text: // Copyright (c) 2019 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 <gflags/gflags.h>
  #include <gtest/gtest.h>
  #include <cmath>
  #include <vector>
  #include "lite/api/paddle_api.h"
  #include "lite/api/test/lite_api_test_helper.h"
  #include "lite/api/test/test_helper.h"
  #include "lite/utils/log/cp_logging.h"
  
  DEFINE_string(data_dir, "", "input image path");
  
  namespace paddle {
  namespace lite {
  
  TEST(OCR_LSTM_INT8_MODEL, test_ocr_lstm_int8_arm) {
    std::shared_ptr<paddle::lite_api::PaddlePredictor> predictor = nullptr;
    // Use the full api with CxxConfig to generate the optimized model
    lite_api::CxxConfig cxx_config;
    cxx_config.set_model_dir(FLAGS_model_dir);
    cxx_config.set_valid_places(
        {lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
         lite_api::Place{TARGET(kARM), PRECISION(kInt32)},
         lite_api::Place{TARGET(kARM), PRECISION(kInt64)}});
    predictor = lite_api::CreatePaddlePredictor(cxx_config);
    predictor->SaveOptimizedModel(FLAGS_model_dir,
                                  paddle::lite_api::LiteModelType::kNaiveBuffer);
  
    // Use the light api with MobileConfig to load and run the optimized model
    paddle::lite_api::MobileConfig mobile_config;
    mobile_config.set_model_from_file(FLAGS_model_dir + ".nb");
    mobile_config.set_threads(FLAGS_threads);
    mobile_config.set_power_mode(
        static_cast<lite_api::PowerMode>(FLAGS_power_mode));
    predictor = paddle::lite_api::CreatePaddlePredictor(mobile_config);
  
    const int batch_size = 1;
    const int channel = 3;
    const int height = 32;
    const int width = 320;
    const int input_size = batch_size * channel * height * width;
  
    auto input_tensor = predictor->GetInput(0);
    input_tensor->Resize({batch_size, channel, height, width});
    auto* input_data = input_tensor->mutable_data<float>();
    ReadTxtFile(FLAGS_data_dir, input_data, input_size);
  
    predictor->Run();
  
    auto out_0 = predictor->GetOutput(0);
    int64_t out_size_0 = ShapeProduction(out_0->shape());
    auto* out_data_0 = out_0->data<int64_t>();
    ASSERT_EQ(out_size_0, 4);
    ASSERT_EQ(out_data_0[0], 389);
    ASSERT_EQ(out_data_0[1], 519);
    ASSERT_EQ(out_data_0[2], 472);
    ASSERT_EQ(out_data_0[3], 519);
  
    auto out_1 = predictor->GetOutput(1);
    int64_t out_size_1 = ShapeProduction(out_1->shape());
    auto* out_data_1 = out_1->data<float>();
    const float eps = 1e-3;
    int num = 0;
    for (int i = 0; i < out_size_1 && num < 10; i++) {
      if (std::abs(out_data_1[i]) > eps) {
        LOG(INFO) << "out_data_1[" << i << "]:" << out_data_1[i];
        ++num;
      }
    }
    ASSERT_EQ(out_size_1, 530000);
    ASSERT_LT(std::abs(out_data_1[6624] - 0.999878), eps);
    ASSERT_LT(std::abs(out_data_1[13249] - 0.999965), eps);
    ASSERT_LT(std::abs(out_data_1[13639] - 0.113657), 1e-2);
    ASSERT_LT(std::abs(out_data_1[20264] - 0.999932), eps);
  }
  
  }  // namespace lite
  }  // namespace paddle
  