update PicoDet inference deploy (#4281)

* update picodet inference deploy

deploy/cpp/CMakeLists.txt

@@ -5,7 +5,7 @@ option(WITH_MKL        "Compile demo with MKL/OpenBlas support,defaultuseMKL."
 option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    ON)
 option(WITH_TENSORRT   "Compile demo with TensorRT."                                    OFF)
 
-option(WITH_KEYPOINT        "Whether to Compile KeyPoint detector"                    ON)
+option(WITH_KEYPOINT        "Whether to Compile KeyPoint detector"                    OFF)
 
 SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
 SET(PADDLE_LIB_NAME "" CACHE STRING "libpaddle_inference")
@@ -22,9 +22,9 @@ include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/i
 link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
 
 if (WITH_KEYPOINT)
-    set(SRCS src/main_keypoint.cc src/preprocess_op.cc src/object_detector.cc src/keypoint_detector.cc src/keypoint_postprocess.cc)
+    set(SRCS src/main_keypoint.cc src/preprocess_op.cc src/object_detector.cc src/picodet_postprocess.cc src/utils.cc src/keypoint_detector.cc src/keypoint_postprocess.cc)
 else ()
-    set(SRCS src/main.cc src/preprocess_op.cc src/object_detector.cc)
+    set(SRCS src/main.cc src/preprocess_op.cc src/object_detector.cc src/picodet_postprocess.cc src/utils.cc)
 endif()
 
 macro(safe_set_static_flag)

deploy/cpp/include/config_parser.h

@@ -99,6 +99,18 @@ class ConfigPaser {
       return false;
     }
 
+    // Get NMS for postprocess
+    if (config["NMS"].IsDefined()) {
+      nms_info_ = config["NMS"];
+    }
+    // Get fpn_stride in PicoDet
+    if (config["fpn_stride"].IsDefined()) {
+      fpn_stride_.clear();
+      for (auto item : config["fpn_stride"]) {
+        fpn_stride_.emplace_back(item.as<int>());
+      }
+    }
+
     return true;
   }
   std::string mode_;
@@ -106,7 +118,9 @@ class ConfigPaser {
   std::string arch_;
   int min_subgraph_size_;
   YAML::Node preprocess_info_;
+  YAML::Node nms_info_;
   std::vector<std::string> label_list_;
+  std::vector<int> fpn_stride_;
   bool use_dynamic_shape_;
 };
 

deploy/cpp/include/object_detector.h

@@ -19,6 +19,7 @@
 #include <memory>
 #include <utility>
 #include <ctime>
+#include <numeric>
 
 #include <opencv2/core/core.hpp>
 #include <opencv2/imgproc/imgproc.hpp>
@@ -28,20 +29,12 @@
 
 #include "include/preprocess_op.h"
 #include "include/config_parser.h"
+#include "include/utils.h"
+#include "include/picodet_postprocess.h"
 
 using namespace paddle_infer;
 
 namespace PaddleDetection {
-// Object Detection Result
-struct ObjectResult {
-  // Rectangle coordinates of detected object: left, right, top, down
-  std::vector<int> rect;
-  // Class id of detected object
-  int class_id;
-  // Confidence of detected object
-  float confidence;
-};
-
 
 // Generate visualization colormap for each class
 std::vector<int> GenerateColorMap(int num_class);
@@ -49,7 +42,7 @@ std::vector<int> GenerateColorMap(int num_class);
 
 // Visualiztion Detection Result
 cv::Mat VisualizeResult(const cv::Mat& img,
-                     const std::vector<ObjectResult>& results,
+                     const std::vector<PaddleDetection::ObjectResult>& results,
                      const std::vector<std::string>& lables,
                      const std::vector<int>& colormap,
                      const bool is_rbox);
@@ -96,7 +89,7 @@ class ObjectDetector {
       const double threshold = 0.5,
       const int warmup = 0,
       const int repeats = 1,
-      std::vector<ObjectResult>* result = nullptr,
+      std::vector<PaddleDetection::ObjectResult>* result = nullptr,
       std::vector<int>* bbox_num = nullptr,
       std::vector<double>* times = nullptr);
 
@@ -121,17 +114,17 @@ class ObjectDetector {
   // Postprocess result
   void Postprocess(
       const std::vector<cv::Mat> mats,
-      std::vector<ObjectResult>* result,
+      std::vector<PaddleDetection::ObjectResult>* result,
       std::vector<int> bbox_num,
+      std::vector<float> output_data_,
       bool is_rbox);
 
   std::shared_ptr<Predictor> predictor_;
   Preprocessor preprocessor_;
   ImageBlob inputs_;
-  std::vector<float> output_data_;
-  std::vector<int> out_bbox_num_data_;
   float threshold_;
   ConfigPaser config_;
+
 };
 
