Cherry-pick add retinanet_detection_output, test=develop (#4157)

lite/kernels/host/CMakeLists.txt

@@ -16,3 +16,4 @@ add_kernel(assign_compute_host Host extra SRCS assign_compute.cc DEPS ${lite_ker
 add_kernel(print_compute_host Host extra SRCS print_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(while_compute_host Host extra SRCS while_compute.cc DEPS ${lite_kernel_deps} program)
 add_kernel(conditional_block_compute_host Host extra SRCS conditional_block_compute.cc DEPS ${lite_kernel_deps} program)
+add_kernel(retinanet_detection_output_compute_host Host extra SRCS retinanet_detection_output_compute.cc DEPS ${lite_kernel_deps})

lite/kernels/host/retinanet_detection_output_compute.cc

+// Copyright (c) 2019 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 "lite/kernels/host/retinanet_detection_output_compute.h"
+#include <cmath>
+#include <map>
+#include <utility>
+#include <vector>
+#include "lite/operators/retinanet_detection_output_op.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+template <class T>
+bool SortScorePairDescend(const std::pair<float, T>& pair1,
+                          const std::pair<float, T>& pair2) {
+  return pair1.first > pair2.first;
+}
+
+template <class T>
+bool SortScoreTwoPairDescend(const std::pair<float, std::pair<T, T>>& pair1,
+                             const std::pair<float, std::pair<T, T>>& pair2) {
+  return pair1.first > pair2.first;
+}
+
+template <class T>
+static inline void GetMaxScoreIndex(
+    const std::vector<T>& scores,
+    const T threshold,
+    int top_k,
+    std::vector<std::pair<T, int>>* sorted_indices) {
+  for (size_t i = 0; i < scores.size(); ++i) {
+    if (scores[i] > threshold) {
+      sorted_indices->push_back(std::make_pair(scores[i], i));
+    }
+  }
+  // Sort the score pair according to the scores in descending order
+  std::stable_sort(sorted_indices->begin(),
+                   sorted_indices->end(),
+                   SortScorePairDescend<int>);
+  // Keep top_k scores if needed.
+  if (top_k > -1 && top_k < static_cast<int>(sorted_indices->size())) {
+    sorted_indices->resize(top_k);
+  }
+}
+
+template <class T>
+static inline T BBoxArea(const std::vector<T>& box, const bool normalized) {
+  if (box[2] < box[0] || box[3] < box[1]) {
+    // If coordinate values are is invalid
+    // (e.g. xmax < xmin or ymax < ymin), return 0.
+    return static_cast<T>(0.);
+  } else {
+    const T w = box[2] - box[0];
+    const T h = box[3] - box[1];
+    if (normalized) {
+      return w * h;
+    } else {
+      // If coordinate values are not within range [0, 1].
+      return (w + 1) * (h + 1);
+    }
+  }
+}
+
+template <class T>
+static inline T JaccardOverlap(const std::vector<T>& box1,
+                               const std::vector<T>& box2,
+                               const bool normalized) {
+  if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
+      box2[3] < box1[1]) {
+    return static_cast<T>(0.);
+  } else {
+    const T inter_xmin = std::max(box1[0], box2[0]);
+    const T inter_ymin = std::max(box1[1], box2[1]);
+    const T inter_xmax = std::min(box1[2], box2[2]);
+    const T inter_ymax = std::min(box1[3], box2[3]);
+    T norm = normalized ? static_cast<T>(0.) : static_cast<T>(1.);
+    T inter_w = inter_xmax - inter_xmin + norm;
+    T inter_h = inter_ymax - inter_ymin + norm;
+    const T inter_area = inter_w * inter_h;
+    const T bbox1_area = BBoxArea<T>(box1, normalized);
+    const T bbox2_area = BBoxArea<T>(box2, normalized);
+    return inter_area / (bbox1_area + bbox2_area - inter_area);
+  }
+}
+
+template <class T>
+void NMSFast(const std::vector<std::vector<T>>& cls_dets,
+             const T nms_threshold,
+             const T eta,
+             std::vector<int>* selected_indices) {
+  int64_t num_boxes = cls_dets.size();
+  std::vector<std::pair<T, int>> sorted_indices;
+  for (int64_t i = 0; i < num_boxes; ++i) {
+    sorted_indices.push_back(std::make_pair(cls_dets[i][4], i));
+  }
+  // Sort the score pair according to the scores in descending order
