PaddleX/libs/ultra-infer/ultra_infer/vision/detection/contrib/yolov8/postprocessor.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/yolov8/postprocessor.h"
#include "ultra_infer/vision/utils/utils.h"

namespace ultra_infer {
namespace vision {
namespace detection {

YOLOv8Postprocessor::YOLOv8Postprocessor() {
  conf_threshold_ = 0.25;
  nms_threshold_ = 0.5;
  multi_label_ = true;
  max_wh_ = 7680.0;
}

bool YOLOv8Postprocessor::Run(
    const std::vector<FDTensor> &tensors, std::vector<DetectionResult> *results,
    const std::vector<std::map<std::string, std::array<float, 2>>> &ims_info) {
  int batch = tensors[0].shape[0];
  // transpose
  std::vector<int64_t> dim{0, 2, 1};
  FDTensor tensor_transpose;
  function::Transpose(tensors[0], &tensor_transpose, dim);

  results->resize(batch);

  for (size_t bs = 0; bs < batch; ++bs) {
    (*results)[bs].Clear();
    if (multi_label_) {
      (*results)[bs].Reserve(tensor_transpose.shape[1] *
                             (tensor_transpose.shape[2] - 4));
    } else {
      (*results)[bs].Reserve(tensor_transpose.shape[1]);
    }
    if (tensor_transpose.dtype != FDDataType::FP32) {
      FDERROR << "Only support post process with float32 data." << std::endl;
      return false;
    }
    const float *data =
        reinterpret_cast<const float *>(tensor_transpose.Data()) +
        bs * tensor_transpose.shape[1] * tensor_transpose.shape[2];
    for (size_t i = 0; i < tensor_transpose.shape[1]; ++i) {
      int s = i * tensor_transpose.shape[2];
      if (multi_label_) {
        for (size_t j = 4; j < tensor_transpose.shape[2]; ++j) {
          float confidence = data[s + j];
          // filter boxes by conf_threshold
          if (confidence <= conf_threshold_) {
            continue;
          }
          int32_t label_id = j - 4;

          // convert from [x, y, w, h] to [x1, y1, x2, y2]
          (*results)[bs].boxes.emplace_back(std::array<float, 4>{
              data[s] - data[s + 2] / 2.0f + label_id * max_wh_,
              data[s + 1] - data[s + 3] / 2.0f + label_id * max_wh_,
              data[s + 0] + data[s + 2] / 2.0f + label_id * max_wh_,
              data[s + 1] + data[s + 3] / 2.0f + label_id * max_wh_});
          (*results)[bs].label_ids.push_back(label_id);
          (*results)[bs].scores.push_back(confidence);
        }
      } else {
        const float *max_class_score = std::max_element(
            data + s + 4, data + s + tensor_transpose.shape[2]);
        float confidence = *max_class_score;
        // filter boxes by conf_threshold
        if (confidence <= conf_threshold_) {
          continue;
        }
        int32_t label_id = std::distance(data + s + 4, max_class_score);
        // convert from [x, y, w, h] to [x1, y1, x2, y2]
        (*results)[bs].boxes.emplace_back(std::array<float, 4>{
            data[s] - data[s + 2] / 2.0f + label_id * max_wh_,
            data[s + 1] - data[s + 3] / 2.0f + label_id * max_wh_,
            data[s + 0] + data[s + 2] / 2.0f + label_id * max_wh_,
            data[s + 1] + data[s + 3] / 2.0f + label_id * max_wh_});
        (*results)[bs].label_ids.push_back(label_id);
        (*results)[bs].scores.push_back(confidence);
      }
    }

    if ((*results)[bs].boxes.size() == 0) {
      return true;
    }

    utils::NMS(&((*results)[bs]), nms_threshold_);

    // scale the boxes to the origin image shape
    auto iter_out = ims_info[bs].find("output_shape");
    auto iter_ipt = ims_info[bs].find("input_shape");
    FDASSERT(iter_out != ims_info[bs].end() && iter_ipt != ims_info[bs].end(),
             "Cannot find input_shape or output_shape from im_info.");
    float out_h = iter_out->second[0];
    float out_w = iter_out->second[1];
    float ipt_h = iter_ipt->second[0];
    float ipt_w = iter_ipt->second[1];
    float scale = std::min(out_h / ipt_h, out_w / ipt_w);
    float pad_h = (out_h - ipt_h * scale) / 2;
    float pad_w = (out_w - ipt_w * scale) / 2;
    for (size_t i = 0; i < (*results)[bs].boxes.size(); ++i) {
      int32_t label_id = ((*results)[bs].label_ids)[i];
      // clip box
      (*results)[bs].boxes[i][0] =
          (*results)[bs].boxes[i][0] - max_wh_ * label_id;
      (*results)[bs].boxes[i][1] =
          (*results)[bs].boxes[i][1] - max_wh_ * label_id;
      (*results)[bs].boxes[i][2] =
          (*results)[bs].boxes[i][2] - max_wh_ * label_id;
      (*results)[bs].boxes[i][3] =
          (*results)[bs].boxes[i][3] - max_wh_ * label_id;
      (*results)[bs].boxes[i][0] =
          std::max(((*results)[bs].boxes[i][0] - pad_w) / scale, 0.0f);
      (*results)[bs].boxes[i][1] =
          std::max(((*results)[bs].boxes[i][1] - pad_h) / scale, 0.0f);
      (*results)[bs].boxes[i][2] =
          std::max(((*results)[bs].boxes[i][2] - pad_w) / scale, 0.0f);
      (*results)[bs].boxes[i][3] =
          std::max(((*results)[bs].boxes[i][3] - pad_h) / scale, 0.0f);
      (*results)[bs].boxes[i][0] = std::min((*results)[bs].boxes[i][0], ipt_w);
      (*results)[bs].boxes[i][1] = std::min((*results)[bs].boxes[i][1], ipt_h);
      (*results)[bs].boxes[i][2] = std::min((*results)[bs].boxes[i][2], ipt_w);
      (*results)[bs].boxes[i][3] = std::min((*results)[bs].boxes[i][3], ipt_h);
    }
  }
  return true;
}

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