Add & to auto to make static analyzer happy

It adds & to make static analyzer happy.
It may help remove copy for non-primitive type.
In addition, some variables of auto turned out not-used variables.
All un-used variables of auto are removed also.

ONE-DCO-1.0-Signed-off-by: Sanggyu Lee <sg5.lee@samsung.com>

diff --git a/runtime/onert/backend/cl_common/include/cl_common/BackendContext.h b/runtime/onert/backend/cl_common/include/cl_common/BackendContext.h
index 76d4039..06aafa1 100644 (file)
--- a/runtime/onert/backend/cl_common/include/cl_common/BackendContext.h
+++ b/runtime/onert/backend/cl_common/include/cl_common/BackendContext.h
@@ -121,7 +121,7 @@ protected:
       if (!tensor_builder->isRegistered(ind))
       {
         // These tensors do not exist in any operation (No use and def)
-        const auto info = obj.info();
+        const auto &info = obj.info();
         const auto layout = _data.operand_layouts.at(ind);
         // TODO Change tensor info to have permuted shape
         registerTensorInfo(ind, info, layout);

diff --git a/runtime/onert/backend/cpu/KernelGenerator.cc b/runtime/onert/backend/cpu/KernelGenerator.cc
index c927bf5..d462daf 100644 (file)
--- a/runtime/onert/backend/cpu/KernelGenerator.cc
+++ b/runtime/onert/backend/cpu/KernelGenerator.cc
@@ -1266,7 +1266,7 @@ void KernelGenerator::visit(const ir::operation::FusedBatchNorm &node)
 
   const auto epsilon = node.param().epsilon;
   const auto is_training = node.param().is_training;
-  const auto data_format = node.param().data_format;
+  const auto &data_format = node.param().data_format;
 
   auto fn = std::make_unique<ops::FusedBatchNormLayer>();
 

diff --git a/runtime/onert/backend/cpu/ops/DetectionPostProcessLayer.cc b/runtime/onert/backend/cpu/ops/DetectionPostProcessLayer.cc
index d89741c..dc9e20e 100644 (file)
--- a/runtime/onert/backend/cpu/ops/DetectionPostProcessLayer.cc
+++ b/runtime/onert/backend/cpu/ops/DetectionPostProcessLayer.cc
@@ -106,7 +106,7 @@ Array<const CornerBox> decodeBoxes(const Array<float> &raw_boxes, const Array<fl
 
     for (size_t i = 0; i < num_boxes; ++i)
     {
-      auto anchor = anchors.at(i);
+      const auto &anchor = anchors.at(i);
       auto &box = decoded_boxes_a.at(i);
       float yc = in_boxes.at(i).y / scales.y * anchor.h + anchor.y;
       float xc = in_boxes.at(i).x / scales.x * anchor.w + anchor.x;
@@ -121,7 +121,7 @@ Array<const CornerBox> decodeBoxes(const Array<float> &raw_boxes, const Array<fl
       assert(box.y2 > box.y1);
     }
 
-    auto decoded_boxes_a_shape = decoded_boxes_a.shape();
+    const auto &decoded_boxes_a_shape = decoded_boxes_a.shape();
 
     return array_cast<const CornerBox>(std::move(decoded_boxes_a), decoded_boxes_a_shape);
   }

diff --git a/runtime/onert/backend/gpu_cl/ClConstantInitializer.h b/runtime/onert/backend/gpu_cl/ClConstantInitializer.h
index 95e228a..ad5b47d 100644 (file)
--- a/runtime/onert/backend/gpu_cl/ClConstantInitializer.h
+++ b/runtime/onert/backend/gpu_cl/ClConstantInitializer.h
@@ -39,7 +39,7 @@ template <typename T>
 static void Init(const onert::ir::Operand &model_obj, onert::backend::ITensor &obj, const bool copy,
                  const onert::ir::Layout frontend_layout = onert::ir::Layout::UNKNOWN)
 {
-  const auto shape = model_obj.shape();
+  const auto &shape = model_obj.shape();
   assert(model_obj.data());
   obj.access([&](::onert::backend::ITensor &tensor) {
     switch (shape.rank())

