function IFCN_COCOStuff(varargin)

run ~/codes/matconvnet-1.0-beta16/matlab/vl_setupnn ;

addpath ~/codes/matconvnet-1.0-beta16/examples ;

addpath(genpath('~/codes/toolbox'));

opts.modelType = 'fcn8s';

opts.imageSize = 512;

opts.layers = 6;

opts.kerSize = 5; % must be odd number

opts.stream = 'all';

opts.recursive = false;

opts.mode = 'val';

opts.resLayer = 101;

opts.newLr = 3; % the times of lr for skipNetwork and contextNetwork

opts.dataset = 'Context';

opts.dataDir = '/home/bshuai/datasets/COCO-Stuff/';

opts.readFromDisk = true;

opts.nh = 512;

opts.rareExponnetial = 2;

opts.nClass = 171;

vgg = ~strncmpi(opts.modelType, 'res', 3);

if vgg

opts.sourceModelPath = '../imagenet/imagenet-vgg-verydeep-16.mat' ;

% Path Setting

networkFolder = sprintf('batch-i%s-%dx%d-%dlayers-1e-3-higher%d', ...'

opts.modelType, opts.kerSize, opts.kerSize, opts.layers, opts.newLr);

opts.sourceModelPath = sprintf('../imagenet/imagenet-resnet-%d-dag.mat', opts.resLayer);

% Path Setting

networkFolder = sprintf('%s-%d-DAG-RNN-%d-%dlayers-1e-3-higher%d', ...'

opts.modelType, opts.nh, opts.resLayer, opts.layers, opts.newLr);

opts.expDir = sprintf('COCOStuff_%d_Val/%s', ...

opts.imageSize, networkFolder);

[opts, varargin] = vl_argparse(opts, varargin) ;

opts.imdbPath = fullfile(opts.dataDir, 'imdb-train-val-disk.mat') ;

opts.numFetchThreads = 1 ; % not used yet

opts.train.batchSize = 10;

opts.train.numSubBatches = 1 ;

opts.train.continue = true ;

opts.train.gpus = [2];

opts.train.prefetch = true ;

opts.train.expDir = opts.expDir ;

opts.train.learningRate = 1e-3*[ones(1, 15) 0.1*ones(1,5) 0.01*ones(1,2)] ;

opts.train.numEpochs = numel(opts.train.learningRate) ;

opts = vl_argparse(opts, varargin) ;

if exist(opts.imdbPath)

imdb = load(opts.imdbPath) ;

imdb = getCOCOStuffImdb(opts) ;

mkdir(opts.expDir) ;

save(opts.imdbPath, '-struct', 'imdb', '-v7.3') ;

train = find(imdb.images.set == 1 ) ;

val = find(imdb.images.set == 2) ;

if vgg

net = fcnInitializeNetwork('sourceModelPath', opts.sourceModelPath,...

'kerSize', opts.kerSize, 'layers', opts.layers,...

'nh', opts.nh,'nClass', opts.nClass, 'recursive', opts.recursive, ...

'newLr', opts.newLr) ;

if any(strcmp(opts.modelType, {'fcn16s', 'fcn8s', 'fcn4s'}))

% upgrade model to FCN16s

net = fcnInitializeNetwork16s(net, 'rnn', false,...

'nh', opts.nh, 'nClass', opts.nClass, 'newLr', opts.newLr) ;

end

if any(strcmp(opts.modelType, {'fcn8s', 'fcn4s'}))

% upgrade model fto FCN8s

net = fcnInitializeNetwork8s(net, 'rnn', false, ...

'nh', opts.nh, 'nClass', opts.nClass, 'newLr', opts.newLr) ;

end

if strcmp(opts.modelType, 'fcn4s')

% upgrade model fto FCN8s

net = fcnInitializeNetwork4s(net, 'rnn', false, ...

'nh', opts.nh, 'nClass', opts.nClass, 'newLr', opts.newLr) ;

end

net = fcnInitializeResNetwork('sourceModelPath', opts.sourceModelPath,...

'rnn', opts.rnn, 'kerSize', opts.kerSize, 'layers', opts.layers,...

'nh', opts.nh,'nClass', opts.nClass, 'recursive', opts.recursive, ...

'resLayer', opts.resLayer, 'newLr', opts.newLr);

if any(strcmp(opts.modelType, {'res16s', 'res8s', 'res4s'}))

% upgrade model to Res16s

net = fcnInitializeResNetwork16s(net, 'rnn', false,...

'nh', opts.nh, 'nClass', opts.nClass, ...

'resLayer', opts.resLayer, 'newLr', opts.newLr) ;

end

if any(strcmp(opts.modelType, {'res8s', 'res4s'}))

% upgrade model to Res16s

net = fcnInitializeResNetwork8s(net, 'rnn', false,...

'nh', opts.nh, 'nClass', opts.nClass, ...

'resLayer', opts.resLayer, 'newLr', opts.newLr) ;

end

if any(strcmp(opts.modelType, {'res4s'}))

% upgrade model to Res16s

net = fcnInitializeResNetwork4s(net, 'rnn', false,...

'nh', opts.nh, 'nClass', opts.nClass, ...

'resLayer', opts.resLayer, 'newLr', opts.newLr) ;

end

bopts.numThreads = opts.numFetchThreads ;

bopts.labelStride = 2 ;

bopts.labelOffset = 1 ;

bopts.classWeights = getWeight(imdb.classFrequency, opts.rareExponnetial);

bopts.useGpu = numel(opts.train.gpus) > 0 ;

bopts.readFromDisk = opts.readFromDisk;

bopts.imageSize = opts.imageSize;

bopts.vgg = vgg;

bopts.stream = opts.stream;

bopts.dataset = opts.dataset;

if strcmp(opts.mode, 'val')

trainSamples = train;

bnSamples = val;

bnSuffix = 'val';

elseif strcmp(opts.mode, 'test')

trainSamples = [train val];

bnSamples = test;

bnSuffix = 'test';

fprintf('---------------------------------------------------\n')

fprintf('Please check the network setting... \n')

fprintf('Network Type: %s \n', opts.modelType);

fprintf('Context Network: Kernel Size %d x %d, %d layers \n', opts.kerSize, ...

opts.kerSize, opts.layers);

fprintf('Batch size: %d \n', opts.train.batchSize);

fprintf('Image Size: %d \n', opts.imageSize);

fprintf('Starting learning rate %8.2e\n', opts.train.learningRate(1));

fprintf('New learning rates for ContextNetwork and SkipNetwork: %d \n', opts.newLr);

fprintf('Rare exponential constant %.1f\n', opts.rareExponnetial);

fprintf('In %s Mode: %d training examples, %d bn samples, with %s bn Suffix \n', ...

opts.mode, numel(trainSamples), numel(bnSamples), bnSuffix);

fprintf('---------------------------------------------------\n')

info = fcn_train_dag(net, imdb, getBatchWrapper(bopts), opts.train, ...

'train', trainSamples, ...

'val', val) ;

load(fullfile(opts.expDir, 'net-epoch-22.mat'), 'net');

net = dagnn.DagNN.loadobj(net) ;

updateBN(net, imdb, getBatchWrapper(bopts), opts.train, ...

'train', bnSamples, 'mode', bnSuffix);

function fn = getBatchWrapper(opts)

fn = @(imdb,batch) getBatch(imdb,batch,opts,'prefetch',nargout==0) ;

function classWeight = getWeight(h, k)

h = h(2:end);

h = h / sum(h);

classWeight = (k).^(max(0,ceil((log10(0.004 ./ h)))));