update mine_hard_examples op

paddle/operators/mine_hard_examples_op.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 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.
@@ -38,7 +38,7 @@ inline bool IsEligibleMining(const MiningType mining_type, const int match_idx,
   }
 }
 
-MiningType GetMiningType(std::string str) {
+inline MiningType GetMiningType(std::string str) {
   if (str == "max_negative") {
     return MiningType::kMaxNegative;
   } else if (str == "hard_example") {
@@ -112,7 +112,7 @@ class MineHardExamplesKernel : public framework::OpKernel<T> {
         neg_sel = std::min(sample_size, neg_sel);
       }
 
-      std::sort(loss_idx.begin(), loss_idx.end(), SortScoreDescend<int>);
+      std::sort(loss_idx.begin(), loss_idx.end(), SortScoreDescend<size_t>);
       std::set<int> sel_indices;
       std::vector<int> neg_indices;
       std::transform(loss_idx.begin(), loss_idx.begin() + neg_sel,
@@ -121,18 +121,27 @@ class MineHardExamplesKernel : public framework::OpKernel<T> {
                        return static_cast<int>(l.second);
                      });
 
-      for (int m = 0; m < prior_num; ++m) {
-        if (match_indices(n, m) > -1) {
-          if (mining_type == MiningType::kHardExample &&
-              sel_indices.find(m) == sel_indices.end()) {
-            match_indices_et(n, m) = -1;
+      if (mining_type == MiningType::kHardExample) {
+        for (int m = 0; m < prior_num; ++m) {
+          if (match_indices(n, m) > -1) {
+            if (sel_indices.find(m) == sel_indices.end()) {
+              match_indices_et(n, m) = -1;
+            }
+          } else {
+            if (sel_indices.find(m) != sel_indices.end()) {
+              neg_indices.push_back(m);
+            }
           }
-        } else {
-          if (sel_indices.find(m) != sel_indices.end()) {
+        }
+      } else {
+        for (int m = 0; m < prior_num; ++m) {
+          if (match_indices(n, m) == -1 &&
+              sel_indices.find(m) != sel_indices.end()) {
             neg_indices.push_back(m);
           }
         }
       }
+
       all_neg_indices.push_back(neg_indices);
       batch_starts.push_back(batch_starts.back() + neg_indices.size());
     }
@@ -253,7 +262,7 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
         "[N, Np], N is the batch size and Np is the number of prior box.");
     AddInput("LocLoss",
              "(Tensor, optional, default Tensor<float>), The localization loss "
-             "wit shape [N, Np], N is the batch size and Np is the number of "
+             "with shape [N, Np], N is the batch size and Np is the number of "
              "prior box.")
         .AsDispensable();
     AddInput("MatchIndices",
@@ -267,15 +276,15 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
              "Np], N is the batch size and Np is the number of prior box.");
     AddAttr<float>("neg_pos_ratio",
                    "(float) The ratio of the negative box to the positive "
-                   "box. Use only when mining_type is equal to max_negative.")
+                   "box. Use only when mining_type is max_negative.")
         .SetDefault(1.0);
     AddAttr<float>("neg_dist_threshold",
-                   "(float) The negative box dis value threshold. "
-                   "Use only when mining_type is equal to max_negative.")
+                   "(float) The negative overlap upper bound for the unmatched "
+                   "predictions. Use only when mining_type is max_negative.")
         .SetDefault(0.5);
     AddAttr<int>("sample_size",
                  "(float) The max sample size of negative box. Use only when "
-                 "mining_type is equal to hard_example.")
+                 "mining_type is hard_example.")
         .SetDefault(0);
     AddAttr<std::string>("mining_type",
                          "(float) The mining algorithm name, the value is "
@@ -295,7 +304,7 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddOutput("UpdatedMatchIndices",
               "(Tensor<int>) The output of updated MatchIndices, a tensor with "
-              "shape [N, Np]. Only update when mining_type is equal to "
+              "shape [N, Np]. Only update when mining_type is "
               "hard_example. The input MatchIndices elements will be update to "
               "-1 when it is not in the candidate high loss list of negative "
               "examples.");
@@ -303,11 +312,12 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Mine hard examples Operator.
 This operator implements hard example mining to select a subset of negative box indices.
-For each image, selects the box with highest losses. subject to the condition that the box cannot have
-an Matcht > neg_dist_threshold when mining_type is equals max_negative. The selected number is 
-min(sample_size, max_negative_box_number) when mining_type is equals hard_example,
-or min(neg_pos_ratio * positive_box_number, max_negative_box_number) when mining_type is 
-equals max_negative, where the max_negative_box_number is the count of MatchIndices elements with value -1.
+For each image, selects the box with highest losses. subject to the condition that the 
+box cannot have an Matcht > neg_dist_threshold when mining_type is max_negative. 
+The selected number is min(sample_size, max_negative_box_number) when mining_type is 
+hard_example, or min(neg_pos_ratio * positive_box_number, max_negative_box_number) 
+when mining_type is max_negative, where the max_negative_box_number is the count of 
+MatchIndices elements with value -1.
 )DOC");
   }
 };