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train.lua
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train.lua
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--------------------------------------------------------
-- Torch Implementation of Looking back at Labels: A Class based Domain Adaptation Technique
--- Written By Vinod Kumar Kurmi ([email protected])
require 'cutorch'
require 'cunn'
require 'optim'
require 'gnuplot'
require 'loadcaffe'
require 'image';
require 'torch';
require 'nn';
require 'xlua'
require 'loadcaffe'
require 'cudnn'
require '../../../../../../../NNLR/misc/nnlr/nnlr' --- for layer wise learnig rate
----------------------------------------------------
local c = require 'trepl.colorize'
local data_tm = torch.Timer()
----------------------------------------------------
opt = {
manual_seed=1, -- Seed
batchSize = 64, -- batch Size
Test_batchSize = 64, -- Test time batch size, it may change after the last epoch of test data
start_Batch_IndexTest=1, --batch index at time of testing
loadSize = 256, -- resize the loaded image to loadsize maintaining aspect ratio. -- see donkey_folder.lua
fineSize = 227, -- size of random crops
nc = 3, -- # of channels in input
nThreads = 1, -- # of data loading threads to use
gpu = 1, -- gpu = 0 is CPU mode. gpu=X is GPU mode on GPU X
save='logs/', -- Saving the logs of trainining
net1_freeze='yes', -- For not updating the first 3 Conv layer
--momentum
number_of_testclass=31, -- number of class in test dataset, in general it is =source class but we can use less class also.
lamda=1, -- Lamda value for gradeint reversal value.(fix)
momentum=0.9,
baseLearningRate=0.0002,
max_epoch=2000,
gamma=0.001, -- for inverse policy : base_lr * (1 + gamma * iter) ^ (- power)
power=0.75, -- for inverse policy : base_lr * (1 + gamma * iter) ^ (- power)
max_epoch_grl=10000, -- For progress in process , calculate the lamda for grl
alpha=10, -- LR schdular (2nd way)
}
cutorch.manualSeed(opt.manual_seed)
torch.manualSeed(opt.manual_seed)
--=====================Tuning Parameters===================================
local prev_accuracy=0
batchSize =opt.batchSize
opt.save=opt.save .. 'batchsize_' .. opt.batchSize
torch.setnumthreads(1)
torch.setdefaulttensortype('torch.FloatTensor')
--==============Ploting Fuction=============================================================================
confusion = optim.ConfusionMatrix({'letter_tray','paper_notebook','printer','bike_helmet','desk_lamp','mobile_phone',
'desk_chair','pen','phone','headphones','ring_binder','tape_dispenser','bookcase','back_pack','laptop_computer','stapler',
'ruler','mouse','projector','trash_can','monitor','file_cabinet','speaker','punchers','desktop_computer','bottle',
'mug','keyboard','scissors','bike','calculator'})
print('Will save at '..opt.save)
paths.mkdir(opt.save)
testLogger = optim.Logger(paths.concat(opt.save, 'test.log'))
testLogger:setNames{'% mean class accuracy (train set)', '% mean class accuracy (test set)'}
testLogger.showPlot = false
errorlog = optim.Logger(paths.concat(opt.save, 'error.log'))
errorlog:setNames{'% Training Error (train set)', '% Testing Error(test set)'}
errorlog.showPlot = false
--==========================================================================================================
----------------------------------------------------
--Path Initilication
prototxt_name = 'pretrained_network/deploy.prototxt'
binary_name = 'pretrained_network/bvlc_alexnet.caffemodel'
