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adversarial_xin_Attr_AE_Stack_perception.lua
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adversarial_xin_Attr_AE_Stack_perception.lua
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require 'torch'
require 'nn'
require 'cunn'
require 'optim'
require 'pl'
local adversarial = {}
function randomChange_Attr(attrs)
local num = attrs:size(1)
local attr_num = attrs:size(2)
local res = attrs:clone()
torch.setdefaulttensortype('torch.FloatTensor')
for i = 1, num do
if res[i][attr_num] == 0 then
local idx = attr_num
res[i][idx] = 1-res[i][idx]
else
while true do
local idx = torch.random(1, ntrain)
idx = torch.LongTensor{idx}
if attrs[i] ~= dataset_Attr:index(1, idx) then
res[i] = dataset_Attr:index(1, idx):clone()
break
end
end
end
end
torch.setdefaulttensortype('torch.CudaTensor')
return res
end
function reArrangeBatch(IDX, data_attr)
local attrs = data_attr:index(1, IDX)
local bs = attrs:size(1)
local nattr = attrs:size(2)
local IDX_Batch = IDX:clone()
local tmp = torch.eq(attrs[{{}, nattr}], 0)
-- print(tmp)
if torch.any(tmp) then
local res = IDX[tmp]
local sz = res:size(1)
IDX_Batch[{{1, sz}}] = IDX[torch.eq(attrs[{{}, nattr}], 0)]:clone()
IDX_Batch[{{sz+1, bs}}] = IDX[torch.eq(attrs[{{}, nattr}], 1)]:clone()
end
return IDX_Batch
end
function rmsprop(opfunc, x, config, state)
-- (0) get/update state
local config = config or {}
local state = state or config
local lr = config.learningRate or 1e-2
local alpha = config.alpha or 0.9
local epsilon = config.epsilon or 1e-8
-- (1) evaluate f(x) and df/dx
local fx, dfdx = opfunc(x)
if config.optimize == true then
-- (2) initialize mean square values and square gradient storage
if not state.m then
state.m = torch.Tensor():typeAs(x):resizeAs(dfdx):zero()
state.tmp = torch.Tensor():typeAs(x):resizeAs(dfdx)
end
-- (3) calculate new (leaky) mean squared values
state.m:mul(alpha)
state.m:addcmul(1.0-alpha, dfdx, dfdx)
-- (4) perform update
state.tmp:sqrt(state.m):add(epsilon)
-- only opdate when optimize is true
if config.numUpdates < 50 then
--io.write(" ", lr/50.0, " ")
x:addcdiv(-lr/50.0, dfdx, state.tmp)
elseif config.numUpdates < 100 then
--io.write(" ", lr/5.0, " ")
x:addcdiv(-lr /5.0, dfdx, state.tmp)
else
--io.write(" ", lr, " ")
x:addcdiv(-lr, dfdx, state.tmp)
end
end
config.numUpdates = config.numUpdates +1
-- return x*, f(x) before optimization
return x, {fx}
end
function adam(opfunc, x, config, state)
--print('ADAM')
-- (0) get/update state
local config = config or {}
local state = state or config
local lr = config.learningRate or 0.001
local beta1 = config.beta1 or 0.9
local beta2 = config.beta2 or 0.999
local epsilon = config.epsilon or 1e-8
-- (1) evaluate f(x) and df/dx
local fx, dfdx = opfunc(x)
if config.optimize == true then
-- Initialization
state.t = state.t or 0
-- Exponential moving average of gradient values
state.m = state.m or x.new(dfdx:size()):zero()
-- Exponential moving average of squared gradient values
state.v = state.v or x.new(dfdx:size()):zero()
-- A tmp tensor to hold the sqrt(v) + epsilon
state.denom = state.denom or x.new(dfdx:size()):zero()
state.t = state.t + 1
-- Decay the first and second moment running average coefficient
state.m:mul(beta1):add(1-beta1, dfdx)
state.v:mul(beta2):addcmul(1-beta2, dfdx, dfdx)
state.denom:copy(state.v):sqrt():add(epsilon)
local biasCorrection1 = 1 - beta1^state.t
local biasCorrection2 = 1 - beta2^state.t
local fac = 1
if config.numUpdates < 10 then
fac = 50.0
elseif config.numUpdates < 30 then
fac = 5.0
else
fac = 1.0
end
io.write(" ", lr/fac, " ")
local stepSize = (lr/fac) * math.sqrt(biasCorrection2)/biasCorrection1
-- (2) update x
x:addcdiv(-stepSize, state.m, state.denom)
end
config.numUpdates = config.numUpdates +1
-- return x*, f(x) before optimization
return x, {fx}
end
-- training function
function adversarial.train(dataset_HR, dataset_LR, dataset_attr)
model_G:training()
model_D:training()
epoch = epoch or 0
local N = N or ntrain
local dataBatchSize = opt.batchSize / 2
local time = sys.clock()
local err_gen = 0
local inputs = torch.Tensor(dataBatchSize*3, opt.geometry[1], opt.geometry[2], opt.geometry[3])
local targets_D = torch.Tensor(dataBatchSize*3)
local inputs_attr_D = torch.Tensor(dataBatchSize*3, nAttr)
local inputs_HR = torch.Tensor(dataBatchSize*2, opt.geometry[1], opt.geometry[2], opt.geometry[3])
local inputs_LR = torch.Tensor(dataBatchSize*2, opt.geometry[1], 16, 16)
local inputs_Attr = torch.Tensor(dataBatchSize*2, nAttr)
local targets_G = torch.Tensor(dataBatchSize*2)
-- do one epoch
print('\n<trainer> on training set:')
print("<trainer> online epoch # " .. epoch .. ' [batchSize = ' .. opt.batchSize .. ' lr = ' .. sgdState_D.learningRate .. ']')
for t = 1,N,opt.batchSize do --dataBatchSize do
IDX_Batch = reArrangeBatch(IDX[{{t, t+dataBatchSize*2-1}}], dataset_attr)
collectgarbage()
inputs_HR = dataset_HR:index(IDX_Batch):clone()
inputs_HR = inputs_HR:index(2, torch.LongTensor{3,2,1}):cuda()
inputs_HR:mul(2):add(-1)
inputs_LR:copy(dataset_LR:index(1, IDX_Batch))
inputs_Attr:copy(dataset_attr:index(1, IDX_Batch))
-- inputs_Attr:mul(2):add(-1)
-- targets_Attr:copy(dataset_attr:index(1, IDX_Batch))
----------------------------------------------------------------------
-- create closure to evaluate f(X) and df/dX of discriminator
local fevalD = function(x)
collectgarbage()
if x ~= parameters_D then -- get new parameters
parameters_D:copy(x)
end
gradParameters_D:zero() -- reset gradients
-- forward pass
local outputs = model_D:forward({inputs, inputs_attr_D})
if sgdState_D.trade_off then
err_R = criterion_D:forward(outputs:narrow(1, 1, dataBatchSize), targets_D:narrow(1, 1, dataBatchSize))
err_F = criterion_D:forward(outputs:narrow(1, (opt.batchSize / 2) + 1, dataBatchSize*2), targets_D:narrow(1, (opt.batchSize / 2) + 1, dataBatchSize*2 ))
local margin = opt.margin -- org = 0.3
sgdState_D.optimize = true
sgdState_G.optimize = true
if err_F < margin or err_R < margin then
sgdState_D.optimize = false
end
if err_F > (1.0-margin) or err_R > (1.0-margin) then
sgdState_G.optimize = false
end
if sgdState_G.optimize == false and sgdState_D.optimize == false then
sgdState_G.optimize = true
sgdState_D.optimize = true
end
end
--io.write("v1_ytc| R:", err_R," F:", err_F, " ")
local f1 = criterion_D:forward(outputs, targets_D)
-- backward pass
local df1_do = criterion_D:backward(outputs, targets_D)
model_D:backward({inputs, inputs_attr_D}, df1_do)
-- penalties (L1 and L2):
if opt.coefL1 ~= 0 or opt.coefL2 ~= 0 then
local norm,sign= torch.norm,torch.sign
-- Loss:
f = f + opt.coefL1 * norm(parameters_D,1)
f = f + opt.coefL2 * norm(parameters_D,2)^2/2
-- Gradients:
gradParameters_D:add( sign(parameters_D):mul(opt.coefL1) + parameters_D:clone():mul(opt.coefL2) )
end
--print('grad D', gradParameters_D:norm())
return f,gradParameters_D
end
----------------------------------------------------------------------
-- create closure to evaluate f(X) and df/dX of generator
local fevalG = function(x)
collectgarbage()
if x ~= parameters_G then -- get new parameters
parameters_G:copy(x)
end
gradParameters_G:zero() -- reset gradients
-- forward pass
local samples = model_G:forward({inputs_LR, inputs_Attr})
local g = criterion_G:forward(samples, inputs_HR)
local samples_percep = preprocess_image(samples:clone())
local inputs_HR_percep = preprocess_image(inputs_HR:clone())
local g_percp = PerceptionLoss:forward(samples_percep, inputs_HR_percep)
err_gen = err_gen + g
local outputs = model_D:forward({samples, inputs_Attr})
local f1 = criterion_D:forward(outputs, targets_G)
-- backward pass
local df1_samples = criterion_D:backward(outputs, targets_G)
model_D:backward({samples, inputs_Attr}, df1_samples)
local df_G_samples = criterion_G:backward(samples, inputs_HR) ---added by xin
local df_vgg = PerceptionLoss:backward(samples_percep, inputs_HR_percep):div(127.5)
local df_do = model_D.modules[1].modules[1].modules[1].gradInput * opt.lambda + df_G_samples + df_vgg * opt.eta
model_G:backward({inputs_LR, inputs_Attr}, df_do)
-- print('gradParameters_G', gradParameters_G:norm())
return f,gradParameters_G
end
----------------------------------------------------------------------
-- (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
-- Get half a minibatch of real, half fake
-- (1.1) Real data
local Part1 = dataBatchSize
local Part2 = dataBatchSize*2
local Part3 = dataBatchSize*3
inputs[{{1,Part1}}] = inputs_HR[{{1,Part1}}]:clone()
inputs_attr_D[{{1, Part1}}] = inputs_Attr[{{1,Part1}}]:clone()
-- generate images: one quater of minibatch
local samples_LR = inputs_LR[{{1,Part1}}]
local samples_Attr = inputs_Attr[{{1,Part1}}]
samples = model_G:forward({samples_LR:cuda(), samples_Attr:cuda()})
inputs[{{Part1+1, Part2}}] = samples:clone()
inputs_attr_D[{{Part1+1, Part2}}] = inputs_Attr[{{1,Part1}}]:clone()
inputs[{{Part2+1, Part3}}] = inputs_HR[{{1, Part1}}]:clone()
inputs_attr_D[{{Part2+1, Part3}}] = randomChange_Attr(inputs_Attr[{{1, Part1}}]):clone()
targets_D[{{1,dataBatchSize}}]:fill(1)
targets_D[{{dataBatchSize+1, dataBatchSize*3}}]:fill(0)
rmsprop(fevalD, parameters_D, sgdState_D)
----------------------------------------------------------------------
-- (2) Update G network: maximize log(D(G(z)))
-- noise_inputs:normal(0, 1)
targets_G:fill(1)
rmsprop(fevalG, parameters_G, sgdState_G)
-- display progress
nIter = nIter + 1
if (nIter-1) % opt.saveFreq == 0 then
netname = string.format('adversarial_net_%s',nIter)
local filename = paths.concat(opt.save, netname)
os.execute('mkdir -p ' .. sys.dirname(filename))
print('<trainer> saving network to '..filename)
-- model_D:clearState()
model_G:clearState()
-- for i, module in ipairs(model_G:listModules()) do
-- module:clearState()
-- end
torch.save(filename, {G = model_G})
--local to_plot = getSamples(valData_LR, dataset_attr, 100)
local to_plot = getSamples_compare_attrflip(valData_LR, dataset_HR, dataset_attr, 50)
torch.setdefaulttensortype('torch.FloatTensor')
local formatted = image.toDisplayTensor({input = to_plot, nrow = 15})
formatted:float()
formatted = formatted:index(1,torch.LongTensor{3,2,1})
image.save(opt.save .. '/UR_example_' .. (nIter or 0) .. '.png', formatted)
torch.setdefaulttensortype('torch.CudaTensor')
end
-- display progress
xlua.progress(t, ntrain)
end -- end for loop over dataset
-- time taken
time = sys.clock() - time
time = time / ntrain
print("<trainer> time to learn 1 sample = " .. (time*1000) .. 'ms')
-- print confusion matrix
--print(confusion)
trainLogger:add{['MSE accuarcy1'] = err_gen/(ntrain/opt.batchSize)}
-- next epoch
epoch = epoch + 1
end
return adversarial