_desc,
{arg='nInputPlane', type='number', help='number of input planes', req=true},
{arg='ratios', type='table', help='list of downsampling ratios', req=true},
{arg='processors', type='table', help='list of processors (each processor sees a single scale)', req=true},
{arg='preProcessors', type='table', help='list of preprocessors (applied before padding)'},
{arg='fov', type='number', help='field of view (== processors\' receptive field)', default=1},
{arg='sub', type='number', help='global subsampling (== processors\' subsampling ratio)', default=1},
{arg='bilinear', type='number', help='bilinear interpolation', default=false},
{arg='cachePrePreproc', type='number', help='beta: cache preprocessed input based on input\' hash', default=false}
)
-- internal modules:
self.downsamplers = {}
self.padders = {}
self.upsamplers = {}
self.preProcessors = self.preProcessors or {}
-- temporary results:
self.pyramid = {}
self.preProcessed = {}
self.padded = {}
self.narrowed = {}
self.processed = {}
self.upsampled = {}
self.gradUpsampled = {}
self.gradProcessed = {}
self.gradNarrowed = {}
self.gradPadded = {}
self.gradPreProcessed = {}
self.gradPyramid = {}
-- inferred params
self.padding = self.fov - self.sub
-- check processors
if #self.processors ~= #self.ratios then
xlua.error('the number of processors provided should == the number of ratios (scales): '
.. #self.ratios, 'nn.SpatialFovea')
end
-- to be compatible with classical container modules
self.modules = self.processors
-- reset
self:reset()
end
function SpatialFovea:focus(x,y,fov)
self.x = x
self.y = y
self.fov = fov or self.fov
if self.x and self.y and self.fov then
self.focused = true
else
self.focused = false
end
end
function SpatialFovea:configure(width,height)
-- init modules
for idx = 1,#self.ratios do
-- down/up ratio
local r = self.ratios[idx]
-- downsamplers
if self.bilinear then
self.downsamplers[idx] = nn.SpatialReSampling(1/r,1/r)
else
self.downsamplers[idx] = nn.SpatialSubSampling(self.nInputPlane, r, r, r, r)
self.downsamplers[idx].weight:fill(1/(r^2))
self.downsamplers[idx].bias:zero()
end
-- padders
if self.padding == 0 then
self.padders[idx] = nn.Identity()
else
local padl = math.floor(self.padding / 2)
local padr = math.floor(self.padding / 2)
self.padders[idx] = nn.SpatialPadding(padl, padr, padl, padr)
end
-- upsamplers
if self.bilinear then
self.upsamplers[idx] = nn.SpatialReSampling(r, r)
else
self.upsamplers[idx] = nn.SpatialUpSampling(r, r)
end
-- set correct types
self.downsamplers[idx]:type(self.output:type())
self.padders[idx]:type(self.output:type())
self.upsamplers[idx]:type(self.output:type())
end
end
function SpatialFovea:updateOutput(input)
-- input must be 3D
if input:nDimension() ~= 3 then
xerror('input must be 3d','nn.SpatialFovea')
end
local width = input:size(3)
local height = input:size(2)
local nmaps = input:size(1)
local nscales = #self.ratios
if input:size(1) ~= self.nInputPlane then
xerror('input must have ' .. self.nInputPlane .. ' input planes' ,'nn.SpatialFovea')
end
self:configure(width,height)
-- (beta) cache preprocessed data based on a unique hash
local retrieved = false
local hash = 0
if self.cachePrePreproc then
-- create or reuse list of cached inputs
self.cachedPreProcessed = self.cachedPreProcessed or {}
-- compute an abritrary hash, should be strong enough
local tohash = input
hash = tostring(tohash:sum())
hash = hash .. tostring(tohash:std())
-- check if input was seend before
if self.cachedPreProcessed[hash] then
for idx = 1,nscales do
self.padded[idx] = self.cachedPreProcessed[hash][idx]
end
retrieved = true
end
end
-- (beta) only compute input if it was not retrieved
if not retrieved then
-- (1) generate pyramid
for idx = 1,nscales do
self.pyramid[idx] = self.downsamplers[idx]:updateOutput(input)
end
-- (2) preprocess
for idx = 1,nscales do
if self.preProcessors[idx] then
self.preProcessed[idx] = self.preProcessors[idx]:updateOutput(self.pyramid[idx])
else
self.preProcessed[idx] = self.pyramid[idx]
end
end
-- (3) pad inputs
for idx = 1,nscales do
self.padded[idx] = self.padders[idx]:updateOutput(self.preProcessed[idx])
end
-- store preprocessed input for future use
if self.cachePrePreproc then
self.cachedPreProcessed[hash] = {}
for idx = 1,nscales do
self.cachedPreProcessed[hash][idx] = self.padded[idx]:clone()
end
end
end
-- (4) is fovea focused ?
