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Removed usage of std::map in DetectionOutput layer

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This commit is contained in:
Aleksandr Rybnikov 2017-06-28 11:31:38 +03:00
parent 82ec76c123
commit ec321e651f
2 changed files with 20 additions and 15 deletions
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23
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -218,7 +218,7 @@ public:
_shareLocation, &allLocationPredictions); _shareLocation, &allLocationPredictions);
// Retrieve all confidences. // 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, GetConfidenceScores(confidenceData, num, numPriors, _numClasses,
&allConfidenceScores); &allConfidenceScores);
@ -240,7 +240,7 @@ public:
for (int i = 0; i < num; ++i) for (int i = 0; i < num; ++i)
{ {
const LabelBBox& decodeBBoxes = allDecodedBBoxes[i]; const LabelBBox& decodeBBoxes = allDecodedBBoxes[i];
const std::map<int, std::vector<float> >& confidenceScores = const std::vector<std::vector<float> >& confidenceScores =
allConfidenceScores[i]; allConfidenceScores[i];
std::map<int, std::vector<int> > indices; std::map<int, std::vector<int> > indices;
int numDetections = 0; int numDetections = 0;
@ -251,13 +251,13 @@ public:
// Ignore background class. // Ignore background class.
continue; continue;
} }
if (confidenceScores.find(c) == confidenceScores.end()) if (confidenceScores.size() <= c)
{ {
// Something bad happened if there are no predictions for current label. // Something bad happened if there are no predictions for current label.
util::make_error<int>("Could not find confidence predictions for label ", c); 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; int label = _shareLocation ? -1 : c;
if (decodeBBoxes.find(label) == decodeBBoxes.end()) if (decodeBBoxes.find(label) == decodeBBoxes.end())
{ {
@ -279,13 +279,13 @@ public:
{ {
int label = it->first; int label = it->first;
const std::vector<int>& labelIndices = it->second; const std::vector<int>& labelIndices = it->second;
if (confidenceScores.find(label) == confidenceScores.end()) if (confidenceScores.size() <= label)
{ {
// Something bad happened for current label. // Something bad happened for current label.
util::make_error<int>("Could not find location predictions for label ", label); util::make_error<int>("Could not find location predictions for label ", label);
continue; 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) for (size_t j = 0; j < labelIndices.size(); ++j)
{ {
size_t idx = labelIndices[j]; size_t idx = labelIndices[j];
@ -328,20 +328,20 @@ public:
int count = 0; int count = 0;
for (int i = 0; i < num; ++i) for (int i = 0; i < num; ++i)
{ {
const std::map<int, std::vector<float> >& confidenceScores = const std::vector<std::vector<float> >& confidenceScores =
allConfidenceScores[i]; allConfidenceScores[i];
const LabelBBox& decodeBBoxes = allDecodedBBoxes[i]; const LabelBBox& decodeBBoxes = allDecodedBBoxes[i];
for (std::map<int, std::vector<int> >::iterator it = allIndices[i].begin(); for (std::map<int, std::vector<int> >::iterator it = allIndices[i].begin();
it != allIndices[i].end(); ++it) it != allIndices[i].end(); ++it)
{ {
int label = it->first; 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. // Something bad happened if there are no predictions for current label.
util::make_error<int>("Could not find confidence predictions for label ", label); util::make_error<int>("Could not find confidence predictions for label ", label);
continue; continue;
} }
const std::vector<float>& scores = confidenceScores.find(label)->second; const std::vector<float>& scores = confidenceScores[label];
int locLabel = _shareLocation ? -1 : label; int locLabel = _shareLocation ? -1 : label;
if (decodeBBoxes.find(locLabel) == decodeBBoxes.end()) if (decodeBBoxes.find(locLabel) == decodeBBoxes.end())
{ {
@ -641,13 +641,14 @@ public:
// confidence prediction for an image. // confidence prediction for an image.
void GetConfidenceScores(const float* confData, const int num, void GetConfidenceScores(const float* confData, const int num,
const int numPredsPerClass, const int numClasses, 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->clear();
confPreds->resize(num); confPreds->resize(num);
for (int i = 0; i < num; ++i) 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) for (int p = 0; p < numPredsPerClass; ++p)
{ {
int startIdx = p * numClasses; int startIdx = p * numClasses;

12
modules/dnn/src/layers/softmax_layer.cpp
View File

@ -123,8 +123,9 @@ public:
for (size_t cnDim = 0; cnDim < channels; cnDim++) for (size_t cnDim = 0; cnDim < channels; cnDim++)
{ {
const int offset = srcOffset + cnDim * cnStep;
for (size_t i = 0; i < innerSize; i++) 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++) for (size_t cnDim = 0; cnDim < channels; cnDim++)
{ {
const int offset = srcOffset + cnDim * cnStep;
for (size_t i = 0; i < innerSize; i++) 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 //divide by computed sum
for (size_t cnDim = 0; cnDim < channels; cnDim++) for (size_t cnDim = 0; cnDim < channels; cnDim++)
{ {
const int offset = srcOffset + cnDim * cnStep;
for (size_t i = 0; i < innerSize; i++) for (size_t i = 0; i < innerSize; i++)
dstPtr[srcOffset + cnDim * cnStep + i] /= bufPtr[bufOffset + i]; dstPtr[offset + i] /= bufPtr[bufOffset + i];
} }
if (logSoftMax) if (logSoftMax)
{ {
for (size_t cnDim = 0; cnDim < channels; cnDim++) for (size_t cnDim = 0; cnDim < channels; cnDim++)
{ {
const int offset = srcOffset + cnDim * cnStep;
for (size_t i = 0; i < innerSize; i++) 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]);
} }
} }
} }