sustaining_gazes/matlab_version/face_validation/DeepLearnToolbox/CAE/scaesetup.m

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2016-04-28 21:40:36 +02:00
function scae = scaesetup(cae, x, opts)
x = x{1};
for l = 1 : numel(cae)
cae = cae{l};
ll= [opts.batchsize size(x{1}, 2) size(x{1}, 3)] + cae.inputkernel - 1;
X = zeros(ll);
cae.M = nbmap(X, cae.scale);
bounds = cae.outputmaps * prod(cae.inputkernel) + numel(x) * prod(cae.outputkernel);
for j = 1 : cae.outputmaps % activation maps
cae.a{j} = zeros(size(x{1}) + cae.inputkernel - 1);
for i = 1 : numel(x) % input map
cae.ik{i}{j} = (rand(cae.inputkernel) - 0.5) * 2 * sqrt(6 / bounds);
cae.ok{i}{j} = (rand(cae.outputkernel) - 0.5) * 2 * sqrt(6 / bounds);
cae.vik{i}{j} = zeros(size(cae.ik{i}{j}));
cae.vok{i}{j} = zeros(size(cae.ok{i}{j}));
end
cae.b{j} = 0;
cae.vb{j} = zeros(size(cae.b{j}));
end
cae.alpha = opts.alpha;
cae.i = cell(numel(x), 1);
cae.o = cae.i;
for i = 1 : numel(cae.o)
cae.c{i} = 0;
cae.vc{i} = zeros(size(cae.c{i}));
end
ss = cae.outputkernel;
cae.edgemask = zeros([opts.batchsize size(x{1}, 2) size(x{1}, 3)]);
cae.edgemask(ss(1) : end - ss(1) + 1, ...
ss(2) : end - ss(2) + 1, ...
ss(3) : end - ss(3) + 1) = 1;
scae{l} = cae;
end
function B = nbmap(X,n)
assert(numel(n)==3,'n should have 3 elements (x,y,z) scaling.');
X = reshape(1:numel(X),size(X,1),size(X,2),size(X,3));
B = zeros(size(X,1)/n(1),prod(n),size(X,2)*size(X,3)/prod(n(2:3)));
u=1;
p=1;
for m=1:size(X,1)
B(u,(p-1)*prod(n(2:3))+1:p*prod(n(2:3)),:) = im2col(squeeze(X(m,:,:)),n(2:3),'distinct');
p=p+1;
if(mod(m,n(1))==0)
u=u+1;
p=1;
end
end
end
end