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models.lua
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require 'nngraph'
function defineG_encoder_decoder(input_nc, output_nc, ngf)
local netG = nil
-- input is (nc) x 256 x 256
local e1 = - nn.SpatialConvolution(input_nc, ngf, 4, 4, 2, 2, 1, 1)
-- input is (ngf) x 128 x 128
local e2 = e1 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf, ngf * 2, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 2)
-- input is (ngf * 2) x 64 x 64
local e3 = e2 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 2, ngf * 4, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 4)
-- input is (ngf * 4) x 32 x 32
local e4 = e3 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 4, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 16 x 16
local e5 = e4 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 8 x 8
local e6 = e5 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 4 x 4
local e7 = e6 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 2 x 2
local e8 = e7 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) -- nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 1 x 1
local d1 = e8 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8) - nn.Dropout(0.5)
-- input is (ngf * 8) x 2 x 2
local d2 = d1 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8) - nn.Dropout(0.5)
-- input is (ngf * 8) x 4 x 4
local d3 = d2 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8) - nn.Dropout(0.5)
-- input is (ngf * 8) x 8 x 8
local d4 = d3 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 16 x 16
local d5 = d4 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8, ngf * 4, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 4)
-- input is (ngf * 4) x 32 x 32
local d6 = d5 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 4, ngf * 2, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 2)
-- input is (ngf * 2) x 64 x 64
local d7 = d6 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 2, ngf, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf)
-- input is (ngf) x128 x 128
local d8 = d7 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf, output_nc, 4, 4, 2, 2, 1, 1)
-- input is (nc) x 256 x 256
local o1 = d8 - nn.Tanh()
netG = nn.gModule({e1},{o1})
return netG
end
function defineG_unet(input_nc, output_nc, ngf)
local netG = nil
-- input is (nc) x 256 x 256
local e1 = - nn.SpatialConvolution(input_nc, ngf, 4, 4, 2, 2, 1, 1)
-- input is (ngf) x 128 x 128
local e2 = e1 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf, ngf * 2, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 2)
-- input is (ngf * 2) x 64 x 64
local e3 = e2 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 2, ngf * 4, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 4)
-- input is (ngf * 4) x 32 x 32
local e4 = e3 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 4, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 16 x 16
local e5 = e4 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 8 x 8
local e6 = e5 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 4 x 4
-- input is (ngf * 8) x 1 x 1
local d1_ = e8 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8) - nn.Dropout(0.5)
-- input is (ngf * 8) x 2 x 2
local d1 = {d1_,e7} - nn.JoinTable(2)
local d2_ = d1 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8 * 2, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8) - nn.Dropout(0.5)
-- input is (ngf * 8) x 4 x 4
local d2 = {d2_,e6} - nn.JoinTable(2)
local d3_ = d2 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8 * 2, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8) - nn.Dropout(0.5)
-- input is (ngf * 8) x 8 x 8
local d3 = {d3_,e5} - nn.JoinTable(2)
local d4_ = d3 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8 * 2, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
-- input is (ngf * 8) x 16 x 16
local d4 = {d4_,e4} - nn.JoinTable(2)
local d5_ = d4 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8 * 2, ngf * 4, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 4)
-- input is (ngf * 4) x 32 x 32
local d5 = {d5_,e3} - nn.JoinTable(2)
local d6_ = d5 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 4 * 2, ngf * 2, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 2)
