update detection_map operator

paddle/operators/detection_map_op.cc

@@ -24,6 +24,29 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Detection"),
+                   "Input(Detection) of DetectionMAPOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"),
+                   "Input(Label) of DetectionMAPOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("MAP"),
+                   "Output(MAP) of DetectionMAPOp should not be null.");
+
+    auto det_dims = ctx->GetInputDim("Detection");
+    PADDLE_ENFORCE_EQ(det_dims.size(), 2UL,
+                      "The rank of Input(Detection) must be 2, "
+                      "the shape is [N, 6].");
+    PADDLE_ENFORCE_EQ(det_dims[1], 6UL,
+                      "The shape is of Input(Detection) [N, 6].");
+    auto label_dims = ctx->GetInputDim("Label");
+    PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
+                      "The rank of Input(Label) must be 2, "
+                      "the shape is [N, 6].");
+    PADDLE_ENFORCE_EQ(label_dims[1], 6UL,
+                      "The shape is of Input(Label) [N, 6].");
+
+    auto ap_type = GetAPType(ctx->Attrs().Get<std::string>("ap_type"));
+    PADDLE_ENFORCE_NE(ap_type, APType::kNone,
+                      "The ap_type should be 'integral' or '11point.");
     auto map_dim = framework::make_ddim({1});
     ctx->SetOutputDim("MAP", map_dim);
   }
@@ -42,25 +65,49 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
   DetectionMAPOpMaker(framework::OpProto* proto,
                       framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("Detect", "The detection output.");
-    AddInput("Label", "The label data.");
-    AddOutput("MAP", "The MAP evaluate result of the detection.");
-
-    AddAttr<float>("overlap_threshold", "The overlap threshold.")
+    AddInput("Label",
+             "(LoDTensor) A 2-D LoDTensor with shape[N, 6] represents the"
+             "Labeled ground-truth data. Each row has 6 values: "
+             "[label, is_difficult, xmin, ymin, xmax, ymax], N is the total "
+             "number of ground-truth data in this mini-batch. For each "
+             "instance, the offsets in first dimension are called LoD, "
+             "the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, "
+             "means there is no ground-truth data.");
+    AddInput("Detection",
+             "(LoDTensor) A 2-D LoDTensor with shape [M, 6] represents the "
+             "detections. Each row has 6 values: "
+             "[label, confidence, xmin, ymin, xmax, ymax], M is the total "
+             "number of detections in this mini-batch. For each instance, "
+             "the offsets in first dimension are called LoD, the number of "
+             "offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
+             "no detected data.");
+    AddOutput("MAP",
+              "(Tensor) A tensor with shape [1], store the mAP evaluate "
+              "result of the detection.");
+
+    AddAttr<float>("overlap_threshold",
+                   "(float) "
+                   "The jaccard overlap threshold of detection output and "
+                   "ground-truth data.")
         .SetDefault(.3f);
     AddAttr<bool>("evaluate_difficult",
+                  "(bool, default true) "
                   "Switch to control whether the difficult data is evaluated.")
         .SetDefault(true);
     AddAttr<std::string>("ap_type",
-                         "The AP algorithm type, 'Integral' or '11point'.")
-        .SetDefault("Integral");
-
+                         "(string, default 'integral') "
+                         "The AP algorithm type, 'integral' or '11point'.")
+        .SetDefault("integral")
+        .InEnum({"integral", "11point"});
     AddComment(R"DOC(
-Detection MAP Operator.
-
-Detection MAP evaluator for SSD(Single Shot MultiBox Detector) algorithm.
-Please get more information from the following papers:
-https://arxiv.org/abs/1512.02325.
+Detection mAP evaluate operator.
+The general steps are as follows. First, calculate the true positive and
+ false positive according to the input of detection and labels, then
+ calculate the mAP evaluate value.
+ Supporting '11 point' and 'integral' mAP algorithm. Please get more information
+ from the following articles:
+ https://sanchom.wordpress.com/tag/average-precision/
+ https://arxiv.org/abs/1512.02325
 
