object_detector.cc

//   Copyright (c) 2020 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 <sstream>
// for setprecision
#include <iomanip>
#include <chrono>
#include "include/object_detector.h"


using namespace paddle_infer;

namespace PaddleDetection {

// Load Model and create model predictor
void ObjectDetector::LoadModel(const std::string& model_dir,
                               const int batch_size,
                               const std::string& run_mode) {
  paddle_infer::Config config;
  std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
  std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
  config.SetModel(prog_file, params_file);
  if (this->device_ == "GPU") {
    config.EnableUseGpu(200, this->gpu_id_);
    config.SwitchIrOptim(true);
    // use tensorrt
    if (run_mode != "fluid") {
      auto precision = paddle_infer::Config::Precision::kFloat32;
      if (run_mode == "trt_fp32") {
        precision = paddle_infer::Config::Precision::kFloat32;
      }
      else if (run_mode == "trt_fp16") {
        precision = paddle_infer::Config::Precision::kHalf;
      }
      else if (run_mode == "trt_int8") {
        precision = paddle_infer::Config::Precision::kInt8;
      } else {
          printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'");
      }
      // set tensorrt
      config.EnableTensorRtEngine(
          1 << 30,
          batch_size,
          this->min_subgraph_size_,
          precision,
          false,
          this->trt_calib_mode_);

      // set use dynamic shape
      if (this->use_dynamic_shape_) {
        // set DynamicShsape for image tensor
        const std::vector<int> min_input_shape = {1, 3, this->trt_min_shape_, this->trt_min_shape_};
        const std::vector<int> max_input_shape = {1, 3, this->trt_max_shape_, this->trt_max_shape_};
        const std::vector<int> opt_input_shape = {1, 3, this->trt_opt_shape_, this->trt_opt_shape_};
        const std::map<std::string, std::vector<int>> map_min_input_shape = {{"image", min_input_shape}};
        const std::map<std::string, std::vector<int>> map_max_input_shape = {{"image", max_input_shape}};
        const std::map<std::string, std::vector<int>> map_opt_input_shape = {{"image", opt_input_shape}};

        config.SetTRTDynamicShapeInfo(map_min_input_shape,
                                      map_max_input_shape,
                                      map_opt_input_shape);
        std::cout << "TensorRT dynamic shape enabled" << std::endl;
      }
    }

  } else if (this->device_ == "XPU"){
    config.EnableXpu(10*1024*1024);
  } else {
    config.DisableGpu();
    if (this->use_mkldnn_) {
      config.EnableMKLDNN();
      // cache 10 different shapes for mkldnn to avoid memory leak
      config.SetMkldnnCacheCapacity(10);
    }
    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
  }
  config.SwitchUseFeedFetchOps(false);
  config.SwitchIrOptim(true);
  config.DisableGlogInfo();
  // Memory optimization
  config.EnableMemoryOptim();
  predictor_ = std::move(CreatePredictor(config));
}

// Visualiztion MaskDetector results
cv::Mat VisualizeResult(const cv::Mat& img,
                        const std::vector<ObjectResult>& results,
                        const std::vector<std::string>& lables,
                        const std::vector<int>& colormap,
                        const bool is_rbox=false) {
  cv::Mat vis_img = img.clone();
  for (int i = 0; i < results.size(); ++i) {
    // Configure color and text size
    std::ostringstream oss;
    oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
    oss << lables[results[i].class_id] << " ";
    oss << results[i].confidence;
    std::string text = oss.str();
    int c1 = colormap[3 * results[i].class_id + 0];
    int c2 = colormap[3 * results[i].class_id + 1];
    int c3 = colormap[3 * results[i].class_id + 2];
    cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
    int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
    double font_scale = 0.5f;
    float thickness = 0.5;
    cv::Size text_size = cv::getTextSize(text,
                                         font_face,
                                         font_scale,
                                         thickness,
                                         nullptr);
    cv::Point origin;

