require 'torch'
require 'nn'
require 'optim'
require 'image'
util = paths.dofile('util.lua')
opt = {
batchSize = 64, -- number of samples to produce
loadSize = 350, -- resize the loaded image to loadsize maintaining aspect ratio. 0 means don't resize. -1 means scale randomly between [0.5,2] -- see donkey_folder.lua
fineSize = 128, -- size of random crops. Only 64 and 128 supported.
nBottleneck = 100, -- # of dim for bottleneck of encoder
nef = 64, -- # of encoder filters in first conv layer
ngf = 64, -- # of gen filters in first conv layer
ndf = 64, -- # of discrim filters in first conv layer
nc = 3, -- # of channels in input
wtl2 = 0, -- 0 means don't use else use with this weight
useOverlapPred = 0, -- overlapping edges (1 means yes, 0 means no). 1 means put 10x more L2 weight on unmasked region.
nThreads = 4, -- # of data loading threads to use
niter = 25, -- # of iter at starting learning rate
lr = 0.0002, -- initial learning rate for adam
beta1 = 0.5, -- momentum term of adam
ntrain = math.huge, -- # of examples per epoch. math.huge for full dataset
display = 1, -- display samples while training. 0 = false
display_id = 10, -- display window id.
display_iter = 50, -- # number of iterations after which display is updated
gpu = 1, -- gpu = 0 is CPU mode. gpu=X is GPU mode on GPU X
name = 'train1', -- name of the experiment you are running
manualSeed = 0, -- 0 means random seed
-- Extra Options:
conditionAdv = 0, -- 0 means false else true
noiseGen = 0, -- 0 means false else true
noisetype = 'normal', -- uniform / normal
nz = 100, -- # of dim for Z
}
for k,v in pairs(opt) do opt[k] = tonumber(os.getenv(k)) or os.getenv(k) or opt[k] end
print(opt)
if opt.display == 0 then opt.display = false end
if opt.conditionAdv == 0 then opt.conditionAdv = false end
if opt.noiseGen == 0 then opt.noiseGen = false end
-- set seed
if opt.manualSeed == 0 then
opt.manualSeed = torch.random(1, 10000)
end
print("Seed: " .. opt.manualSeed)
torch.manualSeed(opt.manualSeed)
torch.setnumthreads(1)
torch.setdefaulttensortype('torch.FloatTensor')
-- create data loader
local DataLoader = paths.dofile('data/data.lua')
local data = DataLoader.new(opt.nThreads, opt)
print("Dataset Size: ", data:size())
---------------------------------------------------------------------------
-- Initialize network variables
---------------------------------------------------------------------------
local function weights_init(m)
local name = torch.type(m)
if name:find('Convolution') then
m.weight:normal(0.0, 0.02)
m.bias:fill(0)
elseif name:find('BatchNormalization') then
if m.weight then m.weight:normal(1.0, 0.02) end
if m.bias then m.bias:fill(0) end
end
end
local nc = opt.nc
local nz = opt.nz
local nBottleneck = opt.nBottleneck
local ndf = opt.ndf
local ngf = opt.ngf
local nef = opt.nef
local real_label = 1
local fake_label = 0
local SpatialBatchNormalization = nn.SpatialBatchNormalization
local SpatialConvolution = nn.SpatialConvolution
local SpatialFullConvolution = nn.SpatialFullConvolution
---------------------------------------------------------------------------
-- Generator net
---------------------------------------------------------------------------
-- Encode Input Context to noise (architecture similar to Discriminator)
local netE = nn.Sequential()
-- input is (nc) x 128 x 128
netE:add(SpatialConvolution(nc, nef, 4, 4, 2, 2, 1, 1))
netE:add(nn.LeakyReLU(0.2, true))
if opt.fineSize == 128 then
-- state size: (nef) x 64 x 64
netE:add(SpatialConvolution(nef, nef, 4, 4, 2, 2, 1, 1))
netE:add(SpatialBatchNormalization(nef)):add(nn.LeakyReLU(0.2, true))
end
