Bug fix

bnorm: don't add bnorm in last layer

rcn.m

@@ -29,6 +29,9 @@
 opts = vl_argparse(opts, varargin);
 
 exp_name = 'exp';
+if opts.useBnorm
+    exp_name = 'exp_bn';
+end
 for problem_iter = 1:numel(opts.problems)
     problem = opts.problems{problem_iter};
     switch problem.type
@@ -43,7 +46,7 @@
 exp_name = sprintf('%s_resid%d_depth%d', exp_name, opts.resid, opts.depth);
 opts.expDir = fullfile('data/exp',exp_name);
 
-rep=20;
+rep=25;
 opts.learningRate = [0.1*ones(1,rep) 0.01*ones(1,rep) 0.001*ones(1,rep) 0.0001*ones(1,rep)];%*0.99 .^ (0:500);
 opts.gradRange = 1e-4;
 if ~exist('data/result', 'dir'), mkdir('data/result'); end

rcn_init.m

@@ -40,7 +40,7 @@
 % optionally switch to batch normalization
 if opts.useBnorm
     d = 1;
-    while d < numel(net.layers)
+    while d+1 < numel(net.layers)
         if strcmp(net.layers{d}.type,'conv')
             net = insertBnorm(net, d);
         end

rcn_train.m

@@ -408,6 +408,7 @@
         end
         if opts.useGpu
             imlow = gpuArray(imlow);
+            imhigh = gpuArray(imhigh);
         end
 
         % predict
@@ -440,13 +441,13 @@
         end
         switch evalType
             case 'PSNR'
-                eval_base(problem_iter) = eval_base(problem_iter) + compute_psnr(imhigh,imlow);
-                eval_ours(problem_iter) = eval_ours(problem_iter) + compute_psnr(imhigh,impred);
+                eval_base(problem_iter) = eval_base(problem_iter) + gather(compute_psnr(imhigh,imlow));
+                eval_ours(problem_iter) = eval_ours(problem_iter) + gather(compute_psnr(imhigh,impred));
         end
 
         if printPic && f_n == 1
-            imwrite(imlow,  strcat(problem.type,'_low.bmp'));
-            imwrite(impred, strcat(problem.type,'_pred.bmp'));
+            imwrite(gather(imlow),  strcat(problem.type,'_low.bmp'));
+            imwrite(gather(impred), strcat(problem.type,'_pred.bmp'));
         end
     end
 end