Function PreluPrepare

tensorflow/lite/kernels/activations.cc:494–525

}

TfLiteStatus PreluPrepare(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  const TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  const TfLiteTensor* alpha = GetInput(context, node, 1);
  PreluOpData* data = reinterpret_cast<PreluOpData*>(node->user_data);

  TF_LITE_ENSURE_EQ(context, input->type, alpha->type);
  output->type = input->type;

  if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) {
    double real_multiplier =
        input->params.scale * alpha->params.scale / output->params.scale;
    QuantizeMultiplierSmallerThanOneExp(
        real_multiplier, &data->output_multiplier, &data->output_shift);
  }

  // PRelu (parameteric Relu) shares the same alpha value on "shared axis".
  // This means it's always required to "broadcast" alpha values in PRelu.
  TfLiteIntArray* output_size = nullptr;
  TF_LITE_ENSURE_OK(
      context, CalculateShapeForBroadcast(context, input, alpha, &output_size));

  TF_LITE_ENSURE_OK(context,
                    context->ResizeTensor(context, output, output_size));
  // After broadcasting, the output shape should always be the same as the
  // input shape.
  TF_LITE_ENSURE(context, HaveSameShapes(input, output));

  return kTfLiteOk;
}

TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, 0);