-- input is (ngf * 2) x 64 x 64
local d6 = {d6_,e2} - nn.JoinTable(2)
local d7_ = d6 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 2 * 2, ngf, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf)
-- input is (ngf) x128 x 128
local d7 = {d7_,e1} - nn.JoinTable(2)
local d8 = d7 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 2, output_nc, 4, 4, 2, 2, 1, 1)
-- input is (nc) x 256 x 256
local o1 = d8 - nn.Tanh()
netG = nn.gModule({e1},{o1})
--graph.dot(netG.fg,'netG')
return netG
end
function defineG_unet_128(input_nc, output_nc, ngf)
-- Two layer less than the default unet to handle 64x64input
local netG = nil
-- input is (nc) x 128X128
local e1 = - nn.SpatialConvolution(input_nc, ngf, 4, 4, 2, 2, 1, 1)
-- input is (ngf) x 64X64
local e2 = e1 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf, ngf * 2, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 2)
-- input is (ngf * 2) x 32X32
local e3 = e2 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 2, ngf * 4, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 4)
-- input is (ngf * 4) x 16x 16
local e4 = e3 - nn.LeakyReLU(0.2, true) - nn.SpatialConvolution(ngf * 4, ngf * 8, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 8)
local d3_ = e4 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 8 , ngf * 4, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 4) - nn.Dropout(0.5)
-- input is (ngf * 4) x 8 x 8
local d3 = {d3_,e3} - nn.JoinTable(2)
local d4_ = d3 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 4*2, ngf * 2, 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf * 2)
-- input is (ngf * 2) x 16 x 16
local d4 = {d4_,e2} - nn.JoinTable(2)
local d5_ = d4 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf * 2 * 2, ngf , 4, 4, 2, 2, 1, 1) - nn.SpatialBatchNormalization(ngf)
-- input is (ngf ) x 32 x 32
local d5 = {d5_,e1} - nn.JoinTable(2)
local d6 = d5 - nn.ReLU(true) - nn.SpatialFullConvolution(ngf*2, output_nc, 4, 4, 2, 2, 1, 1)
-- input is (ngf) x128 x 128
local o1 = d6 - nn.Tanh()
netG = nn.gModule({e1},{o1})
--graph.dot(netG.fg,'netG')
return netG
end
function defineD_basic(input_nc, output_nc, ndf)
n_layers = 2
return defineD_n_layers(input_nc, output_nc, ndf, n_layers)
end
-- rf=1
function defineD_pixelGAN(input_nc, output_nc, ndf)
local netD = nn.Sequential()
-- input is (nc) x 256 x 256
netD:add(nn.SpatialConvolution(input_nc+output_nc, ndf, 1, 1, 1, 1, 0, 0))
netD:add(nn.LeakyReLU(0.2, true))
-- state size: (ndf) x 256 x 256
netD:add(nn.SpatialConvolution(ndf, ndf * 2, 1, 1, 1, 1, 0, 0))
netD:add(nn.SpatialBatchNormalization(ndf * 2)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*2) x 256 x 256
netD:add(nn.SpatialConvolution(ndf * 2, 1, 1, 1, 1, 1, 0, 0))
-- state size: 1 x 256 x 256
netD:add(nn.Sigmoid())
-- state size: 1 x 256 x 256
return netD
end
-- if n=0, then use pixelGAN (rf=1)
-- else rf is 16 if n=1
-- 34 if n=2
-- 70 if n=3
-- 142 if n=4
-- 286 if n=5
-- 574 if n=6
function defineD_n_layers(input_nc, output_nc, ndf, n_layers)
if n_layers==0 then
return defineD_pixelGAN(input_nc, output_nc, ndf)
else
local netD = nn.Sequential()
netD:add(nn.SpatialConvolution(input_nc+output_nc, ndf, 4, 4, 2, 2, 1, 1))
netD:add(nn.LeakyReLU(0.2, true))
local nf_mult = 1
local nf_mult_prev = 1
for n = 1, n_layers-1 do
nf_mult_prev = nf_mult
nf_mult = math.min(2^n,8)
netD:add(nn.SpatialConvolution(ndf * nf_mult_prev, ndf * nf_mult, 4, 4, 2, 2, 1, 1))
netD:add(nn.SpatialBatchNormalization(ndf * nf_mult)):add(nn.LeakyReLU(0.2, true))
end
-- state size: (ndf*M) x N x N
nf_mult_prev = nf_mult
nf_mult = math.min(2^n_layers,8)
netD:add(nn.SpatialConvolution(ndf * nf_mult_prev, ndf * nf_mult, 4, 4, 1, 1, 1, 1))
netD:add(nn.SpatialBatchNormalization(ndf * nf_mult)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*M*2) x (N-1) x (N-1)
netD:add(nn.SpatialConvolution(ndf * nf_mult, 1, 4, 4, 1, 1, 1, 1))
-- state size: 1 x (N-2) x (N-2)
netD:add(nn.Sigmoid())
-- state size: 1 x (N-2) x (N-2)
return netD
end
end
function defineD_n_layers_text(input_nc, output_nc, ndf, n_layers)
if n_layers==0 then
return defineD_pixelGAN(input_nc, output_nc, ndf)
else
local netD = nn.Sequential()
