text: /* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
  
      http://www.apache.org/licenses/LICENSE-2.0
  
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.
  ==============================================================================*/
  #include "tensorflow/lite/tools/evaluation/stages/tflite_inference_stage.h"
  
  #include <cstring>
  #include <fstream>
  
  #include "tensorflow/core/platform/logging.h"
  #include "tensorflow/lite/c/common.h"
  #include "tensorflow/lite/profiling/time.h"
  #include "tensorflow/lite/tools/evaluation/proto/evaluation_stages.pb.h"
  #include "tensorflow/lite/tools/evaluation/utils.h"
  
  namespace tflite {
  namespace evaluation {
  namespace {
  
  TfLiteModelInfo GetTfliteModelInfo(const Interpreter& interpreter) {
    TfLiteModelInfo model_info;
    for (int i : interpreter.inputs()) {
      model_info.inputs.push_back(interpreter.tensor(i));
    }
    for (int i : interpreter.outputs()) {
      model_info.outputs.push_back(interpreter.tensor(i));
    }
    return model_info;
  }
  
  }  // namespace
  
  void TfliteInferenceStage::UpdateModelInfo() {
    model_info_ = GetTfliteModelInfo(*interpreter_);
  
    outputs_.clear();
    outputs_.reserve(interpreter_->outputs().size());
    for (int i : interpreter_->outputs()) {
      TfLiteTensor* tensor = interpreter_->tensor(i);
      outputs_.push_back(tensor->data.raw);
    }
  }
  
  TfLiteStatus TfliteInferenceStage::ApplyCustomDelegate(
      Interpreter::TfLiteDelegatePtr delegate) {
    if (!interpreter_) {
      LOG(ERROR) << "Stage not initialized before calling ApplyCustomDelegate";
      return kTfLiteError;
    }
    // Skip if delegate is a nullptr.
    if (!delegate) {
      LOG(WARNING)
          << "Tried to apply null TfLiteDelegatePtr to TfliteInferenceStage";
      return kTfLiteOk;
    }
    delegates_.push_back(std::move(delegate));
    TF_LITE_ENSURE_STATUS(
        interpreter_->ModifyGraphWithDelegate(delegates_.back().get()));
    UpdateModelInfo();
    return kTfLiteOk;
  }
  
  TfLiteStatus TfliteInferenceStage::Init() {
    if (!config_.specification().has_tflite_inference_params()) {
      LOG(ERROR) << "TfliteInferenceParams not provided";
      return kTfLiteError;
    }
    auto& params = config_.specification().tflite_inference_params();
    if (!params.has_model_file_path()) {
      LOG(ERROR) << "Model path not provided";
      return kTfLiteError;
    }
    std::ifstream model_check(params.model_file_path());
    if (!model_check.good()) {
      LOG(ERROR) << "Model file not found";
      return kTfLiteError;
    }
  
    model_ = FlatBufferModel::BuildFromFile(params.model_file_path().c_str());
    resolver_.reset(new ops::builtin::BuiltinOpResolver);
    InterpreterBuilder(*model_, *resolver_)(&interpreter_);
    if (!interpreter_) {
      LOG(ERROR) << "Could not build interpreter";
      return kTfLiteError;
    }
    interpreter_->SetNumThreads(params.num_threads());
  
    if (params.delegate() == TfliteInferenceParams::NNAPI) {
      Interpreter::TfLiteDelegatePtr delegate = CreateNNAPIDelegate();
      if (delegate) {
        delegates_.push_back(std::move(delegate));
      } else {
        LOG(WARNING) << "NNAPI not supported";
      }
    } else if (params.delegate() == TfliteInferenceParams::GPU) {
      Interpreter::TfLiteDelegatePtr delegate = CreateGPUDelegate(model_.get());
      if (delegate) {
        delegates_.push_back(std::move(delegate));
      } else {
        LOG(WARNING) << "GPU not supported";
      }
    }
    for (int i = 0; i < delegates_.size(); ++i) {
      if (interpreter_->ModifyGraphWithDelegate(delegates_[i].get()) !=
          kTfLiteOk) {
        LOG(FATAL) << "Failed to apply delegate " << i;
      }
    }
    interpreter_->AllocateTensors();
    UpdateModelInfo();
  
    return kTfLiteOk;
  }
  
  TfLiteStatus TfliteInferenceStage::Run() {
    if (!inputs_) {
      LOG(ERROR) << "Input data not set";
      return kTfLiteError;
    }
  
    // Copy input data.
    for (int i = 0; i < interpreter_->inputs().size(); ++i) {
      TfLiteTensor* tensor = interpreter_->tensor(interpreter_->inputs()[i]);
      tensor->data.raw = static_cast<char*>(inputs_->at(i));
    }
  
    // Invoke.
    auto& params = config_.specification().tflite_inference_params();
    for (int i = 0; i < params.invocations_per_run(); ++i) {
      int64_t start_us = profiling::time::NowMicros();
      if (interpreter_->Invoke() != kTfLiteOk) {
        LOG(ERROR) << "TFLite interpreter failed to invoke at run " << i;
        return kTfLiteError;
      }
      latency_stats_.UpdateStat(profiling::time::NowMicros() - start_us);
    }
  
    return kTfLiteOk;
  }
  
  EvaluationStageMetrics TfliteInferenceStage::LatestMetrics() {
    auto& params = config_.specification().tflite_inference_params();
    EvaluationStageMetrics metrics;
    auto* latency_metrics =
        metrics.mutable_process_metrics()->mutable_total_latency();
    latency_metrics->set_last_us(latency_stats_.newest());
    latency_metrics->set_max_us(latency_stats_.max());
    latency_metrics->set_min_us(latency_stats_.min());
    latency_metrics->set_sum_us(latency_stats_.sum());
    latency_metrics->set_avg_us(latency_stats_.avg());
    metrics.set_num_runs(
        static_cast<int>(latency_stats_.count() / params.invocations_per_run()));
    auto* inference_metrics =
        metrics.mutable_process_metrics()->mutable_tflite_inference_metrics();
    inference_metrics->set_num_inferences(latency_stats_.count());
    return metrics;
  }
  
  }  // namespace evaluation
  }  // namespace tflite
  