    if (is_rbox)
    {
        // Draw object, text, and background
        for (int k = 0; k < 4; k++)
        {
            cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
                                      results[i].rect[(k * 2 + 1) % 8]);
            cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
                                      results[i].rect[(k * 2 + 3) % 8]);
            cv::line(vis_img, pt1, pt2, roi_color, 2);
        }
    }
    else
    {
        int w = results[i].rect[2] - results[i].rect[0];
        int h = results[i].rect[3] - results[i].rect[1];
        cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
        // Draw roi object, text, and background
        cv::rectangle(vis_img, roi, roi_color, 2);
    }

    origin.x = results[i].rect[0];
    origin.y = results[i].rect[1];

    // Configure text background
    cv::Rect text_back = cv::Rect(results[i].rect[0],
                                  results[i].rect[1] - text_size.height,
                                  text_size.width,
                                  text_size.height);
    // Draw text, and background
    cv::rectangle(vis_img, text_back, roi_color, -1);
    cv::putText(vis_img,
                text,
                origin,
                font_face,
                font_scale,
                cv::Scalar(255, 255, 255),
                thickness);
  }
  return vis_img;
}

void ObjectDetector::Preprocess(const cv::Mat& ori_im) {
  // Clone the image : keep the original mat for postprocess
  cv::Mat im = ori_im.clone();
  cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
  preprocessor_.Run(&im, &inputs_);
}

void ObjectDetector::Postprocess(
    const std::vector<cv::Mat> mats,
    std::vector<ObjectResult>* result,
    std::vector<int> bbox_num,
    bool is_rbox=false) {
  result->clear();
  int start_idx = 0;
  for (int im_id = 0; im_id < mats.size(); im_id++) {
    cv::Mat raw_mat = mats[im_id];
    int rh = 1;
    int rw = 1;
    if (config_.arch_ == "Face") {
      rh = raw_mat.rows;
      rw = raw_mat.cols;
    }
    for (int j = start_idx; j < start_idx+bbox_num[im_id]; j++) {
      if (is_rbox) {
        // Class id
        int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
        // Confidence score
        float score = output_data_[1 + j * 10];
        int x1 = (output_data_[2 + j * 10] * rw);
        int y1 = (output_data_[3 + j * 10] * rh);
        int x2 = (output_data_[4 + j * 10] * rw);
        int y2 = (output_data_[5 + j * 10] * rh);
        int x3 = (output_data_[6 + j * 10] * rw);
        int y3 = (output_data_[7 + j * 10] * rh);
        int x4 = (output_data_[8 + j * 10] * rw);
        int y4 = (output_data_[9 + j * 10] * rh);
          
        ObjectResult result_item;
        result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
        result_item.class_id = class_id;
        result_item.confidence = score;
        result->push_back(result_item);
      }
      else {
        // Class id
        int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
        // Confidence score
        float score = output_data_[1 + j * 6];
        int xmin = (output_data_[2 + j * 6] * rw);
        int ymin = (output_data_[3 + j * 6] * rh);
        int xmax = (output_data_[4 + j * 6] * rw);
        int ymax = (output_data_[5 + j * 6] * rh);
        int wd = xmax - xmin;
        int hd = ymax - ymin;
          
        ObjectResult result_item;
        result_item.rect = {xmin, ymin, xmax, ymax};
        result_item.class_id = class_id;
        result_item.confidence = score;
        result->push_back(result_item);
      }
    }
    start_idx += bbox_num[im_id];
  }
}

void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
      const double threshold,
      const int warmup,
      const int repeats,
      std::vector<ObjectResult>* result,
      std::vector<int>* bbox_num,
      std::vector<double>* times) {
  auto preprocess_start = std::chrono::steady_clock::now();
  int batch_size = imgs.size();

  // in_data_batch
  std::vector<float> in_data_all;
  std::vector<float> im_shape_all(batch_size * 2);
  std::vector<float> scale_factor_all(batch_size * 2);
  