 }  // namespace PaddleDetection

deploy/cpp/include/picodet_postprocess.h

+//   Copyright (c) 2021 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 <string>
+#include <vector>
+#include <memory>
+#include <utility>
+#include <ctime>
+#include <numeric>
+
+#include "include/utils.h"
+
+namespace PaddleDetection {
+
+void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
+                         std::vector<const float *> outs,
+                         std::vector<int> fpn_stride,
+                         std::vector<float> im_shape,
+                         std::vector<float> scale_factor,
+                         float score_threshold = 0.3,
+                         float nms_threshold = 0.5,
+                         int num_class = 80,
+                         int reg_max = 7);
+
+}  // namespace PaddleDetection
\ No newline at end of file

deploy/cpp/include/preprocess_op.h

@@ -86,7 +86,6 @@ class Resize : public PreprocessOp {
  public:
   virtual void Init(const YAML::Node& item) {
     interp_ = item["interp"].as<int>();
-    //max_size_ = item["target_size"].as<int>();
     keep_ratio_ = item["keep_ratio"].as<bool>();
     target_size_ = item["target_size"].as<std::vector<int>>();
  }

deploy/cpp/include/utils.h

+//   Copyright (c) 2021 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 <string>
+#include <vector>
+#include <memory>
+#include <utility>
+#include <ctime>
+#include <numeric>
+#include <algorithm>
+
+namespace PaddleDetection {
+
+// Object Detection Result
+struct ObjectResult {
+  // Rectangle coordinates of detected object: left, right, top, down
+  std::vector<int> rect;
+  // Class id of detected object
+  int class_id;
+  // Confidence of detected object
+  float confidence;
+};
+
+void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold);
+
+}  // namespace PaddleDetection
\ No newline at end of file

deploy/cpp/src/main.cc

@@ -241,7 +241,7 @@ void PredictImage(const std::vector<std::string> all_img_paths,
     if (run_benchmark) {
       det->Predict(batch_imgs, threshold, 10, 10, &result, &bbox_num,  &det_times);
     } else {
-      det->Predict(batch_imgs, 0.5, 0, 1, &result, &bbox_num, &det_times);
+      det->Predict(batch_imgs, threshold, 0, 1, &result, &bbox_num, &det_times);
       // get labels and colormap
       auto labels = det->GetLabelList();
       auto colormap = PaddleDetection::GenerateColorMap(labels.size());

deploy/cpp/src/main_keypoint.cc

@@ -302,7 +302,7 @@ void PredictImage(const std::vector<std::string> all_img_paths,
     if (run_benchmark) {
       det->Predict(batch_imgs, threshold, 10, 10, &result, &bbox_num,  &det_times);
     } else {
-      det->Predict(batch_imgs, 0.5, 10, 10, &result, &bbox_num, &det_times);
+      det->Predict(batch_imgs, threshold, 0, 1, &result, &bbox_num, &det_times);
     }
     // get labels and colormap
     auto labels = det->GetLabelList();

deploy/cpp/src/object_detector.cc

@@ -17,7 +17,6 @@
 #include <chrono>
 #include "include/object_detector.h"
 
-
 using namespace paddle_infer;
 
 namespace PaddleDetection {
@@ -94,7 +93,7 @@ void ObjectDetector::LoadModel(const std::string& model_dir,
 
 // Visualiztion MaskDetector results
 cv::Mat VisualizeResult(const cv::Mat& img,
-                        const std::vector<ObjectResult>& results,
+                        const std::vector<PaddleDetection::ObjectResult>& results,
                         const std::vector<std::string>& lables,
                         const std::vector<int>& colormap,
                         const bool is_rbox=false) {
@@ -171,8 +170,9 @@ void ObjectDetector::Preprocess(const cv::Mat& ori_im) {
 
 void ObjectDetector::Postprocess(
     const std::vector<cv::Mat> mats,
-    std::vector<ObjectResult>* result,
+    std::vector<PaddleDetection::ObjectResult>* result,
     std::vector<int> bbox_num,
+    std::vector<float> output_data_,
     bool is_rbox=false) {
   result->clear();
   int start_idx = 0;
@@ -199,7 +199,7 @@ void ObjectDetector::Postprocess(
         int x4 = (output_data_[8 + j * 10] * rw);
         int y4 = (output_data_[9 + j * 10] * rh);
           