 )DOC");
   }

paddle/operators/detection_map_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-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 "paddle/operators/detection_map_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    detection_map, ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, float>,
-    ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, double>);

paddle/operators/detection_map_op.h

@@ -13,22 +13,37 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
-#include "paddle/operators/math/detection_util.h"
-#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
 
+enum APType { kNone = 0, kIntegral, k11point };
+
+APType GetAPType(std::string str) {
+  if (str == "integral") {
+    return APType::kIntegral;
+  } else if (str == "11point") {
+    return APType::k11point;
+  } else {
+    return APType::kNone;
+  }
+}
+
+template <typename T>
+inline bool SortScorePairDescend(const std::pair<float, T>& pair1,
+                                 const std::pair<float, T>& pair2) {
+  return pair1.first > pair2.first;
+}
+
 template <typename T>
 inline void GetAccumulation(std::vector<std::pair<T, int>> in_pairs,
                             std::vector<int>* accu_vec) {
-  std::stable_sort(in_pairs.begin(), in_pairs.end(),
-                   math::SortScorePairDescend<int>);
+  std::stable_sort(in_pairs.begin(), in_pairs.end(), SortScorePairDescend<int>);
   accu_vec->clear();
   size_t sum = 0;
   for (size_t i = 0; i < in_pairs.size(); ++i) {
-    // auto score = in_pairs[i].first;
     auto count = in_pairs[i].second;
     sum += count;
     accu_vec->push_back(sum);
@@ -39,126 +54,125 @@ template <typename Place, typename T>
 class DetectionMAPOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* input_label = ctx.Input<framework::LoDTensor>("Label");
-    auto* input_detect = ctx.Input<framework::Tensor>("Detect");
-    auto* map_out = ctx.Output<framework::Tensor>("MAP");
+    auto* in_detect = ctx.Input<framework::LoDTensor>("Detection");
+    auto* in_label = ctx.Input<framework::LoDTensor>("Label");
+    auto* out_map = ctx.Output<framework::Tensor>("MAP");
 
     float overlap_threshold = ctx.Attr<float>("overlap_threshold");
     float evaluate_difficult = ctx.Attr<bool>("evaluate_difficult");
-    std::string ap_type = ctx.Attr<std::string>("ap_type");
+    auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
 
-    auto label_lod = input_label->lod();
+    auto label_lod = in_label->lod();
+    auto detect_lod = in_detect->lod();
     PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
                       "Only support one level sequence now.");
-    auto batch_size = label_lod[0].size() - 1;
-
-    std::vector<std::map<int, std::vector<operators::math::BBox<T>>>> gt_bboxes;
-
-    std::vector<
-        std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>
-        detect_bboxes;
-
-    if (platform::is_gpu_place(ctx.GetPlace())) {
-      framework::LoDTensor input_label_cpu;
-      framework::Tensor input_detect_cpu;
-      input_label_cpu.set_lod(input_label->lod());
-      input_label_cpu.Resize(input_label->dims());
-      input_detect_cpu.Resize(input_detect->dims());
-      input_label_cpu.mutable_data<T>(platform::CPUPlace());
-      input_detect_cpu.mutable_data<T>(platform::CPUPlace());
-      framework::CopyFrom(*input_label, platform::CPUPlace(),
-                          ctx.device_context(), &input_label_cpu);
-      framework::CopyFrom(*input_detect, platform::CPUPlace(),
-                          ctx.device_context(), &input_detect_cpu);
-      GetBBoxes(input_label_cpu, input_detect_cpu, gt_bboxes, detect_bboxes);
-    } else {
-      GetBBoxes(*input_label, *input_detect, gt_bboxes, detect_bboxes);
-    }
+    PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
+                      "The batch_size of input(Label) and input(Detection) "
+                      "must be the same.");
+
+    std::vector<std::map<int, std::vector<Box>>> gt_boxes;
+    std::vector<std::map<int, std::vector<std::pair<T, Box>>>> detect_boxes;
+
+    GetBoxes(*in_label, *in_detect, gt_boxes, detect_boxes);
 
     std::map<int, int> label_pos_count;
     std::map<int, std::vector<std::pair<T, int>>> true_pos;
     std::map<int, std::vector<std::pair<T, int>>> false_pos;
 
