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--[[
Input: A length-2 Table
Input[1]: Responses, batchSize x nChannels x iH x iW
Input[2]: Pairs (minW, minH, maxW, maxH), batchSize x 2 x 2
Output: Scaled responses with zero paddings
batchSize x nChannels x iH x iW
--]]
require 'image'
local Scale, parent = torch.class('nn.Scale', 'nn.Module')
function Scale:__init(sum)
parent.__init(self)
self.sum = sum
self.gradInput = {}
end
function Scale:updateOutput(input)
assert(#input == 2)
assert(input[1]:size(1) == input[2]:size(1))
local batchSize = input[1]:size(1)
local nChannels = input[1]:size(2)
local iH = input[1]:size(3)
local iW = input[1]:size(4)
self.output:resize(batchSize, nChannels, iH, iW):zero()
self.buffer = self.buffer or input[1].new()
self.buffer:resize(batchSize, nChannels)
for i = 1, batchSize do
local minW = input[2][i][1][1]
local minH = input[2][i][1][2]
local maxW = input[2][i][2][1]
local maxH = input[2][i][2][2]
local ratio = (maxW - minW + 1) * (maxH - minH + 1) / (iW * iH)
self.output[{i, {}, {minH, maxH}, {minW, maxW}}] = toNNTensor(
image.scale(input[1][i]:double(), maxW - minW + 1, maxH - minH + 1)):mul(1/ratio)
----[[
for j = 1, nChannels do
if self.output[i][j]:sum() == 0 then
self.buffer[i][j] = 0
else
self.buffer[i][j] = math.min(self.sum / self.output[i][j]:sum(), 100)
self.output[i][j]:mul(self.buffer[i][j])
end
if self.output[i][j]:ne(self.output[i][j]):sum() > 0 then
print(self.buffer[i][j])
print(i..' '..j)
print('!!!!!!!!!!!!!!!!!!!!!!! Found NaN in output !!!!!!!!!!!!!!!!!!!!!!!')
end
end
--[[
print(i..' input sum: '..input[1][i]:sum())
print(i..' output sum: '..self.output[i]:sum())
--]]
end
return self.output
end
function Scale:updateGradInput(input, gradOutput)
for i = 1, #input do
if self.gradInput[i] == nil then
self.gradInput[i] = input[i].new()
end
self.gradInput[i]:resizeAs(input[i]):zero()
end
local batchSize = input[1]:size(1)
local nChannels = input[1]:size(2)
local iH = input[1]:size(3)
local iW = input[1]:size(4)
for i = 1, batchSize do
local minW = input[2][i][1][1]
local minH = input[2][i][1][2]
local maxW = input[2][i][2][1]
local maxH = input[2][i][2][2]
local ratio = (maxW - minW + 1) * (maxH - minH + 1) / (iW * iH)
self.gradInput[1][i] = toNNTensor(image.scale(
gradOutput[{i, {}, {minH, maxH}, {minW, maxW}}]:double(), iW, iH))--:mul(ratio)
----[[
for j = 1, nChannels do
self.gradInput[1][i][j]:mul(self.buffer[i][j])
end
--]]
end
return self.gradInput
end
function Scale:__tostring__()
return string.format('%s()', torch.type(self))
end
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