-        ObjectResult result_item;
+        PaddleDetection::ObjectResult result_item;
         result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
         result_item.class_id = class_id;
         result_item.confidence = score;
@@ -217,7 +217,7 @@ void ObjectDetector::Postprocess(
         int wd = xmax - xmin;
         int hd = ymax - ymin;
           
-        ObjectResult result_item;
+        PaddleDetection::ObjectResult result_item;
         result_item.rect = {xmin, ymin, xmax, ymax};
         result_item.class_id = class_id;
         result_item.confidence = score;
@@ -232,7 +232,7 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
       const double threshold,
       const int warmup,
       const int repeats,
-      std::vector<ObjectResult>* result,
+      std::vector<PaddleDetection::ObjectResult>* result,
       std::vector<int>* bbox_num,
       std::vector<double>* times) {
   auto preprocess_start = std::chrono::steady_clock::now();
@@ -242,6 +242,8 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
   std::vector<float> in_data_all;
   std::vector<float> im_shape_all(batch_size * 2);
   std::vector<float> scale_factor_all(batch_size * 2);
+  std::vector<const float *> output_data_list_;
+  std::vector<int> out_bbox_num_data_;
   
   // Preprocess image
   for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
@@ -277,77 +279,90 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
   }
   
   // Run predictor
+  std::vector<std::vector<float>> out_tensor_list;
+  std::vector<std::vector<int>> output_shape_list;
+  bool is_rbox = false;
+  int reg_max = 7;
+  int num_class = 80;
   // warmup
-  for (int i = 0; i < warmup; i++)
-  {
+  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;
+    for (int j = 0; j < output_names.size(); j++) {
+      auto output_tensor = predictor_->GetOutputHandle(output_names[j]);
+      std::vector<int> output_shape = output_tensor->shape();
+      int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 
+                            1, std::multiplies<int>());
+      if (output_tensor->type() == paddle_infer::DataType::INT32) {
+        out_bbox_num_data_.resize(out_num);
+        output_tensor->CopyToCpu(out_bbox_num_data_.data());
+      } else {
+        std::vector<float> out_data;
+        out_data.resize(out_num);
+        output_tensor->CopyToCpu(out_data.data());
+        out_tensor_list.push_back(out_data);
       }
-    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++)
-  {
+  for (int i = 0; i < repeats; i++) {
     predictor_->Run();
     // Get output tensor
+    out_tensor_list.clear();
+    output_shape_list.clear();
     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;
+    for (int j = 0; j < output_names.size(); j++) {
+      auto output_tensor = predictor_->GetOutputHandle(output_names[j]);
+      std::vector<int> output_shape = output_tensor->shape();
+      int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 
+                            1, std::multiplies<int>());
+      output_shape_list.push_back(output_shape);
+      if (output_tensor->type() == paddle_infer::DataType::INT32) {
+        out_bbox_num_data_.resize(out_num);
+        output_tensor->CopyToCpu(out_bbox_num_data_.data());
+      } else {
+        std::vector<float> out_data;
+        out_data.resize(out_num);
+        output_tensor->CopyToCpu(out_data.data());
+        out_tensor_list.push_back(out_data);
       }
-    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++) {
+  if (config_.arch_ == "PicoDet") {
+    for (int i = 0; i < out_tensor_list.size(); i++) {
+      if (i == 0) {
+        num_class = output_shape_list[i][2];
+      }
+      if (i == config_.fpn_stride_.size()) {
+        reg_max = output_shape_list[i][2] / 4 - 1;
+      }
+      float *buffer = new float[out_tensor_list[i].size()];
+      memcpy(buffer, &out_tensor_list[i][0], 
+                out_tensor_list[i].size()*sizeof(float));
+      output_data_list_.push_back(buffer);
+    }
+    PaddleDetection::PicoDetPostProcess(
+        result, output_data_list_, config_.fpn_stride_, 
+        inputs_.im_shape_, inputs_.scale_factor_,
+        config_.nms_info_["score_threshold"].as<float>(), 
+        config_.nms_info_["nms_threshold"].as<float>(), num_class, reg_max);
+    bbox_num->push_back(result->size());
+  } else {
+    is_rbox = output_shape_list[0][output_shape_list[0].size()-1] % 10 == 0;
+    Postprocess(imgs, result, out_bbox_num_data_, out_tensor_list[0], is_rbox);
+    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;

