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SCSNet_init_independent.m
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SCSNet_init_independent.m
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function net = SCSNet_init_independent( varargin )
%CNN_DDCN_INIT Summary of this function goes here
% Detailed explanation goes here
net = dagnn.DagNN();
d=128;
s=32;
rng('default');
rng(0) ;
reluLeak = 0;
% bnormal =false;
net.meta.solver = 'Adam';
net.meta.inputSize = [96 96] ;
% net.meta.trainOpts.weightDecay = 0.0001 ;
% net.meta.trainOpts.momentum = 0.9;
net.meta.trainOpts.batchSize = 64;
net.meta.trainOpts.learningRate = [logspace(-3,-3,50) logspace(-4,-4,30) logspace(-5,-5,20)];
net.meta.trainOpts.numEpochs = numel(net.meta.trainOpts.learningRate) ;
net.meta.adjGradClipping = false;
net.meta.gradthresh = 0.005;%[0.005*ones(1,20) 0.003*ones(1,20) 0.001*ones(1,40)];
% net.meta.derOutputs = {'s01pdist',1,'s02pdist',1,'s03pdist',1,'pdistbasic',1,'pdistInits02',1,'pdistInits03',1};
net.meta.derOutputs = {'s07pdist',1,'pdistInits07',1};%'s02pdist',1,'s03pdist',1,'s04pdist',1,'s05pdist',1};
%for raw
net.meta.imdbPath ='E:\wzhshi_code\ScalableCSNet\data\model_64_96_Adam-1400\imdb.mat';
net1 = load('.\model\net-04.mat');
net1 = dagnn.DagNN.loadobj(net1.net);
%-------------------------------------------------------------------
%basic layer
% sampling
block = dagnn.Conv('size', [32 32 1 10], 'hasBias', false, ...
'stride', 32, 'pad', [0 0 0 0]);
lName = 's01sampling';
net.addLayer(lName, block, 'input', lName, {[lName '_f']});
%initial reconstruction
block = dagnn.Conv('size', [1 1 10 1024], 'hasBias', false, ...
'stride', 1, 'pad', [0 0 0 0]);
lName = 's01initRecon';
net.addLayer(lName, block, 's01sampling', lName, {[lName '_f']});
block = dagnn.bcs_init_rec_dag('dims',[32 32]);
lName = 's01combine';
net.addLayer(lName,block,'s01initRecon',lName);
% %deep reconstruction
% block = dagnn.Conv('size', [3 3 1 d], 'hasBias', true, ...
% 'stride', 1, 'pad', [1 1 1 1]);
% lName1 = 's01dr1';
% net.addLayer(lName1, block, 's01combine', lName1, {[lName1 '_f'], [lName1 '_b']});
% block = dagnn.ReLU('leak',reluLeak);
% net.addLayer([lName1 '_relu'], block, lName1, [lName1 '_relu']);
%
% i=2;
% block = dagnn.Conv('size', [1 1 d s], 'hasBias', true, ...
% 'stride', 1, 'pad', 0, 'dilate', 1);
% lName = ['s01dr' num2str(i)];
% net.addLayer(lName, block, ['s01dr' num2str(i-1) '_relu'], lName, {[lName '_f'], [lName '_b']});
% block = dagnn.ReLU('leak',reluLeak);
% net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
%
% % net.addLayer([lName1 '_cat'],dagnn.Concat('dim',3),{[lName1 '_relu'],'s01combine'},[lName1 '_cat']);
%
% for i=3:1:15
%
% block = dagnn.Conv('size', [3 3 s s], 'hasBias', true, ...
% 'stride', 1, 'pad', 1, 'dilate', 1);
% lName = ['s01dr' num2str(i)];
% net.addLayer(lName, block, ['s01dr' num2str(i-1) '_relu'], lName, {[lName '_f'], [lName '_b']});
% block = dagnn.ReLU('leak',reluLeak);
% net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
%
% % net.addLayer([lName '_cat'],dagnn.Concat('dim',3),{[lName '_relu'],'s01combine'},[lName '_cat']);
%
% end
%
% i=16;
% block = dagnn.Conv('size', [1 1 s d], 'hasBias', true, ...