net_orignal = loadcaffe.load(prototxt_name, binary_name,'cudnn');
print(' net_orignal', net_orignal)
---------------------------------------------------------
-- create Train data loader
local DataLoader = paths.dofile('data/data.lua')
local data = DataLoader.new(opt.nThreads, opt)
print("Train Dataset Size: ", data:size())
-- create Val data loader
local DataLoaderVal = paths.dofile('data/data_target.lua')
local dataVal = DataLoaderVal.new(opt.nThreads, opt)
print("Val Dataset Size: ", dataVal:size())
-- create Test data loader
local DataLoaderTest = paths.dofile('data_test/data.lua')
local dataTest = DataLoaderTest.new(0, opt)
print("test new Dataset Size: ", dataTest:size())
----------------------------------------------------
--===FUNCTIONS==============
function uti(filename)
local net = torch.load(filename)
net:apply(function(m) if m.weight then
m.gradWeight = m.weight:clone():zero();
m.gradBias = m.bias:clone():zero(); end end)
return net
end
function check_accuracy(scores, targets)
local num_test = (#targets)[1]
local no_correct = 0
local confidences, indices = torch.sort(scores, true)
local predicted_classes = indices[{{},{1}}]:long()
targets = targets:long()
no_correct = no_correct + ((torch.squeeze(predicted_classes):eq(targets)):sum())
local accuracy = no_correct / num_test
return accuracy
end
function check_accuracyTest(scores, targets)
local num_test = (#targets)[1]
local no_correct = 0
local confidences, indices = torch.sort(scores, true)
local predicted_classes = indices[{{},{1}}]:long()
targets = targets:long()
no_correct = no_correct + ((torch.squeeze(predicted_classes):eq(targets)):sum())
local accuracy = no_correct
return accuracy
end
--=======================Model==========================================================================
--------------Load Alexnet Pretrained Netwok
model = nn.Sequential()
--------Map table for two stream input(one for source data another for target data)---------------
net1= nn.MapTable()
net2= nn.MapTable()
net3= nn.MapTable()
net4= nn.MapTable()
netB= nn.MapTable()
netD= nn.MapTable()
net11= nn.Sequential()
net22= nn.Sequential()
net33= nn.Sequential()
net44= nn.Sequential()
netDD= nn.Sequential()
netBB= nn.Sequential()
-- Layer by layer copy from the pretrained Alexnet Netwrok
for i, module in ipairs( net_orignal.modules) do
if(i<11) then
if(i==1) then
module:setMode(1,1,1) -- For making determinstic the cudnn convolution layers
net11:add(module):learningRate('weight', 1) -- Layer wise learning rate
:learningRate('bias', 2)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --conv1
elseif (i==4) then
max1 = nn.SpatialMaxPooling(3, 3, 2,2) --Replace cudnn.maxpooling-->nn.maxpooling for deterministic response
net11:add(max1)
elseif (i==5) then
module:setMode(1,1,1) -- For making determinstic the cudnn convolution layers
net11:add(module):learningRate('weight', 1)
:learningRate('bias', 2)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --conv2
elseif (i==8) then
max2 = nn.SpatialMaxPooling(3, 3, 2,2) --Replace cudnn.maxpooling-->nn.maxpooling for deterministic response
net11:add(max2)
elseif (i==9) then
module:setMode(1,1,1) -- For making determinstic the cudnn convolution layers
net11:add(module):learningRate('weight', 1)
:learningRate('bias', 2)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --conv3
else
if (i ~= 4 and i ~=8) then
net11:add(module)
end
end
elseif (i>10 and i<17) then
if(i==11) then