  // Preprocess image
  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
    cv::Mat im = imgs.at(bs_idx);
    Preprocess(im);
    im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
    im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];

    scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
    scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];

    // TODO: reduce cost time
    in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
  }

  // Prepare input tensor
  
  auto input_names = predictor_->GetInputNames();
  for (const auto& tensor_name : input_names) {
    auto in_tensor = predictor_->GetInputHandle(tensor_name);
    if (tensor_name == "image") {
      int rh = inputs_.in_net_shape_[0];
      int rw = inputs_.in_net_shape_[1];
      in_tensor->Reshape({batch_size, 3, rh, rw});
      in_tensor->CopyFromCpu(in_data_all.data());
    } else if (tensor_name == "im_shape") {
      in_tensor->Reshape({batch_size, 2});
      in_tensor->CopyFromCpu(im_shape_all.data());
    } else if (tensor_name == "scale_factor") {
      in_tensor->Reshape({batch_size, 2});
      in_tensor->CopyFromCpu(scale_factor_all.data());
    }
  }
  
  auto preprocess_end = std::chrono::steady_clock::now();
  // Run predictor
  // warmup
  for (int i = 0; i < warmup; i++)
  {
    predictor_->Run();
    // Get output tensor
    auto output_names = predictor_->GetOutputNames();
    auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
    std::vector<int> output_shape = out_tensor->shape();
    auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
    std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
    // Calculate output length
    int output_size = 1;
    for (int j = 0; j < output_shape.size(); ++j) {
      output_size *= output_shape[j];
    }

    if (output_size < 6) {
      std::cerr << "[WARNING] No object detected." << std::endl;
    }
    output_data_.resize(output_size);
    out_tensor->CopyToCpu(output_data_.data()); 

    int out_bbox_num_size = 1;
    for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
      out_bbox_num_size *= out_bbox_num_shape[j];
    }
    out_bbox_num_data_.resize(out_bbox_num_size);
    out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
  }
  
  bool is_rbox = false;
  auto inference_start = std::chrono::steady_clock::now();
  for (int i = 0; i < repeats; i++)
  {
    predictor_->Run();
    // Get output tensor
    auto output_names = predictor_->GetOutputNames();
    auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
    std::vector<int> output_shape = out_tensor->shape();
    auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
    std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
    // Calculate output length
    int output_size = 1;
    for (int j = 0; j < output_shape.size(); ++j) {
      output_size *= output_shape[j];
    }
    is_rbox = output_shape[output_shape.size()-1] % 10 == 0;

    if (output_size < 6) {
      std::cerr << "[WARNING] No object detected." << std::endl;
    }
    output_data_.resize(output_size);
    out_tensor->CopyToCpu(output_data_.data()); 

    int out_bbox_num_size = 1;
    for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
      out_bbox_num_size *= out_bbox_num_shape[j];
    }
    out_bbox_num_data_.resize(out_bbox_num_size);
    out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
  }
  auto inference_end = std::chrono::steady_clock::now();
  auto postprocess_start = std::chrono::steady_clock::now();
  // Postprocessing result
  result->clear();
  Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
  bbox_num->clear();
  for (int k=0; k<out_bbox_num_data_.size(); k++) {
    int tmp = out_bbox_num_data_[k];
    bbox_num->push_back(tmp);
  }
  auto postprocess_end = std::chrono::steady_clock::now();

  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
  times->push_back(double(preprocess_diff.count() * 1000));
  std::chrono::duration<float> inference_diff = inference_end - inference_start;
  times->push_back(double(inference_diff.count() / repeats * 1000));
  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
  times->push_back(double(postprocess_diff.count() * 1000));
  
}

std::vector<int> GenerateColorMap(int num_class) {
  auto colormap = std::vector<int>(3 * num_class, 0);
  for (int i = 0; i < num_class; ++i) {
    int j = 0;
    int lab = i;
    while (lab) {
      colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j));
      colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j));
      colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j));
      ++j;
      lab >>= 3;
    }
  }
  return colormap;
}

}  // namespace PaddleDetection