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train_ytc_xin_128_stn_diff_layers_Dwithdecay_TAE_decoder.lua
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train_ytc_xin_128_stn_diff_layers_Dwithdecay_TAE_decoder.lua
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------------------------------------------------------------
--- This code is based on the eyescream code released at
--- https://github.com/facebook/eyescream
--- If you find it usefull consider citing
--- http://arxiv.org/abs/1506.05751
------------------------------------------------------------
require 'hdf5'
require 'nngraph'
require 'torch'
require 'nn'
require 'cunn'
require 'optim'
require 'image'
require 'pl'
require 'paths'
require 'cudnn'
ok, disp = pcall(require, 'display')
if not ok then print('display not found. unable to plot') end
--adversarial = require 'adverserial_xin_v1_D_revise'
URnet = require 'adverserial_xin_v1_D_revise'
stn_L1 = require 'stn_L1_org'
stn_L2 = require 'stn_L2_org'
----------------------------------------------------------------------
-- parse command-line options
opt = lapp[[
-s,--save (default "logs128_ytc16_stn_D_decay_TAE_decoder") subdirectory to save logs
--saveFreq (default 1) save every saveFreq epochs
-n,--network (default "") reload pretrained network
-p,--plot plot while training
-r,--learningRate (default 0.001) learning rate
-b,--batchSize (default 64) batch size
-m,--momentum (default 0) momentum, for SGD only
--coefL1 (default 0) L1 penalty on the weights
--coefL2 (default 0) L2 penalty on the weights
-t,--threads (default 4) number of threads
-g,--gpu (default 0) gpu to run on (default cpu)
-d,--noiseDim (default 512) dimensionality of noise vector
--K (default 1) number of iterations to optimize D for
-w, --window (default 3) windsow id of sample image
--scale (default 128) scale of images to train on
--lambda (default 0.01) trade off D and Euclidean distance
--margin (default 0.3) trade off D and G
]]
if opt.gpu < 0 or opt.gpu > 3 then opt.gpu = false end
print(opt)
ntrain = 29952
nval = 1024
local highHd5 = hdf5.open('datasets/YTC_HR_rotate_v5.hdf5', 'r')
local data_HR = highHd5:read('YTC'):all()
data_HR:mul(2):add(-1)
highHd5:close()
trainData_HR = data_HR[{{1, ntrain}}]
valData_HR = data_HR[{{ntrain+nval+1, 2*nval+ntrain}}]
-- output of TAE, generate from the training dataset by TAE
local lowHd5 = hdf5.open('datasets/YTC_TAE_intermediate.hdf5', 'r')
local data_LR = lowHd5:read('YTC'):all()
data_LR:mul(2):add(-1)
lowHd5:close()
trainData_LR = data_LR[{{1, ntrain}}]
valData_LR = data_LR[{{ntrain+nval+1, 2*nval+ntrain}}]
-- fix seed
torch.manualSeed(1)
-- threads
torch.setnumthreads(opt.threads)
print('<torch> set nb of threads to ' .. torch.getnumthreads())
if opt.gpu then
cutorch.setDevice(opt.gpu + 1)
print('<gpu> using device ' .. opt.gpu)
torch.setdefaulttensortype('torch.CudaTensor')
else
torch.setdefaulttensortype('torch.FloatTensor')
end
input_scale = 16
opt.scale = valData_HR:size(4)
print(opt.scale)
opt.geometry = {3, opt.scale, opt.scale}
local input_sz = opt.geometry[1] * opt.geometry[2] * opt.geometry[3]
if opt.network == '' then
model_D = nn.Sequential()
model_D:add(cudnn.SpatialConvolution(3, 32, 5, 5, 1, 1, 2, 2))
model_D:add(cudnn.SpatialMaxPooling(2,2))
model_D:add(cudnn.ReLU(true))
model_D:add(nn.SpatialDropout(0.2))
model_D:add(cudnn.SpatialConvolution(32, 64, 5, 5, 1, 1, 2, 2))
model_D:add(cudnn.SpatialMaxPooling(2,2))
model_D:add(cudnn.ReLU(true))
model_D:add(nn.SpatialDropout(0.2))
model_D:add(cudnn.SpatialConvolution(64, 128, 5, 5, 1, 1, 2, 2))
model_D:add(cudnn.SpatialMaxPooling(2,2))