module:setMode(1,1,1)
net22:add(module):learningRate('weight', 1)
:learningRate('bias', 2)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --conv4
elseif (i==13) then
module:setMode(1,1,1)
net22:add(module):learningRate('weight', 1)
:learningRate('bias', 2)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --conv5
elseif (i==15) then
max3 = nn.SpatialMaxPooling(3, 3, 2,2) --Replace cudnn.maxpooling-->nn.maxpooling for deterministic response
net22:add(max3)
else
if (i ~= 15) then
net22:add(module)
end
end
else
if(i==17) then
net33:add(module):learningRate('weight', 1)
:learningRate('bias', 2)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --FC6
elseif (i==20) then
net33:add(module):learningRate('weight', 1)
:learningRate('bias', 2)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --FC7
elseif (i==23) then
net33:add(module):learningRate('weight', 0)
:learningRate('bias', 0)
:weightDecay('weight', 0)
:weightDecay('bias', 0) --FC8
else
net33:add(module)
end
end
end
net33:remove(#net33) --removed softmax
net33:remove(#net33) --removed FC8
-- Bottlenec Network------
netBB:add(nn.Linear( 4096, 256)):learningRate('weight', 10)
:learningRate('bias', 20)
:weightDecay('weight', 1)
:weightDecay('bias', 0)
netBB:add(nn.ReLU(true))
-- Gradient Reversal Domain classifier Network
module = nn.GradientReversal(lambda)
netDD:add(module)
netDD:add( nn.Linear( 256, 1024)):learningRate('weight', 10)
:learningRate('bias', 20)
netDD:add(nn.ReLU(true))
netDD:add(nn.Dropout(0.5))
netDD:add( nn.Linear( 1024, 1024)):learningRate('weight', 10)
:learningRate('bias', 20)
netDD:add(nn.ReLU(true))
netDD:add(nn.Dropout(0.5))
netDD:add( nn.Linear( 1024, 32)):learningRate('weight', 10)
:learningRate('bias', 20)
--netDD:add(nn.Sigmoid()) -- removed this layer if we are using the nn.CrossEntropyCriterion()
-- Classifier Network------------------
net44:add( nn.Linear( 256, 31)):learningRate('weight', 10)
:learningRate('bias', 20)
:weightDecay('weight', 1)
:weightDecay('bias', 0) --FC6
net44:add(nn.LogSoftMax())
-- Map Tabel for two input----
net1:add(net11)
net2:add(net22)
net3:add(net33)
net4:add(net44)
netB:add(netBB)
netD:add(netDD)
--Initially Lamda set =0
module:setLambda(0)
--============ Criterion=================
local criterion = nn.ClassNLLCriterion()
local criterionNLL = nn.ClassNLLCriterion()
local criterionNLL_parallel = nn.ParallelCriterion():add(criterionNLL):add(criterionNLL)
local criterionCrossE = nn.CrossEntropyCriterion()
local criterionCrossE_parallel = nn.ParallelCriterion():add(criterionCrossE,0.1):add(criterionCrossE,0.1)
--==========================================
-----------------------------------------------------------------------------------------------
if opt.gpu >=0 then
net1:cuda()
net2:cuda()
net3:cuda()
netB:cuda()
net4:cuda()
netD:cuda()
criterion:cuda()
criterionNLL_parallel:cuda()
criterionCrossE_parallel:cuda()
end
--=== Different Learning rate for weigth and bias
local temp_baseWeightDecay=0.001 --no meaningin my case
local learningRates_Net1, weightDecays_Net1 = net1:getOptimConfig(opt.baseLearningRate,temp_baseWeightDecay)
local learningRates_Net2, weightDecays_Net2 = net2:getOptimConfig(opt.baseLearningRate, temp_baseWeightDecay)
local learningRates_Net3, weightDecays_Net3 = net3:getOptimConfig(opt.baseLearningRate, temp_baseWeightDecay)
local learningRates_NetB, weightDecays_NetB = netB:getOptimConfig(opt.baseLearningRate, temp_baseWeightDecay)