deploy/cpp/src/picodet_postprocess.cc

+//   Copyright (c) 2021 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 "include/picodet_postprocess.h"
+
+namespace PaddleDetection {
+
+float fast_exp(float x) {
+    union {
+        uint32_t i;
+        float f;
+    } v{};
+    v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
+    return v.f;
+}
+
+template <typename _Tp>
+int activation_function_softmax(const _Tp *src, _Tp *dst, int length) {
+    const _Tp alpha = *std::max_element(src, src + length);
+    _Tp denominator{0};
+
+    for (int i = 0; i < length; ++i) {
+        dst[i] = fast_exp(src[i] - alpha);
+        denominator += dst[i];
+    }
+
+    for (int i = 0; i < length; ++i) {
+        dst[i] /= denominator;
+    }
+
+    return 0;
+}
+
+// PicoDet decode
+PaddleDetection::ObjectResult disPred2Bbox(const float *&dfl_det, int label, float score,
+                      int x, int y, int stride, std::vector<float> im_shape,
+                      int reg_max) {
+    float ct_x = (x + 0.5) * stride;
+    float ct_y = (y + 0.5) * stride;
+    std::vector<float> dis_pred;
+    dis_pred.resize(4);
+    for (int i = 0; i < 4; i++) {
+      float dis = 0;
+      float* dis_after_sm = new float[reg_max + 1];
+      activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm, reg_max + 1);
+      for (int j = 0; j < reg_max + 1; j++) {
+          dis += j * dis_after_sm[j];
+      }
+      dis *= stride;
+      dis_pred[i] = dis;
+      delete[] dis_after_sm;
+    }
+    int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f);
+    int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f);
+    int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]);
+    int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]);
+
+    PaddleDetection::ObjectResult result_item;
+    result_item.rect = {xmin, ymin, xmax, ymax};
+    result_item.class_id = label;
+    result_item.confidence = score;
+
+    return result_item;
+}
+
+
+void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
+                         std::vector<const float *> outs,
+                         std::vector<int> fpn_stride,
+                         std::vector<float> im_shape,
+                         std::vector<float> scale_factor,
+                         float score_threshold,
+                         float nms_threshold,
+                         int num_class,
+                         int reg_max) {
+  std::vector<std::vector<PaddleDetection::ObjectResult>> bbox_results;
+  bbox_results.resize(num_class);
+  int in_h = im_shape[0], in_w = im_shape[1];
+  for (int i = 0; i < fpn_stride.size(); ++i) {
+    int feature_h = in_h / fpn_stride[i];
+    int feature_w = in_w / fpn_stride[i];
+    for (int idx = 0; idx < feature_h * feature_w; idx++) {
+      const float *scores = outs[i] + (idx * num_class);
+
+      int row = idx / feature_w;
+      int col = idx % feature_w;
+      float score = 0;
+      int cur_label = 0;
+      for (int label = 0; label < num_class; label++) {
+        if (scores[label] > score) {
+          score = scores[label];
+          cur_label = label;
+        }
+      }
+      if (score > score_threshold) {
+        const float *bbox_pred = outs[i + fpn_stride.size()]
+              + (idx * 4 * (reg_max + 1));
+        bbox_results[cur_label].push_back(disPred2Bbox(bbox_pred, 
+              cur_label, score, col, row, fpn_stride[i], im_shape, reg_max));
+      }
+    }
+  }
+  for (int i = 0; i < (int)bbox_results.size(); i++) {
+    PaddleDetection::nms(bbox_results[i], nms_threshold);
+
+    for (auto box : bbox_results[i]) {
+        box.rect[0] = box.rect[0] / scale_factor[1];
+        box.rect[2] = box.rect[2] / scale_factor[1];
+        box.rect[1] = box.rect[1] / scale_factor[0];
+        box.rect[3] = box.rect[3] / scale_factor[0];
+        results->push_back(box);
+    }
+  }
+}
+
+}  // namespace PaddleDetection
\ No newline at end of file

deploy/cpp/src/utils.cc

+//   Copyright (c) 2021 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 "include/utils.h"
+
+namespace PaddleDetection {
+
+void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold) {
+  std::sort(input_boxes.begin(),
+  input_boxes.end(), 
+  [](ObjectResult a, ObjectResult b) { return a.confidence > b.confidence; });
+  std::vector<float> vArea(input_boxes.size());
+  for (int i = 0; i < int(input_boxes.size()); ++i) {
+    vArea[i] = (input_boxes.at(i).rect[2] - input_boxes.at(i).rect[0] + 1) 
+            * (input_boxes.at(i).rect[3] - input_boxes.at(i).rect[1] + 1);
+  }
+  for (int i = 0; i < int(input_boxes.size()); ++i) {
+    for (int j = i + 1; j < int(input_boxes.size());) {
+      float xx1 = (std::max)(input_boxes[i].rect[0], input_boxes[j].rect[0]);
+      float yy1 = (std::max)(input_boxes[i].rect[1], input_boxes[j].rect[1]);
+      float xx2 = (std::min)(input_boxes[i].rect[2], input_boxes[j].rect[2]);
+      float yy2 = (std::min)(input_boxes[i].rect[3], input_boxes[j].rect[3]);
+      float w = (std::max)(float(0), xx2 - xx1 + 1);
+      float h = (std::max)(float(0), yy2 - yy1 + 1);
+      float inter = w * h;
+      float ovr = inter / (vArea[i] + vArea[j] - inter);
+      if (ovr >= nms_threshold) {
+          input_boxes.erase(input_boxes.begin() + j);
+          vArea.erase(vArea.begin() + j);
+      }
+      else {
+          j++;
+      }
+    }
+  }
+}
+
+}  // namespace PaddleDetection

