[MXNET-703] Minor refactor of TensorRT code

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 = static_cast<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

@@ -62,9 +62,10 @@ namespace nnvm_to_onnx {
 op::ONNXParam ConvertNnvmGraphToOnnx(
     const nnvm::Graph& g,
     std::unordered_map<std::string, NDArray>* const shared_buffer) {
-    op::ONNXParam onnx_param;
-    op::nnvm_to_onnx::NameToIdx_t onnx_input_map;
-    op::nnvm_to_onnx::InferenceMap_t onnx_output_map;
+
+  op::ONNXParam onnx_param;
+  op::nnvm_to_onnx::NameToIdx_t onnx_input_map;
+  op::nnvm_to_onnx::InferenceMap_t onnx_output_map;
 
   const nnvm::IndexedGraph& ig = g.indexed_graph();
   const auto& storage_types = g.GetAttr<StorageTypeVector>("storage_type");
@@ -242,23 +243,23 @@ 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));
   }
 
   // pads
   AttributeProto* const pads = node_proto->add_attribute();
   pads->set_name("pads");
   pads->set_type(AttributeProto::INTS);
-  for (int kval : pad) {
+  for (dim_t kval : pad) {
     pads->add_ints(static_cast<int64>(kval));
   }
 
   // strides
   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));
   }
 
@@ -469,7 +470,7 @@ void ConvertConstant(
   const NDArray nd = shared_buffer->find(node_name)->second;
   const TBlob& blob = nd.data();
   const TShape shape = blob.shape_;
-  const int32_t size = shape.Size();
+  const size_t size = shape.Size();
 
   std::shared_ptr<float> shared_data_ptr(new float[size]);
   float* const data_ptr = shared_data_ptr.get();
@@ -485,7 +486,7 @@ void ConvertConstant(
     tensor_proto->add_dims(static_cast<int64>(dim));
   }
 
-  for (int blob_idx = 0; blob_idx < size; ++blob_idx) {
+  for (size_t blob_idx = 0; blob_idx < size; ++blob_idx) {
     tensor_proto->add_float_data(data_ptr[blob_idx]);
   }
 }