local learningRates_NetD, weightDecays_NetD = netD:getOptimConfig(opt.baseLearningRate, temp_baseWeightDecay)
local learningRates_Net4, weightDecays_Net4 = net4:getOptimConfig(opt.baseLearningRate, temp_baseWeightDecay)
--===========Parameters===================================
parameters1, gradParameters1 = net1:getParameters()
parameters2, gradParameters2 = net2:getParameters()
parameters3, gradParameters3 = net3:getParameters()
parameters4, gradParameters4 = net4:getParameters()
parametersB, gradParametersB = netB:getParameters()
parametersD, gradParametersD = netD:getParameters()
--============================================================
--===== weight initilization==========
local method = 'xavier'
net4 = require('misc/weight-init')(net4, method)
netB = require('misc/weight-init')(netB, method)
netD = require('misc/weight-init')(netD, method)
----------------------------------------------------------------------------------------------------
print('=> New Model')
print(model)
print('net1', net1)
print('net2', net2)
print('net3', net3)
print('netB', netB)
print('net4', net4)
print(criterion)
collectgarbage()
local updated_learningrate=opt.baseLearningRate
--===================Training Fuctions======================================
function train()
net1:training()
net2:training()
net3:training()
net4:training()
netB:training()
netD:training()
epoch = epoch or 1
if(epoch>1) then
print(c.blue '==>'.." online epoch # " .. epoch .. ' [batchSize = ' .. batchSize .. ']')
local p=epoch/opt.max_epoch_grl
local baseWeightDecay = torch.pow((1 + epoch * opt.gamma), (-1 * opt.power))
updated_learningrate=opt.baseLearningRate*baseWeightDecay
print('Learnig Rate',updated_learningrate)
print('Lamda',opt.lamda)
module:setLambda(opt.lamda)
end
local avg_loss=0
local avg_acc=0
local count =0
for i = 1, data:size(), opt.batchSize do
-----Classifier Network-------------------
data_tm:reset(); data_tm:resume()
local batchInputs_source,label = data:getBatch()
local batchInputs_target = dataVal:getBatch()
local SlabelDomain=torch.Tensor(opt.batchSize):fill(32)
local TlabelDomain=torch.Tensor(opt.batchSize):fill(32)
if opt.gpu >=0 then
label=label:cuda()
SlabelDomain=SlabelDomain:cuda()
TlabelDomain=TlabelDomain:cuda()
batchInputs_source=batchInputs_source:cuda()
batchInputs_target=batchInputs_target:cuda()
end
--------------------------------------------------------------------------
-- forwardNetwork
outputs1 = net1:forward({batchInputs_source,batchInputs_target})
outputs2 = net2:forward(outputs1)
outputs3 = net3:forward(outputs2)
outputsB = netB:forward(outputs3)
outputs4 = net4:forward(outputsB)
outputsD = netD:forward(outputsB)
err = criterion:forward(outputs4[1], label)
errDomain = criterionCrossE_parallel:forward(outputsD, {label,TlabelDomain})
--------------------------------------------------------------------------
-- backward Network
gradParametersD:zero()
gradParameters4:zero()
gradParametersB:zero()
gradParameters3:zero()
gradParameters2:zero()
gradParameters1:zero()
local dgradOutputsS=torch.CudaTensor() --Declaration of dgradOutputsS for source class
dgradOutputsS:resize(outputs4[1]:size())
dgradOutputsS:zero()
dgradOutputsS = criterion:backward(outputs4[1], label)
local zeros = torch.CudaTensor() -- Zero gradient for Target data Classification(we dont have target label)
zeros:resize(dgradOutputsS:size())
zeros:zero()
dgradOutputs={dgradOutputsS, zeros}
---- Optimization Net4-------
feval_net4 = function(x)
