Method AddTfOp

tensorflow/lite/delegates/flex/test_util.cc:117–163

}

void FlexModelTest::AddTfOp(TfOpType op, const std::vector<int>& inputs,
                            const std::vector<int>& outputs) {
  tf_ops_.push_back(next_op_index_);
  ++next_op_index_;

  auto attr = [](const string& key, const string& value) {
    return " attr{ key: '" + key + "' value {" + value + "}}";
  };

  string type_attribute;
  switch (interpreter_->tensor(inputs[0])->type) {
    case kTfLiteInt32:
      type_attribute = attr("T", "type: DT_INT32");
      break;
    case kTfLiteFloat32:
      type_attribute = attr("T", "type: DT_FLOAT");
      break;
    case kTfLiteString:
      type_attribute = attr("T", "type: DT_STRING");
      break;
    default:
      // TODO(b/113613439): Use nodedef string utilities to properly handle all
      // types.
      LOG(FATAL) << "Type not supported";
      break;
  }

  if (op == kUnpack) {
    string attributes =
        type_attribute + attr("num", "i: 2") + attr("axis", "i: 0");
    AddTfOp("FlexUnpack", "Unpack", attributes, inputs, outputs);
  } else if (op == kIdentity) {
    string attributes = type_attribute;
    AddTfOp("FlexIdentity", "Identity", attributes, inputs, outputs);
  } else if (op == kAdd) {
    string attributes = type_attribute;
    AddTfOp("FlexAdd", "Add", attributes, inputs, outputs);
  } else if (op == kMul) {
    string attributes = type_attribute;
    AddTfOp("FlexMul", "Mul", attributes, inputs, outputs);
  } else if (op == kNonExistent) {
    AddTfOp("NonExistentOp", "NonExistentOp", "", inputs, outputs);
  } else if (op == kIncompatibleNodeDef) {
    // "Cast" op is created without attributes - making it incompatible.
    AddTfOp("FlexCast", "Cast", "", inputs, outputs);
  }
}

void FlexModelTest::AddTfOp(const char* tflite_name, const string& tf_name,
                            const string& nodedef_str,