Method BuildGraph

tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:3189–3272

}

TfLiteStatus NNAPIDelegateKernel::BuildGraph(
    TfLiteContext* context, const TfLiteIntArray* input_tensors,
    const TfLiteIntArray* output_tensors) {
  // Build the ops and tensors.
  TF_LITE_ENSURE_STATUS(AddOpsAndTensors(context));
  // Map input and output tensor indices to ANN
  std::vector<uint32_t> inputs;
  inputs.reserve(input_tensors->size);
  std::vector<uint32_t> outputs;
  outputs.reserve(output_tensors->size);

  size_t total_input_byte_size = 0;
  // Make the TensorFlow Lite inputs and outputs to ann_indices.
  for (int i : TfLiteIntArrayView(input_tensors)) {
    // Constant tensors are not NNAPI inputs.
    if (i != kOptionalTensor &&
        context->tensors[i].allocation_type != kTfLiteMmapRo &&
        // The delegate might not have mapped this input (this can
        // happen if one tensor is split in several ones)
        operand_mapping_.lite_index_to_ann(i) != -1) {
      inputs.push_back(operand_mapping_.lite_index_to_ann(i));
      if (context->tensors[i].buffer_handle != kTfLiteNullBufferHandle) {
        continue;
      }
      const TfLiteType nn_type_conversion =
          operand_mapping_.lite_index_to_ann_type_conversion(i);
      int tensor_size = 0;
      if (nn_type_conversion == kTfLiteNoType) {
        tensor_size = context->tensors[i].bytes;
      } else {
        size_t type_size;
        TF_LITE_ENSURE_OK(
            context, GetSizeOfType(context, nn_type_conversion, &type_size));
        tensor_size = NumElements(&context->tensors[i]) * type_size;
      }
      total_input_byte_size += tensor_size;
      total_input_byte_size += getNumPaddingBytes(tensor_size);
    }
  }

  size_t total_output_byte_size = 0;
  for (int i : TfLiteIntArrayView(output_tensors)) {
    const int output_tensor_ann_index = operand_mapping_.lite_index_to_ann(i);
    // Unmapped outputs are not added
    if (output_tensor_ann_index != -1) {
      outputs.push_back(output_tensor_ann_index);
    }
    if (context->tensors[i].buffer_handle != kTfLiteNullBufferHandle) {
      continue;
    }
    total_output_byte_size += context->tensors[i].bytes;
    total_output_byte_size += getNumPaddingBytes(context->tensors[i].bytes);
  }

  // Add state output tensors as model outputs.
  for (int i : model_state_outputs_) {
    outputs.push_back(i);
  }