Function DumpOperatorEdges

tensorflow/lite/toco/dump_graphviz.cc:525–570

}

void DumpOperatorEdges(const Model& model, string* output_file, int op_index) {
  // Inputs
  const Operator& op = *model.operators[op_index];
  string op_id = GetOpId(op_index);
  for (int i = 0; i < op.inputs.size(); i++) {
    const auto& input = op.inputs[i];
    if (!model.HasArray(input)) {
      // Connected arrays should _always_ exist. Except, perhaps, during
      // development.
      continue;
    }
    float log2_buffer_size = GetLog2BufferSize(model, input);
    // Draw lines that transport more data thicker (Otherwise, where would the
    // data fit? right?).
    float line_width = std::max(0.5f, log2_buffer_size / 3.0f);
    // Keep edges that transport more data shorter than those with less.
    float weight = std::max(1.0f, log2_buffer_size);
    if (!IsInputArray(model, input) &&
        GetOpWithOutput(model, input) == nullptr) {
      // Give the main line of data flow a straighter path by penalizing edges
      // to standalone buffers. Weights are generally very large buffers that
      // would otherwise skew the layout.
      weight = 1.0f;
    }
    string compass_pt = GetArrayCompassPt(model, input);
    AppendF(output_file, kInputEdgeFmt, input, compass_pt, op_id, i, line_width,
            weight);
  }
  // Outputs
  for (int i = 0; i < op.outputs.size(); i++) {
    const auto& output = op.outputs[i];
    if (!model.HasArray(output)) {
      continue;
    }
    float log2_buffer_size = GetLog2BufferSize(model, output);
    // See comments above regarding weight and line_width calculations.
    float line_width = std::max(0.5f, log2_buffer_size / 3.0f);
    float weight = std::max(1.0f, log2_buffer_size);
    if (!IsArrayConsumed(model, output)) {
      weight = 1.0f;
    }
    string compass_pt = GetArrayCompassPt(model, output);
    AppendF(output_file, kOutputEdgeFmt, op_id, i, output, compass_pt,
            line_width, weight);
  }
}

struct Node {
  Node() : math_ops(0) {}