-- state size: (nef) x 32 x 32
netE:add(SpatialConvolution(nef, nef * 2, 4, 4, 2, 2, 1, 1))
netE:add(SpatialBatchNormalization(nef * 2)):add(nn.LeakyReLU(0.2, true))
-- state size: (nef*2) x 16 x 16
netE:add(SpatialConvolution(nef * 2, nef * 4, 4, 4, 2, 2, 1, 1))
netE:add(SpatialBatchNormalization(nef * 4)):add(nn.LeakyReLU(0.2, true))
-- state size: (nef*4) x 8 x 8
netE:add(SpatialConvolution(nef * 4, nef * 8, 4, 4, 2, 2, 1, 1))
netE:add(SpatialBatchNormalization(nef * 8)):add(nn.LeakyReLU(0.2, true))
-- state size: (nef*8) x 4 x 4
netE:add(SpatialConvolution(nef * 8, nBottleneck, 4, 4))
-- state size: (nBottleneck) x 1 x 1
local netG = nn.Sequential()
local nz_size = nBottleneck
if opt.noiseGen then
local netG_noise = nn.Sequential()
-- input is Z: (nz) x 1 x 1, going into a convolution
netG_noise:add(SpatialConvolution(nz, nz, 1, 1, 1, 1, 0, 0))
-- state size: (nz) x 1 x 1
local netG_pl = nn.ParallelTable();
netG_pl:add(netE)
netG_pl:add(netG_noise)
netG:add(netG_pl)
netG:add(nn.JoinTable(2))
netG:add(SpatialBatchNormalization(nBottleneck+nz)):add(nn.LeakyReLU(0.2, true))
-- state size: (nBottleneck+nz) x 1 x 1
nz_size = nBottleneck+nz
else
netG:add(netE)
netG:add(SpatialBatchNormalization(nBottleneck)):add(nn.LeakyReLU(0.2, true))
nz_size = nBottleneck
end
-- Decode noise to generate image
-- input is Z: (nz_size) x 1 x 1, going into a convolution
netG:add(SpatialFullConvolution(nz_size, ngf * 8, 4, 4))
netG:add(SpatialBatchNormalization(ngf * 8)):add(nn.ReLU(true))
-- state size: (ngf*8) x 4 x 4
netG:add(SpatialFullConvolution(ngf * 8, ngf * 4, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf * 4)):add(nn.ReLU(true))
-- state size: (ngf*4) x 8 x 8
netG:add(SpatialFullConvolution(ngf * 4, ngf * 2, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf * 2)):add(nn.ReLU(true))
-- state size: (ngf*2) x 16 x 16
netG:add(SpatialFullConvolution(ngf * 2, ngf, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf)):add(nn.ReLU(true))
-- state size: (ngf) x 32 x 32
if opt.fineSize == 128 then
netG:add(SpatialFullConvolution(ngf, ngf, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf)):add(nn.ReLU(true))
-- state size: (ngf) x 64 x 64
end
netG:add(SpatialFullConvolution(ngf, nc, 4, 4, 2, 2, 1, 1))
netG:add(nn.Tanh())
-- state size: (nc) x 128 x 128
netG:apply(weights_init)
---------------------------------------------------------------------------
-- Adversarial discriminator net
---------------------------------------------------------------------------
local netD = nn.Sequential()
if opt.conditionAdv then
print('conditional adv not implemented')
exit()
local netD_ctx = nn.Sequential()
-- input Context: (nc) x 128 x 128, going into a convolution
netD_ctx:add(SpatialConvolution(nc, ndf, 5, 5, 2, 2, 2, 2))
-- state size: (ndf) x 64 x 64
local netD_pred = nn.Sequential()
-- input pred: (nc) x 64 x 64, going into a convolution
netD_pred:add(SpatialConvolution(nc, ndf, 5, 5, 2, 2, 2+32, 2+32)) -- 32: to keep scaling of features same as context
-- state size: (ndf) x 64 x 64
local netD_pl = nn.ParallelTable();
netD_pl:add(netD_ctx)
netD_pl:add(netD_pred)
netD:add(netD_pl)
netD:add(nn.JoinTable(2))
netD:add(nn.LeakyReLU(0.2, true))
-- state size: (ndf * 2) x 64 x 64
netD:add(SpatialConvolution(ndf*2, ndf, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf) x 32 x 32
else
-- input is (nc) x 128 x 128, going into a convolution
netD:add(SpatialConvolution(nc, ndf, 4, 4, 2, 2, 1, 1))
netD:add(nn.LeakyReLU(0.2, true))
-- state size: (ndf) x 64 x 64
end
if opt.fineSize == 128 then
netD:add(SpatialConvolution(ndf, ndf, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf) x 32 x 32
