sustaining_gazes/matlab_version/face_validation/WriteOutFaceCheckersCNNbinary.m
2016-04-28 15:40:36 -04:00

126 lines
No EOL
4.3 KiB
Matlab

function WriteOutFaceCheckersCNNbinary(locationTxt, faceCheckers)
addpath('..\PDM_helpers\');
% use little-endian
faceCheckerFile = fopen(locationTxt, 'w', 'l');
views = numel(faceCheckers);
% Type 0 - linear SVR, 1 - feed forward neural net, 2 - CNN
fwrite(faceCheckerFile, 2, 'uint'); % 4 bytes
% Number of face checkers
fwrite(faceCheckerFile, views, 'uint'); % 4 bytes
% Matrices representing view orientations
for i=1:views
% this indicates that we're writing a 3x1 double matrix
writeMatrixBin(faceCheckerFile, faceCheckers(i).centres', 6);
end
for i = 1:views
% The normalisation models
% Mean of images
writeMatrixBin(faceCheckerFile, faceCheckers(i).mean_ex, 6);
% Standard deviation of images
writeMatrixBin(faceCheckerFile, faceCheckers(i).std_ex, 6);
cnn = faceCheckers(i).cnn;
num_depth_layers = size(cnn.layers,1);
% Get the number of layers
fwrite(faceCheckerFile, num_depth_layers, 'uint'); % 4 bytes
for layers=2:num_depth_layers
% write layer type: 0 - convolutional, 1 - subsampling, 2-
% fully connected
if(cnn.layers{layers}.type == 'c')
% write the type (convolutional)
fwrite(faceCheckerFile, 0, 'uint'); % 4 bytes
num_in_map = size(cnn.layers{layers}.k,2);
% write the number of input maps
fwrite(faceCheckerFile, num_in_map, 'uint'); % 4 bytes
num_out_kerns = cnn.layers{layers}.outputmaps;
% write the number of kernels for each output map
fwrite(faceCheckerFile, num_out_kerns, 'uint'); % 4 bytes
% Write output map bias terms
for k2=1:num_out_kerns
fwrite(faceCheckerFile, cnn.layers{layers}.b{k2}, 'float32'); % 4 bytes
end
for k=1:num_in_map
for k2=1:num_out_kerns
% Write out the bias term
W = cnn.layers{layers}.k{k}{k2};
writeMatrixBin(faceCheckerFile, W, 5);
end
end
else
fwrite(faceCheckerFile, 1, 'uint'); % 4 bytes
% size of scaling
fwrite(faceCheckerFile, cnn.layers{layers}.scale, 'uint'); % 4 bytes
end
end
% This is the fully connected layer
fwrite(faceCheckerFile, 2, 'uint'); % 4 bytes
% the bias term
fwrite(faceCheckerFile, cnn.ffb, 'float32');
% the weights
writeMatrixBin(faceCheckerFile, cnn.ffW, 5);
%sigm(net.ffW * net.fv + repmat(net.ffb, 1, size(net.fv, 2)));
% Piecewise affine warp
nPix = faceCheckers(i).nPix;
minX = faceCheckers(i).minX;
minY = faceCheckers(i).minY;
destination = reshape(faceCheckers(i).destination, numel(faceCheckers(i).destination), 1);
triangulation = faceCheckers(i).triangulation;
triX = faceCheckers(i).triX;
mask = faceCheckers(i).mask;
alphas = faceCheckers(i).alphas;
betas = faceCheckers(i).betas;
fwrite(faceCheckerFile, nPix, 'uint'); % 4 bytes
fwrite(faceCheckerFile, minX, 'float64'); % 8 bytes
fwrite(faceCheckerFile, minY, 'float64'); % 8 bytes
% Destination shape
writeMatrixBin(faceCheckerFile, destination, 6);
% Triangulation
writeMatrixBin(faceCheckerFile, triangulation, 4);
% Triangle map
writeMatrixBin(faceCheckerFile, triX, 4);
% Mask
writeMatrixBin(faceCheckerFile, mask, 4);
% Alphas
writeMatrixBin(faceCheckerFile, alphas, 6);
% Betas
writeMatrixBin(faceCheckerFile, betas, 6);
end
fclose(faceCheckerFile);
end