diff --git a/runtime/onert/backend/gpu_cl/KernelGenerator.cc b/runtime/onert/backend/gpu_cl/KernelGenerator.cc
index a24c4f5..31d3134 100644 (file)
--- a/runtime/onert/backend/gpu_cl/KernelGenerator.cc
+++ b/runtime/onert/backend/gpu_cl/KernelGenerator.cc
@@ -166,7 +166,7 @@ absl::Status KernelGenerator::readConstTensor(
   absl::variant<tflite::gpu::Tensor<tflite::gpu::Linear, tflite::gpu::DataType::FLOAT32>,
                 tflite::gpu::Tensor<tflite::gpu::HWC, tflite::gpu::DataType::FLOAT32>> *alpha)
 {
-  const auto shape = _ctx.at(index).shape();
+  const auto &shape = _ctx.at(index).shape();
   if (CheckIfLinearConvertible(&shape))
   {
     tflite::gpu::Tensor<tflite::gpu::Linear, tflite::gpu::DataType::FLOAT32> tensor;
@@ -304,7 +304,7 @@ void KernelGenerator::visit(const ir::operation::Conv2D &node)
   auto kernel{node.getInputs().at(ir::operation::Conv2D::KERNEL)};
   auto bias{node.getInputs().at(ir::operation::Conv2D::BIAS)};
 
-  const auto param = node.param();
+  const auto &param = node.param();
 
   tflite::gpu::OperationDef op_def;
   op_def.precision = tflite::gpu::CalculationsPrecision::F32;
@@ -375,7 +375,7 @@ void KernelGenerator::visit(const ir::operation::Conv2D &node)
     {
       std::unique_ptr<tflite::gpu::GPUOperation> gpu_op_1;
       tflite::gpu::OperationDef op_def_1;
-      const auto shape = _ctx.at(output).shape();
+      const auto &shape = _ctx.at(output).shape();
       auto new_ind = _tensor_reg->addNewClTensor(shape);
 
       addClNode({input}, {new_ind}, std::move(gpu_op));
@@ -418,7 +418,7 @@ void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
 
   const auto stride = node.param().stride;
   const auto dilation = node.param().dilation;
-  const auto padding = node.param().padding;
+  const auto &padding = node.param().padding;
 
   const auto multiplier = node.param().multiplier;
 

diff --git a/runtime/onert/backend/gpu_cl/operand/ICLTensor.cc b/runtime/onert/backend/gpu_cl/operand/ICLTensor.cc
index ef71bbc..1e61b99 100644 (file)
--- a/runtime/onert/backend/gpu_cl/operand/ICLTensor.cc
+++ b/runtime/onert/backend/gpu_cl/operand/ICLTensor.cc
@@ -60,7 +60,7 @@ void ICLTensor::writeConvertInit(tflite::gpu::TensorObjectConverterBuilder *conv
   TensorObjectDef permute_def = input_def;
   permute_def.object_def.object_type = ToObjectType(handle()->GetStorageType());
 
-  auto dims = permute_def.dimensions;
+  const auto &dims = permute_def.dimensions;
   const BHWC shape(dims.b, dims.h, dims.w, dims.c);
   const TensorDescriptor desc{
     permute_def.object_def.data_type,
@@ -105,7 +105,7 @@ void ICLTensor::readConvertInit(tflite::gpu::TensorObjectConverterBuilder *conve
   permute_def.object_def.data_type = DataType::FLOAT32;
   permute_def.object_def.user_provided = true;
 
-  auto dims = permute_def.dimensions;
+  const auto &dims = permute_def.dimensions;
   const BHWC shape(dims.b, dims.h, dims.w, dims.c);
   const TensorDescriptor desc{
     permute_def.object_def.data_type,

diff --git a/runtime/onert/backend/ruy/KernelGenerator.cc b/runtime/onert/backend/ruy/KernelGenerator.cc
index ae7ec28..735a948 100644 (file)
--- a/runtime/onert/backend/ruy/KernelGenerator.cc
+++ b/runtime/onert/backend/ruy/KernelGenerator.cc
@@ -55,7 +55,7 @@ std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationI
   assert(_return_fn); // _return_fn must have been generated
   ret->append(std::move(_return_fn));
 
