imagenet_experiments #4
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| function [net, info] = cnn_pass_imagenet_mat(net, imdb, getBatch, outputDir, varargin) | ||
| %CNN_PASS_IMAGENET_MAT pass data through the network | ||
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| opts.train = [] ; | ||
| opts.val = [] ; | ||
| opts.numEpochs = 300 ; | ||
| opts.batchSize = 256 ; | ||
| opts.useGpu = false ; | ||
| opts.learningRate = 0.001 ; | ||
| opts.continue = false ; | ||
| opts.expDir = fullfile('data','exp') ; | ||
| opts.figuresPath = fullfile('data','figures','fig') ; | ||
| opts.conserveMemory = true ; | ||
| opts.sync = true ; | ||
| opts.prefetch = false ; | ||
| opts.weightDecay = 0.0005 ; | ||
| opts.useGpu = false; | ||
| opts.momentum = 0.9 ; | ||
| opts.errorType = 'multiclass' ; | ||
| opts.plotDiagnostics = false ; | ||
| opts = vl_argparse(opts, varargin) ; | ||
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| 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 | ||
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| % ------------------------------------------------------------------------- | ||
| % Network initialization | ||
| % ------------------------------------------------------------------------- | ||
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| % ------------------------------------------------------------------------- | ||
| % Validate | ||
| % ------------------------------------------------------------------------- | ||
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| rng(0) ; | ||
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| if opts.useGpu | ||
| one = gpuArray(single(1)) ; | ||
| else | ||
| one = single(1) ; | ||
| end | ||
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| startTime = tic; | ||
| info.train.objective = [] ; | ||
| info.train.error = [] ; | ||
| info.train.topFiveError = [] ; | ||
| info.train.speed = [] ; | ||
| info.val.objective = [] ; | ||
| info.val.error = [] ; | ||
| info.val.topFiveError = [] ; | ||
| info.val.speed = [] ; | ||
| info.time = [] ; | ||
| opts.expDir | ||
| res = [] ; | ||
| %---------------data saving data | ||
| dataDirMimic = outputDir; | ||
| if ~exist(dataDirMimic, 'dir') | ||
| mkdir(dataDirMimic); | ||
| end | ||
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| if opts.useGpu | ||
| net = vl_simplenn_move(net, 'gpu') ; | ||
| end | ||
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| for epoch=1:opts.numEpochs | ||
| val = opts.val ; | ||
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| info.train.objective(end+1) = 0 ; | ||
| info.train.error(end+1) = 0 ; | ||
| info.train.topFiveError(end+1) = 0 ; | ||
| info.train.speed(end+1) = 0 ; | ||
| info.val.objective(end+1) = 0 ; | ||
| info.val.error(end+1) = 0 ; | ||
| info.val.topFiveError(end+1) = 0 ; | ||
| info.val.speed(end+1) = 0 ; | ||
| info.time(end+1) = 0; | ||
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| % evaluation on validation set | ||
| lastProcessBatch = 1; | ||
| curBatchnumber = 0; | ||
| for t=lastProcessBatch:opts.batchSize:numel(val) | ||
| curBatchnumber = curBatchnumber + 1; | ||
| batch_time = tic ; | ||
| batch = val(t:min(t+opts.batchSize-1, numel(val))) ; | ||
| fprintf('validation: epoch %02d: processing batch %3d of %3d ...', epoch, ... | ||
| fix(t/opts.batchSize)+1, ceil(numel(val)/opts.batchSize)) ; | ||
| [im, labels] = getBatch(imdb, batch) ; | ||
| if opts.prefetch | ||
| nextBatch = val(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(val))) ; | ||
| getBatch(imdb, nextBatch) ; | ||
| end | ||
| if opts.useGpu | ||
| im = gpuArray(im) ; | ||
| end | ||
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| net.layers{end}.class = labels ; | ||
| opts.useGpu | ||
| [res, data_img] = vl_simplenn_imagenet_mat(net, im, [], res, ... | ||
| 'disableDropout', true, ... | ||
| 'conserveMemory', opts.conserveMemory, ... | ||
| 'sync', opts.sync) ; %#ok | ||
| lastProcessBatch = t; %#ok | ||
| save(fullfile(outputDir,'curBatch.mat'),'lastProcessBatch') | ||
| save(fullfile(outputDir, strcat('data_img', num2str(curBatchnumber))), 'data_img'); | ||
| % print information | ||
| batch_time = toc(batch_time) ; | ||
| speed = numel(batch)/batch_time; | ||
| info.val = updateError(opts, info.val, net, res, batch_time) ; | ||
| fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ; | ||
| n = t + numel(batch) - 1 ; | ||
| if strcmp(opts.errorType, 'mse') | ||
| fprintf(' err %.5f\n', info.val.error(end)/n) ; | ||
| else | ||
| fprintf(' err %.1f err5 %.1f', ... | ||
| info.val.error(end)/n*100, info.val.topFiveError(end)/n*100) ; | ||
| fprintf('\n') ; | ||
| end | ||
| end | ||
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| end | ||
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| % ------------------------------------------------------------------------- | ||
| function info = updateError(opts, info, net, res, speed) | ||
| % ------------------------------------------------------------------------- | ||
| predictions = gather(res(end-1).x) ; | ||
| sz = size(predictions) ; | ||
| n = prod(sz(1:2)) ; | ||
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| labels = net.layers{end}.class ; | ||
| info.objective(end) = info.objective(end) + sum(double(gather(res(end).x))) ; | ||
| info.speed(end) = info.speed(end) + speed ; | ||
| switch opts.errorType | ||
| case 'multiclass' | ||
| [~,predictions] = sort(predictions, 3, 'descend') ; | ||
| error = ~bsxfun(@eq, predictions, reshape(labels, 1, 1, 1, [])) ; | ||
| info.error(end) = info.error(end) +.... | ||
| sum(sum(sum(error(:,:,1,:))))/n ; | ||
| info.topFiveError(end) = info.topFiveError(end) + ... | ||
| sum(sum(sum(min(error(:,:,1:5,:),[],3))))/n ; | ||
| case 'binary' | ||
| error = bsxfun(@times, predictions, labels) < 0 ; | ||
| info.error(end) = info.error(end) + sum(error(:))/n ; | ||
| case 'mse' | ||
| error = predictions - labels; | ||
| info.error(end) = info.error(end) + sum(error(:).^2); | ||
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| end | ||
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| % ------------------------------------------------------------------------- | ||
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| function info = evaluate_imagenet_tt(varargin) | ||
| % evaluate_imagenet_tt evauate vgg16-tt models on imagenet | ||
| opts.data_path = fullfile('data', 'imagenet12','data_path') ; | ||
| opts.dataDir = fullfile('data', 'imagenet12') ; | ||
| opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ; | ||
| opts.imdbPath = fullfile(opts.expDir, 'imdb.mat'); | ||
| opts.modelPath = fullfile('data', 'models', 'imagenet-vgg-f.mat') ; | ||
| opts.lite = false ; | ||
| opts.numFetchThreads = 8 ; | ||
| opts.train.batchSize = 64 ; | ||
| opts.train.numEpochs = 1 ; | ||
| opts.train.gpus = [1]; | ||
| opts.train.prefetch = false ; | ||
| opts.train.expDir = opts.expDir ; | ||
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| opts = vl_argparse(opts, varargin) ; | ||
| display(opts); | ||
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| % ------------------------------------------------------------------------- | ||
| % Database initialization | ||
| % ------------------------------------------------------------------------- | ||
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| if exist(opts.imdbPath) | ||
| imdb = load(opts.imdbPath) ; | ||
| else | ||
| imdb = cnn_imagenet_setup_data('dataDir', opts.dataDir, 'lite', opts.lite) ; | ||
| mkdir(opts.expDir) ; | ||
| save(opts.imdbPath, '-struct', 'imdb') ; | ||
| end | ||
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| % ------------------------------------------------------------------------- | ||
| % Network initialization | ||
| % ------------------------------------------------------------------------- | ||
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| net = load(opts.modelPath) ; | ||
| net.layers{end}.type = 'softmaxloss' ; % softmax -> softmaxloss | ||
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| % Synchronize label indexes between the model and the image database | ||
| imdb = cnn_imagenet_sync_labels(imdb, net); | ||
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| % ------------------------------------------------------------------------- | ||
| % Stochastic gradient descent | ||
| % ------------------------------------------------------------------------- | ||
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| data_path = fullfile(opts.data_path, 'data_img'); | ||
