139 lines
5.2 KiB
Matlab
139 lines
5.2 KiB
Matlab
function WriteOutFaceCheckersCNNbinary(locationTxt, faceCheckers)
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addpath('../PDM_helpers\');
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% use little-endian
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faceCheckerFile = fopen(locationTxt, 'w', 'l');
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views = numel(faceCheckers);
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% Type 0 - linear SVR, 1 - feed forward neural net, 2 - CNN, 3 - new
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% CNN
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fwrite(faceCheckerFile, 3, 'uint'); % 4 bytes
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% Number of face checkers
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fwrite(faceCheckerFile, views, 'uint'); % 4 bytes
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% Matrices representing view orientations
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for i=1:views
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% this indicates that we're writing a 3x1 double matrix
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writeMatrixBin(faceCheckerFile, faceCheckers(i).centres', 6);
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end
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for i = 1:views
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% The normalisation models
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% Mean of images
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writeMatrixBin(faceCheckerFile, faceCheckers(i).mean_ex, 6);
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% Standard deviation of images
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writeMatrixBin(faceCheckerFile, faceCheckers(i).std_ex, 6);
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cnn = faceCheckers(i).cnn;
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num_depth_layers = size(cnn.layers,2);
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% Get the number of layers
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fwrite(faceCheckerFile, num_depth_layers, 'uint'); % 4 bytes
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% For disambiguation between FC and conv layers
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res = vl_simplenn(cnn, single(faceCheckers(i).mask), [], []);
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for layers=1:num_depth_layers
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% write layer type: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 -
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% fully connected, 3 - relu, 4 - sigmoid
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if(cnn.layers{layers}.type == 'conv')
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% First check if it is an FC layer (they are represented
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% like that in matconvnet)
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if(numel(res(layers).x) == numel(cnn.layers{layers}.weights{1}(:,:,:,1)))
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% This is the fully connected layer
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fwrite(faceCheckerFile, 2, 'uint'); % 4 bytes
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% the bias term
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writeMatrixBin(faceCheckerFile, cnn.layers{layers}.weights{2}(:), 5);
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% the weights
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% Convert the filters to a matrix
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weights_c = cnn.layers{layers}.weights{1};
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size_w = size(weights_c);
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weights = zeros(size_w(1)*size_w(2)*size_w(3), size_w(4));
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weights(:) = weights_c;
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writeMatrixBin(faceCheckerFile, weights, 5);
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else
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% write the type (convolutional)
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fwrite(faceCheckerFile, 0, 'uint'); % 4 bytes
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num_in_map = size(cnn.layers{layers}.weights{1},3);
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% write the number of input maps
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fwrite(faceCheckerFile, num_in_map, 'uint'); % 4 bytes
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num_out_kerns = size(cnn.layers{layers}.weights{1},4);
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% write the number of kernels for each output map
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fwrite(faceCheckerFile, num_out_kerns, 'uint'); % 4 bytes
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% Write output map bias terms
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for k2=1:num_out_kerns
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fwrite(faceCheckerFile, cnn.layers{layers}.weights{2}(k2), 'float32'); % 4 bytes
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end
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for k=1:num_in_map
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for k2=1:num_out_kerns
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% Write out the bias term
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W = squeeze(cnn.layers{layers}.weights{1}(:,:,k,k2));
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writeMatrixBin(faceCheckerFile, W, 5);
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end
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end
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end
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elseif(cnn.layers{layers}.type == 'pool')
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fwrite(faceCheckerFile, 1, 'uint'); % 4 bytes, indicate max pooling layer, no params, assume (2x2 stride 2)
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elseif(cnn.layers{layers}.type == 'relu')
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fwrite(faceCheckerFile, 3, 'uint'); % 4 bytes, indicate relu layer, no params
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end
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end
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% Piecewise affine warp
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nPix = faceCheckers(i).nPix;
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minX = faceCheckers(i).minX;
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minY = faceCheckers(i).minY;
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destination = reshape(faceCheckers(i).destination, numel(faceCheckers(i).destination), 1);
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triangulation = faceCheckers(i).triangulation;
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triX = faceCheckers(i).triX;
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mask = faceCheckers(i).mask;
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alphas = faceCheckers(i).alphas;
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betas = faceCheckers(i).betas;
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fwrite(faceCheckerFile, nPix, 'uint'); % 4 bytes
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fwrite(faceCheckerFile, minX, 'float64'); % 8 bytes
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fwrite(faceCheckerFile, minY, 'float64'); % 8 bytes
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% Destination shape
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writeMatrixBin(faceCheckerFile, destination, 6);
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% Triangulation
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writeMatrixBin(faceCheckerFile, triangulation, 4);
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% Triangle map
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writeMatrixBin(faceCheckerFile, triX, 4);
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% Mask
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writeMatrixBin(faceCheckerFile, mask, 4);
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% Alphas
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writeMatrixBin(faceCheckerFile, alphas, 6);
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% Betas
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writeMatrixBin(faceCheckerFile, betas, 6);
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end
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fclose(faceCheckerFile);
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end |