dgradOutputs_mod4 = net4:backward(outputsB, dgradOutputs)
return err, gradParameters4
end
optim.sgd(feval_net4, parameters4, {
learningRates = learningRates_Net4,
weightDecays = weightDecays_Net4,
learningRate = updated_learningrate,
momentum = opt.momentum,
})
dgradOutputsDomain = criterionCrossE_parallel:backward(outputsD, {label,TlabelDomain}) -- classification loss grad
---- Optimization Domain Confusion Branch -------
feval_netD = function(x)
dgradOutputs_modD = netD:backward(outputsB, dgradOutputsDomain)
return err, gradParametersD
end
optim.sgd(feval_netD, parametersD, {
learningRates = learningRates_NetD,
weightDecays = weightDecays_NetD,
learningRate = updated_learningrate,
momentum = opt.momentum,
})
---- Optimization netB(bottleneck_ Branch -------
local total_grad={}
total_grad[1] = dgradOutputs_mod4[1]+ dgradOutputs_modD[1]
total_grad[2] = dgradOutputs_mod4[2]+ dgradOutputs_modD[2]
feval_netB = function(x)
dgradOutputs_modB = netB:backward(outputs3,total_grad)
return err, gradParametersB
end
optim.sgd(feval_netB, parametersB, {
learningRates = learningRates_NetB,
weightDecays = weightDecays_NetB,
learningRate = updated_learningrate,
momentum = opt.momentum,
})
---- Optimization net3(FC6,FC7) Branch -------
gradParameters3:zero()
feval_net3 = function(x)
dgradOutputs_mod3= net3:backward(outputs2,dgradOutputs_modB)
return err, gradParameters3
end
optim.sgd(feval_net3, parameters3, {
learningRates = learningRates_Net3,
weightDecays = weightDecays_Net3,
learningRate = updated_learningrate,
momentum = opt.momentum,
})
---- Optimization net2(Conv4 -Pool5) Branch -------
gradParameters2:zero()
feval_net2 = function(x)
dgradOutputs_mod2= net2:backward(outputs1,dgradOutputs_mod3)
return err, gradParameters2
end
optim.sgd(feval_net2, parameters2, {
learningRates = learningRates_Net2,
weightDecays = weightDecays_Net2,
learningRate = updated_learningrate,
momentum = opt.momentum,
})
---- if required then Net1 optimization----
if opt.net1_freeze =='no' then
gradParameters1:zero()
feval_net1 = function(x)
model.modules[1]:backward((batchInputs_source),dgradOutputs_mod2)
return gradOutputs_mod1, gradParameters1
end
optim.sgd(feval_net1, parameters1, {
learningRates = learningRates_Net1,
weightDecays = weightDecays_Net1,
learningRate = updated_learningrate,
momentum = opt.momentum,
})
end
local train_acc = check_accuracy(outputs4[1], label)
avg_loss=avg_loss+err
avg_acc=avg_acc+train_acc
train_acc =nil
err=nil
count=count+1
end
epoch = epoch + 1
return (avg_loss)/count,avg_acc/count
end
--==============================Testing===================================================================
function test()
-- disable flips, dropouts and batch normalization
net1:evaluate()
net2:evaluate()
net3:evaluate()
netB:evaluate()
net4:evaluate()
netD:evaluate()
local err_val=0
local avg_test_acc=0
local count=0
opt.Test_batchSize=opt.batchSize
for i = 1,dataTest:size(), opt.Test_batchSize do
data_tm:reset(); data_tm:resume()
opt.start_Batch_IndexTest=i
local batchInputs_test,validLabel = dataTest:getBatch(opt.start_Batch_IndexTest,dataTest:size())
if opt.gpu >=0 then
batchInputs_test=batchInputs_test:cuda()
validLabel=validLabel:cuda()
end
local outputs1 = net1:forward({batchInputs_test:cuda(),batchInputs_test:cuda()})
local outputs2 = net2:forward(outputs1)
local outputs3 = net3:forward(outputs2)
local outputsB = netB:forward(outputs3)
local outputs = net4:forward(outputsB)
confusion:batchAdd(outputs[1], validLabel)
