Fix anchor index calculation

Fix anchor feature map source index calculation. Now returning a larger
portion of the feature rect in Localizer:inputToFeatureRect (inflating
rect by kernel width). Added debug-normalization function (to view
feature maps or weights).

Anchors.lua

@@ -118,13 +118,11 @@ function Anchors:findPositive(roi_list, clip_rect, pos_threshold, neg_threshold,
         for x=1,r.xs:size()[1] do
           -- create rect, add layer & aspect info
           local anchor_rect = Rect.new(r.xs[{x, 1}], minY, r.xs[{x, 2}], maxY)
-          --print(anchor_rect)
           anchor_rect.layer = r.layer
           anchor_rect.aspect = r.aspect 
-          anchor_rect.index = { { r.aspect * 6 - 5, r.aspect * 6 }, r.ly + y - 1, r.ly + x - 1 }
+          anchor_rect.index = { { r.aspect * 6 - 5, r.aspect * 6 }, r.ly + y - 1, r.lx + x - 1 }
 
           local v = Rect.IoU(roi.rect, anchor_rect)
-          --print(v)
           if v > pos_threshold then
             table.insert(matches, { anchor_rect, roi })
             best = nil

Localizer.lua

@@ -43,7 +43,7 @@ function Localizer:inputToFeatureRect(rect, layer_index)
   for i=1,layer_index do
     local l = self.layers[i]
     if l.dW < l.kW then
-      rect = rect:inflate((l.kW-l.dW)/2, (l.kH-l.dH)/2)
+      rect = rect:inflate((l.kW-l.dW), (l.kH-l.dH))
     end
 
     rect = rect:offset(l.padW, l.padH)

main.lua

@@ -48,9 +48,6 @@ if opt.seed ~= 0 then
   cutorch.manualSeed(opt.seed)
 end
 
--- precompute positive examples
-
-
 function read_csv_file(fn)
   -- format of RoI file:
   -- filename, left, top, right, bottom, model_class_name, model_class_index, material_name, material_index
@@ -221,9 +218,10 @@ function create_optimization_target(pnet, cnet, weights, gradient, training_data
       for i,x in ipairs(p) do
         local anchor = x[1]
         local roi = x[2]
+        local l = x[1].layer
 
-        local out = outputs[x[1].layer]
-        local delta_out = delta_outputs[x[1].layer]
+        local out = outputs[l]
+        local delta_out = delta_outputs[l]
 
         local idx = x[1].index
         local v = out[idx]
@@ -250,8 +248,9 @@ function create_optimization_target(pnet, cnet, weights, gradient, training_data
 
       -- process negative
       for i,x in ipairs(n) do
-        local out = outputs[x.layer]
-        local delta_out = delta_outputs[x.layer]
+        local l = x.layer
+        local out = outputs[l]
+        local delta_out = delta_outputs[l]
         local idx = x.index
         local v = out[idx]
         local d = delta_out[idx]
@@ -419,7 +418,6 @@ function graph_evaluate(training_data_filename, network_filename, normalize)
   for n,fn in ipairs(test_images) do
 
     -- load image
-    print(fn)
     local input = load_image_auto_size(fn, training_data.target_smaller_side, training_data.max_pixel_size, 'yuv')
     local input_size = input:size()
     input = normalize_image(input):cuda()
@@ -512,10 +510,10 @@ function graph_evaluate(training_data_filename, network_filename, normalize)
 
     for i,m in ipairs(winners) do
       local color
-      --if m.class ~= 17 and math.exp(m.confidence) > 0.25 then
+      if m.class ~= 17 and math.exp(m.confidence) > 0.25 then
         draw_rectangle(img, m.r, blue)
-        draw_rectangle(img, m.r2, green)
-      --end
+        --draw_rectangle(img, m.r2, green)
+      end
     end
 
     image.saveJPG(string.format('dummy%d.jpg', n), img)
@@ -588,5 +586,5 @@ function graph_training(training_data_filename, network_filename)
 end
 
 --precompute_positive_list('training_data.t7', 0.6, 0.3)
-graph_training('training_data.t7', 'full2_001000.t7') 
---graph_evaluate('training_data.t7', 'full2_001000.t7', true)
+graph_training('training_data.t7') 
+--graph_evaluate('training_data.t7', 'full2_022000.t7', true)

utilities.lua

@@ -141,3 +141,8 @@ end
 function remove_quotes(s)
   return s:gsub('^"(.*)"$', "%1")
 end
+
+function normalize_debug(t)
+  local lb, ub = t:min(), t:max()
+  return (t -lb):div(ub-lb+1e-10)
+end