-  for (auto &&ind : (op.getInputs() | ir::Remove::UNDEFINED) + op.getOutputs())
+  for (const auto &&ind : (op.getInputs() | ir::Remove::UNDEFINED) + op.getOutputs())
   {
     auto portable_tensor = _tensor_reg->getPortableTensor(ind);
     if (portable_tensor)
@@ -101,7 +101,7 @@ void KernelGenerator::visit(const ir::operation::Conv2D &node)
 
   const auto stride = node.param().stride;
   const auto activation = node.param().activation;
-  const auto param_padding = node.param().padding;
+  const auto &param_padding = node.param().padding;
   const auto dilation = node.param().dilation;
   auto fn = std::make_unique<ops::ConvolutionLayer>();
 

diff --git a/runtime/onert/backend/xnnpack/KernelGenerator.cc b/runtime/onert/backend/xnnpack/KernelGenerator.cc
index 25f3fd2..b721491 100644 (file)
--- a/runtime/onert/backend/xnnpack/KernelGenerator.cc
+++ b/runtime/onert/backend/xnnpack/KernelGenerator.cc
@@ -102,7 +102,7 @@ void KernelGenerator::visit(const ir::operation::Conv2D &node)
 
   const auto stride = node.param().stride;
   const auto activation = node.param().activation;
-  const auto param_padding = node.param().padding;
+  const auto &param_padding = node.param().padding;
   const auto dilation = node.param().dilation;
   auto fn = std::make_unique<ops::ConvolutionLayer>(_external_context);
 
@@ -142,7 +142,7 @@ void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
   const auto ker_width = ker_shape.dim(2);
   const auto dilation_width = node.param().dilation.width_factor;
   const auto dilation_height = node.param().dilation.height_factor;
-  const auto param_padding = node.param().padding;
+  const auto &param_padding = node.param().padding;
   const auto padding = ir::calculatePadding(param_padding, ifm_shape, ofm_shape, stride, ker_width,
                                             ker_height, dilation_width, dilation_height);
   const auto multiplier = node.param().multiplier;

diff --git a/runtime/onert/core/include/backend/basic/BackendContextHelpers.h b/runtime/onert/core/include/backend/basic/BackendContextHelpers.h
index 9992ca1..7588d42 100644 (file)
--- a/runtime/onert/core/include/backend/basic/BackendContextHelpers.h
+++ b/runtime/onert/core/include/backend/basic/BackendContextHelpers.h
@@ -63,7 +63,7 @@ template <typename T_BackendContext> void planTensors(const T_BackendContext &ct
     if (!tensor_builder->isRegistered(ind))
     {
       // These tensors do not exist in any  (No use and def)
-      const auto info = obj.info();
+      const auto &info = obj.info();
       // NOTE Currently we only support NHWC tensors for cpu-common tensors.
       //      There is no way to get the layout info from the backend context for now.
       //      When we support NCHW tensors as well, we also need to change tensor info to be

diff --git a/runtime/onert/core/src/backend/basic/TensorBuilder.cc b/runtime/onert/core/src/backend/basic/TensorBuilder.cc
index f9d8387..4912af1 100644 (file)
--- a/runtime/onert/core/src/backend/basic/TensorBuilder.cc
+++ b/runtime/onert/core/src/backend/basic/TensorBuilder.cc
@@ -62,7 +62,7 @@ void TensorBuilder::registerTensorInfo(const ir::OperandIndex &ind, const ir::Op
 void TensorBuilder::notifyFirstUse(const ir::OperandIndex &ind)
 {
   assert(_tensor_info_map.find(ind) != _tensor_info_map.end());
-  const auto tensor_info = _tensor_info_map.at(ind);
+  const auto &tensor_info = _tensor_info_map.at(ind);
 
   if (!_tensor_reg->getNativeTensor(ind)->is_dynamic())
   {

diff --git a/runtime/onert/core/src/backend/builtin/TensorBuilder.cc b/runtime/onert/core/src/backend/builtin/TensorBuilder.cc
index fefae40..a2f7af3 100644 (file)
--- a/runtime/onert/core/src/backend/builtin/TensorBuilder.cc
+++ b/runtime/onert/core/src/backend/builtin/TensorBuilder.cc
@@ -57,7 +57,7 @@ void TensorBuilder::notifyFirstUse(const ir::OperandIndex &ind)
   if (_tensor_info_map.find(ind) == _tensor_info_map.end()) // Do not proceed for user tensors
     return;
 
-  const auto tensor_info = _tensor_info_map.at(ind);
+  const auto &tensor_info = _tensor_info_map.at(ind);
 
   if (!nativeOwnTensorAt(ind)->is_dynamic())
   {

diff --git a/runtime/onert/core/src/compiler/HEScheduler.cc b/runtime/onert/core/src/compiler/HEScheduler.cc
index f662ef5..56e2208 100644 (file)
--- a/runtime/onert/core/src/compiler/HEScheduler.cc
+++ b/runtime/onert/core/src/compiler/HEScheduler.cc
@@ -409,7 +409,7 @@ int64_t HEScheduler::DFSChildrenMaxRank(const ir::OperationIndex &index)
 int64_t HEScheduler::backendAvailableTime(const backend::Backend *backend,
                                           const int64_t &starting_time, const int64_t &time_amount)
 {
-  const auto backend_times = _backends_avail_time.at(backend);
+  const auto &backend_times = _backends_avail_time.at(backend);
   // finishing and starting times of an op, that will come after current op
   auto next_op_fst = backend_times.upper_bound(starting_time);
   // finishing time of an op, that will come before current op

diff --git a/runtime/onert/core/src/compiler/ShapeValidator.cc b/runtime/onert/core/src/compiler/ShapeValidator.cc
index 3e940f0..5c25ea1 100644 (file)
--- a/runtime/onert/core/src/compiler/ShapeValidator.cc
+++ b/runtime/onert/core/src/compiler/ShapeValidator.cc
@@ -227,9 +227,9 @@ void ShapeValidator::visit(const ir::operation::Reduce &node)
   if (operands.at(output_index).info().isDynamic())
     return;
 
-  const auto input_index{node.getInputs().at(ir::operation::Reduce::Input::INPUT)};
-  const auto input_shape = operands.at(input_index).shape();
-  const auto output_shape = operands.at(output_index).shape();
+  const auto &input_index{node.getInputs().at(ir::operation::Reduce::Input::INPUT)};
+  const auto &input_shape = operands.at(input_index).shape();
+  const auto &output_shape = operands.at(output_index).shape();
 
   OP_REQUIRES(input_shape.rank() <= 4);
   OP_REQUIRES(output_shape.rank() <= input_shape.rank());
@@ -516,9 +516,9 @@ void ShapeValidator::visit(const ir::operation::Gather &node)
   const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)};
   const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)};
 
-  const auto ifm_shape = operands.at(ifm_index).shape();
-  const auto indices_shape = operands.at(indices_index).shape();
-  const auto ofm_shape = operands.at(ofm_index).shape();
+  const auto &ifm_shape = operands.at(ifm_index).shape();
+  const auto &indices_shape = operands.at(indices_index).shape();
+  const auto &ofm_shape = operands.at(ofm_index).shape();
 
   OP_REQUIRES(ifm_shape.rank() <= 4);
   OP_REQUIRES(indices_shape.rank() <= 3);
@@ -566,7 +566,7 @@ void ShapeValidator::visit(const ir::operation::Pack &node)
   const auto output_rank = static_cast<int32_t>(output_shape.rank());
 
   const auto input1_index{node.getInputs().at(0)};
-  const auto input_shape = operands.at(input1_index).shape();
+  const auto &input_shape = operands.at(input1_index).shape();
 
