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bass-net_model.lua
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bass-net_model.lua
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require 'nn'
require 'optim'
matio = require 'matio'
require 'xlua'
require 'pl'
require 'paths'
require 'torch'
require 'math'
local opt = lapp[[
--path_dir (default "./data/") Path to the data directory
-d, --data (default "Indian_pines") Dataset to use
--development (default 1) Use development dataset/ Whole training dataset
-s,--save (default "logs/") subdirectory to save logs
-p,--plot plot while training
-o,--optimization (default "Adam") optimization: SGD | LBFGS | Adam
-l,--learningRate (default 0.0005) learning rate, for SGD only
-b,--batchSize (default 200) batch size
-m,--momentum (default 0) momentum, for SGD only
-i,--maxIter (default 8000) maximum nb of iterations per batch, for LBFGS
--coefL1 (default 0) L1 penalty on the weights
--coefL2 (default 0) L2 penalty on the weights
-t,--type (default "cpu") GPU or CPU
--network (default "MLP") MLP or CNN
--patch_size (default 3) patch size of tthe image
--nbands (default 10) number of bands
--block1_conv1 (default 3333) number of filters in the 1*1 convolution (here, 3333 is the sentinel value)
]]
if opt.development == 1 then
opt.dev = ""
if opt.data == "Salinas" then
nval = 400
else
nval = 200
end
else
opt.dev = "_Full"
nval = 0
end
if opt.data == "Salinas" then
opt.nclasses = 16
else
opt.nclasses = 9
end
-- Loading Data
test_data = matio.load(opt.path_dir .. opt.data .. "_Test_patch_" .. tostring(opt.patch_size) .. ".mat").test_patch
test_labels = matio.load(opt.path_dir ..opt.data .. "_Test_patch_" .. tostring(opt.patch_size) .. ".mat").test_labels:transpose(1,2)
opt.channels = test_data:size(2)
train_data = matio.load(opt.path_dir ..opt.data .. opt.dev .. "_Train_patch_" .. tostring(opt.patch_size) .. ".mat").train_patch:reshape(opt.nclasses*200-nval, opt.channels, opt.patch_size, opt.patch_size)
train_labels = matio.load(opt.path_dir ..opt.data .. opt.dev .. "_Train_patch_" .. tostring(opt.patch_size) .. ".mat").train_labels:transpose(1,2)
val_data = matio.load(opt.path_dir ..opt.data .. "_Val_patch_" .. tostring(opt.patch_size) .. ".mat").val_patch
val_labels = matio.load(opt.path_dir ..opt.data .. "_Val_patch_" .. tostring(opt.patch_size) .. ".mat").val_labels:transpose(1, 2)
if opt.block1_conv1 == 3333 then
opt.block1_conv1 = opt.channels
end
while (opt.block1_conv1 % opt.nbands ~= 0) do
opt.nbands = opt.nbands + 1
print("Number of parallel networks reinitialized to " .. tostring(opt.nbands))
end
trainset = {}
trainset.data = train_data
trainset.labels = train_labels
testset = {}
testset.data = test_data
testset.labels = test_labels
valset = {}
valset.data = val_data
valset.labels = val_labels
print(trainset)
print(testset)
print(valset)
setmetatable(trainset, {__index = function(self, index)
local input = self.data[index]
local class = self.labels[index]
local labelvector = torch.zeros(opt.nclasses)
local label = labelvector
label[class[1]+1] = 1
local example = {input, label}
return example
end})
setmetatable(testset, {__index = function(self, index)
local input = self.data[index]
local class = self.labels[index]
local labelvector = torch.zeros(opt.nclasses)
local label = labelvector
label[class[1]+1] = 1
local example = {input, label}
return example
end})
setmetatable(valset, {__index = function(self, index)
local input = self.data[index]
local class = self.labels[index]
