cnn_train_dag_seg.m

function [net,stats] = cnn_train_dag_seg(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) 2014-15 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).

% Todo: save momentum with checkpointing (a waste?)

% -------------------------------------------------------------------------
%                                                          Generate options
% -------------------------------------------------------------------------
opts = generate_default_opts();
opts = vl_argparse(opts, varargin);


% -------------------------------------------------------------------------
%                                                            Initialization
% -------------------------------------------------------------------------

state.getBatch = getBatch ;
evaluateMode = isempty(opts.train) ;
if ~evaluateMode
  if isempty(opts.derOutputs)
    error('DEROUTPUTS must be specified when training.\n') ;
  end
end
stats = [] ;

% setup GPUs
numGpus = numel(opts.gpus) ;
if numGpus > 1
  if isempty(gcp('nocreate')),
    parpool('local',numGpus) ;
    spmd, gpuDevice(opts.gpus(labindex)), end
  end
  if exist(opts.memoryMapFile)
    delete(opts.memoryMapFile) ;
  end
elseif numGpus == 1
  gpuDevice(opts.gpus)
end

% -------------------------------------------------------------------------
%                                                        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('resuming by loading epoch %d\n', start) ;
  [net, stats] = loadState(modelPath(start)) ;
  net.conserveMemory = false;
end

for epoch=start+1:opts.numEpochs

  % train one epoch
  save('epochNum.mat','epoch');
  state.epoch = epoch ;
  state.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ;
  state.train = opts.train(randperm(numel(opts.train))) ; % shuffle
  state.val = opts.val ;
  state.imdb = imdb ;

  if numGpus <= 1
    stats.train(epoch) = process_epoch(net, state, opts, 'train') ;
    stats.val(epoch) = process_epoch(net, state, opts, 'val') ;
  else
    savedNet = net.saveobj() ;
    spmd
      net_ = dagnn.DagNN.loadobj(savedNet) ;
      stats_.train = 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 ;
    clear net_ stats_ stats__ savedNet_ ;
  end

  if ~evaluateMode
    saveState(modelPath(epoch), net, stats) ;
  end

% -------------------------------------------------------------------------
%                                                  Visualize results & save
% -------------------------------------------------------------------------
  stats.epoch = epoch;
  opts.fcn_visualize(stats, imdb, net, opts);

end

% -------------------------------------------------------------------------
function stats = process_epoch(net, state, opts, mode)
% -------------------------------------------------------------------------

if strcmp(mode,'train')
  state.momentum = num2cell(zeros(1, numel(net.params))) ;
end

numGpus = numel(opts.gpus) ;
if numGpus >= 1
  net.move('gpu') ;
  if strcmp(mode,'train')
    sate.momentum = cellfun(@gpuArray,state.momentum,'UniformOutput',false) ;
  end
end
if numGpus > 1
  mmap = map_gradients(opts.memoryMapFile, net, numGpus) ;
else
  mmap = [] ;
end

stats.time = 0 ;
stats.num = 0 ;
stats.num_batches = 0;
subset = state.(mode) ;
start = tic ;
num = 0 ;

for t=1:opts.batchSize:numel(subset)
  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

  % extract learning stats
  stats.num_batches = stats.num_batches + 1;
  stats = opts.extractStatsFn(net, stats) ;

  % accumulate gradient
  if strcmp(mode, 'train')
    if ~isempty(mmap)
      write_gradients(mmap, net) ;
      labBarrier() ;
    end
    state = accumulate_gradients(state, net, opts, batchSize, mmap) ;
  end

  % print learning statistics
  time = toc(start) ;
  stats.num = num ;
  stats.time = toc(start) ;

  fprintf('%s:\t epoch %02d: %3d/%3d: %.1f Hz', mode, state.epoch, fix(t/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize), stats.num/stats.time * max(numGpus, 1));
  fprintf('\t[obj-loss = %.3f,\t overall acc.=%.3f%%,\t perclass acc.=%.3f%%]', stats.objective, stats.overall, stats.perclass);
  fprintf('\n') ;
end

net.reset() ;
net.move('cpu') ;

% -------------------------------------------------------------------------
function state = accumulate_gradients(state, net, opts, batchSize, mmap)
% -------------------------------------------------------------------------
for p=1:numel(net.params)

  % bring in gradients from other GPUs if any
  if ~isempty(mmap)
    numGpus = numel(mmap.Data) ;
    tmp = zeros(size(mmap.Data(labindex).(net.params(p).name)), 'single') ;
    for g = setdiff(1:numGpus, labindex)
      tmp = tmp + mmap.Data(g).(net.params(p).name) ;
    end
    net.params(p).der = net.params(p).der + tmp ;
  else
    numGpus = 1 ;
  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).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 stats = accumulateStats(stats_)
% -------------------------------------------------------------------------

stats = struct() ;

for s = {'train', 'val'}
  s = char(s) ;
  total = 0 ;

  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) ;

      if g == 1
        stats.(s).(f) = 0 ;
      end
      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 saveState(fileName, net, stats)
% -------------------------------------------------------------------------
net_ = net ;
net = net_.saveobj() ;
save(fileName, 'net', 'stats') ;

% -------------------------------------------------------------------------
function [net, stats] = loadState(fileName)
% -------------------------------------------------------------------------
load(fileName, 'net', 'stats') ;
net = dagnn.DagNN.loadobj(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]) ;