From ec321e651fdcc13ccdc16678f2e3a70e9eb97a8e Mon Sep 17 00:00:00 2001
From: Aleksandr Rybnikov <arrybn@gmail.com>
Date: Wed, 28 Jun 2017 11:31:38 +0300
Subject: [PATCH] Removed usage of std::map in DetectionOutput layer

---
 .../dnn/src/layers/detection_output_layer.cpp | 23 ++++++++++---------
 modules/dnn/src/layers/softmax_layer.cpp      | 12 ++++++----
 2 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/modules/dnn/src/layers/detection_output_layer.cpp b/modules/dnn/src/layers/detection_output_layer.cpp
index 463d0a093d..6b449bfea6 100644
--- a/modules/dnn/src/layers/detection_output_layer.cpp
+++ b/modules/dnn/src/layers/detection_output_layer.cpp
@@ -218,7 +218,7 @@ public:
                           _shareLocation, &allLocationPredictions);
 
         // Retrieve all confidences.
-        std::vector<std::map<int, std::vector<float> > > allConfidenceScores;
+        std::vector<std::vector<std::vector<float> > > allConfidenceScores;
         GetConfidenceScores(confidenceData, num, numPriors, _numClasses,
                             &allConfidenceScores);
 
@@ -240,7 +240,7 @@ public:
         for (int i = 0; i < num; ++i)
         {
             const LabelBBox& decodeBBoxes = allDecodedBBoxes[i];
-            const std::map<int, std::vector<float> >& confidenceScores =
+            const std::vector<std::vector<float> >& confidenceScores =
             allConfidenceScores[i];
             std::map<int, std::vector<int> > indices;
             int numDetections = 0;
@@ -251,13 +251,13 @@ public:
                     // Ignore background class.
                     continue;
                 }
-                if (confidenceScores.find(c) == confidenceScores.end())
+                if (confidenceScores.size() <= c)
                 {
                     // Something bad happened if there are no predictions for current label.
                     util::make_error<int>("Could not find confidence predictions for label ", c);
                 }
 
-                const std::vector<float>& scores = confidenceScores.find(c)->second;
+                const std::vector<float>& scores = confidenceScores[c];
                 int label = _shareLocation ? -1 : c;
                 if (decodeBBoxes.find(label) == decodeBBoxes.end())
                 {
@@ -279,13 +279,13 @@ public:
                 {
                     int label = it->first;
                     const std::vector<int>& labelIndices = it->second;
-                    if (confidenceScores.find(label) == confidenceScores.end())
+                    if (confidenceScores.size() <= label)
                     {
                         // Something bad happened for current label.
                         util::make_error<int>("Could not find location predictions for label ", label);
                         continue;
                     }
-                    const std::vector<float>& scores = confidenceScores.find(label)->second;
+                    const std::vector<float>& scores = confidenceScores[label];
                     for (size_t j = 0; j < labelIndices.size(); ++j)
                     {
                         size_t idx = labelIndices[j];
@@ -328,20 +328,20 @@ public:
         int count = 0;
         for (int i = 0; i < num; ++i)
         {
-            const std::map<int, std::vector<float> >& confidenceScores =
+            const std::vector<std::vector<float> >& confidenceScores =
             allConfidenceScores[i];
             const LabelBBox& decodeBBoxes = allDecodedBBoxes[i];
             for (std::map<int, std::vector<int> >::iterator it = allIndices[i].begin();
                  it != allIndices[i].end(); ++it)
             {
                 int label = it->first;
-                if (confidenceScores.find(label) == confidenceScores.end())
+                if (confidenceScores.size() <= label)
                 {
                     // Something bad happened if there are no predictions for current label.
                     util::make_error<int>("Could not find confidence predictions for label ", label);
                     continue;
                 }
-                const std::vector<float>& scores = confidenceScores.find(label)->second;
+                const std::vector<float>& scores = confidenceScores[label];
                 int locLabel = _shareLocation ? -1 : label;
                 if (decodeBBoxes.find(locLabel) == decodeBBoxes.end())
                 {
@@ -641,13 +641,14 @@ public:
     //      confidence prediction for an image.
     void GetConfidenceScores(const float* confData, const int num,
                              const int numPredsPerClass, const int numClasses,
-                             std::vector<std::map<int, std::vector<float> > >* confPreds)
+                             std::vector<std::vector<std::vector<float> > >* confPreds)
     {
         confPreds->clear();
         confPreds->resize(num);
         for (int i = 0; i < num; ++i)
         {
-            std::map<int, std::vector<float> >& labelScores = (*confPreds)[i];
+            std::vector<std::vector<float> >& labelScores = (*confPreds)[i];
+            labelScores.resize(numClasses);
             for (int p = 0; p < numPredsPerClass; ++p)
             {
                 int startIdx = p * numClasses;
diff --git a/modules/dnn/src/layers/softmax_layer.cpp b/modules/dnn/src/layers/softmax_layer.cpp
index fc80fc1cd5..23a470cd18 100644
--- a/modules/dnn/src/layers/softmax_layer.cpp
+++ b/modules/dnn/src/layers/softmax_layer.cpp
@@ -123,8 +123,9 @@ public:
 
             for (size_t cnDim = 0; cnDim < channels; cnDim++)
             {
+                const int offset = srcOffset + cnDim * cnStep;
                 for (size_t i = 0; i < innerSize; i++)
-                    dstPtr[srcOffset + cnDim * cnStep + i] = srcPtr[srcOffset + cnDim * cnStep + i] - bufPtr[bufOffset + i];
+                    dstPtr[offset + i] = srcPtr[offset + i] - bufPtr[bufOffset + i];
             }
         }
 
@@ -141,22 +142,25 @@ public:
 
             for (size_t cnDim = 0; cnDim < channels; cnDim++)
             {
+                const int offset = srcOffset + cnDim * cnStep;
                 for (size_t i = 0; i < innerSize; i++)
-                    bufPtr[bufOffset + i] += dstPtr[srcOffset + cnDim * cnStep + i];
+                    bufPtr[bufOffset + i] += dstPtr[offset + i];
             }
 
             //divide by computed sum
             for (size_t cnDim = 0; cnDim < channels; cnDim++)
             {
+                const int offset = srcOffset + cnDim * cnStep;
                 for (size_t i = 0; i < innerSize; i++)
-                    dstPtr[srcOffset + cnDim * cnStep + i] /= bufPtr[bufOffset + i];
+                    dstPtr[offset + i] /= bufPtr[bufOffset + i];
             }
             if (logSoftMax)
             {
                 for (size_t cnDim = 0; cnDim < channels; cnDim++)
                 {
+                    const int offset = srcOffset + cnDim * cnStep;
                     for (size_t i = 0; i < innerSize; i++)
-                        dstPtr[srcOffset + cnDim * cnStep + i] = log(dstPtr[srcOffset + cnDim * cnStep + i]);
+                        dstPtr[offset + i] = log(dstPtr[offset + i]);
                 }
             }
         }