netD:add(nn.SpatialConvolution(input_nc+output_nc, ndf, 4, 4, 2, 2, 1, 1))
netD:add(nn.LeakyReLU(0.2, true))
for n = 1, 3 do
netD:add(nn.SpatialConvolution(ndf , ndf , 4, 4, 2, 2, 1, 1))
netD:add(nn.SpatialBatchNormalization(ndf)):add(nn.LeakyReLU(0.2, true))
end
netD:add(nn.SpatialConvolution(ndf , ndf, 4, 4, 2, 2, 1, 1))
netD:add(nn.SpatialBatchNormalization(ndf)):add(nn.LeakyReLU(0.2, true))
netD:add(nn.SpatialConvolution(ndf , ndf, 4, 4, 2, 2, 1, 1))
-- state size: (ndf*M*2) x (N-1) x (N-1)
netD:add(nn.View(4*4*ndf))
-- state size: 1 x (N-2) x (N-2)
netD:add(nn.Linear(4*4*ndf,1))
netD:add(nn.Sigmoid())
-- state size: 1 x (N-2) x (N-2)
return netD
end
end
require 'nn'
function create_cnn_10(nc,nz,ngf)
local netG = nn.Sequential()
netG = nn.Sequential()
netG:add(nn.SpatialConvolution(nc, ngf, 5, 5,1,1,2,2))
netG:add(nn.SpatialBatchNormalization(ngf ))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, 2*ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.SpatialBatchNormalization(2*ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(2*ngf, 4*ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.SpatialBatchNormalization(4*ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(4*ngf, 8*ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.Dropout(0.2))
netG:add(nn.SpatialBatchNormalization(8*ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(8*ngf, 16*ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.Dropout(0.2))
netG:add(nn.SpatialBatchNormalization(16*ngf ))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(16*ngf , 8*ngf, 5, 5, 1, 1,2,2))
netG:add(nn.SpatialBatchNormalization(8*ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(8*ngf, 4*ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.SpatialBatchNormalization(4*ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(4*ngf, 2*ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.SpatialBatchNormalization(2*ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(2*ngf, ngf, 5, 5, 1, 1, 2,2))
netG:add(nn.SpatialBatchNormalization(ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, nc, 5, 5, 1, 1, 2,2))
netG:add(nn.Tanh())
return netG
end
function create_cnn(nc,nz,ngf)
local netG = nn.Sequential()
netG = nn.Sequential()
netG:add(nn.SpatialConvolution(nc, ngf, 7, 7, 1, 1, 3, 3))
netG:add(nn.SpatialBatchNormalization(ngf ))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, ngf, 7, 7, 1, 1, 3, 3))
netG:add(nn.SpatialBatchNormalization(ngf ))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, ngf, 7, 7, 1, 1, 3, 3))
netG:add(nn.Dropout(0.2))
netG:add(nn.SpatialBatchNormalization(ngf ))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf , ngf, 7,7, 1, 1,3,3))
netG:add(nn.SpatialBatchNormalization(ngf ))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, nc, 7, 7, 1, 1, 3, 3))
netG:add(nn.Tanh())
return netG
end
function create_cnn1(nc,nz,ngf)
local netRevG = nn.Sequential()
netRevG = nn.Sequential()
netRevG:add(nn.SpatialConvolution(nc, ngf, 5, 5,1,1,2,2))
netRevG:add(nn.SpatialBatchNormalization(ngf ))
netRevG:add(nn.ReLU(true))
netRevG:add(nn.SpatialConvolution(ngf, ngf, 5, 5, 1, 1, 2, 2))
netRevG:add(nn.SpatialBatchNormalization(ngf ))
netRevG:add(nn.ReLU(true))
netRevG:add(nn.SpatialConvolution(ngf, ngf, 5, 5, 1, 1, 2, 2))
netRevG:add(nn.SpatialBatchNormalization(ngf ))
netRevG:add(nn.ReLU(true))
netRevG:add(nn.SpatialConvolution(ngf , ngf, 5, 5, 1, 1,2,2))
netRevG:add(nn.SpatialBatchNormalization(ngf ))
netRevG:add(nn.ReLU(true))
netRevG:add(nn.SpatialConvolution(ngf, nc, 5, 5, 1, 1, 2,2))
netRevG:add(nn.Tanh())
return netRevG
end
function create_cnn_circles(nc,nz,ngf)
local netG = nn.Sequential()
netG = nn.Sequential()
netG:add(nn.SpatialConvolution(nc, ngf, 5, 5,1,1,2,2))
netG:add(nn.SpatialBatchNormalization(ngf ))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.SpatialBatchNormalization(ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.SpatialBatchNormalization(ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, ngf, 5, 5, 1, 1, 2, 2))
netG:add(nn.Dropout(0.2))
netG:add(nn.SpatialBatchNormalization(ngf))
netG:add(nn.ReLU(true))
netG:add(nn.SpatialConvolution(ngf, nc, 5, 5, 1, 1, 2, 2))
netG:add(nn.Tanh())
return netG
end