deploy/python/infer.py

@@ -25,6 +25,7 @@ from paddle.inference import Config
 from paddle.inference import create_predictor
 
 from benchmark_utils import PaddleInferBenchmark
+from picodet_postprocess import PicoDetPostProcess
 from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, LetterBoxResize
 from visualize import visualize_box_mask
 from utils import argsparser, Timer, get_current_memory_mb
@@ -277,6 +278,111 @@ class DetectorSOLOv2(Detector):
             boxes_num=np_boxes_num)
 
 
+class DetectorPicoDet(Detector):
+    """
+    Args:
+        config (object): config of model, defined by `Config(model_dir)`
+        model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
+        device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
+        run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
+        batch_size (int): size of pre batch in inference
+        trt_min_shape (int): min shape for dynamic shape in trt
+        trt_max_shape (int): max shape for dynamic shape in trt
+        trt_opt_shape (int): opt shape for dynamic shape in trt
+        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
+            calibration, trt_calib_mode need to set True
+        cpu_threads (int): cpu threads
+        enable_mkldnn (bool): whether to open MKLDNN 
+    """
+
+    def __init__(self,
+                 pred_config,
+                 model_dir,
+                 device='CPU',
+                 run_mode='fluid',
+                 batch_size=1,
+                 trt_min_shape=1,
+                 trt_max_shape=1280,
+                 trt_opt_shape=640,
+                 trt_calib_mode=False,
+                 cpu_threads=1,
+                 enable_mkldnn=False):
+        self.pred_config = pred_config
+        self.predictor, self.config = load_predictor(
+            model_dir,
+            run_mode=run_mode,
+            batch_size=batch_size,
+            min_subgraph_size=self.pred_config.min_subgraph_size,
+            device=device,
+            use_dynamic_shape=self.pred_config.use_dynamic_shape,
+            trt_min_shape=trt_min_shape,
+            trt_max_shape=trt_max_shape,
+            trt_opt_shape=trt_opt_shape,
+            trt_calib_mode=trt_calib_mode,
+            cpu_threads=cpu_threads,
+            enable_mkldnn=enable_mkldnn)
+        self.det_times = Timer()
+        self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
+
+    def predict(self, image, threshold=0.5, warmup=0, repeats=1):
+        '''
+        Args:
+            image (str/np.ndarray): path of image/ np.ndarray read by cv2
+            threshold (float): threshold of predicted box' score
+        Returns:
+            results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
+                            matix element:[class, score, x_min, y_min, x_max, y_max]
+        '''
+        self.det_times.preprocess_time_s.start()
+        inputs = self.preprocess(image)
+        self.det_times.preprocess_time_s.end()
+        input_names = self.predictor.get_input_names()
+        for i in range(len(input_names)):
+            input_tensor = self.predictor.get_input_handle(input_names[i])
+            input_tensor.copy_from_cpu(inputs[input_names[i]])
+        np_score_list, np_boxes_list = [], []
+        for i in range(warmup):
+            self.predictor.run()
+            np_score_list.clear()
+            np_boxes_list.clear()
+            output_names = self.predictor.get_output_names()
+            num_outs = int(len(output_names) / 2)
+            for out_idx in range(num_outs):
+                np_score_list.append(
+                    self.predictor.get_output_handle(output_names[out_idx])
+                    .copy_to_cpu())
+                np_boxes_list.append(
+                    self.predictor.get_output_handle(output_names[
+                        out_idx + num_outs]).copy_to_cpu())
+
+        self.det_times.inference_time_s.start()
+        for i in range(repeats):
+            self.predictor.run()
+            np_score_list.clear()
+            np_boxes_list.clear()
+            output_names = self.predictor.get_output_names()
+            num_outs = int(len(output_names) / 2)
+            for out_idx in range(num_outs):
+                np_score_list.append(
+                    self.predictor.get_output_handle(output_names[out_idx])
+                    .copy_to_cpu())
+                np_boxes_list.append(
+                    self.predictor.get_output_handle(output_names[
+                        out_idx + num_outs]).copy_to_cpu())
+        self.det_times.inference_time_s.end(repeats=repeats)
+        self.det_times.img_num += 1
+        self.det_times.postprocess_time_s.start()
+        self.postprocess = PicoDetPostProcess(
+            inputs['image'].shape[2:],
+            inputs['im_shape'],
+            inputs['scale_factor'],
+            strides=self.pred_config.fpn_stride,
+            nms_threshold=self.pred_config.nms['nms_threshold'])
+        np_boxes, np_boxes_num = self.postprocess(np_score_list, np_boxes_list)
+        self.det_times.postprocess_time_s.end()
+        return dict(boxes=np_boxes, boxes_num=np_boxes_num)
+
+
 def create_inputs(imgs, im_info):
     """generate input for different model type
     Args:
@@ -341,6 +447,10 @@ class PredictConfig():
         self.tracker = None
         if 'tracker' in yml_conf:
             self.tracker = yml_conf['tracker']
+        if 'NMS' in yml_conf:
+            self.nms = yml_conf['NMS']
+        if 'fpn_stride' in yml_conf:
+            self.fpn_stride = yml_conf['fpn_stride']
         self.print_config()
 
