refactor(op): polish generate_proposals_op

Polish styles in generate_proposals_op.

1. inline lambda functions rathar than use std::function to save var.
2. add `static inline` to template functions .cc
   * Make them static to prevent generating symbols.
   * Make them inline to give compiler a hit inline them as possible.
   * Not if the function is not static, they cannot be inlined since the
     symbols should be exported.
3. add `static` to global functions in .cc
   * Make them static to prevent generating symbols.
4. Use Vector<uint64> instead manually manange storage between devices.
5. Prefer to use platform::ForRange, so we can optimize `ForRange` by
   just changing `for_range.h` if it is needed.
6. Do not change shape of inputs

test=develop

Author: reyoung

paddle/fluid/operators/detection/generate_proposals_op.cc

@@ -12,10 +12,12 @@ 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 <cmath>
+#include <cstring>
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/var_type.h"
+#include "paddle/fluid/operators/detail/safe_ref.h"
 #include "paddle/fluid/operators/gather.h"
 #include "paddle/fluid/operators/math/math_function.h"
 
@@ -25,21 +27,17 @@ namespace operators {
 using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 
-struct AppendProposalsFunctor {
-  LoDTensor *out_;
-  int64_t offset_;
-  Tensor *to_add_;
+static const double kBBoxClipDefault = std::log(1000.0 / 16.0);
 
-  AppendProposalsFunctor(LoDTensor *out, int64_t offset, Tensor *to_add)
-      : out_(out), offset_(offset), to_add_(to_add) {}
-
-  template <typename T>
-  void apply() const {
-    auto *out_data = out_->data<T>();
-    auto *to_add_data = to_add_->data<T>();
-    memcpy(out_data + offset_, to_add_data, to_add_->numel() * sizeof(T));
-  }
-};
+static void AppendProposals(Tensor *dst, int64_t offset, const Tensor &src) {
+  auto *out_data = dst->data<void>();
+  auto *to_add_data = src.data<void>();
+  size_t size_of_t = framework::SizeOfType(src.type());
+  offset *= size_of_t;
+  std::memcpy(
+      reinterpret_cast<void *>(reinterpret_cast<uintptr_t>(out_data) + offset),
+      to_add_data, src.numel() * size_of_t);
+}
 
 class GenerateProposalsOp : public framework::OperatorWithKernel {
  public:
@@ -75,8 +73,9 @@ class GenerateProposalsOp : public framework::OperatorWithKernel {
 };
 
 template <class T>
-void BoxCoder(const platform::DeviceContext &ctx, Tensor *all_anchors,
-              Tensor *bbox_deltas, Tensor *variances, Tensor *proposals) {
+static inline void BoxCoder(const platform::DeviceContext &ctx,
+                            Tensor *all_anchors, Tensor *bbox_deltas,
+                            Tensor *variances, Tensor *proposals) {
   T *proposals_data = proposals->mutable_data<T>(ctx.GetPlace());
 
   int64_t row = all_anchors->dims()[0];
@@ -108,22 +107,22 @@ void BoxCoder(const platform::DeviceContext &ctx, Tensor *all_anchors,
                       anchor_center_y;
       bbox_width = std::exp(std::min<T>(variances_data[i * len + 2] *
                                             bbox_deltas_data[i * len + 2],
-                                        std::log(1000.0 / 16.0))) *
+                                        kBBoxClipDefault)) *
                    anchor_width;
       bbox_height = std::exp(std::min<T>(variances_data[i * len + 3] *
                                              bbox_deltas_data[i * len + 3],
-                                         std::log(1000.0 / 16.0))) *
+                                         kBBoxClipDefault)) *
                     anchor_height;
     } else {
       bbox_center_x =
           bbox_deltas_data[i * len] * anchor_width + anchor_center_x;
       bbox_center_y =
           bbox_deltas_data[i * len + 1] * anchor_height + anchor_center_y;
       bbox_width = std::exp(std::min<T>(bbox_deltas_data[i * len + 2],
-                                        std::log(1000.0 / 16.0))) *
+                                        kBBoxClipDefault)) *
                    anchor_width;
       bbox_height = std::exp(std::min<T>(bbox_deltas_data[i * len + 3],
-                                         std::log(1000.0 / 16.0))) *
+                                         kBBoxClipDefault)) *
                     anchor_height;
     }
 
