// 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 <vector>
#include "lite/api/cxx_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/api/test_helper.h"
#include "lite/core/op_registry.h"

namespace paddle {
namespace lite {

#ifdef LITE_WITH_FPGA
TEST(ResNet50, test) {
  lite::Predictor predictor;
  std::vector<Place> valid_places(
      {Place{TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)}});

  predictor.Build(FLAGS_model_dir,
                  "",
                  "",
                  Place{TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)},
                  valid_places);

  auto* input_tensor = predictor.GetInput(0);
  input_tensor->Resize(DDim(std::vector<DDim::value_type>({1, 3, 224, 224})));
  auto* data = input_tensor->mutable_data<float>();
  auto item_size = input_tensor->dims().production();
  for (int i = 0; i < item_size; i++) {
    data[i] = 1;
  }

  for (int i = 0; i < FLAGS_warmup; ++i) {
    predictor.Run();
  }

  auto start = GetCurrentUS();
  for (int i = 0; i < FLAGS_repeats; ++i) {
    predictor.Run();
  }

  LOG(INFO) << "================== Speed Report ===================";
}
#endif

}  // namespace lite
}  // namespace paddle