-    CalcTrueAndFalsePositive(batch_size, evaluate_difficult, overlap_threshold,
-                             gt_bboxes, detect_bboxes, label_pos_count,
-                             true_pos, false_pos);
+    CalcTrueAndFalsePositive(gt_boxes, detect_boxes, evaluate_difficult,
+                             overlap_threshold, label_pos_count, true_pos,
+                             false_pos);
 
     T map = CalcMAP(ap_type, label_pos_count, true_pos, false_pos);
 
-    T* map_data = nullptr;
-    framework::Tensor map_cpu;
-    map_out->mutable_data<T>(ctx.GetPlace());
-    if (platform::is_gpu_place(ctx.GetPlace())) {
-      map_data = map_cpu.mutable_data<T>(map_out->dims(), platform::CPUPlace());
-      map_data[0] = map;
-      framework::CopyFrom(map_cpu, platform::CPUPlace(), ctx.device_context(),
-                          map_out);
+    T* map_data = out_map->mutable_data<T>(ctx.GetPlace());
+    map_data[0] = map;
+  }
+
+ protected:
+  struct Box {
+    Box(T xmin, T ymin, T xmax, T ymax)
+        : xmin(xmin), ymin(ymin), xmax(xmax), ymax(ymax), is_difficult(false) {}
+
+    T xmin, ymin, xmax, ymax;
+    bool is_difficult;
+  };
+
+  inline T JaccardOverlap(const Box& box1, const Box& box2) const {
+    if (box2.xmin > box1.xmax || box2.xmax < box1.xmin ||
+        box2.ymin > box1.ymax || box2.ymax < box1.ymin) {
+      return 0.0;
     } else {
-      map_data = map_out->mutable_data<T>(ctx.GetPlace());
-      map_data[0] = map;
+      T inter_xmin = std::max(box1.xmin, box2.xmin);
+      T inter_ymin = std::max(box1.ymin, box2.ymin);
+      T inter_xmax = std::min(box1.xmax, box2.xmax);
+      T inter_ymax = std::min(box1.ymax, box2.ymax);
+
+      T inter_width = inter_xmax - inter_xmin;
+      T inter_height = inter_ymax - inter_ymin;
+      T inter_area = inter_width * inter_height;
+
+      T bbox_area1 = (box1.xmax - box1.xmin) * (box1.ymax - box1.ymin);
+      T bbox_area2 = (box2.xmax - box2.xmin) * (box2.ymax - box2.ymin);
+
+      return inter_area / (bbox_area1 + bbox_area2 - inter_area);
     }
   }
 
- protected:
-  void GetBBoxes(
-      const framework::LoDTensor& input_label,
-      const framework::Tensor& input_detect,
-      std::vector<std::map<int, std::vector<operators::math::BBox<T>>>>&
-          gt_bboxes,
-      std::vector<
-          std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>&
-          detect_bboxes) const {
-    const T* label_data = input_label.data<T>();
-    const T* detect_data = input_detect.data<T>();
+  void GetBoxes(const framework::LoDTensor& input_label,
+                const framework::LoDTensor& input_detect,
+                std::vector<std::map<int, std::vector<Box>>>& gt_boxes,
+                std::vector<std::map<int, std::vector<std::pair<T, Box>>>>&
+                    detect_boxes) const {
+    auto labels = framework::EigenTensor<T, 2>::From(input_label);
+    auto detect = framework::EigenTensor<T, 2>::From(input_detect);
 
     auto label_lod = input_label.lod();
-    auto batch_size = label_lod[0].size() - 1;
+    auto detect_lod = input_detect.lod();
+
+    int batch_size = label_lod[0].size() - 1;
     auto label_index = label_lod[0];
 