| getBatchWrapper = @(imdb,batch) getBatch(imdb, batch, data_path); | ||
| [net,info] = cnn_train(net, imdb, getBatchWrapper, opts.train, ... | ||
| 'conserveMemory', true, ... | ||
| 'train', NaN, ... | ||
| 'val', find(imdb.images.set==2)) ; | ||
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| % ------------------------------------------------------------------------- | ||
| function [im,labels] = getBatch(imdb, batch, data_path) | ||
| % ------------------------------------------------------------------------- | ||
| %data_path = '../../data_permanent/data_imagenet_mimic_deep/data_img'; | ||
| sizeBatch = 64; | ||
| %batch(1) | ||
| for i = 1 : numel(batch) | ||
| %for val without train | ||
| nFile = ceil((batch(i) - 1281167) / 64); | ||
| %nEl = batch(i) - 64 * floor((batch(i) - 1281167) / 64); | ||
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| % nFile = ceil(batch(i) / sizeBatch); | ||
| % fprintf('nFile %d\n', nFile); | ||
| batchData = load(strcat(data_path, num2str(nFile), '.mat')); | ||
| %batchData | ||
| batchData = batchData.data_img; | ||
| if i == 1 | ||
| im_size = size(batchData); | ||
| im_size(4) = numel(batch); | ||
| im =single(zeros(im_size)); | ||
| end | ||
| % batch(i) - (nFile + 20019-1 - 1) * sizeBatch | ||
| % batch(i) | ||
| %size(batchData) | ||
| %for val without train | ||
| im(:,:,:,i) = batchData(:,:,:, batch(i) - 15 - (nFile + 20019-1 - 1) * sizeBatch); | ||
| end | ||
| labels = imdb.images.label(batch) ; | ||
| %size(labels) | ||
| %size(im) |
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| function info = make_data_mat(varargin) | ||
| % MAKE_DATA_MAT pass data though the network and save it | ||
| opts.outputDir = fullfile('data', 'imagenet_TT','outputDir') ; | ||
| opts.dataDir = fullfile('data', 'imagenet_TT') ; | ||
| opts.expDir = fullfile('data', 'imagenet_TT') ; | ||
| opts.imdbPath = fullfile(opts.expDir, 'imdb.mat'); | ||
| opts.modelPath = fullfile('data', 'models', 'imagenet-vgg-deep-16.mat') ; | ||
| opts.lite = false ; | ||
| opts.numFetchThreads = 8 ; | ||
| opts.train.batchSize = 64 ; | ||
| opts.train.numEpochs = 1 ; | ||
| opts.train.useGpu = true; | ||
| opts.train.prefetch = false ; | ||
| opts.train.expDir = opts.expDir ; | ||
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| opts = vl_argparse(opts, varargin) ; | ||
| display(opts); | ||
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| % ------------------------------------------------------------------------- | ||
| % Database initialization | ||
| % ------------------------------------------------------------------------- | ||
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| if exist(opts.imdbPath) | ||
| imdb = load(opts.imdbPath) ; | ||
| else | ||
| imdb = cnn_imagenet_setup_data('dataDir', opts.dataDir, 'lite', opts.lite) ; | ||
| mkdir(opts.expDir) ; | ||
| save(opts.imdbPath, '-struct', 'imdb') ; | ||
| end | ||
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| % ------------------------------------------------------------------------- | ||
| % Network initialization | ||
| % ------------------------------------------------------------------------- | ||
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| net = load(opts.modelPath) ; | ||
| net.layers{end}.type = 'softmaxloss' ; % softmax -> softmaxloss | ||
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| % Synchronize label indexes between the model and the image database | ||
| imdb = cnn_imagenet_sync_labels(imdb, net); | ||
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| % ------------------------------------------------------------------------- | ||
| % Stochastic gradient descent | ||
| % ------------------------------------------------------------------------- | ||
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| fn = getBatchWrapper(net.normalization, opts.numFetchThreads) ; | ||
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| [~,info] = cnn_pass_imagenet_mat(net, imdb, fn, opts.outputDir, opts.train, ... | ||
| 'conserveMemory', true, ... | ||
| 'train', NaN, ... | ||
| 'val', find(imdb.images.set == 2)) ; | ||
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| % ------------------------------------------------------------------------- | ||
| function fn = getBatchWrapper(opts, numThreads) | ||
