Function GetArrayColorAndShape

tensorflow/lite/toco/dump_graphviz.cc:123–168

}

void GetArrayColorAndShape(const Model& model, const string& array_name,
                           Color* color, string* shape) {
  // All colors in this file are from:
  // https://material.io/guidelines/style/color.html
  // Arrays involved in RNN back-edges have a different color
  for (const auto& rnn_state : model.flags.rnn_states()) {
    // RNN state, fed by a back-edge. Bold color.
    if (array_name == rnn_state.state_array()) {
      *color = Color(0x0F, 0x9D, 0x58);
      *shape = "invhouse";
      return;
    }
    // RNN back-edge source, feeding a RNN state.
    // Light tone of the same color as RNN states.
    if (array_name == rnn_state.back_edge_source_array()) {
      *color = Color(0xB7, 0xE1, 0xCD);
      *shape = "house";
      return;
    }
  }
  // Constant parameter arrays have their own bold color
  if (model.GetArray(array_name).buffer) {
    *color = Color(0x42, 0x85, 0xF4);
    *shape = "cylinder";
    return;
  }
  // Remaining arrays are activations.
  // We use gray colors for them because they are the majority
  // of arrays so we want to highlight other arrays instead of them.
  // First, we use a bolder gray for input/output arrays:
  if (IsInputArray(model, array_name)) {
    *color = Color(0x9E, 0x9E, 0x9E);
    *shape = "invhouse";
    return;
  }
  if (IsOutputArray(model, array_name)) {
    *color = Color(0x9E, 0x9E, 0x9E);
    *shape = "house";
    return;
  }
  // Remaining arrays are intermediate activation arrays.
  // Lighter tone of the same grey as for input/output arrays:
  // We want these to be very discrete.
  *color = Color(0xF5, 0xF5, 0xF5);
  *shape = "box";
}

string GetArrayCompassPt(const Model& model, const string& array_name) {
  // The "compass point" is the point on the node where edge connections are