+  std::stable_sort(
+      sorted_indices.begin(), sorted_indices.end(), SortScorePairDescend<int>);
+  selected_indices->clear();
+  T adaptive_threshold = nms_threshold;
+
+  while (sorted_indices.size() != 0) {
+    const int idx = sorted_indices.front().second;
+    bool keep = true;
+    for (size_t k = 0; k < selected_indices->size(); ++k) {
+      if (keep) {
+        const int kept_idx = (*selected_indices)[k];
+        T overlap = T(0.);
+
+        overlap = JaccardOverlap<T>(cls_dets[idx], cls_dets[kept_idx], false);
+        keep = overlap <= adaptive_threshold;
+      } else {
+        break;
+      }
+    }
+    if (keep) {
+      selected_indices->push_back(idx);
+    }
+    sorted_indices.erase(sorted_indices.begin());
+    if (keep && eta < 1 && adaptive_threshold > 0.5) {
+      adaptive_threshold *= eta;
+    }
+  }
+}
+
+template <class T>
+void DeltaScoreToPrediction(
+    const std::vector<T>& bboxes_data,
+    const std::vector<T>& anchors_data,
+    T im_height,
+    T im_width,
+    T im_scale,
+    int class_num,
+    const std::vector<std::pair<T, int>>& sorted_indices,
+    std::map<int, std::vector<std::vector<T>>>* preds) {
+  im_height = static_cast<T>(std::round(im_height / im_scale));
+  im_width = static_cast<T>(std::round(im_width / im_scale));
+  T zero(0);
+  int i = 0;
+  for (const auto& it : sorted_indices) {
+    T score = it.first;
+    int idx = it.second;
+    int a = idx / class_num;
+    int c = idx % class_num;
+
+    int box_offset = a * 4;
+    T anchor_box_width =
+        anchors_data[box_offset + 2] - anchors_data[box_offset] + 1;
+    T anchor_box_height =
+        anchors_data[box_offset + 3] - anchors_data[box_offset + 1] + 1;
+    T anchor_box_center_x = anchors_data[box_offset] + anchor_box_width / 2;
+    T anchor_box_center_y =
+        anchors_data[box_offset + 1] + anchor_box_height / 2;
+    T target_box_center_x = 0, target_box_center_y = 0;
+    T target_box_width = 0, target_box_height = 0;
+    target_box_center_x =
+        bboxes_data[box_offset] * anchor_box_width + anchor_box_center_x;
+    target_box_center_y =
+        bboxes_data[box_offset + 1] * anchor_box_height + anchor_box_center_y;
+    target_box_width = std::exp(bboxes_data[box_offset + 2]) * anchor_box_width;
+    target_box_height =
+        std::exp(bboxes_data[box_offset + 3]) * anchor_box_height;
+    T pred_box_xmin = target_box_center_x - target_box_width / 2;
+    T pred_box_ymin = target_box_center_y - target_box_height / 2;
+    T pred_box_xmax = target_box_center_x + target_box_width / 2 - 1;
+    T pred_box_ymax = target_box_center_y + target_box_height / 2 - 1;
+    pred_box_xmin = pred_box_xmin / im_scale;
+    pred_box_ymin = pred_box_ymin / im_scale;
+    pred_box_xmax = pred_box_xmax / im_scale;
+    pred_box_ymax = pred_box_ymax / im_scale;
+
+    pred_box_xmin = std::max(std::min(pred_box_xmin, im_width - 1), zero);
+    pred_box_ymin = std::max(std::min(pred_box_ymin, im_height - 1), zero);
+    pred_box_xmax = std::max(std::min(pred_box_xmax, im_width - 1), zero);
+    pred_box_ymax = std::max(std::min(pred_box_ymax, im_height - 1), zero);
+
+    std::vector<T> one_pred;
+    one_pred.push_back(pred_box_xmin);
+    one_pred.push_back(pred_box_ymin);
+    one_pred.push_back(pred_box_xmax);
+    one_pred.push_back(pred_box_ymax);
+    one_pred.push_back(score);
+    (*preds)[c].push_back(one_pred);
+    i++;
+  }
+}
+
+template <class T>
+void MultiClassNMS(const std::map<int, std::vector<std::vector<T>>>& preds,
+                   int class_num,
+                   const int keep_top_k,
+                   const T nms_threshold,
+                   const T nms_eta,
+                   std::vector<std::vector<T>>* nmsed_out,
+                   int* num_nmsed_out) {
+  std::map<int, std::vector<int>> indices;
+  int num_det = 0;
+  for (int c = 0; c < class_num; ++c) {
+    if (static_cast<bool>(preds.count(c))) {
+      const std::vector<std::vector<T>> cls_dets = preds.at(c);
+      NMSFast(cls_dets, nms_threshold, nms_eta, &(indices[c]));
