Function UseArraysExtraInfo

tensorflow/lite/toco/tooling_util.cc:2349–2385

}

void UseArraysExtraInfo(Model* model, bool quantize_output) {
  for (const auto& entry : model->flags.arrays_extra_info().entries()) {
    const auto matches = ScanArrayNames(*model, entry);
    for (const auto& matched_name : matches) {
      auto& array = model->GetArray(matched_name);
      if (entry.has_min() || entry.has_max()) {
        CHECK_EQ(entry.has_min(), entry.has_max());
        auto& minmax = array.GetOrCreateMinMax();
        minmax.min = entry.min();
        minmax.max = entry.max();
      }
      if (entry.has_data_type() && quantize_output) {
        array.final_data_type =
            ConvertIODataTypeToArrayDataType(entry.data_type());
      }
      if (entry.has_shape()) {
        array.clear_shape();
        // Make sure to create the shape even if there are no dims, to
        // correctly record 0-D shapes.
        array.mutable_shape();
        for (const auto& dim : entry.shape().dims()) {
          array.mutable_shape()->mutable_dims()->push_back(dim);
        }
      }
      if (entry.has_constant_float_value()) {
        CHECK(array.has_shape());
        if (array.data_type == ArrayDataType::kFloat) {
          auto& data = array.GetMutableBuffer<ArrayDataType::kFloat>().data;
          data.resize(RequiredBufferSizeForShape(array.shape()));
          for (float& f : data) {
            f = entry.constant_float_value();
          }
        }
      }
    }
  }
}

void UndoWeightsShuffling(Model* model) {
  for (const auto& op : model->operators) {