First C++ version of DCLM

matlab_version/models/dpn/write_patch_expert_bin.m

@@ -0,0 +1,73 @@
+function write_patch_expert_bin(location, trainingScale, centers, visiIndex, patch_experts)
+          
+    patches_file = fopen(location, 'w');        
+    
+    [n_views, n_landmarks, ~] = size(patch_experts.correlations);
+
+    % write out the scaling factor as this is what will be used when
+    % fitting on the window
+    fwrite(patches_file, trainingScale, 'float64');
+    
+    fwrite(patches_file, n_views, 'int');
+    
+    % Write out the information about the view's and centers here
+    for i=1:n_views
+        % this indicates that we're writing a 3x1 double matrix
+        writeMatrixBin(patches_file, centers(i,:)', 6);
+    end
+            
+    % Write out the visibilities
+    for i=1:n_views
+        % this indicates that we're writing a 3x1 double matrix
+        writeMatrixBin(patches_file, visiIndex(i,:)', 4);
+    end
+            
+    for i=1:n_views
+        for j=1:n_landmarks
+
+            % Write out that we're writing a dpn patch expert of 11x11 support region
+            fwrite(patches_file, 6, 'int');
+            fwrite(patches_file, 11, 'int');
+            fwrite(patches_file, 11, 'int');
+            
+            if(~visiIndex(i,j))       
+                % Write out that there won't be any neurons for this
+                % landmark
+                fwrite(patches_file, 0, 'int');
+                fwrite(patches_file, 0, 'int');
+            else
+                num_layers = numel(patch_experts.patch_experts{i,j})/2;
+
+                fwrite(patches_file, num_layers, 'int');
+
+                for n=1:num_layers
+                    
+                    % output the actual layer
+                    
+                    % Layer type, bias, weights
+                    
+                    % Type of layer, first two are relu, the final one is a
+                    % sigmoid (0 - sigmoid, 1 - tanh_opt, 2 - ReLU)
+                    if(n < 3)
+                        fwrite(patches_file, 2, 'int');
+                    else
+                        fwrite(patches_file, 0, 'int');
+                    end
+                                                            
+                    bias = patch_experts.patch_experts{i,j}{n*2};
+                    
+                    weights = patch_experts.patch_experts{i,j}{n*2-1};
+
+                    % the actual bias/weight matrix
+                    writeMatrixBin(patches_file, bias, 5);
+                    writeMatrixBin(patches_file, weights, 5);
+                end
+                
+                % finally write out the confidence
+                fwrite(patches_file, patch_experts.correlations(i,j), 'float64');
+                
+            end    
+        end
+    end
+
+    fclose(patches_file);