+      num_det += indices[c].size();
+    }
+  }
+
+  std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
+  for (const auto& it : indices) {
+    int label = it.first;
+    const std::vector<int>& label_indices = it.second;
+    for (size_t j = 0; j < label_indices.size(); ++j) {
+      int idx = label_indices[j];
+      score_index_pairs.push_back(
+          std::make_pair(preds.at(label)[idx][4], std::make_pair(label, idx)));
+    }
+  }
+  // Keep top k results per image.
+  std::stable_sort(score_index_pairs.begin(),
+                   score_index_pairs.end(),
+                   SortScoreTwoPairDescend<int>);
+  if (num_det > keep_top_k) {
+    score_index_pairs.resize(keep_top_k);
+  }
+
+  // Store the new indices.
+  std::map<int, std::vector<int>> new_indices;
+  for (const auto& it : score_index_pairs) {
+    int label = it.second.first;
+    int idx = it.second.second;
+    std::vector<T> one_pred;
+    one_pred.push_back(label);
+    one_pred.push_back(preds.at(label)[idx][4]);
+    one_pred.push_back(preds.at(label)[idx][0]);
+    one_pred.push_back(preds.at(label)[idx][1]);
+    one_pred.push_back(preds.at(label)[idx][2]);
+    one_pred.push_back(preds.at(label)[idx][3]);
+    nmsed_out->push_back(one_pred);
+  }
+
+  *num_nmsed_out = (num_det > keep_top_k ? keep_top_k : num_det);
+}
+
+template <class T>
+void RetinanetDetectionOutput(
+    const operators::RetinanetDetectionOutputParam& param,
+    const std::vector<Tensor>& scores,
+    const std::vector<Tensor>& bboxes,
+    const std::vector<Tensor>& anchors,
+    const Tensor& im_info,
+    std::vector<std::vector<T>>* nmsed_out,
+    int* num_nmsed_out) {
+  int64_t nms_top_k = param.nms_top_k;
+  int64_t keep_top_k = param.keep_top_k;
+  T nms_threshold = static_cast<T>(param.nms_threshold);
+  T nms_eta = static_cast<T>(param.nms_eta);
+  T score_threshold = static_cast<T>(param.score_threshold);
+
+  int64_t class_num = scores[0].dims()[1];
+  std::map<int, std::vector<std::vector<T>>> preds;
+  for (size_t l = 0; l < scores.size(); ++l) {
+    // Fetch per level score
+    Tensor scores_per_level = scores[l];
+    // Fetch per level bbox
+    Tensor bboxes_per_level = bboxes[l];
+    // Fetch per level anchor
+    Tensor anchors_per_level = anchors[l];
+
+    int64_t scores_num = scores_per_level.numel();
+    int64_t bboxes_num = bboxes_per_level.numel();
+    std::vector<T> scores_data(scores_num);
+    std::vector<T> bboxes_data(bboxes_num);
+    std::vector<T> anchors_data(bboxes_num);
+    std::copy_n(scores_per_level.data<T>(), scores_num, scores_data.begin());
+    std::copy_n(bboxes_per_level.data<T>(), bboxes_num, bboxes_data.begin());
+    std::copy_n(anchors_per_level.data<T>(), bboxes_num, anchors_data.begin());
+    std::vector<std::pair<T, int>> sorted_indices;
+
+    // For the highest level, we take the threshold 0.0
+    T threshold = (l < (scores.size() - 1) ? score_threshold : 0.0);
+    GetMaxScoreIndex(scores_data, threshold, nms_top_k, &sorted_indices);
+    auto* im_info_data = im_info.data<T>();
+    auto im_height = im_info_data[0];
+    auto im_width = im_info_data[1];
+    auto im_scale = im_info_data[2];
+    DeltaScoreToPrediction(bboxes_data,
+                           anchors_data,
+                           im_height,
+                           im_width,
+                           im_scale,
+                           class_num,
+                           sorted_indices,
+                           &preds);
+  }
+
+  MultiClassNMS(preds,
+                class_num,
+                keep_top_k,
+                nms_threshold,
+                nms_eta,
+                nmsed_out,
+                num_nmsed_out);
+}
+
+template <class T>
+void MultiClassOutput(const std::vector<std::vector<T>>& nmsed_out,
+                      Tensor* outs) {
+  auto* odata = outs->mutable_data<T>();
+  int count = 0;
+  int64_t out_dim = 6;
+  for (size_t i = 0; i < nmsed_out.size(); ++i) {
+    odata[count * out_dim] = nmsed_out[i][0] + 1;  // label
+    odata[count * out_dim + 1] = nmsed_out[i][1];  // score
