Preallocate the node structure std::vector

PiperOrigin-RevId: 209830234

tensorflow/contrib/lite/interpreter.cc

@@ -476,6 +476,10 @@ TfLiteStatus Interpreter::ResetVariableTensorsToZero() {
   return kTfLiteOk;
 }
 
+void Interpreter::ReserveNodes(int count) {
+  nodes_and_registration_.reserve(count);
+}
+
 TfLiteStatus Interpreter::AddNodeWithParameters(
     const std::vector<int>& inputs, const std::vector<int>& outputs,
     const char* init_data, size_t init_data_size, void* builtin_data,

tensorflow/contrib/lite/interpreter.h

@@ -136,6 +136,11 @@ class Interpreter {
   // interpreter.
   TfLiteStatus SetVariables(std::vector<int> variables);
 
+  // Ensure the internal node storage memory allocates at least `count`
+  // spots for node. NOTE, this doesn't actually add operators. This is an
+  // efficiency optimization that is subject to change.
+  void ReserveNodes(int count);
+
   // Adds a node with the given parameters and returns the index of the new
   // node in `node_index` (optionally). Interpreter will take ownership of
   // `builtin_data` and destroy it with `free`. Ownership of 'init_data'

tensorflow/contrib/lite/model.cc

@@ -802,6 +802,10 @@ TfLiteStatus InterpreterBuilder::ParseNodes(
     const flatbuffers::Vector<flatbuffers::Offset<Operator>>* operators,
     Interpreter* interpreter) {
   TfLiteStatus status = kTfLiteOk;
+
+  // Reduce the number of redundant allocations
+  interpreter->ReserveNodes(operators->Length());
+
   for (int i = 0; i < operators->Length(); ++i) {
     const auto* op = operators->Get(i);
     int index = op->opcode_index();