end
netD:add(SpatialConvolution(ndf, ndf * 2, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf * 2)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*2) x 16 x 16
netD:add(SpatialConvolution(ndf * 2, ndf * 4, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf * 4)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*4) x 8 x 8
netD:add(SpatialConvolution(ndf * 4, ndf * 8, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf * 8)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*8) x 4 x 4
netD:add(SpatialConvolution(ndf * 8, 1, 4, 4))
netD:add(nn.Sigmoid())
-- state size: 1 x 1 x 1
netD:add(nn.View(1):setNumInputDims(3))
-- state size: 1
netD:apply(weights_init)
---------------------------------------------------------------------------
-- Loss Metrics
---------------------------------------------------------------------------
local criterion = nn.BCECriterion()
local criterionMSE
if opt.wtl2~=0 then
criterionMSE = nn.MSECriterion()
end
---------------------------------------------------------------------------
-- Setup Solver
---------------------------------------------------------------------------
print('LR of Gen is ',(opt.wtl2>0 and opt.wtl2<1) and 10 or 1,'times Adv')
optimStateG = {
learningRate = (opt.wtl2>0 and opt.wtl2<1) and opt.lr*10 or opt.lr,
beta1 = opt.beta1,
}
optimStateD = {
learningRate = opt.lr,
beta1 = opt.beta1,
}
---------------------------------------------------------------------------
-- Initialize data variables
---------------------------------------------------------------------------
local mask_global = torch.ByteTensor(opt.batchSize, opt.fineSize, opt.fineSize)
local input_ctx_vis = torch.Tensor(opt.batchSize, nc, opt.fineSize, opt.fineSize)
local input_ctx = torch.Tensor(opt.batchSize, nc, opt.fineSize, opt.fineSize)
local input_center = torch.Tensor(opt.batchSize, nc, opt.fineSize, opt.fineSize)
local input_real_center
if opt.wtl2~=0 then
input_real_center = torch.Tensor(opt.batchSize, nc, opt.fineSize, opt.fineSize)
end
local noise = torch.Tensor(opt.batchSize, nz, 1, 1)
local label = torch.Tensor(opt.batchSize)
local errD, errG, errG_l2
local epoch_tm = torch.Timer()
local tm = torch.Timer()
local data_tm = torch.Timer()
if pcall(require, 'cudnn') and pcall(require, 'cunn') and opt.gpu>0 then
print('Using CUDNN !')
end
if opt.gpu > 0 then
require 'cunn'
cutorch.setDevice(opt.gpu)
input_ctx_vis = input_ctx_vis:cuda(); input_ctx = input_ctx:cuda(); input_center = input_center:cuda()
noise = noise:cuda(); label = label:cuda()
netG = util.cudnn(netG); netD = util.cudnn(netD)
netD:cuda(); netG:cuda(); criterion:cuda();
if opt.wtl2~=0 then
criterionMSE:cuda(); input_real_center = input_real_center:cuda();
end
end
print('NetG:',netG)
print('NetD:',netD)
-- Generating random pattern
local res = 0.06 -- the lower it is, the more continuous the output will be. 0.01 is too small and 0.1 is too large
local density = 0.25
local MAX_SIZE = 10000
local low_pattern = torch.Tensor(res*MAX_SIZE, res*MAX_SIZE):uniform(0,1):mul(255)
local pattern = image.scale(low_pattern, MAX_SIZE, MAX_SIZE,'bicubic')
low_pattern = nil
pattern:div(255);
pattern = torch.lt(pattern,density):byte() -- 25% 1s and 75% 0s
pattern = pattern:byte()
print('...Random pattern generated')
local parametersD, gradParametersD = netD:getParameters()
local parametersG, gradParametersG = netG:getParameters()
if opt.display then disp = require 'display' end
noise_vis = noise:clone()
if opt.noisetype == 'uniform' then
noise_vis:uniform(-1, 1)
elseif opt.noisetype == 'normal' then
