Use lambda obj in the ignore case and do some refactoring (#2466)

dlib/dnn/loss.h

@@ -3641,7 +3641,7 @@ namespace dlib
                                                              h / (1 - h) * anchors[a].height));
                                 for (long k = 0; k < num_classes; ++k)
                                 {
-                                    const float conf = out_data[tensor_index(output_tensor, n, a * num_feats + 5 + k, r, c)] * obj;
+                                    const float conf = obj * out_data[tensor_index(output_tensor, n, a * num_feats + 5 + k, r, c)];
                                     if (conf > adjust_threshold)
                                         det.labels.emplace_back(conf, options.labels[k]);
                                 }
@@ -3716,7 +3716,7 @@ namespace dlib
 
                             // Incur loss for the boxes that are below a certain IoU threshold with any truth box
                             if (best_iou < options.iou_ignore_threshold)
-                                g[o_idx] = out_data[o_idx];
+                                g[o_idx] = options.lambda_obj * out_data[o_idx];
                         }
                     }
                 }
@@ -3780,13 +3780,13 @@ namespace dlib
                             const double th = truth_box.rect.height() / (anchors[a].height + truth_box.rect.height());
 
                             // Scale regression error according to the truth size
-                            const double scale_box = 2 - truth_box.rect.area() / input_area;
+                            const double scale_box = options.lambda_box * (2 - truth_box.rect.area() / input_area);
 
                             // Compute the gradient for the box coordinates
-                            g[x_idx] = options.lambda_box * scale_box * (out_data[x_idx] - tx);
-                            g[y_idx] = options.lambda_box * scale_box * (out_data[y_idx] - ty);
-                            g[w_idx] = options.lambda_box * scale_box * (out_data[w_idx] - tw);
-                            g[h_idx] = options.lambda_box * scale_box * (out_data[h_idx] - th);
+                            g[x_idx] = scale_box * (out_data[x_idx] - tx);
+                            g[y_idx] = scale_box * (out_data[y_idx] - ty);
+                            g[w_idx] = scale_box * (out_data[w_idx] - tw);
+                            g[h_idx] = scale_box * (out_data[h_idx] - th);
 
                             // Compute the classification error
                             for (long k = 0; k < num_classes; ++k)