fix workflow of the pool5 feature explorer

experiments/rcnn_exp_train_and_test.m

 function [res_test, res_train] = rcnn_exp_train_and_test()
+% Runs an experiment that trains an R-CNN model and tests it.
 
 % -------------------- CONFIG --------------------
 net_file     = './data/caffe_nets/finetune_voc_2007_trainval_iter_70k';

startup.m

@@ -4,7 +4,7 @@ if exist('selective_search/SelectiveSearchCodeIJCV')
   addpath('selective_search/SelectiveSearchCodeIJCV/Dependencies');
 else
   fprintf('Warning: you will need the selective search IJCV code.\n');
-  fprintf('Press any key to download it (runs ./selective_search/fetch_selective_search.sh)');
+  fprintf('Press any key to download it (runs ./selective_search/fetch_selective_search.sh)> ');
   pause;
   system('./selective_search/fetch_selective_search.sh');
   addpath('selective_search/SelectiveSearchCodeIJCV');
@@ -19,8 +19,9 @@ addpath('bbox_regression');
 if exist('external/caffe/matlab/caffe')
   addpath('external/caffe/matlab/caffe');
 else
-  warning('Please install caffe in ./external/caffe');
+  warning('Please install Caffe in ./external/caffe');
 end
 addpath('experiments');
 addpath('imdb');
+addpath('vis/pool5-explorer');
 fprintf('R-CNN startup done\n');

vis/pool5-explorer/pool5_explorer.m

@@ -11,11 +11,7 @@ function pool5_explorer(imdb, cache_name)
 
 conf = rcnn_config('sub_dir', imdb.name);
 
-index_file = sprintf('%s/pool5_explorer_index_%s', ...
-    conf.cache_dir, cache_name);
-
-% load pre-computed index into variable 'index'
-load(index_file);
+index = pool5_explorer_build_index(imdb, cache_name);
 
 figures = [1 2];
 vf_sz = [8 12];

vis/pool5-explorer/pool5_explorer_build_index.m

-function pool5_explorer_build_index(imdb, cache_name)
+function index = pool5_explorer_build_index(imdb, cache_name)
 % AUTORIGHTS
 % ---------------------------------------------------------
 % Copyright (c) 2014, Ross Girshick
@@ -23,73 +23,81 @@ conf = rcnn_config('sub_dir', imdb.name);
 save_file = sprintf('%s/pool5_explorer_index_%s', ...
     conf.cache_dir, cache_name);
 
-TOP_K = 1000;
+try
+  % Load already computed index
+  fprintf('trying to load: %s\n', save_file);
+  load(save_file);
+catch
+  warning('Building the explorer index. This will take a long time.');
 
-ids = imdb.image_ids;
+  TOP_K = 1000;
 
-% select features
-sel_features = 1:(6*6*256);
+  ids = imdb.image_ids;
 
-index.imdb_name = imdb.name;
-index.images = ids;
-features = cell(length(sel_features), 1);
+  % select features
+  sel_features = 1:(6*6*256);
 
-for i = 1:length(features)
-  features{i}.image_inds = [];
-  features{i}.scores = [];
-  features{i}.boxes = zeros(0, 4);
-end
-
-for i = 1:length(ids)
-  tic_toc_print('%d/%d', i, length(ids));
-  th = tic();
-  d = rcnn_load_cached_pool5_features(cache_name, ...
-      imdb.name, ids{i});
+  index.imdb_name = imdb.name;
+  index.images = ids;
+  features = cell(length(sel_features), 1);
 
-  feat = d.feat;
+  for i = 1:length(features)
+    features{i}.image_inds = [];
+    features{i}.scores = [];
+    features{i}.boxes = zeros(0, 4);
+  end
 
-  parfor f = sel_features
-    threshold = min(features{f}.scores);
-    if isempty(threshold)
-      threshold = -inf;
-    end
-    sel_0 = find(feat(:,f) > threshold);
-    if isempty(sel_0)
-      continue;
+  for i = 1:length(ids)
+    tic_toc_print('%d/%d', i, length(ids));
+    th = tic();
+    d = rcnn_load_cached_pool5_features(cache_name, ...
+        imdb.name, ids{i});
+
+    feat = d.feat;
+
+    parfor f = sel_features
+      threshold = min(features{f}.scores);
+      if isempty(threshold)
+        threshold = -inf;
+      end
+      sel_0 = find(feat(:,f) > threshold);
+      if isempty(sel_0)
+        continue;
+      end
+      bs = [d.boxes(sel_0,:) feat(sel_0,f)];
+      sel = nms(bs, 0.1);
+
+      sel = sel_0(sel);
+      sz = length(sel);
+
+      new_image_inds = i*ones(sz,1);
+      new_scores = feat(sel,f);
+      new_boxes = d.boxes(sel,:);
+
+      features{f}.image_inds = cat(1, features{f}.image_inds, ...
+                                      new_image_inds);
+      features{f}.scores = cat(1, features{f}.scores, ...
+                                  new_scores);
+      features{f}.boxes = cat(1, features{f}.boxes, ...
+                                 new_boxes);
+
+      [~, ord] = sort(features{f}.scores, 'descend');
+      if length(ord) > TOP_K 
+        ord = ord(1:TOP_K);
+      end
+      features{f}.image_inds = features{f}.image_inds(ord);
+      features{f}.scores = features{f}.scores(ord);
+      features{f}.boxes = features{f}.boxes(ord, :);
     end
-    bs = [d.boxes(sel_0,:) feat(sel_0,f)];
-    sel = nms(bs, 0.1);
-
-    sel = sel_0(sel);
-    sz = length(sel);
-
-    new_image_inds = i*ones(sz,1);
-    new_scores = feat(sel,f);
-    new_boxes = d.boxes(sel,:);
-
-    features{f}.image_inds = cat(1, features{f}.image_inds, ...
-                                    new_image_inds);
-    features{f}.scores = cat(1, features{f}.scores, ...
-                                new_scores);
-    features{f}.boxes = cat(1, features{f}.boxes, ...
-                               new_boxes);
-
-    [~, ord] = sort(features{f}.scores, 'descend');
-    if length(ord) > TOP_K 
-      ord = ord(1:TOP_K);
+    fprintf(' %.3fs\n', toc(th));
+
+    if mod(i, 50) == 0
+      index.features = features;
+      save(save_file, 'index');
+      fprintf('checkpoint %d\n', i);
     end
-    features{f}.image_inds = features{f}.image_inds(ord);
-    features{f}.scores = features{f}.scores(ord);
-    features{f}.boxes = features{f}.boxes(ord, :);
   end
-  fprintf(' %.3fs\n', toc(th));
 
-  if mod(i, 50) == 0
-    index.features = features;
-    save(save_file, 'index');
-    fprintf('checkpoint %d\n', i);
-  end
+  index.features = features;
+  save(save_file, 'index');
 end
-
-index.features = features;
-save(save_file, 'index');