@@ -136,30 +135,32 @@ void BoxCoder(const platform::DeviceContext &ctx, Tensor *all_anchors,
 }
 
 template <class T>
-void ClipTiledBoxes(const platform::DeviceContext &ctx, const Tensor &im_info,
-                    Tensor *boxes) {
+static inline void ClipTiledBoxes(const platform::DeviceContext &ctx,
+                                  const Tensor &im_info, Tensor *boxes) {
   T *boxes_data = boxes->mutable_data<T>(ctx.GetPlace());
   const T *im_info_data = im_info.data<T>();
+  T zero(0);
   for (int64_t i = 0; i < boxes->numel(); ++i) {
     if (i % 4 == 0) {
       boxes_data[i] =
-          std::max(std::min(boxes_data[i], im_info_data[1] - 1), 0.0f);
+          std::max(std::min(boxes_data[i], im_info_data[1] - 1), zero);
     } else if (i % 4 == 1) {
       boxes_data[i] =
-          std::max(std::min(boxes_data[i], im_info_data[0] - 1), 0.0f);
+          std::max(std::min(boxes_data[i], im_info_data[0] - 1), zero);
     } else if (i % 4 == 2) {
       boxes_data[i] =
-          std::max(std::min(boxes_data[i], im_info_data[1] - 1), 0.0f);
+          std::max(std::min(boxes_data[i], im_info_data[1] - 1), zero);
     } else {
       boxes_data[i] =
-          std::max(std::min(boxes_data[i], im_info_data[0] - 1), 0.0f);
+          std::max(std::min(boxes_data[i], im_info_data[0] - 1), zero);
     }
   }
 }
 
 template <class T>
-void FilterBoxes(const platform::DeviceContext &ctx, Tensor *boxes,
-                 float min_size, const Tensor &im_info, Tensor *keep) {
+static inline void FilterBoxes(const platform::DeviceContext &ctx,
+                               Tensor *boxes, float min_size,
+                               const Tensor &im_info, Tensor *keep) {
   const T *im_info_data = im_info.data<T>();
   T *boxes_data = boxes->mutable_data<T>(ctx.GetPlace());
   T im_scale = im_info_data[2];
@@ -185,24 +186,24 @@ void FilterBoxes(const platform::DeviceContext &ctx, Tensor *boxes,
   keep->Resize({keep_len});
 }
 
-bool SortScorePairDescend(const std::pair<float, int> &pair1,
-                          const std::pair<float, int> &pair2) {
-  return pair1.first > pair2.first;
-}
-
 template <class T>
-void GetMaxScoreIndex(const std::vector<T> &scores,
-                      std::vector<std::pair<T, int>> *sorted_indices) {
+static inline std::vector<std::pair<T, int>> GetSortedScoreIndex(
+    const std::vector<T> &scores) {
+  std::vector<std::pair<T, int>> sorted_indices;
+  sorted_indices.reserve(scores.size());
   for (size_t i = 0; i < scores.size(); ++i) {
-    sorted_indices->push_back(std::make_pair(scores[i], i));
+    sorted_indices.emplace_back(scores[i], i);
   }
   // Sort the score pair according to the scores in descending order
-  std::stable_sort(sorted_indices->begin(), sorted_indices->end(),
-                   SortScorePairDescend);
+  std::stable_sort(sorted_indices.begin(), sorted_indices.end(),
+                   [](const std::pair<T, int> &a, const std::pair<T, int> &b) {
+                     return a.first < b.first;
+                   });
+  return sorted_indices;
 }
 
 template <class T>
-T BBoxArea(const T *box, const bool normalized) {
+static inline T BBoxArea(const T *box, 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.
@@ -220,7 +221,7 @@ T BBoxArea(const T *box, const bool normalized) {
 }
 
 template <class T>
-T JaccardOverlap(const T *box1, const T *box2, const bool normalized) {
+static inline T JaccardOverlap(const T *box1, const T *box2, bool normalized) {
   if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
       box2[3] < box1[1]) {
     return static_cast<T>(0.);
@@ -229,39 +230,49 @@ T JaccardOverlap(const T *box1, const T *box2, const bool normalized) {
     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]);
-    const T inter_w = std::max(0.0f, inter_xmax - inter_xmin + 1);
-    const T inter_h = std::max(0.0f, inter_ymax - inter_ymin + 1);
+    const T inter_w = std::max(T(0), inter_xmax - inter_xmin + 1);
+    const T inter_h = std::max(T(0), inter_ymax - inter_ymin + 1);
     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 <typename T>
+static inline Tensor VectorToTensor(const std::vector<T> &selected_indices,
+                                    int selected_num) {
+  Tensor keep_nms;
+  keep_nms.Resize({selected_num});
+  auto *keep_data = keep_nms.mutable_data<T>(platform::CPUPlace());
+  for (int i = 0; i < selected_num; ++i) {
+    keep_data[i] = selected_indices[i];
+  }
+  return keep_nms;
+}
+
 template <class T>
-Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox, Tensor *scores,
-           const T nms_threshold, const float eta) {
+static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
+                         Tensor *scores, T nms_threshold, float eta) {
   PADDLE_ENFORCE_NOT_NULL(bbox);
   int64_t num_boxes = bbox->dims()[0];
   // 4: [xmin ymin xmax ymax]
   int64_t box_size = bbox->dims()[1];
 
