Imported Upstream version 1.18.0

[platform/core/ml/nnfw.git] / runtime / onert / backend / cpu / KernelGenerator.cc

diff --git a/runtime/onert/backend/cpu/KernelGenerator.cc b/runtime/onert/backend/cpu/KernelGenerator.cc
index 59fb68d..75274dc 100644 (file)
--- a/runtime/onert/backend/cpu/KernelGenerator.cc
+++ b/runtime/onert/backend/cpu/KernelGenerator.cc
@@ -35,6 +35,7 @@
 #include "ops/GatherLayer.h"
 #include "ops/LSTMLayer.h"
 #include "ops/MeanLayer.h"
+#include "ops/DetectionPostProcessLayer.h"
 #include "ops/OneHotLayer.h"
 #include "ops/OperationUtils.h"
 #include "ops/PackLayer.h"
@@ -1177,6 +1178,51 @@ void KernelGenerator::visit(const ir::operation::MatrixBandPart &node)
   _return_fn = std::move(fn);
 }
 
+void KernelGenerator::visit(const ir::operation::DetectionPostProcess &node)
+{
+  using NMS = ir::operation::DetectionPostProcess;
+
+  ops::DetectionPostProcessLayer::DetectionPostProcessParameters parameters;
+  parameters.scales.y = node.param().scale.y_scale;
+  parameters.scales.x = node.param().scale.x_scale;
+  parameters.scales.w = node.param().scale.w_scale;
+  parameters.scales.h = node.param().scale.h_scale;
+
+  parameters.iou_threshold = node.param().iou_threshold;
+  parameters.score_threshold = node.param().score_threshold;
+  parameters.max_boxes_per_class = node.param().max_boxes_per_class;
+  parameters.max_detections = node.param().max_detections;
+  parameters.num_classes = node.param().num_classes;
+  parameters.center_box_format = node.param().center_size_boxes;
+  parameters.max_classes_per_detection = node.param().max_classes_per_detection;
+
+  auto boxes_index = node.getInputs().at(NMS::Input::BOXES);
+  auto scores_index = node.getInputs().at(NMS::Input::SCORES);
+  auto anchors_index = node.getInputs().at(NMS::Input::INPUT_ANCHORS);
+
+  auto o_classes_index = node.getOutputs().at(NMS::Output::BOX_CLASSES);
+  auto o_coords_index = node.getOutputs().at(NMS::Output::BOX_COORDS);
+  auto o_scores_index = node.getOutputs().at(NMS::Output::BOX_SCORES);
+  auto o_num_selected_index = node.getOutputs().at(NMS::Output::NUM_SELECTED);
+
+  parameters.boxes_descr = _ctx.at(boxes_index).shape().dims();
+  parameters.scrores_descr = _ctx.at(scores_index).shape().dims();
+
+  parameters.boxes_input = _tensor_reg->getPortableTensor(boxes_index);
+  parameters.scores_input = _tensor_reg->getPortableTensor(scores_index);
+  parameters.anchors_input = _tensor_reg->getPortableTensor(anchors_index);
+
+  parameters.box_classes_output = _tensor_reg->getPortableTensor(o_classes_index);
+  parameters.box_coords_output = _tensor_reg->getPortableTensor(o_coords_index);
+  parameters.box_scores_output = _tensor_reg->getPortableTensor(o_scores_index);
+  parameters.num_selections_output = _tensor_reg->getPortableTensor(o_num_selected_index);
+
+  auto fn = std::make_unique<ops::DetectionPostProcessLayer>();
+  fn->configure(std::move(parameters));
+
+  _return_fn = std::move(fn);
+}
+
 void KernelGenerator::visit(const ir::operation::BatchMatMul &node)
 {
   const auto output_index{node.getOutputs().at(0)};