PaddleX/deploy/ultra-infer/ultra_infer/vision/detection/contrib/yolov5/yolov5.cc

// 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.

#include "ultra_infer/vision/detection/contrib/yolov5/yolov5.h"

namespace ultra_infer {
namespace vision {
namespace detection {

YOLOv5::YOLOv5(const std::string &model_file, const std::string &params_file,
               const RuntimeOption &custom_option,
               const ModelFormat &model_format) {
  if (model_format == ModelFormat::ONNX) {
    valid_cpu_backends = {Backend::OPENVINO, Backend::ORT};
    valid_gpu_backends = {Backend::ORT, Backend::TRT};
  } else if (model_format == ModelFormat::SOPHGO) {
    valid_sophgonpu_backends = {Backend::SOPHGOTPU};
  } else {
    valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
    valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
    valid_kunlunxin_backends = {Backend::LITE};
    valid_timvx_backends = {Backend::LITE};
    valid_ascend_backends = {Backend::LITE};
  }
  runtime_option = custom_option;
  runtime_option.model_format = model_format;
  runtime_option.model_file = model_file;
  runtime_option.params_file = params_file;
  initialized = Initialize();
}

bool YOLOv5::Initialize() {
  if (!InitRuntime()) {
    FDERROR << "Failed to initialize ultra_infer backend." << std::endl;
    return false;
  }
  return true;
}

bool YOLOv5::Predict(cv::Mat *im, DetectionResult *result, float conf_threshold,
                     float nms_threshold) {
  postprocessor_.SetConfThreshold(conf_threshold);
  postprocessor_.SetNMSThreshold(nms_threshold);
  if (!Predict(*im, result)) {
    return false;
  }
  return true;
}

bool YOLOv5::Predict(const cv::Mat &im, DetectionResult *result) {
  std::vector<DetectionResult> results;
  if (!BatchPredict({im}, &results)) {
    return false;
  }
  *result = std::move(results[0]);
  return true;
}

bool YOLOv5::BatchPredict(const std::vector<cv::Mat> &images,
                          std::vector<DetectionResult> *results) {
  std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
  std::vector<FDMat> fd_images = WrapMat(images);

  if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, &ims_info)) {
    FDERROR << "Failed to preprocess the input image." << std::endl;
    return false;
  }

  reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
  if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
    FDERROR << "Failed to inference by runtime." << std::endl;
    return false;
  }

  if (!postprocessor_.Run(reused_output_tensors_, results, ims_info)) {
    FDERROR << "Failed to postprocess the inference results by runtime."
            << std::endl;
    return false;
  }

  return true;
}

} // namespace detection
} // namespace vision
} // namespace ultra_infer