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IFCN_PascalContext_2.m
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IFCN_PascalContext_2.m
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function IFCN_PascalContext_2(varargin)
%FNCTRAIN Train IFCN model using MatConvNet
% distcomp.feature( 'LocalUseMpiexec', false )
restoredefaultpath
dbstop if error
run ~/codes/matconvnet-1.0-beta24/matlab/vl_setupnn ;
addpath ~/codes/matconvnet-1.0-beta24/examples ;
addpath(genpath('~/codes/toolbox'));
addpath(genpath('feedback'));
%--------------------------------------------------------------------------
% Set the parameters
opts.modelType = 'res4s';
opts.rnn = false;
opts.imageSize = 512;
opts.layers = 6;
opts.kerSize = 5; % must be odd number
opts.stream = 'all';
opts.recursive = false;
opts.mode = 'val';
opts.resLayer = 50;
opts.newLr = 3; % the times of lr for skipNetwork and contextNetwork
%--------------------------------------------------------------------------
% -------------------------------------------------------------------------
% Almost fixed
opts.dataset = 'Context';
opts.dataDir = '/home/bshuai/datasets/VOC2010/';
opts.readFromDisk = true;
opts.nh = 512;
opts.rareExponnetial = 2;
if strcmp(opts.stream, 'stuff')
opts.nClass = 36;
end
if strcmp(opts.stream, 'object')
opts.nClass = 115;
end
if strcmp(opts.stream, 'all')
opts.nClass = 59;
end
%--------------------------------------------------------------------------
vgg = ~strncmpi(opts.modelType, 'res', 3);
if vgg
opts.sourceModelPath = '/home/bshuai/codes/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);
else
opts.sourceModelPath = sprintf('/home/bshuai/codes/imagenet/imagenet-resnet-%d-dag.mat', opts.resLayer);
% Path Setting
networkFolder = sprintf('CVPR18-shared-Gated_gausBDR(25x25-sigma5-w=2)-batch-i%s-%dx%d-%dlayers-%d-dag-1e-3-higher%d', ...'
opts.modelType, opts.kerSize, opts.kerSize, opts.layers, opts.resLayer, opts.newLr);
end
if strcmp(opts.mode, 'val')
opts.expDir = sprintf('PascalContext_%d_Val/%s', ...
opts.imageSize, networkFolder);
else
opts.expDir = sprintf('PascalContext_%d_TrainVal/%s', ...
opts.imageSize, networkFolder);
end
if opts.rnn, opts.expDir = [opts.expDir, '-rnn']; end
[opts, varargin] = vl_argparse(opts, varargin) ;
% experiment setup
if ~opts.readFromDisk
opts.imdbPath = fullfile(opts.dataDir, 'imdb-plain.mat') ;
else
opts.imdbPath = fullfile(opts.dataDir, 'imdb-train-val-disk.mat') ;
end
opts.numFetchThreads = 1 ; % not used yet
% training options (SGD)
opts.train.batchSize = 10;
opts.train.numSubBatches = 1 ;
opts.train.continue = true ;
opts.train.gpus = [1];
opts.train.prefetch = true ;
opts.train.expDir = opts.expDir ;
opts.train.learningRate = 1.5e-3*[ones(1, 15) 0.1*ones(1,5) 0.01*ones(1,5)] ;
% opts.train.learningRate = 1e-4*getLr() ;
opts.train.numEpochs = numel(opts.train.learningRate) ;
% -------------------------------------------------------------------------
% Setup data
% -------------------------------------------------------------------------
if exist(opts.imdbPath)
imdb = load(opts.imdbPath) ;
else
imdb = getPascalContextImdb_disk(opts) ;
mkdir(opts.expDir) ;
save(opts.imdbPath, '-struct', 'imdb', '-v7.3') ;
end
% Get training and test/validation subsets
train = find(imdb.images.set == 1 ) ;
val = find(imdb.images.set == 3) ;
test = find(imdb.images.set == 3) ;
TrainNum=ceil(numel(train)/opts.train.batchSize);
IterarionsNum=opts.train.numEpochs*TrainNum;
for i=1:IterarionsNum
opts.train.PolylearningRate(i)=2e-3*(1-(i-1)/IterarionsNum)^0.9;
end
opts = vl_argparse(opts, varargin) ;
% -------------------------------------------------------------------------
% Setup model
% -------------------------------------------------------------------------
% Get initial model from VGG-VD-16
if vgg
net = fcnInitializeNetwork('sourceModelPath', opts.sourceModelPath,...
'rnn', opts.rnn, '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
else
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_2(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_2(net, 'rnn', false,...
'nh', opts.nh, 'nClass', opts.nClass, ...
'resLayer', opts.resLayer, 'newLr', opts.newLr) ;
end
end
% -------------------------------------------------------------------------
% Train
% -------------------------------------------------------------------------
% Setup data fetching options
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;
% -------------------------------------------------------------------------
% Network setting
% -------------------------------------------------------------------------
% Launch SGD
if strcmp(opts.mode, 'val')
trainSamples = train;
bnSamples = val;%[train; val];
bnSuffix = 'val';
elseif strcmp(opts.mode, 'test')
trainSamples = [train val];
bnSamples = test;
bnSuffix = 'test';
end
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), TrainNum, opts.train, ...
'train', trainSamples, ...
'val', val) ;
% if ~vgg
% update BN statistics
load(fullfile(opts.expDir, sprintf('net-epoch-%d.mat',opts.train.numEpochs)), 'net');
net = dagnn.DagNN.loadobj(net) ;
updateBN(net, imdb, getBatchWrapper(bopts), opts.train, ...
'train', bnSamples, 'mode', bnSuffix);
% newAutotestIFCN_PascalContext(opts.expDir)
AutotestIFCN_PascalContext(opts.expDir)
% AutotestIFCN_PascalContext_multiscale(opts.expDir)
% opts.train.batchSize = 15;
% bopts.imageSize =416;
% opts = vl_argparse(opts, varargin) ;
% load(fullfile(opts.expDir, sprintf('net-epoch-%d.mat',opts.train.numEpochs)), 'net');
% net = dagnn.DagNN.loadobj(net) ;
% updateBN(net, imdb, getBatchWrapper(bopts), opts.train, ...
% 'train', bnSamples, 'mode', bnSuffix);
% AutotestIFCN_PascalContext(opts.expDir)
% newAutotestIFCN_PascalContext(opts.expDir)
% AutotestIFCN_PascalContext_multiscale(opts.expDir)
% end
% -------------------------------------------------------------------------
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(0.01 ./ h)))));
% classWeight = ones(1, numel(h));