+    odata[count * out_dim + 2] = nmsed_out[i][2];  // xmin
+    odata[count * out_dim + 3] = nmsed_out[i][3];  // xmin
+    odata[count * out_dim + 4] = nmsed_out[i][4];  // xmin
+    odata[count * out_dim + 5] = nmsed_out[i][5];  // xmin
+    count++;
+  }
+}
+
+void RetinanetDetectionOutputCompute::Run() {
+  auto& param = Param<operators::RetinanetDetectionOutputParam>();
+  auto& boxes = param.bboxes;
+  auto& scores = param.scores;
+  auto& anchors = param.anchors;
+  auto* im_info = param.im_info;
+  auto* outs = param.out;
+
+  std::vector<Tensor> boxes_list(boxes.size());
+  std::vector<Tensor> scores_list(scores.size());
+  std::vector<Tensor> anchors_list(anchors.size());
+  for (size_t j = 0; j < boxes_list.size(); ++j) {
+    boxes_list[j] = *boxes[j];
+    scores_list[j] = *scores[j];
+    anchors_list[j] = *anchors[j];
+  }
+  auto score_dims = scores_list[0].dims();
+  int64_t batch_size = score_dims[0];
+  auto box_dims = boxes_list[0].dims();
+  int64_t box_dim = box_dims[2];
+  int64_t out_dim = box_dim + 2;
+
+  std::vector<std::vector<std::vector<float>>> all_nmsed_out;
+  std::vector<uint64_t> batch_starts = {0};
+  for (int i = 0; i < batch_size; ++i) {
+    int num_nmsed_out = 0;
+    std::vector<Tensor> box_per_batch_list(boxes_list.size());
+    std::vector<Tensor> score_per_batch_list(scores_list.size());
+    for (size_t j = 0; j < boxes_list.size(); ++j) {
+      auto score_dims = scores_list[j].dims();
+      score_per_batch_list[j] = scores_list[j].Slice<float>(i, i + 1);
+      score_per_batch_list[j].Resize({score_dims[1], score_dims[2]});
+      box_per_batch_list[j] = boxes_list[j].Slice<float>(i, i + 1);
+      box_per_batch_list[j].Resize({score_dims[1], box_dim});
+    }
+    Tensor im_info_slice = im_info->Slice<float>(i, i + 1);
+
+    std::vector<std::vector<float>> nmsed_out;
+    RetinanetDetectionOutput(param,
+                             score_per_batch_list,
+                             box_per_batch_list,
+                             anchors_list,
+                             im_info_slice,
+                             &nmsed_out,
+                             &num_nmsed_out);
+    all_nmsed_out.push_back(nmsed_out);
+    batch_starts.push_back(batch_starts.back() + num_nmsed_out);
+  }
+
+  uint64_t num_kept = batch_starts.back();
+  if (num_kept == 0) {
+    outs->Resize({0, out_dim});
+  } else {
+    outs->Resize({static_cast<int64_t>(num_kept), out_dim});
+    for (int i = 0; i < batch_size; ++i) {
+      int64_t s = static_cast<int64_t>(batch_starts[i]);
+      int64_t e = static_cast<int64_t>(batch_starts[i + 1]);
+      if (e > s) {
+        Tensor out = outs->Slice<float>(s, e);
+        MultiClassOutput(all_nmsed_out[i], &out);
+      }
+    }
+  }
+
+  LoD lod;
+  lod.emplace_back(batch_starts);
+  outs->set_lod(lod);
+}
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_KERNEL(
+    retinanet_detection_output,
+    kHost,
+    kFloat,
+    kNCHW,
+    paddle::lite::kernels::host::RetinanetDetectionOutputCompute,
+    def)
+    .BindInput("BBoxes",
+               {LiteType::GetTensorTy(TARGET(kHost),
+                                      PRECISION(kFloat),
+                                      DATALAYOUT(kNCHW))})
+    .BindInput("Scores",
+               {LiteType::GetTensorTy(TARGET(kHost),
+                                      PRECISION(kFloat),
+                                      DATALAYOUT(kNCHW))})
+    .BindInput("Anchors",
+               {LiteType::GetTensorTy(TARGET(kHost),
+                                      PRECISION(kFloat),
+                                      DATALAYOUT(kNCHW))})
+    .BindInput("ImInfo",
+               {LiteType::GetTensorTy(TARGET(kHost),
+                                      PRECISION(kFloat),
+                                      DATALAYOUT(kNCHW))})
+    .BindOutput("Out",
+                {LiteType::GetTensorTy(TARGET(kHost),
+                                       PRECISION(kFloat),
+                                       DATALAYOUT(kNCHW))})
+    .Finalize();