-    for (size_t n = 0; n < batch_size; ++n) {
-      std::map<int, std::vector<operators::math::BBox<T>>> bboxes;
+    for (int n = 0; n < batch_size; ++n) {
+      std::map<int, std::vector<Box>> boxes;
       for (int i = label_index[n]; i < label_index[n + 1]; ++i) {
-        std::vector<operators::math::BBox<T>> bbox;
-        math::GetBBoxFromLabelData<T>(label_data + i * 6, 1, bbox);
-        int label = static_cast<int>(label_data[i * 6]);
-        bboxes[label].push_back(bbox[0]);
+        Box box(labels(i, 2), labels(i, 3), labels(i, 4), labels(i, 5));
+        int label = labels(i, 0);
+        auto is_difficult = labels(i, 1);
+        if (std::abs(is_difficult - 0.0) < 1e-6)
+          box.is_difficult = false;
+        else
+          box.is_difficult = true;
+        boxes[label].push_back(box);
       }
-      gt_bboxes.push_back(bboxes);
+      gt_boxes.push_back(boxes);
     }
 
-    size_t n = 0;
-    size_t detect_box_count = input_detect.dims()[0];
-    for (size_t img_id = 0; img_id < batch_size; ++img_id) {
-      std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>> bboxes;
-      size_t cur_img_id = static_cast<size_t>((detect_data + n * 7)[0]);
-      while (cur_img_id == img_id && n < detect_box_count) {
-        std::vector<T> label;
-        std::vector<T> score;
-        std::vector<operators::math::BBox<T>> bbox;
-        math::GetBBoxFromDetectData<T>(detect_data + n * 7, 1, label, score,
-                                       bbox);
-        bboxes[label[0]].push_back(std::make_pair(score[0], bbox[0]));
-        ++n;
-        cur_img_id = static_cast<size_t>((detect_data + n * 7)[0]);
+    auto detect_index = detect_lod[0];
+    for (int n = 0; n < batch_size; ++n) {
+      std::map<int, std::vector<std::pair<T, Box>>> boxes;
+      for (int i = detect_index[n]; i < detect_index[n + 1]; ++i) {
+        Box box(detect(i, 2), detect(i, 3), detect(i, 4), detect(i, 5));
+        int label = detect(i, 0);
+        auto score = detect(i, 1);
+        boxes[label].push_back(std::make_pair(score, box));
       }
-      detect_bboxes.push_back(bboxes);
+      detect_boxes.push_back(boxes);
     }
   }
 
   void CalcTrueAndFalsePositive(
-      size_t batch_size, bool evaluate_difficult, float overlap_threshold,
-      const std::vector<std::map<int, std::vector<operators::math::BBox<T>>>>&
-          gt_bboxes,
-      const std::vector<
-          std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>&
-          detect_bboxes,
+      const std::vector<std::map<int, std::vector<Box>>>& gt_boxes,
+      const std::vector<std::map<int, std::vector<std::pair<T, Box>>>>&
+          detect_boxes,
+      bool evaluate_difficult, float overlap_threshold,
       std::map<int, int>& label_pos_count,
       std::map<int, std::vector<std::pair<T, int>>>& true_pos,
       std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
-    for (size_t n = 0; n < batch_size; ++n) {
-      auto image_gt_bboxes = gt_bboxes[n];
-      for (auto it = image_gt_bboxes.begin(); it != image_gt_bboxes.end();
-           ++it) {
+    int batch_size = gt_boxes.size();
+    for (int n = 0; n < batch_size; ++n) {
+      auto image_gt_boxes = gt_boxes[n];
+      for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
         size_t count = 0;
         auto labeled_bboxes = it->second;
         if (evaluate_difficult) {
@@ -179,16 +193,16 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
       }
     }
 
-    for (size_t n = 0; n < detect_bboxes.size(); ++n) {
-      auto image_gt_bboxes = gt_bboxes[n];
-      auto detections = detect_bboxes[n];
+    for (size_t n = 0; n < detect_boxes.size(); ++n) {
+      auto image_gt_boxes = gt_boxes[n];
+      auto detections = detect_boxes[n];
 
-      if (image_gt_bboxes.size() == 0) {
+      if (image_gt_boxes.size() == 0) {
         for (auto it = detections.begin(); it != detections.end(); ++it) {
-          auto pred_bboxes = it->second;
+          auto pred_boxes = it->second;
           int label = it->first;
-          for (size_t i = 0; i < pred_bboxes.size(); ++i) {
-            auto score = pred_bboxes[i].first;
+          for (size_t i = 0; i < pred_boxes.size(); ++i) {
+            auto score = pred_boxes[i].first;
             true_pos[label].push_back(std::make_pair(score, 0));
             false_pos[label].push_back(std::make_pair(score, 1));
           }
@@ -198,28 +212,27 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
 
       for (auto it = detections.begin(); it != detections.end(); ++it) {
         int label = it->first;
-        auto pred_bboxes = it->second;
-        if (image_gt_bboxes.find(label) == image_gt_bboxes.end()) {
-          for (size_t i = 0; i < pred_bboxes.size(); ++i) {
-            auto score = pred_bboxes[i].first;
+        auto pred_boxes = it->second;
+        if (image_gt_boxes.find(label) == image_gt_boxes.end()) {
+          for (size_t i = 0; i < pred_boxes.size(); ++i) {
+            auto score = pred_boxes[i].first;
             true_pos[label].push_back(std::make_pair(score, 0));
             false_pos[label].push_back(std::make_pair(score, 1));
           }
           continue;
         }
 
-        auto matched_bboxes = image_gt_bboxes.find(label)->second;
+        auto matched_bboxes = image_gt_boxes.find(label)->second;
         std::vector<bool> visited(matched_bboxes.size(), false);
         // Sort detections in descend order based on scores
-        std::sort(pred_bboxes.begin(), pred_bboxes.end(),
-                  math::SortScorePairDescend<operators::math::BBox<T>>);
-        for (size_t i = 0; i < pred_bboxes.size(); ++i) {
-          float max_overlap = -1.0;
+        std::sort(pred_boxes.begin(), pred_boxes.end(),
+                  SortScorePairDescend<Box>);
+        for (size_t i = 0; i < pred_boxes.size(); ++i) {
+          T max_overlap = -1.0;
           size_t max_idx = 0;
-          auto score = pred_bboxes[i].first;
+          auto score = pred_boxes[i].first;
           for (size_t j = 0; j < matched_bboxes.size(); ++j) {
-            float overlap =
-                JaccardOverlap(pred_bboxes[i].second, matched_bboxes[j]);
+            T overlap = JaccardOverlap(pred_boxes[i].second, matched_bboxes[j]);
             if (overlap > max_overlap) {
               max_overlap = overlap;
               max_idx = j;
@@ -249,7 +262,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
   }
 
   T CalcMAP(
-      std::string ap_type, const std::map<int, int>& label_pos_count,
+      APType ap_type, const std::map<int, int>& label_pos_count,
       const std::map<int, std::vector<std::pair<T, int>>>& true_pos,
       const std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
     T mAP = 0.0;
@@ -266,18 +279,18 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
       GetAccumulation<T>(label_true_pos, &tp_sum);
       std::vector<int> fp_sum;
       GetAccumulation<T>(label_false_pos, &fp_sum);
-      std::vector<float> precision, recall;
+      std::vector<T> precision, recall;
       size_t num = tp_sum.size();
       // Compute Precision.
       for (size_t i = 0; i < num; ++i) {
         // CHECK_LE(tpCumSum[i], labelNumPos);
-        precision.push_back(static_cast<float>(tp_sum[i]) /
-                            static_cast<float>(tp_sum[i] + fp_sum[i]));
-        recall.push_back(static_cast<float>(tp_sum[i]) / label_num_pos);
+        precision.push_back(static_cast<T>(tp_sum[i]) /
+                            static_cast<T>(tp_sum[i] + fp_sum[i]));
+        recall.push_back(static_cast<T>(tp_sum[i]) / label_num_pos);
       }
       // VOC2007 style
-      if (ap_type == "11point") {
-        std::vector<float> max_precisions(11, 0.0);
+      if (ap_type == APType::k11point) {
+        std::vector<T> max_precisions(11, 0.0);
         int start_idx = num - 1;
         for (int j = 10; j >= 0; --j)
           for (int i = start_idx; i >= 0; --i) {
@@ -292,7 +305,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
           }
         for (int j = 10; j >= 0; --j) mAP += max_precisions[j] / 11;
         ++count;
-      } else if (ap_type == "Integral") {
+      } else if (ap_type == APType::kIntegral) {
         // Nature integral
         float average_precisions = 0.;
         float prev_recall = 0.;

