[MXNET-703] Minor refactor of TensorRT code (#13311)

src/executor/onnx_to_tensorrt.cc

@@ -100,8 +100,8 @@ nvinfer1::ICudaEngine* onnxToTrtCtx(
  }
 
  if ( !trt_parser->parse(onnx_model.c_str(), onnx_model.size()) ) {
- int nerror = trt_parser->getNbErrors();
- for ( int i=0; i < nerror; ++i ) {
+ size_t nerror = trt_parser->getNbErrors();
+ for ( size_t i=0; i < nerror; ++i ) {
  nvonnxparser::IParserError const* error = trt_parser->getError(i);
  if ( error->node() != -1 ) {
  ::ONNX_NAMESPACE::NodeProto const& node =

src/executor/trt_graph_executor.cc

@@ -133,7 +133,7 @@ void TrtGraphExecutor::Init(nnvm::Symbol symbol,
  }
 
  auto trt_groups = GetTrtCompatibleSubsets(g, shared_buffer);
- for (auto trt_group : trt_groups) {
+ for (const auto &trt_group : trt_groups) {
  if (trt_group.size() > 1) {
  g = ReplaceSubgraph(std::move(g), trt_group, shared_buffer);
  g = ReinitGraph(std::move(g), default_ctx, ctx_map, in_arg_ctxes, arg_grad_ctxes,
@@ -142,7 +142,6 @@ void TrtGraphExecutor::Init(nnvm::Symbol symbol,
  }
  }
 
-
  InitArguments(g.indexed_graph(), g.GetAttr<nnvm::ShapeVector>("shape"),
  g.GetAttr<nnvm::DTypeVector>("dtype"),
  g.GetAttr<StorageTypeVector>("storage_type"),
@@ -188,7 +187,7 @@ void TrtGraphExecutor::InitArguments(const nnvm::IndexedGraph& idx,
  const uint32_t eid = idx.entry_id(nid, 0);
  const TShape& inferred_shape = inferred_shapes[eid];
  const int inferred_dtype = inferred_dtypes[eid];
- const NDArrayStorageType inferred_stype = (NDArrayStorageType) inferred_stypes[eid];
+ const auto inferred_stype = (NDArrayStorageType) inferred_stypes[eid];
  const std::string& arg_name = idx[nid].source->attrs.name;
  // aux_states
  if (mutable_nodes.count(nid)) {
@@ -427,7 +426,7 @@ Executor *TrtGraphExecutor::TensorRTBind(nnvm::Symbol symbol,
  std::unordered_map<std::string, NDArray> *shared_buffer,
  Executor *shared_exec) {
  auto exec = new exec::TrtGraphExecutor();
- exec->Init(symbol, default_ctx, group2ctx,
+ exec->Init(std::move(symbol), default_ctx, group2ctx,
  in_arg_ctxes, arg_grad_ctxes, aux_state_ctxes,
  arg_shape_map, arg_dtype_map, arg_stype_map,
  grad_req_types, param_names,

src/operator/contrib/nnvm_to_onnx-inl.h

@@ -70,7 +70,7 @@ struct ONNXParam : public dmlc::Parameter<ONNXParam> {
  nnvm_to_onnx::InferenceMap_t output_map;
  ::onnx::ModelProto onnx_pb_graph;
 
- ONNXParam() {}
+ ONNXParam() = default;
 
  ONNXParam(const ::onnx::ModelProto& onnx_graph,
  const nnvm_to_onnx::InferenceMap_t& input_map,
@@ -104,14 +104,14 @@ std::unordered_map<std::string, uint32_t> GetOutputLookup(const nnvm::IndexedGra
 void ConvertPlaceholder(
  const std::string& node_name,
  const std::unordered_map<std::string, TShape>& placeholder_shapes,
- GraphProto* const graph_proto);
+ GraphProto* graph_proto);
 
-void ConvertConstant(GraphProto* const graph_proto,
+void ConvertConstant(GraphProto* graph_proto,
  const std::string& node_name,
- std::unordered_map<std::string, NDArray>* const shared_buffer);
+ std::unordered_map<std::string, NDArray>* shared_buffer);
 
-void ConvertOutput(op::nnvm_to_onnx::InferenceMap_t* const trt_output_map,
- GraphProto* const graph_proto,
+void ConvertOutput(op::nnvm_to_onnx::InferenceMap_t* trt_output_map,
+ GraphProto* graph_proto,
  const std::unordered_map<std::string, uint32_t>::iterator& out_iter,
  const std::string& node_name,
  const nnvm::Graph& g,
@@ -169,7 +169,7 @@ void ConvertElementwiseAdd(NodeProto *node_proto,
 
 ONNXParam ConvertNnvmGraphToOnnx(
  const nnvm::Graph &g,
- std::unordered_map<std::string, NDArray> *const shared_buffer);
+ std::unordered_map<std::string, NDArray>* shared_buffer);
 
 static const std::unordered_map<std::string, ConverterFunction> converter_map = {
  {"Convolution", ConvertConvolution},

src/operator/contrib/nnvm_to_onnx.cc

@@ -263,7 +263,7 @@ void ConvertPooling(NodeProto* node_proto, const NodeAttrs& attrs,
  AttributeProto* const kernel_shape = node_proto->add_attribute();
  kernel_shape->set_name("kernel_shape");
  kernel_shape->set_type(AttributeProto::INTS);
- for (int kval : kernel) {
+ for (dim_t kval : kernel) {
  kernel_shape->add_ints(static_cast<int64>(kval));
  }
 
@@ -283,7 +283,7 @@ void ConvertPooling(NodeProto* node_proto, const NodeAttrs& attrs,
  AttributeProto* const strides = node_proto->add_attribute();
  strides->set_name("strides");
  strides->set_type(AttributeProto::INTS);
- for (int kval : stride) {
+ for (dim_t kval : stride) {
  strides->add_ints(static_cast<int64>(kval));
  }