lite/kernels/host/retinanet_detection_output_compute.h

+// Copyright (c) 2019 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 <algorithm>
+#include "lite/core/kernel.h"
+#include "lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+class RetinanetDetectionOutputCompute
+    : public KernelLite<TARGET(kHost), PRECISION(kFloat), DATALAYOUT(kNCHW)> {
+ public:
+  void Run() override;
+
+  virtual ~RetinanetDetectionOutputCompute() = default;
+};
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/operators/CMakeLists.txt

@@ -137,6 +137,7 @@ add_operator(topk_op extra SRCS topk_op.cc DEPS ${op_DEPS})
 add_operator(increment_op extra SRCS increment_op.cc DEPS ${op_DEPS})
 add_operator(layer_norm_op extra SRCS layer_norm_op.cc DEPS ${op_DEPS})
 add_operator(sequence_softmax_op extra SRCS sequence_softmax_op.cc DEPS ${op_DEPS})
+add_operator(retinanet_detection_output_op extra SRCS retinanet_detection_output_op.cc DEPS ${op_DEPS})
 # for content-dnn specific
 add_operator(search_aligned_mat_mul_op extra SRCS search_aligned_mat_mul_op.cc DEPS ${op_DEPS})
 add_operator(search_seq_fc_op extra SRCS search_seq_fc_op.cc DEPS ${op_DEPS})

lite/operators/op_params.h

@@ -1537,6 +1537,19 @@ struct PrintParam : ParamBase {
   bool is_forward{true};
 };
 
+struct RetinanetDetectionOutputParam : ParamBase {
+  std::vector<Tensor*> bboxes{};
+  std::vector<Tensor*> scores{};
+  std::vector<Tensor*> anchors{};
+  Tensor* im_info{};
+  Tensor* out{};
+  float score_threshold{};
+  int nms_top_k{};
+  float nms_threshold{};
+  float nms_eta{};
+  int keep_top_k{};
+};
+
 }  // namespace operators
 }  // namespace lite
 }  // namespace paddle