paddle/operators/math/detection_util.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-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 "paddle/operators/math/detection_util.h"
-#include "paddle/operators/math/math_function.h"
-
-namespace paddle {
-namespace operators {
-namespace math {}  // namespace math
-}  // namespace operators
-}  // namespace paddle

paddle/operators/math/detection_util.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-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 "paddle/operators/math/detection_util.h"
-#include "paddle/operators/math/math_function.h"
-#include "paddle/platform/cuda_helper.h"
-
-namespace paddle {
-namespace operators {
-namespace math {}  // namespace math
-}  // namespace operators
-}  // namespace paddle

paddle/operators/math/detection_util.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-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 "paddle/framework/selected_rows.h"
-#include "paddle/platform/device_context.h"
-
-namespace paddle {
-namespace operators {
-namespace math {
-
-template <typename T>
-struct BBox {
-  BBox(T x_min, T y_min, T x_max, T y_max)
-      : x_min(x_min),
-        y_min(y_min),
-        x_max(x_max),
-        y_max(y_max),
-        is_difficult(false) {}
-
-  BBox() {}
-
-  T get_width() const { return x_max - x_min; }
-
-  T get_height() const { return y_max - y_min; }
-
-  T get_center_x() const { return (x_min + x_max) / 2; }
-
-  T get_center_y() const { return (y_min + y_max) / 2; }
-
-  T get_area() const { return get_width() * get_height(); }
-
-  // coordinate of bounding box
-  T x_min;
-  T y_min;
-  T x_max;
-  T y_max;
-  // whether difficult object (e.g. object with heavy occlusion is difficult)
-  bool is_difficult;
-};
-
-template <typename T>
-void GetBBoxFromDetectData(const T* detect_data, const size_t num_bboxes,
-                           std::vector<T>& labels, std::vector<T>& scores,
-                           std::vector<BBox<T>>& bboxes) {
-  size_t out_offset = bboxes.size();
-  labels.resize(out_offset + num_bboxes);
-  scores.resize(out_offset + num_bboxes);
-  bboxes.resize(out_offset + num_bboxes);
-  for (size_t i = 0; i < num_bboxes; ++i) {
-    labels[out_offset + i] = *(detect_data + i * 7 + 1);
-    scores[out_offset + i] = *(detect_data + i * 7 + 2);
-    BBox<T> bbox;
-    bbox.x_min = *(detect_data + i * 7 + 3);
-    bbox.y_min = *(detect_data + i * 7 + 4);
-    bbox.x_max = *(detect_data + i * 7 + 5);
-    bbox.y_max = *(detect_data + i * 7 + 6);
-    bboxes[out_offset + i] = bbox;
-  };
-}
-
-template <typename T>
-void GetBBoxFromLabelData(const T* label_data, const size_t num_bboxes,
-                          std::vector<BBox<T>>& bboxes) {
-  size_t out_offset = bboxes.size();
-  bboxes.resize(bboxes.size() + num_bboxes);
-  for (size_t i = 0; i < num_bboxes; ++i) {
-    BBox<T> bbox;
-    bbox.x_min = *(label_data + i * 6 + 1);
-    bbox.y_min = *(label_data + i * 6 + 2);
-    bbox.x_max = *(label_data + i * 6 + 3);
-    bbox.y_max = *(label_data + i * 6 + 4);
-    T is_difficult = *(label_data + i * 6 + 5);
-    if (std::abs(is_difficult - 0.0) < 1e-6)
-      bbox.is_difficult = false;
-    else
-      bbox.is_difficult = true;
-    bboxes[out_offset + i] = bbox;
-  }
-}
-
-template <typename T>
-inline float JaccardOverlap(const BBox<T>& bbox1, const BBox<T>& bbox2) {
-  if (bbox2.x_min > bbox1.x_max || bbox2.x_max < bbox1.x_min ||
-      bbox2.y_min > bbox1.y_max || bbox2.y_max < bbox1.y_min) {
-    return 0.0;
-  } else {
-    float inter_x_min = std::max(bbox1.x_min, bbox2.x_min);
-    float inter_y_min = std::max(bbox1.y_min, bbox2.y_min);
-    float inter_x_max = std::min(bbox1.x_max, bbox2.x_max);
-    float inter_y_max = std::min(bbox1.y_max, bbox2.y_max);
-
-    float inter_width = inter_x_max - inter_x_min;
-    float inter_height = inter_y_max - inter_y_min;
-    float inter_area = inter_width * inter_height;
-
-    float bbox_area1 = bbox1.get_area();
-    float bbox_area2 = bbox2.get_area();
-
-    return inter_area / (bbox_area1 + bbox_area2 - inter_area);
-  }
-}
-
-template <typename T>
-bool SortScorePairDescend(const std::pair<float, T>& pair1,
-                          const std::pair<float, T>& pair2) {
-  return pair1.first > pair2.first;
-}
-
-// template <>
-// bool SortScorePairDescend(const std::pair<float, NormalizedBBox>& pair1,
-//                           const std::pair<float, NormalizedBBox>& pair2) {
-//   return pair1.first > pair2.first;
-// }
-
-}  // namespace math
-}  // namespace operators
-}  // namespace paddle

python/paddle/v2/fluid/tests/test_detection_map_op.py

@@ -10,14 +10,14 @@ class TestDetectionMAPOp(OpTest):
     def set_data(self):
         self.init_test_case()
 
-        self.mAP = [self.calc_map(self.tf_pos)]
+        self.mAP = [self.calc_map(self.tf_pos, self.tf_pos_lod)]
         self.label = np.array(self.label).astype('float32')
         self.detect = np.array(self.detect).astype('float32')
         self.mAP = np.array(self.mAP).astype('float32')
 
         self.inputs = {
             'Label': (self.label, self.label_lod),
-            'Detect': self.detect
+            'Detection': (self.detect, self.detect_lod)
         }
 
         self.attrs = {
@@ -31,29 +31,29 @@ class TestDetectionMAPOp(OpTest):
     def init_test_case(self):
         self.overlap_threshold = 0.3
         self.evaluate_difficult = True
-        self.ap_type = "Integral"
+        self.ap_type = "integral"
 
         self.label_lod = [[0, 2, 4]]
-        # label xmin ymin xmax ymax difficult
-        self.label = [[1, 0.1, 0.1, 0.3, 0.3, 0], [1, 0.6, 0.6, 0.8, 0.8, 1],
-                      [2, 0.3, 0.3, 0.6, 0.5, 0], [1, 0.7, 0.1, 0.9, 0.3, 0]]
+        # label difficult xmin ymin xmax ymax
+        self.label = [[1, 0, 0.1, 0.1, 0.3, 0.3], [1, 1, 0.6, 0.6, 0.8, 0.8],
+                      [2, 0, 0.3, 0.3, 0.6, 0.5], [1, 0, 0.7, 0.1, 0.9, 0.3]]
 
-        # image_id label score xmin ymin xmax ymax difficult
+        # label score xmin ymin xmax ymax difficult
+        self.detect_lod = [[0, 3, 7]]
         self.detect = [
-            [0, 1, 0.3, 0.1, 0.0, 0.4, 0.3], [0, 1, 0.7, 0.0, 0.1, 0.2, 0.3],
-            [0, 1, 0.9, 0.7, 0.6, 0.8, 0.8], [1, 2, 0.8, 0.2, 0.1, 0.4, 0.4],
-            [1, 2, 0.1, 0.4, 0.3, 0.7, 0.5], [1, 1, 0.2, 0.8, 0.1, 1.0, 0.3],
-            [1, 3, 0.2, 0.8, 0.1, 1.0, 0.3]
+            [1, 0.3, 0.1, 0.0, 0.4, 0.3], [1, 0.7, 0.0, 0.1, 0.2, 0.3],
+            [1, 0.9, 0.7, 0.6, 0.8, 0.8], [2, 0.8, 0.2, 0.1, 0.4, 0.4],
+            [2, 0.1, 0.4, 0.3, 0.7, 0.5], [1, 0.2, 0.8, 0.1, 1.0, 0.3],
+            [3, 0.2, 0.8, 0.1, 1.0, 0.3]
         ]
 
