formatting

src/include/migraphx/op/nonmaxsuppression.hpp

@@ -70,7 +70,8 @@ struct nonmaxsuppression
     };
 
     template <class T>
-    box batch_box(const migraphx::tensor_view<T>& boxes, std::size_t box_ind, std::size_t box_idx) const
+    box
+    batch_box(const migraphx::tensor_view<T>& boxes, std::size_t box_ind, std::size_t box_idx) const
     {
         box result{};
         auto start = box_ind + 4 * box_idx;
@@ -92,7 +93,6 @@ struct nonmaxsuppression
         return result;
     }
 
-
     inline bool suppress_by_iou(box b1, box b2, float iou_threshold) const
     {
         b1.sort();
@@ -128,18 +128,21 @@ struct nonmaxsuppression
         return intersection_over_union > iou_threshold;
     }
 
-    
     argument compute(const shape& output_shape, std::vector<argument> args) const
     {
         argument result{output_shape};
 
-        std::size_t max_output_boxes_per_class = (args.size() > 2) ? (args.at(2).at<std::size_t>()) : 0;
-        if(max_output_boxes_per_class == 0) { return result; }
+        std::size_t max_output_boxes_per_class =
+            (args.size() > 2) ? (args.at(2).at<std::size_t>()) : 0;
+        if(max_output_boxes_per_class == 0)
+        {
+            return result;
+        }
         float iou_threshold   = (args.size() > 3) ? (args.at(3).at<float>()) : 0.0f;
         float score_threshold = (args.size() > 4) ? (args.at(4).at<float>()) : 0.0f;
 
-        result.visit([&](auto output){
-            visit_all(args[0], args[1])([&](auto boxes, auto scores){
+        result.visit([&](auto output) {
+            visit_all(args[0], args[1])([&](auto boxes, auto scores) {
                 std::fill(output.begin(), output.end(), 0);
                 const auto& lens       = scores.get_shape().lens();
                 const auto num_batches = lens[0];
@@ -155,11 +158,13 @@ struct nonmaxsuppression
                     auto batch_idx = idx[0];
                     auto class_idx = idx[1];
                     // index offset for this class
-                        std::size_t score_offset_ind = (batch_idx * num_classes + class_idx) * num_boxes;
+                    std::size_t score_offset_ind =
+                        (batch_idx * num_classes + class_idx) * num_boxes;
                     // index to first value of this batch
                     std::size_t batch_boxes_ind = batch_idx * num_boxes * 4;
                     std::priority_queue<std::pair<float, int64_t>> boxes_heap;
-                        auto insert_to_boxes_heap = make_function_output_iterator([&](const auto& x) { boxes_heap.push(x); });
+                    auto insert_to_boxes_heap =
+                        make_function_output_iterator([&](const auto& x) { boxes_heap.push(x); });
                     int64_t box_idx = 0;
 
                     // filter boxes below score_threshold
@@ -180,8 +185,8 @@ struct nonmaxsuppression
                     {
                         const std::pair<float, int64_t>& next_top_score = boxes_heap.top();
 
-                            // Check with existing selected boxes for this class, remove box if it exceeds the IOU
-                            // (Intersection Over Union) threshold
+                        // Check with existing selected boxes for this class, remove box if it
+                        // exceeds the IOU (Intersection Over Union) threshold
                         bool not_selected = std::any_of(
                             selected_boxes_inside_class.begin(),
                             selected_boxes_inside_class.end(),
@@ -203,8 +208,7 @@ struct nonmaxsuppression
                     }
                 });
                 std::copy(selected_indices.begin(), selected_indices.end(), output.begin());
-            }
-            );
+            });
         });
 
         return result;