lite/operators/retinanet_detection_output_op.cc

+// Copyright (c) 2019 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 "lite/operators/retinanet_detection_output_op.h"
+#include <vector>
+#include "lite/core/op_lite.h"
+#include "lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+bool RetinanetDetectionOutputOpLite::CheckShape() const {
+  CHECK_OR_FALSE(param_.bboxes.size() > 0);
+  CHECK_OR_FALSE(param_.scores.size() > 0);
+  CHECK_OR_FALSE(param_.anchors.size() > 0);
+  CHECK_OR_FALSE(param_.bboxes.size() == param_.scores.size());
+  CHECK_OR_FALSE(param_.bboxes.size() == param_.anchors.size());
+  CHECK_OR_FALSE(param_.im_info);
+  CHECK_OR_FALSE(param_.out);
+
+  DDim bbox_dims = param_.bboxes.front()->dims();
+  DDim score_dims = param_.scores.front()->dims();
+  DDim anchor_dims = param_.anchors.front()->dims();
+  DDim im_info_dims = param_.im_info->dims();
+
+  CHECK_OR_FALSE(bbox_dims.size() == 3);
+  CHECK_OR_FALSE(score_dims.size() == 3);
+  CHECK_OR_FALSE(anchor_dims.size() == 2);
+  CHECK_OR_FALSE(bbox_dims[2] == 4);
+  CHECK_OR_FALSE(bbox_dims[1] == score_dims[1]);
+  CHECK_OR_FALSE(anchor_dims[0] == bbox_dims[1]);
+  CHECK_OR_FALSE(im_info_dims.size() == 2);
+
+  return true;
+}
+
+bool RetinanetDetectionOutputOpLite::InferShapeImpl() const {
+  DDim bbox_dims = param_.bboxes.front()->dims();
+  param_.out->Resize({bbox_dims[1], bbox_dims[2] + 2});
+  return true;
+}
+
+bool RetinanetDetectionOutputOpLite::AttachImpl(const cpp::OpDesc &op_desc,
+                                                lite::Scope *scope) {
+  for (auto arg_name : op_desc.Input("BBoxes")) {
+    param_.bboxes.push_back(
+        scope->FindVar(arg_name)->GetMutable<lite::Tensor>());
+  }
+  for (auto arg_name : op_desc.Input("Scores")) {
+    param_.scores.push_back(
+        scope->FindVar(arg_name)->GetMutable<lite::Tensor>());
+  }
+  for (auto arg_name : op_desc.Input("Anchors")) {
+    param_.anchors.push_back(
+        scope->FindVar(arg_name)->GetMutable<lite::Tensor>());
+  }
+  AttachInput(op_desc, scope, "ImInfo", false, &param_.im_info);
+  AttachOutput(op_desc, scope, "Out", false, &param_.out);
+
+  param_.score_threshold = op_desc.GetAttr<float>("score_threshold");
+  param_.nms_top_k = op_desc.GetAttr<int>("nms_top_k");
+  param_.nms_threshold = op_desc.GetAttr<float>("nms_threshold");
+  param_.nms_eta = op_desc.GetAttr<float>("nms_eta");
+  param_.keep_top_k = op_desc.GetAttr<int>("keep_top_k");
+
+  return true;
+}
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_OP(retinanet_detection_output,
+                 paddle::lite::operators::RetinanetDetectionOutputOpLite);

lite/operators/retinanet_detection_output_op.h

+// Copyright (c) 2019 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 "lite/core/op_lite.h"
+#include "lite/core/scope.h"
+#include "lite/operators/op_params.h"
+#include "lite/utils/all.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+class RetinanetDetectionOutputOpLite : public OpLite {
+ public:
+  RetinanetDetectionOutputOpLite() {}
+
+  explicit RetinanetDetectionOutputOpLite(const std::string &op_type)
+      : OpLite(op_type) {}
+
+  bool CheckShape() const override;
+
+  bool InferShapeImpl() const override;
+
+  bool AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) override;
+
+  void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
+
+  std::string DebugString() const override {
+    return "retinanet_detection_output";
+  }
+
+#ifdef LITE_WITH_PROFILE
+  void GetOpRuntimeInfo(paddle::lite::profile::OpCharacter *ch) {}
+#endif
+
+ private:
+  mutable RetinanetDetectionOutputParam param_;
+};
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle