342 lines
10 KiB
Mathematica
342 lines
10 KiB
Mathematica
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function [state,stats] = cnn_test_dag( imdb, getBatch, varargin)
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%CNN_TEST_DAG Demonstrates test a CNN using the DagNN wrapper
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% CNN_TEST_DAG() is a slim version to CNN_TRAIN_DAG(), just do the
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% testing of the final net in the export
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opts.expDir = fullfile('data','exp') ;
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opts.batchSize = 256 ;
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opts.numSubBatches = 1 ;
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opts.train = [] ;
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opts.val = [] ;
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opts.test = [];
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opts.gpus = [] ;
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opts.prefetch = false ;
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opts.testEpoch = inf;
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opts.testSelect = [1, 1, 1]; % (1) training; (2)validation; (3), testing
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opts.saveResult = true;
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opts.bnRefine = false;
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opts.randomSeed = 0 ;
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opts.stegoShuffle = false;
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opts.cudnn = true ;
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opts.extractStatsFn = @extractStats ;
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opts = vl_argparse(opts, varargin) ;
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if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
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if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end
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if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end
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if isempty(opts.test), opts.test = find(imdb.images.set==3); end
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if isnan(opts.train), opts.train = [] ; end
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% -------------------------------------------------------------------------
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% Initialization
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% -------------------------------------------------------------------------
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state.getBatch = getBatch ;
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% -------------------------------------------------------------------------
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% Train and validate
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% -------------------------------------------------------------------------
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if ( opts.bnRefine )
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modelPath = @(ep) fullfile(opts.expDir, sprintf('bn-epoch-%d.mat', ep));
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resultPath = @(ep) fullfile(opts.expDir, sprintf('test-bn-epoch-%d.mat', ep));
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else
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modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
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resultPath = @(ep) fullfile(opts.expDir, sprintf('test-net-epoch-%d.mat', ep));
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end
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start = findLastCheckpoint(opts.expDir) ;
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if( start < 1 )
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error( 'Found no net' );
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end
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if start >= 1
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start = min(start, opts.testEpoch);
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fprintf('%s: testing by loading epoch name %s\n', mfilename, modelPath(start) );
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net = loadState(modelPath(start)) ;
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end
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% Make sure that we use the estimated BN moments
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for i = 1:numel(net.layers)
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if ( isa( net.layers(i).block, 'dagnn.BatchNorm') )
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net.layers(i).block.computeMoment = false;
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end
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end
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for epoch = start
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% Set the random seed based on the epoch and opts.randomSeed.
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% This is important for reproducibility, including when training
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% is restarted from a checkpoint.
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rng(epoch + opts.randomSeed) ;
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prepareGPUs(opts, true ) ;
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% Train for one epoch.
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state.epoch = epoch ;
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% shuffle
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if( opts.stegoShuffle )
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N = numel(opts.train); % TRN
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M = numel(opts.val); % VAL
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K = numel(opts.test); % TST
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Lab = max( 1, numel(opts.gpus));
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% M and N must be even, and multiple Lab
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assert( ( rem( N, 2*Lab ) == 0 ) & ...
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( rem( M, 2*Lab ) == 0 ) & ...
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( rem( K, 2*Lab ) == 0 ) );
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seq = opts.train( 2*randperm(N/2) - 1 );
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seq = reshape( seq, Lab, N/(2*Lab) );
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state.train = reshape( [seq; seq+1], 1, N );
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seq = opts.val( 2*randperm(M/2) - 1 );
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seq = reshape( seq, Lab, M/(2*Lab) );
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state.val = reshape( [seq; seq+1], 1, M );
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seq = opts.test( 2*randperm(K/2) - 1 );
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seq = reshape( seq, Lab, K/(2*Lab) );
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state.test = reshape( [seq; seq+1], 1, K );
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else
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state.train = opts.train(randperm(numel(opts.train))) ;
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state.val = opts.val(randperm(numel(opts.val))) ;
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state.test = opts.test(randperm(numel(opts.test))) ;
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% N = numel(opts.train); % TRN
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% M = numel(opts.val); % VAL
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% K = numel(opts.test); % TST
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%
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%
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% state.train = opts.train([1:2:N, 2:2:N]);
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% state.val = opts.val([1:2:M, 2:2:M]);
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% state.test = opts.test([1:2:K, 2:2:K]);
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end
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state.imdb = imdb ;
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if numel(opts.gpus) <= 1
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if( opts.testSelect(1) )
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stats.train = process_epoch(net, state, opts, 'train') ;
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end
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if( opts.testSelect(2) )
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stats.val = process_epoch(net, state, opts, 'val') ;
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end
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if( opts.testSelect(3) )
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stats.test = process_epoch(net, state, opts, 'test');
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end
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else
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savedNet = net.saveobj() ;
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spmd
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net_ = dagnn.DagNN.loadobj(savedNet) ;
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if( opts.testSelect(1) )
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stats_.train = process_epoch(net_, state, opts, 'train') ;
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end
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if( opts.testSelect(2) )
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stats_.val = process_epoch(net_, state, opts, 'val') ;
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end
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if( opts.testSelect(3) )
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stats_.test = process_epoch(net_, state, opts, 'test');
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end
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if labindex == 1, savedNet_ = net_.saveobj() ; end
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end
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net = dagnn.DagNN.loadobj(savedNet_{1}) ;
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stats__ = accumulateStats(stats_) ;
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if( opts.testSelect(1) )
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stats.train = stats__.train ;
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end
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if( opts.testSelect(2) )
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stats.val = stats__.val ;
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end
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if( opts.testSelect(3) )
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stats.test = stats__.test;
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end
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clear net_ stats_ stats__ savedNet savedNet_ ;
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end
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% save
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if( opts.saveResult == true )
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saveState(resultPath(epoch), net, stats, state) ;
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end
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end
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% -------------------------------------------------------------------------
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function stats = process_epoch(net, state, opts, mode)
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% -------------------------------------------------------------------------
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% move CNN to GPU as needed
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numGpus = numel(opts.gpus) ;
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if numGpus >= 1
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net.move('gpu') ;
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end
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subset = state.(mode) ;
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num = 0 ;
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stats.num = 0 ; % return something even if subset = []
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stats.time = 0 ;
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adjustTime = 0 ;
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start = tic ;
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for t=1:opts.batchSize:numel(subset)
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fprintf('%s: epoch %02d: %3d/%3d:', mode, state.epoch, ...
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fix((t-1)/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize)) ;
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batchSize = min(opts.batchSize, numel(subset) - t + 1) ;
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for s=1:opts.numSubBatches
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% get this image batch and prefetch the next
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batchStart = t + (labindex-1) + (s-1) * numlabs ;
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batchEnd = min(t+opts.batchSize-1, numel(subset)) ;
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batch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ;
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num = num + numel(batch) ;
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if numel(batch) == 0, continue ; end
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inputs = state.getBatch(state.imdb, batch) ;
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if opts.prefetch
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if s == opts.numSubBatches
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batchStart = t + (labindex-1) + opts.batchSize ;
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batchEnd = min(t+2*opts.batchSize-1, numel(subset)) ;
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else
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batchStart = batchStart + numlabs ;
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end
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nextBatch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ;
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state.getBatch(state.imdb, nextBatch) ;
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end
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net.mode = 'test' ;
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net.eval(inputs) ;
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end
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% get statistics
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time = toc(start) + adjustTime ;
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batchTime = time - stats.time ;
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stats = opts.extractStatsFn(net) ;
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stats.num = num ;
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stats.time = time ;
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currentSpeed = batchSize / batchTime ;
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averageSpeed = (t + batchSize - 1) / time ;
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if t == opts.batchSize + 1
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% compensate for the first iteration, which is an outlier
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adjustTime = 2*batchTime - time ;
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stats.time = time + adjustTime ;
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end
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fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ;
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for f = setdiff(fieldnames(stats)', {'num', 'time'})
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f = char(f) ;
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fprintf(' %s:', f) ;
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fprintf(' %.3f', stats.(f)) ;
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end
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fprintf('\n') ;
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end
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net.reset() ;
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net.move('cpu') ;
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% -------------------------------------------------------------------------
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function stats = accumulateStats(stats_)
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% -------------------------------------------------------------------------
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for s = {'train', 'val', 'test'}
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s = char(s) ;
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total = 0 ;
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% initialize stats stucture with same fields and same order as
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% stats_{1}
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stats__ = stats_{1} ;
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if ( ~isfield(stats__, s) )
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continue;
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end
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names = fieldnames(stats__.(s))' ;
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values = zeros(1, numel(names)) ;
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fields = cat(1, names, num2cell(values)) ;
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stats.(s) = struct(fields{:}) ;
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for g = 1:numel(stats_)
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stats__ = stats_{g} ;
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num__ = stats__.(s).num ;
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total = total + num__ ;
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for f = setdiff(fieldnames(stats__.(s))', 'num')
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f = char(f) ;
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stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ;
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if g == numel(stats_)
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stats.(s).(f) = stats.(s).(f) / total ;
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end
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end
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end
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stats.(s).num = total ;
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end
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% -------------------------------------------------------------------------
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function stats = extractStats(net)
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% -------------------------------------------------------------------------
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sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ;
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stats = struct() ;
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for i = 1:numel(sel)
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stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ;
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end
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% -------------------------------------------------------------------------
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function saveState(fileName, net, stats, state )
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% -------------------------------------------------------------------------
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net_ = net ;
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net = net_.saveobj() ;
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save(fileName, 'net', 'stats', 'state') ;
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% -------------------------------------------------------------------------
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function [net, stats] = loadState(fileName)
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% -------------------------------------------------------------------------
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load(fileName, 'net', 'stats') ;
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net = dagnn.DagNN.loadobj(net) ;
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% -------------------------------------------------------------------------
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function epoch = findLastCheckpoint(modelDir)
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% -------------------------------------------------------------------------
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list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ;
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tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ;
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epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ;
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epoch = max([epoch 0]) ;
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% -------------------------------------------------------------------------
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function prepareGPUs(opts, cold)
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% -------------------------------------------------------------------------
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numGpus = numel(opts.gpus) ;
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if numGpus > 1
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% check parallel pool integrity as it could have timed out
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pool = gcp('nocreate') ;
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if ~isempty(pool) && pool.NumWorkers ~= numGpus
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delete(pool) ;
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end
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pool = gcp('nocreate') ;
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if isempty(pool)
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parpool('local', numGpus) ;
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cold = true ;
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end
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end
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if numGpus >= 1 && cold
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fprintf('%s: resetting GPU\n', mfilename)
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if numGpus == 1
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gpuDevice(opts.gpus)
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else
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spmd, gpuDevice(opts.gpus(labindex)), end
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end
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end
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%end
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