local labelvector = torch.zeros(opt.nclasses)
local label = labelvector
label[class[1]+1] = 1
local example = {input, label}
return example
end})
function trainset:size()
return trainset.data:size(1)
end
function testset:size()
return testset.data:size(1)
end
function valset:size()
return valset.data:size(1)
end
opt.band_size = opt.block1_conv1/opt.nbands
model = nn.Sequential()
model:add(nn.Reshape(opt.band_size, opt.patch_size*opt.patch_size))
model:add(nn.TemporalConvolution(opt.patch_size*opt.patch_size, 20, 3, 1))
model:add(nn.ReLU())
model:add(nn.TemporalConvolution(20, 20, 3, 1))
model:add(nn.ReLU())
model:add(nn.TemporalConvolution(20, 10, 3, 1))
model:add(nn.ReLU())
model:add(nn.TemporalConvolution(10, 5, 5, 1))
model:add(nn.ReLU())
model:add(nn.Reshape((opt.band_size-10)*5, 1))
parallel_model = nn.Parallel(2, 2)
for i = 1, opt.nbands do
parallel_model:add(model:clone())
end
net = nn.Sequential()
net:add(nn.SpatialConvolution(opt.channels, opt.block1_conv1, 1, 1))
net:add(nn.ReLU())
net:add(nn.Reshape(opt.nbands, opt.band_size, opt.patch_size*opt.patch_size))
net:add(parallel_model)
net:add(nn.Reshape(opt.nbands*(opt.band_size-10)*5))
net:add(nn.Linear(opt.nbands*(opt.band_size-10)*5, 100))
net:add(nn.ReLU())
net:add(nn.Dropout())
net:add(nn.Linear(100, opt.nclasses))
net:add(nn.LogSoftMax())
-- Parameter Sharing
parallel_model = net:get(4)
for band = 2, opt.nbands do
local current_module = parallel_model:get(band)
current_module:share(parallel_model:get(1), 'weight', 'bias',
'gradWeight', 'gradBias')
end
net:training()
criterion = nn.ClassNLLCriterion()
if opt.data == "Salinas" then
classes = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'}
else
classes = {'0', '1', '2', '3', '4', '5', '6', '7', '8'}
end
parameters, gradParameters = net:getParameters()
confusion = optim.ConfusionMatrix(classes)
trainLogger = optim.Logger(paths.concat("./" .. opt.save .. opt.data, 'train.log'))
valLogger = optim.Logger(paths.concat("./" .. opt.save .. opt.data, 'val.log'))
testLogger = optim.Logger(paths.concat("./" .. opt.save .. opt.data, 'test.log'))
function train(dataset)
-- epoch tracker
epoch = epoch or 1
-- local vars
local time = sys.clock()
-- do one epoch
print('<trainer> on training set:')
print("<trainer> online epoch # " .. epoch .. ' [batchSize = ' .. opt.batchSize .. ']')
for t = 1,dataset:size(),opt.batchSize do
local inputs = torch.Tensor(opt.batchSize, opt.channels, opt.patch_size, opt.patch_size)
local targets = torch.Tensor(opt.batchSize)
if opt.type == "cuda" then
inputs:cuda()
targets:cuda()
end
local k = 1
for i = t,math.min(t+opt.batchSize-1,dataset:size()) do
-- load new sample
local sample = dataset[i]
local input = sample[1]:clone()
local _,target = sample[2]:clone():max(1)
target = target:squeeze()
inputs[k] = input
targets[k] = target
k = k + 1
end
-- create closure to evaluate f(X) and df/dX
local feval = function(x)
-- just in case:
collectgarbage()
-- get new parameters
if x ~= parameters then
parameters:copy(x)
end
-- reset gradients
gradParameters:zero()
-- evaluate function for complete mini batch
local outputs = net:forward(inputs)
local f = criterion:forward(outputs, targets)
-- estimate df/dW
local df_do = criterion:backward(outputs, targets)
net:backward(inputs, df_do)
-- penalties (L1 and L2):
if opt.coefL1 ~= 0 or opt.coefL2 ~= 0 then
-- locals:
local norm,sign= torch.norm,torch.sign
-- Loss:
f = f + opt.coefL1 * norm(parameters,1)
f = f + opt.coefL2 * norm(parameters,2)^2/2
-- Gradients:
