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local SpatialPyramid, parent = torch.class('nn.SpatialPyramid', 'nn.Module')
local help_desc = [[
Simplified (and more flexible regarding sizes) fovea:
From a given image, generates a pyramid of scales, and process each scale
with the given list of processors.
The result of each module/scale is then
upsampled to produce a homogenous list of 3D feature maps (a table of 3D tensors)
grouping the different scales.
There are two operating modes: focused [mostly training], and global [inference].
In global mode,
the entire input is processed.
In focused mode, the fovea is first focused on a particular (x,y) point.
This function has two additional parameters, w and h, that represent the size
of the OUTPUT of the processors.
To focus the fovea, simply call fovea:focus(x,y,w,h) before doing a forward.
A call to fovea:focus(nil) makes it unfocus (go back to global mode).
If prescaled_input is true, then the input has to be a table of pre-downscaled
3D tensors. It does not work in focus mode.
]]
function SpatialPyramid:__init(ratios, processors, kW, kH, dW, dH, xDimIn, yDimIn,
xDimOut, yDimOut, prescaled_input)
parent.__init(self)
self.prescaled_input = prescaled_input or false
assert(#ratios == #processors)
self.ratios = ratios
self.kH = kH
self.kW = kW
self.dH = dH
self.dW = dW
self.focused = false
self.x = 0
self.y = 0
self.wFocus = 0
self.hFocus = 0
self.processors = processors
local wPad = kW-dW
local hPad = kH-dH
local padLeft = math.floor(wPad/2)
local padRight = math.ceil (wPad/2)
local padTop = math.floor(hPad/2)
local padBottom = math.ceil (hPad/2)
-- focused
self.focused_pipeline = nn.ConcatTable()
for i = 1,#self.ratios do
local seq = nn.Sequential()
seq:add(nn.SpatialPadding(0,0,0,0, yDimIn, xDimIn))
seq:add(nn.SpatialReSamplingEx{rwidth=1.0/self.ratios[i], rheight=1.0/self.ratios[i],
xDim = xDimIn, yDim = yDimIn, mode='average'})
seq:add(processors[i])
self.focused_pipeline:add(seq)
end
-- unfocused
if prescaled_input then
self.unfocused_pipeline = nn.ParallelTable()
else
self.unfocused_pipeline = nn.ConcatTable()
end
for i = 1,#self.ratios do
local seq = nn.Sequential()
if not prescaled_input then
seq:add(nn.SpatialReSamplingEx{rwidth=1.0/self.ratios[i], rheight=1.0/self.ratios[i],
xDim = xDimIn, yDim = yDimIn, mode='average'})
seq:add(nn.SpatialPadding(padLeft, padRight, padTop, padBottom, yDimIn, xDimIn))
end
seq:add(processors[i])
seq:add(nn.SpatialReSamplingEx{rwidth=self.ratios[i], rheight=self.ratios[i],
xDim=xDimOut, yDim=yDimOut, mode='simple'})
self.unfocused_pipeline:add(seq)
end
end
function SpatialPyramid:focus(x, y, w, h)
w = w or 1
h = h or 1
if x and y then
self.x = x
self.y = y
self.focused = true
self.winWidth = {}
self.winHeight = {}
for i = 1,#self.ratios do
self.winWidth[i] = self.ratios[i] * ((w-1) * self.dW + self.kW)
self.winHeight[i] = self.ratios[i] * ((h-1) * self.dH + self.kH)
end
else
self.focused = false
end
end
function SpatialPyramid:configureFocus(wImg, hImg)
for i = 1,#self.ratios do
local padder = self.focused_pipeline.modules[i].modules[1]
padder.pad_l = -self.x + math.ceil (self.winWidth[i] /2)
padder.pad_r = self.x + math.floor(self.winWidth[i] /2) - wImg
padder.pad_t = -self.y + math.ceil (self.winHeight[i]/2)
padder.pad_b = self.y + math.floor(self.winHeight[i]/2) - hImg
end
end
function SpatialPyramid:checkSize(input)
for i = 1,#self.ratios do
if (math.fmod(input:size(2), self.ratios[i]) ~= 0) or
(math.fmod(input:size(3), self.ratios[i]) ~= 0) then
error('SpatialPyramid: input sizes must be multiple of ratios')
end
end
end
function SpatialPyramid:updateOutput(input)
if not self.prescaled_input then
self:checkSize(input)
end
if self.focused then
self:configureFocus(input:size(3), input:size(2))
self.output = self.focused_pipeline:updateOutput(input)
else
self.output = self.unfocused_pipeline:updateOutput(input)
end
return self.output
end
function SpatialPyramid:updateGradInput(input, gradOutput)
if self.focused then
self.gradInput = self.focused_pipeline:updateGradInput(input, gradOutput)
else
self.gradInput = self.unfocused_pipeline:updateGradInput(input, gradOutput)
end
return self.gradInput
end
function SpatialPyramid:zeroGradParameters()
self.focused_pipeline:zeroGradParameters()
self.unfocused_pipeline:zeroGradParameters()
end
function SpatialPyramid:accGradParameters(input, gradOutput, scale)
if self.focused then
self.focused_pipeline:accGradParameters(input, gradOutput, scale)
else
self.unfocused_pipeline:accGradParameters(input, gradOutput, scale)
end
end
function SpatialPyramid:updateParameters(learningRate)
if self.focused then
self.focused_pipeline:updateParameters(learningRate)
else
self.unfocused_pipeline:updateParameters(learningRate)
end
end
function SpatialPyramid:type(type)
parent.type(self, type)
self.focused_pipeline:type(type)
self.unfocused_pipeline:type(type)
return self
end
function SpatialPyramid:parameters()
if self.focused then
return self.focused_pipeline:parameters()
else
return self.unfocused_pipeline:parameters()
end
end
function SpatialPyramid:__tostring__()
if self.focused then
local dscr = tostring(self.focused_pipeline):gsub('\n', '\n | ')
return 'SpatialPyramid (focused)\n' .. dscr
else
local dscr = tostring(self.unfocused_pipeline):gsub('\n', '\n | ')
return 'SpatialPyramid (unfocused)\n' .. dscr
end
end
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