| % ------------------------------------------------------------------------- | ||
| fn = @(imdb,batch) getBatch(imdb,batch,opts,numThreads) ; | ||
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| % ------------------------------------------------------------------------- | ||
| function [im,labels] = getBatch(imdb, batch, opts, numThreads) | ||
| % ------------------------------------------------------------------------- | ||
| images = strcat([imdb.imageDir filesep], imdb.images.name(batch)) ; | ||
| im = cnn_imagenet_get_batch(images, opts, ... | ||
| 'numThreads', numThreads, ... | ||
| 'prefetch', nargout == 0) ; | ||
| labels = imdb.images.label(batch) ; |
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| #!/bin/bash | ||
| # sudo apt-get update | ||
| # sudo apt-get install imagemagick | ||
| # Start in folder with original .tar files | ||
| #Before launch set PACKEDTRAINDIR and DATA_OUT | ||
| # Variables | ||
| PACKEDTRAINDIR=/home/ubuntu/tmp/train_packed | ||
| DATA_OUT=/home/ubuntu/imagenet_mimic | ||
| TRAINDIR=$DATA_OUT/images/train | ||
| VALDIR=$DATA_OUT/images/val | ||
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| mkdir $DATA_OUT | ||
| mkdir $DATA_OUT/images | ||
| # Unpack main train archive | ||
| mkdir $PACKEDTRAINDIR | ||
| tar -xvf ILSVRC2012_img_train.tar -C $PACKEDTRAINDIR | ||
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| # Unpack & resize nested train archives | ||
| mkdir $TRAINDIR | ||
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| for NAME in $PACKEDTRAINDIR/*.tar; do | ||
| INDEX=$(basename $NAME .tar) | ||
| echo $INDEX | ||
| if test -d $TRAINDIR/$INDEX; then | ||
| echo "Folder "$TRAINDIR/$INDEX" exists" | ||
| else | ||
| mkdir $TRAINDIR/$INDEX | ||
| tar -xf $PACKEDTRAINDIR/$INDEX.tar -C $TRAINDIR/$INDEX | ||
| # Resize to height to 256, preserving the aspect ratio | ||
| mogrify -resize 256x256^ "$TRAINDIR/$INDEX/*.JPEG" | ||
| fi | ||
| done | ||
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| # Validation | ||
| mkdir $VALDIR | ||
| tar -xf ILSVRC2012_img_val.tar -C $VALDIR | ||
| mogrify -resize 256x256^ "$VALDIR/*.JPEG" | ||
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| # CMYK -> RGB. Important | ||
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Owner
There was a problem hiding this comment. Can you please add a short comment explaining why this is needed? |
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| mogrify -colorspace rgb $TRAINDIR/n03062245/n03062245_4620.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n04264628/n04264628_27969.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03961711/n03961711_5286.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n01739381/n01739381_1309.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n04258138/n04258138_17003.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03018349/n03018349_4028.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n04336792/n04336792_7448.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n02492035/n02492035_15739.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03544143/n03544143_17228.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03467068/n03467068_12171.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03633091/n03633091_5218.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n02447366/n02447366_23489.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03347037/n03347037_9675.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n02077923/n02077923_14822.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n02747177/n02747177_10752.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n04371774/n04371774_5854.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n07583066/n07583066_647.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n04596742/n04596742_4225.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n13037406/n13037406_4650.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03529860/n03529860_11437.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n03710637/n03710637_5125.JPEG | ||
| mogrify -colorspace rgb $TRAINDIR/n04033995/n04033995_2932.JPEG | ||
| mogrify -colorspace rgb $VALDIR/ILSVRC2012_val_00019877.JPEG | ||
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I think it's obsolete.