fix loss in multibox_target

operator/multibox_target.cc

@@ -198,20 +198,18 @@ inline void MultiBoxTargetForward(const Tensor<cpu, 2, DType> &loc_target,
                 anchor_flags[j] == -1) {
                 // calcuate class predictions
               DType max_val = p_cls_preds[j];
-              DType max_val_pos = max_val;
               for (int k = 1; k < num_classes; ++k) {
                 DType tmp = p_cls_preds[j + num_anchors * k];
-                if (tmp > max_val_pos) max_val_pos = tmp;
+                if (tmp > max_val) max_val = tmp;
               }
               DType sum = 0.f;
               for (int k = 0; k < num_classes; ++k) {
                 DType tmp = p_cls_preds[j + num_anchors * k];
                 sum += std::exp(tmp - max_val);
               }
-              max_val_pos = std::exp(max_val_pos - max_val) / sum;
-              // loss should be -log(x), but value does not matter, so skip log
-              DType loss = -max_val_pos;
-              temp.push_back(SortElemDescend(loss, j));
+              DType prob = std::exp(p_cls_preds[j] - max_val) / sum;
+              // loss should be -log(x), but value does not matter, skip log
+              temp.push_back(SortElemDescend(-prob, j));
             }
           }  // end iterate anchors
 

operator/multibox_target.cu

@@ -189,25 +189,24 @@ __global__ void NegativeMining(const DType *overlaps, const DType *cls_preds,
     if (anchor_flags[i] < 0) {
       // compute max class prediction score
       DType max_val = cls_preds[i];
-      DType max_val_pos = max_val;  // regarding background
       for (int j = 1; j < num_classes; ++j) {
         DType temp = cls_preds[i + num_anchors * j];
-        if (temp > max_val_pos) max_val_pos = temp;
+        if (temp > max_val) max_val = temp;
       }
       DType sum = 0.f;
       for (int j = 0; j < num_classes; ++j) {
         DType temp = cls_preds[i + num_anchors * j];
         sum += exp(temp - max_val);
       }
-      max_val_pos = exp(max_val_pos - max_val) / sum;
+      DType prob = exp(cls_preds[i] - max_val) / sum;
       DType max_iou = -1.f;
       for (int j = 0; j < num_labels; ++j) {
         DType temp = overlaps[i * num_labels + j];
         if (temp > max_iou) max_iou = temp;
       }
       if (max_iou < negative_mining_thresh) {
         // only do it for anchors with iou < thresh
-        buffer[i] = -max_val_pos;  // -log(x) actually, but value does not matter
+        buffer[i] = -prob;  // -log(x) actually, but value does not matter
       }
     }
   }

symbol/common.py

@@ -173,7 +173,7 @@ def multibox_layer(from_layers, num_classes, sizes=[.2, .95],
         else:
             step = '(-1.0, -1.0)'
         anchors = mx.symbol.MultiBoxPrior(from_layer, sizes=size_str, ratios=ratio_str, \
-            clip=clip, name="{}_anchors".format(from_name), steps=steps)
+            clip=clip, name="{}_anchors".format(from_name), steps=step)
         anchors = mx.symbol.Flatten(data=anchors)
         anchor_layers.append(anchors)
 

train.py

@@ -15,8 +15,8 @@ def parse_args():
                         default=os.path.join(os.getcwd(), 'data', 'val.rec'), type=str)
     parser.add_argument('--val-list', dest='val_list', help='validation list to use',
                         default="", type=str)
-    parser.add_argument('--network', dest='network', type=str, default='ssd_300',
-                        choices=['vgg16_reduced', 'ssd_300'], help='which network to use')
+    parser.add_argument('--network', dest='network', type=str, default='vgg16_ssd_300',
+                        choices=['vgg16_ssd_300', 'vgg16_ssd_512'], help='which network to use')
     parser.add_argument('--batch-size', dest='batch_size', type=int, default=32,
                         help='training batch size')
     parser.add_argument('--resume', dest='resume', type=int, default=-1,