210 lines
6.5 KiB
Matlab
210 lines
6.5 KiB
Matlab
function train_CNN_model(varargin)
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setup('useGPU', true);
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tic
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% Load the data
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root_loc = 'D:\Datasets\detection_validation';
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location = [root_loc, '/prep_data/'];
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faceCheckersLoc_train = dir([location 'face_validator_train_*']);
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faceCheckersLoc_test = dir([location 'face_validator_test_*']);
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corrs_all = [];
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rmses_all = [];
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faceCheckers = struct;
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% As we will be training a classifier that will act as a regressor,
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% binarize it
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num_bins = 30;
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for i=1:numel(faceCheckersLoc_train)
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load([location faceCheckersLoc_train(i).name]);
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% set a max value to the error
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errors(errors > 3) = 3;
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errors_train = quantizeContinuous(errors, 0, 3, num_bins);
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examples_train = single(examples);
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clear examples
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mean_ex = mean(examples_train);
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std_ex = std(examples_train);
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std_ex = std_ex / 256;
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examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex);
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num_examples_train = size(examples_train,1);
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% Add rows and columns untill we have 60 x 60 image
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% keep adding rows
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while(size(mask,1) < 60)
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mask = cat(1, mask, false(1, size(mask,2)));
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triX = cat(1, triX, -ones(1, size(mask,2)));
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end
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% keep adding cols
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while(size(mask,2) < 60)
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mask = cat(2, mask, false(size(mask,1),1));
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triX = cat(2, triX, -ones(size(triX,1),1));
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end
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examples_r = single(zeros(size(mask, 1), size(mask, 2), num_examples_train));
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img_curr = zeros(size(mask));
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for e=1:num_examples_train
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img_curr(mask) = examples_train(e,:);
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examples_r(:, :, e) = img_curr;
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end
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imdb.images.data = examples_r;
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clear examples_r
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imdb.images.label = errors_train';
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imdb.images.id = 1:numel(errors_train);
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% Split data for training and validation (20%)
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imdb.images.set = ones(1, numel(errors_train));
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imdb.images.set(round(4*end/5):end) = 2;
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% Visualize some of the data
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figure(10) ; clf ; colormap gray ;
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subplot(1,2,1) ;
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pos_samples = squeeze(imdb.images.data(:,:,imdb.images.label==1));
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vl_imarraysc(pos_samples(:,:,1:20)) ;
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axis image off ;
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title('Positive training data') ;
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subplot(1,2,2) ;
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neg_samples = squeeze(imdb.images.data(:,:,imdb.images.label>5));
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vl_imarraysc(neg_samples(:,:,1:20)) ;
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axis image off ;
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title('Negative training data') ;
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net = initializeFaceCNN(num_bins) ;
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trainOpts.batchSize = 100 ;
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trainOpts.numEpochs = 20;
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trainOpts.continue = true ;
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trainOpts.gpus = [1] ;
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trainOpts.learningRate = 0.001 ;
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trainOpts.expDir = ['trained/intermediate/face_validator_' num2str(i)];
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trainOpts.errorFunction = 'regression';
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trainOpts = vl_argparse(trainOpts, varargin);
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% Call training function in MatConvNet
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[net,info] = cnn_train_reg(net, imdb, @getBatch, trainOpts) ;
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% Move the CNN back to the CPU if it was trained on the GPU
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if numel(trainOpts.gpus) > 0
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net = vl_simplenn_move(net, 'cpu') ;
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end
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% Save the result for later use
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net.layers(end) = [] ;
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net.imageMean = mean_ex ;
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net.imageStd = std_ex;
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% save('data/face-experiment/facecnn.mat', '-struct', 'net') ;
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% Evaluate on the test data, also evaluate using correlation and RMSE
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load([location faceCheckersLoc_test(i).name]);
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% set a max value to the error
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errors(errors > 3) = 3;
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errors_test = errors;
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errors_q = quantizeContinuous(errors, 0, 3, num_bins);
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errors_test_q = errors_q;
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examples_test = single(examples);
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examples_test = bsxfun(@times, bsxfun(@minus, examples_test, net.imageMean), 1./net.imageStd);
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num_examples_test = size(examples_test,1);
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% keep adding rows
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while(size(mask,1) < 60)
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mask = cat(1, mask, false(1, size(mask,2)));
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triX = cat(1, triX, -ones(1, size(mask,2)));
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end
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% keep adding cols
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while(size(mask,2) < 60)
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mask = cat(2, mask, false(size(mask,1),1));
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triX = cat(2, triX, -ones(size(triX,1),1));
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end
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examples_r = single(zeros(size(mask, 1), size(mask, 2), num_examples_test));
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img_curr = zeros(size(mask));
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for e=1:num_examples_test
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img_curr(mask) = examples_test(e,:);
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examples_r(:, :, e) = img_curr;
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end
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examples_test = zeros(size(examples_r,1), size(examples_r,2), 1, size(examples_r,3));
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examples_test(:) = single(examples_r(:));
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ids = 1:99:num_examples_test;
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errors_all_test = zeros(num_examples_test,1);
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for k=1:numel(ids)-1
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examples_test_sm = single(examples_test(:,:,:,ids(k):ids(k+1)-1));
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res = vl_simplenn(net, examples_test_sm, [], []);
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res = gather(res(end).x);
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[~,res] = sort(res, 3, 'descend') ;
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res = squeeze(res);
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res = res(1,:);
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errors_test_rec = unQuantizeContinuous(res, 0, 3, num_bins)';
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errors_all_test(ids(k):ids(k+1)-1) = errors_test_rec;
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end
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errors_all_test = errors_all_test(1:ids(end));
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corrs_test = corr(errors_all_test, errors_test(1:numel(errors_all_test)));
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rmse_test = sqrt(mean((errors_all_test-errors_test(1:numel(errors_all_test))).^2));
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corrs_all = cat(1, corrs_all, corrs_test);
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rmses_all = cat(1, rmses_all, rmse_test);
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net.corrs = corrs_test;
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net.rmse = rmse_test;
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save([trainOpts.expDir, '/facecnn.mat'], '-struct', 'net');
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% The CNN
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faceCheckers(i).cnn = net;
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% The orientation
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faceCheckers(i).centres = centres;
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% The info for preprocessing
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faceCheckers(i).mask = mask;
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faceCheckers(i).nPix = nPix;
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faceCheckers(i).minX = minX;
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faceCheckers(i).minY = minY;
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faceCheckers(i).destination = shape(:,1:2);
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tri = load([location faceCheckersLoc_train(i).name], 'triangulation');
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faceCheckers(i).triangulation = tri.triangulation;
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faceCheckers(i).triX = triX;
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faceCheckers(i).mask = mask;
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faceCheckers(i).alphas = alphas;
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faceCheckers(i).betas = betas;
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faceCheckers(i).mean_ex = mean_ex;
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faceCheckers(i).std_ex = std_ex;
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WriteOutFaceCheckersCNNbinary("trained/validator_cnn.txt", faceCheckers);
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end
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save('trained/faceCheckers.mat', 'faceCheckers', 'corrs_all', 'rmses_all');
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end
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% --------------------------------------------------------------------
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function [im, labels] = getBatch(imdb, batch)
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% --------------------------------------------------------------------
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im = imdb.images.data(:,:,batch) ;
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im = reshape(im, 60, 60, 1, []) ;
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labels = imdb.images.label(1,batch) ;
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end
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