   OP_REQUIRES(axis >= -output_rank && axis < output_rank);
   for (const auto &index : node.getInputs())

diff --git a/runtime/onert/core/src/compiler/StaticShapeInferer.cc b/runtime/onert/core/src/compiler/StaticShapeInferer.cc
index a25b326..68cff7e 100644 (file)
--- a/runtime/onert/core/src/compiler/StaticShapeInferer.cc
+++ b/runtime/onert/core/src/compiler/StaticShapeInferer.cc
@@ -524,11 +524,11 @@ void StaticShapeInferer::visit(const ir::operation::Fill &op)
   assert(dims_buf);
 
   const auto &dims_shape = shape.info().shape();
-  auto new_shape = ((dims_type == ir::DataType::INT32)
-                      ? shape_inference::inferFillShape<int32_t>(
-                          dims_shape, reinterpret_cast<const int32_t *>(dims_buf))
-                      : shape_inference::inferFillShape<int64_t>(
-                          dims_shape, reinterpret_cast<const int64_t *>(dims_buf)));
+  const auto &new_shape = ((dims_type == ir::DataType::INT32)
+                             ? shape_inference::inferFillShape<int32_t>(
+                                 dims_shape, reinterpret_cast<const int32_t *>(dims_buf))
+                             : shape_inference::inferFillShape<int64_t>(
+                                 dims_shape, reinterpret_cast<const int64_t *>(dims_buf)));
 
   output.info().shape(new_shape);
 }
@@ -1088,8 +1088,8 @@ void StaticShapeInferer::visit(const ir::operation::SpaceToBatchND &op)
 
   const auto output_index = op.getOutputs().at(0);
   const auto input_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)};
-  const auto block_shape_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)};
-  const auto padding_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)};
+  const auto &block_shape_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)};
+  const auto &padding_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)};
 
   ir::Operand &output = operands.at(output_index);
   const auto &input = operands.at(input_idx);
@@ -1103,9 +1103,9 @@ void StaticShapeInferer::visit(const ir::operation::SpaceToBatchND &op)
     return;
   }
 
-  auto input_shape = input.info().shape();
-  auto block_shape_shape = block_shape.info().shape();
-  auto padding_shape = padding.info().shape();
+  const auto &input_shape = input.info().shape();
+  const auto &block_shape_shape = block_shape.info().shape();
+  const auto &padding_shape = padding.info().shape();
 
   auto block_shape_data = reinterpret_cast<const int32_t *>(block_shape.data()->base());
   auto padding_data = reinterpret_cast<const int32_t *>(padding.data()->base());
@@ -1325,7 +1325,7 @@ void StaticShapeInferer::visit(const ir::operation::While &op)
   auto body_input_observer = _subg_input_observers.at(op.param().body_subg_index).get();
   auto cond_input_observer = _subg_input_observers.at(op.param().cond_subg_index).get();
   // re-sizing input shapes of body subgraph
-  const auto inputs = op.getInputs();
+  const auto &inputs = op.getInputs();
   std::vector<ir::OperandInfo> inputs_info;
   const auto &graph = _lowered_subg->graph();
   for (size_t i = 0; i < inputs.size(); ++i)
@@ -1401,9 +1401,7 @@ void StaticShapeInferer::visit(const ir::operation::Bulk &op)
   const auto output_idx = op.getOutputs().at(0);
   ir::Operand &output = operands.at(output_idx);
 
-  auto cur_input_shape = input.info().shape();
-  auto origin_input_shape = op.param().origin_input_shapes[0];
-  auto cur_output_shape = output.info().shape();
+  const auto &cur_input_shape = input.info().shape();
   auto origin_output_shape = op.param().origin_output_shapes[0];
 