     def check_model(self, yml_conf):
@@ -595,21 +705,13 @@ def predict_video(detector, camera_id):
 
 def main():
     pred_config = PredictConfig(FLAGS.model_dir)
-    detector = Detector(
-        pred_config,
-        FLAGS.model_dir,
-        device=FLAGS.device,
-        run_mode=FLAGS.run_mode,
-        batch_size=FLAGS.batch_size,
-        trt_min_shape=FLAGS.trt_min_shape,
-        trt_max_shape=FLAGS.trt_max_shape,
-        trt_opt_shape=FLAGS.trt_opt_shape,
-        trt_calib_mode=FLAGS.trt_calib_mode,
-        cpu_threads=FLAGS.cpu_threads,
-        enable_mkldnn=FLAGS.enable_mkldnn)
+    detector_func = 'Detector'
     if pred_config.arch == 'SOLOv2':
-        detector = DetectorSOLOv2(
-            pred_config,
+        detector_func = 'DetectorSOLOv2'
+    elif pred_config.arch == 'PicoDet':
+        detector_func = 'DetectorPicoDet'
+
+    detector = eval(detector_func)(pred_config,
                                    FLAGS.model_dir,
                                    device=FLAGS.device,
                                    run_mode=FLAGS.run_mode,

deploy/python/picodet_postprocess.py

+# Copyright (c) 2021 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.
+
+import numpy as np
+from scipy.special import softmax
+
+
+def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
+    """
+    Args:
+        box_scores (N, 5): boxes in corner-form and probabilities.
+        iou_threshold: intersection over union threshold.
+        top_k: keep top_k results. If k <= 0, keep all the results.
+        candidate_size: only consider the candidates with the highest scores.
+    Returns:
+         picked: a list of indexes of the kept boxes
+    """
+    scores = box_scores[:, -1]
+    boxes = box_scores[:, :-1]
+    picked = []
+    indexes = np.argsort(scores)
+    indexes = indexes[-candidate_size:]
+    while len(indexes) > 0:
+        current = indexes[-1]
+        picked.append(current)
+        if 0 < top_k == len(picked) or len(indexes) == 1:
+            break
+        current_box = boxes[current, :]
+        indexes = indexes[:-1]
+        rest_boxes = boxes[indexes, :]
+        iou = iou_of(
+            rest_boxes,
+            np.expand_dims(
+                current_box, axis=0), )
+        indexes = indexes[iou <= iou_threshold]
+
+    return box_scores[picked, :]
+
+
+def iou_of(boxes0, boxes1, eps=1e-5):
+    """Return intersection-over-union (Jaccard index) of boxes.
+    Args:
+        boxes0 (N, 4): ground truth boxes.
+        boxes1 (N or 1, 4): predicted boxes.
+        eps: a small number to avoid 0 as denominator.
+    Returns:
+        iou (N): IoU values.
+    """
+    overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
+    overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
+
+    overlap_area = area_of(overlap_left_top, overlap_right_bottom)
+    area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
+    area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
+    return overlap_area / (area0 + area1 - overlap_area + eps)
+
+
+def area_of(left_top, right_bottom):
+    """Compute the areas of rectangles given two corners.
+    Args:
+        left_top (N, 2): left top corner.
+        right_bottom (N, 2): right bottom corner.
+    Returns:
+        area (N): return the area.
+    """
+    hw = np.clip(right_bottom - left_top, 0.0, None)
+    return hw[..., 0] * hw[..., 1]
+
+
+class PicoDetPostProcess(object):
+    """
+    Args:
+        input_shape (int): network input image size
+        ori_shape (int): ori image shape of before padding
+        scale_factor (float): scale factor of ori image
+        enable_mkldnn (bool): whether to open MKLDNN
+    """
+
+    def __init__(self,
+                 input_shape,
+                 ori_shape,
+                 scale_factor,
+                 strides=[8, 16, 32, 64],
+                 score_threshold=0.4,
+                 nms_threshold=0.5,
+                 nms_top_k=1000,
+                 keep_top_k=100):
+        self.ori_shape = ori_shape
+        self.input_shape = input_shape
+        self.scale_factor = scale_factor
+        self.strides = strides
+        self.score_threshold = score_threshold
+        self.nms_threshold = nms_threshold
+        self.nms_top_k = nms_top_k
+        self.keep_top_k = keep_top_k
+
+    def warp_boxes(self, boxes, ori_shape):
+        """Apply transform to boxes
+        """
+        width, height = ori_shape[1], ori_shape[0]
+        n = len(boxes)
+        if n:
+            # warp points
+            xy = np.ones((n * 4, 3))
+            xy[:, :2] = boxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(
+                n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