% 'stride', 1, 'pad', 0, 'dilate', 1);
% lName = ['s01dr' num2str(i)];
% net.addLayer(lName, block, ['s01dr' num2str(i-1) '_relu'], lName, {[lName '_f'], [lName '_b']});
% block = dagnn.ReLU('leak',reluLeak);
% net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
%
% % i=5;
% % block = dagnn.Conv('size', [3 3 d+1 d], 'hasBias', true, ...
% % 'stride', 1, 'pad', 1, 'dilate', 1);
% % lName = ['s01dr' num2str(i)];
% % net.addLayer(lName, block, ['s01dr' num2str(i-1) '_cat'], lName, {[lName '_f'], [lName '_b']});
% % block = dagnn.ReLU('leak',reluLeak);
% % net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
%
% % net.addLayer('s01dr',dagnn.Concat('dim',3),{'s01dr1_relu','s01dr2_relu','s01dr3_relu','s01dr4_relu'},'s01dr');
%
% block = dagnn.Conv('size', [3 3 d 1], 'hasBias', true, ...
% 'stride', 1, 'pad', [1 1 1 1]);
% lName = 's01prediction';
% net.addLayer(lName, block, 's01dr16_relu', lName, {[lName '_f'], [lName '_b']});
%
% net.addLayer('s01dr_pred',dagnn.Sum(),{'s01prediction','s01combine'},'s01dr_pred');
% % net.addLayer('s01pdist',dagnn.L2TVLoss('lamda', 0.001),{ 's01dr_pred','label'},'s01pdist');
% % net.addLayer('pdistInits01',dagnn.L2TVLoss('lamda', 0.001),{'s01combine','label'},'pdistInits01');
% net.addLayer('pdistInits01',dagnn.EuclidLoss(),{'s01combine','label'},'pdistInits01');
% net.addLayer('s01pdist',dagnn.EuclidLoss(),{ 's01dr_pred','label'},'s01pdist');
% % net.addLayer('s01pdist',dagnn.vllab_dag_loss('lambda', 1, 'loss_type', 'L1','average_by_size',0),{'s01dr_pred','label'},'s01pdist');
% % net.addLayer('pdistInits01',dagnn.vllab_dag_loss ('lambda', 1, 'loss_type', 'L1','average_by_size',0),{'s01combine','label'},'pdistInits01');
% %---------------------------------------------------------------------------
%enhancement layer1
% sampling
block = dagnn.Conv('size', [32 32 1 41], 'hasBias', false, ...
'stride', 32, 'pad', [0 0 0 0]);
lName = 's02sampling';
net.addLayer(lName, block, 'input', lName, {[lName '_f']});
%initial reconstruction
% net.addLayer('s02samplingE',dagnn.Concat('dim',3),{'s01sampling','s02sampling'},'s02samplingE');
block = dagnn.Conv('size', [1 1 41 1024], 'hasBias', false, ...
'stride', 1, 'pad', [0 0 0 0]);
lName = 's02initRecon';
net.addLayer(lName, block, 's02sampling', lName, {[lName '_f']});
block = dagnn.bcs_init_rec_dag('dims',[32 32]);
lName = 's02combine';
net.addLayer(lName,block,'s02initRecon',lName);
% %---------------------------------------------------------------------------
%enhancement layer2
% sampling
block = dagnn.Conv('size', [32 32 1 51], 'hasBias', false, ...
'stride', 32, 'pad', [0 0 0 0]);
lName = 's03sampling';
net.addLayer(lName, block, 'input', lName, {[lName '_f']});
%initial reconstruction
% net.addLayer('s02samplingE',dagnn.Concat('dim',3),{'s01sampling','s02sampling'},'s02samplingE');
block = dagnn.Conv('size', [1 1 51 1024], 'hasBias', false, ...
'stride', 1, 'pad', [0 0 0 0]);
lName = 's03initRecon';
net.addLayer(lName, block, 's03sampling', lName, {[lName '_f']});
block = dagnn.bcs_init_rec_dag('dims',[32 32]);
lName = 's03combine';
net.addLayer(lName,block,'s03initRecon',lName);
%---------------------------------------------------------------------------
%enhancement layer3
% sampling
block = dagnn.Conv('size', [32 32 1 103], 'hasBias', false, ...
'stride', 32, 'pad', [0 0 0 0]);
lName = 's04sampling';
net.addLayer(lName, block, 'input', lName, {[lName '_f']});
%initial reconstruction
% net.addLayer('s02samplingE',dagnn.Concat('dim',3),{'s01sampling','s02sampling'},'s02samplingE');
block = dagnn.Conv('size', [1 1 103 1024], 'hasBias', false, ...
'stride', 1, 'pad', [0 0 0 0]);
lName = 's04initRecon';
net.addLayer(lName, block, 's04sampling', lName, {[lName '_f']});
block = dagnn.bcs_init_rec_dag('dims',[32 32]);
lName = 's04combine';
net.addLayer(lName,block,'s04initRecon',lName);
%enhancement layer4
% sampling
block = dagnn.Conv('size', [32 32 1 102], 'hasBias', false, ...
'stride', 32, 'pad', [0 0 0 0]);
lName = 's05sampling';
net.addLayer(lName, block, 'input', lName, {[lName '_f']});
%initial reconstruction
% net.addLayer('s02samplingE',dagnn.Concat('dim',3),{'s01sampling','s02sampling'},'s02samplingE');
block = dagnn.Conv('size', [1 1 102 1024], 'hasBias', false, ...
'stride', 1, 'pad', [0 0 0 0]);
lName = 's05initRecon';
net.addLayer(lName, block, 's05sampling', lName, {[lName '_f']});
block = dagnn.bcs_init_rec_dag('dims',[32 32]);
lName = 's05combine';
net.addLayer(lName,block,'s05initRecon',lName);
%enhancement layer5
% sampling
block = dagnn.Conv('size', [32 32 1 103], 'hasBias', false, ...
'stride', 32, 'pad', [0 0 0 0]);
lName = 's06sampling';
net.addLayer(lName, block, 'input', lName, {[lName '_f']});
%initial reconstruction
% net.addLayer('s02samplingE',dagnn.Concat('dim',3),{'s01sampling','s02sampling'},'s02samplingE');
block = dagnn.Conv('size', [1 1 103 1024], 'hasBias', false, ...
'stride', 1, 'pad', [0 0 0 0]);
lName = 's06initRecon';
net.addLayer(lName, block, 's06sampling', lName, {[lName '_f']});
block = dagnn.bcs_init_rec_dag('dims',[32 32]);
lName = 's06combine';
net.addLayer(lName,block,'s06initRecon',lName);
%enhancement layer6
% sampling
block = dagnn.Conv('size', [32 32 1 102], 'hasBias', false, ...
'stride', 32, 'pad', [0 0 0 0]);
lName = 's07sampling';
net.addLayer(lName, block, 'input', lName, {[lName '_f']});
%initial reconstruction
% net.addLayer('s02samplingE',dagnn.Concat('dim',3),{'s01sampling','s02sampling'},'s02samplingE');
block = dagnn.Conv('size', [1 1 102 1024], 'hasBias', false, ...
'stride', 1, 'pad', [0 0 0 0]);
lName = 's07initRecon';
net.addLayer(lName, block, 's07sampling', lName, {[lName '_f']});
block = dagnn.bcs_init_rec_dag('dims',[32 32]);
lName = 's07combine';
net.addLayer(lName,block,'s07initRecon',lName);
% % net.addLayer('s02dr0',dagnn.Concat('dim',3),{'s01dr_pred','s02combine'},'s02dr0');
enl = 7;
% net.addLayer('s07combineAdds06',dagnn.Sum(),{'s01combine','s02combine','s03combine','s04combine','s05combine','s06combine','s07combine'},'s07combineAdds06');
net.addLayer(['s0' num2str(enl) 'combineAdds0' num2str(enl-1)],dagnn.Sum(),{'s01combine','s02combine','s03combine','s04combine','s05combine','s06combine','s07combine'},['s0' num2str(enl) 'combineAdds0' num2str(enl-1)]);
net.addLayer(['pdistInits0' num2str(enl)],dagnn.EuclidLoss(),{['s0' num2str(enl) 'combineAdds0' num2str(enl-1)],'label'},['pdistInits0' num2str(enl)]);
%deep reconstruction
block = dagnn.Conv('size', [3 3 1 d], 'hasBias', true, ...