gradParameters:add( sign(parameters):mul(opt.coefL1) + parameters:clone():mul(opt.coefL2) )
end
-- update confusion
-- print(inputs:size(1))
for i = 1,opt.batchSize do
confusion:add(outputs[i], targets[i])
end
-- return f and df/dX
return f,gradParameters
end
-- optimize on current mini-batch
if opt.optimization == 'LBFGS' then
-- Perform LBFGS step:
lbfgsState = lbfgsState or {
maxIter = opt.maxIter,
lineSearch = optim.lswolfe
}
optim.lbfgs(feval, parameters, lbfgsState)
-- disp report:
print('LBFGS step')
print(' - progress in batch: ' .. t .. '/' .. dataset:size())
print(' - nb of iterations: ' .. lbfgsState.nIter)
print(' - nb of function evalutions: ' .. lbfgsState.funcEval)
elseif opt.optimization == 'SGD' then
-- Perform SGD step:
sgdState = sgdState or {
learningRate = opt.learningRate,
momentum = opt.momentum,
learningRateDecay = 5e-7
}
optim.sgd(feval, parameters, sgdState)
elseif opt.optimization == "Adam" then
adamState = adamState or {
learningRate = opt.learningRate,
momentum = opt.momentum,
learningRateDecay = 5e-9
}
optim.adam(feval, parameters, adamState)
-- disp progress
xlua.progress(t, dataset:size())
else
error('unknown optimization method')
end
end
-- time taken
time = sys.clock() - time
time = time / dataset:size()
-- print confusion matrix
print(confusion)
print('% mean class accuracy (train set)' .. tostring(confusion.totalValid*100))
trainLogger:add{['% mean class accuracy (train set)'] = confusion.totalValid * 100}
confusion:zero()
epoch = epoch + 1
return (1 - confusion.totalValid)*100
end
best_val = 0
function val(dataset)
net:evaluate()
for t = 1, dataset:size(), opt.batchSize do
xlua.progress(t, dataset:size())
local inputs = torch.Tensor(opt.batchSize, opt.channels, opt.patch_size, opt.patch_size)
local targets = torch.Tensor(opt.batchSize)
local k = 1
for i = t, math.min(t+opt.batchSize-1, dataset:size()) do
local sample = dataset[i]
local input = sample[1]:clone()
local _,target = sample[2]:clone():max(1)
target = target:squeeze()
inputs[k] = input
targets[k] = target
k = k+1
end
local preds = net:forward(inputs) -- Computing Loss
for l = 1, k - 1 do
confusion:add(preds[l], targets[l])
end
end
print(confusion)
if confusion.totalValid > best_val then
best_val = confusion.totalValid
end
print("Best validation accuracy yet :" .. tostring(best_val*100) .. "%")
valLogger:add{['% mean class accuracy (val set)'] = confusion.totalValid * 100}
confusion:zero()
net:training()
end
best_test = 0
function test(dataset)
net:evaluate()
for t = 1, dataset:size(), opt.batchSize do
xlua.progress(t, dataset:size())
local inputs = torch.Tensor(opt.batchSize, opt.channels, opt.patch_size, opt.patch_size)
local targets = torch.Tensor(opt.batchSize)
local k = 1
for i = t, math.min(t+opt.batchSize-1, dataset:size()) do
local sample = dataset[i]
local input = sample[1]:clone()
local _,target = sample[2]:clone():max(1)
target = target:squeeze()
inputs[k] = input
targets[k] = target
k = k+1
end
local preds = net:forward(inputs) -- Computing Loss
for l = 1, k - 1 do
confusion:add(preds[l], targets[l])
end
end
print(confusion)
if confusion.totalValid > best_test then
best_test = confusion.totalValid
torch.save("./pretrained/final_Block_2_no_fc.t7", net)
end
print("Best test accuracy yet :" .. tostring(best_test*100) .. "%")
testLogger:add{['% mean class accuracy (test set)'] = confusion.totalValid * 100}
confusion:zero()
net:training()
end
for i = 1, opt.maxIter do
train(trainset)
if opt.development == 1 then
val(valset)
else
test(testset)
end
end