   // TODO: more check for valid batch request

diff --git a/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.cc b/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.cc
index 39eb803..1657c0c 100644 (file)
--- a/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.cc
+++ b/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.cc
@@ -87,7 +87,7 @@ void PermutationInsertionPass::callback(const ir::OperandIndex &index, ir::Opera
       const auto op_layout = op_li->layout();
       const backend::Backend *backend = op_li->backend();
       assert(backend);
-      auto use_node_inputs = operation.getInputs();
+      const auto &use_node_inputs = operation.getInputs();
       assert(use_node_inputs.contains(index));
 
       auto new_index = factor_to_index.at({backend, op_layout});

diff --git a/runtime/onert/core/src/exec/DynamicShapeInferer.cc b/runtime/onert/core/src/exec/DynamicShapeInferer.cc
index 78b21cf..4cbf2fe 100644 (file)
--- a/runtime/onert/core/src/exec/DynamicShapeInferer.cc
+++ b/runtime/onert/core/src/exec/DynamicShapeInferer.cc
@@ -423,11 +423,11 @@ void DynamicShapeInferer::visit(const ir::operation::Fill &op)
   assert(dims_buf);
 
   const auto &dims_shape = shape->getShape();
-  auto output_shape = ((dims_type == ir::DataType::INT32)
-                         ? shape_inference::inferFillShape<int32_t>(
-                             dims_shape, reinterpret_cast<const int32_t *>(dims_buf))
-                         : shape_inference::inferFillShape<int64_t>(
-                             dims_shape, reinterpret_cast<const int64_t *>(dims_buf)));
+  const auto &output_shape = ((dims_type == ir::DataType::INT32)
+                                ? shape_inference::inferFillShape<int32_t>(
+                                    dims_shape, reinterpret_cast<const int32_t *>(dims_buf))
+                                : shape_inference::inferFillShape<int64_t>(
+                                    dims_shape, reinterpret_cast<const int64_t *>(dims_buf)));
 
   output->applyShape(output_shape);
   assert(output->buffer() != nullptr);

diff --git a/runtime/onert/core/src/exec/Execution.cc b/runtime/onert/core/src/exec/Execution.cc
index 1384c9f..f51bed8 100644 (file)
--- a/runtime/onert/core/src/exec/Execution.cc
+++ b/runtime/onert/core/src/exec/Execution.cc
@@ -48,7 +48,7 @@ void Execution::changeInputShape(const ir::IOIndex &index, const ir::Shape &new_
 void Execution::setInput(const ir::IOIndex &index, const void *buffer, size_t length,
                          ir::Layout layout)
 {
-  const auto info = _executors->inputInfo(index);
+  const auto &info = _executors->inputInfo(index);
 
   // TODO handle when (!buffer && length != 0) : setting the input as an optional tensor
 
@@ -88,7 +88,7 @@ void Execution::setInput(const ir::IOIndex &index, const ir::TypeInfo &type, con
 // TODO Remove default parameter
 void Execution::setOutput(const ir::IOIndex &index, void *buffer, size_t length, ir::Layout layout)
 {
-  const auto info = _executors->outputInfo(index);
+  const auto &info = _executors->outputInfo(index);
 
   if (length < info.total_size())
   {
@@ -102,7 +102,7 @@ void Execution::setOutput(const ir::IOIndex &index, void *buffer, size_t length,
 void Execution::setOutput(const ir::IOIndex &index, const ir::TypeInfo &type,
                           const ir::Shape &shape, void *buffer, size_t length, ir::Layout layout)
 {
-  auto info = ir::OperandInfo::createStaticInfo(shape, type);
+  const auto &info = ir::OperandInfo::createStaticInfo(shape, type);
 
   if (length < info.total_size())
   {

diff --git a/runtime/onert/core/src/exec/ExecutorBase.cc b/runtime/onert/core/src/exec/ExecutorBase.cc
index ad00734..0bc088b 100644 (file)
--- a/runtime/onert/core/src/exec/ExecutorBase.cc
+++ b/runtime/onert/core/src/exec/ExecutorBase.cc
@@ -66,8 +66,8 @@ void ExecutorBase::execute(const std::vector<backend::IPortableTensor *> &inputs
     assert(input_tensor != nullptr);
     if (input != nullptr)
     {
-      const auto orig_input_shape = input_tensor->orig_info().shape();
-      const auto changed_input_shape =
+      const auto &orig_input_shape = input_tensor->orig_info().shape();
+      const auto &changed_input_shape =
         convertShape(input->getShape(), input->layout(), input_tensor->orig_layout());
       if (input_tensor->get_info().shape() != changed_input_shape)
       {

