Method Interpreter

tensorflow/lite/interpreter.cc:73–101

}  // namespace

Interpreter::Interpreter(ErrorReporter* error_reporter)
    : error_reporter_(error_reporter ? error_reporter
                                     : DefaultErrorReporter()) {
  // TODO(b/128420794): Include the TFLite runtime version in the log.
  // Prod logging is useful for mobile platforms where scraping console logs is
  // critical for debugging.
#if defined(TFLITE_IS_MOBILE_PLATFORM)
  TFLITE_LOG_PROD_ONCE(TFLITE_LOG_INFO, "Initialized TensorFlow Lite runtime.");
#else
  TFLITE_LOG_ONCE(TFLITE_LOG_INFO, "Initialized TensorFlow Lite runtime.");
#endif

  // There's always at least 1 subgraph which is the primary subgraph.
  AddSubgraphs(1);
  context_ = primary_subgraph().context();

  // Reserve some space for the tensors to avoid excessive resizing.
  for (int i = 0; i < kTfLiteMaxExternalContexts; ++i) {
    external_contexts_[i] = nullptr;
  }

  // This operation is cheap because we allocate the CPU context resources (i.e.
  // threads) lazily.
  own_external_cpu_backend_context_.reset(new ExternalCpuBackendContext());
  external_contexts_[kTfLiteCpuBackendContext] =
      own_external_cpu_backend_context_.get();

  UseNNAPI(false);
}

Interpreter::~Interpreter() {}