text: /* Copyright (c) 2018 PaddlePaddle 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 <iostream>
  #include "../test_helper.h"
  #include "../test_include.h"
  
  int main() {
  #ifdef PADDLE_MOBILE_FPGA
    paddle_mobile::PaddleMobile<paddle_mobile::FPGA> paddle_mobile;
  #endif
  
  #ifdef PADDLE_MOBILE_CPU
    paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
  #endif
    paddle_mobile.SetThreadNum(4);
    auto time1 = time();
    if (paddle_mobile.Load(g_resnet, true)) {
      auto time2 = time();
      std::cout << "load cost :" << time_diff(time1, time1) << "ms" << std::endl;
      std::vector<int64_t> dims{1, 3, 32, 32};
      Tensor input_tensor;
      SetupTensor<float>(&input_tensor, {1, 3, 32, 32}, static_cast<float>(0),
                         static_cast<float>(1));
  
      std::vector<float> input(input_tensor.data<float>(),
                               input_tensor.data<float>() + input_tensor.numel());
      // 预热一次
      paddle_mobile.Predict(input, dims);
      auto time3 = time();
      for (int i = 0; i < 10; ++i) {
        paddle_mobile.Predict(input, dims);
      }
      auto time4 = time();
      std::cout << "predict cost :" << time_diff(time3, time4) << "ms"
                << std::endl;
    }
  
    return 0;
  }
  