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WS_CNN_Train.m
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WS_CNN_Train.m
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function [net,stats] = WS_CNN_Train(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.expDir = fullfile('data','exp') ;
opts.continue = true ;
opts.batchSize = 256 ;
opts.numSubBatches = 1 ;
opts.train = [] ;
opts.val = [] ;
opts.gpus = [] ;
opts.prefetch = false ;
opts.numEpochs = 300 ;
opts.learningRate = 0.001 ;
opts.weightDecay = 0.0005 ;
opts.momentum = 0.9 ;
opts.randomSeed = 0 ;
opts.memoryMapFile = fullfile(tempdir, 'matconvnet.bin') ;
opts.profile = false ;
opts.derOutputs = {'objective', 1} ;
opts.extractStatsFn = @extractStats ;
opts.plotStatistics = true;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end
if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end
if isnan(opts.train), opts.train = [] ; end
% -------------------------------------------------------------------------
% Initialization
% -------------------------------------------------------------------------
evaluateMode = isempty(opts.train) ;
if ~evaluateMode
if isempty(opts.derOutputs)
error('DEROUTPUTS must be specified when training.\n') ;
end
end
state.getBatch = getBatch ;
stats = [] ;
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
start = opts.continue * findLastCheckpoint(opts.expDir) ;
if start >= 1
fprintf('%s: resuming by loading epoch %d\n', mfilename, start) ;
[net, stats] = loadState(modelPath(start)) ;
end
h = figure('units','normalized','outerposition',[0 0 1 1]);
for epoch=start+1:opts.numEpochs
% Set the random seed based on the epoch and opts.randomSeed.
% This is important for reproducibility, including when training
% is restarted from a checkpoint.
rng(epoch + opts.randomSeed);
prepareGPUs(opts, epoch == start+1) ;
% Train for one epoch.
state.epoch = epoch;
state.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ;
state.imdb = imdb ;
if numel(opts.gpus) <= 1
[stats.train(epoch),prof] = process_epoch(net, state, opts, 'train') ;
stats.val(epoch) = process_epoch(net, state, opts, 'val') ;
if opts.profile
profview(0,prof) ;
keyboard ;
end
else
savedNet = net.saveobj() ;
spmd
net_ = dagnn.DagNN.loadobj(savedNet) ;
[stats_.train, prof_] = process_epoch(net_, state, opts, 'train') ;
stats_.val = process_epoch(net_, state, opts, 'val') ;
if labindex == 1, savedNet_ = net_.saveobj() ; end
end
net = dagnn.DagNN.loadobj(savedNet_{1}) ;
stats__ = accumulateStats(stats_) ;
stats.train(epoch) = stats__.train ;
stats.val(epoch) = stats__.val ;
if opts.profile
mpiprofile('viewer', [prof_{:,1}]) ;
keyboard ;
end
clear net_ stats_ stats__ savedNet savedNet_ ;
end
% save
if ~evaluateMode
saveState(modelPath(epoch), net, stats) ;
end
if opts.plotStatistics
switchFigure(1) ; clf ;
plots = setdiff(...
cat(2,...
fieldnames(stats.train)', ...
fieldnames(stats.val)'), {'num', 'time'}) ;
for p = plots
p = char(p) ;
values = zeros(0, epoch) ;
leg = {} ;
for f = {'train', 'val'}
f = char(f) ;
if isfield(stats.(f), p)
tmp = [stats.(f).(p)] ;
values(end+1,:) = tmp(1,:)' ;
leg{end+1} = f ;
end
end
subplot(1,numel(plots),find(strcmp(p,plots))) ;
plot(1:epoch, values','o-') ;
xlabel('epoch') ;
title(p) ;
legend(leg{:}) ;
grid on ;
end
drawnow ;
% print(1, modelFigPath, '-dpdf') ;
end
end
if ~isempty(epoch)
print(1, modelFigPath, '-dpdf') ;
end
% -------------------------------------------------------------------------
function [stats, prof] = process_epoch(net, state, opts, mode)
% -------------------------------------------------------------------------
% initialize empty momentum
if strcmp(mode,'train')
state.momentum = num2cell(zeros(1, numel(net.params))) ;
end
% move CNN to GPU as needed
numGpus = numel(opts.gpus) ;
if numGpus >= 1
net.move('gpu') ;
if strcmp(mode,'train')
state.momentum = cellfun(@gpuArray,state.momentum,'UniformOutput',false) ;
end
end
if numGpus > 1
mmap = map_gradients(opts.memoryMapFile, net, numGpus) ;
else
mmap = [] ;
end
% profile
if opts.profile
if numGpus <= 1
profile clear ;
profile on ;
else
mpiprofile reset ;
mpiprofile on ;
end
end
DataID = opts.(mode);
DataImgageLabel = state.imdb.ImageLabelList(DataID);
PositiveDataID = DataID(DataImgageLabel == 1);
NegativeDataID = DataID(DataImgageLabel == 0);
NumPositiveData = numel(PositiveDataID);
NumNegativeData = numel(NegativeDataID);
NumData = NumPositiveData + NumNegativeData;
assert(NumPositiveData == NumNegativeData, 'Currently Assume NumPositiveData = NumNegativeData!!!')