'stride', 1, 'pad', [1 1 1 1]);
lName1 = ['s0' num2str(enl) 'dr1'];
net.addLayer(lName1, block, ['s0' num2str(enl) 'combineAdds0' num2str(enl-1)], lName1, {[lName1 '_f'], [lName1 '_b']});
block = dagnn.ReLU('leak',reluLeak);
net.addLayer([lName1 '_relu'], block, lName1, [lName1 '_relu']);
% net.addLayer([lName1 '_cat'],dagnn.Concat('dim',3),{[lName1 '_relu'],'s01dr_pred'},[lName1 '_cat']);
i=2;
block = dagnn.Conv('size', [1 1 d s], 'hasBias', true, ...
'stride', 1, 'pad', 0, 'dilate', 1);
lName = ['s0' num2str(enl) 'dr' num2str(i)];
net.addLayer(lName, block, ['s0' num2str(enl) 'dr' num2str(i-1) '_relu'], lName, {[lName '_f'], [lName '_b']});
block = dagnn.ReLU('leak',reluLeak);
net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
% net.addLayer([lName1 '_cat'],dagnn.Concat('dim',3),{[lName1 '_relu'],'s01combine'},[lName1 '_cat']);
for i=3:1:15
block = dagnn.Conv('size', [3 3 s s], 'hasBias', true, ...
'stride', 1, 'pad', 1, 'dilate', 1);
lName = ['s0' num2str(enl) 'dr' num2str(i)];
net.addLayer(lName, block, ['s0' num2str(enl) 'dr' num2str(i-1) '_relu'], lName, {[lName '_f'], [lName '_b']});
block = dagnn.ReLU('leak',reluLeak);
net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
% net.addLayer([lName '_cat'],dagnn.Concat('dim',3),{[lName '_relu'],'s01dr_pred'},[lName '_cat']);
end
i=16;
block = dagnn.Conv('size', [1 1 s d], 'hasBias', true, ...
'stride', 1, 'pad', 0, 'dilate', 1);
lName = ['s0' num2str(enl) 'dr' num2str(i)];
net.addLayer(lName, block, ['s0' num2str(enl) 'dr' num2str(i-1) '_relu'], lName, {[lName '_f'], [lName '_b']});
block = dagnn.ReLU('leak',reluLeak);
net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
% i=5;
% block = dagnn.Conv('size', [3 3 d+1 d], 'hasBias', true, ...
% 'stride', 1, 'pad', 1, 'dilate', 1);
% lName = ['s02dr' num2str(i)];
% net.addLayer(lName, block, ['s02dr' num2str(i-1) '_cat'], lName, {[lName '_f'], [lName '_b']});
% block = dagnn.ReLU('leak',reluLeak);
% net.addLayer([lName '_relu'], block, lName, [lName '_relu']);
% net.addLayer('s02dr',dagnn.Concat('dim',3),{'s02dr1_relu','s02dr2_relu','s02dr3_relu','s02dr4_relu'},'s02dr');
block = dagnn.Conv('size', [3 3 d 1], 'hasBias', true, ...
'stride', 1, 'pad', [1 1 1 1]);
lName = ['s0' num2str(enl) 'prediction'];
net.addLayer(lName, block, ['s0' num2str(enl) 'dr16_relu'], lName, {[lName '_f'], [lName '_b']});
%
% net.addLayer('s01dr_pred',dagnn.Sum(),{'s01prediction','s01combine'},'s01dr_pred');
net.addLayer(['s0' num2str(enl) 'dr_pred'],dagnn.Sum(),{['s0' num2str(enl) 'prediction'],['s0' num2str(enl) 'combineAdds0' num2str(enl-1)]},['s0' num2str(enl) 'dr_pred']);
% net.addLayer('s02dr_pred',dagnn.Sum(),{'s01dr_pred','s02dr_res'},'s02dr_pred');
% net.addLayer('pdistInits02',dagnn.EuclidLoss(),{'s02combine','label'},'pdistInits02');
net.addLayer(['s0' num2str(enl) 'pdist'],dagnn.EuclidLoss(),{['s0' num2str(enl) 'dr_pred'],'label'},['s0' num2str(enl) 'pdist']);
net.initParams();
for i=1:12
net.params(i).value = net1.params(i).value;
net.params(i).learningRate = 0;
net.params(i).weightDecay = 0;
end
for i=13:46
net.params(i+2).value = net1.params(i).value;
end