   std::vector<T> scores_data(num_boxes);
   std::copy_n(scores->data<T>(), num_boxes, scores_data.begin());
-  std::vector<std::pair<T, int>> sorted_indices;
-  GetMaxScoreIndex<T>(scores_data, &sorted_indices);
+  std::vector<std::pair<T, int>> sorted_indices =
+      GetSortedScoreIndex<T>(scores_data);
 
   std::vector<int> selected_indices;
   int selected_num = 0;
   T adaptive_threshold = nms_threshold;
   const T *bbox_data = bbox->data<T>();
-  bool flag;
   while (sorted_indices.size() != 0) {
-    int idx = sorted_indices.front().second;
-    flag = true;
-    for (size_t k = 0; k < selected_indices.size(); ++k) {
+    int idx = sorted_indices.back().second;
+    bool flag = true;
+    for (int kept_idx : selected_indices) {
       if (flag) {
-        const int kept_idx = selected_indices[k];
         T overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
                                       bbox_data + kept_idx * box_size, false);
         flag = (overlap <= adaptive_threshold);
@@ -271,32 +282,29 @@ Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox, Tensor *scores,
     }
     if (flag) {
       selected_indices.push_back(idx);
-      selected_num++;
+      ++selected_num;
     }
-    sorted_indices.erase(sorted_indices.begin());
+    sorted_indices.erase(sorted_indices.end());
     if (flag && eta < 1 && adaptive_threshold > 0.5) {
       adaptive_threshold *= eta;
     }
   }
-  Tensor keep_nms;
-  keep_nms.Resize({selected_num});
-  int *keep_data = keep_nms.mutable_data<int>(ctx.GetPlace());
-  for (int i = 0; i < selected_num; ++i) {
-    keep_data[i] = selected_indices[i];
-  }
-
-  return keep_nms;
+  return VectorToTensor(selected_indices, selected_num);
 }
 
-template <typename DeviceContext, typename T>
+template <typename T>
 class GenerateProposalsKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &context) const override {
     auto *scores = context.Input<Tensor>("Scores");
     auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
     auto *im_info = context.Input<Tensor>("ImInfo");
-    auto *anchors = context.Input<Tensor>("Anchors");
-    auto *variances = context.Input<Tensor>("Variances");
+    auto anchors = detail::Ref(context.Input<Tensor>("Anchors"),
+                               "Cannot find input Anchors(%s) in scope",
+                               context.Inputs("Anchors")[0]);
+    auto variances = detail::Ref(context.Input<Tensor>("Variances"),
+                                 "Cannot find input Variances(%s) in scope",
+                                 context.Inputs("Variances")[0]);
 
     auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
     auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");
@@ -307,15 +315,16 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
     float min_size = context.Attr<float>("min_size");
     float eta = context.Attr<float>("eta");
 
-    auto &dev_ctx = context.template device_context<DeviceContext>();
+    auto &dev_ctx =
+        context.template device_context<platform::CPUDeviceContext>();
 
-    auto scores_dim = scores->dims();
+    auto &scores_dim = scores->dims();
     int64_t num = scores_dim[0];
     int64_t c_score = scores_dim[1];
     int64_t h_score = scores_dim[2];
     int64_t w_score = scores_dim[3];
 
-    auto bbox_dim = bbox_deltas->dims();
+    auto &bbox_dim = bbox_deltas->dims();
     int64_t c_bbox = bbox_dim[1];
     int64_t h_bbox = bbox_dim[2];
     int64_t w_bbox = bbox_dim[3];
@@ -330,17 +339,17 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
     scores_swap.mutable_data<T>({num, h_score, w_score, c_score},
                                 dev_ctx.GetPlace());
 
