yolov6.h

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.  //NOLINT
//
// 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 "fastdeploy/fastdeploy_model.h"
#include "fastdeploy/vision/common/processors/transform.h"
#include "fastdeploy/vision/common/result.h"

namespace fastdeploy {

namespace vision {

namespace detection {
class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
 public:
  YOLOv6(const std::string& model_file, const std::string& params_file = "",
         const RuntimeOption& custom_option = RuntimeOption(),
         const ModelFormat& model_format = ModelFormat::ONNX);

  ~YOLOv6();

  std::string ModelName() const { return "YOLOv6"; }
  virtual bool Predict(cv::Mat* im, DetectionResult* result,
                       float conf_threshold = 0.25,
                       float nms_iou_threshold = 0.5);


  void UseCudaPreprocessing(int max_img_size = 3840 * 2160);

  std::vector<int> size;
  // padding value, size should be the same as channels

  std::vector<float> padding_value;
  // only pad to the minimum rectange which height and width is times of stride
  bool is_mini_pad;
  // while is_mini_pad = false and is_no_pad = true,
  // will resize the image to the set size
  bool is_no_pad;
  // if is_scale_up is false, the input image only can be zoom out,
  // the maximum resize scale cannot exceed 1.0
  bool is_scale_up;
  // padding stride, for is_mini_pad
  int stride;
  // for offseting the boxes by classes when using NMS,
  // default 4096 in meituan/YOLOv6
  float max_wh;

 private:
  bool Initialize();

  bool Preprocess(Mat* mat, FDTensor* outputs,
                  std::map<std::string, std::array<float, 2>>* im_info);

  bool CudaPreprocess(Mat* mat, FDTensor* output,
                      std::map<std::string, std::array<float, 2>>* im_info);

  bool Postprocess(FDTensor& infer_result, DetectionResult* result,
                   const std::map<std::string, std::array<float, 2>>& im_info,
                   float conf_threshold, float nms_iou_threshold);

  bool IsDynamicInput() const { return is_dynamic_input_; }

  void LetterBox(Mat* mat, std::vector<int> size, std::vector<float> color,
                 bool _auto, bool scale_fill = false, bool scale_up = true,
                 int stride = 32);

  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape
  // or not.)
  // meituan/YOLOv6 official 'export_onnx.py' script will export static ONNX by
  // default.
  // while is_dynamic_input if 'false', is_mini_pad will force 'false'. This
  // value will
  // auto check by fastdeploy after the internal Runtime already initialized.
  bool is_dynamic_input_;
// CUDA host buffer for input image
  uint8_t* input_img_cuda_buffer_host_ = nullptr;
  // CUDA device buffer for input image
  uint8_t* input_img_cuda_buffer_device_ = nullptr;
  // CUDA device buffer for TRT input tensor
  float* input_tensor_cuda_buffer_device_ = nullptr;
  // Whether to use CUDA preprocessing
  bool use_cuda_preprocessing_ = false;
  // CUDA stream
  void* cuda_stream_ = nullptr;
};

}  // namespace detection
}  // namespace vision
}  // namespace fastdeploy