load cached results if they exist

bbox_regression/rcnn_test_bbox_regressor.m

@@ -24,67 +24,75 @@ else
   end
 end
 
-aboxes = cell(num_classes, 1);
-box_inds = cell(num_classes, 1);
-for i = 1:num_classes
-  load([conf.cache_dir rcnn_model.classes{i} '_boxes_' imdb.name]);
-  aboxes{i} = boxes;
-  box_inds{i} = inds;
-  clear boxes inds;
-end
-
-for i = 1:length(image_ids)
-  fprintf('%s: bbox reg test (%s) %d/%d\n', procid(), imdb.name, i, length(image_ids));
-  d = rcnn_load_cached_pool5_features(feat_opts.cache_name, ...
-      imdb.name, image_ids{i});
-  if isempty(d.feat)
-    continue;
+try
+  aboxes = cell(num_classes, 1);
+  for i = 1:num_classes
+    load([conf.cache_dir rcnn_model.classes{i} '_boxes_' imdb.name suffix]);
+    aboxes{i} = boxes;
+  end
+catch
+  aboxes = cell(num_classes, 1);
+  box_inds = cell(num_classes, 1);
+  for i = 1:num_classes
+    load([conf.cache_dir rcnn_model.classes{i} '_boxes_' imdb.name]);
+    aboxes{i} = boxes;
+    box_inds{i} = inds;
+    clear boxes inds;
   end
 
-  d.feat = rcnn_pool5_to_fcX(d.feat, feat_opts.layer, rcnn_model);
-  d.feat = rcnn_scale_features(d.feat, feat_opts.feat_norm_mean);
+  for i = 1:length(image_ids)
+    fprintf('%s: bbox reg test (%s) %d/%d\n', procid(), imdb.name, i, length(image_ids));
+    d = rcnn_load_cached_pool5_features(feat_opts.cache_name, ...
+        imdb.name, image_ids{i});
+    if isempty(d.feat)
+      continue;
+    end
 
-  if feat_opts.binarize
-    d.feat = single(d.feat > 0);
-  end
+    d.feat = rcnn_pool5_to_fcX(d.feat, feat_opts.layer, rcnn_model);
+    d.feat = rcnn_scale_features(d.feat, feat_opts.feat_norm_mean);
 
-  for j = 1:num_classes
-    I = box_inds{j}{i};
-    boxes = aboxes{j}{i};
-    if ~isempty(boxes)
-      scores = boxes(:,end);
-      boxes = boxes(:,1:4);
-      assert(sum(sum(abs(d.boxes(I,:) - boxes))) == 0);
-      boxes = rcnn_predict_bbox_regressor(bbox_reg.models{j}, d.feat(I,:), boxes);
-      boxes(:,1) = max(boxes(:,1), 1);
-      boxes(:,2) = max(boxes(:,2), 1);
-      boxes(:,3) = min(boxes(:,3), imdb.sizes(i,2));
-      boxes(:,4) = min(boxes(:,4), imdb.sizes(i,1));
-      aboxes{j}{i} = cat(2, single(boxes), single(scores));
+    if feat_opts.binarize
+      d.feat = single(d.feat > 0);
+    end
+
+    for j = 1:num_classes
+      I = box_inds{j}{i};
+      boxes = aboxes{j}{i};
+      if ~isempty(boxes)
+        scores = boxes(:,end);
+        boxes = boxes(:,1:4);
+        assert(sum(sum(abs(d.boxes(I,:) - boxes))) == 0);
+        boxes = rcnn_predict_bbox_regressor(bbox_reg.models{j}, d.feat(I,:), boxes);
+        boxes(:,1) = max(boxes(:,1), 1);
+        boxes(:,2) = max(boxes(:,2), 1);
+        boxes(:,3) = min(boxes(:,3), imdb.sizes(i,2));
+        boxes(:,4) = min(boxes(:,4), imdb.sizes(i,1));
+        aboxes{j}{i} = cat(2, single(boxes), single(scores));
 
-      if 0
-        % debugging visualizations
-        im = imread(imdb.image_at(i));
-        keep = nms(aboxes{j}{i}, 0.3);
-        for k = 1:min(10, length(keep))
-          if aboxes{j}{i}(keep(k),end) > -0.9
-            showboxes(im, aboxes{j}{i}(keep(k),1:4));
-            title(sprintf('%s %d score: %.3f\n', rcnn_model.classes{j}, ...
-                k, aboxes{j}{i}(keep(k),end)));
-            pause;
+        if 0
+          % debugging visualizations
+          im = imread(imdb.image_at(i));
+          keep = nms(aboxes{j}{i}, 0.3);
+          for k = 1:min(10, length(keep))
+            if aboxes{j}{i}(keep(k),end) > -0.9
+              showboxes(im, aboxes{j}{i}(keep(k),1:4));
+              title(sprintf('%s %d score: %.3f\n', rcnn_model.classes{j}, ...
+                  k, aboxes{j}{i}(keep(k),end)));
+              pause;
+            end
           end
         end
       end
     end
   end
-end
 
-for i = 1:num_classes
-  save_file = [conf.cache_dir rcnn_model.classes{i} '_boxes_' imdb.name suffix];
-  boxes = aboxes{i};
-  inds = box_inds{i};
-  save(save_file, 'boxes', 'inds');
-  clear boxes inds;
+  for i = 1:num_classes
+    save_file = [conf.cache_dir rcnn_model.classes{i} '_boxes_' imdb.name suffix];
+    boxes = aboxes{i};
+    inds = box_inds{i};
+    save(save_file, 'boxes', 'inds');
+    clear boxes inds;
+  end
 end
 
 % ------------------------------------------------------------------------
@@ -92,7 +100,13 @@ end
 % ------------------------------------------------------------------------
 for model_ind = 1:num_classes
   cls = rcnn_model.classes{model_ind};
-  res(model_ind) = imdb.eval_func(cls, aboxes{model_ind}, imdb, suffix);
+  try
+    ld = load([conf.cache_dir cls '_pr_' imdb.name suffix]);
+    fprintf('!!! %s : %.4f %.4f\n', cls, ld.res.ap, ld.res.ap_auc);
+    res(model_ind) = ld.res;
+  catch
+    res(model_ind) = imdb.eval_func(cls, aboxes{model_ind}, imdb, suffix);
+  end
 end
 
 fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');

imdb/imdb_eval_voc.m

@@ -84,13 +84,14 @@ if do_eval
 end
 fprintf('!!! %s : %.4f %.4f\n', cls, ap, ap_auc);
 
-save([conf.cache_dir cls '_pr_' imdb.name suffix], ...
-    'recall', 'prec', 'ap', 'ap_auc');
-
 res.recall = recall;
 res.prec = prec;
 res.ap = ap;
 res.ap_auc = ap_auc;
+
+save([conf.cache_dir cls '_pr_' imdb.name suffix], ...
+    'res', 'recall', 'prec', 'ap', 'ap_auc');
+
 if rm_res
   delete(res_fn);
 end