-        # image_id label score false_pos false_pos
-        # [-1, 1, 3, -1, -1],
-        # [-1, 2, 1, -1, -1]
-        self.tf_pos = [[0, 1, 0.9, 1, 0], [0, 1, 0.7, 1, 0], [0, 1, 0.3, 0, 1],
-                       [1, 1, 0.2, 1, 0], [1, 2, 0.8, 0, 1], [1, 2, 0.1, 1, 0],
-                       [1, 3, 0.2, 0, 1]]
+        # label score true_pos false_pos
+        self.tf_pos_lod = [[0, 3, 7]]
+        self.tf_pos = [[1, 0.9, 1, 0], [1, 0.7, 1, 0], [1, 0.3, 0, 1],
+                       [1, 0.2, 1, 0], [2, 0.8, 0, 1], [2, 0.1, 1, 0],
+                       [3, 0.2, 0, 1]]
 
-    def calc_map(self, tf_pos):
+    def calc_map(self, tf_pos, tf_pos_lod):
         mAP = 0.0
         count = 0
 
@@ -71,7 +71,7 @@ class TestDetectionMAPOp(OpTest):
             return accu_list
 
         label_count = collections.Counter()
-        for (label, xmin, ymin, xmax, ymax, difficult) in self.label:
+        for (label, difficult, xmin, ymin, xmax, ymax) in self.label:
             if self.evaluate_difficult:
                 label_count[label] += 1
             elif not difficult:
@@ -79,7 +79,7 @@ class TestDetectionMAPOp(OpTest):
 
         true_pos = collections.defaultdict(list)
         false_pos = collections.defaultdict(list)
-        for (image_id, label, score, tp, fp) in tf_pos:
+        for (label, score, tp, fp) in tf_pos:
             true_pos[label].append([score, tp])
             false_pos[label].append([score, fp])
 
@@ -103,22 +103,22 @@ class TestDetectionMAPOp(OpTest):
                 recall.append(float(accu_tp_sum[i]) / label_pos_num)
 
             if self.ap_type == "11point":
-                max_precisions = [11.0, 0.0]
+                max_precisions = [0.0] * 11
                 start_idx = len(accu_tp_sum) - 1
-                for j in range(10, 0, -1):
-                    for i in range(start_idx, 0, -1):
-                        if recall[i] < j / 10.0:
+                for j in range(10, -1, -1):
+                    for i in range(start_idx, -1, -1):
+                        if recall[i] < float(j) / 10.0:
                             start_idx = i
                             if j > 0:
                                 max_precisions[j - 1] = max_precisions[j]
                                 break
-                            else:
-                                if max_precisions[j] < accu_precision[i]:
-                                    max_precisions[j] = accu_precision[i]
-                for j in range(10, 0, -1):
+                        else:
+                            if max_precisions[j] < precision[i]:
+                                max_precisions[j] = precision[i]
+                for j in range(10, -1, -1):
                     mAP += max_precisions[j] / 11
                 count += 1
-            elif self.ap_type == "Integral":
+            elif self.ap_type == "integral":
                 average_precisions = 0.0
                 prev_recall = 0.0
                 for i in range(len(accu_tp_sum)):
@@ -147,8 +147,17 @@ class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp):
 
         self.evaluate_difficult = False
 
-        self.tf_pos = [[0, 1, 0.7, 1, 0], [0, 1, 0.3, 0, 1], [1, 1, 0.2, 1, 0],
-                       [1, 2, 0.8, 0, 1], [1, 2, 0.1, 1, 0], [1, 3, 0.2, 0, 1]]
+        self.tf_pos_lod = [[0, 2, 6]]
+        # label score true_pos false_pos
+        self.tf_pos = [[1, 0.7, 1, 0], [1, 0.3, 0, 1], [1, 0.2, 1, 0],
+                       [2, 0.8, 0, 1], [2, 0.1, 1, 0], [3, 0.2, 0, 1]]
+
+
+class TestDetectionMAPOp11Point(TestDetectionMAPOp):
+    def init_test_case(self):
+        super(TestDetectionMAPOp11Point, self).init_test_case()
+
+        self.ap_type = "11point"
 
 
 if __name__ == '__main__':