subset = zeros(1, NumData);
subset(1:2:end) = PositiveDataID(randperm(NumPositiveData));
subset(2:2:end) = NegativeDataID(randperm(NumPositiveData));
num = 0 ;
stats.num = 0 ; % return something even if subset = []
stats.time = 0 ;
adjustTime = 0 ;
start = tic ;
for t=1:opts.batchSize:numel(subset)
fprintf('%s: epoch %02d: %3d/%3d:', mode, state.epoch, ...
fix((t-1)/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize)) ;
batchSize = min(opts.batchSize, numel(subset) - t + 1) ;
for s=1:opts.numSubBatches
% get this image batch and prefetch the next
batchStart = t + (labindex-1) + (s-1) * numlabs ;
batchEnd = min(t+opts.batchSize-1, numel(subset)) ;
batch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ;
num = num + numel(batch) ;
if numel(batch) == 0, continue ; end
inputs = state.getBatch(state.imdb, batch) ;
if opts.prefetch
if s == opts.numSubBatches
batchStart = t + (labindex-1) + opts.batchSize ;
batchEnd = min(t+2*opts.batchSize-1, numel(subset)) ;
else
batchStart = batchStart + numlabs ;
end
nextBatch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ;
state.getBatch(state.imdb, nextBatch) ;
end
if strcmp(mode, 'train')
net.mode = 'normal' ;
net.accumulateParamDers = (s ~= 1) ;
net.eval(inputs, opts.derOutputs) ;
else
net.mode = 'test' ;
net.eval(inputs) ;
end
end
% accumulate gradient
if strcmp(mode, 'train')
if ~isempty(mmap)
write_gradients(mmap, net) ;
labBarrier() ;
end
state = accumulate_gradients(state, net, opts, batchSize, mmap) ;
end
% get statistics
time = toc(start) + adjustTime ;
batchTime = time - stats.time ;
stats = opts.extractStatsFn(net) ;
stats.num = num ;
stats.time = time ;
currentSpeed = batchSize / batchTime ;
averageSpeed = (t + batchSize - 1) / time ;
if t == opts.batchSize + 1
% compensate for the first iteration, which is an outlier
adjustTime = 2*batchTime - time ;
stats.time = time + adjustTime ;
end
fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ;
for f = setdiff(fieldnames(stats)', {'num', 'time'})
f = char(f) ;
fprintf(' %s:', f) ;
fprintf(' %.3f', stats.(f)) ;
end
fprintf('\n') ;
end
if ~isempty(mmap)
unmap_gradients(mmap) ;
end
if opts.profile
if numGpus <= 1
prof = profile('info') ;
profile off ;
else
prof = mpiprofile('info');
mpiprofile off ;
end
else
prof = [] ;
end
net.reset() ;
net.move('cpu') ;
% -------------------------------------------------------------------------
function state = accumulate_gradients(state, net, opts, batchSize, mmap)
% -------------------------------------------------------------------------
numGpus = numel(opts.gpus) ;
otherGpus = setdiff(1:numGpus, labindex) ;
for p=1:numel(net.params)
% accumualte gradients from multiple labs (GPUs) if needed
if numGpus > 1
tag = net.params(p).name ;
for g = otherGpus
tmp = gpuArray(mmap.Data(g).(tag)) ;
net.params(p).der = net.params(p).der + tmp ;
end
end
switch net.params(p).trainMethod
case 'average' % mainly for batch normalization
thisLR = net.params(p).learningRate ;
net.params(p).value = ...
(1 - thisLR) * net.params(p).value + ...