if self.focused then
for idx = 1,nscales do
local fov = self.fov
local ox = math.floor(math.floor((self.x-1) / self.ratios[idx]) / self.sub) * self.sub + 1
local oy = math.floor(math.floor((self.y-1) / self.ratios[idx]) / self.sub) * self.sub + 1
self.narrowed[idx] = self.padded[idx]:narrow(3,ox,fov):narrow(2,oy,fov)
end
else
for idx = 1,nscales do
self.narrowed[idx] = self.padded[idx]
end
end
-- (5) apply processors to pyramid
for idx = 1,nscales do
self.processed[idx] = self.processors[idx]:updateOutput(self.narrowed[idx])
end
-- (6) upscale, only if fovea is not focused
if self.focused then
for idx = 1,nscales do
self.upsampled[idx] = self.processed[idx]
end
else
for idx = 1,nscales do
self.upsampled[idx] = self.upsamplers[idx]:updateOutput(self.processed[idx])
end
end
-- (7) concatenate all maps into a single 3D volume
local currentslice = 1
for idx = 1,nscales do
currentslice = currentslice + self.processed[idx]:size(1)
end
self.output:resize(currentslice-1, self.upsampled[1]:size(2), self.upsampled[1]:size(3))
currentslice = 1
for idx = 1,nscales do
local omap = self.output:narrow(1, currentslice, self.upsampled[idx]:size(1))
omap:copy( self.upsampled[idx] )
currentslice = currentslice + self.upsampled[idx]:size(1)
end
return self.output
end
function SpatialFovea:updateGradInput(input, gradOutput)
-- nb of scales
local nscales = #self.ratios
-- (7) extract different scales
local currentslice = 1
for idx = 1,nscales do
self.gradUpsampled[idx] = gradOutput:narrow(1, currentslice, self.processed[idx]:size(1))
currentslice = currentslice + self.upsampled[idx]:size(1)
end
-- (6) bprop through upsamplers
if self.focused then
for idx = 1,nscales do
self.gradProcessed[idx] = self.gradUpsampled[idx]
end
else
for idx = 1,nscales do
self.gradProcessed[idx] = self.upsamplers[idx]:updateGradInput(self.processed[idx], self.gradUpsampled[idx])
end
end
-- (5) bprop through processors
for idx = 1,nscales do
self.gradNarrowed[idx] = self.processors[idx]:updateGradInput(self.narrowed[idx], self.gradProcessed[idx])
end
-- (beta) if caching preprocessed input, no need to compute
-- backward past this point
if self.cachePrePreproc then
return self.gradNarrowed
end
-- (4) is fovea focused ?
if self.focused then
for idx = 1,nscales do
self.gradPadded[idx] = self.gradPadded[idx] or torch.Tensor():typeAs(self.output)
self.gradPadded[idx]:resizeAs(self.padded[idx]):zero()
local fov = self.fov
local ox = math.floor(math.floor((self.x-1) / self.ratios[idx]) / self.sub) * self.sub + 1
local oy = math.floor(math.floor((self.y-1) / self.ratios[idx]) / self.sub) * self.sub + 1
self.gradPadded[idx]:narrow(3,ox,fov):narrow(2,oy,fov):copy(self.gradNarrowed[idx])
end
else
for idx = 1,nscales do
self.gradPadded[idx] = self.gradNarrowed[idx]
end
end
-- (3) bprop through padders
for idx = 1,nscales do
self.gradPreProcessed[idx] = self.padders[idx]:updateGradInput(self.preProcessed[idx], self.gradPadded[idx])
end
-- (2) bprop through preProcessors
for idx = 1,nscales do
if self.preProcessors[idx] then
self.gradPyramid[idx] = self.preProcessors[idx]:updateGradInput(self.pyramid[idx], self.gradPreProcessed[idx])
else
self.gradPyramid[idx] = self.gradPreProcessed[idx]
end
end
-- (1) bprop through pyramid
self.gradInput:resizeAs(self.gradPyramid[1]):zero()
for idx = 1,nscales do
self.gradInput:add( self.downsamplers[idx]:updateGradInput(input, self.gradPyramid[idx]) )
end
return self.gradInput
end
function SpatialFovea:reset(stdv)
for idx = 1,#self.processors do
if self.processors[idx].reset then
self.processors[idx]:reset(stdv)
end
end
end
function SpatialFovea:zeroGradParameters()
for idx = 1,#self.processors do
self.processors[idx]:zeroGradParameters()
end
end
function SpatialFovea:accGradParameters(input, gradOutput, scale)
-- accumulate gradients for all processors
for idx = 1,#self.processors do
self.gradNarrowed[idx] = self.processors[idx]:accGradParameters(self.narrowed[idx], self.gradProcessed[idx], scale)
end
end
function SpatialFovea:updateParameters(learningRate)
for idx = 1,#self.processors do
self.processors[idx]:updateParameters(learningRate)
end
end
function SpatialFovea:type(type)
parent.type(self,type)
for idx = 1,#self.processors do
self.processors[idx]:type(type)
self.upsamplers[idx]:type(type)
self.downsamplers[idx]:type(type)
self.padders[idx]:type(type)
end
for idx = 1,#self.preProcessors do
self.preProcessors[idx]:type(type)
end
return self
end
function SpatialFovea: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 SpatialFovea:__tostring__()
local tab = ' '
local line = '\n'
local next = ' |`-> '
local ext = ' | '
local last = ' ... -> '
local str = 'nn.SpatialFovea'
str = str .. ' {' .. line .. tab .. 'input'
for i=1,#self.processors do
local pipeline = nn.Sequential()
if self.preProcessors[i] then
pipeline:add(self.preProcessors[i])
end
pipeline:add(self.processors[i])
str = str .. line .. tab .. next .. '(' .. i .. '): ' .. tostring(pipeline):gsub(line, line .. tab .. ext)
end
str = str .. line .. tab .. last .. 'output'
str = str .. line .. '}'
return str
end
|