diff --git a/runtime/onert/core/src/ir/Graph.cc b/runtime/onert/core/src/ir/Graph.cc
index ef0f988..306572c 100644 (file)
--- a/runtime/onert/core/src/ir/Graph.cc
+++ b/runtime/onert/core/src/ir/Graph.cc
@@ -168,7 +168,7 @@ void Graph::verify(void) const
 void Graph::initializeUseDef()
 {
   operations().iterate([&](const OperationIndex &index, const IOperation &node) -> void {
-    auto outputs = node.getOutputs();
+    const auto &outputs = node.getOutputs();
     for (auto &&output : outputs | ir::Remove::UNDEFINED)
     {
       operands().at(output).setDef(index);

diff --git a/runtime/onert/core/src/ir/OperationValidator.cc b/runtime/onert/core/src/ir/OperationValidator.cc
index cf7323d..09f773c 100644 (file)
--- a/runtime/onert/core/src/ir/OperationValidator.cc
+++ b/runtime/onert/core/src/ir/OperationValidator.cc
@@ -213,7 +213,7 @@ void OperationValidator::visit(const operation::DepthToSpace &node)
 
 void OperationValidator::visit(const operation::DetectionPostProcess &node)
 {
-  auto param = node.param();
+  const auto &param = node.param();
 
   // FIXME: number of classes should be 1 for now.
   OP_REQUIRES(param.num_classes == 1);

diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc
index 21c7cdd..2265e99 100644 (file)
--- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc
+++ b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc
@@ -64,7 +64,7 @@ bool ANeuralNetworksExecution::compareDataType(const ANeuralNetworksOperandType
 {
   try
   {
-    const auto operand_type = _execution->primary_subgraph().operands().at(index).typeInfo();
+    const auto &operand_type = _execution->primary_subgraph().operands().at(index).typeInfo();
     const auto typeInfo = NNAPIConvert::getTypeInfo(type);
 
     if (operand_type != typeInfo)
@@ -111,7 +111,7 @@ bool ANeuralNetworksExecution::IsOptionalInput(const onert::ir::OperandIndex ind
 
 bool ANeuralNetworksExecution::hasUnspecifiedDims(const onert::ir::OperandIndex index) noexcept
 {
-  const auto operand_shape = _execution->primary_subgraph().operands().at(index).shape();
+  const auto &operand_shape = _execution->primary_subgraph().operands().at(index).shape();
 
   return operand_shape.hasUnspecifiedDims();
 }
@@ -138,7 +138,7 @@ bool ANeuralNetworksExecution::setInput(uint32_t index, const ANeuralNetworksOpe
     onert::ir::IOIndex input_index{index};
     const auto operand_index = getInputOperandIndex(index);
 
-    const auto type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo();
+    const auto &type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo();
     const auto shape = (type != nullptr)
                          ? NNAPIConvert::getShape(type)
                          : _execution->primary_subgraph().operands().at(operand_index).shape();
@@ -171,7 +171,6 @@ bool ANeuralNetworksExecution::setOptionalInput(uint32_t index,
     onert::ir::IOIndex input_index{index};
     const auto operand_index = getInputOperandIndex(index);
 
-    const auto type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo();
     const auto shape = (type != nullptr)
                          ? NNAPIConvert::getShape(type)
                          : _execution->primary_subgraph().operands().at(operand_index).shape();
@@ -206,7 +205,7 @@ bool ANeuralNetworksExecution::setOutput(uint32_t index, const ANeuralNetworksOp
     onert::ir::IOIndex output_index{index};
     const auto operand_index = getOutputOperandIndex(index);
 
-    const auto type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo();
+    const auto &type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo();
     const auto shape = (type != nullptr)
                          ? NNAPIConvert::getShape(type)
                          : _execution->primary_subgraph().operands().at(operand_index).shape();