noise_vis:normal(0, 1)
end
---------------------------------------------------------------------------
-- Define generator and adversary closures
---------------------------------------------------------------------------
-- create closure to evaluate f(X) and df/dX of discriminator
local fDx = function(x)
netD:apply(function(m) if torch.type(m):find('Convolution') then m.bias:zero() end end)
netG:apply(function(m) if torch.type(m):find('Convolution') then m.bias:zero() end end)
gradParametersD:zero()
-- train with real
data_tm:reset(); data_tm:resume()
local real_ctx = data:getBatch()
real_center = real_ctx -- view
input_center:copy(real_center)
if opt.wtl2~=0 then
input_real_center:copy(real_center)
end
-- get random mask
local mask, wastedIter
wastedIter = 0
while true do
local x = torch.uniform(1, MAX_SIZE-opt.fineSize)
local y = torch.uniform(1, MAX_SIZE-opt.fineSize)
mask = pattern[{{y,y+opt.fineSize-1},{x,x+opt.fineSize-1}}] -- view, no allocation
local area = mask:sum()*100./(opt.fineSize*opt.fineSize)
if area>20 and area<30 then -- want it to be approx 75% 0s and 25% 1s
-- print('wasted tries: ',wastedIter)
break
end
wastedIter = wastedIter + 1
end
torch.repeatTensor(mask_global,mask,opt.batchSize,1,1)
real_ctx[{{},{1},{},{}}][mask_global] = 2*117.0/255.0 - 1.0
real_ctx[{{},{2},{},{}}][mask_global] = 2*104.0/255.0 - 1.0
real_ctx[{{},{3},{},{}}][mask_global] = 2*123.0/255.0 - 1.0
input_ctx:copy(real_ctx)
data_tm:stop()
label:fill(real_label)
local output
if opt.conditionAdv then
output = netD:forward({input_ctx,input_center})
else
output = netD:forward(input_center)
end
local errD_real = criterion:forward(output, label)
local df_do = criterion:backward(output, label)
if opt.conditionAdv then
netD:backward({input_ctx,input_center}, df_do)
else
netD:backward(input_center, df_do)
end
-- train with fake
if opt.noisetype == 'uniform' then -- regenerate random noise
noise:uniform(-1, 1)
elseif opt.noisetype == 'normal' then
noise:normal(0, 1)
end
local fake
if opt.noiseGen then
fake = netG:forward({input_ctx,noise})
else
fake = netG:forward(input_ctx)
end
input_center:copy(fake)
label:fill(fake_label)
local output
if opt.conditionAdv then
output = netD:forward({input_ctx,input_center})
else
output = netD:forward(input_center)
end
local errD_fake = criterion:forward(output, label)
local df_do = criterion:backward(output, label)
if opt.conditionAdv then
netD:backward({input_ctx,input_center}, df_do)
else
netD:backward(input_center, df_do)
end
errD = errD_real + errD_fake
return errD, gradParametersD
end
-- create closure to evaluate f(X) and df/dX of generator
local fGx = function(x)
netD:apply(function(m) if torch.type(m):find('Convolution') then m.bias:zero() end end)
netG:apply(function(m) if torch.type(m):find('Convolution') then m.bias:zero() end end)
gradParametersG:zero()
--[[ the three lines below were already executed in fDx, so save computation
noise:uniform(-1, 1) -- regenerate random noise
local fake = netG:forward({input_ctx,noise})
input_center:copy(fake) ]]--
label:fill(real_label) -- fake labels are real for generator cost
local output = netD.output -- netD:forward({input_ctx,input_center}) was already executed in fDx, so save computation
errG = criterion:forward(output, label)
local df_do = criterion:backward(output, label)
local df_dg
if opt.conditionAdv then
df_dg = netD:updateGradInput({input_ctx,input_center}, df_do)
df_dg = df_dg[2] -- df_dg[2] because conditional GAN
else
df_dg = netD:updateGradInput(input_center, df_do)
end
local errG_total = errG
if opt.wtl2~=0 then
errG_l2 = criterionMSE:forward(input_center, input_real_center)