+            # xy = xy @ M.T  # transform
+            xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)  # rescale
+            # create new boxes
+            x = xy[:, [0, 2, 4, 6]]
+            y = xy[:, [1, 3, 5, 7]]
+            xy = np.concatenate(
+                (x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+            # clip boxes
+            xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
+            xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
+            return xy.astype(np.float32)
+        else:
+            return boxes
+
+    def __call__(self, scores, raw_boxes):
+        batch_size = raw_boxes[0].shape[0]
+        reg_max = int(raw_boxes[0].shape[-1] / 4 - 1)
+        out_boxes_num = []
+        out_boxes_list = []
+        for batch_id in range(batch_size):
+            # generate centers
+            decode_boxes = []
+            select_scores = []
+            for stride, box_distribute, score in zip(self.strides, raw_boxes,
+                                                     scores):
+                box_distribute = box_distribute[batch_id]
+                score = score[batch_id]
+                # centers
+                fm_h = self.input_shape[0] / stride
+                fm_w = self.input_shape[1] / stride
+                h_range = np.arange(fm_h)
+                w_range = np.arange(fm_w)
+                ww, hh = np.meshgrid(w_range, h_range)
+                ct_row = (hh.flatten() + 0.5) * stride
+                ct_col = (ww.flatten() + 0.5) * stride
+                center = np.stack((ct_col, ct_row, ct_col, ct_row), axis=1)
+
+                # box distribution to distance
+                reg_range = np.arange(reg_max + 1)
+                box_distance = box_distribute.reshape((-1, reg_max + 1))
+                box_distance = softmax(box_distance, axis=1)
+                box_distance = box_distance * np.expand_dims(reg_range, axis=0)
+                box_distance = np.sum(box_distance, axis=1).reshape((-1, 4))
+                box_distance = box_distance * stride
+
+                # top K candidate
+                topk_idx = np.argsort(score.max(axis=1))[::-1]
+                topk_idx = topk_idx[:self.nms_top_k]
+                center = center[topk_idx]
+                score = score[topk_idx]
+                box_distance = box_distance[topk_idx]
+
+                # decode box
+                decode_box = center + [-1, -1, 1, 1] * box_distance
+
+                select_scores.append(score)
+                decode_boxes.append(decode_box)
+
+            # nms
+            bboxes = np.concatenate(decode_boxes, axis=0)
+            confidences = np.concatenate(select_scores, axis=0)
+            picked_box_probs = []
+            picked_labels = []
+            for class_index in range(0, confidences.shape[1]):
+                probs = confidences[:, class_index]
+                mask = probs > self.score_threshold
+                probs = probs[mask]
+                if probs.shape[0] == 0:
+                    continue
+                subset_boxes = bboxes[mask, :]
+                box_probs = np.concatenate(
+                    [subset_boxes, probs.reshape(-1, 1)], axis=1)
+                box_probs = hard_nms(
+                    box_probs,
+                    iou_threshold=self.nms_threshold,
+                    top_k=self.keep_top_k, )
+                picked_box_probs.append(box_probs)
+                picked_labels.extend([class_index] * box_probs.shape[0])
+            if not picked_box_probs:
+                return np.array([]), np.array([]), np.array([])
+            picked_box_probs = np.concatenate(picked_box_probs)
+
+            # resize output boxes
+            picked_box_probs[:, :4] = self.warp_boxes(picked_box_probs[:, :4],
+                                                      self.ori_shape[batch_id])
+            im_scale = np.concatenate([
+                self.scale_factor[batch_id][::-1],
+                self.scale_factor[batch_id][::-1]
+            ])
+            picked_box_probs[:, :4] /= im_scale
+            # clas score box
+            out_boxes_list.append(
+                np.concatenate(
+                    [
+                        np.expand_dims(
+                            np.array(picked_labels), axis=-1), np.expand_dims(
+                                picked_box_probs[:, 4], axis=-1),
+                        picked_box_probs[:, :4]
+                    ],
+                    axis=1))
+            out_boxes_num.append(len(picked_labels))
+
+        out_boxes_list = np.concatenate(out_boxes_list, axis=0)
+        out_boxes_num = np.asarray(out_boxes_num).astype(np.int32)
+        return out_boxes_list, out_boxes_num

