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-- Xception model | |
-- a Torch7 implementation of: https://arxiv.org/abs/1610.02357 | |
-- E. Culurciello, October 2016 | |
require 'nn' | |
local nClasses = 1000 | |
function nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH) | |
local block = nn.Sequential() | |
block:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(nInputPlane), kW,kH, 1,1, 1,1)) | |
block:add(nn.SpatialConvolution(nInputPlane, nOutputPlane, 1,1, 1,1, 1,1)) | |
return block | |
end | |
function SepConvBypass(nInputPlane, nOutputPlane, kW,kH, maxW,maxH, maxSW,maxSH, flow, first) | |
local sum = nn.ConcatTable() | |
local main = nn.Sequential() | |
local other = nn.Sequential() | |
sum:add(main):add(other) | |
if flow == 'entry' then | |
if not first then main:add(nn.ReLU()) end | |
main:add(nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH)) | |
main:add(nn.ReLU()) | |
main:add(nn.SpatialSeparableConvolution(nOutputPlane, nOutputPlane, kW, kH)) | |
main:add(nn.SpatialMaxPooling(maxW, maxH, maxSW, maxSH)) | |
main:add(nn.Padding(3,2)) | |
main:add(nn.Padding(4,2)) | |
other:add(nn.SpatialConvolution(nInputPlane, nOutputPlane, 1,1, 2,2, 1,1)) | |
elseif flow == 'middle' then | |
main:add(nn.ReLU()) | |
main:add(nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW,kH)) | |
main:add(nn.ReLU()) | |
main:add(nn.SpatialSeparableConvolution(nOutputPlane, nOutputPlane, kW,kH)) | |
main:add(nn.ReLU()) | |
main:add(nn.SpatialSeparableConvolution(nOutputPlane, nOutputPlane, kW,kH)) | |
other:add(nn.Identity()) | |
elseif flow == 'exit'then | |
main:add(nn.SpatialSeparableConvolution(nInputPlane, nInputPlane, kW,kH)) | |
main:add(nn.ReLU()) | |
main:add(nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW,kH)) | |
main:add(nn.SpatialMaxPooling(maxW, maxH, maxSW, maxSH)) | |
main:add(nn.Padding(3,2)) | |
main:add(nn.Padding(4,2)) | |
other:add(nn.SpatialConvolution(nInputPlane, nOutputPlane, 1,1, 2,2, 1,1)) | |
else | |
print('Error: flow must be either: entry, middle, exit') | |
return 0 | |
end | |
return nn.Sequential():add(sum):add(nn.CAddTable()) | |
end | |
local model = nn.Sequential() | |
-- Entry flow: | |
model:add(nn.SpatialConvolution(3, 32, 3,3, 2,2, 1,1)) -- input: 3x299x299 | |
model:add(nn.ReLU()) | |
model:add(nn.SpatialConvolution(32, 64, 3,3, 2,2, 1,1)) -- output: 64x75x75 | |
model:add(nn.ReLU()) | |
model:add(SepConvBypass(64, 128, 3,3, 3,3, 2,2, 'entry', true)) -- 75 --> 39 | |
model:add(SepConvBypass(128, 256, 3,3, 3,3, 2,2, 'entry', false)) -- 39 --> 21 | |
model:add(SepConvBypass(256, 768, 3,3, 3,3, 2,2, 'entry', false)) -- 21 --> 12 | |
-- Middle flow | |
for i=1,8 do | |
model:add(SepConvBypass(768, 768, 3,3, 3,3, 2,2, 'middle')) -- 12 --> 12 | |
end | |
-- Exit flow | |
model:add(SepConvBypass(768, 1024, 3,3, 3,3, 2,2, 'exit')) -- 12 --> 7 | |
model:add(nn.SpatialSeparableConvolution(1024, 1536, 3,3)) | |
model:add(nn.ReLU()) | |
model:add(nn.SpatialSeparableConvolution(1536, 2048, 3,3)) -- 7 --> 7 | |
model:add(nn.ReLU()) | |
model:add(nn.SpatialAveragePooling(7,7)) -- 7 --> 1 | |
model:add(nn.View(2048)) | |
model:add(nn.Linear(2048, nClasses)) | |
model:add(nn.LogSoftMax()) | |
-- test code: | |
print(model) | |
local a = torch.Tensor(1,3,299,299) -- input image test | |
local b = model:forward(a) -- test network | |
print(b:size()) |
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