(thisLR/batchSize/net.params(p).fanout) * net.params(p).der ;
case 'gradient'
thisDecay = opts.weightDecay * net.params(p).weightDecay ;
thisLR = state.learningRate * net.params(p).learningRate ;
state.momentum{p} = opts.momentum * state.momentum{p} ...
- thisDecay * net.params(p).value ...
- (1 / batchSize) * net.params(p).der ;
net.params(p).value = net.params(p).value + thisLR * state.momentum{p} ;
case 'otherwise'
error('Unknown training method ''%s'' for parameter ''%s''.', ...
net.params(p).trainMethod, ...
net.params(p).name) ;
end
end
% -------------------------------------------------------------------------
function mmap = map_gradients(fname, net, numGpus)
% -------------------------------------------------------------------------
format = {} ;
for i=1:numel(net.params)
format(end+1,1:3) = {'single', size(net.params(i).value), net.params(i).name} ;
end
format(end+1,1:3) = {'double', [3 1], 'errors'} ;
if ~exist(fname) && (labindex == 1)
f = fopen(fname,'wb') ;
for g=1:numGpus
for i=1:size(format,1)
fwrite(f,zeros(format{i,2},format{i,1}),format{i,1}) ;
end
end
fclose(f) ;
end
labBarrier() ;
mmap = memmapfile(fname, ...
'Format', format, ...
'Repeat', numGpus, ...
'Writable', true) ;
% -------------------------------------------------------------------------
function write_gradients(mmap, net)
% -------------------------------------------------------------------------
for i=1:numel(net.params)
mmap.Data(labindex).(net.params(i).name) = gather(net.params(i).der) ;
end
% -------------------------------------------------------------------------
function unmap_gradients(mmap)
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
function stats = accumulateStats(stats_)
% -------------------------------------------------------------------------
for s = {'train', 'val'}
s = char(s) ;
total = 0 ;
% initialize stats stucture with same fields and same order as
% stats_{1}
stats__ = stats_{1} ;
names = fieldnames(stats__.(s))' ;
values = zeros(1, numel(names)) ;
fields = cat(1, names, num2cell(values)) ;
stats.(s) = struct(fields{:}) ;
for g = 1:numel(stats_)
stats__ = stats_{g} ;
num__ = stats__.(s).num ;
total = total + num__ ;
for f = setdiff(fieldnames(stats__.(s))', 'num')
f = char(f) ;
stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ;
if g == numel(stats_)
stats.(s).(f) = stats.(s).(f) / total ;
end
end
end
stats.(s).num = total ;
end
% -------------------------------------------------------------------------
function stats = extractStats(net)
% -------------------------------------------------------------------------
sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ;
stats = struct() ;
for i = 1:numel(sel)
stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ;
end
% -------------------------------------------------------------------------
function saveState(fileName, net, stats)
% -------------------------------------------------------------------------
net_ = net ;
net = net_.saveobj() ;
save(fileName, 'net', 'stats') ;
% -------------------------------------------------------------------------
function [net, stats] = loadState(fileName)
% -------------------------------------------------------------------------
net = load(fileName, 'net', 'stats') ;
stats = net.stats;
net = dagnn.DagNN.loadobj(net.net) ;
% -------------------------------------------------------------------------
function epoch = findLastCheckpoint(modelDir)
% -------------------------------------------------------------------------
list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ;
tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ;
epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ;
epoch = max([epoch 0]) ;
% -------------------------------------------------------------------------
function switchFigure(n)
% -------------------------------------------------------------------------
if get(0,'CurrentFigure') ~= n
try
set(0,'CurrentFigure',n) ;
catch
figure(n) ;
end
end
% -------------------------------------------------------------------------
function prepareGPUs(opts, cold)
% -------------------------------------------------------------------------
numGpus = numel(opts.gpus) ;
if numGpus > 1
% check parallel pool integrity as it could have timed out
pool = gcp('nocreate') ;
if ~isempty(pool) && pool.NumWorkers ~= numGpus
delete(pool) ;
end
pool = gcp('nocreate') ;
if isempty(pool)
parpool('local', numGpus) ;
cold = true ;
end
if exist(opts.memoryMapFile)
delete(opts.memoryMapFile) ;
end
end
if numGpus >= 1 && cold
fprintf('%s: resetting GPU\n', mfilename)
if numGpus == 1
gpuDevice(opts.gpus)
else
spmd, gpuDevice(opts.gpus(labindex)), end
end
end
%end