ppdet/engine/export_utils.py

@@ -162,5 +162,13 @@ def _dump_infer_config(config, path, image_shape, model):
     infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader(
         reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape[1:])
 
+    if infer_arch == 'PicoDet':
+        infer_cfg['NMS'] = config['PicoHead']['nms']
+        # In order to speed up the prediction, the threshold of nms 
+        # is adjusted here, which can be changed in infer_cfg.yml
+        config['PicoHead']['nms']["score_threshold"] = 0.3
+        config['PicoHead']['nms']["nms_threshold"] = 0.5
+        infer_cfg['fpn_stride'] = config['PicoHead']['fpn_stride']
+
     yaml.dump(infer_cfg, open(path, 'w'))
     logger.info("Export inference config file to {}".format(os.path.join(path)))

ppdet/engine/trainer.py

@@ -561,8 +561,6 @@ class Trainer(object):
         if hasattr(self.model, 'fuse_norm'):
             self.model.fuse_norm = self.cfg['TestReader'].get('fuse_normalize',
                                                               False)
-        if hasattr(self.cfg, 'lite_deploy'):
-            self.model.lite_deploy = self.cfg.lite_deploy
 
         # Save infer cfg
         _dump_infer_config(self.cfg,

ppdet/modeling/architectures/picodet.py

@@ -41,7 +41,7 @@ class PicoDet(BaseArch):
         self.backbone = backbone
         self.neck = neck
         self.head = head
-        self.lite_deploy = False
+        self.deploy = False
 
     @classmethod
     def from_config(cls, cfg, *args, **kwargs):
@@ -62,8 +62,8 @@ class PicoDet(BaseArch):
     def _forward(self):
         body_feats = self.backbone(self.inputs)
         fpn_feats = self.neck(body_feats)
-        head_outs = self.head(fpn_feats)
-        if self.training or self.lite_deploy:
+        head_outs = self.head(fpn_feats, self.deploy)
+        if self.training or self.deploy:
             return head_outs
         else:
             im_shape = self.inputs['im_shape']
@@ -83,7 +83,7 @@ class PicoDet(BaseArch):
         return loss
 
     def get_pred(self):
-        if self.lite_deploy:
+        if self.deploy:
             return {'picodet': self._forward()[0]}
         else:
             bbox_pred, bbox_num = self._forward()

ppdet/modeling/heads/pico_head.py

@@ -226,7 +226,7 @@ class PicoHead(GFLHead):
                         bias_attr=ParamAttr(initializer=Constant(value=0))))
                 self.head_reg_list.append(head_reg)
 
-    def forward(self, fpn_feats):
+    def forward(self, fpn_feats, deploy=False):
         assert len(fpn_feats) == len(
             self.fpn_stride
         ), "The size of fpn_feats is not equal to size of fpn_stride"
@@ -243,11 +243,19 @@ class PicoHead(GFLHead):
             else:
                 cls_score = self.head_cls_list[i](conv_cls_feat)
                 bbox_pred = self.head_reg_list[i](conv_reg_feat)
+
             if self.dgqp_module:
                 quality_score = self.dgqp_module(bbox_pred)
                 cls_score = F.sigmoid(cls_score) * quality_score
 
-            if not self.training:
+            if deploy:
+                # Now only supports batch size = 1 in deploy
+                # TODO(ygh): support batch size > 1
+                cls_score = F.sigmoid(cls_score).reshape(
+                    [1, self.cls_out_channels, -1]).transpose([0, 2, 1])
+                bbox_pred = bbox_pred.reshape([1, (self.reg_max + 1) * 4,
+                                               -1]).transpose([0, 2, 1])
+            elif not self.training:
                 cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1]))
                 bbox_pred = bbox_pred.transpose([0, 2, 3, 1])