// Copyright (c) 2022 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.

#pragma once
#include "ultra_infer/ultra_infer_model.h"
#include "ultra_infer/vision/common/processors/transform.h"
#include "ultra_infer/vision/common/result.h"

// The namespace should be
// ultra_infer::vision::classification (ultra_infer::vision::${task})
namespace ultra_infer {
namespace vision {
/** \brief All object classification model APIs are defined inside this
 * namespace
 *
 */
namespace classification {
/*! @brief Torchvision ResNet series model
 */
class ULTRAINFER_DECL ResNet : public UltraInferModel {
public:
  /** \brief  Set path of model file and the configuration of runtime.
   *
   * \param[in] model_file Path of model file, e.g ./resnet50.onnx
   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams,
   * if the model format is ONNX, this parameter will be ignored \param[in]
   * custom_option RuntimeOption for inference, the default will use cpu, and
   * choose the backend defined in "valid_cpu_backends" \param[in] model_format
   * Model format of the loaded model, default is ONNX format
   */
  ResNet(const std::string &model_file, const std::string &params_file = "",
         const RuntimeOption &custom_option = RuntimeOption(),
         const ModelFormat &model_format = ModelFormat::ONNX);

  virtual std::string ModelName() const { return "ResNet"; }
  /** \brief Predict for the input "im", the result will be saved in "result".
   *
   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array
   * with layout HWC, BGR format \param[in] result Saving the inference result.
   * \param[in] topk The length of return values, e.g., if topk==2, the result
   * will include the 2 most possible class label for input image.
   */
  virtual bool Predict(cv::Mat *im, ClassifyResult *result, int topk = 1);
  /*! @brief
  Argument for image preprocessing step, tuple of (width, height), decide the
  target size after resize, default size = {224, 224}
  */
  std::vector<int> size;
  /*! @brief
  Mean parameters for normalize, size should be the the same as channels,
  default mean_vals = {0.485f, 0.456f, 0.406f}
  */
  std::vector<float> mean_vals;
  /*! @brief
  Std parameters for normalize, size should be the the same as channels, default
  std_vals = {0.229f, 0.224f, 0.225f}
  */
  std::vector<float> std_vals;

private:
  /*! @brief Initialize for ResNet model, assign values to the global variables
   * and call InitRuntime()
   */
  bool Initialize();
  /// PreProcessing for the input "mat", the result will be saved in "outputs".
  bool Preprocess(Mat *mat, FDTensor *outputs);
  /*! @brief PostProcessing for the input "infer_result", the result will be
   * saved in "result".
   */
  bool Postprocess(FDTensor &infer_result, ClassifyResult *result,
                   int topk = 1);
};
} // namespace classification
} // namespace vision
} // namespace ultra_infer