-    math::Transpose<DeviceContext, T, 4> trans;
+    math::Transpose<platform::CPUDeviceContext, T, 4> trans;
     std::vector<int> axis = {0, 2, 3, 1};
     trans(dev_ctx, *bbox_deltas, &bbox_deltas_swap, axis);
     trans(dev_ctx, *scores, &scores_swap, axis);
 
     framework::LoD lod;
-    std::vector<size_t> lod0(1, 0);
-    Tensor *anchor = const_cast<framework::Tensor *>(anchors);
-    anchor->Resize({anchors->numel() / 4, 4});
-    Tensor *var = const_cast<framework::Tensor *>(variances);
-    var->Resize({var->numel() / 4, 4});
+    lod.resize(1);
+    auto &lod0 = lod[0];
+    lod0.push_back(0);
+    anchors.Resize({anchors.numel() / 4, 4});
+    variances.Resize({variances.numel() / 4, 4});
 
     int64_t num_proposals = 0;
     for (int64_t i = 0; i < num; ++i) {
@@ -352,32 +361,25 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
       scores_slice.Resize({h_score * w_score * c_score, 1});
 
       std::pair<Tensor, Tensor> tensor_pair =
-          ProposalForOneImage(dev_ctx, im_info_slice, *anchor, *var,
+          ProposalForOneImage(dev_ctx, im_info_slice, anchors, variances,
                               bbox_deltas_slice, scores_slice, pre_nms_top_n,
                               post_nms_top_n, nms_thresh, min_size, eta);
-      Tensor proposals = tensor_pair.first;
-      Tensor scores = tensor_pair.second;
-
-      framework::VisitDataType(
-          framework::ToDataType(rpn_rois->type()),
-          AppendProposalsFunctor(rpn_rois, 4 * num_proposals, &proposals));
-      framework::VisitDataType(
-          framework::ToDataType(rpn_roi_probs->type()),
-          AppendProposalsFunctor(rpn_roi_probs, num_proposals, &scores));
+      Tensor &proposals = tensor_pair.first;
+      Tensor &scores = tensor_pair.second;
 
+      AppendProposals(rpn_rois, 4 * num_proposals, proposals);
+      AppendProposals(rpn_roi_probs, num_proposals, scores);
       num_proposals += proposals.dims()[0];
-      lod0.emplace_back(num_proposals);
+      lod0.push_back(num_proposals);
     }
-
-    lod.emplace_back(lod0);
     rpn_rois->set_lod(lod);
     rpn_roi_probs->set_lod(lod);
     rpn_rois->Resize({num_proposals, 4});
     rpn_roi_probs->Resize({num_proposals, 1});
   }
 
   std::pair<Tensor, Tensor> ProposalForOneImage(
-      const DeviceContext &ctx, const Tensor &im_info_slice,
+      const platform::CPUDeviceContext &ctx, const Tensor &im_info_slice,
       const Tensor &anchors, const Tensor &variances,
       const Tensor &bbox_deltas_slice,  // [M, 4]
       const Tensor &scores_slice,       // [N, 1]
@@ -392,10 +394,9 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
     for (int i = 0; i < scores_slice.numel(); ++i) {
       index[i] = i;
     }
-    std::function<bool(const int64_t &, const int64_t &)> compare =
-        [scores_data](const int64_t &i, const int64_t &j) {
-          return scores_data[i] > scores_data[j];
-        };
+    auto compare = [scores_data](const int64_t &i, const int64_t &j) {
+      return scores_data[i] > scores_data[j];
+    };
 
     if (pre_nms_top_n <= 0 || pre_nms_top_n >= scores_slice.numel()) {
       std::sort(index, index + scores_slice.numel(), compare);
@@ -469,12 +470,12 @@ class GenerateProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
 Generate Proposals OP
 
 This operator proposes rois according to each box with their probability to be a foreground object and 
-the box can be calculated by anchors. Bbox_deltais and scores are the output of RPN. Final proposals
+the box can be calculated by anchors. Bbox_details and scores are the output of RPN. Final proposals
 could be used to train detection net.
 
 Scores is the probability for each box to be an object. In format of (N, A, H, W) where N is batch size, A is number
 of anchors, H and W are height and width of the feature map.
-BboxDeltas is the differece between predicted box locatoin and anchor location. In format of (N, 4*A, H, W)
+BboxDeltas is the differece between predicted box location and anchor location. In format of (N, 4*A, H, W)
 
 For generating proposals, this operator transposes and resizes scores and bbox_deltas in size of (H*W*A, 1) and (H*W*A, 4) and 
  calculate box locations as proposals candidates. Then clip boxes to image and remove predicted boxes with small area. 
@@ -490,6 +491,5 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(generate_proposals, ops::GenerateProposalsOp,
                   ops::GenerateProposalsOpMaker,
                   paddle::framework::EmptyGradOpMaker);
-REGISTER_OP_CPU_KERNEL(
-    generate_proposals,
-    ops::GenerateProposalsKernel<paddle::platform::CPUDeviceContext, float>);
+REGISTER_OP_CPU_KERNEL(generate_proposals, ops::GenerateProposalsKernel<float>,
+                       ops::GenerateProposalsKernel<double>);