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URnet.lua
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URnet.lua
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require 'torch'
require 'nn'
require 'cunn'
require 'optim'
require 'pl'
local URnet = {}
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 URnet.train(dataset_HR, dataset_LR_HDF5, dataset_attr)
model_G: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(opt.batchSize, opt.geometry[1], opt.geometry[2], opt.geometry[3])
local targets = torch.Tensor(opt.batchSize)
local inputs_HR = torch.Tensor(opt.batchSize, opt.geometry[1], opt.geometry[2], opt.geometry[3])
local inputs_LR = torch.Tensor(opt.batchSize, opt.geometry[1], 16, 16)
-- do one epoch
print('\n<trainer> on training set:')
print("<trainer> online epoch # " .. epoch .. ' [batchSize = ' .. opt.batchSize .. ' lr = ' .. sgdState_G.learningRate .. ']')
for t = 1,N,opt.batchSize do --dataBatchSize do
----------------------------------------------------------------------
-- create closure to evaluate f(X) and df/dX of discriminator
----------------------------------------------------------------------
-- 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)
local g = criterion_G:forward(samples, inputs_HR)
err_gen = err_gen + g
-- local samples_percep = preprocess_image(samples:clone())
-- local inputs_HR_percep = preprocess_image(inputs_HR:clone())
-- local err_percep = PerceptionLoss:forward(samples_percep, inputs_HR_percep)
--io.write("G:",f+g, " G:", tostring(sgdState_G.optimize)," D:",tostring(sgdState_D.optimize)," ", sgdState_G.numUpdates, " ", sgdState_D.numUpdates , "\n")
--io.flush()
-- backward pass
local df_G_samples = criterion_G:backward(samples, inputs_HR) ---added by xin
-- local df_vgg = PerceptionLoss:backward(samples_percep, inputs_HR_percep)
local df_do = df_G_samples --+ df_vgg * opt.eta
model_G:backward(inputs_LR, df_do)
-- print('gradParameters_G', gradParameters_G:norm())
return f,gradParameters_G
end
----------------------------------------------------------------------
----------------------------------------------------------------------
-- (2) Update G network: maximize log(D(G(z)))
-- noise_inputs:normal(0, 1)
local sample_HR = dataset_HR:index(IDX[{{t, t+opt.batchSize-1}}])
sample_HR:mul(2):add(-1)
sample_HR = sample_HR:index(2, torch.LongTensor{3,2,1})
local k = 1
for i = t, t+opt.batchSize-1 do
inputs_HR[k] = sample_HR[k]:clone()
inputs_LR[k] = dataset_LR_HDF5[IDX[i]]:clone()
k = k+1
end
targets:fill(1)
rmsprop(fevalG, parameters_G, sgdState_G)
nIter = nIter + 1
if (nIter-1) % opt.saveFreq == 0 then
netname = string.format('UR_net_%s',nIter)
local filename = paths.concat(opt.save, netname)
os.execute('mkdir -p ' .. sys.dirname(filename))
print('<trainer> saving network to '..filename)
model_G:clearState()
torch.save(filename, {G = model_G})
local to_plot = getSamples_compare(valData_LR, dataset_HR, dataset_attr, 50)
torch.setdefaulttensortype('torch.FloatTensor')
local formatted = image.toDisplayTensor({input = to_plot, nrow = 10})
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 URnet