Method cook_operands

compiler/tflchef/core/src/ModelChef.cpp:276–645

}

template <typename T> void ModelChef::cook_operands(const T &graph)
{
  int32_t buffer_start = _buffer_vec.size();
  int32_t buffer_index = 0;

  // Create buffer(s) 1~n(I) for input(s)
  const auto size_input = graph.input_size();
  for (int ci = 0; ci < size_input; ++ci)
  {
    tflite::BufferBuilder buffer_builder{*_flatbuffer_builder};
    _buffer_vec.emplace_back(buffer_builder.Finish());
  }
  // Create buffer(s) n(I)+1~n(I)+n(O) for output(s)
  const auto size_output = graph.output_size();
  for (int co = 0; co < size_output; ++co)
  {
    tflite::BufferBuilder buffer_builder{*_flatbuffer_builder};
    _buffer_vec.emplace_back(buffer_builder.Finish());
  }

  auto input_names = as_dataset(graph.input()).vectorize();
  auto output_names = as_dataset(graph.output()).vectorize();

  for (const auto &operand : graph.operand())
  {
    assert(operand.has_name());
    assert(operand.has_type());

    flatbuffers::Offset<tflite::SparsityParameters> sparsity_index;

    flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape;
    std::vector<int32_t> dims;
    if (operand.has_shape())
    {
      dims = as_dims(operand.shape());
      shape = _flatbuffer_builder->CreateVector(dims);
    }

    auto name = _flatbuffer_builder->CreateString(operand.name());

    buffer_index = 0;

    // Create Buffer if filler is specified
    if (operand.has_filler())
    {
      const auto &filler = operand.filler();

      assert(filler.has_tag());

      auto args = ranged_arguments(filler.arg().begin(), filler.arg().end());
      auto chef = data_chef_registry(operand.type()).lookup(filler.tag()).create(args);

      assert(chef != nullptr);

      // Create Data
      int32_t count = (element_count(dims) > 0) ? element_count(dims) : filler.arg_size();
      auto data_vec = chef->generate(count);