model_D:add(cudnn.ReLU(true))
model_D:add(nn.SpatialDropout(0.2))
model_D:add(cudnn.SpatialConvolution(128, 96, 5, 5, 1, 1, 2, 2))
model_D:add(cudnn.ReLU(true))
model_D:add(cudnn.SpatialMaxPooling(2,2))
model_D:add(nn.SpatialDropout(0.2))
model_D:add(nn.Reshape(8*8*96))
model_D:add(nn.Linear(8*8*96, 1024))
model_D:add(cudnn.ReLU(true))
model_D:add(nn.Dropout())
model_D:add(nn.Linear(1024,1))
model_D:add(nn.Sigmoid())
----------------------------------------------------------------------
model_G = nn.Sequential()
model_G:add(cudnn.SpatialConvolution(3, 512, 3, 3, 1, 1, 1, 1))
model_G:add(cudnn.SpatialBatchNormalization(512))
model_G:add(cudnn.ReLU(true))
model_G:add(nn.SpatialUpSamplingNearest(2))
model_G:add(cudnn.SpatialConvolution(512,256, 3, 3, 1, 1, 1, 1))
model_G:add(cudnn.SpatialBatchNormalization(256))
model_G:add(cudnn.ReLU(true))
model_G:add(nn.SpatialUpSamplingNearest(2))
model_G:add(cudnn.SpatialConvolution(256, 128, 5, 5, 1, 1, 2, 2))
model_G:add(cudnn.SpatialBatchNormalization(128))
model_G:add(cudnn.ReLU(true))
model_G:add(nn.SpatialUpSamplingNearest(2))
model_G:add(cudnn.SpatialConvolution(128, 64, 5, 5, 1, 1, 2, 2))
model_G:add(cudnn.SpatialBatchNormalization(64))
model_G:add(cudnn.ReLU(true))
model_G:add(cudnn.SpatialConvolution(64, 3, 5, 5, 1, 1, 2, 2))
else
print('<trainer> reloading previously trained network: ' .. opt.network)
tmp = torch.load(opt.network)
model_D = tmp.D
model_G = tmp.G
end
model_G:cuda() -- convert model to CUDA
-- loss function: negative log-likelihood
criterion_D = nn.BCECriterion()
criterion_G = nn.MSECriterion()
-- retrieve parameters and gradients
parameters_D,gradParameters_D = model_D:getParameters()
parameters_G,gradParameters_G = model_G:getParameters()
-- print networks
print('Discriminator network:')
print(model_D)
print('Generator network:')
print(model_G)
-- log results to files
trainLogger = optim.Logger(paths.concat(opt.save, 'train.log'))
--testLogger = optim.Logger(paths.concat(opt.save, 'test.log'))
if opt.gpu then
print('Copy model to gpu')
model_D:cuda()
model_G:cuda()
end
-- Training parameters
sgdState_D = {
learningRate = opt.learningRate,
momentum = opt.momentum,
optimize=true,
numUpdates = 0
}
sgdState_G = {
learningRate = opt.learningRate,
momentum = opt.momentum,
optimize=true,
numUpdates=0
}
-- Get examples to plot
function getSamples(dataset, N)
local N = N or 10
local dataset_HR = dataset
local inputs = torch.Tensor(N,3,16,16)
for i = 1,N do
--idx = math.random(nval)
--inputs[i] = image.scale(torch.squeeze(dataset_HR[i]),16,16)
inputs[i] = dataset[i]
end
local samples = model_G:forward(inputs)
local samples = nn.HardTanh():forward(samples)
local to_plot = {}
for i = 1,N do
to_plot[#to_plot+1] = samples[i]:float()
end
return to_plot
end
--while true do
for ii = 1,200 do
local to_plot = getSamples(valData_LR,100)
torch.setdefaulttensortype('torch.FloatTensor')
trainLogger:style{['MSE accuarcy1'] = '-'}
-- trainLogger:style{['MSE accuarcy'] = '-'}
trainLogger:plot()
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_' .. (epoch or 0) .. '.png', formatted)
IDX = torch.randperm(ntrain)
if opt.gpu then
torch.setdefaulttensortype('torch.CudaTensor')
else
torch.setdefaulttensortype('torch.FloatTensor')
end
URnet.train(trainData_LR,trainData_HR)
sgdState_D.momentum = math.min(sgdState_D.momentum + 0.0008, 0.7)
sgdState_D.learningRate = math.max(opt.learningRate*0.99^epoch, 0.000001)
sgdState_G.momentum = math.min(sgdState_G.momentum + 0.0008, 0.7)
sgdState_G.learningRate = math.max(opt.learningRate*0.99^epoch, 0.000001)
opt.lambda = math.max(opt.lambda*0.99, 0.005) -- or 0.995
end