err_val = err_val+ criterion:forward(outputs[1],validLabel) -- Classification Loss
count=count+1
local test_batch_acc = check_accuracyTest(outputs[1], validLabel)
avg_test_acc=avg_test_acc+test_batch_acc
test_batch_acc=nil
end
confusion:updateValids()
test_accuracy=confusion.totalValid
if not testLogger then
confusion:zero()
end
return err_val/count, test_accuracy,avg_test_acc/dataTest:size()
end
function save_html(train_acc,test_acc,train_err,test_err)
if testLogger then
paths.mkdir(opt.save)
testLogger:add{train_acc, test_acc}
testLogger:style{'-','-'}
testLogger:plot()
errorlog:add{train_err, test_err}
errorlog:style{'-','-'}
errorlog:plot()
if paths.filep(opt.save..'/test.log.eps') then
local base64im
do
os.execute(('convert -density 200 %s/test.log.eps %s/test.png'):format(opt.save,opt.save))
os.execute(('openssl base64 -in %s/test.png -out %s/test.base64'):format(opt.save,opt.save))
local f = io.open(opt.save..'/test.base64')
if f then base64im = f:read'*all' end
end
local base64im_error
do
os.execute(('convert -density 200 %s/error.log.eps %s/error.png'):format(opt.save,opt.save))
os.execute(('openssl base64 -in %s/error.png -out %s/error.base64'):format(opt.save,opt.save))
local f = io.open(opt.save..'/error.base64')
if f then base64im_error = f:read'*all' end
end
local file = io.open(opt.save..'/report.html','w')
file:write('<h5>Training data size: '..data:size()..'\n')
file:write('<h5>Validation data size: '..dataTest:size()..'\n')
file:write('<h5>batchSize: '..batchSize..'\n')
file:write('<h5>Network upto conv3 is freeze: '..opt.net1_freeze..'\n')
file:write('<h5>Base Learning Rate: '..opt.baseLearningRate..'\n')
file:write('<h5>momentum: '..opt.momentum..'\n')
file:write('<h5>Seed : '..opt.manual_seed..'\n')
file:write('<h5>lamda : '..opt.lamda..'\n')
file:write('<h5>number of test Class : '..opt.number_of_testclass..'\n')
file:write'</table><pre>\n'
file:write(tostring(confusion)..'\n')
file:write(tostring(net4)..'\n')
file:write'</pre></body></html>'
file:write(([[
<!DOCTYPE html>
<html>
<body>
<title>%s - %s</title>
<img src="data:image/png;base64,%s">
<table>
]]):format(opt.save,epoch,base64im))
file:write(([[
<!DOCTYPE html>
<html>
<body>
<title>%s - %s</title>
<img src="data:image/png;base64,%s">
<table>
]]):format(opt.save,epoch,base64im_error))
file:close()
end
confusion:zero()
end
if prev_accuracy< test_acc then
print('Model is saving')
collectgarbage()
net1:clearState()
net2:clearState()
net3:clearState()
netB:clearState()
net4:clearState()
netD:clearState()
torch.save(paths.concat(opt.save, 'Accuracy' .. test_acc .. 'net1_' .. epoch .. '.t7'),net1) -- defined in util.lua
torch.save(paths.concat(opt.save, 'Accuracy' .. test_acc .. 'net2_' .. epoch .. '.t7'),net2)
torch.save(paths.concat(opt.save, 'Accuracy' .. test_acc .. 'net3_' .. epoch .. '.t7'),net3)
torch.save(paths.concat(opt.save, 'Accuracy' .. test_acc .. 'netB_' .. epoch .. '.t7'),netB)
torch.save(paths.concat(opt.save, 'Accuracy' .. test_acc .. 'net4_' .. epoch .. '.t7'),net4)
torch.save(paths.concat(opt.save, 'Accuracy' .. test_acc .. 'netD_' .. epoch .. '.t7'),netD)
print('Model is Saved')
prev_accuracy=test_acc
end
end
for i=1,opt.max_epoch do
train_loss,train_acc=train()
print('Train_acc',train_acc,'Train_loss',train_loss)
collectgarbage()
test_loss,test_acc,test_acc_2=test()
print('test_acc',test_acc, 'test_acc_2',test_acc_2,'Test_loss',test_loss)
save_html(train_acc,test_acc,train_loss,test_loss)
collectgarbage()
end