local df_dg_l2 = criterionMSE:backward(input_center, input_real_center)
if opt.useOverlapPred==0 then
if (opt.wtl2>0 and opt.wtl2<1) then
df_dg:mul(1-opt.wtl2):add(opt.wtl2,df_dg_l2)
errG_total = (1-opt.wtl2)*errG + opt.wtl2*errG_l2
else
df_dg:add(opt.wtl2,df_dg_l2)
errG_total = errG + opt.wtl2*errG_l2
end
else
local overlapL2Weight = 10
local wtl2Matrix = df_dg_l2:clone():fill(overlapL2Weight*opt.wtl2)
for i=1,3 do
wtl2Matrix[{{},{i},{},{}}][mask_global] = opt.wtl2
end
if (opt.wtl2>0 and opt.wtl2<1) then
df_dg:mul(1-opt.wtl2):addcmul(1,wtl2Matrix,df_dg_l2)
errG_total = (1-opt.wtl2)*errG + opt.wtl2*errG_l2
else
df_dg:addcmul(1,wtl2Matrix,df_dg_l2)
errG_total = errG + opt.wtl2*errG_l2
end
end
end
if opt.noiseGen then
netG:backward({input_ctx,noise}, df_dg)
else
netG:backward(input_ctx, df_dg)
end
return errG_total, gradParametersG
end
---------------------------------------------------------------------------
-- Train Context Encoder
---------------------------------------------------------------------------
for epoch = 1, opt.niter do
epoch_tm:reset()
local counter = 0
for i = 1, math.min(data:size(), opt.ntrain), opt.batchSize do
tm:reset()
-- (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
optim.adam(fDx, parametersD, optimStateD)
-- (2) Update G network: maximize log(D(G(z)))
optim.adam(fGx, parametersG, optimStateG)
-- display
counter = counter + 1
if counter % opt.display_iter == 0 and opt.display then
local real_ctx = data:getBatch()
-- disp.image(real_ctx, {win=opt.display_id * 6, title=opt.name})
local mask, wastedIter
wastedIter = 0
while true do
local x = torch.uniform(1, MAX_SIZE-opt.fineSize)
local y = torch.uniform(1, MAX_SIZE-opt.fineSize)
mask = pattern[{{y,y+opt.fineSize-1},{x,x+opt.fineSize-1}}] -- view, no allocation
local area = mask:sum()*100./(opt.fineSize*opt.fineSize)
if area>20 and area<30 then -- want it to be approx 75% 0s and 25% 1s
-- print('wasted tries: ',wastedIter)
break
end
wastedIter = wastedIter + 1
end
mask=torch.repeatTensor(mask,opt.batchSize,1,1)
real_ctx[{{},{1},{},{}}][mask] = 2*117.0/255.0 - 1.0
real_ctx[{{},{2},{},{}}][mask] = 2*104.0/255.0 - 1.0
real_ctx[{{},{3},{},{}}][mask] = 2*123.0/255.0 - 1.0
input_ctx_vis:copy(real_ctx)
local fake
if opt.noiseGen then
fake = netG:forward({input_ctx_vis,noise_vis})
else
fake = netG:forward(input_ctx_vis)
end
disp.image(fake, {win=opt.display_id, title=opt.name})
real_ctx[{{},{1},{},{}}][mask] = 1.0
real_ctx[{{},{2},{},{}}][mask] = 1.0
real_ctx[{{},{3},{},{}}][mask] = 1.0
disp.image(real_ctx, {win=opt.display_id * 3, title=opt.name})
end
-- logging
if ((i-1) / opt.batchSize) % 1 == 0 then
print(('Epoch: [%d][%8d / %8d]\t Time: %.3f DataTime: %.3f '
.. ' Err_G_L2: %.4f Err_G: %.4f Err_D: %.4f'):format(
epoch, ((i-1) / opt.batchSize),
math.floor(math.min(data:size(), opt.ntrain) / opt.batchSize),
tm:time().real, data_tm:time().real, errG_l2 or -1,
errG and errG or -1, errD and errD or -1))
end
end
paths.mkdir('checkpoints')
parametersD, gradParametersD = nil, nil -- nil them to avoid spiking memory
parametersG, gradParametersG = nil, nil
if epoch % 20 == 0 then
util.save('checkpoints/' .. opt.name .. '_' .. epoch .. '_net_G.t7', netG, opt.gpu)
util.save('checkpoints/' .. opt.name .. '_' .. epoch .. '_net_D.t7', netD, opt.gpu)
end
parametersD, gradParametersD = netD:getParameters() -- reflatten the params and get them
parametersG, gradParametersG = netG:getParameters()
print(('End of epoch %d / %d \t Time Taken: %.3f'):format(
epoch, opt.niter, epoch_tm:time().real))
end
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